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  • Views and Copies in Pandas
    • @@ -238,6 +237,7 @@
  • Editing Specific Locations
  • Cleaning Data Types
  • Missing Data
    • +
  • The Object and Categorical Data Types
  • Data Manipulations
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a/_build/html/_sources/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb b/_build/html/_sources/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb index dcf2a931..75bb85bc 100644 --- a/_build/html/_sources/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb +++ b/_build/html/_sources/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb @@ -812,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1926,6 +1926,1089 @@ "execution_count": 9, "metadata": {}, "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-28 07:21:51,309 - tornado.application - ERROR - Exception in callback >\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/ioloop.py\", line 919, in _run\n", + " val = self.callback()\n", + " ^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/system_monitor.py\", line 168, in update\n", + " net_ioc = psutil.net_io_counters()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/psutil/__init__.py\", line 2119, in net_io_counters\n", + " rawdict = _psplatform.net_io_counters()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "OSError: [Errno 12] Cannot allocate memory\n", + "2023-11-29 12:24:43,023 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,086 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,125 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,130 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,280 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:32:25,398 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,405 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,753 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 369): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 140): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1043): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 634): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 405): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1308): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:32:25,749 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,864 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:32:25,946 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:32:38,591 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:38,659 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,011 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,181 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,299 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', 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self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:51,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,043 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,257 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,540 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,664 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:32:52,661 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:04,636 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:04,728 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:04,862 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:04,895 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 1331): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 248): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 19): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 922): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 513): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 284): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1416): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1187): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 778): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1452): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 369): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 140): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1043): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 634): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 405): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1308): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:33:05,272 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:05,301 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:15,177 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:15,350 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:15,994 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:16,318 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:16,497 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', 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self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File 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last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:29,643 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:29,963 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:29,964 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:29,974 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:40,320 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:40,536 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,055 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,131 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,253 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 1331): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 248): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 19): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 922): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 513): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 284): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1416): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1187): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 778): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1452): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 369): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 140): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1043): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 634): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 405): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1308): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:33:41,247 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,214 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:53,215 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,290 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,402 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:53,563 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 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distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:05,777 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:05,800 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:05,900 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:06,047 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:06,100 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,458 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,682 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:17,740 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,961 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:34:18,184 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n" + ] } ], "source": [ diff --git a/_build/html/_sources/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb b/_build/html/_sources/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb index 7354d5af..d8e69cd4 100644 --- a/_build/html/_sources/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb +++ b/_build/html/_sources/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb @@ -46,7 +46,7 @@ "years_of_education = [12, 16, 11, 22, 12, 8, 12]\n", "\n", "survey = np.array([incomes, ages, years_of_education]).T\n", - "survey\n" + "survey" ] }, { @@ -78,7 +78,7 @@ ], "source": [ "mean_age = np.mean(survey[:, 1])\n", - "np.round(mean_age, 1)\n" + "np.round(mean_age, 1)" ] }, { @@ -140,7 +140,7 @@ ], "source": [ "mean_income = np.mean(survey[:, 0])\n", - "mean_income\n" + "mean_income" ] }, { @@ -160,7 +160,7 @@ } ], "source": [ - "np.round(mean_income, 1)\n" + "np.round(mean_income, 1)" ] }, { @@ -241,12 +241,12 @@ ], "source": [ "survey[survey[:, 0] < 20_000, 0] = survey[survey[:, 0] < 20_000, 0] + 1_500\n", - "survey\n" + "survey" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -255,13 +255,13 @@ "40214.3" ] }, - "execution_count": 27, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(np.mean(survey[:, 0]), 1)\n" + "np.round(np.mean(survey[:, 0]), 1)" ] }, { @@ -325,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -334,7 +334,7 @@ "(122603, 5)" ] }, - "execution_count": 11, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -346,28 +346,7 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "26.0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hourly = cps[:, 0] / cps[:, 1]\n", - "np.round(np.mean(hourly), 1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -376,19 +355,19 @@ "27.883329990399556" ] }, - "execution_count": 13, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "men = np.mean(hourly[cps[:, 2] == 1])\n", - "men\n" + "men" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -397,18 +376,18 @@ "27.9" ] }, - "execution_count": 14, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(men, 1)\n" + "np.round(men, 1)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -417,19 +396,19 @@ "23.803157653012704" ] }, - "execution_count": 15, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "women = np.mean(hourly[cps[:, 2] == 2])\n", - "women\n" + "women" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -438,18 +417,18 @@ "23.8" ] }, - "execution_count": 16, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(women, 1)\n" + "np.round(women, 1)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -458,18 +437,18 @@ "0.8536698328789393" ] }, - "execution_count": 17, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "women / men\n" + "women / men" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -478,13 +457,13 @@ "0.854" ] }, - "execution_count": 18, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(women / men, 3)\n" + "np.round(women / men, 3)" ] }, { @@ -518,7 +497,7 @@ ], "source": [ "black = np.mean(hourly[cps[:, 3] == 2])\n", - "black\n" + "black" ] }, { @@ -538,7 +517,7 @@ } ], "source": [ - "np.round(black, 1)\n" + "np.round(black, 1)" ] }, { @@ -559,7 +538,7 @@ ], "source": [ "white = np.mean(hourly[cps[:, 3] == 1])\n", - "white\n" + "white" ] }, { @@ -579,7 +558,7 @@ } ], "source": [ - "np.round(white, 1)\n" + "np.round(white, 1)" ] }, { @@ -599,7 +578,7 @@ } ], "source": [ - "black / white\n" + "black / white" ] }, { @@ -619,7 +598,7 @@ } ], "source": [ - "np.round(black / white, 3)\n" + "np.round(black / white, 3)" ] }, { @@ -651,7 +630,7 @@ "metadata": {}, "outputs": [], "source": [ - "true_average = np.sum((hourly * cps[:, 4]) / np.sum(cps[:, 4]))\n" + "true_average = np.sum((hourly * cps[:, 4]) / np.sum(cps[:, 4]))" ] }, { @@ -671,7 +650,7 @@ } ], "source": [ - "np.round(true_average, 2)\n" + "np.round(true_average, 2)" ] } ], @@ -691,7 +670,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/_build/html/_sources/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb b/_build/html/_sources/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb index 0a589235..8d044ba4 100644 --- a/_build/html/_sources/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb +++ b/_build/html/_sources/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb @@ -9,7 +9,7 @@ "\n", "**Note**: \n", "\n", - "> This exercise has been written out in something called a Jupyter Notebook. We'll discuss Jupyter Notebooks in more detail later in the course—they are a very powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don't need to *do* anything with the notebook except read its contents—just use write your Python code in a regular `.py` file.\n", + "> This exercise has been written out in something called a Jupyter Notebook. We'll discuss Jupyter Notebooks in more detail later in the course—they are very a powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don't need to *do* anything with the notebook except read its contents—just use write your Python code in a regular `.py` file.\n", "\n", "\n", "As previously discussed, we frequently use matrices and data science because they are a natural format for representing data generated by collecting the same type of information from numerous entities. For example, below is a toy dataset that you could imagine was created by collecting information about employees at a company—each column is a different type of information being collected (income, age, years of education), and each row is the information about a different employee.\n", @@ -47,7 +47,7 @@ "years_of_education = [12, 16, 11, 22, 12, 8, 12]\n", "\n", "survey = np.array([incomes, ages, years_of_education]).T\n", - "survey\n" + "survey" ] }, { @@ -56,9 +56,17 @@ "source": [ "## Part 1: Summarizing Data\n", "\n", - "1. What is the average (mean) age of all employees (rounded to 1 decimal place)?\n", - "2. What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?\n", - "3. What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?" + "### Question 1 \n", + "\n", + "What is the average (mean) age of all employees (rounded to 1 decimal place)?\n", + "\n", + "### Question 2\n", + "\n", + "What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?\n", + "\n", + "### Question 3\n", + "\n", + "What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?" ] }, { @@ -70,7 +78,9 @@ "\n", "The US government is thinking about offering a 1,500 tax credit to anyone making less than 20,000 a year. You can think of this tax credit as effectively an additional $1,500 of income to each person receiving the credit.\n", "\n", - "4. Using the data from `survey`, modify income values to calculate a new estimate of the employees' incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?\n", + "### Question 4 \n", + "\n", + "Using the data from `survey`, modify income values to calculate a new estimate of the employees' incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?\n", " - Do so by subsetting and editing values programmatically, *not* just typing values by hand. (Yes, writing out a new vector by hand is easy to do in this example, but you couldn't do it with a large, real dataset!)" ] }, @@ -82,7 +92,7 @@ "\n", "In this exercise, we'll be working with data from the [US Current Population Survey, provided by the National Bureau of Economic Research (NBER)](https://www.nber.org/research/data/current-population-survey-cps-merged-outgoing-rotation-group-earnings-data). This is a regular survey conducted by the US Bureau of Labor to calculate the US employment rate.\n", "\n", - "In this exercise, we'll use these data to study gender and racial wage inequality in the US.\n", + "In this exercise, we'll use this data to study gender and racial wage inequality in the US.\n", "\n", "Load data from the 2018 CPS survey with the following command:\n", "\n", @@ -90,16 +100,20 @@ "cps = np.loadtxt(\"data/cps.txt\")\n", "```\n", "\n", - "This data are a *subset* of the full CPS survey and contains only data on **employed respondents working at least 35 hours a week (e.g., full-time).**\n" + "This data is a *subset* of the full CPS survey and contains only data on **employed respondents working at least 35 hours a week (e.g., full-time).**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "5. How many rows does this matrix have?\n", + "### Question 5 \n", + "\n", + "How many rows does this matrix have?\n", + "\n", + "### Question 6 \n", "\n", - "6. The five columns of this matrix correspond to:\n", + "The five columns of this matrix correspond to:\n", " - Column 1: Weekly income in dollars.\n", " - Column 2: Usual hours respondent works per week.\n", " - Column 3: Gender. 2 is \"Female\", 1 is \"Male\"\n", @@ -115,7 +129,9 @@ "\n", "For the moment, let's ignore survey weights—they don't impact results here significantly.\n", "\n", - "**What is the *average hourly wage* for all workers in this dataset (round it to one decimal place)?**\n", + "**What is the *average hourly wage* for all workers in this data?** \n", + "\n", + "Please round your *final* answer to one decimal place, but *don't round any intermediate results!* (if you round at each stage of the calculation, those roundings may compound in odd ways).\n", "\n", "**Hint:** This will require more than just using `mean` on a single column!" ] @@ -124,11 +140,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "7. What is the average hourly wage of working men (rounded to one decimal place)? \n", + "### Question 7 \n", + "\n", + "What is the average hourly wage of working men? (Please round your *final* answer to one decimal place, but *don't round any intermediate results!*)\n", + "\n", + "### Question 8 \n", + "\n", + "What is the average hourly wage of working women? (Please round your *final* answer to one decimal place, but *don't round any intermediate results!*)\n", "\n", - "8. What is the average hourly wage of working women (rounded to one decimal place)?\n", + "### Question 9 \n", "\n", - "9. What share (e.g., a value between 0 and 1) of men's average hourly wage is women's average hourly wage? (rounded to three decimal places). In other words, what is women's average hourly wage divided by men's average hourly wage? *Don't round anything until you have your final answer!*\n", + "What share (e.g., a value between 0 and 1) of men's average hourly wage is women's average hourly wage? (Please round your *final* answer to three decimal place, but *don't round any intermediate results!*). In other words, what is women's average hourly wage divided by men's average hourly wage?\n", "\n", "Congratulations! You've just calculated the US gender wage gap, on your own, using real data! I mean... I guess \"congratulations\" is a weird thing to say after directly measuring one of the more egregious inequities in US society, but one of the reasons many of us study data science is so that we will have the ability to directly measure these types of phenomena in the hopes of being able to better understand and address them." ] @@ -137,11 +159,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "10. Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?\n", + "### Question 10 \n", "\n", - "11. What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?\n", + "Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?\n", "\n", - "12. What share (e.g., a value between 0 and 1) of respondents who identify as White's average hourly wage is respondents who identify as Black's average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black's average hourly wage divided by respondents who identify as White's average hourly wage? *Don't round anything until you have your final answer!*\n", + "### Question 11 \n", + "\n", + "What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?\n", + "\n", + "### Question 12 \n", + "\n", + "What share (e.g., a value between 0 and 1) of respondents who identify as White's average hourly wage is respondents who identify as Black's average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black's average hourly wage divided by respondents who identify as White's average hourly wage? *Don't round anything until you have your final answer!*\n", "\n", "Note that this will only be an approximation—one would normally also include all respondents of mixed-race into non-mutually exclusive categories like \"Any Part Black\" or \"Any Part White\", and we would also break out Hispanic and non-Hispanic respondents. But as most respondents only pick on racial category, this will still give us a reasonable approximation." ] @@ -154,7 +182,7 @@ "\n", "As noted above, the fifth column of our data contains something called \"sampling weights.\" That's because when the government conducted this survey, they didn't draw a random sample of respondents from the US population where everyone had the same probability of being interviewed. As a result, when we calculate the average hourly wage of the people in the survey, it isn't *quite* the best estimate we have for the average hourly wage for everyone working in the United States. To calculate this number as accurately as possible, we have to take into account the fact that some respondents in the data were more likely to be included than others, and thus can be thought of as standing in for a smaller group of people in the US.\n", "\n", - "Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than what we would get if we just took a random sample where everyone in the United States had the same probability of being included.\n", + "Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than when we would get if we just took a random sample where everyone in the United States had the same probability of being included.\n", "\n", "As I mentioned above, for the questions above, the sampling weights don't make a very big difference to our answers, but the way to get the *most accurate* estimates would be to take them into account. So let's give that a try!\n", "\n", @@ -162,9 +190,11 @@ "\n", "For a *weighted* average, we take the value for each observation $i$ and multiply it by\n", "\n", - "$$weight_i / \\sum_{j=1}^N weight_j$$\n", + "$$weight_i / \\sum_{j=1}^N weights_j$$\n", + "\n", + "where $weight_i$ is the observation's weight, and $\\sum_{j=1}^N weights_j$ is the total of all the weights in the population being averaged. Then we just add up those values!\n", "\n", - "where $weight_i$ is the observation's weight, and $\\sum_{j=1}^N weight_j$ is the total of all the weights in the population being averaged. Then we just add up those values!\n", + "### Question 13\n", "\n", "Given that, what is the average hourly wage of Americans working full-time jobs (i.e., the group in this survey) taking into account survey weights (rounded to **two** decimal places)?\n" ] @@ -172,7 +202,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.6.5 ('base')", + "display_name": "Python 3.10.6 ('base')", "language": "python", "name": "python3" }, @@ -186,12 +216,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.10.6" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "f54e0b9f92ceb50229b3b7ea26b5d02f05ce8845dd8d997c014a0efede4c9c20" + "hash": "718fed28bf9f8c7851519acf2fb923cd655120b36de3b67253eeb0428bd33d2d" } } }, diff --git a/_build/html/_sources/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb b/_build/html/_sources/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb index 9a06eecf..327594d4 100644 --- a/_build/html/_sources/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb +++ b/_build/html/_sources/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb @@ -276,8 +276,8 @@ " 704 907 46 752 31 916 578 58 588 206 338 358 782 953 336 682 640 278\n", " 526 324 62 331 751 442 463 605 239 957 522 335 389 179 657 486 565 258\n", " 140 997 882 388 866 13 925 854 489 402]\n", - "894.7222222222222\n", - "894.0\n" + "425.078431372549\n", + "423.0\n" ] } ], @@ -291,11 +291,11 @@ "\n", "def remove_greater_than(array, threshold):\n", " '''remove entries in `array' greater than `threshold' '''\n", - " return array[array > threshold]\n", + " return array[array <= threshold]\n", "\n", "def remove_less_than(array, threshold):\n", " '''remove entries in `array' less than `threshold' ''' \n", - " return array[array < threshold]\n", + " return array[array >= threshold]\n", "\n", "def remove_if_equal(array, value_list):\n", " '''remove entries in `array' that equal any value in `value_list' '''\n", @@ -311,6 +311,7 @@ "data = remove_greater_than(data,800)\n", "even_integers_through_1000 = np.arange(2, 1001, 2)\n", "data = remove_if_equal(data,even_integers_through_1000)\n", + "data = remove_less_than(data,25)\n", "data = remove_duplicates(data)\n", "\n", "filtered_mean = np.mean(data)\n", @@ -322,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -330,21 +331,81 @@ "output_type": "stream", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 0 1 2 3\n", - " 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n", - "[11 12 13 14 15 16 17 18 19 11 12 13 14 15 16 17 18 19]\n", - "[0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]\n", - "[ 0 4 5 6 7 8 16 17 18 19 0 4 5 6 7 8 16 17 18 19]\n", - "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" + " 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" ] } ], "source": [ "test = np.concatenate((np.arange(0,20),np.arange(0,20)))\n", - "print(test)\n", - "print(remove_greater_than(test,10))\n", - "print(remove_less_than(test,10))\n", - "print(remove_if_equal(test,[1,2,3,9,10,11,12,13,14,15]))\n", - "print(remove_duplicates(test))\n" + "print(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10]\n" + ] + } + ], + "source": [ + "print(remove_greater_than(test,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_less_than(test,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 4 5 6 7 8 16 17 18 19 0 4 5 6 7 8 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_if_equal(test,[1,2,3,9,10,11,12,13,14,15]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_duplicates(test))" ] } ], @@ -364,7 +425,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.12.0" }, "orig_nbformat": 4, "vscode": { diff --git a/_build/html/_sources/notebooks/class_3/week_2/00_intro_to_pandas.ipynb b/_build/html/_sources/notebooks/class_3/week_2/00_intro_to_pandas.ipynb index fc7665f6..b6255028 100644 --- a/_build/html/_sources/notebooks/class_3/week_2/00_intro_to_pandas.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_2/00_intro_to_pandas.ipynb @@ -8,10 +8,11 @@ "\n", "In the last class, we discussed how to work with numerical data through arrays and matrices in cases when *all the data were of the same type*. You encounter this often, so `numpy` is a powerful tool for working with such homogeneous data (data of the same type) and in such cases is often the preferred tool if you want to do numeric computations because of its speed. However, there are several frequently encountered situations in which `numpy` tools may not be ideal and where instead the `pandas` library may be best. These include:\n", "\n", - "1. If you do not have purely numerical data (i.e. if your data are heterogeneous), and therefore have a mix of data types, `numpy` may not be appropriate, while `pandas` can easily handle and analyze mixed data types\n", - "2. If you have tabular data, `pandas` makes life much easier to **describe**, **summarize**, **query**, and **visualize** the data than the equivalent processes with `numpy`.\n", + "1. Your data includes not only numerical data, but also text, images, geometric objects, or any other type of data (i.e., your data are heterogeneous). In these situations, `numpy` may not be appropriate, while `pandas` can easily organize, manipulate, and analyze this kid of \"mixed data.\"\n", + "2. If you have tabular data — that is, data organized into rows and columns, as in a spreadsheet — it is often easier in `pandas` to **describe**, **summarize**, **query**, and **visualize** the data than the equivalent processes with `numpy`.\n", "\n", "## Mixed data types\n", + "\n", "Often our data are of mixed types (e.g. integers and strings). This happens all the time. Imagine that you are collecting basic medical information from a patient. You may ask for height and weight (numerical, floating point numbers), age (integer), and blood type (categorical, string). While numpy can store these together in an array, there's not much you're going to be able to do with it computationally. Consider the following example where `numpy` throws an error:" ] }, @@ -410,7 +411,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -419,7 +420,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -469,7 +470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/_build/html/_sources/notebooks/class_3/week_2/10_pandas_series.ipynb b/_build/html/_sources/notebooks/class_3/week_2/10_pandas_series.ipynb index a8b7c855..b3184b0c 100644 --- a/_build/html/_sources/notebooks/class_3/week_2/10_pandas_series.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_2/10_pandas_series.ipynb @@ -302,10 +302,11 @@ "\n", "Before we dive too far into Series manipulations, it's important to talk about datatypes. Every Series, as we will see, has a \"dtype\" (short for datatype). The `dtype` of a Series is important to understand because a Series' `dtype` determines what manipulations you can apply to that series. \n", "\n", - "There are, broadly, two types of Series: \n", + "There are, broadly, three types of Series: \n", "\n", "- Numeric: these hold numbers that `pandas` understands are numbers. Specific numeric datatypes include things like `int64`, and `int32` (integers), or `float64` and `float32` (floating point numbers).\n", - "- Object: these are Series that can hold any Python object, like strings, numbers, Sets, you name it. They have dtype `O` for \"objects\". They are flexible, but also very slow and actually harder to work with." + "- Object: these are Series that can hold any Python object, like strings, numbers, sets, you name it. They have dtype `O` for \"objects\". They are flexible, but also very slow and actually harder to work with.\n", + "- Categorical: this is a special data type (analogous to factors in R, if that means anything to you) that we'll discuss in a future reading. Don't worry about them for now." ] }, { diff --git a/_build/html/_sources/notebooks/class_3/week_2/15_manipulating_series.ipynb b/_build/html/_sources/notebooks/class_3/week_2/15_manipulating_series.ipynb index ed5e05c9..a0ec1323 100644 --- a/_build/html/_sources/notebooks/class_3/week_2/15_manipulating_series.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_2/15_manipulating_series.ipynb @@ -495,7 +495,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb b/_build/html/_sources/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb index ab7ed7e0..b7936bba 100644 --- a/_build/html/_sources/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -152,7 +152,7 @@ "dtype: object" ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -173,7 +173,7 @@ "'dog'" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -209,7 +209,7 @@ "dtype: int64" ] }, - "execution_count": 16, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -220,16 +220,24 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_22245/1336667577.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " attendance[0]\n" + ] + }, { "data": { "text/plain": [ "132" ] }, - "execution_count": 17, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -245,6 +253,21 @@ "As a result, for someone reading your code to understand what `[]` is doing, they have to also be keeping track of the data type of the index of the pandas object being manipulated. And while you may think \"yeah, but my code is just for me, so who cares?\" let me let you in on a secret: that \"other person\" who may struggle to understand your code maybe you when you come back to your code in a week to fix something and no longer remember everything that feels obvious to you right now!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, as you may have noticed from the `FutureWarning` generated when we ran that code:\n", + "\n", + "```python\n", + "FutureWarning: Series.__getitem__ treating keys as positions is deprecated. \n", + "In a future version, integer keys will always be treated as labels (consistent \n", + "with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + "```\n", + "\n", + "the developers of pandas concur that this behavior is *very* weird, and at some point soon it will be depreciated (probably in `pandas 3.0`). But for now, it remains a \"gotcha\" to be aware of!" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -273,7 +296,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_2/20_Exercise_series.ipynb b/_build/html/_sources/notebooks/class_3/week_2/20_Exercise_series.ipynb index 0996b043..deddccf5 100644 --- a/_build/html/_sources/notebooks/class_3/week_2/20_Exercise_series.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_2/20_Exercise_series.ipynb @@ -12,13 +12,22 @@ "\n", "```python\n", "import pandas as pd\n", - "gdppercap = pd.Series([34605, 34493, 12393, 44200, 10041, \n", - " 58138, 4709, 49284, 10109, 42536],\n", - " index=['Bahrain', 'Belgium', 'Bulgaria',\n", - " 'Ireland', 'Macedonia', 'Norway', \n", - " 'Paraguay', 'Singapore', \n", - " 'South Africa', 'Switzerland']\n", - " ) \n", + "\n", + "gdppercap = pd.Series(\n", + " [34605, 34493, 12393, 44200, 10041, 58138, 4709, 49284, 10109, 42536],\n", + " index=[\n", + " \"Bahrain\",\n", + " \"Belgium\",\n", + " \"Bulgaria\",\n", + " \"Ireland\",\n", + " \"Macedonia\",\n", + " \"Norway\",\n", + " \"Paraguay\",\n", + " \"Singapore\",\n", + " \"South Africa\",\n", + " \"Switzerland\",\n", + " ],\n", + ")\n", "```" ] }, @@ -112,15 +121,32 @@ "To test this hypothesis, let's do a small analysis of the dynamics of income inequality in our sample. Create the following Series in your Python session, which provides the average growth rate of GDP per capita for all the countries in our sample from 2000 to 2018. \n", "\n", "```python\n", - "avg_growth = pd.Series([-0.29768835, 0.980299584, 4.52991925, \n", - " 3.686556736, 2.621416804, 0.775132075, \n", - " 2.015489468, 3.345793635, 1.349993318, \n", - " 0.982775018],\n", - " index=['Bahrain', 'Belgium', 'Bulgaria', \n", - " 'Ireland', 'Macedonia', 'Norway', \n", - " 'Paraguay', 'Singapore', \n", - " 'South Africa', 'Switzerland']\n", - " )\n", + "avg_growth = pd.Series(\n", + " [\n", + " -0.29768835,\n", + " 0.980299584,\n", + " 4.52991925,\n", + " 3.686556736,\n", + " 2.621416804,\n", + " 0.775132075,\n", + " 2.015489468,\n", + " 3.345793635,\n", + " 1.349993318,\n", + " 0.982775018,\n", + " ],\n", + " index=[\n", + " \"Bahrain\",\n", + " \"Belgium\",\n", + " \"Bulgaria\",\n", + " \"Ireland\",\n", + " \"Macedonia\",\n", + " \"Norway\",\n", + " \"Paraguay\",\n", + " \"Singapore\",\n", + " \"South Africa\",\n", + " \"Switzerland\",\n", + " ],\n", + ")\n", "```" ] }, @@ -152,7 +178,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb b/_build/html/_sources/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb index 62c8f6cf..c2d96c63 100644 --- a/_build/html/_sources/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb @@ -162,7 +162,7 @@ "source": [ "## `[]` Square brackets\n", "\n", - "As with Series, single square brackets in `pandas` change their behavior depending on the values you pass them. Again, it is worth emphasizing that there is *nothing* that one can do with square brackets that you can't do with `.loc` and `.iloc`, so if they seem to strange, you don't have to use them. \n", + "As with Series, single square brackets in `pandas` change their behavior depending on the values you pass them. Again, it is worth emphasizing that there is *nothing* that one can do with square brackets that you can't do with `.loc` and `.iloc`, so if they seem too strange, you don't have to use them. \n", "\n", "With that said, as summarized below, `[]` is actually much safer on DataFrames than on Series. \n", "\n", @@ -833,7 +833,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_14379/4041630850.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_22305/4041630850.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", " world.rank_doubled = range(0, 2 * len(world), 2)\n" ] }, @@ -963,7 +963,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_3/00_plaintext_files.ipynb b/_build/html/_sources/notebooks/class_3/week_3/00_plaintext_files.ipynb index 08baadd3..57dbdfac 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/00_plaintext_files.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/00_plaintext_files.ipynb @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -146,13 +146,14 @@ "5 Ukraine C&E Europe 7271 16" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", + "\n", "pd.read_csv(\"data/world-very-small.csv\")" ] }, @@ -171,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -180,13 +181,12 @@ "{'pets': ['dogs', 'cats'], 'wild animals': ['zebras', 'penguins']}" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\n", "my_dict = {\"pets\": [\"dogs\", \"cats\"], \"wild animals\": [\"zebras\", \"penguins\"]}\n", "my_dict" ] @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -225,9 +225,9 @@ "\n", "# Print out what the json\n", "# representation of our dictionary would look like.\n", - "# Note that `indent=4` results in \n", + "# Note that `indent=4` results in\n", "# this nice looking indentation—without that option\n", - "# the contents all appear on one line. \n", + "# the contents all appear on one line.\n", "print(json.dumps(my_dict, indent=4))" ] }, @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -314,7 +314,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/_build/html/_sources/notebooks/class_3/week_3/05_binary_files.ipynb b/_build/html/_sources/notebooks/class_3/week_3/05_binary_files.ipynb index 2ed888c3..a2e5aab1 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/05_binary_files.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/05_binary_files.ipynb @@ -27,6 +27,7 @@ "outputs": [], "source": [ "import pandas as pd\n", + "\n", "world = pd.read_csv(\"data/world-very-small.csv\")\n", "world.to_stata(\"data/world-very-small.dta\")" ] diff --git a/_build/html/_sources/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb b/_build/html/_sources/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb index 98caa4f3..9ff0848d 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb @@ -206,7 +206,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb b/_build/html/_sources/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb index a0159db2..4c773bf2 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb @@ -361,11 +361,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Adding pd.set_option(\"mode.copy_on_write\", True) Easily\n", + "## Adding pd.set_option(\"mode.copy_on_write\", True) Easily\n", "\n", - "The easiest way to consistently add the `pd.set_option(\"mode.copy_on_write\", True)` is probably to create a custom import snippet — basically, a short keyboard shortcut that automatically import pandas, numpy, *and* sets this setting.\n", + "The easiest way to consistently add the `pd.set_option(\"mode.copy_on_write\", True)` is probably to create a custom \"snippet\" — basically, a keyboard shortcut that your editor expands into a block of text you type regularly.\n", "\n", - "For example, I've set up so when I type `ipd` followed by `tab`, I get:\n", + "For example, I've set up my VS Code installation so that when I type `ipd` followed by pressing `tab`, I get:\n", "\n", "```python\n", "import pandas as pd\n", @@ -373,9 +373,10 @@ "\n", "pd.set_option(\"mode.copy_on_write\", True)\n", "```\n", + "\n", "(in my head, `ipd` is short for **i**mport pandas as **pd** --> `ipd`)\n", "\n", - "To set up your own, just look for documentation on adding a snippet to your preferred editor. Use VS Code? [Here's how to do it!](https://code.visualstudio.com/docs/editor/userdefinedsnippets#_create-your-own-snippets) My snippet is:\n", + "To set up your own, just look for documentation on adding a snippet to your preferred editor. Use VS Code? [Here's how to do it!](https://code.visualstudio.com/docs/editor/userdefinedsnippets#_create-your-own-snippets) My VS Code snippet is:\n", "\n", "```json\n", "\"import pandas, numpy, activate CoW\": {\n", @@ -390,7 +391,7 @@ "}\n", "```\n", "\n", - "(note the need to escape quotation marks)" + "(note the need to escape quotation marks by putting a backslash right before them)" ] }, { diff --git a/_build/html/_sources/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb b/_build/html/_sources/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb index d263ecd3..041fdbeb 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb @@ -379,6 +379,14 @@ "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/2520817220.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '3.14' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.iloc[1, 1] = 3.14\n" + ] + }, { "data": { "text/html": [ @@ -628,6 +636,14 @@ "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/673548005.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'a' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.iloc[0, 0] = \"a\"\n" + ] + }, { "data": { "text/html": [ @@ -771,7 +787,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_64429/164737205.py:1: SettingWithCopyWarning: \n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/164737205.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -812,237 +828,6 @@ "\n", "Which leads me to what I will admit is an infuriating piece of advice to have to offer: **if you take a subset for any purpose other than immediately analyzing, you should add .copy() to that subsetting.** Seriously. Just when in doubt, `.copy()`." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### An Aside: No, the problem doesn't *only* emerge when you change the data type of a column\n", - "\n", - "Some readers may have noticed a pattern in the illustrations I've presented, and from them developed an intuition that a column will only lose it's \"view-ness\" when one changes the datatype of that column. Though this will always cause problems, it is not the only place problems can arise. What follows isn't something you *need* to know, but may be useful if you're deeply interested. \n", - "\n", - "In the examples above, each column was it's own object, and so behaved independently. But this is not always the case in `pandas`. If a DataFrame is created from a single numpy matrix with multiple columns, `pandas` will try to be efficient by just keeping that matrix intact. \n", - "\n", - "But as a result, if you do something (like change the type) of *one* of the columns that is tied to that matrix, `pandas` will create new arrays to back *all* the columns that were once tied to the matrix. As a result, a view of a single column can stop being a view due to changes to a different column. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1],\n", - " [2, 3],\n", - " [4, 5]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "my_matrix = np.arange(6).reshape(3, 2)\n", - "my_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " a b\n", - "0 0 1\n", - "1 2 3\n", - "2 4 5" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(my_matrix, columns=[\"a\", \"b\"])\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 -42\n", - "1 2\n", - "2 4\n", - "Name: a, dtype: int64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Column_a starts of it's life as a view\n", - "column_a = df[\"a\"]\n", - "df.iloc[0, 0] = -42\n", - "column_a" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " a b\n", - "0 -42 new entry\n", - "1 2 3\n", - "2 4 5" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# But if I make a change to column b...\n", - "df.loc[0, \"b\"] = \"new entry\"\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 999999\n", - "1 2\n", - "2 4\n", - "Name: a, dtype: int64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Then the same type of change to column a of `df` will no longer\n", - "# be shared\n", - "\n", - "df.iloc[0, 0] = 999999\n", - "column_a" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, as noted before: it is best to never to try and infer whether a subset of a DataFrame if a view or a copy until you have *explicitly* made a copy with `.copy()`." - ] } ], "metadata": { @@ -1061,7 +846,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_3/20_cleaning_identifying.ipynb b/_build/html/_sources/notebooks/class_3/week_3/20_cleaning_identifying.ipynb index 6cf1a269..9a4b21bc 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/20_cleaning_identifying.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/20_cleaning_identifying.ipynb @@ -15,7 +15,7 @@ "\n", "These parts of data science are not nearly as cool or widely discussed as new machine learning packages or the latest learning algorithm, but as anyone who has done data science in the real world will tell you, data cleaning and wrangling will take up a very large portion of your working life. Indeed, it is often said that \"data scientists spend 80% of their time cleaning data and only 20% doing analyses\" (or, in the version I prefer, \"data scientists spend 80% of their time cleaning data and the other 20% of their time complaining about cleaning data.\") That's probably a little high on average—a [recent survey by Anaconda](https://www.anaconda.com/state-of-data-science-2020) found on average data scientists reported data wrangling took up about 45% of their time—but what is undisputed is its importance to being a successful data scientist.\n", "\n", - "So in this reading we will discuss tools in pandas for identifying problems before we turn to tools for correcting problems in our next reading!" + "So in this reading, we will discuss tools in pandas for identifying problems before we turn to tools for correcting problems in our next reading!" ] }, { @@ -29,7 +29,7 @@ "\n", "### Your Challenge\n", "\n", - "Suppose you have been asked to calculate the overall US employment rate and average incomes for men and women, as well as the employment rate and average income of people in their 30s and 40s. To accomplish this task, you have been handed a 0.1% sample of the US American Community Survey—a representative survey of residents of the United States published by the US Census Bureau. This is real data, and so like all real data comes with lots of quirk and oddities we'll need to navigate.\n", + "Suppose you have been asked to calculate the overall US employment rate and average incomes for men and women, as well as the employment rate and average income of people in their 30s and 40s. To accomplish this task, you have been handed a 0.1% sample of the US American Community Survey—a representative survey of residents of the United States published by the US Census Bureau. This is real data, and so like all real data comes with lots of quirks and oddities we'll need to navigate.\n", "\n", "Moreover, you've even been told that the variable for gender in the dataset is `sex`, the variable for age is `age`, the variable for income is `inctot`, and the variable for whether someone is employed is `empstat`. Should be pretty easy, right? Let's do it!\n", "\n", @@ -43,6 +43,9 @@ "outputs": [], "source": [ "import pandas as pd\n", + "import numpy as np\n", + "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", "\n", "# Here we load the American Community Survey data.\n", "# Note that here we're using a data loading trick\n", @@ -55,12 +58,12 @@ ")\n", "\n", "# For this exercise, we'll focus on just these five variables,\n", - "# which from the official documentation provided by the US census \n", + "# which from the official documentation provided by the US census\n", "# bureau we know consist of the year the survey was conducted,\n", - "# the gender of the respondent, their age, their total income, \n", + "# the gender of the respondent, their age, their total income,\n", "# and their employment status.\n", "#\n", - "# You can find the documenation for this data here: \n", + "# You can find the documenation for this data here:\n", "\n", "acs = acs[[\"year\", \"sex\", \"age\", \"inctot\", \"empstat\"]]" ] @@ -74,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -83,15 +86,34 @@ "1723646.2703978634" ] }, - "execution_count": 2, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Simple output:\n", "acs[\"inctot\"].mean()" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The average income of Americans is $1,723,646.27\n" + ] + } + ], + "source": [ + "# Made pretty and better formatted:\n", + "print(f\"The average income of Americans is ${acs['inctot'].mean():,.2f}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -244,12 +266,12 @@ "import numpy.random as npr\n", "\n", "# The rows that `.sample()` picks will be random.\n", - "# Normally that's advantageous, but in \n", - "# this case it results in getting \n", + "# Normally that's advantageous, but in\n", + "# this case it results in getting\n", "# a slightly different set of rows every time\n", - "# I run this code, so I'm going to set a \n", - "# seed—something we talked about in Course 2 \n", - "# on numpy—so that the rows being picked \n", + "# I run this code, so I'm going to set a\n", + "# seed—something we talked about in Course 2\n", + "# on numpy—so that the rows being picked\n", "# stay the same every time I run this code.\n", "npr.seed(42)\n", "\n", @@ -264,7 +286,7 @@ "\n", "We can also begin to see the causes of some of our problems. For example, it's not clear to see why `acs[\"inctot\"].mean()` didn't get what we wanted. Why? Because in five random rows, we've already gotten three people who report making 9,999,999 dollars, and all three are less than 12 years old. So clearly the variable doesn't just contain actual income data! \n", "\n", - "(What are these `9999999` values? They're something called a \"Sentinel Value\"—a very old-school way of representing missing data, in which a special value that would never occur naturally was used to indicate when the field was \"missing.\" It was used back in the day when computers didn't use data types that had an actual special way of representing missing data, and some people still use it for backwards compatibility. We'll talk more about these and other ways of representing missing data in an upcoming reading.)\n", + "(What are these `9999999` values? They're something called a \"Sentinel Value\"—a very old-school way of representing missing data, in which a special value that would never occur naturally was used to indicate when the field was \"missing.\" It was used back in the day when computers didn't use data types that had an actual special way of representing missing data, and some people still use it for backward compatibility. We'll talk more about these and other ways of representing missing data in an upcoming reading.)\n", "\n", "Similarly, we can also see that while some observations of `empstat` make sense (`employed` or `not in labor force`), there are also values of `n/a` we need to figure out. `n/a` is a common shorthand for \"Not Applicable\", meaning it didn't make sense to ask the respondent that question. Since we see those appearing for respondents who are under 18, that probably means that `n/a` was filled in for all children, but that's something we'll have to investigate more. But it definitely tells us why we can't calculate the employment rate by running `(acs[\"empstat\"] == \"employed\").mean()`, since all the `n/a` values would pull down the share of respondents who are employed even though we don't consider children when calculating the employment rate!\n", "\n", @@ -287,11 +309,12 @@ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "n/a 57843\n", "unemployed 7727\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, "execution_count": 5, @@ -300,7 +323,7 @@ } ], "source": [ - "acs.empstat.value_counts()\n" + "acs.empstat.value_counts()" ] }, { @@ -318,11 +341,12 @@ { "data": { "text/plain": [ + "empstat\n", "employed 0.466320\n", "not in labor force 0.328134\n", "n/a 0.181324\n", "unemployed 0.024222\n", - "Name: empstat, dtype: float64" + "Name: proportion, dtype: float64" ] }, "execution_count": 6, @@ -340,7 +364,7 @@ "source": [ "From this, we can see that in addition to the `n/a` observations that appear to be children, we also have two categories of people who aren't employed: `unemployed` and `not in labor force`. A little googling will tell us that `not in the labor force` connotes someone who is not looking for work, and is thus not considered unemployed when calculating the employment (or unemployment) rate—it turns out that the unemployment rate is only meant to tell us about the share of people *who want to work* who haven't found jobs, not the proportion of the overall population with a job.\n", "\n", - "So now that we know that, we can calculate our unemployment rate by subsetting for people in those two categories, then calculating our average:" + "So now that we know that, we can calculate our unemployment rate by subsetting for people in those two categories, and then calculating our average:" ] }, { @@ -350,7 +374,9 @@ "outputs": [], "source": [ "# Employment Rate\n", - "emp_rate = (acs.loc[acs[\"empstat\"].isin([\"employed\", \"unemployed\"]), \"empstat\"] == \"employed\").mean()" + "emp_rate = (\n", + " acs.loc[acs[\"empstat\"].isin([\"employed\", \"unemployed\"]), \"empstat\"] == \"employed\"\n", + ").mean()" ] }, { @@ -388,18 +414,18 @@ "metadata": {}, "source": [ "Much better!\n", - "\n", - "Of course `.value_counts()` stops being useful when the number of unique values a variable takes on gets too big. For example, it isn't that useful to look at `.value_counts()` of total income, because, as we see below, it takes on over 8,000 unique values! Yes we can see there's a cluster at 0 and a cluster at our old friend `9999999`, but beyond that it isn't very helpful. " + "Of course, `.value_counts()` stops being useful when the number of unique values a variable takes on gets too big. For example, it isn't that useful to look at `.value_counts()` of total income, because, as we see below, it takes on over 8,000 unique values! Yes, we can see there's a cluster at 0 and a cluster at our old friend `9999999`, but beyond that it isn't very helpful. " ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "inctot\n", "9999999 53901\n", "0 33679\n", "30000 4778\n", @@ -411,10 +437,10 @@ "57760 1\n", "200310 1\n", "505400 1\n", - "Name: inctot, Length: 8471, dtype: int64" + "Name: count, Length: 8471, dtype: int64" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -429,7 +455,7 @@ "source": [ "**.describe()**\n", "\n", - "So for continuous numeric variables, we instead use `.describe()`, which provides summary statistics for a variable. Statistics is not required for this course, so not everything here will be familiar to all students, but just looking at the minimum value in the data (`min`), the maximum value in the data (`max`), and the the average value (`mean`) can tell you a lot:" + "For continuous numeric variables, we instead use `.describe()`, which provides summary statistics for a variable. Statistics is not required for this course, so not everything here will be familiar to all students, but just looking at the minimum value in the data (`min`), the maximum value in the data (`max`), and the average value (`mean`) can tell you a lot:" ] }, { @@ -457,7 +483,7 @@ } ], "source": [ - "acs[\"inctot\"].describe()\n" + "acs[\"inctot\"].describe()" ] }, { @@ -499,7 +525,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb b/_build/html/_sources/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb index 6c9e0f00..ff922c07 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb @@ -18,7 +18,7 @@ "- You can use a general cleaning function, or \n", "- you can directly modify problematic values. \n", "\n", - "In this reading, I'll provide an overview of how to do make global edits before we learn to edit specific locations in the next reading.\n", + "In this reading, I'll provide an overview of how to make global edits before we learn to edit specific locations in the next reading.\n", "\n", "In these exercises, we'll work with our `world-very-small` toy dataset. We're doing so because working with a small dataset makes it easier to see everything the functions we use are doing. However, in some of the examples that follow we'll do things that seem a little silly given the size of the dataset (like print out all values of a variable with `.value_counts()` even though we can see all the values!), but we'll do they anyway since you would need them when working with any real data. " ] @@ -139,10 +139,10 @@ "import pandas as pd\n", "\n", "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", - "smallworld\n" + "smallworld" ] }, { @@ -255,7 +255,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Voila! Replace works for numbers too -- so suppose we learned that Polity had mis-coded all scores of 15 as 16, so we wanted to change all 16s to 15s:" + "Voila! Replace works for numbers too—suppose we learned that the Polity organization had mis-coded all scores of 15 as 16, so we wanted to change all 16s to 15s:" ] }, { @@ -470,7 +470,12 @@ } ], "source": [ - "changes = {\"Mexico\": \"México\", \"Russia\": \"Российская Федерация\", \"Ukraine\": \"Україна\", \"Brazil\": \"Brasil\"}\n", + "changes = {\n", + " \"Mexico\": \"México\",\n", + " \"Russia\": \"Российская Федерация\",\n", + " \"Ukraine\": \"Україна\",\n", + " \"Brazil\": \"Brasil\",\n", + "}\n", "\n", "smallworld[\"country\"] = smallworld[\"country\"].replace(changes)\n", "\n", @@ -483,9 +488,9 @@ "source": [ "**.replace() with Regular Expressions**\n", "\n", - "Finally, `.replace()` also works with regular expressions when one passes the `regex=True` keyword argument. We haven't covered regular expressions in this course, but basically they are a way of telling Python you want to look for *patterns* to replace instead of exact matches. If you don't know about regular expressions, feel free to skip this sub-section, but since some students may be familiar with them, here's a quick use of their overview:\n", + "Finally, `.replace()` also works with regular expressions when one passes the `regex=True` keyword argument. We haven't covered regular expressions in this course, but essentially they are a way of telling Python you want to look for *patterns* in text to replace instead of exact matches. If you don't know about regular expressions, feel free to skip this sub-section, but since some students may be familiar with them, here's a quick use of their overview:\n", "\n", - "Basically, when one passes `regex=True` to `.replace`, `.replace()` will treat the first argument as a regular expression rather than a literal value. Note that this will obviously only work when string values are provided.\n", + "When one passes `regex=True` to `.replace`, `.replace()` will treat the first argument as a regular expression rather than a literal value. This will only work when string values are provided.\n", "\n", "(`.replace(regex=True)` uses `re.sub` on each observation in a Series under the hood, and so supports all the same syntax as `re.sub`.)\n", "\n", @@ -817,7 +822,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb b/_build/html/_sources/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb index 8685d509..5f853028 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, when we write `my_list[2]` on the *left* side of the assignment operator (a single equals sign), then whatever we put on the right side of the assignment operator is being assigned *into the entry with index 2* of the list. \n", + "As we can see, when we write `my_list[2]` on the *left* side of the assignment operator (a single equals sign), whatever we put on the right side of the assignment operator is assigned _into_ the entry with index 2 of the list. \n", "\n", "As you may recall, this same logic can also be extended to two dimensions in `numpy` arrays. Consider the following: " ] @@ -208,6 +208,8 @@ "source": [ "import pandas as pd\n", "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", + "\n", "df = pd.DataFrame({\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8]})\n", "df" ] @@ -291,7 +293,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But this alone is only kinda useful. After all, our datasets are usually very large, and we rarely want to make modifications to cells whose row numbers we already know. But thankfully, in `pandas` we can pass boolean vectors to `.loc` to identify all rows that meet certain conditions and assign values to those specific cells. For example, suppose we wanted to set column `b` to 0 for all rows where column `a` is even. We could do:" + "But this alone is only kinda useful. After all, our datasets are usually very large, and we rarely want to make modifications to cells whose row numbers we already know. Thankfully, in `pandas` we can pass boolean vectors to `.loc` to identify all rows that meet certain conditions and assign values to those specific cells. For example, suppose we wanted to set column `b` to 0 for all rows where column `a` is even. We could do:" ] }, { @@ -374,14 +376,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "See how the boolean vector on the left subset for rows where `a` was even (the value of `a % 2` is zero), and the second entry (`b`) subset for the column `b`, then we assigned 0 into those cells? It's just a generalization of the kinds of assignments we did above with lists and numpy arrays, just using boolean vectors and column labels instead of indices!" + "See how the boolean vector on the left subset for rows where `a` was even (the value of `a % 2` is zero), and the second entry (`b`) subset for column `b`, then we assigned 0 into those cells? It's just a generalization of the kinds of assignments we did above with lists and numpy arrays, just using boolean vectors and column labels instead of indices!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Great! But now suppose we don't just want to set certain values to a constant, but instead we wanted to, say, double all the values in odd rows. We can do that to by assigning values that \"fit\" into the cells on the left of the assignment operator (i.e. by making sure the values we assign have the same dimensions as the cells into which we're trying to assign them):" + "Great! But now suppose we don't just want to set certain values to a constant, but instead we wanted to, say, double all the values in odd rows. We can do that too by assigning values that \"fit\" into the cells on the left of the assignment operator (i.e. by making sure the values we assign have the same dimensions as the cells into which we're trying to assign them):" ] }, { @@ -494,7 +496,8 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", + " polityIV\n", " \n", " \n", " \n", @@ -503,49 +506,55 @@ " Brazil\n", " S. America\n", " 10296\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", + " 16\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008\n", - "0 Brazil S. America 10296\n", - "1 Germany W. Europe 35613\n", - "2 Mexico N. America 14495\n", - "3 Mozambique Africa 855\n", - "4 Russia C&E Europe 16139\n", - "5 Ukraine C&E Europe 7271" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 16\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 9, @@ -554,10 +563,11 @@ } ], "source": [ - "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + "smallworld = smallworld = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", + "\n", "smallworld" ] }, @@ -589,7 +599,8 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", + " polityIV\n", " \n", " \n", " \n", @@ -598,49 +609,55 @@ " Brazil\n", " S. America\n", " 10296\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", + " 16\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008\n", - "0 Brazil S. America 10296\n", - "1 Germany W. Europe 35613\n", - "2 Mexico N. America 14495\n", - "3 Mozambique Africa 855\n", - "4 Russia C&E Europe 16139\n", - "5 Ukraine C&E Europe 7271" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 16\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 10, @@ -687,7 +704,7 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", " polityIV\n", " \n", " \n", @@ -697,55 +714,55 @@ " Brazil\n", " S. America\n", " 10296\n", - " NaN\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", - " NaN\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", - " NaN\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", - " 5.0\n", + " 5\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", - " NaN\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", - " NaN\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008 polityIV\n", - "0 Brazil S. America 10296 NaN\n", - "1 Germany W. Europe 35613 NaN\n", - "2 Mexico N. America 14495 NaN\n", - "3 Mozambique Africa 855 5.0\n", - "4 Russia C&E Europe 16139 NaN\n", - "5 Ukraine C&E Europe 7271 NaN" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 11, @@ -790,9 +807,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_37248/2079543889.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_27525/2079543889.py:1: ChainedAssignmentError: A value is trying to be set on a copy of a DataFrame or Series through chained assignment.\n", + "When using the Copy-on-Write mode, such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " smallworld[smallworld.country == \"Mozambique\"][\"polityIV\"] = 5\n" @@ -803,11 +821,126 @@ "smallworld[smallworld.country == \"Mozambique\"][\"polityIV\"] = 5" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smallworld" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You will get the `SettingWithCopyWarning` we discussed in our reading on views and copies. That's because it's possible that when you run `smallworld[smallworld.country == \"Mozambique\"]`, pandas **may** return an entirely new DataFrame, and the next part of the operation (changing the values of `polityIV`) will run against a completely new DataFrame, not `smallworld`, and in the end your original `smallworld` DataFrame won't end up being modified at all. This kind of chained assignment will SOMETIMES work, but not ALWAYS, which is why you get that warning. " + "Provided you have Copy-on-Write activated, this will result in a `ChainedAssignmentError`:\n", + "\n", + "```python\n", + "\n", + "ChainedAssignmentError: A value is trying to be set on a copy of a DataFrame \n", + "or Series through chained assignment.\n", + "When using the Copy-on-Write mode, such chained assignment never works to \n", + "update the original DataFrame or Series, because the intermediate object on \n", + "which we are setting values always behaves as a copy.\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the \n", + "assignment in a single step.\n", + "```\n", + "\n", + "That's because when you run `smallworld[smallworld.country == \"Mozambique\"]`, pandas returns an entirely new DataFrame, and the next part of the operation (changing the values of `polityIV`) is being excuted against that completely new DataFrame, not the one associated with the variable `smallworld`. As a result, `smallworld` DataFrame won't end up being modified at all.\n", + "\n", + "\n", + "(If you don't have Copy-on-Write activated, you will get a `SettingWithCopyWarning` warning instead. Whether the assignment will work in that case is basically unpredictable!)\n", + "\n" ] }, { @@ -823,13 +956,111 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "smallworld[\"region\"] = pd.Categorical(smallworld[\"region\"])" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smallworld" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -844,33 +1075,20 @@ "/Users/Nick/github/mids_coursera/class_3/week_3/32_cleaning_editing_specific_locations.ipynb Cell 29 in ()\n", "----> 1 smallworld.loc[smallworld[\"country\"] == \"Mexico\", \"region\"] = \"C America\"\n", "\n", - "File ~/opt/miniconda3/lib/python3.9/site-packages/pandas/core/indexing.py:716, in _LocationIndexer.__setitem__(self, key, value)\n", - " 713 self._has_valid_setitem_indexer(key)\n", - " 715 iloc = self if self.name == \"iloc\" else self.obj.iloc\n", - "--> 716 iloc._setitem_with_indexer(indexer, value, self.name)\n", - "\n", "[...]\n", "\n", - "File ~/opt/miniconda3/lib/python3.9/site-packages/pandas/core/arrays/categorical.py:1484, in Categorical._validate_scalar(self, fill_value)\n", - " 1482 fill_value = self._unbox_scalar(fill_value)\n", - " 1483 else:\n", - "-> 1484 raise TypeError(\n", - " 1485 \"Cannot setitem on a Categorical with a new \"\n", - " 1486 f\"category ({fill_value}), set the categories first\"\n", - " 1487 )\n", - " 1488 return fill_value\n", "\n", "TypeError: Cannot setitem on a Categorical with a new category (C America), set the categories first\n", "```\n", "\n", "So what do we do?\n", "\n", - "One option is to modify the allowable categories for the variable with `.cat.add_categories()`, then making the edit again:" + "One option is to modify the allowable categories for the variable with `.cat.add_categories()`, then make the edit again:" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -887,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ diff --git a/_build/html/_sources/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb b/_build/html/_sources/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb index 503b15bd..db153b76 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb @@ -111,8 +111,10 @@ } ], "source": [ - "import numpy as np\n", "import pandas as pd\n", + "import numpy as np\n", + "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", "\n", "world = pd.read_csv(\"data/world-very-small-type-errors.csv\")\n", "world" @@ -194,7 +196,7 @@ "source": [ "Oops! Yup, there it is! Indeed, we can also see that `polityIV` is also an `object`, something we'll deal with later. \n", "\n", - "So this kind of phenomenon is such a common problem that pandas has a tool to deal with it: `.str.isnumeric()`. This method returns `True` is an observation can be converted to a number without issues, and `False` otherwise, so by using its logical negation, we can find all observations that aren't convertible (which we may then need to fix):" + "So this kind of phenomenon is such a common problem that pandas has a tool to deal with it: `.str.isnumeric()`. This method returns `True` if an observation can be converted to a number without issues, and `False` otherwise, so by using its logical negation, we can find all observations that aren't convertible (which we may then need to fix):" ] }, { @@ -225,7 +227,7 @@ "source": [ "(Recall we have to use `~` instead of `not` to invert `True` values to `False` in a numpy array or pandas Series)\n", "\n", - "There we are! One observation of `\"na\"` is the source of our problems. Note that you could also use `.unique()` instead of `.value_counts()` if you don't care about the number of observations are causing problems. \n", + "There we are! One observation of `\"na\"` is the source of our problems. Note that you could also use `.unique()` instead of `.value_counts()` if you don't care about the number of observations that are causing problems. \n", "\n", "OK, so how do we fix this? In this case, it seems like `na` is probably just short for `np.nan`, so we can replace it with `np.nan`, which *is* a number:" ] @@ -271,7 +273,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, there's one other option in these situations: if `na` is appearing in lots of places in your data as a representation for missing data, we can also communicate this fact to `read_csv`. Remember when we said that `read_csv` has more options than you could ever imagine? Well one is `na_values`, where you can specify how missing data is represented in a dataset. If we use that to tell `read_csv` that `\"na\"` means data is missing, it'll make this correction on the fly!" + "Now, there's one other option in these situations: if `na` is appearing in lots of places in your data as a representation for missing data, we can also communicate this fact to `read_csv`. Remember when we said that `read_csv` has more options than you could ever imagine? Well, one is `na_values`, where you can specify how missing data is represented in a dataset. If we use that to tell `read_csv` that `\"na\"` means data is missing, it'll make this correction on the fly!" ] }, { @@ -430,7 +432,7 @@ "source": [ "## Deliberate Non-Numerics\n", "\n", - "While in the example above `\"na\"` wasn't really meant to be interpreted as a string, sometimes non-numeric values are inserted into datasets to communicate something important to the user. For example, in this toy example `polityIV` has a non-numeric value:" + "While in the example above `\"na\"` wasn't really meant to be interpreted as a string, sometimes non-numeric values are inserted into datasets to communicate something important to the user. For example, in this toy example, `polityIV` has a non-numeric value:" ] }, { @@ -495,7 +497,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here, the value is to indicate that the value of policy could be 20 *or any value above 20*. Why might this occur? In this case I've made it up, but in many surveys there will be a maximum value allowed (e.g., some surveys have a top income value they can record). These are called \"top-codes\", and they are different from the case above because it's not immediately clear what value you should put into the field. You *could* put in 20, but... the value might not be 20, it could be 21!\n", + "Here, the value is to indicate that the value of `polityIV` could be 20 *or any value above 20*. Why might this occur? In this case, I've made it up, but in many surveys, there will be a maximum value allowed (e.g., some surveys have a top income value they can record). These are called \"top-codes\", and they are different from the case above because it's not immediately clear what value you should put into the field. You *could* put in 20, but... the value might not be 20, it could be 21!\n", "\n", "So when you find a top-code like this, you need to think carefully about what to do with it, as the answer will depend on the type of analysis you want to do. But the answer will almost always lie not in the data, but in the documentation for the data. " ] diff --git a/_build/html/_sources/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb b/_build/html/_sources/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb index e437372e..0717c5ad 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ "False" ] }, - "execution_count": 10, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -143,7 +143,7 @@ "5 Ukraine C&E Europe 7271 16.0" ] }, - "execution_count": 11, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -151,9 +151,9 @@ "source": [ "import pandas as pd\n", "\n", - "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + "smallworld = smallworld = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", "smallworld.loc[smallworld.country == \"Mozambique\", \"polityIV\"] = np.nan\n", "smallworld" @@ -168,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -209,13 +209,13 @@ "Index: []" ] }, - "execution_count": 12, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[smallworld.polityIV == np.nan]\n" + "smallworld.loc[smallworld.polityIV == np.nan]" ] }, { @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -276,13 +276,13 @@ "3 Mozambique Africa 855 NaN" ] }, - "execution_count": 13, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[pd.isnull(smallworld.polityIV)]\n" + "smallworld.loc[pd.isnull(smallworld.polityIV)]" ] }, { @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -341,13 +341,13 @@ "3 Mozambique Africa 855 NaN" ] }, - "execution_count": 14, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[smallworld.polityIV.isnull()]\n" + "smallworld.loc[smallworld.polityIV.isnull()]" ] }, { @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -368,13 +368,13 @@ "True" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.isnull(None)\n" + "pd.isnull(None)" ] }, { @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -473,14 +473,14 @@ "5 Ukraine C&E Europe 7271 16.0" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "smallworld.loc[smallworld.polityIV.isnull(), \"polityIV\"] = 5\n", - "smallworld\n" + "smallworld" ] }, { @@ -494,20 +494,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "unemployed 7727\n", "n/a 0\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -531,21 +532,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "NaN 57843\n", "unemployed 7727\n", "n/a 0\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, - "execution_count": 21, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -615,7 +617,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/_sources/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb b/_build/html/_sources/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb new file mode 100644 index 00000000..699f3b8f --- /dev/null +++ b/_build/html/_sources/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb @@ -0,0 +1,637 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Object and Categorical Data Types\n", + "\n", + "In our previous readings, we introduced the idea that not only can DataFrames and Series hold any of the numeric data types we've come to know and love from numpy — like `float64` or `int64` — but that they can also hold arbitrary Python objects in an `object`-type Series. Moreover, we also acknowledged (though didn't go into) the existence of another relatively unique pandas data type — the `Categorical` Series. \n", + "\n", + "In this reading, we will discuss the purpose of these two data types, as well as their advantages and disadvantages.\n", + "\n", + "## The object dtype\n", + "\n", + "The `object` type Series gives pandas _incredible_ flexibility as it allows any type of data to be stored in a table. The most common use of the `object` data type is to store text — for example, names, addresses, written answers, etc. — but the flexibility can also be used for applications like geospatial analysis, in which a single row of a DataFrame may represent a single country, the columns may represent features of the country (name, income, population), and the last column stores geometric Python objects that describe the shape and location of the country. \n", + "\n", + "But this flexibility also comes at a cost — performance and memory efficiency.\n", + "\n", + "### The object Performance Penalty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To understand why `object` Series are slow, it helps to first discuss why numeric Series (and numeric numpy arrays) are fast. \n", + "When you work with a numeric pandas Series or numpy array, all of the entries in those arrays live in the same place in memory (in your computer's RAM). This is possible because all those integers (or all those floating point numbers) are written with the same number of 0s and 1s. In an `int64` Series, for example, every integer is represented by 64 1s and 0s. This makes it easy for the computer to lay them out sequentially. Moreover, it makes it possible for the computer to find specific entries quickly, since it knows that the third integer in the Series will start 64 * 2 spots from the start of the Series and end 64 spots later.\n", + "\n", + "But an *object* Series is a little different. Python objects vary in size — some may only take up 128 0s and 1s, while others may require thousands — and so the actual data in an object Series can't be laid down in a nice regularly spaced sequence. Instead, every entry in an object Series gets put in a different location in your computer's memory (RAM), and only the *address* of that information is placed in a nice organized Series. These addresses are all the same size, and so the addresses can be organized in a regular manner, even if the actual content you want to store is irregular.\n", + "\n", + "The cost of this arrangement is that if you ask for the second entry of an object Series (e.g., `my_series.iloc[1]`), you're computer has first go to the second location in the array, read the address stored there, *then go to that address* to find the actual content you want. And those added steps waste time.\n", + "\n", + "The other problem with object Series is that because they can store anything, Python doesn't know before it looks up an entry whether it will find a string, an integer, or a Python set. As a result, when it sees code like: \n", + "\n", + "```python \n", + "my_array * 2\n", + "```\n", + "\n", + "Python can't be sure what is meant by `*` — it could mean \"do integer multiplication\" (if a given entry in `my_array` is an integer), but it might also mean \"double up the list you find\" (if the entry is a Python list)!\n", + "\n", + "Indeed we can see this if we make a numpy object array full of ints and compare it to a numpy integer array. The both have the same content, but they are organized in memory differently:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "object_numbers = pd.Series(np.arange(1000000), dtype=\"object\")\n", + "numbers = pd.Series(np.arange(1000000), dtype=\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15.9 ms ± 70.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%timeit object_numbers * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "771 µs ± 9.62 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit numbers * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See? Same operation (doubling each entry of arrays with the integers from 0 to 1,000,000), but the object array operation is ~20x slower. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So yes, `object` dtypes are absolutely wonderful and introduce unbelievable flexibility to pandas; but remember there is a cost to using them, so stick to numeric data types when possible!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Categoricals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `category` data type is a delightful little hack that allows us to avoid most of the problems with `object` Series *in certain circumstances*. \n", + "\n", + "To illustrate how `category` Series work, suppose we have a DataFrame with information on hundreds of thousands of customers in the United States, and that one column of that DataFrame contains the name of each customer's state of residence (substitute Province or any other sub-national administrative unit if states don't resonate for you). \n", + "\n", + "Because those state names are words, they are being stored in an `object` Series. That, in turn, means that Python has created hundreds of thousands of Python objects — each containing the name of a customer's state — *and* a vector containing addresses for each of those objects. \n", + "\n", + "But as there are only 50 states in the United States, this might strike you as absurd, since most of those hundreds of thousands of Python objects are holding the same text! Surely we can do something more efficient than that? \n", + "\n", + "Enter Categoricals. The idea of a `category` Series is to take a `object` Series that contains frequently repeated strings and:\n", + "\n", + "1. Replace each unique string with a number (for example, `\"Colorado\"` could become `1` and `\"Tennessee\"` could become `2`), and\n", + "2. Create a small \"lookup table\" that keeps track of what string is associated with each number. \n", + "\n", + "Now pandas doesn't need to create hundreds of thousands of Python strings to record each customer's state, it can just make a numeric Series with state names replaced by the numbers 1-50, and a new small vector with the fifty names of states.\n", + "\n", + "Moreover, in addition to saving memory, this also dramatically improves the *performance* of pandas. Suppose, for example, you want to subset for customers living in North Carolina. When these states were in an `object` Series, pandas would have to go to each entry, figure out where the associated Python object is stored, get that object, and check to see if it was the string `\"North Carolina\"`. \n", + "\n", + "But now, pandas can just go to the lookup table, see that `\"North Carolina\"` is the 33rd entry (and so is represented by the number 33 in our encoded Series of numbers), and look for values of 33 in that Series. Hooray!\n", + "\n", + "But the best part is that, in most cases, the fact your data has been split into a numeric vector and a lookup table is actually entirely hidden from you, the user. For *most* operations, using a Categorical Series is just like using an `object` Series, just faster. \n", + "\n", + "### Categorical Series in Practice\n", + "\n", + "To illustrate how one works with Categorical Series, let's make a toy version of this customer dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "1 Aditya Tennessee\n", + "2 Francisco Colorado\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "customers = pd.DataFrame(\n", + " {\n", + " \"customer\": [\"Bob\", \"Aditya\", \"Francisco\", \"Shufan\"],\n", + " \"state\": [\"Colorado\", \"Tennessee\", \"Colorado\", \"Virginia\"],\n", + " }\n", + ")\n", + "customers" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "customer object\n", + "state object\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, `state` begins its life as a standard `object` Series, but we can convert it to a Categorical with `.astype(\"category\")`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "1 Aditya Tennessee\n", + "2 Francisco Colorado\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"] = customers[\"state\"].astype(\"category\")\n", + "customers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, at first glance nothing about this column has changed. But if we pull it out, you can see it's dtype is `category` and that the Categories associated with the Series (the lookup table) contains three values: `\"Colorado\"`, `\"Tennessee\"` and \"`Virginia\"`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Colorado\n", + "1 Tennessee\n", + "2 Colorado\n", + "3 Virginia\n", + "Name: state, dtype: category\n", + "Categories (3, object): ['Colorado', 'Tennessee', 'Virginia']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And if you want to, you can see the two underlying pieces directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Colorado', 'Tennessee', 'Virginia'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"].cat.categories" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 0\n", + "3 2\n", + "dtype: int8" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"].cat.codes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But as we said, in most cases `category` Series will operate just like `object` Series. Subsetting, for example, will work just as it would with an `object` Series:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
      2FranciscoColorado
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "2 Francisco Colorado" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.loc[customers[\"state\"] == \"Colorado\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only place that problems *may* arise is that one cannot make arbitrary edits to a `category` Series — if you try and set a cell to have a value that isn't in the current `Categories` table, you will get an error:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "\n", + "customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "\n", + "TypeError Traceback (most recent call last)\n", + "/Users/nce8/github/practicaldatascience_book/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb Cell 22 line 1\n", + "----> 1 customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "\n", + "[...]\n", + "\n", + "TypeError: Cannot setitem on a Categorical with a new category (Kansas), set the categories first\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You *can* add novel values, to be clear, you just have to add the category first:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Colorado\n", + "1 Tennessee\n", + "2 Colorado\n", + "3 Virginia\n", + "Name: state, dtype: category\n", + "Categories (4, object): ['Colorado', 'Tennessee', 'Virginia', 'Kansas']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"] = customers[\"state\"].cat.add_categories([\"Kansas\"])\n", + "customers[\"state\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobKansas
      1AdityaTennessee
      2FranciscoKansas
      3ShufanVirginia
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Kansas\n", + "1 Aditya Tennessee\n", + "2 Francisco Kansas\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "customers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why Not Always Use Categoricals?\n", + "\n", + "Categoricals are great, but they are only useful when your `object` Series has a relatively small number of unique values. If you tried to convert an `object` Series with hundreds of thousands of addresses — and nearly all of them were unique — into a `category` Series, then pandas would have to create a lookup table that had... hundreds of thousands of unique entries (essentially, it would just be recreating your original `object` Series). And so there would be no real performance benefit." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_build/html/_sources/notebooks/class_3/week_3/40_EX_cleaning.ipynb b/_build/html/_sources/notebooks/class_3/week_3/40_EX_cleaning.ipynb index 1f7ed419..685ee402 100644 --- a/_build/html/_sources/notebooks/class_3/week_3/40_EX_cleaning.ipynb +++ b/_build/html/_sources/notebooks/class_3/week_3/40_EX_cleaning.ipynb @@ -25,8 +25,11 @@ "outputs": [], "source": [ "import pandas as pd\n", - "acs = pd.read_stata(\"https://github.com/nickeubank/MIDS_Data/blob/master\"\n", - " \"/US_AmericanCommunitySurvey/US_ACS_2017_10pct_sample.dta?raw=true\")" + "\n", + "acs = pd.read_stata(\n", + " \"https://github.com/nickeubank/MIDS_Data/blob/master\"\n", + " \"/US_AmericanCommunitySurvey/US_ACS_2017_10pct_sample.dta?raw=true\"\n", + ")" ] }, { @@ -65,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -97,7 +100,7 @@ "Categories (97, object): ['less than 1 year old' < '1' < '2' < '3' ... '93' < '94' < '95' < '96']" ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -108,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -126,7 +129,7 @@ "41.30384885455982" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -134,7 +137,7 @@ "source": [ "acs.loc[acs[\"age\"] == \"less than 1 year old\", \"age\"] = 0\n", "acs.loc[acs[\"age\"] == \"90 (90+ in 1980 and 1990)\", \"age\"] = 90\n", - "acs[\"age\"] = acs[\"age\"].astype('float')\n", + "acs[\"age\"] = acs[\"age\"].astype(\"float\")\n", "acs[\"age\"].mean()" ] }, @@ -268,7 +271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/html/genindex.html b/_build/html/genindex.html index d9ca0e94..0ca750b8 100644 --- a/_build/html/genindex.html +++ b/_build/html/genindex.html @@ -224,7 +224,6 @@
    • Views and Copies in Pandas
      • @@ -237,6 +236,7 @@
    • Editing Specific Locations
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    • The Object and Categorical Data Types
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      @@ -2315,7 +2315,1086 @@

      Layer16: series-groupby-sum-agg

      - +
      2023-11-28 07:21:51,309 - tornado.application - ERROR - Exception in callback <bound method SystemMonitor.update of <SystemMonitor: cpu: 16 memory: 197 MB fds: 180>>
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/ioloop.py", line 919, in _run
+    val = self.callback()
+          ^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/system_monitor.py", line 168, in update
+    net_ioc = psutil.net_io_counters()
+              ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/psutil/__init__.py", line 2119, in net_io_counters
+    rawdict = _psplatform.net_io_counters()
+              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+OSError: [Errno 12] Cannot allocate memory
+2023-11-29 12:24:43,023 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:24:43,086 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:24:43,125 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:24:43,130 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:24:43,280 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:32:25,398 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55482>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:25,405 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55483>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:25,753 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1406): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1177): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 94): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 768): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 359): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 130): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1262): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1033): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 624): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1527): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1298): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 215): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 889): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1563): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 480): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 251): 'memory', 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+2023-11-29 12:32:25,749 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55480>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:25,864 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55481>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:32:25,946 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55484>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:32:38,591 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55650>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:38,659 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55651>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:39,011 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55647>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:39,181 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55648>: ConnectionResetError: [Errno 54] Connection reset by peer
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+2023-11-29 12:32:39,295 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55649>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:51,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55658>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:52,043 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55659>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:52,257 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55656>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:52,540 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55655>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:52,664 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:32:52,661 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55657>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:04,636 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55667>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:04,728 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55663>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:04,862 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55666>: ConnectionResetError: [Errno 54] Connection reset by peer
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+2023-11-29 12:33:05,272 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55664>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:05,301 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55665>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:15,177 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55672>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:15,350 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55673>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:15,994 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55669>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:16,318 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55670>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:16,497 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:33:16,494 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55671>: ConnectionResetError: [Errno 54] Connection reset by peer
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+2023-11-29 12:33:29,522 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55678>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:29,643 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55679>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:29,963 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55676>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:29,964 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55675>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:29,974 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55677>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:40,320 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55687>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:40,536 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55686>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:41,055 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55684>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:41,131 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55683>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:41,253 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:33:41,247 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55685>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:53,214 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55695>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:53,215 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55691>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:53,290 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55694>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:53,402 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55692>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:53,563 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:33:53,845 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55693>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:05,777 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55722>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:05,800 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55720>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:05,900 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55723>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:06,047 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55719>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:06,100 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:34:06,432 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55721>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:17,458 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55765>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:17,682 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55764>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:17,740 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55761>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:17,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55762>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:17,961 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:34:18,184 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55763>: ConnectionResetError: [Errno 54] Connection reset by peer
+
      +
      +

      Or you can plot out a task graph. You can do this at a high level with .dask.visualize(), or in excruciating detail for a task this big with just .visualize(). Note visualize() does require you install additional libraries, so install ipycytoscape and python-graphviz with conda first. But these get so big I’m not gonna include one here—you can find examples here if you’d like to see more.

      diff --git a/_build/html/notebooks/PDS_not_yet_in_coursera/30_big_data/distributed_starting_dask_cluster.html b/_build/html/notebooks/PDS_not_yet_in_coursera/30_big_data/distributed_starting_dask_cluster.html index 1d01c3b7..756ec308 100644 --- a/_build/html/notebooks/PDS_not_yet_in_coursera/30_big_data/distributed_starting_dask_cluster.html +++ b/_build/html/notebooks/PDS_not_yet_in_coursera/30_big_data/distributed_starting_dask_cluster.html @@ -225,7 +225,6 @@
    • Views and Copies in Pandas
      • @@ -238,6 +237,7 @@
    • Editing Specific Locations
    • Cleaning Data Types
    • Missing Data
      • +
    • The Object and Categorical Data Types
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      @@ -495,10 +495,31 @@

      Contents

      @@ -514,7 +535,7 @@

      Contents

      Week 3 Quiz#

      Note:

      -

      This exercise has been written out in something called a Jupyter Notebook. We’ll discuss Jupyter Notebooks in more detail later in the course—they are a very powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don’t need to do anything with the notebook except read its contents—just use write your Python code in a regular .py file.

      +

      This exercise has been written out in something called a Jupyter Notebook. We’ll discuss Jupyter Notebooks in more detail later in the course—they are very a powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don’t need to do anything with the notebook except read its contents—just use write your Python code in a regular .py file.

      As previously discussed, we frequently use matrices and data science because they are a natural format for representing data generated by collecting the same type of information from numerous entities. For example, below is a toy dataset that you could imagine was created by collecting information about employees at a company—each column is a different type of information being collected (income, age, years of education), and each row is the information about a different employee.

      In the following questions, you will be asked to answer a number of questions about this toy dataset. As with other exercises in this class, you will find the directions for this graded exercise here, but please submit your actual answers in the graded quiz.

      @@ -545,81 +566,102 @@

      Week 3 Quiz

      Part 1: Summarizing Data#

      -
        -

      1. What is the average (mean) age of all employees (rounded to 1 decimal place)?

        2. -

      2. What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?

        3. -

      3. What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?

        4. -
      +
      +

      Question 1#

      +

      What is the average (mean) age of all employees (rounded to 1 decimal place)?

      +
      +
      +

      Question 2#

      +

      What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?

      +
      +
      +

      Question 3#

      +

      What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?

      +

      Part 2: Editing Data#

      The US government is thinking about offering a 1,500 tax credit to anyone making less than 20,000 a year. You can think of this tax credit as effectively an additional $1,500 of income to each person receiving the credit.

      -
        -

      1. Using the data from survey, modify income values to calculate a new estimate of the employees’ incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?

        -
          -

        • Do so by subsetting and editing values programmatically, not just typing values by hand. (Yes, writing out a new vector by hand is easy to do in this example, but you couldn’t do it with a large, real dataset!)

          • -
        -
        2. -
      +
      +

      Question 4#

      +

      Using the data from survey, modify income values to calculate a new estimate of the employees’ incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)? +- Do so by subsetting and editing values programmatically, not just typing values by hand. (Yes, writing out a new vector by hand is easy to do in this example, but you couldn’t do it with a large, real dataset!)

      +

      Part 3: Measuring Income Inequality (with Real Data!)#

      In this exercise, we’ll be working with data from the US Current Population Survey, provided by the National Bureau of Economic Research (NBER). This is a regular survey conducted by the US Bureau of Labor to calculate the US employment rate.

      -

      In this exercise, we’ll use these data to study gender and racial wage inequality in the US.

      +

      In this exercise, we’ll use this data to study gender and racial wage inequality in the US.

      Load data from the 2018 CPS survey with the following command:

      cps = np.loadtxt("data/cps.txt")
      -

      This data are a subset of the full CPS survey and contains only data on employed respondents working at least 35 hours a week (e.g., full-time).

      -
        -

      1. How many rows does this matrix have?

        2. -

      2. The five columns of this matrix correspond to:

        -
          -

        • Column 1: Weekly income in dollars.

          • -

        • Column 2: Usual hours respondent works per week.

          • -

        • Column 3: Gender. 2 is “Female”, 1 is “Male”

          • -

        • Column 4: Race. This can take on a lot of values for those who identify as mixed race, but for simplicity, in this exercise, we’ll just focus on a few values. For those interested, the full set of codes can be found on page 19 of the CPS codebook.

          -
            -

          • 1: White

            • -

          • 2: Black

            • -

          • 3: American Indian

            • -

          • 4: Asian only

            • -

          • 5: Hawaiian/Pacific Islander only

            • -
          -
          • -

        • Column 5: Survey weights.

          • -
        -
        3. -
      +

      This data is a subset of the full CPS survey and contains only data on employed respondents working at least 35 hours a week (e.g., full-time).

      +
      +

      Question 5#

      +

      How many rows does this matrix have?

      +
      +
      +

      Question 6#

      +

      The five columns of this matrix correspond to: +- Column 1: Weekly income in dollars. +- Column 2: Usual hours respondent works per week. +- Column 3: Gender. 2 is “Female”, 1 is “Male” +- Column 4: Race. This can take on a lot of values for those who identify as mixed race, but for simplicity, in this exercise, we’ll just focus on a few values. For those interested, the full set of codes can be found on page 19 of the CPS codebook. +- 1: White +- 2: Black +- 3: American Indian +- 4: Asian only +- 5: Hawaiian/Pacific Islander only +- Column 5: Survey weights.

      Note that race does not break out Hispanic / non-Hispanic identities. In US government surveys, Hispanic / non-Hispanic is usually recorded in a separate ethnicity variable, so many people who identify as Hispanic are identified as White or Black in the race variable.

      For the moment, let’s ignore survey weights—they don’t impact results here significantly.

      -

      What is the average hourly wage for all workers in this dataset (round it to one decimal place)?

      +

      What is the average hourly wage for all workers in this data?

      +

      Please round your final answer to one decimal place, but don’t round any intermediate results! (if you round at each stage of the calculation, those roundings may compound in odd ways).

      Hint: This will require more than just using mean on a single column!

      -
        -

      1. What is the average hourly wage of working men (rounded to one decimal place)?

        2. -

      2. What is the average hourly wage of working women (rounded to one decimal place)?

        3. -

      3. What share (e.g., a value between 0 and 1) of men’s average hourly wage is women’s average hourly wage? (rounded to three decimal places). In other words, what is women’s average hourly wage divided by men’s average hourly wage? Don’t round anything until you have your final answer!

        4. -
      +
      +
      +

      Question 7#

      +

      What is the average hourly wage of working men? (Please round your final answer to one decimal place, but don’t round any intermediate results!)

      +
      +
      +

      Question 8#

      +

      What is the average hourly wage of working women? (Please round your final answer to one decimal place, but don’t round any intermediate results!)

      +
      +
      +

      Question 9#

      +

      What share (e.g., a value between 0 and 1) of men’s average hourly wage is women’s average hourly wage? (Please round your final answer to three decimal place, but don’t round any intermediate results!). In other words, what is women’s average hourly wage divided by men’s average hourly wage?

      Congratulations! You’ve just calculated the US gender wage gap, on your own, using real data! I mean… I guess “congratulations” is a weird thing to say after directly measuring one of the more egregious inequities in US society, but one of the reasons many of us study data science is so that we will have the ability to directly measure these types of phenomena in the hopes of being able to better understand and address them.

      -
        -

      1. Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?

        2. -

      2. What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?

        3. -

      3. What share (e.g., a value between 0 and 1) of respondents who identify as White’s average hourly wage is respondents who identify as Black’s average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black’s average hourly wage divided by respondents who identify as White’s average hourly wage? Don’t round anything until you have your final answer!

        4. -
      +
      +
      +

      Question 10#

      +

      Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?

      +
      +
      +

      Question 11#

      +

      What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?

      +
      +
      +

      Question 12#

      +

      What share (e.g., a value between 0 and 1) of respondents who identify as White’s average hourly wage is respondents who identify as Black’s average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black’s average hourly wage divided by respondents who identify as White’s average hourly wage? Don’t round anything until you have your final answer!

      Note that this will only be an approximation—one would normally also include all respondents of mixed-race into non-mutually exclusive categories like “Any Part Black” or “Any Part White”, and we would also break out Hispanic and non-Hispanic respondents. But as most respondents only pick on racial category, this will still give us a reasonable approximation.

      +

      Bonus Question#

      As noted above, the fifth column of our data contains something called “sampling weights.” That’s because when the government conducted this survey, they didn’t draw a random sample of respondents from the US population where everyone had the same probability of being interviewed. As a result, when we calculate the average hourly wage of the people in the survey, it isn’t quite the best estimate we have for the average hourly wage for everyone working in the United States. To calculate this number as accurately as possible, we have to take into account the fact that some respondents in the data were more likely to be included than others, and thus can be thought of as standing in for a smaller group of people in the US.

      -

      Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than what we would get if we just took a random sample where everyone in the United States had the same probability of being included.

      +

      Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than when we would get if we just took a random sample where everyone in the United States had the same probability of being included.

      As I mentioned above, for the questions above, the sampling weights don’t make a very big difference to our answers, but the way to get the most accurate estimates would be to take them into account. So let’s give that a try!

      When we calculate the average of a variable, we do so by multiplying all the values of the variable by \(1/N\) (where \(N\) is the number of observations we have) and then adding up those multiplied values.

      For a weighted average, we take the value for each observation \(i\) and multiply it by

      -\[weight_i / \sum_{j=1}^N weight_j\]
      -

      where \(weight_i\) is the observation’s weight, and \(\sum_{j=1}^N weight_j\) is the total of all the weights in the population being averaged. Then we just add up those values!

      +\[weight_i / \sum_{j=1}^N weights_j\] +

      where \(weight_i\) is the observation’s weight, and \(\sum_{j=1}^N weights_j\) is the total of all the weights in the population being averaged. Then we just add up those values!

      +
      +

      Question 13#

      Given that, what is the average hourly wage of Americans working full-time jobs (i.e., the group in this survey) taking into account survey weights (rounded to two decimal places)?

      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
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      + + + Ctrl+K +
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      +

      The Object and Categorical Data Types

      + +
      + +
      +
      + + + + +
      + +
      +

      The Object and Categorical Data Types#

      +

      In our previous readings, we introduced the idea that not only can DataFrames and Series hold any of the numeric data types we’ve come to know and love from numpy — like float64 or int64 — but that they can also hold arbitrary Python objects in an object-type Series. Moreover, we also acknowledged (though didn’t go into) the existence of another relatively unique pandas data type — the Categorical Series.

      +

      In this reading, we will discuss the purpose of these two data types, as well as their advantages and disadvantages.

      +
      +

      The object dtype#

      +

      The object type Series gives pandas incredible flexibility as it allows any type of data to be stored in a table. The most common use of the object data type is to store text — for example, names, addresses, written answers, etc. — but the flexibility can also be used for applications like geospatial analysis, in which a single row of a DataFrame may represent a single country, the columns may represent features of the country (name, income, population), and the last column stores geometric Python objects that describe the shape and location of the country.

      +

      But this flexibility also comes at a cost — performance and memory efficiency.

      +
      +

      The object Performance Penalty#

      +

      To understand why object Series are slow, it helps to first discuss why numeric Series (and numeric numpy arrays) are fast. +When you work with a numeric pandas Series or numpy array, all of the entries in those arrays live in the same place in memory (in your computer’s RAM). This is possible because all those integers (or all those floating point numbers) are written with the same number of 0s and 1s. In an int64 Series, for example, every integer is represented by 64 1s and 0s. This makes it easy for the computer to lay them out sequentially. Moreover, it makes it possible for the computer to find specific entries quickly, since it knows that the third integer in the Series will start 64 * 2 spots from the start of the Series and end 64 spots later.

      +

      But an object Series is a little different. Python objects vary in size — some may only take up 128 0s and 1s, while others may require thousands — and so the actual data in an object Series can’t be laid down in a nice regularly spaced sequence. Instead, every entry in an object Series gets put in a different location in your computer’s memory (RAM), and only the address of that information is placed in a nice organized Series. These addresses are all the same size, and so the addresses can be organized in a regular manner, even if the actual content you want to store is irregular.

      +

      The cost of this arrangement is that if you ask for the second entry of an object Series (e.g., my_series.iloc[1]), you’re computer has first go to the second location in the array, read the address stored there, then go to that address to find the actual content you want. And those added steps waste time.

      +

      The other problem with object Series is that because they can store anything, Python doesn’t know before it looks up an entry whether it will find a string, an integer, or a Python set. As a result, when it sees code like:

      +
      my_array * 2
+
      +
      +

      Python can’t be sure what is meant by * — it could mean “do integer multiplication” (if a given entry in my_array is an integer), but it might also mean “double up the list you find” (if the entry is a Python list)!

      +

      Indeed we can see this if we make a numpy object array full of ints and compare it to a numpy integer array. The both have the same content, but they are organized in memory differently:

      +
      +
      +
      import pandas as pd
+import numpy as np
+
+object_numbers = pd.Series(np.arange(1000000), dtype="object")
+numbers = pd.Series(np.arange(1000000), dtype="int64")
+
      +
      +
      +
      +
      +
      +
      %timeit object_numbers * 2
+
      +
      +
      +
      +
      15.9 ms ± 70.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
      +
      +
      +
      +
      +
      +
      %timeit numbers * 2
+
      +
      +
      +
      +
      771 µs ± 9.62 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
+
      +
      +
      +
      +

      See? Same operation (doubling each entry of arrays with the integers from 0 to 1,000,000), but the object array operation is ~20x slower.

      +

      So yes, object dtypes are absolutely wonderful and introduce unbelievable flexibility to pandas; but remember there is a cost to using them, so stick to numeric data types when possible!

      +
      +
      +
      +

      Categoricals#

      +

      The category data type is a delightful little hack that allows us to avoid most of the problems with object Series in certain circumstances.

      +

      To illustrate how category Series work, suppose we have a DataFrame with information on hundreds of thousands of customers in the United States, and that one column of that DataFrame contains the name of each customer’s state of residence (substitute Province or any other sub-national administrative unit if states don’t resonate for you).

      +

      Because those state names are words, they are being stored in an object Series. That, in turn, means that Python has created hundreds of thousands of Python objects — each containing the name of a customer’s state — and a vector containing addresses for each of those objects.

      +

      But as there are only 50 states in the United States, this might strike you as absurd, since most of those hundreds of thousands of Python objects are holding the same text! Surely we can do something more efficient than that?

      +

      Enter Categoricals. The idea of a category Series is to take a object Series that contains frequently repeated strings and:

      +
        +

      1. Replace each unique string with a number (for example, "Colorado" could become 1 and "Tennessee" could become 2), and

        2. +

      2. Create a small “lookup table” that keeps track of what string is associated with each number.

        3. +
      +

      Now pandas doesn’t need to create hundreds of thousands of Python strings to record each customer’s state, it can just make a numeric Series with state names replaced by the numbers 1-50, and a new small vector with the fifty names of states.

      +

      Moreover, in addition to saving memory, this also dramatically improves the performance of pandas. Suppose, for example, you want to subset for customers living in North Carolina. When these states were in an object Series, pandas would have to go to each entry, figure out where the associated Python object is stored, get that object, and check to see if it was the string "North Carolina".

      +

      But now, pandas can just go to the lookup table, see that "North Carolina" is the 33rd entry (and so is represented by the number 33 in our encoded Series of numbers), and look for values of 33 in that Series. Hooray!

      +

      But the best part is that, in most cases, the fact your data has been split into a numeric vector and a lookup table is actually entirely hidden from you, the user. For most operations, using a Categorical Series is just like using an object Series, just faster.

      +
      +

      Categorical Series in Practice#

      +

      To illustrate how one works with Categorical Series, let’s make a toy version of this customer dataset:

      +
      +
      +
      import pandas as pd
+import numpy as np
+
+customers = pd.DataFrame(
+    {
+        "customer": ["Bob", "Aditya", "Francisco", "Shufan"],
+        "state": ["Colorado", "Tennessee", "Colorado", "Virginia"],
+    }
+)
+customers
+
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      +
      +
      +
      +
      +
      customers.dtypes
+
      +
      +
      +
      +
      customer    object
+state       object
+dtype: object
+
      +
      +
      +
      +

      As we can see, state begins its life as a standard object Series, but we can convert it to a Categorical with .astype("category"):

      +
      +
      +
      customers["state"] = customers["state"].astype("category")
+customers
+
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      +
      +
      +

      As you can see, at first glance nothing about this column has changed. But if we pull it out, you can see it’s dtype is category and that the Categories associated with the Series (the lookup table) contains three values: "Colorado", "Tennessee" and “Virginia":

      +
      +
      +
      customers["state"]
+
      +
      +
      +
      +
      0     Colorado
+1    Tennessee
+2     Colorado
+3     Virginia
+Name: state, dtype: category
+Categories (3, object): ['Colorado', 'Tennessee', 'Virginia']
+
      +
      +
      +
      +

      And if you want to, you can see the two underlying pieces directly:

      +
      +
      +
      customers["state"].cat.categories
+
      +
      +
      +
      +
      Index(['Colorado', 'Tennessee', 'Virginia'], dtype='object')
+
      +
      +
      +
      +
      +
      +
      customers["state"].cat.codes
+
      +
      +
      +
      +
      0    0
+1    1
+2    0
+3    2
+dtype: int8
+
      +
      +
      +
      +

      But as we said, in most cases category Series will operate just like object Series. Subsetting, for example, will work just as it would with an object Series:

      +
      +
      +
      customers.loc[customers["state"] == "Colorado"]
+
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + +
      customerstate
      0BobColorado
      2FranciscoColorado
      +
      +
      +

      The only place that problems may arise is that one cannot make arbitrary edits to a category Series — if you try and set a cell to have a value that isn’t in the current Categories table, you will get an error:

      +
      
+customers.loc[customers["state"] == "Colorado", "state"] = "Kansas"
+
+TypeError                                 Traceback (most recent call last)
+/Users/nce8/github/practicaldatascience_book/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb Cell 22 line 1
+----> 1 customers.loc[customers["state"] == "Colorado", "state"] = "Kansas"
+
+[...]
+
+TypeError: Cannot setitem on a Categorical with a new category (Kansas), set the categories first
+
      +
      +

      You can add novel values, to be clear, you just have to add the category first:

      +
      +
      +
      customers["state"] = customers["state"].cat.add_categories(["Kansas"])
+customers["state"]
+
      +
      +
      +
      +
      0     Colorado
+1    Tennessee
+2     Colorado
+3     Virginia
+Name: state, dtype: category
+Categories (4, object): ['Colorado', 'Tennessee', 'Virginia', 'Kansas']
+
      +
      +
      +
      +
      +
      +
      customers.loc[customers["state"] == "Colorado", "state"] = "Kansas"
+customers
+
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      customerstate
      0BobKansas
      1AdityaTennessee
      2FranciscoKansas
      3ShufanVirginia
      +
      +
      +
      +
      +

      Why Not Always Use Categoricals?#

      +

      Categoricals are great, but they are only useful when your object Series has a relatively small number of unique values. If you tried to convert an object Series with hundreds of thousands of addresses — and nearly all of them were unique — into a category Series, then pandas would have to create a lookup table that had… hundreds of thousands of unique entries (essentially, it would just be recreating your original object Series). And so there would be no real performance benefit.

      +
      +
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      + + + + +
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      + + + +
      + +
      +
      + Contents +
      +
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      + + +
      +
      + +
      + +
      + +

      +By Kyle Bradbury & Nick Eubank +

      + +
      + +
      + +

      + + © Copyright 2023. +
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      + + \ No newline at end of file diff --git a/_build/html/notebooks/class_3/week_3/40_EX_cleaning.html b/_build/html/notebooks/class_3/week_3/40_EX_cleaning.html index 2f306bb7..fad871ec 100644 --- a/_build/html/notebooks/class_3/week_3/40_EX_cleaning.html +++ b/_build/html/notebooks/class_3/week_3/40_EX_cleaning.html @@ -227,7 +227,6 @@
    • Views and Copies in Pandas
      • @@ -240,6 +239,7 @@
    • Editing Specific Locations
    • Cleaning Data Types
    • Missing Data
      • +
    • The Object and Categorical Data Types

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  • Data Manipulations
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    • Cleaning Data Types
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  • Data Manipulations
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,15,17,18,20,23,25,26,27,28,31,32,33,34,35,36,40,42,43,44,46,47,48,49,55,58,59,61,62,64,68,70,71,75,77,79,81,89,90,93,94,95,97,98,101,102,103,104,105,107,108,109,112,113,114,115,117,118,121,123,124,126],"var":[17,22,23,32,42,48,68,89,94,101,104,118,119,126],"voil\u00e0":[43,62,101],"while":[2,6,7,8,9,10,12,13,14,16,17,18,19,20,23,25,26,27,30,31,32,34,35,36,39,40,41,44,46,47,48,49,54,55,56,57,60,62,65,67,73,74,75,76,77,79,88,89,90,91,92,93,96,97,99,100,102,103,105,106,107,108,109,110,111,114,118,120,123,126,127,133],A:[12,13,14,17,18,20,23,24,25,30,32,34,35,36,41,42,43,48,51,52,56,60,61,62,65,73,75,78,79,81,84,90,92,97,102,104,106,109,110,111,113,114,121,123,124,125,126,131,132],AND:[11,18,29,76,104,112,126,127],And:[6,8,11,12,13,14,15,16,17,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,40,41,42,43,48,49,54,55,60,62,63,64,67,68,74,76,90,91,93,94,96,97,98,99,101,102,104,105,106,109,110,111,112,117,118,120,121,124,125,126,127,128,133],As:[3,6,9,10,12,14,15,18,20,22,23,25,26,27,29,30,31,32,34,36,40,41,42,43,44,48,49,51,52,53,55,57,60,61,62,63,66,67,68,70,71,72,73,74,76,77,79,81,88,89,90,91,93,94,95,96,97,98,99,100,101,102,103,104,106,107,108,110,111,113,118,120,121,126,127,133],At:[27,29,32,34,35,54,76,97,100,102,123,126],BUT:[30,80],BUt:30,Be:[27,51,52,86,92,116],Being:[15,18,74,90,111],But:[2,6,7,8,9,11,12,13,15,17,18,19,20,22,23,24,26,27,29,30,31,32,34,35,36,37,39,40,42,43,44,48,49,50,53,54,55,61,62,67,68,70,71,75,76,89,90,91,92,93,95,96,97,98,99,100,101,102,104,105,106,107,109,111,112,113,117,118,120,121,123,124,126,127,128,129],By:[0,7,10,11,15,18,20,30,31,51,52,74,81,89,97,98,102,104,108,111,113,125],FOR:11,For:[0,8,9,10,11,12,13,14,15,16,17,18,20,22,23,24,25,27,29,30,31,32,36,37,38,40,41,42,43,44,46,47,48,49,51,52,54,60,61,62,63,64,66,68,70,71,73,75,76,77,80,81,82,83,89,92,93,94,95,97,99,100,101,102,103,104,105,106,107,108,110,111,113,115,118,121,124,126,127,128,129,133],IF:11,IN:[24,32,110],If:[3,6,7,8,9,10,11,12,13,14,15,16,17,18,20,23,24,25,27,29,30,31,33,34,35,36,39,41,42,43,45,46,47,48,49,51,52,55,58,59,60,61,63,65,67,68,70,71,72,75,76,77,78,79,82,83,88,89,90,91,92,93,94,95,97,103,104,105,107,108,109,111,113,117,118,120,126,127,133],In:[2,3,6,7,8,9,11,12,13,14,15,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,37,38,39,40,41,42,43,46,47,48,49,51,52,53,54,55,57,60,61,62,67,70,71,72,73,74,75,76,77,78,79,80,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,117,118,121,122,123,124,125,126,127,128,129,133],Is:[29,46,47,49,51,52,63,107,116,121],It:[3,4,8,9,10,11,12,13,14,15,16,17,19,20,21,22,23,28,30,31,32,33,34,35,38,42,51,52,54,74,75,77,81,88,94,97,98,99,100,102,104,107,111,113,118,120,121,122,126],Its:118,NOT:[9,13,55,82,93,123],Near:[98,127],No:[17,20,25,32,36,126],Not:[6,9,15,19,30,43,47,51,88,102,106,107,111,126,127,128,129],OF:[80,133],ON:[11,12],OR:110,Of:[18,35,41,43,55,60,67,89,94,97,102,103,118,123],On:[3,8,9,12,16,41,80,94,100,118],One:[9,10,12,14,24,25,27,30,34,42,43,47,61,66,68,77,89,92,95,104,105,106,109,110,118,121,124,126,127],Ones:41,Or:[12,17,27,32,35,41,43,51,52,63,64,67,68,93,95,97,102,103,106,120],Such:79,THAT:31,THE:[24,32,124],THEN:126,TO:[11,99,126,127],That:[3,9,10,11,12,13,17,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,40,43,44,55,61,70,71,75,76,77,90,91,95,97,98,102,104,107,117,118,123,125,126,128,129],The:[0,5,9,10,13,14,15,17,18,21,22,23,24,25,27,28,30,31,32,33,34,35,41,42,43,45,48,49,51,52,54,55,56,57,60,61,62,63,65,66,68,69,70,71,74,75,77,79,80,82,83,84,86,89,90,92,93,94,95,96,97,102,106,107,108,109,110,111,112,113,114,115,120,121,123,124,126,127,128,129,134],Their:117,Then:[3,6,7,8,9,10,12,16,17,19,20,23,27,28,29,30,31,32,33,41,42,43,46,47,48,49,51,52,55,62,68,70,71,75,76,80,91,93,100,101,105,107,111,114,118,121,123],There:[4,6,9,11,13,14,15,17,18,22,23,25,26,27,29,30,33,34,36,38,40,44,46,47,48,60,66,73,74,75,76,77,80,81,89,90,92,93,95,103,105,106,109,110,113,115,118,121,123,124,126,133],These:[6,12,13,14,15,17,20,25,31,32,46,47,51,52,55,72,73,75,88,89,92,93,95,97,99,102,103,105,115,118,121,123,126,133],To:[2,3,4,6,7,8,9,10,11,12,14,15,16,17,19,20,21,23,25,27,30,31,32,39,40,43,48,49,51,52,54,55,61,62,63,66,67,68,70,71,74,75,77,79,81,82,83,84,89,92,95,100,102,103,104,106,107,108,109,110,111,112,113,114,118,120,121,122,123,126,127,128,129,133],Will:30,With:[7,9,10,12,18,24,30,34,35,39,41,57,78,80,88,91,92,93,94,96,108,120,123,124,125,126,128,129,133],_____:80,______:80,_______:80,_________:95,__call__:[118,119],__name__:17,__nonzero__:17,__setitem__:104,__traceback__:30,_build:0,_compile_for_arg:30,_concatenate_chunk:126,_core:118,_decor:126,_engin:[94,126],_ensure_numer:105,_event_pip:30,_fit_predict:[118,119],_growth:92,_has_valid_setitem_index:104,_is_master_process:30,_lib:[94,126],_locationindex:104,_lsprof:30,_mean:107,_merg:[19,111],_of:107,_pandas_api:28,_rate:92,_read:126,_read_row:126,_reader:126,_schedule_flush:30,_setitem_with_index:104,_stat:[118,119],_sync_cluster_info:33,_thread:30,_tokenize_row:126,_unbox_scalar:104,_validate_scalar:104,_valu:92,_wait_for_tstate_lock:30,_x:107,a1:89,a52aba7de6d5:13,a93c:32,a9efa4ff5a9d6612f62c70f567177170:[],a_0:77,a_1:77,a_2:77,a_:[11,78],a_boolean_vector:41,a_d:132,a_fast_funct:30,a_float_vector:41,a_fold:12,a_folder_with_stuff:11,a_medium_funct:30,a_slow_funct:30,a_string_vector:41,a_toi:78,ab:62,abandon:15,abbrevi:[110,115],abcdefghiklmnopqrstuwx1:12,abcfghloprstuw:12,abil:[11,13,14,27,31,34,35,42,63,70,71,88,89,90,118,121,123,133],abl:[9,12,13,14,15,17,24,25,26,27,29,31,32,34,35,36,40,41,53,63,70,71,73,74,76,84,88,89,90,107,108,110,111,112,113,114,116,117,118,123,124,125,126,127,133],abort:17,about:[2,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29,30,31,35,36,39,40,41,42,43,44,46,47,48,49,51,52,53,54,55,61,62,63,66,67,68,69,70,71,74,75,76,77,79,80,81,82,87,88,89,91,93,94,95,97,98,99,100,101,102,103,104,105,106,109,111,112,113,115,117,118,120,121,122,123,126,127,129],abov:[3,13,15,17,18,25,27,30,31,32,33,34,36,40,41,42,43,44,46,47,48,49,51,52,54,55,60,61,62,63,66,67,70,71,73,74,75,79,80,82,89,90,93,96,97,99,100,101,102,104,105,107,108,109,110,111,112,118,120,121,124,126,127,133],absolut:[3,17,18,19,20,25,27,32,35,62,100,111],abstractli:15,abus:18,ac:[46,47,51,52,95,102,106,107],academ:[34,125],acceler:114,accelerationord:[],accent:103,accept:[6,12,30,32,40,98,103,111],access:[6,8,10,11,14,15,16,17,23,26,29,30,31,32,38,40,41,48,73,76,89,90,92,94,97,98,103,118,121],accid:19,accident:[17,29,55,76,105],acclaim:17,accommod:[49,110,121,126],accomplish:[13,15,17,18,20,24,39,48,49,53,62,82,83,84,92,102,108,109,110,112,114,123,124,128,129],accord:[111,112],accordingli:[68,73],account:[10,11,15,33,46,47,70,71,107,110],accru:[51,52],accumul:[30,32,95],accur:[23,40,46,47,70,71,95,102,107],accuraci:[70,71],accustom:[17,62,92],achiev:[13,14,15,18,25,30,34,114,115,118],acquir:[30,34],across:[17,20,26,27,30,31,32,35,42,46,47,48,51,52,61,73,74,77,78,92,97,98,103,107,108,109,110,111,112,115,121,123,124,127],acs_5yr_hh_incom:130,act:[34,42,94,124],action:[2,19,31,109,114,118,119,123,133,134],action_ind:32,activ:[3,8,10,12,14,16,18,27,30,31,34,39,58,59,76,77,100,123,125],actual:[2,3,6,7,8,9,10,12,13,15,17,20,21,22,23,24,25,26,27,28,29,30,31,32,33,35,36,40,43,46,47,48,51,52,54,55,57,61,62,64,67,68,70,71,76,78,87,89,92,93,94,95,98,99,100,101,102,106,107,113,117,118,120,121,122,128,129],acut:26,ad:[6,9,10,12,14,20,21,25,26,29,30,40,46,47,49,60,62,68,70,71,76,79,80,88,89,94,97,109,127,134],adapt:[78,92,123],add:[0,9,10,12,13,14,17,18,19,20,22,23,24,25,26,29,30,35,38,40,42,46,47,60,62,66,68,70,71,74,75,76,77,80,88,89,96,97,100,101,107,108,109,110,111,113,114,118,119,120,121,123,124,125,127,128,129,133],add_artist:123,add_categori:104,add_select:121,add_up_entri:13,addict:35,adding_tot:30,addit:[2,3,9,12,14,15,17,22,23,29,30,31,32,34,35,36,42,51,52,53,60,68,70,71,76,77,79,94,96,97,102,106,109,110,112,118,121,123,124,126],addition:[14,82,83,84,127],addr:33,address:[9,15,17,29,32,37,48,53,57,68,70,71,101,104,107,110,111,113,118,120,126,128],adeli:[118,119],adequ:34,adjust:[13,60,64,73,107,123,126,134],adjusted_incom:64,admin:[7,33],administr:127,admit:[35,96,101,118],admittedli:[62,93],admonit:0,adob:62,adopt:[17,124,133],adult:102,advanc:[9,11,29,30,34,36,76,97,123],advantag:[14,17,20,29,76,88,92,102,104,106,107,112],advic:[34,80,91,101],advis:[51,52],advoc:[18,24,92,101],ae:120,aesthet:[17,117,124],af:33,affect:[6,20,23,27,95],affili:32,afghanistan:[126,127,128,129],afraid:28,africa:[17,88,92,93,94,97,98,103,104,105,106,126,127,128,129],african:[93,127],after:[3,8,10,12,13,15,17,20,24,27,29,32,33,34,37,38,42,48,49,53,55,58,59,60,62,66,70,71,74,82,83,84,85,86,95,97,101,103,104,111,114,117,118,123,126,133],after_tax:42,after_tax_incom:66,afterward:12,ag:[15,41,43,49,60,63,64,66,70,71,88,89,95,102,107,118,121,124,133],again:[6,8,9,11,13,17,26,31,32,43,48,58,59,62,73,74,77,79,80,93,94,104,108,110,121,124,126,127],against:[8,12,16,17,20,40,104,113,124],agenc:[34,95,115],agent:32,agg:32,aggfunc:112,aggreg:[112,113,114],agnost:[96,121],ago:[19,106,120],agre:[9,10,15,32],ah:118,ahead:[6,7,15,32,55,62,92,102,110],ai:88,aid:98,aim:34,air:[35,109,110,118,121],airport:[6,7],ak:110,aka:36,al:110,alabama:110,alaina:[112,114],alameda:113,alaska:110,albania:[93,94,126,127,128,129],albeit:118,alert:[17,101],algebra:[31,40,60,77],algeria:[93,94,126,127],algorithm:[13,15,20,31,36,72,102],alia:40,alias:38,align:[86,90,91,93,94,109],aliv:118,all:[0,2,3,4,6,7,8,9,10,11,12,13,14,15,17,18,19,20,21,23,24,25,26,27,29,30,31,32,33,34,35,37,39,40,41,42,43,44,45,46,47,48,49,51,52,54,55,57,60,61,62,63,64,65,66,67,70,71,73,74,76,77,78,79,80,81,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,109,110,111,112,113,114,115,116,118,121,122,123,124,126,127,128,129,133,134],all_prescript:32,all_rows_w_even_a:28,alloc:[20,27,41,51,52,89,118],allow:[6,7,9,10,11,12,13,14,15,25,27,28,29,30,31,32,34,36,39,40,42,43,50,53,65,67,68,74,76,78,80,89,90,91,94,100,101,104,105,108,10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lifornia:[19,62,110,111,113],californian:113,call:[2,4,6,7,8,9,11,12,13,14,15,16,17,20,21,22,23,24,26,27,29,30,31,32,33,34,36,39,40,42,43,44,46,47,48,49,51,52,54,56,57,58,59,61,62,63,66,68,70,71,76,77,78,79,80,81,86,88,89,92,93,94,96,97,98,99,100,101,102,103,104,105,109,110,111,112,113,117,118,121,123,126,128,129],callabl:94,cambodia:127,came:[9,32,40,54,80,97,99,111],cameroon:127,campaign:41,can:[0,2,3,4,6,7,8,9,10,11,12,13,14,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,120,121,122,123,124,125,126,127,128,129,133,134],can_swim:93,canada:127,cancel:[13,51,52],cancellederror:33,cancer:[15,36],candid:[32,104,127],cannot:[19,30,31,32,36,46,47,61,93,104,123,127],canon:118,canva:123,cap:103,capabl:[8,16,31,36,123],capac:115,cape:127,capfac:115,capit:[51,52,103,111],capita:[17,88,92,93,103,110,121,125,126,127,128,129],captur:[77,78,95,123,133],car:[10,42,55,73,112,121],carbon:[13,125,127,128,129],card:79,cardin:[73,118],care:[12,17,20,25,27,31,73,79,89,91,92,104,105,109,110,111,118,123,124],career:[9,10,12,34,36,41,98],carefulli:[9,14,15,18,29,32,34,63,76,91,105,113,118],careless:17,carlo:[82,83],carolina:110,carpentri:11,carri:[17,111],carrol:32,cash:35,cast:[89,104,105],casted_kei:94,casual:12,cat:[12,48,80,91,93,97,104,109,110],categor:[27,28,36,86,88,105,118],categori:[19,28,70,71,102,104,107,113,118,119,124,127],categoricaldtyp:118,cathol:121,caught:17,caus:[9,10,14,17,18,19,29,30,33,39,49,76,94,101,102,105,106,111,118,119,124],causal:111,cautiou:36,caveat:[101,104],cbgufgtrki:28,cbserial:[107,130],cd:[0,6,7,11,12,116],cdef:30,cdot:[13,78],cede:10,celebr:80,cell:[13,14,25,39,41,49,61,67,74,88,89,94,104,109,121,123],censu:[15,41,43,46,47,51,52,95,102,107,110,113],census:[107,113],cent:[33,60],center:[43,67,74,110,123,124,126],centimet:125,central:[31,32,89,93,97,104,127],centric:9,ceo:49,cerebellum:67,certain:[9,17,23,30,58,59,67,82,83,84,97,104,105,106,111,113,117,118],certainli:[13,34,67,74,109,120,124,134],certif:[34,60,95],chad:[93,127],chain:[],challeng:[32,34,78,88,95,109,123],chanc:[9,17,18,34,53,79,93,97],chang:[0,2,7,10,11,12,13,14,17,18,19,20,21,22,23,24,25,27,29,31,34,36,41,43,49,51,52,53,54,55,56,58,59,61,62,64,66,76,78,79,82,83,84,88,89,90,93,94,96,97,99,100,103,104,111,113,114,116,118,119,124,126,128,129,133,134],change_contrast:62,channel:[8,9,74,121],channel_prior:9,chao:32,chapter:[20,82],charact:[41,44,48,97,103,126,127,133],character:[41,43,79,121],characterist:[15,73,79,89,106,112],charg:102,charl:20,chart:[122,124,127,129],chatgpt:88,chdir:[17,32],cheaper:35,cheatsheet:[0,25,32],check:[12,13,14,17,18,19,20,26,28,29,30,31,32,36,41,42,44,51,52,58,59,60,64,76,77,80,86,87,89,93,95,98,100,102,105,109,110,113,118,121,122,126],checklist:14,chem:110,chevel:[],chevroelt:[],chide:17,child:[14,80,118],childhood:118,children:[95,102,106,107,118],chile:[118,127],china:[17,127,128,129],chines:41,chip:[31,35],chl:118,choic:[6,7,11,17,18,28,40,79,93,118,120,124],cholesterol:108,choos:[9,13,14,73,123,124],chose:[10,41],chosen:[32,80,117,121,124],chunk:[18,24,27,32,116,126],chunksiz:[27,32,126],ci:[116,121],ci_high:[118,119,121],ci_low:[118,119,121],ciat:12,circl:[3,10,13,54,67,99,104,118,123,128,129],circumst:[55,77,85,104],cite:[46,47,51,52],citi:75,citizen:[15,103,118],civil:111,clarifi:[21,125],clariti:[76,77,124],class_2:[49,130],class_3:[88,104,109],classifi:[113,124],classroom:[15,34,41],clean:[9,10,13,15,17,19,24,28,34,51,52,87,102,104,106,108,109,110,118,124,127],cleanli:[24,102,103,105],clear:[14,15,17,24,25,26,30,31,34,36,49,55,63,77,87,89,91,101,102,105,107,110,111,118,126,128,129],clearer:[15,77,110],clearli:[13,15,17,19,36,40,67,76,79,100,101,102,109,112,114,118,124,126,128,129,134],clever:[44,62],click:[0,3,6,7,8,9,10,11,12,14,16,17,21,25,39,46,47,51,52,126],client:[3,12,32,33,110],climat:[41,55,67],clinic:36,clip:123,clip_on:123,close:[6,10,17,19,20,26,31,32,33,35,43,51,52,54,58,59,62,74,79,93,98,103,125,128,129],closer:[31,62,67],closest:127,cloth:17,cloud:[6,7,9,10,11,27,31,35,36,75],cloudcov:75,cloudi:75,clump:[128,129],clumsi:102,clunki:63,cluster:[11,27,31,32,102,107,130],clutter:[17,121],cmap:[62,67,74],cmd:[3,7,9,11,12],cmder:[3,11,12],co2:[118,121,125,126,127,128,129],co2_al:126,co2_emissions_tonnes_per_person:126,co:[10,17,110,123,125],coal:115,coauthorship:34,code:[2,6,8,9,13,16,17,18,20,23,24,26,27,28,29,31,32,33,35,36,37,38,40,41,46,47,48,49,51,52,55,57,58,59,60,63,64,66,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,91,92,94,95,97,98,100,101,102,103,105,107,109,110,111,113,114,118,120,121,123,124,126,133,134],codebook:[70,71,111,113,115],codec:113,coeffici:[15,51,52,92],coerc:[32,49],coercion:49,coher:[25,110],coil:32,coin:10,col1:104,col2:104,col:28,col_dict:126,col_index:104,colab:9,collabor:116,collaps:[27,32,111,113,121],colleagu:[2,82,83,97,98,112,124],collect:[15,23,29,30,32,34,37,40,41,42,43,45,46,47,51,52,53,55,60,67,70,71,73,75,81,82,83,85,86,88,89,98,107,109,111,113,123,128,129],colleg:[17,34,70,71,95],colloqui:[11,13],colombia:127,colon:[13,40,97,99,133],color:[10,17,18,41,60,67,72,97,113,118,120,121,123,128,129,134],color_list:[128,129],colorado:110,columbia:110,column:[13,14,15,25,27,28,32,40,41,42,60,61,64,65,66,68,70,71,73,74,75,78,86,88,95,96,97,104,105,107,108,109,110,111,112,113,118,121,123,126,127,128,129],column_a:101,columnar:[],com:[6,9,17,19,30,32,93,94,102,103,104,106,107,118,121,126],combin:[15,17,25,27,41,42,44,48,49,61,67,87,89,112,114,120,123,125,126,127,134],combined_labeler_nam:32,come:[3,6,9,10,11,12,13,15,17,20,21,22,23,26,27,28,29,30,31,32,35,36,39,40,41,43,46,47,51,52,53,61,62,63,64,67,74,76,80,90,91,92,95,96,97,98,99,100,101,102,103,104,107,108,110,117,118,120,122,133],comedi:62,comer:32,comfort:[9,10,12,18,36,41,62,118],comm:[32,33],comma:[10,25,63,67,68,93,97,126],command:[3,4,8,9,10,16,17,23,25,27,29,32,33,35,40,46,47,51,52,55,63,70,71,81,84,95,100,101,107,110,113,123,133],command_lin:[11,12],commandlin:[8,16],commandlinetool:3,commandnam:12,commensur:34,comment:[13,17,18,21,25,126],commerc:15,commit:[0,15,21,34,111,113],common:[4,8,12,13,14,15,16,21,22,23,32,38,40,41,54,67,72,73,77,79,87,89,93,94,97,99,102,103,105,106,109,110,111,112,114,115,116,120,123,126,134],commonli:[17,21,30,41,42,51,52,60,95,97,98,102,109,123,127,133],commun:[15,17,25,34,46,47,51,52,58,59,70,71,76,95,102,105,106,107,111,116,117,118,120,124,133],comoro:[126,127],compact:[38,114],compani:[2,11,15,25,27,32,33,35,36,49,55,70,71],compar:[12,15,17,28,29,30,31,32,37,51,52,55,60,62,68,73,77,78,97,110,123,124,125,127,133],comparison:[6,15,20,32,42,123,124,133],compartment:17,compat:[4,10,30,68,81,97,98,102,117],compel:[34,124,134],compens:34,compet:26,compil:[29,30,32,36,76,121],complain:102,complet:[3,6,10,15,29,31,32,34,36,40,46,47,51,52,58,59,63,76,77,82,83,84,85,95,104,107,110,111,116,118,123],complex:[14,23,35,36,41,75,85,87,105,108,111,114,128,129],complic:[10,13,14,16,17,31,41,48,62,81,88,94,97,98,99,121],complimentari:93,compon:[14,31,34,35,38,41,74,77,87,89,123,124],compos:[25,41,117,118,120,123],composit:[19,113],compound:92,comprehens:[85,110],compress:[12,27,28,32,97,98,126],compris:[],comput:[4,6,7,9,10,11,12,13,16,17,22,23,24,25,26,28,29,30,34,36,37,39,40,41,53,54,57,60,61,62,72,73,74,76,77,78,79,81,82,83,84,85,86,87,88,96,97,98,100,102,105,108,110,112,113],computation:[35,88,114],con:[97,98],concat:[28,109],concaten:[29,66,76,83,88,108,110,133],concatent:109,concept:[9,23,32,38,48,57,73,85,86,90,108,114,124],conceptu:[15,22,32,34],concern:[34,41,94,113,120,126],concert:27,concis:[30,40,42,53,57,77],conclud:[85,116],conclus:[2,36,98,111,124],concord:[],concret:[15,61,68],conda:[3,6,9,10,12,14,31,32,33,38,40,51,52],condabin:12,condit:[13,14,17,25,30,31,48,49,75,79,104,118,121],conduct:[17,41,43,60,70,71,95,102,110,111],conf_int:[118,119,121],confid:[17,36,82,84,127],config:[9,123,124,128],configur:[5,10,12,14,20,39],configure_legend:129,configure_mark:129,confirm:[10,54,60,105],conflict:[9,10,94],confound:15,confus:[11,13,17,31,49,89,93,123,124],congo:[93,94,127],congratul:[15,36,51,52,70,71,95],connect:[11,17,32,33,34,57,101,111,114,120,123],connect_timeout:33,connecticut:110,connector:33,connot:102,consecut:48,consequ:[18,54,61,118],conserv:17,consid:[12,13,15,20,27,29,30,31,32,34,43,48,49,55,67,88,102,104,109,110,112,113,121,124,127],consider:[49,97,127],consist:[10,25,31,34,43,44,65,67,77,79,91,100,101,102,111,124],consisten:124,consol:[14,39],consolid:34,constant:[18,31,104,107,118,121,123],constantli:[17,18,75,116],constitu:[89,118],constitut:[15,36,113],constrain:25,constraint:30,construct:[21,34,48,79,82,88,89,107,115,123],constructor:[85,89],construt:93,consult:[88,117],contact:[17,46,47,51,52],contain:[10,12,25,41,43,44,46,47,48,54,57,60,62,63,67,70,71,75,81,88,89,91,93,97,102,103,108,110,115,121,123,124,126,127],content:[0,3,6,12,14,17,25,29,37,40,46,47,51,52,57,58,59,68,70,71,73,85,88,89,93,95,97,108,110,112,117,124,125,126,127,128,129,133,134],context:[15,17,25,26,36,51,52],contextu:41,contin:[125,128,129],continu:[13,14,17,20,53,92,102,103,113],contour:123,contrast:[6,12,15,24,32,34,41,43,74,92,96,97,102,118],contribut:[18,34,62,74,111,125,128],contriv:19,control:[10,13,14,15,17,20,26,41,79,86,110,118,124,133],conveni:[20,46,47,51,52,58,59,70,71,74,76,88,133],convent:[22,27],converg:[10,92],convers:[89,116],convert:[13,17,27,29,30,40,44,56,60,61,62,72,76,96,97,100,103,105,118,125,126,127,133],convert_dtyp:96,convert_feet_and_inches_to_inch:17,convolut:94,cool:[19,38,42,49,61,89,102],coolor:124,coordin:[8,9,63,67,74,128,129],copi:[0,6,12,13,17,18,22,23,27,29,31,38,51,52,57,62,66,73,77,79,86,89,104,111,114,121,126,127],copilot:[36,88],copy_on_writ:[32,101,118,119],copyright:62,corbon:126,cord:67,core:[9,10,17,26,30,31,32,33,35,73,85,89,93,94,96,104,105,108,118],corner:[36,39,63],correct:[9,13,14,17,36,41,46,47,49,58,59,75,77,78,80,97,102,105,107,111,123,124,126,127],correction_no:32,correctli:[3,6,13,20,36,48,87,111],correctly_signed_and_squar:62,correl:[67,77,111,112,118],correspond:[12,41,43,57,62,63,70,71,74,75,79,80,81,89,91,95,109,110,114,123,126,127,133],corrupt:[19,24,31,55,94,98,100,111],cost:[20,27,31,34,35,42,46,47,49,66,89,117],costa:[93,127],cote:127,could:[8,9,11,12,14,15,17,18,20,24,25,26,29,30,32,34,37,40,41,42,43,44,46,47,48,49,51,52,54,55,60,61,63,64,67,68,70,71,73,74,75,76,82,89,90,93,97,101,102,103,104,105,106,108,109,110,111,112,113,114,118,121,124,126,127,128,129,133],couldn:[23,31,43,62,66,70,71,105,112],count:[12,17,22,38,43,73,74,79,86,88,89,92,93,94,95,96,102,105,106,107,109,110,111,112,118,121,126,130,133],count_nonzero:73,count_valu:75,count_values_in_each_row:75,counter:[13,25],counterpart:118,counti:[15,32,111,113],countri:[17,46,47,51,52,88,92,93,94,97,98,102,103,104,105,106,118,121,125,126,127,128,129],coup:[103,104],coupl:[8,9,12,18,24,25,27,30,32,34,40,43,48,55,60,62,94,95,107,111,115,118,121],cours:[6,7,9,10,11,12,15,17,18,20,21,26,32,35,36,39,40,41,42,43,46,47,49,51,52,53,54,55,58,59,60,63,67,70,71,73,77,82,83,84,88,89,95,97,99,102,103,108,110,114,116,118,121,123],coursera:[39,40,46,47],cousin:6,cover:[12,17,20,27,33,34,43,54,56,62,63,75,99,103,108,118,120],coverag:30,cow:100,cp:[0,12,70,71],cpu:[31,32,33],cpu_count:[31,32],cpython:30,craft:18,cram:49,crash:[27,32],crazi:18,creat:[2,3,4,9,11,12,13,14,17,18,20,22,23,24,27,31,32,33,36,39,40,42,43,44,45,46,47,48,49,51,52,54,55,56,57,60,61,62,63,64,65,66,67,68,70,71,73,74,75,77,78,79,80,81,82,83,84,85,86,87,88,90,92,93,94,95,96,97,98,99,100,101,109,110,111,114,116,117,118,119,120,121,123,124,125,126,127,128,129,133,134],creater:32,creation:[49,96],creatur:[25,74],credenti:[33,34],credibl:34,credit:[41,64,66,70,71,108,110],credit_a:51,credit_b:51,credit_cutoff_:51,credit_cutoff_d:51,cri:[],crime:[15,17,19,113],crimin:111,criteria:[15,82,83,84,108],critic:[18,35,36,104,108],croatia:127,crop:[67,101,102],cross:67,crowd:118,crucial:[2,118],crunch:33,crush:34,cryptic:42,crystal:[89,128,129],cs:34,csv:[12,17,19,27,28,32,86,88,93,94,96,97,98,103,104,105,106,110,111,113,118,121,126,130],ct:[67,74,110],cuba:127,cudf:32,culpa:18,cumbersom:[14,17,123],cumtim:30,cumul:92,cup:79,curiou:[25,32],curl:6,curli:40,currenc:[46,47,60],current:[6,7,8,10,12,14,15,16,18,24,29,31,36,39,40,41,58,59,60,62,64,70,71,77,92,95,96,104,105,107,117,118,120,121,133],cursor:14,curt:18,curv:[43,79,118,119,123],custom:[6,10,14,15,41,60,100,109,117,118,123,128,129,134],customiz:117,cut:[46,47,67],cute:118,cutoff:[17,46,47,121,127],cutoff_a:51,cutoff_b:51,cyber:20,cylind:[],cynthia:34,cypfoxeuok:28,cypru:127,cython_avg_numbers_up_to:30,cython_w_overflow_avg_numbers_up_to:30,czech:127,czechia:127,d010:[46,47,51,52],d3:[117,121],d5a6d15d0b1463d80fbfbb777dc826f7:[],d6ab8c8f87e7:30,d:[4,6,7,9,10,11,12,14,15,20,26,29,31,32,35,38,42,49,51,52,60,61,63,64,72,75,76,79,81,88,90,97,98,100,102,103,104,106,110,111,113,118,120,121,123,124,126,127,131,132,133],dai:[9,12,17,18,24,25,26,30,31,35,40,41,42,73,75,80,89,90,102,121],daili:[6,7,34,36,73],dakota:110,damag:111,danger:[0,9,18,20,111,118],dark:[6,12,41,62,67],darker:62,darn:[],darussalam:127,dash:12,dashboard:[32,33],dashboard_link:33,dask:[16,27,31,116],dask_env:16,data:[2,6,7,8,9,10,11,12,13,14,16,17,18,19,20,22,23,25,28,29,30,31,32,33,34,42,44,45,48,53,54,55,56,57,58,59,60,61,62,63,64,65,67,68,69,72,73,74,75,76,78,79,82,83,84,85,89,90,91,92,94,96,99,100,104,109,110,112,115,117,119,120,122,123,124,125,128,129,130,133,134],data_copi:114,data_serv:121,data_transform:121,databas:[27,93,108,115,118],datacamp:[11,12],dataclass:[118,119],datafram:[13,17,25,28,31,32,60,86,87,89,95,96,99,100,101,104,105,106,108,109,110,111,112,113,114,118,119,121,126,127,128,129,131,132],dataframe_typ:32,dataframeiolay:32,dataframetreereduct:32,datanum:107,datasauru:116,dataset:[15,17,20,25,26,27,28,30,31,32,33,35,46,47,48,51,52,56,60,66,70,71,75,87,88,90,92,93,95,98,102,103,104,105,106,108,111,113,114,115,118,119,123,124,125,127,128,129],datatyp:[20,27,30,49,96,101,106],date:[32,79,88,97,109],date_pars:126,datum:121,dayfirst:126,dbc2199fcf7e6302cdd1ea2955dc378605b52659:19,dbl:121,dc:110,dd:32,ddot:78,de:[2,30,32,110],deactiv:[8,14,16],dead:31,deal:[9,14,20,27,35,46,47,68,79,85,105,106,111,113,134],dealer:111,dealership:[42,112],death:110,debat:118,debt:[51,52,102],debug:[9,12,24,30],debugging_in_vscod:13,decad:[35,111],decemb:12,decid:[9,11,15,17,24,29,30,32,34,35,49,51,52,76,111,118],decim:[17,20,23,25,40,41,49,51,52,60,70,71,89,97,126,133],decis:[73,95,108],declar:[29,30,76,120],declin:[19,55,92,111,113],decod:113,decompos:[74,118],decomposit:118,decompress:[27,28,32],decor:30,decreas:[51,52,62,111,118,134],dedic:[18,30],deduc:30,deem:25,deep:[25,35,38,57,117,126],deeper:[13,73,85,87,123,124,126],deepli:[18,34,41,93,96,101],def:[13,17,23,30,31,62,75,77,78,80,82,83,93,118,119,121,123,126,127],defaultformatt:25,defen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13,15,18,19,20,21,22,25,26,27,30,31,32,33,34,35,36,39,40,41,42,43,46,47,48,49,51,52,54,55,58,59,60,62,63,67,70,71,77,79,82,83,89,90,91,93,94,95,97,98,100,102,103,104,105,106,110,111,112,113,116,118,121,123,124,126,127,134],done:[3,6,7,8,10,11,15,24,25,27,29,30,32,40,43,48,67,73,76,77,94,95,98,99,101,102,104,109,118,123,126,127,128,129],dont:[8,16,30],door:35,dos_str:32,dosage_unit:32,dot:[6,14,19,38,118,119,120,124],dotfil:12,doubl:[0,3,12,23,25,29,30,31,35,37,46,47,55,62,77,96,104,126],double_nonvector:77,double_vector:77,doublequot:126,doubt:101,dove:85,down:[9,11,17,20,27,28,30,31,36,38,43,46,47,51,52,55,61,73,81,95,102,110,111,112,113,128,129],download:[3,6,7,9,10,12,15,24,32,95,111,113,126],downstream:13,downward:[14,57],dozen:[2,9,17,18],dpi:124,dqcfywcfmx:[],dracula:6,draft:36,drag:121,dragon:80,dramat:53,drastic:77,draw:[15,48,70,71,79,80,123,124],drawn:62,dream:[96,123],drew:[38,80,86],drift:17,drive:[26,27,32,35,41,55],driven:[51,52,73,111],driver:125,drop:[17,27,32,34,46,47,48,51,52,89,93,95,97,106,107,109,110,118,119],drop_dupl:130,dropbox:[17,32,88],dropdown:121,dropna:[106,110,118,119],drug:[32,36,113],drug_cod:32,drug_nam:32,ds:126,ds_store:12,dt:32,dta:[28,93,95,98,102,106,107],dtype:[20,27,28,29,32,41,44,48,49,60,62,88,89,90,91,93,94,96,101,102,103,104,105,106,107,110,111,112,113,118,126,127,130],dtype_backend:96,dubiou:96,duct:13,due:[15,20,31,36,72,101,106,125],dug:19,duke:[9,10,11,12,25,34,35,36],dumb:31,dump:97,duplic:[12,31,77,80,82,83,84,113,127],durat:35,duration_with_copi:55,duration_with_view:55,dure:[8,9,12,15,16,17,19,25,27,30,33,34,42,46,47,51,52,79,126],dutch:[],dynam:[29,30,57,76,89,92],dz:126,dza:126,dzvsmydwbp:28,e:[2,3,4,7,8,9,11,12,13,14,16,17,20,24,25,26,27,29,30,31,32,34,38,40,41,42,43,46,47,48,49,51,52,56,62,63,65,67,68,70,71,76,79,80,81,88,89,90,92,93,94,96,97,98,99,102,103,104,105,106,107,108,109,110,113,116,118,121,122,127,132],each:[8,10,12,13,14,15,16,17,18,20,25,26,27,28,29,30,31,32,34,37,40,41,42,43,45,46,47,48,49,51,52,55,57,60,61,62,63,67,68,70,71,73,74,75,76,77,78,79,80,81,82,83,84,88,89,90,91,92,93,94,95,97,100,101,102,103,107,108,109,110,111,112,113,114,116,118,119,121,123,124,126,127,128,129,134],ear:80,earli:[2,8,12,15,17,19,20,34,36,40,96,126],earlier:[12,14,17,73,75,79,80,114,124,126],earn:[25,41,43,46,47,49,51,52,63,92,95,107],earner:[43,51,52],earth:[13,125],eas:[0,40,80,126],easi:[2,9,10,13,15,17,20,21,24,27,29,30,31,32,36,37,39,40,42,54,61,66,67,70,71,76,88,93,94,95,97,99,100,102,113,117,118,120,121,124,129],easier:[6,8,12,16,17,18,24,25,30,32,33,35,38,42,60,62,63,73,75,77,86,87,88,89,91,92,94,95,97,100,103,104,110,112,113,114,118,120,121,124,128,129,133],easiest:[12,33,89,93,95,100,133],easili:[2,9,10,12,13,14,17,18,24,25,27,28,35,36,40,41,42,45,49,60,63,67,74,79,87,88,89,97,103,108,109,111,118,120,121,124,126,133],east:[17,93,94],eastern:[113,126],echo:12,econ:110,econom:[17,70,71,92,95,102],economi:118,economist:[92,118],ecosystem:[2,3,38,88,97,116],ecuador:127,edg:[10,20,43,67,79,120,123,128,129],edgealpha:[],edgecolor:[120,123,128,129],edgestyl:[],edgewidth:[],edit:[2,10,12,13,14,17,38,49,54,62,65,99,100,103,106,116,118,126],editor:[2,3,9,10,13,25,97,98,100],edu:[46,47,51,52],educ:[9,34,36,60,63,64,66,70,71,118],edward:124,eerili:39,ef233c:124,effect:[9,13,14,15,17,20,27,32,36,39,43,51,52,62,70,71,77,93,95,100,111,112,118,125],effici:[13,15,18,27,30,31,32,38,40,45,55,60,75,77,85,87,96,101,108,112,114,118,123,124],efficiency_data:55,efficieni:124,effort:[2,17,34,51,52,70,71,91,93,98,110,111,118,124,126,127],effortlessli:96,eg:126,egi:126,egregi:[70,71],egrid2016:115,egrid:115,egweyn:[112,114],egypt:[17,126,127],eh:126,eight:[40,75],either:[9,10,11,12,13,14,15,17,27,31,34,35,39,42,48,49,54,56,60,73,89,90,93,95,99,100,101,102,105,109,110,112,118,121,124],el:127,elaps:77,elder:80,elect:41,electr:55,element:[29,38,42,48,49,50,54,61,63,64,68,73,77,78,89,90],eleph:[109,110],eleven:60,elid:30,elif:126,elimin:[79,127],elon:43,els:[13,18,24,27,30,34,48,96,97,104,123,124,126],elsewher:[18,121],emac:12,email:[6,7,18,34],embarass:17,embarassingli:31,embark:34,embodi:[10,120],embrac:42,emerg:[17,18,36],emir:[126,127,128,129],emiss:[118,121,127,128,129],emoji:41,emp_rat:102,emphas:[10,17,21,22,23,27,32,49,86,93,94,118,121],emphasi:124,empir:[36,80],emploi:[70,71,79,95,102,106,107],employ:[70,71,95,102,106],employe:[15,42,49,70,71,95,110,112,114],employees_by_year:112,empow:9,empstat:[95,102,106,107],empti:[16,17,27,48,75,77,126,133],en:[0,47],enabl:[10,25,39,77,89,96,100,101,104,116,121,124],enact:[51,52],enclos:[80,97],encod:[62,97,98,102,113,116,120,121,126,128,129],encoding_error:126,encount:[14,17,19,23,30,31,34,40,56,79,85,88,89,93,97,98,106,108,109,110,114,127,133],encourag:[13,27,82,118,123,127],end:[6,9,10,11,13,15,18,20,22,23,24,29,30,31,32,34,35,37,40,42,48,49,55,60,61,63,67,70,71,78,89,90,93,96,97,103,104,108,110,111,114,118,119,124,125,126],endless:106,endog:[118,119],endpoint:55,enemi:77,energi:[9,10,15,55,115,125],engag:[21,34,36],engin:[8,9,15,16,17,29,30,76,85,87,116,123,126],enhanc:[89,90,123],enjoi:35,enough:[9,11,14,20,24,25,27,29,32,35,36,40,51,52,63,70,71,74,81,93,97,107,120,121],enrol:34,ensu:32,ensur:[6,9,10,15,17,18,20,21,25,30,32,34,49,55,60,70,71,80,82,83,93,126],entail:[34,36],enter:[6,10,32,60,74,82,110],enterpris:32,entir:[2,6,9,17,18,23,24,29,30,31,36,41,48,62,63,67,76,87,99,104,120,123,124,127],entireti:93,entiti:[15,41,70,71],entri:[13,17,19,22,23,26,27,29,37,42,45,48,49,50,54,55,56,57,60,61,62,63,65,68,69,73,75,77,79,82,83,90,91,93,94,96,97,99,100,101,102,103,104,106,109,110,112,113,126,128,129],enumer:[13,123,124,128,129],env:126,environ:[3,4,5,6,7,9,12,13,14,33,38,81],environment:115,eon:80,epa:115,epoch:40,epvgfgzts:28,equal:[13,18,23,30,48,61,67,68,74,78,79,82,83,84,94,95,104,109,118,127],equatori:127,equit:118,equival:[11,32,38,60,67,77,88,115,133],eras:[12,17,27],erika:25,eritrea:[93,94,127],err:[36,94,105],erron:14,error:[3,12,14,17,18,19,25,27,30,32,33,36,42,48,49,61,68,82,83,87,88,89,93,94,98,101,102,104,105,111,113,116,118,121,126],error_bad_lin:126,error_rewrit:30,errorband:121,errorbar:123,escap:[12,100,103,133],escapechar:126,esh:126,esoter:54,especi:[3,11,12,17,20,25,27,31,34,35,36,40,41,57,60,64,112,113,118,123],esperanza:[112,114],essai:36,essenti:[10,26,88,89,100,101,109,110],establish:[34,35,49,55,104],estim:[17,26,32,40,41,42,46,47,51,52,60,70,71,72,82,83,92,107,108,118,119,123],estonia:127,estout:17,eswatini:127,etc:[0,2,3,6,7,9,11,12,13,15,17,24,25,28,29,30,32,34,35,36,39,40,41,42,51,52,60,61,68,75,76,81,82,83,84,85,86,89,97,98,100,101,103,105,108,110,113,115,116,118,120,123,133],ethiopia:127,ethnic:[70,71,107],etiquett:116,eubank:[1,25],euituavzq:28,europ:[88,93,94,97,98,103,104,105,106,126,127,128,129],eval:[114,126],evalu:[4,12,13,14,15,17,24,29,36,40,48,49,51,52,55,76,77,79,81,91,108],evang:121,even:[3,8,9,10,12,13,14,15,18,19,20,21,24,25,26,27,30,31,32,33,34,39,40,41,42,43,44,47,48,49,51,52,54,55,60,62,69,73,77,79,82,83,84,88,89,90,91,94,97,99,102,103,104,106,107,108,112,113,118,121,123,124,133,134],even_integers_through_1000:83,even_row:28,evenli:[51,52,92],event:34,eventu:[32,111,126],ever:[10,11,12,13,17,18,23,24,29,30,31,42,55,63,67,80,89,90,94,96,97,100,102,105,117,118,121],everi:[9,10,12,14,15,17,18,23,24,29,30,31,34,40,42,46,47,51,52,55,73,75,76,77,89,93,95,97,98,100,102,111,113,117,118,121,123,134],everyon:[9,10,15,17,25,31,32,34,41,46,47,49,51,52,60,63,70,71,89,94,107,118],everyth:[6,9,12,13,18,19,21,24,25,26,27,29,30,31,35,36,38,40,41,43,44,47,49,53,60,62,69,74,89,91,95,97,100,102,103,105,109,113,114,125,126,127],everywher:[8,12,17,33,49],evid:[39,51,52,68,95,105,111,134],evidenc:[14,34,68,121],evil:49,evolut:[53,67],evolutionari:118,evolv:[13,31,36,67,96,118],ewcskolhuv:[],ex10_gini_policy_d:51,ex11_gini_policy_:51,ex4_gini:51,ex4_share_below_poverti:51,ex6_gini_policy_a:51,ex6_gini_policy_b:51,ex7_gini_policy_c:51,ex8_revenue_rais:51,ex9_transf:51,ex:3,exact:[6,12,20,23,29,32,40,41,51,52,62,77,103,120,127],exactli:[3,13,15,17,18,29,30,32,33,37,48,49,77,93,94,97,102,110,111,113,117,123,125],exam:34,examin:[107,111,113,118],exampl:[2,8,9,10,11,12,13,14,15,19,20,21,22,23,24,26,27,28,29,32,34,35,37,38,40,41,42,43,44,46,47,48,49,51,52,54,55,58,59,60,61,62,63,64,66,67,68,70,71,72,73,74,77,78,79,80,81,82,85,86,89,90,93,94,96,97,98,99,101,102,103,104,105,106,107,109,111,112,113,114,115,117,118,121,122,123,124,126,127,128,129,133],example_csv:[11,12],example_data:[11,12,17,19,93,94,103,104,106,118,121],exc:33,exceed:[118,121],exceedingli:[24,56],excel:[15,18,26,40,60,76,82,83,84,93,97,98,124,127],except:[14,17,20,21,23,25,30,32,33,46,47,51,52,58,59,65,70,71,86,90,93,94,102,105,113,114,127],exception:79,exchang:[21,40,46,47],excit:[10,15],exclud:107,exclus:[70,71],excruci:32,exec:[30,127,128,129],execut:[3,4,10,12,13,14,17,25,26,29,30,31,32,33,39,76,77,78,79,81,82,83,90,100,101,110,114],exercis:[6,15,21,25,35,36,43,45,70,71,75,77,79,84,90,93,95,102,103,106,111,113,126,127],exhaust:[31,98],exhibit:[97,106],exist:[4,10,12,17,18,20,22,23,24,27,31,34,36,49,54,63,81,89,90,93,94,95,97,101,110,118,125],exit:[3,12,34],exog:[118,119],expand:[2,35],expect:[13,17,18,19,24,32,35,46,47,51,52,64,73,74,79,89,91,95,97,100,103,109,110,111,113,118,121,124,126,127],expens:[26,34,35],experi:[4,7,9,11,15,17,24,27,31,32,54,55,70,71,74,79,81,91,93,115,120,124],experienc:[18,42,104],experiment1:[51,52],experiment:[79,120],expert:[13,97],expertis:[36,112],explain:[36,40,41,43,48,49,100,101,118,123,124],explan:20,explanatori:126,explicit:[21,42,79,89,91,101,113],explicitli:[8,13,15,16,20,29,58,59,61,76,77,88,89,94,101,107,113,121,124],explor:[12,13,14,32,51,52,67,72,73,74,75,77,78,79,82,83,84,85,87,89,90,92,93,94,102,108,109,110,111,112,115,116,118,121,123,124,125,127,128],exploratori:[116,118,121,122],explos:17,expon:[62,124],exponenti:129,exported_statist:17,expos:[55,68,118,121],exposit:[93,116],express:[14,17,31,34,48,76,77,82,83,103,108,112,114],ext_modul:30,extend:[41,43,74,104],extens:[14,25,32,34,36,38,39,60,81,97,98,11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118,119,121,128,129],scale_x_continu:[],scaler:68,scan:[41,67,74],scandinavia:[93,94],scarc:118,scari:9,scatter:[111,116,118,119,120,121,122,123,128,129],scatterplot:[120,121,128,129],scenario:[79,82,83],scene:[32,39,49,55,62,96,99,100,120],schedul:[30,32,33],schema:121,scheme:60,school:[34,36,64,70,71,95,102,107,118],schouweil:[46,47,51,52],sci:[29,76],scienc:[2,6,7,8,9,11,12,15,16,17,18,20,24,25,26,27,30,31,38,41,42,43,45,46,47,48,51,52,53,54,58,59,60,67,70,71,72,73,75,79,82,83,84,85,87,88,89,90,93,102,108,111,112,115,117,118,120,123,127],scientif:[31,109],scientist:[6,7,8,9,10,12,15,17,18,20,24,27,29,30,31,32,34,35,36,40,60,62,67,74,76,77,85,87,89,95,97,98,102,106,116,117,120,121,134],scikit:[30,32,79,116],scipi:[4,18,81],scope:[14,34],score:[93,103,104,106,127],scrap:123,scratch:32,screen:[12,123,128,129],screw:[12,17],script:[11,12,13,30,86],scroll:[113,121],sd:[110,126],sdn:126,se:[32,121],sea:125,seaborn:[19,123],search:[9,10,12,14,18,25,36,39,80,87,95,126],second:[12,15,17,20,22,24,25,26,29,30,31,33,34,37,40,41,42,43,48,49,54,60,61,63,65,66,67,68,73,74,75,77,85,87,88,89,90,93,94,97,98,99,101,104,105,108,110,111,113,115,117,118,121,126,127],second_column:113,second_seri:90,second_subset:55,secondari:118,secret:[12,29,76,91],section:[3,11,12,17,18,20,30,31,62,67,76,79,88,89,101,103,104,108,110,114,115],secur:[20,31,36,113],see:[2,3,4,6,7,8,9,10,12,13,14,15,16,17,18,19,20,22,23,25,29,30,31,32,33,35,36,37,38,39,40,41,42,43,46,47,48,49,51,52,53,54,55,57,60,61,62,64,67,68,73,74,75,76,77,79,80,81,82,83,84,89,90,91,93,94,95,96,97,98,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,118,120,121,122,123,124,126,127,128,129,133],seed:[20,78,79,82,83,84,102,123],seek:[41,79,102],seem:[10,12,15,18,19,20,21,31,32,34,40,43,46,47,51,52,54,89,93,94,95,103,105,107,118,120,127],seen:[21,25,30,32,38,39,40,41,42,46,47,62,67,77,79,113,114],segment:57,select:[3,6,7,8,9,10,14,15,16,27,39,46,47,49,51,52,63,72,74,79,80,88,90,92,94,99,107,108,114,121,123,124,127],selection_interv:121,selection_singl:121,selector:[16,121],selectornam:121,self:[17,30,47,94,104,118,119,126,134],sell:[42,111],semest:[8,9,25],semi:[97,128,129],semicolon:126,semiolog:118,sen:[],send:[27,30,33,34,39,86],send_recv_from_rpc:33,seneg:127,senior:108,sens:[6,9,11,13,19,20,29,32,34,36,40,42,43,44,53,54,61,72,73,76,82,83,88,95,96,97,98,102,107,111,115,118],sensit:[12,35,74,118],sensor:[74,106],sent:98,sentec:15,sentenc:[15,80,133],sentinel:[102,106,107],sep:[32,97,126],separ:[2,3,12,31,46,47,51,52,55,63,67,70,71,81,82,83,90,93,97,99,104,107,109,110,120,123,124,126,128,129,133],seper:30,septemb:10,seqplt16:115,sequenc:[20,23,31,61,79,115,120],sequencematch:127,sequenti:[24,31,99],serbia:127,seri:[12,17,20,29,32,35,37,41,43,67,79,80,86,87,91,94,99,100,103,105,106,123,124,127],serial:[31,107,130],series_dtyp:32,series_w_numeric_index:91,seriou:[19,29,35,76,120],serious:[15,18,21,27,32,62,66,97,101,102],serv:[],server:[11,12,31],servic:[11,17,32,33,36,118],session:[3,6,7,8,10,12,14,16,24,31,32,33,39,40,58,59,92,100],set:[0,2,6,7,8,12,14,15,16,17,20,23,24,25,26,27,30,31,32,33,34,35,36,38,39,40,42,48,49,51,52,54,62,67,70,71,72,77,78,79,82,83,84,85,86,89,93,95,100,101,102,103,104,106,107,108,109,111,112,113,114,117,118,121,123,124,126,127,128,129],set_axisbelow:[128,129],set_bound:124,set_opt:[32,101,118,119],set_ticks_posit:124,set_titl:123,set_vis:[123,124],set_xlabel:[123,124],set_xlim:123,set_xtick:124,set_ylabel:[123,124],set_ylim:123,setitem:104,settingwithcopywarn:[101,104],settl:62,setup:[3,5,6,8,10,14,30,32,33],seven:32,sever:[6,7,11,12,17,19,25,26,30,36,40,41,48,49,55,60,88,111,113,117,118,120,121],sex:[102,118],sexist:15,seychel:127,sf:8,sfvmhgqeyn:[],sh:[6,7],shade:124,shall:80,shallow:38,shape:[15,18,35,36,49,60,61,62,65,67,68,69,70,74,78,82,83,93,118,123,126,130],share:[2,17,19,20,25,41,42,43,46,47,51,52,54,55,56,70,71,87,89,92,99,101,102,109,110,111,112,113,118,121,123,124,125,134],share_feloni:19,sharpli:19,she:[17,34,41,49,80],sheepskin:95,shelf:[22,23,57],shell:[6,10,11,12],shift:[3,10,55,62,94,113],shifted_and_scaled_back:62,shine:[74,112],shipment:32,shockingli:36,shoot:29,short_str:127,shortag:123,shortcom:31,shortcut:[6,7,13,100,108],shorter:[29,37],shortest:[42,89],shorthand:[102,126,127],shortli:0,shot:92,should:[3,4,6,8,10,12,13,15,16,18,24,25,26,27,28,29,30,32,34,35,36,39,40,43,49,51,52,55,57,58,59,61,62,65,73,76,78,80,81,89,91,93,95,97,100,101,102,105,106,107,111,113,116,117,118,119,121,123,124,126,127,133],shouldn:[61,67,113],shout:80,shove:32,show:[3,6,7,10,12,14,15,17,18,26,27,30,31,34,47,57,61,67,73,77,79,85,86,93,95,102,109,111,112,113,114,121,123,124,128,133],showcas:123,shower:79,shown:[14,17,20,31,36,43,57,79,89,102,109,110,112,114,123,125,128,129,133],shrink:73,shrunk:125,shuffl:[20,79,89,93],shy:67,si:126,sid:126,side:[10,29,33,36,37,41,43,48,49,55,60,67,89,92,94,104,133],sidewai:42,sierra:[93,127],sign:[12,13,17,46,47,62,94,104,108,110,124],signal:[19,41,95,123],signatur:[10,88],signific:[15,25,31,36,51,52,72,87,94,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,ty:100,type:[3,4,6,10,11,12,14,15,17,18,20,22,25,27,28,29,31,32,34,36,38,39,40,42,45,46,47,48,53,55,62,63,65,66,67,68,70,71,73,74,76,77,79,81,87,91,93,96,97,98,99,100,102,104,109,111,113,115,118,120,121,123,126,127,129,133,134],typeerror:[13,93,94,104,105],typeset:124,typic:[14,73,75,77,81,93,108,109,110,123,124,128,129],typingerror:30,typo:[13,111],typographi:124,u1:126,u21:44,u32:88,u4:48,u7:41,u:[41,46,47,51,52,110,115],uae:17,ubiquit:[6,7,108],ufunc:88,ufunctypeerror:88,uganda:127,ugh:[90,100,101],ugli:13,uh:[19,62,102],ui:10,uint16:20,uint32:20,uint64:20,uint:20,ukrain:[88,93,97,98,103,104,105,106,127],ultim:47,ultra:[13,35],umn:[46,47,51,52],un:[34,126],unabl:[32,114],unavail:[11,81,106],unawar:[34,111],unbeknownst:15,uncaught:14,uncertain:55,unchang:[73,101],uncheck:[10,14],unclear:94,uncomfort:36,uncommon:[31,54,106],uncompress:[27,28],uncov:118,undata:126,undefin:62,under:[10,27,32,36,46,47,51,52,57,88,93,102,103,118,121],underestim:[51,52,107],underflow:20,undergradu:[15,34],underli:[2,3,8,9,10,13,19,22,23,31,32,55,60,62,93,96,99,118,134],underscor:[12,22],understand:[2,6,12,13,14,15,18,19,20,21,23,25,27,29,31,32,34,35,36,38,42,53,54,55,57,58,59,61,70,71,75,76,77,86,87,88,89,91,96,97,100,101,102,107,112,113,114,116,117,118,124,126,129],understood:[128,129],undertak:[51,52],underwai:2,undesir:14,undisput:102,unemploi:[95,102,106],unemploy:102,unemployment_percentag:17,unequ:92,unexpect:[57,126],unfamiliar:123,unfold:17,unfortun:[18,20,23,29,76,100],ungrad:62,unhthmzuna:28,unicod:[41,97,98],unicodedecodeerror:113,unifi:[2,30],uniform:[18,41,43,51,52,79,80,123],uniformli:[20,79],unimagin:26,unimport:12,unintend:13,unintenti:100,union:8,uniqu:[17,23,31,36,42,74,83,89,90,100,102,104,105,106,107,110,113,121,123,124,126,127],unique_charact:126,unit:[17,25,31,32,38,41,43,46,47,51,52,60,70,71,93,94,95,102,104,107,110,113,124,126,127,128,129],united_nations_contin:126,univers:[11,15,25,33,34,47,97,98],unix:[20,40],unknown:34,unlabel:15,unless:[11,12,14,17,35,79,97,126],unlik:[4,9,12,17,23,25,28,29,30,32,36,40,49,51,52,58,59,61,63,65,74,76,79,90,93,97,102,106,120],unmatch:127,unnecessari:25,unneed:27,unnot:17,unpack:31,unpredict:100,unproduct:17,unread:[118,121],unreason:18,unsign:20,unsubscrib:18,unsuccess:61,unsupervis:116,unsupport:[13,30,93],unsupportederror:30,unsurprisingli:103,until:[10,12,14,17,23,29,32,46,47,54,70,71,74,76,100,101,118,120,127],unto:[31,98],unusu:[97,98],unwant:[18,110],unwittingli:13,unzip:7,up:[4,6,7,11,12,13,14,17,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,39,40,41,42,43,44,45,46,47,48,49,51,52,55,57,60,61,62,63,67,68,70,71,72,74,76,77,79,81,87,89,90,91,92,93,94,95,96,97,100,101,102,103,104,105,108,109,110,111,112,113,119,120,121,123,124,126,133],upcom:[42,51,52,79,102,110],updat:[0,4,14,17,21,25,32,49,51,52,64,66,81,86,88,113,118,126],updated_them:118,upfront:[17,27],upon:[8,14,123],upper:[17,20,23,67,103,123],uppercas:23,upsid:[17,40],upward:118,urban:[34,124],urg:80,uri:[],url:[102,121],uruguai:127,us:[0,2,3,4,6,7,9,10,11,13,14,15,17,18,20,21,22,23,26,27,31,33,34,35,41,42,43,45,48,49,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,68,70,71,72,73,74,75,77,78,79,80,81,82,83,84,85,86,87,88,91,92,93,95,97,100,102,103,104,105,106,107,109,110,111,112,113,114,115,116,117,118,119,120,121,122,125,126,127,128,129,133],us_acs_2017_10pct_sampl:[95,102,106,107],us_americancommunitysurvei:[102,106,107],us_household_incom:[43,46,47,51,52,97,130],usa:[46,47,51,52],usag:[12,93,96],usbempwyah:[],usd:[128,129],use_inf_as_na:118,usecol:[27,32,126],user:[2,6,7,9,10,11,12,13,15,17,18,20,23,25,28,29,30,31,32,33,35,36,39,46,47,49,51,52,54,88,89,91,96,97,99,100,101,104,105,109,111,117,118,120,121],user_guid:[101,104],usernam:18,userwarn:94,usr:3,usual:[4,11,12,15,18,20,21,24,27,29,30,32,33,41,53,55,60,67,70,71,76,81,90,93,97,98,104,105,117],ut:110,utah:110,utc:40,utf:113,util:[6,9,27,63,97,98,103,106,111,117,126],uzbekistan:127,v0_8:[19,118],v11:[46,47,51,52],v1:68,v2:[27,32,68],v5:121,v:[12,13,38,46,47,83,86,116,130],va:[110,123],vacat:62,vagu:15,valid:[12,13,19,79,111,118],validate_integ:126,valign:[],valu:[2,12,13,14,15,17,18,19,20,23,25,29,30,31,32,35,39,40,41,42,43,44,46,47,48,49,50,51,52,54,55,57,58,59,62,64,65,66,67,68,70,71,73,74,77,78,79,80,82,83,84,86,92,93,94,95,96,97,99,100,101,102,104,105,106,107,108,109,110,111,112,113,115,118,119,121,123,124,125,126,127,128,129],valuabl:[14,17,27,34,42,43,60,74,93,118],value_count:[89,95,102,103,105,107,111],value_list:[82,83],valueerror:[17,42,48,49,61,68,89,105],vanessa:21,vanilla:[9,13,20,30,45,48,53],vanish:118,vanuatu:127,vapor:125,var212:17,vari:[77,80,107,113,124],variabl:[6,7,9,13,14,15,17,21,24,27,29,30,32,40,46,47,48,51,52,54,55,64,70,71,76,77,79,81,85,89,92,93,94,95,99,102,103,104,105,107,109,110,111,113,114,115,118,121,133],varianc:[42,89],variant:80,variat:[15,51,52,118],variou:[6,7,10,15,24,32,41,73,88,97,111,118],vast:[43,125],vconcat:[],vct:[],vdot:78,ve:[2,3,6,9,10,11,12,14,15,17,18,19,22,23,24,25,26,28,30,31,32,34,36,38,39,4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Data Science","Practical Data Science in Python","The Jupyter Project","Running R Jupyter Notebooks in VS Code","Managing Python Packages","Set Up an Augmented Command Line","Improving Your Command Line Experience (macOS)","The Command Line on Windows / Installing Bash","Installing Geopandas","Installing Python and Miniconda","Setting Up Visual Studio Code","The Command Line, Basics","Advanced Command Line Tutorial","Principles and Tools for Debugging","Debugging Python in VS Code","Backwards Design","Managing Conda Environments","Defensive Programming","Getting Help Online","The Iceberg Principle","Numbers in Computers","Reviewing Code on Github","Python vs. R: Important Differences To Be Aware Of","Python: Variables vs. Objects","Workflow Management","Writing Good Jupyter Notebooks","What is Big Data?","Big Data Strategies","Parquet","Speed in Python","Solving Performance Issues","Parallel Computing","Distributed Computing with dask","Starting a Cloud Dask Cluster","So You\u2019re Thinking About a PhD","Advice on Buying Computers for Data Science Students","chatGPT and You","The Object dtype and Performance","Class Two: Representing Data: Objects, collections, and their uses for computation (Numpy)","Running Python in the VS Code Interactive Window","Numpy Arrays: The Workhorse of Data Science","Working with Vectors","Doing Math With Vectors","Visualizing Your Data with Histograms","Type Promotion in numpy","Vector Recap","(Ungraded) Vector Exercises","(Ungraded) Vector Exercises","Subsetting Vectors","Modifying Subsets of Vectors","Recap","Week 2 Numpy Re-cap Exercises","Week 2 Numpy Re-cap Exercises","Welcome to Matrices","Views and Copies","When Do I Get a View and When Do I Get a Copy?","Review of Views and Copies","Variables are pointers to objects","Views and Copies Exercises","Views and Copies Exercises","Intro to Matrices","Folding and Reshaping Matrices","Matrices as Images","Subsetting Matrices","Editing Subsets","Review of Matrices","Matrix Exercises","Arrays","Broadcasting","Review of ND-Arrays","Week 3 Quiz","Week 3 Quiz","Welcome to Week 4!","Summarizing Arrays","Color Images as Arrays","Practical Example: comparing weather by counting like-values","The Python Ease of Use / Speed Tradeoff","Vectorization in Python","Exercise: Speeding up matrix operations","Random numbers","Exercise 1: Calculating the mean and variance of common random variables","Understanding and Managing Python Packages","Week 4 Assessment","Week 4 Assessment Solutions (NOT FOR POSTING)","Assessment: filtering and querying a dataset","Course Wrap-up","Class Three: Loading and manipulating data","Programming with data","Working with tabular data","Pandas Series","Subsetting and Indexing Series","(OPTIONAL) Subsetting and Indexing with Single Square Brackets ([])","Series Exercises","Working with tabular data through Dataframes","Subsetting DataFrame Tricks and Gotchas","Estimating Labor Market Returns to Education","PyArrow: An Alternative to Numpy 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Python: customizing plots in matplotlib","Project: Global greenhouse gas emissions","Project: gathering, inspecting, and cleaning the data","Project: Data merging","Project: Plotting","Project: Plotting","<no title>","<no title>","CONCAT","Combining numbers and text into strings: f-strings","Why not just use Excel or 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0,53,54,55,61,62,67,68,70,71,75,76,89,90,91,92,93,95,96,97,98,99,100,101,102,104,105,106,107,108,110,112,113,114,118,119,121,122,124,125,127,128,129,130],By:[0,7,10,11,15,18,20,30,31,51,52,74,81,89,97,98,102,104,109,112,114,126],FOR:11,For:[0,8,9,10,11,12,13,14,15,16,17,18,20,22,23,24,25,27,29,30,31,32,36,37,38,40,41,42,43,44,46,47,48,49,51,52,54,60,61,62,63,64,66,68,70,71,73,75,76,77,80,81,82,83,89,92,93,94,95,97,99,100,102,103,104,105,106,107,108,109,111,112,114,116,119,122,125,127,128,129,130,134],IF:11,IN:[24,32,111],If:[3,6,7,8,9,10,11,12,13,14,15,16,17,18,20,23,24,25,27,29,30,31,33,34,35,36,39,41,42,43,45,46,47,48,49,51,52,55,58,59,60,61,63,65,67,68,70,71,72,75,76,77,78,79,82,83,88,89,90,91,92,93,94,95,97,103,104,105,107,108,109,110,112,114,118,119,121,127,128,134],In:[2,3,6,7,8,9,11,12,13,14,15,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,37,38,39,40,41,42,43,46,47,48,49,51,52,53,54,55,57,60,61,62,67,70,71,72,73,74,75,76,77,78,79,80,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,118,119,122,123,124,125,126,127,128,129,130,134],Is:[29,46,47,49,51,52,63,108,117,122],It:[3,4,8,9,10,11,12,13,14,15,16,17,19,20,21,22,23,28,30,31,32,33,34,35,38,42,51,52,54,74,75,77,81,88,94,97,98,99,100,102,104,108,112,114,119,121,122,123,127],Its:119,NOT:[9,13,55,82,93,124],Near:[98,128],No:[17,20,25,32,36,127],Not:[6,9,15,19,30,43,47,51,88,102,106,108,112,127,128,129,130],OF:[80,134],ON:[11,12],OR:111,Of:[18,35,41,43,55,60,67,89,94,97,102,103,119,124],On:[3,8,9,12,16,41,80,94,100,119],One:[9,10,12,14,24,25,27,30,34,42,43,47,61,66,68,77,89,92,95,104,105,106,110,111,119,122,125,127,128],Ones:41,Or:[12,17,27,32,35,41,43,51,52,63,64,67,68,93,95,97,102,103,106,121],Such:79,THAT:31,THE:[24,32,125],THEN:127,TO:[11,99,127,128],That:[3,9,10,11,12,13,17,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,40,43,44,55,61,70,71,75,76,77,90,91,95,97,98,102,104,107,108,118,119,124,126,127,129,130],The:[0,5,9,10,13,14,15,17,18,21,22,23,24,25,27,28,30,31,32,33,34,35,41,42,43,45,48,49,51,52,54,55,56,57,60,61,62,63,65,66,68,69,70,71,74,75,77,79,80,82,83,84,86,89,90,92,93,94,95,96,97,102,106,108,109,110,111,112,113,114,115,116,121,122,124,125,127,128,129,130,135],Their:118,Then:[3,6,7,8,9,10,12,16,17,19,20,23,27,28,29,30,31,32,33,41,42,43,46,47,48,49,51,52,55,62,68,70,71,75,76,80,91,93,100,105,108,112,115,119,122,124],There:[4,6,9,11,13,14,15,17,18,22,23,25,26,27,29,30,33,34,36,38,40,44,46,47,48,60,66,73,74,75,76,77,80,81,89,90,92,93,95,103,105,106,110,111,114,116,119,122,124,125,127,134],These:[6,12,13,14,15,17,20,25,31,32,46,47,51,52,55,72,73,75,88,89,92,93,95,97,99,102,103,105,107,116,119,122,124,127,134],To:[2,3,4,6,7,8,9,10,11,12,14,15,16,17,19,20,21,23,25,27,30,31,32,39,40,43,48,49,51,52,54,55,61,62,63,66,67,68,70,71,74,75,77,79,81,82,83,84,89,91,92,95,100,102,103,104,106,107,108,109,110,111,112,113,114,115,119,121,122,123,124,127,128,129,130,134],Will:30,With:[7,9,10,12,18,24,30,34,35,39,41,57,78,80,88,91,92,93,94,96,109,121,124,125,126,127,129,130,134],_____:80,______:80,_______:80,_________:95,__call__:[119,120],__class__:32,__getitem__:91,__init__:32,__name__:[17,32],__nonzero__:17,__setitem__:[],__traceback__:30,_build:0,_compile_for_arg:30,_concatenate_chunk:127,_core:119,_decor:127,_engin:[94,127],_ensure_numer:105,_event_pip:30,_fit_predict:[119,120],_growth:92,_handle_writ:32,_has_valid_setitem_index:[],_is_master_process:30,_lib:[94,127],_locationindex:[],_lsprof:30,_mean:108,_merg:[19,112],_of:108,_pandas_api:28,_psplatform:32,_rate:92,_read:127,_read_row:127,_read_to_buff:32,_reader:127,_run:32,_schedule_flush:30,_setitem_with_index:[],_stat:[119,120],_sync_cluster_info:33,_thread:30,_tokenize_row:127,_unbox_scalar:[],_validate_scalar:[],_valu:92,_wait_for_tstate_lock:30,_write_buff:32,_x:108,a1:89,a52aba7de6d5:13,a93c:32,a9efa4ff5a9d6612f62c70f567177170:[],a_0:77,a_1:77,a_2:77,a_:[11,78],a_boolean_vector:41,a_d:133,a_fast_funct:30,a_float_vector:41,a_fold:12,a_folder_with_stuff:11,a_medium_funct:30,a_slow_funct:30,a_string_vector:41,a_toi:78,ab:62,abandon:15,abbrevi:[111,116],abcdefghiklmnopqrstuwx1:12,abcfghloprstuw:12,abil:[11,13,14,27,31,34,35,42,63,70,71,88,89,90,119,122,124,134],abl:[9,12,13,14,15,17,24,25,26,27,29,31,32,34,35,36,40,41,53,63,70,71,73,74,76,84,88,89,90,108,109,111,112,113,114,115,117,118,119,124,125,126,127,128,134],abort:17,about:[2,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29,30,31,35,36,39,40,41,42,43,44,46,47,48,49,51,52,53,54,55,61,62,63,66,67,68,69,70,71,74,75,76,77,79,80,81,82,87,88,89,91,93,94,95,97,98,99,100,101,102,103,104,105,106,107,110,112,113,114,116,118,119,121,122,123,124,127,128,130],abov:[3,13,15,17,18,25,27,30,31,32,33,34,36,40,41,42,43,44,46,47,48,49,51,52,54,55,60,61,62,63,66,67,70,71,73,74,75,79,80,82,89,90,93,96,97,99,100,102,104,105,108,109,110,111,112,113,119,121,122,125,127,128,134],absolut:[3,17,18,19,20,25,27,32,35,62,100,107,112],abstractli:15,absurd:107,abus:18,ac:[46,47,51,52,95,102,106,108],academ:[34,126],acceler:115,accelerationord:[],accent:103,accept:[6,12,30,32,40,98,103,112],access:[6,8,10,11,14,15,16,17,23,26,29,30,31,32,38,40,41,48,73,76,89,90,91,92,94,97,98,103,119,122],accid:19,accident:[17,29,55,76,105],acclaim:17,accommod:[49,111,122,127],accomplish:[13,15,17,18,20,24,39,48,49,53,62,82,83,84,92,102,109,110,111,113,115,124,125,129,130],accord:[112,113],accordingli:[68,73],account:[10,11,15,33,46,47,70,71,108,111],accru:[51,52],accumul:[30,32,95],accur:[23,40,46,47,70,71,95,102,108],accuraci:[70,71],accustom:[17,62,92],achiev:[13,14,15,18,25,30,34,115,116,119],acknowledg:107,acquir:[30,34],across:[17,20,26,27,30,31,32,35,42,46,47,48,51,52,61,73,74,77,78,92,97,98,103,108,109,110,111,112,113,116,122,124,125,128],acs_5yr_hh_incom:131,act:[34,42,94,125],action:[2,19,31,110,115,119,120,124,134,135],action_ind:32,activ:[3,8,10,12,14,16,18,27,30,31,34,39,58,59,76,77,100,104,124,126],actual:[2,3,6,7,8,9,10,12,13,15,17,20,21,22,23,24,25,26,27,28,29,30,31,32,33,35,36,40,43,46,47,48,51,52,54,55,57,61,62,64,67,68,70,71,76,78,87,89,92,93,94,95,98,99,100,101,102,106,107,108,114,118,119,121,122,123,129,130],acut:26,ad:[6,9,10,12,14,20,21,25,26,29,30,40,46,47,49,60,62,68,70,71,76,79,80,88,89,94,97,107,110,128,135],adapt:[78,92,124],add:[0,9,10,12,13,14,17,18,19,20,22,23,24,25,26,29,30,35,38,40,42,46,47,60,62,66,68,70,71,74,75,76,77,80,88,89,96,97,100,101,107,108,109,110,111,112,114,115,119,120,121,122,124,125,126,128,129,130,134],add_artist:124,add_categori:[104,107],add_select:122,add_up_entri:13,addict:35,adding_tot:30,addit:[2,3,9,12,14,15,17,22,23,29,30,31,32,34,35,36,42,51,52,53,60,68,70,71,76,77,79,94,96,97,102,106,107,110,111,113,119,122,124,125,127],addition:[14,82,83,84,128],addr:33,address:[9,15,17,29,32,37,48,53,57,68,70,71,101,104,107,108,111,112,114,119,121,127,129],adeli:[119,120],adequ:34,aditya:107,adjust:[13,60,64,73,108,124,127,135],adjusted_incom:64,admin:[7,33],administr:[107,128],admit:[35,96,101,119],admittedli:[62,93],admonit:0,adob:62,adopt:[17,125,134],adult:102,advanc:[9,11,29,30,34,36,76,97,124],advantag:[14,17,20,29,76,88,92,102,104,106,107,108,113],advic:[34,80,91,101],advis:[51,52],advoc:[18,24,92,101],ae:121,aesthet:[17,118,125],af:33,affect:[6,20,23,27,95],affili:32,afghanistan:[127,128,129,130],afraid:28,africa:[17,88,92,93,94,97,98,103,104,105,106,127,128,129,130],african:[93,128],after:[3,8,10,12,13,15,17,20,24,27,29,32,33,34,37,38,42,48,49,53,55,58,59,60,62,66,70,71,74,82,83,84,85,86,95,97,101,103,104,112,115,118,119,124,127,134],after_tax:42,after_tax_incom:66,afterward:12,ag:[15,41,43,49,60,63,64,66,70,71,88,89,95,102,108,119,122,125,134],again:[6,8,9,11,13,17,26,31,32,43,48,58,59,62,73,74,77,79,80,93,94,104,109,111,122,125,127,128],against:[8,12,16,17,20,40,104,114,125],agenc:[34,95,116],agent:32,agg:32,aggfunc:113,aggreg:[113,114,115],agnost:[96,122],ago:[19,106,121],agre:[9,10,15,32],ah:119,ahead:[6,7,15,32,55,62,92,102,111],ai:88,aid:98,aim:34,air:[35,110,111,119,122],airport:[6,7],ak:111,aka:36,al:111,alabama:111,alaina:[113,115],alameda:114,alaska:111,albania:[93,94,127,128,129,130],albeit:119,alert:[17,101],algebra:[31,40,60,77],algeria:[93,94,127,128],algorithm:[13,15,20,31,36,72,102],alia:40,alias:38,align:[86,90,91,93,94,110],aliv:119,all:[0,2,3,4,6,7,8,9,10,11,12,13,14,15,17,18,19,20,21,23,24,25,26,27,29,30,31,32,33,34,35,37,39,40,41,42,43,44,45,46,47,48,49,51,52,54,55,57,60,61,62,63,64,65,66,67,70,71,73,74,76,77,78,79,80,81,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,110,111,112,113,114,115,116,117,119,122,123,124,125,127,128,129,130,134,135],all_prescript:32,all_rows_w_even_a:28,alloc:[20,27,32,41,51,52,89,119],allow:[6,7,9,10,11,12,13,14,15,25,27,28,29,30,31,32,34,36,39,40,42,43,50,53,65,67,68,74,76,78,80,89,90,91,94,100,101,104,105,107,109,110,111,112,113,115,118,119,121,122,129,130],alma:47,almost:[6,7,11,12,17,18,21,23,27,29,31,34,36,43,55,63,70,71,74,76,89,95,97,105,121,122],alon:[8,32,104],along:[12,15,18,21,30,34,40,41,43,48,53,60,61,63,65,67,68,73,74,77,98,113,119,121,134],alongsid:43,alpaca:80,alpha2:127,alpha3:127,alpha:[119,120,121,122,129,130],alphabet:[93,115,127],alread:115,alreadi:[3,6,9,10,11,12,15,17,18,24,29,30,32,39,40,48,49,60,68,76,88,92,93,97,102,104,109,114,119,125],alright:125,also:[0,2,4,6,7,8,9,10,11,12,13,14,15,16,17,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,40,41,42,43,45,48,49,51,52,54,55,56,60,61,62,63,64,65,66,67,70,71,72,73,74,75,76,77,79,81,82,83,84,85,86,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,118,119,120,121,122,123,125,127,129,130],alt:[122,130],altair:[8,117,118,119],altair_data_serv:122,altair_figur:[],altair_sav:122,altern:[11,25,34,40,73,89,91,109,117],although:[9,14,15,51,52,62,89,119,124],altitud:41,altogeth:111,alwai:[4,8,9,11,12,13,15,16,17,18,19,20,21,22,23,25,27,28,30,31,32,34,35,36,38,43,44,48,49,54,55,57,60,61,63,65,73,77,78,86,89,90,91,93,94,97,98,99,100,103,104,105,106,109,110,111,11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n:128,bent:18,berin:119,berkelei:127,best:[9,10,12,13,14,15,17,18,22,23,25,28,31,32,34,35,36,38,41,57,70,71,73,77,80,87,88,96,107,113,118,119,121,127],bet:30,betrai:54,better:[5,6,9,11,13,17,18,19,21,26,27,31,32,34,35,36,43,49,51,52,70,71,75,81,89,95,102,105,112,119,125],between:[10,11,13,17,20,22,23,24,26,27,29,30,31,32,33,34,35,41,42,43,45,46,47,48,51,52,54,56,57,62,65,67,68,69,70,71,74,75,76,77,78,79,80,89,90,92,95,96,99,100,102,108,111,114,119,124,125,128,129],betwen:95,bewar:104,beyond:[89,102,119,135],bezo:43,bhutan:[17,128],bhvesyguml:28,bi:127,bia:15,bias:15,bibl:17,bidirect:34,big:[8,9,11,15,18,19,20,22,23,24,29,30,31,32,35,36,39,40,43,54,57,70,71,85,89,93,97,102,104,105,114,119,122,124,129,130],big_arrai:77,bigger:[19,20,27,34,60,104,112,119,129,130],biggest:[22,30,31,32,89,112],bill:119,bill_depth_mm:119,bill_length_mm:[119,120],billi:80,billion:[30,40,127,129,130],bin:[3,12,51,52,79,122,123],bin_edg:79,binari:[29,74,76,86,97],bind:122,binding_rang:122,biologist:119,bird:[93,110,111],birth:[15,110,119,122],birthdai:79,bisco:[],bissau:[93,94,128],bit:[8,9,10,13,15,18,20,26,27,29,30,37,41,48,54,57,74,76,88,89,97,98,99,113,116,122,124,127,128,129,130],bite:18,bitumin:116,bizarr:17,black:[2,25,41,62,67,70,71,74,108,114,129,130],blank:[48,80,110,116,129,130,134],blaze:[29,30,76],blend:[],blob:[102,106,108],block:[0,6,17,25,29,30,32,41,48,75,89,100,121],blockwis:32,blog:32,blood:[67,88,109,110],blow:18,blr:[],blue:[41,62,67,74,110,111,122,124],blueberri:80,blurri:125,bmatrix:[60,78],bntbtkxbdq:28,board:18,bob:107,bodi:[67,100,119],body_mass_g:[119,120],bokeh:118,bold:[6,124,125],bolivarian:128,bolivia:128,bolt:122,book:[22,23,57,100,119,124,125],bool:[17,23,41,48,49,90,93,111,114,128],bool_vector:49,bootcamp:9,bosnia:128,both:[8,9,10,11,13,15,17,19,20,22,23,25,27,29,30,31,32,34,37,40,49,51,52,54,56,63,64,65,67,68,76,79,89,90,91,95,97,99,100,102,103,104,106,107,108,109,110,111,112,114,118,119,124,127,129,130],bother:11,botswana:128,bottom:[3,6,7,8,10,13,16,17,20,27,35,43,63,93,97,118,119,124,125],bound:[32,94],boundari:[129,130],box:[0,10,14,17,30,48,62,122,123,124],boxplot:123,brace:40,bracket:[48,63,65,67,69,80,90,134],bradburi:1,brain:[41,67],brainer:32,branch:[6,17],brasil:103,brazil:[88,93,97,98,103,104,105,106,128],breakdown:[93,114],breakpoint:14,breath:17,breviti:104,brewer:125,brief:56,briefli:57,bright:[67,74],bring:[12,14,15,18,27,31,55,82,83,84,87,110,111,115,117,119,125,126,129,130,134],britain:128,british:[60,127],broad:15,broadcast:[42,49,69],broadli:[20,89,94,103,125],broken:[31,32,94],brokenpipeerror:32,brook:34,brought:17,brows:[17,46,47,51,52],browser:[0,10,122],brunei:128,brush:122,bsd:12,btw:[],bubbl:[4,81],buck:[27,35],buddhist:122,budget:35,buf:32,bug:[10,14,87],buggi:33,bui:[15,26,27,46,47,73,125],build:[9,15,21,30,31,32,34,41,60,66,75,77,85,88,89,90,109,119,121,122,126],built:[2,9,14,15,17,28,30,32,63,77,79,82,83,84,88,97,98,101,103,118,121,122,123,124,133],builtin:30,bulgaria:[92,128],bulk:103,bullet:[18,31],bunch:[15,18,29,31,76,93,119],bundl:[3,10,28,118],bureau:[15,19,41,43,46,47,51,52,70,71,95,102,108,111,114],burkina:[17,128],burn:[35,127],burundi:[127,128],busi:[15,17,32,36,41,109,112,119],busy_dai:90,button:[3,6,8,10,13,14,16,39,46,47,51,52,127],buyer_addl_co_info:32,buyer_address1:32,buyer_address2:32,buyer_bus_act:32,buyer_c:32,buyer_counti:32,buyer_dea_no:32,buyer_nam:32,buyer_st:32,buyer_zip:32,bytearrai:12,bytearray_inst:12,bytes_inst:12,bytes_read:32,c0:124,c1:[110,111,124,132,133],c2:[110,111,132,133],c3:[110,111,132,133],c4:124,c:[3,6,7,8,12,13,14,20,25,29,30,31,40,49,51,52,76,77,78,79,88,89,93,94,95,96,97,98,103,104,105,106,110,111,112,113,119,122,124,125,127,129,130,132,133],c_:78,c_d:133,c_parser_wrapp:127,ca:[19,32,111,114],ca_arrests_2009:112,ca_felonies_2001_2010:19,ca_misdomeaners_2001_2010:19,cabo:128,cach:122,cache_d:127,calc:111,calc_base_wt_in_gm:32,calcul:[13,17,20,26,32,40,41,42,43,46,47,49,51,52,60,63,70,71,73,74,78,86,92,102,105,108,109,111,112,113,114,115],california:[19,62,111,112,114],californian:114,call:[2,4,6,7,8,9,11,12,13,14,15,16,17,20,21,22,23,24,26,27,29,30,31,32,33,34,36,39,40,42,43,44,46,47,48,49,51,52,54,56,57,58,59,61,62,63,66,68,70,71,76,77,78,79,80,81,86,88,89,92,93,94,96,97,98,99,100,101,102,103,104,105,107,110,111,112,113,114,118,119,122,124,127,129,130],callabl:94,callback:32,cambodia:128,came:[9,32,40,54,80,97,99,112],cameroon:128,campaign:41,can:[0,2,3,4,6,7,8,9,10,11,12,13,14,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,121,122,123,124,125,126,127,128,129,130,134,135],can_swim:93,canada:128,cancel:[13,51,52],cancellederror:33,cancer:[15,36],candid:[32,104,128],cannot:[19,30,31,32,36,46,47,61,93,104,107,124,128],canon:119,canva:124,cap:103,capabl:[8,16,31,36,124],capac:116,cape:128,capfac:116,capit:[51,52,103,112],capita:[17,88,92,93,103,111,122,126,127,128,129,130],captur:[77,78,95,124,134],car:[10,42,55,73,113,122],carbon:[13,126,128,129,130],card:79,cardin:[73,119],care:[12,17,20,25,27,31,73,79,89,91,92,104,105,110,111,112,119,124,125],career:[9,10,12,34,36,41,98],carefulli:[9,14,15,18,29,32,34,63,76,91,105,114,119],careless:17,carlo:[82,83],carolina:[107,111],carpentri:11,carri:[17,112],carrol:32,cash:35,cast:[89,101,104,105],casted_kei:94,casual:12,cat:[12,48,80,91,93,97,104,107,110,111],categor:[27,28,36,86,88,89,105,119],categori:[19,28,70,71,102,104,107,108,114,119,120,125,128],categoricaldtyp:119,cathol:122,caught:17,caus:[9,10,14,17,18,19,29,30,32,33,39,49,76,94,101,102,105,106,112,119,120,125],causal:112,cautiou:36,caveat:[101,104],cbgufgtrki:28,cbserial:[108,131],cd:[0,6,7,11,12,117],cdef:30,cdot:[13,78],cede:10,celebr:80,cell:[13,14,25,39,41,49,61,67,74,88,89,94,104,107,110,122,124],censu:[15,41,43,46,47,51,52,95,102,108,111,114],census:[108,114],cent:[33,60],center:[43,67,74,111,124,125,127],centimet:126,central:[31,32,89,93,97,104,128],centric:9,ceo:49,cerebellum:67,certain:[9,17,23,30,58,59,67,82,83,84,97,104,105,106,107,112,114,118,119],certainli:[13,34,67,74,110,121,125,135],certif:[34,60,95],chad:[93,128],chain:[],chainedassignmenterror:104,challeng:[32,34,78,88,95,110,124],chanc:[9,17,18,34,53,79,93,97],chang:[0,2,7,10,11,12,13,14,17,18,19,20,21,22,23,24,25,27,29,31,34,36,41,43,49,51,52,53,54,55,56,58,59,61,62,64,66,76,78,79,82,83,84,88,89,90,93,94,96,97,99,100,101,103,104,107,112,114,115,117,119,120,125,127,129,130,134,135],change_contrast:62,channel:[8,9,74,122],channel_prior:9,chao:32,chapter:[20,82],charact:[41,44,48,97,103,127,128,134],character:[41,43,79,122],characterist:[15,73,79,89,106,113],charg:102,charl:20,chart:[123,125,128,130],chatgpt:88,chdir:[17,32],cheaper:35,cheatsheet:[0,25,32],check:[12,13,14,17,18,19,20,26,28,29,30,31,32,36,41,42,44,51,52,58,59,60,64,76,77,80,86,87,89,93,95,98,100,102,105,107,110,111,114,119,122,123,127],checklist:14,chem:111,chevel:[],chevroelt:[],chide:17,child:[14,80,119],childhood:119,children:[95,102,106,108,119],chile:[119,128],china:[17,128,129,130],chines:41,chip:[31,35],chl:119,choic:[6,7,11,17,18,28,40,79,93,119,121,125],cholesterol:109,choos:[9,13,14,73,124,125],chose:[10,41],chosen:[32,80,118,122,125],chunk:[18,24,27,32,117,127],chunksiz:[27,32,127],ci:[117,122],ci_high:[119,120,122],ci_low:[119,120,122],ciat:12,circl:[3,10,13,54,67,99,104,119,124,129,130],circumst:[55,77,85,104,107],cite:[46,47,51,52],citi:75,citizen:[15,103,119],civil:112,clarifi:[21,126],clariti:[76,77,125],class_2:[49,131],class_3:[88,103,104,106,107,110],classifi:[114,125],classroom:[15,34,41],clean:[9,10,13,15,17,19,24,28,34,51,52,87,102,104,106,109,110,111,119,125,128],cleanli:[24,102,103,105],clear:[14,15,17,24,25,26,30,31,34,36,49,55,63,77,87,89,91,101,102,105,107,108,111,112,119,127,129,130],clearer:[15,77,111],clearli:[13,15,17,19,36,40,67,76,79,100,101,102,110,113,115,119,125,127,129,130,135],clever:[44,62],click:[0,3,6,7,8,9,10,11,12,14,16,17,21,25,39,46,47,51,52,127],client:[3,12,32,33,111],climat:[41,55,67],clinic:36,clip:124,clip_on:124,close:[6,10,17,19,20,26,31,32,33,35,43,51,52,54,58,59,62,74,79,93,98,103,126,129,130],closer:[31,62,67],closest:128,cloth:17,cloud:[6,7,9,10,11,27,31,35,36,75],cloudcov:75,cloudi:75,clump:[129,130],clumsi:102,clunki:63,cluster:[11,27,31,32,102,108,131],clutter:[17,122],cmap:[62,67,74],cmd:[3,7,9,11,12],cmder:[3,11,12],co2:[119,122,126,127,128,129,130],co2_al:127,co2_emissions_tonnes_per_person:127,co:[10,17,111,124,126],coal:116,coauthorship:34,code:[2,6,8,9,13,16,17,18,20,23,24,26,27,28,29,31,32,33,35,36,37,38,40,41,46,47,48,49,51,52,55,57,58,59,60,63,64,66,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,91,92,94,95,97,98,100,101,102,103,105,107,108,110,111,112,114,115,119,121,122,124,125,127,134,135],codebook:[70,71,112,114,116],codec:114,coeffici:[15,51,52,92],coerc:[32,49],coercion:49,coher:[25,111],coil:32,coin:10,col1:104,col2:104,col:28,col_dict:127,col_index:104,colab:9,collabor:117,collaps:[27,32,112,114,122],colleagu:[2,82,83,97,98,113,125],collect:[15,23,29,30,32,34,37,40,41,42,43,45,46,47,51,52,53,55,60,67,70,71,73,75,81,82,83,85,86,88,89,98,108,110,112,114,124,129,130],colleg:[17,34,70,71,95],colloqui:[11,13],colombia:128,colon:[13,40,97,99,134],color:[10,17,18,41,60,67,72,97,114,119,121,122,124,129,130,135],color_list:[129,130],colorado:[107,111],columbia:111,column:[13,14,15,25,27,28,32,40,41,42,60,61,64,65,66,68,70,71,73,74,75,78,86,88,95,96,97,101,104,105,107,108,109,110,111,112,113,114,119,122,124,127,128,129,130],column_a:101,columnar:[],com:[6,9,17,19,30,32,93,94,102,103,104,106,108,119,122,127],combin:[15,17,25,27,41,42,44,48,49,61,67,87,89,113,115,121,124,126,127,128,135],combined_labeler_nam:32,come:[3,6,9,10,11,12,13,15,17,20,21,22,23,26,27,28,29,30,31,32,35,36,39,40,41,43,46,47,51,52,53,61,62,63,64,67,74,76,80,90,91,92,95,96,97,98,99,100,101,102,103,104,107,108,109,111,118,119,121,123,134],comedi:62,comer:32,comfort:[9,10,12,18,36,41,62,119],comm:[32,33],comma:[10,25,63,67,68,93,97,127],command:[3,4,8,9,10,16,17,23,25,27,29,32,33,35,40,46,47,51,52,55,63,70,71,81,84,95,100,101,108,111,114,124,134],command_lin:[11,12],commandlin:[8,16],commandlinetool:3,commandnam:12,commclosederror:32,commensur:34,comment:[13,17,18,21,25,127],commerc:15,commit:[0,15,21,34,112,114],common:[4,8,12,13,14,15,16,21,22,23,32,38,40,41,54,67,72,73,77,79,87,89,93,94,97,99,102,103,105,106,107,110,111,112,113,115,116,117,121,124,127,135],commonli:[17,21,30,41,42,51,52,60,95,97,98,102,110,124,128,134],commun:[15,17,25,34,46,47,51,52,58,59,70,71,76,95,102,105,106,108,112,117,118,119,121,125,134],comoro:[127,128],compact:[38,115],compani:[2,11,15,25,27,32,33,35,36,49,55,70,71],compar:[12,15,17,28,29,30,31,32,37,51,52,55,60,62,68,73,77,78,97,107,111,124,125,126,128,134],comparison:[6,15,20,32,42,124,125,134],compartment:17,compat:[4,10,30,68,81,97,98,101,102,118],compel:[34,125,135],compens:34,compet:26,compil:[29,30,32,36,76,122],complain:102,complet:[3,6,10,15,29,31,32,34,36,40,46,47,51,52,58,59,63,76,77,82,83,84,85,95,104,108,111,112,117,119,124],complex:[14,23,35,36,41,75,85,87,105,109,112,115,129,130],complic:[10,13,14,16,17,31,41,48,62,81,88,94,97,98,99,122],complimentari:93,compon:[14,31,34,35,38,41,74,77,87,89,124,125],compos:[25,41,118,119,121,124],composit:[19,114],compound:[71,92],comprehens:[85,111],compress:[12,27,28,32,97,98,127],compris:[],comput:[4,6,7,9,10,11,12,13,16,17,22,23,24,25,26,28,29,30,34,36,37,39,40,41,53,54,57,60,61,62,72,73,74,76,77,78,79,81,82,83,84,85,86,87,88,96,97,98,100,102,105,107,109,111,113,114],computation:[35,88,115],con:[97,98],concat:[28,110],concaten:[29,66,76,83,88,109,111,134],concatent:110,concept:[9,23,32,38,48,57,73,85,86,90,109,115,125],conceptu:[15,22,32,34],concern:[34,41,94,114,121,127],concert:27,concis:[30,40,42,53,57,77],conclud:[85,117],conclus:[2,36,98,112,125],concord:[],concret:[15,61,68],concur:91,conda:[3,6,9,10,12,14,31,32,33,38,40,51,52],condabin:12,condit:[13,14,17,25,30,31,48,49,75,79,104,119,122],conduct:[17,41,43,60,70,71,95,102,111,112],conf_int:[119,120,122],confid:[17,36,82,84,128],config:[9,124,125,129],configur:[5,10,12,14,20,39],configure_legend:130,configure_mark:130,confirm:[10,54,60,105],conflict:[9,10,94],confound:15,confus:[11,13,17,31,49,89,93,124,125],congo:[93,94,128],congratul:[15,36,51,52,70,71,95],connect:[11,17,32,33,34,57,101,112,115,121,124],connect_timeout:33,connecticut:111,connectionreseterror:32,connector:33,connot:102,consecut:48,consequ:[18,54,61,119],conserv:17,consid:[12,13,15,20,27,29,30,31,32,34,43,48,49,55,67,88,102,104,110,111,113,114,122,125,128],consider:[49,97,128],consist:[10,25,31,34,43,44,65,67,77,79,91,100,101,102,112,125],consisten:125,consol:[14,39],consolid:34,constant:[18,31,104,108,119,122,124],constantli:[17,18,75,117],constitu:[89,119],constitut:[15,36,114],constrain:25,constraint:30,construct:[21,34,48,79,82,88,89,108,116,124],constructor:[85,89],construt:93,consult:[88,118],contact:[17,46,47,51,52],contain:[10,12,25,41,43,44,46,47,48,54,57,60,62,63,67,70,71,75,81,88,89,91,93,97,102,103,107,109,111,116,122,124,125,127,128],content:[0,3,6,12,14,17,25,29,37,40,46,47,51,52,57,58,59,68,70,71,73,85,88,89,93,95,97,107,109,111,113,118,125,126,127,128,129,130,134,135],context:[15,17,25,26,36,51,52],contextu:41,contin:[126,129,130],continu:[13,14,17,20,53,92,102,103,114],contour:124,contrast:[6,12,15,24,32,34,41,43,74,92,96,97,102,119],contribut:[18,34,62,74,112,126,129],contriv:19,control:[10,13,14,15,17,20,26,41,79,86,111,119,125,134],conveni:[20,46,47,51,52,58,59,70,71,74,76,88,134],convent:[22,27],converg:[10,92],convers:[89,117],convert:[13,17,27,29,30,40,44,56,60,61,62,72,76,96,97,100,103,105,107,119,126,127,128,134],convert_dtyp:96,convert_feet_and_inches_to_inch:17,convert_stream_closed_error:32,convolut:94,cool:[19,38,42,49,61,89,102],coolor:125,coordin:[8,9,63,67,74,129,130],copi:[0,6,12,13,17,18,22,23,27,29,31,38,51,52,57,62,66,73,77,79,86,89,104,112,115,122,127,128],copilot:[36,88],copy_on_writ:[32,101,102,104,105,119,120],copyright:62,corbon:127,cord:67,core:[9,10,17,26,30,31,32,33,35,73,85,89,93,94,96,105,109,119],corner:[36,39,63],correct:[9,13,14,17,36,41,46,47,49,58,59,75,77,78,80,97,102,105,108,112,124,125,127,128],correction_no:32,correctli:[3,6,13,20,36,48,87,112],correctly_signed_and_squar:62,correl:[67,77,112,113,119],correspond:[12,41,43,57,62,63,70,71,74,75,79,80,81,89,91,95,110,111,115,124,127,128,134],corrupt:[19,24,31,55,94,98,100,112],cost:[20,27,31,34,35,42,46,47,49,66,89,107,118],costa:[93,128],cote:128,could:[8,9,11,12,14,15,17,18,20,24,25,26,29,30,32,34,37,40,41,42,43,44,46,47,48,49,51,52,54,55,60,61,63,64,67,68,70,71,73,74,75,76,82,89,90,93,97,101,102,103,104,105,106,107,109,110,111,112,113,114,115,119,122,125,127,128,129,130,134],couldn:[23,31,32,43,62,66,70,71,105,113],count:[12,17,22,38,43,73,74,79,86,88,89,92,93,94,95,96,102,105,106,108,110,111,112,113,119,122,127,131,134],count_nonzero:73,count_valu:75,count_values_in_each_row:75,counter:[13,25],counterpart:119,counti:[15,32,112,114],countri:[17,46,47,51,52,88,92,93,94,97,98,102,103,104,105,106,107,119,122,126,127,128,129,130],coup:[103,104],coupl:[8,9,12,18,24,25,27,30,32,34,40,43,48,55,60,62,94,95,108,112,116,119,122],cours:[6,7,9,10,11,12,15,17,18,20,21,26,32,35,36,39,40,41,42,43,46,47,49,51,52,53,54,55,58,59,60,63,67,70,71,73,77,82,83,84,88,89,95,97,99,102,103,109,111,115,117,119,122,124],coursera:[39,40,46,47],cousin:6,cover:[12,17,20,27,33,34,43,54,56,62,63,75,99,103,109,119,121],coverag:30,cow:100,cp:[0,12,70,71],cpu:[31,32,33],cpu_count:[31,32],cpython:30,craft:18,cram:49,crash:[27,32],crazi:18,creat:[2,3,4,9,11,12,13,14,17,18,20,22,23,24,27,31,32,33,36,39,40,42,43,44,45,46,47,48,49,51,52,54,55,56,57,60,61,62,63,64,65,66,67,68,70,71,73,74,75,77,78,79,80,81,82,83,84,85,86,87,88,90,92,93,94,95,96,97,98,99,100,101,107,110,111,112,115,117,118,119,120,121,122,124,125,126,127,128,129,130,134,135],creater:32,creation:[49,96],creatur:[25,74],credenti:[33,34],credibl:34,credit:[41,64,66,70,71,109,111],credit_a:51,credit_b:51,credit_cutoff_:51,credit_cutoff_d:51,cri:[],crime:[15,17,19,114],crimin:112,criteria:[15,82,83,84,109],critic:[18,35,36,104,109],croatia:128,crop:[67,101,102],cross:67,crowd:119,crucial:[2,119],crunch:33,crush:34,cryptic:42,crystal:[89,129,130],cs:34,csv:[12,17,19,27,28,32,86,88,93,94,96,97,98,103,104,105,106,111,112,114,119,122,127,131],ct:[67,74,111],cuba:128,cudf:32,culpa:18,cumbersom:[14,17,124],cumtim:30,cumul:92,cup:79,curiou:[25,32],curl:6,curli:40,currenc:[46,47,60],current:[6,7,8,10,12,14,15,16,18,24,29,31,36,39,40,41,58,59,60,62,64,70,71,77,92,95,96,104,105,107,108,118,119,121,122,134],cursor:14,curt:18,curv:[43,79,119,120,124],custom:[6,10,14,15,41,60,100,107,110,118,119,124,129,130,135],customiz:118,cut:[46,47,67],cute:119,cutoff:[17,46,47,122,128],cutoff_a:51,cutoff_b:51,cyber:20,cylind:[],cynthia:34,cypfoxeuok:28,cypru:128,cython_avg_numbers_up_to:30,cython_w_overflow_avg_numbers_up_to:30,czech:128,czechia:128,d010:[46,47,51,52],d3:[118,122],d5a6d15d0b1463d80fbfbb777dc826f7:[],d6ab8c8f87e7:30,d:[4,6,7,9,10,11,12,14,15,20,26,29,31,32,35,38,42,49,51,52,60,61,63,64,72,75,76,79,81,88,90,97,98,100,102,103,104,106,111,112,114,119,121,122,124,125,127,128,132,133,134],dai:[9,12,17,18,24,25,26,30,31,35,40,41,42,73,75,80,89,90,102,122],daili:[6,7,34,36,73],dakota:111,damag:112,danger:[0,9,18,20,112,119],dark:[6,12,41,62,67],darker:62,darn:[],darussalam:128,dash:12,dashboard:[32,33],dashboard_link:33,dask:[16,27,31,117],dask_env:[16,32],data:[2,6,7,8,9,10,11,12,13,14,16,17,18,19,20,22,23,25,28,29,30,31,32,33,34,42,44,45,48,53,54,55,56,57,58,59,60,61,62,63,64,65,67,68,69,72,73,74,75,76,78,79,82,83,84,85,89,90,91,92,94,96,99,100,101,104,110,111,113,116,118,120,121,123,124,125,126,129,130,131,134,135],data_copi:115,data_serv:122,data_transform:122,databas:[27,93,109,116,119],datacamp:[11,12],dataclass:[119,120],datafram:[13,17,25,28,31,32,60,86,87,89,91,95,96,99,100,101,104,105,106,107,109,110,111,112,113,114,115,119,120,122,127,128,129,130,132,133],dataframe_typ:32,dataframeiolay:32,dataframetreereduct:32,datanum:108,datasauru:117,dataset:[15,17,20,25,26,27,28,30,31,32,33,35,46,47,48,51,52,56,60,66,70,71,75,87,88,90,92,93,95,98,102,103,104,105,106,107,109,112,114,115,116,119,120,124,125,126,128,129,130],datatyp:[20,27,30,49,96,106],date:[32,79,88,97,110],date_pars:127,datum:122,dayfirst:127,dbc2199fcf7e6302cdd1ea2955dc378605b52659:19,dbl:122,dc:111,dd:32,ddot:78,de:[2,30,32,111],deactiv:[8,14,16],dead:31,deal:[9,14,20,27,35,46,47,68,79,85,105,106,112,114,135],dealer:112,dealership:[42,113],death:111,debat:119,debt:[51,52,102],debug:[9,12,24,30],debugging_in_vscod:13,decad:[35,112],decemb:12,decid:[9,11,15,17,24,29,30,32,34,35,49,51,52,76,112,119],decim:[17,20,23,25,40,41,49,51,52,60,70,71,89,97,127,134],decis:[73,95,109],declar:[29,30,76,121],declin:[19,55,92,112,114],decod:114,decompos:[74,119],decomposit:119,decompress:[27,28,32],decor:30,decreas:[51,52,62,112,119,135],dedic:[18,30],deduc:30,deem:25,deep:[25,35,38,57,118,127],deeper:[13,73,85,87,124,125,127],deepli:[18,34,41,93,96,101],def:[13,17,23,30,31,62,75,77,78,80,82,83,93,119,120,122,124,127,128],defaultformatt:25,defens:[],defer:62,defin:[6,7,13,23,30,34,46,47,73,92,110,119,125,134],definit:[12,25,26,30,34,35,43,51,52,54,81,95,98,102,104,108,127],deg:121,degre:[17,31,35,62,92,95,121,124],degredation_over_tim:55,dejavu:125,del:27,delai:[31,32,77],delawar:111,delet:[9,11,12,13,17,24,27,32],deliber:[10,42,70,71,106],delight:[99,107,124],delim_whitespac:127,delimit:[17,32,127],delin:97,deliv:[29,33,76],deliver:77,deliveri:119,dell:35,dem:128,demand:49,democraci:[93,103],democrat:128,demograph:[15,114],demonstr:[30,32,34,38,40,73,79,88,89,115,117,119,124,129,130,134],demystifi:[26,38],denmark:128,denot:[56,95,97,106,108],densiti:[121,122,123],depart:24,depend:[3,5,6,8,9,10,12,13,15,18,20,31,32,34,36,41,51,52,53,54,62,70,71,77,79,89,91,93,94,97,98,99,100,101,105,119,122],depict:23,deploi:[33,86,88],deprec:[28,91,101,119],depreci:91,depth:98,dequ:30,derefer:[29,76],dereferenc:29,deriv:[40,111,112,126],descend:97,describ:[11,14,15,27,38,41,47,73,76,86,89,93,102,107,113,115,119,126,127,131],descript:[10,12,18,24,86,100,116,127],deseri:32,design:[2,10,12,13,17,20,27,29,30,31,32,34,36,40,41,74,76,77,88,89,96,97,98,101,111,119,121,124,125],desir:[55,79,104,119],desit:127,desktop:[12,17,27,31,35],despit:[26,36,55,62,88,90,97],destruct:127,detach:10,detail:[11,12,14,20,21,32,33,42,43,46,47,51,52,54,58,59,60,61,66,67,70,71,79,89,91,99,102,109,116,119,122,124],detect:[13,19,74,102],determin:[10,15,29,34,43,48,51,52,55,75,76,80,89,92,93,108,111,112,116,124,125,127],determinist:[10,20,79],detriment:36,deutschland:103,dev:[20,29,30,37,107],devel:[119,120],develop:[3,13,15,17,18,30,31,32,34,36,40,54,73,81,85,90,91,92,111,115,125,127],deviat:[42,73,82,83,89],devot:[34,87,125],df1:[110,112],df1_var:112,df1f62c87a9d21a7c2e1959a33e7735a:[],df2:[28,110,112],df2_correct:112,df2_var:112,df3:110,df4:110,df:[17,28,32,93,94,96,97,98,100,101,104,105,114,123,131],df_ax0:110,df_ax1:110,df_concat:110,df_concat_axis1:110,df_concat_reindex:110,df_new:18,df_w_pyarrow_backend:96,dfa:111,dfb:111,di:32,diagnos:[12,14,15,109,112],diagnost:[15,112],diagon:63,diagram:99,dialect:127,dice:79,dict:[38,119,120],dictat:[30,31,98,127],dictionari:[23,40,85,93,97,103,119],did:[9,13,17,28,32,34,36,42,44,49,53,57,67,88,90,96,99,104,112,114,119,127],didn:[12,13,15,19,24,29,34,36,41,53,62,64,70,71,76,101,102,105,106,107,112],diff_nonvector:78,diff_vector:78,diffcar:108,differ:[2,3,4,6,8,9,10,11,12,13,14,15,16,17,20,23,25,26,27,29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,46,47,49,51,52,53,54,55,57,60,61,62,63,64,67,68,69,70,71,73,74,76,77,78,79,81,82,83,85,87,88,89,90,91,92,93,95,96,97,98,99,101,102,103,105,107,109,111,112,113,114,115,118,119,121,122,124,127,128,129,130,134],differenti:[25,34,67],diffey:108,diffhear:108,difficult:[51,52,91,119,125,129,130],difficulti:13,difflib:128,diffmob:108,diffphi:108,diffrem:108,diffsen:108,digit:[20,25,41,51,62,125,134],digitalocean:33,digress:[26,53],dimens:[40,41,48,53,60,63,65,66,67,68,69,73,74,79,88,93,104,110],dimension:[38,41,60,61,62,65,72,73,74,85,88,89,93,117,124],dioxid:[126,128,129,130],diploma:[70,71,95],direct:[3,6,8,9,11,12,16,18,32,33,34,35,44,61,62,70,71,119,127,128],directli:[8,10,11,12,13,14,15,17,24,26,28,30,32,33,34,46,47,51,52,57,60,70,71,77,87,96,102,103,104,105,107,111,118,123,126,127],directori:[6,7,10,11,12,32,112],dirti:[29,76],disabl:30,disable_max_row:122,disadvantag:107,disappear:[27,94,119],disappoint:122,disast:[86,91],disciplin:[31,98],discount:35,discov:[15,17,19,34,36,95,105,119],discret:[51,52,92,95,102],discuss:[3,6,9,10,12,13,14,17,18,20,21,22,27,28,29,31,32,34,35,38,42,43,46,47,48,49,51,52,58,59,60,62,70,71,72,73,76,77,79,80,82,83,84,85,88,89,91,93,96,99,100,102,103,107,108,109,110,111,112,114,115,118,119,121,124,127,134],diseas:111,disgust:18,disk:[17,96,122],dispatch:30,dispers:119,displac:111,displai:[2,3,6,12,17,39,43,62,74,97,118,124,125,129],disproportion:62,dissatisfi:34,dissemin:98,dissert:34,distant:121,distinct:[20,22,31,32,34,49,54,68,90,99,100,103,108,111,114,118],distinguish:25,distract:125,distribut:[16,20,28,29,30,33,41,43,51,52,75,76,79,80,82,83,92,95,98,102,108,112,119,122],district:111,distutil:30,dive:[15,22,31,34,40,42,54,73,87,88,89,93,104,118,119,122,124,125,126,127],divers:93,divid:[20,32,34,40,51,52,55,62,70,71,79,87,104,109,111,112,115,118,129,130],divis:[34,48],djibouti:128,dl:32,doc:[0,9,17,30,79,94,96,101,103,104],doctor:[27,110],documen:102,document:[2,6,12,17,23,25,30,34,42,46,47,66,88,95,97,99,100,101,102,104,105,110,116,118,119,121,122,123,124],doe:[7,10,11,12,13,15,17,20,22,24,27,29,30,31,32,35,38,40,43,46,47,48,51,52,55,62,63,64,67,70,71,74,77,81,82,83,90,91,93,94,95,97,100,101,105,106,108,109,110,112,114,119,121,122,127,135],doesn:[3,7,9,10,12,13,15,17,18,19,20,23,25,26,27,28,29,30,31,32,34,40,41,44,49,55,62,77,79,88,89,90,94,95,96,100,101,102,106,107,108,114,119,121,125,129,130],dog:[48,91,93,97,110,111],dogmat:25,doi:[46,47,51,52],doke:125,dollar:[12,13,27,35,41,42,43,46,47,49,51,52,60,64,70,71,102,108,111,115,127,129],domain:[0,15,27,36,130],domest:[92,119,127,129],domin:[30,129,130],dominica:128,dominican:128,don:[3,6,7,8,9,10,11,12,13,15,18,19,20,21,22,25,26,27,30,31,32,33,34,35,36,39,40,41,42,43,46,47,48,49,51,52,54,55,58,59,60,62,63,67,70,71,77,79,82,83,89,90,91,93,94,95,97,98,100,102,103,104,105,106,107,111,112,113,114,117,119,122,124,125,127,128,135],done:[3,6,7,8,10,11,15,24,25,27,29,30,32,40,43,48,67,73,76,77,94,95,98,99,101,102,104,110,119,124,127,128,129,130],dont:[8,16,30],door:35,dos_str:32,dosage_unit:32,dot:[6,14,19,38,119,120,121,125],dotfil:12,doubl:[0,3,12,23,25,29,30,31,35,37,46,47,55,62,77,96,104,107,127],double_nonvector:77,double_vector:77,doublequot:127,doubt:101,dove:85,down:[9,11,17,20,27,28,30,31,36,38,43,46,47,51,52,55,61,73,81,95,102,107,111,112,113,114,129,130],download:[3,6,7,9,10,12,15,24,32,95,112,114,127],downstream:13,downward:[14,57],dozen:[2,9,17,18],dpi:125,dqcfywcfmx:[],dracula:6,draft:36,drag:122,dragon:80,dramat:[53,107],drastic:77,draw:[15,48,70,71,79,80,124,125],drawn:62,dream:[96,124],drew:[38,80,86],drift:17,drive:[26,27,32,35,41,55],driven:[51,52,73,112],driver:126,drop:[17,27,32,34,46,47,48,51,52,89,93,95,97,106,108,110,111,119,120],drop_dupl:131,dropbox:[17,32,88],dropdown:122,dropna:[106,111,119,120],drug:[32,36,114],drug_cod:32,drug_nam:32,ds:127,ds_store:12,dt:32,dta:[28,93,95,98,102,106,108],dtype:[20,27,28,29,32,41,44,48,49,60,62,88,89,90,91,93,94,96,101,102,103,104,105,106,108,111,112,113,114,119,127,128,131],dtype_backend:96,dubiou:96,duct:13,due:[15,20,31,36,72,106,126],dug:19,duke:[9,10,11,12,25,34,35,36],dumb:31,dump:97,duplic:[12,31,77,80,82,83,84,114,128],durat:35,duration_with_copi:55,duration_with_view:55,dure:[8,9,12,15,16,17,19,25,27,30,32,33,34,42,46,47,51,52,79,127],dutch:[],dynam:[29,30,57,76,89,92],dz:127,dza:127,dzvsmydwbp:28,e:[2,3,4,7,8,9,11,12,13,14,16,17,20,24,25,26,27,29,30,31,32,34,38,40,41,42,43,46,47,48,49,51,52,56,62,63,65,67,68,70,71,76,79,80,81,88,89,90,92,93,94,96,97,98,99,102,103,104,105,106,107,108,109,110,111,114,117,119,122,123,128,133],each:[8,10,12,13,14,15,16,17,18,20,25,26,27,28,29,30,31,32,34,37,40,41,42,43,45,46,47,48,49,51,52,55,57,60,61,62,63,67,68,70,71,73,74,75,76,77,78,79,80,81,82,83,84,88,89,90,91,92,93,94,95,97,100,102,103,107,108,109,110,111,112,113,114,115,117,119,120,122,124,125,127,128,129,130,135],ear:80,earli:[2,8,12,15,17,19,20,34,36,40,96,127],earlier:[12,14,17,73,75,79,80,115,125,127],earn:[25,41,43,46,47,49,51,52,63,92,95,108],earner:[43,51,52],earth:[13,126],eas:[0,40,80,127],easi:[2,9,10,13,15,17,20,21,24,27,29,30,31,32,36,37,39,40,42,54,61,66,67,70,71,76,88,93,94,95,97,99,100,102,107,114,118,119,121,122,125,130],easier:[6,8,12,16,17,18,24,25,30,32,33,35,38,42,60,62,63,73,75,77,86,87,88,89,91,92,94,95,97,100,103,104,111,113,114,115,119,121,122,125,129,130,134],easiest:[12,33,89,93,95,100,134],easili:[2,9,10,12,13,14,17,18,24,25,27,28,35,36,40,41,42,45,49,60,63,67,74,79,87,88,89,97,103,109,110,112,119,121,122,125,127,134],east:[17,93,94],eastern:[114,127],echo:12,econ:111,econom:[17,70,71,92,95,102],economi:119,economist:[92,119],ecosystem:[2,3,38,88,97,117],ecuador:128,edg:[10,20,43,67,79,121,124,129,130],edgealpha:[],edgecolor:[121,124,129,130],edgestyl:[],edgewidth:[],edit:[2,10,12,13,14,17,38,49,54,62,65,99,100,103,106,107,117,119,127],editor:[2,3,9,10,13,25,97,98,100],edu:[46,47,51,52],educ:[9,34,36,60,63,64,66,70,71,119],edward:125,eerili:39,ef233c:125,effect:[9,13,14,15,17,20,27,32,36,39,43,51,52,62,70,71,77,93,95,100,112,113,119,126],effici:[13,15,18,27,30,31,32,38,40,45,55,60,75,77,85,87,96,107,109,113,115,119,124,125],efficiency_data:55,efficieni:125,effort:[2,17,34,51,52,70,71,91,93,98,111,112,119,125,127,128],effortlessli:96,eg:127,egi:127,egregi:[70,71],egrid2016:116,egrid:116,egweyn:[113,115],egypt:[17,127,128],eh:127,eight:[40,75],either:[9,10,11,12,13,14,15,17,27,31,34,35,39,42,48,49,54,56,60,73,89,90,93,95,99,100,101,102,105,110,111,113,119,122,125],el:128,elaps:77,elder:80,elect:41,electr:55,element:[29,38,42,48,49,50,54,61,63,64,68,73,77,78,89,90],eleph:[110,111],eleven:60,elid:30,elif:127,elimin:[79,128],elon:43,els:[13,18,24,27,30,34,48,96,97,124,125,127],elsewher:[18,122],emac:12,email:[6,7,18,34],embarass:17,embarassingli:31,embark:34,embodi:[10,121],embrac:42,emerg:[17,18,36],emir:[127,128,129,130],emiss:[119,122,128,129,130],emoji:41,emp_rat:102,emphas:[10,17,21,22,23,27,32,49,86,93,94,119,122],emphasi:125,empir:[36,80],emploi:[70,71,79,95,102,106,108],employ:[70,71,95,102,106],employe:[15,42,49,70,71,95,111,113,115],employees_by_year:113,empow:9,empstat:[95,102,106,108],empti:[16,17,27,48,75,77,127,134],en:[0,47],enabl:[10,25,39,77,89,96,100,101,104,117,122,125],enact:[51,52],enclos:[80,97],encod:[62,97,98,102,107,114,117,121,122,127,129,130],encoding_error:127,encount:[14,17,19,23,30,31,34,40,56,79,85,88,89,93,97,98,106,109,110,111,115,128,134],encourag:[13,27,82,119,124,128],end:[6,9,10,11,13,15,18,20,22,23,24,29,30,31,32,34,35,37,40,42,48,49,55,60,61,63,67,70,71,78,89,90,93,96,97,103,104,107,109,111,112,115,119,120,125,126,127],endless:106,endog:[119,120],endpoint:55,enemi:77,energi:[9,10,15,55,116,126],engag:[21,34,36],engin:[8,9,15,16,17,29,30,76,85,87,117,124,127],enhanc:[89,90,124],enjoi:35,enough:[9,11,14,20,24,25,27,29,32,35,36,40,51,52,63,70,71,74,81,93,97,108,121,122],enrol:34,ensu:32,ensur:[6,9,10,15,17,18,20,21,25,30,32,34,49,55,60,70,71,80,82,83,93,127],entail:[34,36],enter:[6,10,32,60,74,82,107,111],enterpris:32,entir:[2,6,9,17,18,23,24,29,30,31,36,41,48,62,63,67,76,87,99,104,107,121,124,125,128],entireti:93,entiti:[15,41,70,71],entri:[13,17,19,22,23,26,27,29,37,42,45,48,49,50,54,55,56,57,60,61,62,63,65,68,69,73,75,77,79,82,83,90,91,93,94,96,97,99,100,101,102,103,104,106,107,110,111,113,114,127,129,130],enumer:[13,124,125,129,130],env:[32,127],environ:[3,4,5,6,7,9,12,13,14,33,38,81],environment:116,eon:80,epa:116,epoch:40,epvgfgzts:28,equal:[13,18,23,30,48,61,67,68,74,78,79,82,83,84,94,95,104,110,119,128],equatori:128,equit:119,equival:[11,32,38,60,67,77,88,116,134],eras:[12,17,27],erika:25,eritrea:[93,94,128],err:[36,94,105],errno:32,erron:14,error:[3,12,14,17,18,19,25,27,30,32,33,36,42,48,49,61,68,82,83,87,88,89,93,94,98,101,102,104,105,107,112,114,117,119,122,127],error_bad_lin:127,error_rewrit:30,errorband:122,errorbar:124,escap:[12,100,103,134],escapechar:127,esh:127,esoter:54,especi:[3,11,12,17,20,25,27,31,34,35,36,40,41,57,60,64,113,114,119,124],esperanza:[113,115],essai:36,essenti:[10,26,88,89,100,101,103,107,110,111],establish:[34,35,49,55,104],estim:[17,26,32,40,41,42,46,47,51,52,60,70,71,72,82,83,92,108,109,119,120,124],estonia:128,estout:17,eswatini:128,etc:[0,2,3,6,7,9,11,12,13,15,17,24,25,28,29,30,32,34,35,36,39,40,41,42,51,52,60,61,68,75,76,81,82,83,84,85,86,89,97,98,100,101,103,105,107,109,111,114,116,117,119,121,124,134],ethiopia:128,ethnic:[70,71,108],etiquett:117,eubank:[1,25],euituavzq:28,europ:[88,93,94,97,98,103,104,105,106,127,128,129,130],eval:[115,127],evalu:[4,12,13,14,15,17,24,29,36,40,48,49,51,52,55,76,77,79,81,91,109],evang:122,even:[3,8,9,10,12,13,14,15,18,19,20,21,24,25,26,27,30,31,32,33,34,39,40,41,42,43,44,47,48,49,51,52,54,55,60,62,69,73,77,79,82,83,84,88,89,90,91,94,97,99,102,103,104,106,107,108,109,113,114,119,122,124,125,134,135],even_integers_through_1000:83,even_row:28,evenli:[51,52,92],event:34,eventu:[32,112,127],ever:[10,11,12,13,17,18,23,24,29,30,31,42,55,63,67,80,89,90,94,96,97,100,102,105,118,119,122],everi:[9,10,12,14,15,17,18,23,24,29,30,31,34,40,42,46,47,51,52,55,73,75,76,77,89,93,95,97,98,100,102,107,112,114,118,119,122,124,135],everyon:[9,10,15,17,25,31,32,34,41,46,47,49,51,52,60,63,70,71,89,94,108,119],everyth:[6,9,12,13,18,19,21,24,25,26,27,29,30,31,35,36,38,40,41,43,44,47,49,53,60,62,69,74,89,91,95,97,100,102,103,105,110,114,115,126,127,128],everywher:[8,12,17,33,49],evid:[39,51,52,68,95,105,112,135],evidenc:[14,34,68,122],evil:49,evolut:[53,67],evolutionari:119,evolv:[13,31,36,67,96,119],ewcskolhuv:[],ex10_gini_policy_d:51,ex11_gini_policy_:51,ex4_gini:51,ex4_share_below_poverti:51,ex6_gini_policy_a:51,ex6_gini_policy_b:51,ex7_gini_policy_c:51,ex8_revenue_rais:51,ex9_transf:51,ex:3,exact:[6,12,20,23,29,32,40,41,51,52,62,77,103,121,128],exactli:[3,13,15,17,18,29,30,32,33,37,48,49,77,93,94,97,102,111,112,114,118,124,126],exam:34,examin:[108,112,114,119],exampl:[2,8,9,10,11,12,13,14,15,19,20,21,22,23,24,26,27,28,29,32,34,35,37,38,40,41,42,43,44,46,47,48,49,51,52,54,55,58,59,60,61,62,63,64,66,67,68,70,71,72,73,74,77,78,79,80,81,82,85,86,89,90,93,94,96,97,98,99,101,102,103,104,105,106,107,108,110,112,113,114,115,116,118,119,122,123,124,125,127,128,129,130,134],example_csv:[11,12],example_data:[11,12,17,19,93,94,119,122],exc:[32,33],exceed:[119,122],exceedingli:[24,56],excel:[15,18,26,40,60,76,82,83,84,93,97,98,125,128],except:[14,17,20,21,23,25,30,32,33,46,47,51,52,58,59,65,70,71,86,90,93,94,102,105,114,115,128],exception:79,exchang:[21,40,46,47],excit:[10,15],exclud:108,exclus:[70,71],excruci:32,excut:104,exec:[30,128,129,130],execut:[3,4,10,12,13,14,17,25,26,29,30,31,32,33,39,76,77,78,79,81,82,83,90,100,101,111,115],exercis:[6,15,21,25,35,36,43,45,70,71,75,77,79,84,90,93,95,102,103,106,112,114,127,128],exhaust:[31,98],exhibit:[97,106],exist:[4,10,12,17,18,20,22,23,24,27,31,34,36,49,54,63,81,89,90,93,94,95,97,101,107,111,119,126],exit:[3,12,34],exog:[119,120],expand:[2,35,100],expect:[13,17,18,19,24,32,35,46,47,51,52,64,73,74,79,89,91,95,97,100,103,110,111,112,114,119,122,125,127,128],expens:[26,34,35],experi:[4,7,9,11,15,17,24,27,31,32,54,55,70,71,74,79,81,91,93,116,121,125],experienc:[18,42,104],experiment1:[51,52],experiment:[79,121],expert:[13,97],expertis:[36,113],explain:[36,40,41,43,48,49,100,101,119,124,125],explan:20,explanatori:127,explicit:[21,42,79,89,91,101,114],explicitli:[8,13,15,16,20,29,58,59,61,76,77,88,89,94,101,108,114,122,125],explor:[12,13,14,32,51,52,67,72,73,74,75,77,78,79,82,83,84,85,87,89,90,92,93,94,102,109,110,111,112,113,116,117,119,122,124,125,126,128,129],exploratori:[117,119,122,123],explos:17,expon:[62,125],exponenti:130,exported_statist:17,expos:[55,68,119,122],exposit:[93,117],express:[14,17,31,34,48,76,77,82,83,103,109,113,115],ext_modul:30,extend:[41,43,74,104],extens:[14,25,32,34,36,38,39,60,81,97,98,114],extent:122,extern:35,extra:[0,27,32,61,68,89,111,113,125],extract:[32,46,47,48,49,50,51,52,63,64,75,90,93,127],extran:111,extraordinari:80,extrapol:92,extrem:[8,9,12,14,16,31,34,36,41,43,48,51,52,62,79,88,89,90,94,95,105,108,118,119,134],ey:[13,17,19,74,80,95,114,119,122],ezlquomybn:28,f2777b5ca39c9fe965d9e5058695203d:32,f30d4b630726:114,f485e25f94f5664c192e73df6d42002d:[],f623161a15ec:32,f:[13,14,17,25,30,32,40,51,55,73,80,102,105,119,120,122,127,128,129,130,133],f_drugoff:112,face:[34,85,110],facebook:27,facecolor:12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Data Science","Practical Data Science in Python","The Jupyter Project","Running R Jupyter Notebooks in VS Code","Managing Python Packages","Set Up an Augmented Command Line","Improving Your Command Line Experience (macOS)","The Command Line on Windows / Installing Bash","Installing Geopandas","Installing Python and Miniconda","Setting Up Visual Studio Code","The Command Line, Basics","Advanced Command Line Tutorial","Principles and Tools for Debugging","Debugging Python in VS Code","Backwards Design","Managing Conda Environments","Defensive Programming","Getting Help Online","The Iceberg Principle","Numbers in Computers","Reviewing Code on Github","Python vs. R: Important Differences To Be Aware Of","Python: Variables vs. Objects","Workflow Management","Writing Good Jupyter Notebooks","What is Big Data?","Big Data Strategies","Parquet","Speed in Python","Solving Performance Issues","Parallel Computing","Distributed Computing with dask","Starting a Cloud Dask Cluster","So You\u2019re Thinking About a PhD","Advice on Buying Computers for Data Science Students","chatGPT and You","The Object dtype and Performance","Class Two: Representing Data: Objects, collections, and their uses for computation (Numpy)","Running Python in the VS Code Interactive Window","Numpy Arrays: The Workhorse of Data Science","Working with Vectors","Doing Math With Vectors","Visualizing Your Data with Histograms","Type Promotion in numpy","Vector Recap","(Ungraded) Vector Exercises","(Ungraded) Vector Exercises","Subsetting Vectors","Modifying Subsets of Vectors","Recap","Week 2 Numpy Re-cap Exercises","Week 2 Numpy Re-cap Exercises","Welcome to Matrices","Views and Copies","When Do I Get a View and When Do I Get a Copy?","Review of Views and Copies","Variables are pointers to objects","Views and Copies Exercises","Views and Copies Exercises","Intro to Matrices","Folding and Reshaping Matrices","Matrices as Images","Subsetting Matrices","Editing Subsets","Review of Matrices","Matrix 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as Pandas Backend","Ways To Store and Read Data","Ways To Store and Read Data: Binary Files","Views and Copies in Pandas","Views and Copies in pandas","The View/Copy Headache in pandas without Copy on Write","Identifying Data Problems","Fixing Data Value Problems","Editing Specific Locations","Cleaning Data Types","Missing Data","The Object and Categorical Data Types","Exercise: Data Cleaning","Combining, grouping, and querying data","Combining datasets: concatenating","Combining datasets: merging","Merging Data to Understand the Relationship between Drug Legalization and Violent Crime","Grouping datasets","Groupby and Arrest Data","Queries reprised: asking your data questions","Exercise: queries into the state of electricity production and CO2 emissions in the United States","Class 5: Towards the practice of data science","Plotting in Python","seaborn and the Grammar of Graphics","seaborn.objects Recipes","matplotlib","Advanced Plotting with Altair","Plotting with Pandas","Plotting 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a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_augmented_commandline.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_augmented_commandline.ipynb index f3fc0126..16b5de82 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_augmented_commandline.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_augmented_commandline.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "6216c2db", + "id": "93d0a5b0", "metadata": {}, "source": [ "# Set Up an Augmented Command Line\n", diff --git a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_conda_envs.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_conda_envs.ipynb index 6c4b42bb..988db8fe 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_conda_envs.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_conda_envs.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d61c54ad", + "id": "00ad5fc3", "metadata": {}, "source": [ "# Installing Geopandas" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "ef3e48ae", + "id": "61e65633", "metadata": {}, "source": [ "Early in the semester, I told you that conda was generally preferred over pip by data scientists because it could install more than just python packages? Well here's an example of a place that pays off big!\n", diff --git a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_python.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_python.ipynb index b615756d..79811c2f 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_python.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_python.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "017d5e93", + "id": "f9570ce9", "metadata": {}, "source": [ "# Installing Python and Miniconda" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "739c30f6", + "id": "974d304a", "metadata": {}, "source": [ "One of the major learning goals of this class is for you to be comfortable managing all the software and settings required for you to do data science on your own computer. \n", @@ -36,7 +36,7 @@ }, { "cell_type": "markdown", - "id": "73bc8e1f", + "id": "dacfd6c4", "metadata": {}, "source": [ "## Reading These Setup Instructions\n", @@ -46,7 +46,7 @@ }, { "cell_type": "markdown", - "id": "593e5383", + "id": "d68f3399", "metadata": {}, "source": [ "## Installing Python with Miniconda\n", @@ -131,7 +131,7 @@ }, { "cell_type": "markdown", - "id": "d7daf733", + "id": "dbf580c8", "metadata": {}, "source": [ "## And that's it!\n", diff --git a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_vscode.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_vscode.ipynb index 2e6000fb..13cbe3f7 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_vscode.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/00_setup_env/setup_vscode.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7a236ea8", + "id": "419a7c95", "metadata": {}, "source": [ "# Setting Up Visual Studio Code" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "7f2903a8", + "id": "c0eb8584", "metadata": {}, "source": [ "> This is written assuming you've already installed and setup Python using miniconda [as instructed here](setup_python.ipynb). If you haven't, please go do that now!" @@ -18,7 +18,7 @@ }, { "cell_type": "markdown", - "id": "f88836e3", + "id": "4ab7434a", "metadata": {}, "source": [ "As noted in our previous reading, here in Duke MIDS we're converging on using VS Code in our courses as our \"preferred\" tool for editing and running code. That's not because we necessarily because we think it's \"the objectively best editor\"—what editor is best for an individual depends a lot on their own background, programming style, and work flow!— but rather because we think it's a good fit for most students. Moreover, by all supporting the same editor, we can help you get *really good* with VS Code—not everyone will agree VS Code is the best editor, but I think all data scientists will agree that getting really good with *an* editor is an important skill!\n", @@ -158,7 +158,7 @@ }, { "cell_type": "markdown", - "id": "8fcd7ce6", + "id": "47e3e39f", "metadata": {}, "source": [ "## Set terminal.integrated.inheritEnv to False in VS Code\n", @@ -183,7 +183,7 @@ }, { "cell_type": "markdown", - "id": "f4eb25ad", + "id": "53062e57", "metadata": {}, "source": [ "## Run Some Python Code!\n", diff --git a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/10_debugging_principles.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/10_debugging_principles.ipynb index b3bdf77b..e2e52417 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/10_debugging_principles.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/10_debugging_principles.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "ae3f8c76", + "id": "a8200bec", "metadata": {}, "source": [ "# Principles and Tools for Debugging\n", @@ -63,7 +63,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "88b31a3c", + "id": "3e9a075a", "metadata": {}, "outputs": [ { @@ -90,7 +90,7 @@ }, { "cell_type": "markdown", - "id": "fbf69116", + "id": "8f11aa9b", "metadata": {}, "source": [ "That's clearly not 25 and we can see why - we didn't put the inputs in the correct order, so we computed $2^5$ instead of $5^2$. In this case, however, the code *runs* fine, it just doesn't run *correctly*. These types of errors can be far more pernicious than syntax errors since the code doesn't throw an immediate fit. This assumes that the underlying logic of the algorithm is correct, but that there's a typo in the code or an issue with some of the data structures or packages that were used. Consider the following example that's a bit trickier. Let's say we wanted to calculate both the sum of the values in a list ($\\sum_{i=1}^{N} x_i$) and the sum of the squares in the list ($\\sum_{i=1}^{N} x_i$). And as input we give it $[1,2,3]$. What we would want to see as output are:\n", @@ -107,7 +107,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "78862fcb", + "id": "55425fd4", "metadata": {}, "outputs": [ { @@ -139,7 +139,7 @@ }, { "cell_type": "markdown", - "id": "b4d82bce", + "id": "8d8d9391", "metadata": {}, "source": [ "Clearly the first sum is incorrect since we expected the output to be $[6, 14]$. This case has a more subtle error. Did you spot it? It's because of the line `B=A`, which passes a reference to `A`, but does not make a *copy* of `A`. So when we square each of the entries of `B` using `B[i] = b**2`, we are *actually* squaring the values of *both* `A` and `B`. We can fix this by making a copy of `A` instead:" @@ -148,7 +148,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "467531de", + "id": "a4ef0c28", "metadata": {}, "outputs": [ { @@ -180,7 +180,7 @@ }, { "cell_type": "markdown", - "id": "fc2fc540", + "id": "5043e66e", "metadata": {}, "source": [ "Because fixing logical errors (e.g., fixing our bugs, or in the lingo *debugging* our code) requires developing a deeper understanding of what our code is actually doing, we generally can't solve the problem just by staring at our code—we need tools that will allow us to poke around and better understand what's going on *as the code executes.* And that's the realm of debugging tools!" @@ -188,7 +188,7 @@ }, { "cell_type": "markdown", - "id": "c3e6be7d", + "id": "641ae2b9", "metadata": {}, "source": [ "### 3. When logical errors cause syntax errors\n", @@ -201,7 +201,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "aa75a948", + "id": "7d248a2d", "metadata": {}, "outputs": [], "source": [ @@ -216,7 +216,7 @@ }, { "cell_type": "markdown", - "id": "1935ada7", + "id": "825d31a5", "metadata": {}, "source": [ "If I run this with only numbers in a list, we don't have any problems:" @@ -225,7 +225,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "4c8650e7", + "id": "c2b40476", "metadata": {}, "outputs": [ { @@ -245,7 +245,7 @@ }, { "cell_type": "markdown", - "id": "1862b877", + "id": "b7847f4d", "metadata": {}, "source": [ "But if I include a number stored as a string in the list (which the function is supposed to convert into a `float`), I get this error:\n", @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "441e2fe5", + "id": "63e0b797", "metadata": {}, "source": [ "## Debugging Tools" @@ -284,7 +284,7 @@ }, { "cell_type": "markdown", - "id": "4897a1e8", + "id": "1e28d426", "metadata": {}, "source": [ "How you choose to debug your code depends a lot on both personal taste and the type of code you're writing. For example, if you're writing a simple linear script—for example, loading a DataFrame, cleaning some columns, doing some merges, etc.—then you can generally find your problems just by running your code one line at a time. \n", @@ -296,7 +296,7 @@ }, { "cell_type": "markdown", - "id": "efadde98", + "id": "3d82be32", "metadata": {}, "source": [ "### 1. Debugging with `print()` statements\n", @@ -309,7 +309,7 @@ }, { "cell_type": "markdown", - "id": "743eda78", + "id": "d306d32e", "metadata": {}, "source": [ "For example, the factorial of the integers $1$ through $4$ are given as:\n", @@ -327,7 +327,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "2dc6eb58", + "id": "fafbecdb", "metadata": {}, "outputs": [], "source": [ @@ -343,7 +343,7 @@ }, { "cell_type": "markdown", - "id": "3750f68e", + "id": "07629a28", "metadata": {}, "source": [ "And let's test to see whether that function works by evaluating the factorial of the integers 1 through 4:" @@ -352,7 +352,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "22c28789", + "id": "268a5725", "metadata": {}, "outputs": [ { @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "ebe1c880", + "id": "cb5cfd59", "metadata": {}, "source": [ "It clearly does NOT work for integers 3, 4, and 5. Let's see if we can find out why using `print()`. What we're interested in, is what's going on in lines 5-8 since those conditions are incorrect. Let's add in some `print()` statements:" @@ -382,7 +382,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "10130ca2", + "id": "fa3f7401", "metadata": {}, "outputs": [], "source": [ @@ -400,7 +400,7 @@ }, { "cell_type": "markdown", - "id": "078a3eb4", + "id": "2e918dfc", "metadata": {}, "source": [ "Now let's run the program again for one of the problematic values, 2:" @@ -409,7 +409,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "236d76ca", + "id": "8151760a", "metadata": {}, "outputs": [ { @@ -431,7 +431,7 @@ }, { "cell_type": "markdown", - "id": "eccd6c0e", + "id": "da0716d3", "metadata": {}, "source": [ "Now we can see that the problem here occurs because `i` begins at 0 rather than 1 and therefore is multiplying everything by 0, canceling the results. Now we can correct that by using $i + 1$ instead of $i$. Let's make that adjustment and see how it works:" @@ -440,7 +440,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "f911ff0d", + "id": "2882ca64", "metadata": {}, "outputs": [], "source": [ @@ -459,7 +459,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "ac8b3345", + "id": "8e57a816", "metadata": {}, "outputs": [ { @@ -481,7 +481,7 @@ }, { "cell_type": "markdown", - "id": "b8e1e601", + "id": "634dbb05", "metadata": {}, "source": [ "This is encouraging! Now let's try the rest of the values, but remove the `print()` statements first so we can easily read the results:" @@ -490,7 +490,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "c92f7b99", + "id": "63426d23", "metadata": {}, "outputs": [], "source": [ @@ -507,7 +507,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "99bb4c51", + "id": "186279d3", "metadata": {}, "outputs": [ { @@ -528,7 +528,7 @@ }, { "cell_type": "markdown", - "id": "558830fe", + "id": "24104c10", "metadata": {}, "source": [ "So this approach can certainly work, as it did here. Sometime for quick errors in small programs this can work perfectly well, since the best debugging tool is the one that helps you to achieve your solution. However, we had to frequently made adjustments directly within the code and we either had to delete or comment those out to remove them. This is not ideal for large programs as this can introduce the possibility of introducing errors through those edits that are made." @@ -536,7 +536,7 @@ }, { "cell_type": "markdown", - "id": "daa86d5d", + "id": "76d54289", "metadata": {}, "source": [ "**Pro-Tip:** It's so common when doing debugging to want to print out something like `The value of x is NUMBER` that Python has an inbuilt shortcut for writing this: `f\"{x=}\"`! Just add an equal sign after a variable name and Python will interpolate *both* the variable name *and* the value of the variable:" @@ -545,7 +545,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "00525fa6", + "id": "88741249", "metadata": {}, "outputs": [ { @@ -563,7 +563,7 @@ }, { "cell_type": "markdown", - "id": "97299aac", + "id": "aff8941c", "metadata": {}, "source": [ "### 2. Debuggers\n", diff --git a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/20_debugging_in_vscode.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/20_debugging_in_vscode.ipynb index bf3f5711..3220b345 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/20_debugging_in_vscode.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/20_debugging_in_vscode.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d8086d27", + "id": "5ac7bdad", "metadata": {}, "source": [ "# Debugging Python in VS Code\n" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "f5f68e09", + "id": "a946e9c3", "metadata": {}, "source": [ "As noted in our [previous reading](10_debugging_in_vscode.ipynb), when working with more complicated programs, we often need something more sophisticated than `print()` to understand why our code isn't working. And for that, we have debuggers! \n", @@ -20,7 +20,7 @@ }, { "cell_type": "markdown", - "id": "754289f7", + "id": "f6c95bd9", "metadata": {}, "source": [ "1. Debugging `.py` files with the built in debugger\n", @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "418e5f4f", + "id": "9d2e231e", "metadata": {}, "source": [ "## 0. Preliminaries \n", @@ -44,7 +44,7 @@ }, { "cell_type": "markdown", - "id": "5a186c3d", + "id": "c122396e", "metadata": {}, "source": [ "![numbered image of vscode buttons](images/preliminaries.png)" @@ -52,7 +52,7 @@ }, { "cell_type": "markdown", - "id": "66fe6ac2", + "id": "7c89db89", "metadata": {}, "source": [ "### 1. VS Code Debugger for `.py` files\n", @@ -62,7 +62,7 @@ }, { "cell_type": "markdown", - "id": "ffe211b8", + "id": "52139140", "metadata": {}, "source": [ "![debug start](images/debug_start.png)" @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "f9d77917", + "id": "a086c22f", "metadata": {}, "source": [ "To resolve this issue, we need to set breakpoint (#1). Breakpoints are conditions that if the code reaches such a condition, the code will pause. The default breakpoint is for \"Uncaught Exceptions\" although we can add to that list breakpoints at any point in the code. For example if we click on the \"gutter\" (the space to the left of the line numbers, as shown in #2), we activate a breakpoint which means that when the execution of the code reaches that line it will pause and show you the state of all the variables in the code, which is incredibly useful (giving us the information we previously achieved through `print()` statements). We can activate multiple breakpoints and the program will pause execution anytime a line with a breakpoint is encountered. To remove a breakpoint, just click on the red dot or uncheck the box in the breakpoints panel (#1) to temporarily deactivate it." @@ -78,7 +78,7 @@ }, { "cell_type": "markdown", - "id": "b13b71f8", + "id": "1bf0bf12", "metadata": {}, "source": [ "![break points](images/debug_breakpoints.png)\n" @@ -86,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "adcbe36d", + "id": "c05f3394", "metadata": {}, "source": [ "The first time we click \"Run and Debug\" it will ask us to choose a debug configuration. While these can be customized, we typically want to use the standard \"Python File\" option." @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "299865cb", + "id": "a30585b9", "metadata": {}, "source": [ "![config](images/debug_select_configuration.png)\n" @@ -102,7 +102,7 @@ }, { "cell_type": "markdown", - "id": "481f336d", + "id": "cf96962c", "metadata": {}, "source": [ "Once we've launched a debug session, that's when the real power of the tool becomes clear as shown in the figure below. Let's walk through each of the components as shown in the figure below:\n", @@ -117,7 +117,7 @@ }, { "cell_type": "markdown", - "id": "a0a0767b", + "id": "4e532891", "metadata": {}, "source": [ "![debug menu](images/debug_menu.png)\n" @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "bf8d5db5", + "id": "ecb7c51c", "metadata": {}, "source": [ "Let's walk through an example of using the debugging tool as shown below. Here we can see that the debugger pauses execution at the breakpoint set at line 6. As we step through execution, we can see the variables `fact` and `i` changing as execution continues and it becomes clear that i takes on the value of 0 initially, forcing `fact` to 0 and that value propagates through until it is returned. This allows us to easily see the source of the error without the need of modifying the content of the code at all!" @@ -133,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "d285913d", + "id": "fd5ca977", "metadata": {}, "source": [ "![debug step](images/anim_debugger_step.gif)\n" @@ -141,7 +141,7 @@ }, { "cell_type": "markdown", - "id": "634e9772", + "id": "0c3b0b73", "metadata": {}, "source": [ "Additionally, we could set a watch on variables `i` and `fact` or even on the product of `i` and `fact`:" @@ -149,7 +149,7 @@ }, { "cell_type": "markdown", - "id": "e07e426b", + "id": "f0e434a7", "metadata": {}, "source": [ "![watch](images/anim_debugging_watch.gif)\n" @@ -157,7 +157,7 @@ }, { "cell_type": "markdown", - "id": "06bd3915", + "id": "08cd9cfa", "metadata": {}, "source": [ "This tool can be particularly useful when dealing exploring matrices, which we can explore through the data viewer in the variable explorer. " @@ -165,7 +165,7 @@ }, { "cell_type": "markdown", - "id": "33ce639f", + "id": "91e91215", "metadata": {}, "source": [ "![data_viewer](images/data_viewer.png)\n", @@ -174,7 +174,7 @@ }, { "cell_type": "markdown", - "id": "ec1c6bf5", + "id": "baf42195", "metadata": {}, "source": [ "The data viewer can be used to explore the data in a matrix including sorting and filtering by column:" @@ -182,7 +182,7 @@ }, { "cell_type": "markdown", - "id": "69dd1c93", + "id": "e03e4062", "metadata": {}, "source": [ "![debug viewer](images/anim_debugging_data_viewer.gif)" @@ -190,7 +190,7 @@ }, { "cell_type": "markdown", - "id": "0ad46643", + "id": "36937b7c", "metadata": {}, "source": [ "Additionally, we can interact with any of the variables in the program using the debugger console as shown here evaluating `i`, `fact`, and `i*fact`. We can also introduce new variables directly into the current scope. To give an example, we'll add `y = 27` and `z = [1, 2, 3, 4]`. We can even edit those variables in the variable explorer and if any other variables relied upon them in the program, it would use the new values we input." @@ -198,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "8a6a331c", + "id": "a5fa6fee", "metadata": {}, "source": [ "![debug console](images/anim_debugging_console.gif)\n" @@ -206,7 +206,7 @@ }, { "cell_type": "markdown", - "id": "cf23c758", + "id": "6c8b9a72", "metadata": {}, "source": [ "One last handy feature of the debugger is that one a breakpoint is activated, if you hover over a variable, you will be able to see it's contents in a small tooltip box that appears near your cursor. For example:" @@ -214,7 +214,7 @@ }, { "cell_type": "markdown", - "id": "178436cf", + "id": "c700a143", "metadata": {}, "source": [ "![debug hover](images/anim_debugging_hover.gif)\n" @@ -222,7 +222,7 @@ }, { "cell_type": "markdown", - "id": "dd04fd22", + "id": "9b31238f", "metadata": {}, "source": [ "This suite of debugging tools is incredibly useful for debugging complicated programs and as you can see, it requires no additional code to be added! What's most valuable about these tools is that they quickly give you insights into how a program is running and allows you to explore the logic of the code interactively." @@ -230,7 +230,7 @@ }, { "cell_type": "markdown", - "id": "e4e3cf0c", + "id": "3452f07b", "metadata": {}, "source": [ "### 2. VS Code Debugger for `.ipynb` files (Jupyter notebooks)\n", @@ -240,7 +240,7 @@ }, { "cell_type": "markdown", - "id": "befb1a63", + "id": "a168da50", "metadata": {}, "source": [ "![debug jupyter](images/debugger_jupyter.png)\n" @@ -248,7 +248,7 @@ }, { "cell_type": "markdown", - "id": "f2b9f42a", + "id": "878b0097", "metadata": {}, "source": [ "Let's replicate the debugging process that we used previously in this Jupyter session showing the interactive tools, the debugging console, the variable watch list, and the ability to hover over values and see their contents:" @@ -256,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "dbb6d5ad", + "id": "b3b65466", "metadata": {}, "source": [ "![jupyter animation](images/anim_debugging_jupyter.gif)\n" @@ -264,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "beb6b101", + "id": "5a933bd0", "metadata": {}, "source": [ "The last Jupyter debugging feature to mention here is the line-by-line execution button. While not incorporating the use of breakpoints as the previously described features, this tool gives you the abilities to view and watch variables through line-by-line execution, just as before. We also show another Jupyter tool that can be helpful for debugging, which is the Jupyter:variables console. This is similar to the debugging variable explorer, but is available outside of a debugging session, which makes it highly useful for debugging issues in Jupyter notebooks. Jupyter:variables consoles also have access to the data viewer tool we discussed earlier - extremely helpful for exploring matrices." @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "08a8d4c1", + "id": "cd5bcfd1", "metadata": {}, "source": [ "![jupyter line](images/anim_debugging_jupyter_line.gif)\n" @@ -280,7 +280,7 @@ }, { "cell_type": "markdown", - "id": "14e653d5", + "id": "166821c2", "metadata": {}, "source": [ "## Debugging Summary\n", diff --git a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/conda_environments.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/conda_environments.ipynb index ea1b3a83..86ad58ce 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/conda_environments.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/conda_environments.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "a7dbefd5", + "id": "ed5fd72e", "metadata": {}, "source": [ "# Managing Conda Environments" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "4ce49327", + "id": "915da79e", "metadata": {}, "source": [ "It's extremely common in software engineering and data science to need different sets of packages (and potentially different versions of Python) for different projects. To make this easier, conda is capable of creating isolated \"environments\", each with its own version of Python and related packages.\n", @@ -57,7 +57,7 @@ }, { "cell_type": "markdown", - "id": "c83c34f5", + "id": "3d2798bb", "metadata": {}, "source": [ "### Installing Packages After Setup\n", @@ -67,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "fe5aa7b3", + "id": "c0b6c4b8", "metadata": {}, "source": [ "\n", diff --git a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/writing_good_jupyter_notebooks.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/writing_good_jupyter_notebooks.ipynb index e81f6e5a..0b4f6173 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/writing_good_jupyter_notebooks.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/20_programming_concepts/writing_good_jupyter_notebooks.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "6dc40980", + "id": "a6b8ddb4", "metadata": {}, "source": [ "# Writing Good Jupyter Notebooks\n", @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "b96a498e", + "id": "fe03f24c", "metadata": {}, "source": [ "\n", @@ -33,7 +33,7 @@ }, { "cell_type": "markdown", - "id": "cf37c036", + "id": "877444d2", "metadata": {}, "source": [ "\n", @@ -102,7 +102,7 @@ }, { "cell_type": "markdown", - "id": "12091c2d", + "id": "9810da15", "metadata": {}, "source": [ "## Format Your Code\n", @@ -128,7 +128,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "852c06f6", + "id": "1be0d442", "metadata": {}, "outputs": [ { @@ -178,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "b1502fda", + "id": "ab998100", "metadata": {}, "source": [ "## Other Odds and Ends\n", @@ -190,7 +190,7 @@ }, { "cell_type": "markdown", - "id": "130d13f0", + "id": "5c31eb98", "metadata": {}, "source": [ "## Some Examples\n", @@ -204,7 +204,7 @@ }, { "cell_type": "markdown", - "id": "95a4b07a", + "id": "d74fc116", "metadata": {}, "source": [ "## Homework Grading\n", diff --git a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb index ad94a828..d8507574 100644 --- a/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb +++ b/_build/jupyter_execute/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb @@ -812,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1926,6 +1926,1089 @@ "execution_count": 9, "metadata": {}, "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-28 07:21:51,309 - tornado.application - ERROR - Exception in callback >\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/ioloop.py\", line 919, in _run\n", + " val = self.callback()\n", + " ^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/system_monitor.py\", line 168, in update\n", + " net_ioc = psutil.net_io_counters()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/psutil/__init__.py\", line 2119, in net_io_counters\n", + " rawdict = _psplatform.net_io_counters()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "OSError: [Errno 12] Cannot allocate memory\n", + "2023-11-29 12:24:43,023 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,086 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,125 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,130 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,280 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:32:25,398 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,405 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,753 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', 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962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,864 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:32:25,946 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:32:38,591 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:38,659 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,011 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,181 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,299 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 369): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 140): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1043): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 634): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 405): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1308): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:32:39,295 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:51,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,043 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,257 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,540 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,664 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:32:52,661 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:04,636 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:04,728 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:04,862 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:04,895 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:33:05,272 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:05,301 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:15,177 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:15,350 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:15,994 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:16,318 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:16,497 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', 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self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File 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('assign-786d0153597eb09d7a8413baa8e8f555', 1187): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 778): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1452): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 369): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 140): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1043): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 634): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 405): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1308): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:33:29,522 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:29,643 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:29,963 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:29,964 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:29,974 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:40,320 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:40,536 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,055 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,131 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,253 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1406): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1177): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 94): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 768): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 359): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 130): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1262): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1033): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 624): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1527): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1298): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 215): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 889): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1563): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 480): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 251): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1154): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 745): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 516): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:33:41,247 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,214 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:53,215 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,290 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,402 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:53,563 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 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distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:05,777 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:05,800 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:05,900 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:06,047 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:06,100 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 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('assign-786d0153597eb09d7a8413baa8e8f555', 140): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1043): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 634): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 405): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1308): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:34:06,432 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,458 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,682 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:17,740 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,961 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:34:18,184 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n" + ] } ], "source": [ diff --git a/_build/jupyter_execute/notebooks/class_2/week_2/30_subsetting_vectors.ipynb b/_build/jupyter_execute/notebooks/class_2/week_2/30_subsetting_vectors.ipynb index 47485c19..e76cbdc1 100644 --- a/_build/jupyter_execute/notebooks/class_2/week_2/30_subsetting_vectors.ipynb +++ b/_build/jupyter_execute/notebooks/class_2/week_2/30_subsetting_vectors.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "9d5c9785", + "id": "d18ca1dc", "metadata": {}, "source": [ "# Subsetting Vectors" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "59a76b2b", + "id": "fafcde3f", "metadata": {}, "source": [ "In our last reading, we learned what vectors are, and how to do operations on entire vectors. But often times we want to work with *subsets* of a vector. Indeed, extracting a subset of elements from a vector is an extremely important task, not least because it generalizes nicely to datasets (which are at the heart of data science). This process --- whether applied to a vector or a dataset --- is often referred to as \"taking a subset\", \"subsetting\", or \"filtering\". If there is one skill you need to master as quickly as possible, it's this.\n", @@ -23,7 +23,7 @@ }, { "cell_type": "markdown", - "id": "054f2970", + "id": "69bc8547", "metadata": {}, "source": [ "## What is Subsetting?\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "b962591f", + "id": "fcfca08f", "metadata": {}, "outputs": [ { @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "c56c3ffc", + "id": "4dc30e8d", "metadata": {}, "source": [ "If I wanted to pull out the second entry in that vector, I could do so with *array indexing* using square brackets `[]` (remember that indexes start at 0 in Python, so the second entry is at index `1`):" @@ -65,7 +65,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "2631a255", + "id": "769b9471", "metadata": {}, "outputs": [ { @@ -85,7 +85,7 @@ }, { "cell_type": "markdown", - "id": "ff91feaa", + "id": "5281dd1f", "metadata": {}, "source": [ "And if I want to assign that second entry to a new variable, I can!" @@ -94,7 +94,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "f18451bf", + "id": "e5dfd156", "metadata": {}, "outputs": [ { @@ -115,7 +115,7 @@ }, { "cell_type": "markdown", - "id": "74a21f9b", + "id": "c3954510", "metadata": {}, "source": [ "But what, exactly, is happening when I subset? Let's return to the idea that a variable is just a box holding some data, and walk through the following block of code:" @@ -123,7 +123,7 @@ }, { "cell_type": "markdown", - "id": "f1d46343", + "id": "74007a6f", "metadata": {}, "source": [ "```python\n", @@ -138,7 +138,7 @@ }, { "cell_type": "markdown", - "id": "212f9e13", + "id": "1693601c", "metadata": {}, "source": [ "In the first line of code, we create a new vector with three entries and assign it to the variable `a`. Just as in our previous reading, we can think of the variable `a` as a box that is holding this new vector.\n", @@ -156,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "0232b253", + "id": "3df8d093", "metadata": {}, "source": [ "This `variable[]` notation is something we'll use a *lot* with numpy, and it will always mean the same thing: we're trying to access some data in the data stored in the box `variable`.\n", @@ -166,7 +166,7 @@ }, { "cell_type": "markdown", - "id": "6578ee74", + "id": "5ca9d7ad", "metadata": {}, "source": [ "## Subsetting By Index\n", @@ -183,7 +183,7 @@ }, { "cell_type": "markdown", - "id": "aa1b1218", + "id": "8f757e9c", "metadata": {}, "source": [ "There are several ways one can subset arrays, but the most basic is with *simple indexing*, which is where you use a single index to access a single entry, or a range (e.g., `0:3` to get the first three elements of an array), just like with lists. So in the example above, we grabbed the second element by passing in the index of the entry we wanted:" @@ -192,7 +192,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "68b46026", + "id": "b4a5fd69", "metadata": {}, "outputs": [ { @@ -261,7 +261,7 @@ }, { "cell_type": "markdown", - "id": "f1fa333c", + "id": "85a3ef0f", "metadata": {}, "source": [ "numpy arrays also support something called *fancy indexing*, which is where you pass a list (or another array) containing a set of indices you want to get back. With *fancy indexing*, index values don't have to be consecutive or even in order! Here's an example with a list:" @@ -299,7 +299,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "b05a21f9", + "id": "feaed44e", "metadata": {}, "outputs": [ { @@ -341,7 +341,7 @@ }, { "cell_type": "markdown", - "id": "14f7a020", + "id": "5015c48b", "metadata": {}, "source": [ "Note that with fancy indexing, you don't have to subset entries in order: if you pass indices out of order, you'll get a vector with a new order!" @@ -350,7 +350,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "ff6ba3cf", + "id": "f1c7d651", "metadata": {}, "outputs": [ { @@ -370,7 +370,7 @@ }, { "cell_type": "markdown", - "id": "e7999e21", + "id": "0c23686f", "metadata": {}, "source": [ "Again, this is all working the same way as our example with just one entry -- Python interprets the square brackets as a request for some data in the box `a`, and if we pass multiple indices, it just grabs multiple items from that box. " @@ -378,7 +378,7 @@ }, { "cell_type": "markdown", - "id": "ef921582", + "id": "bc8f97fb", "metadata": {}, "source": [ "## Subsetting with Booleans" @@ -386,7 +386,7 @@ }, { "cell_type": "markdown", - "id": "5e64d6d0", + "id": "c7538d7e", "metadata": {}, "source": [ "Subsetting with Booleans is a little hard to explain, so instead let's jump right into an example. \n", @@ -397,7 +397,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "cb45b16e", + "id": "fed919fc", "metadata": {}, "outputs": [ { @@ -418,7 +418,7 @@ }, { "cell_type": "markdown", - "id": "116ff341", + "id": "bc758a6e", "metadata": {}, "source": [ "Within these brackets is a vector with the same number of Boolean elements as there are elements in the vector you want to subset. Elements across the two vectors are matched by order: elements that match with `True` are kept while elements that match with `False` are dropped.\n", @@ -429,7 +429,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "92e89a49", + "id": "86b57411", "metadata": {}, "outputs": [ { @@ -452,7 +452,7 @@ }, { "cell_type": "markdown", - "id": "f637ffd6", + "id": "809fa231", "metadata": {}, "source": [ "First, we create `a`:\n", @@ -480,7 +480,7 @@ }, { "cell_type": "markdown", - "id": "46bebea4", + "id": "992a7fdd", "metadata": {}, "source": [ "## Subsetting With Logical Operations " @@ -488,7 +488,7 @@ }, { "cell_type": "markdown", - "id": "0a952f14", + "id": "102ab711", "metadata": {}, "source": [ "\n", @@ -621,7 +621,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "70cf7c9c", + "id": "6822fd10", "metadata": {}, "outputs": [ { diff --git a/_build/jupyter_execute/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb b/_build/jupyter_execute/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb index 01480985..06ef94df 100644 --- a/_build/jupyter_execute/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb +++ b/_build/jupyter_execute/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb @@ -46,7 +46,7 @@ "years_of_education = [12, 16, 11, 22, 12, 8, 12]\n", "\n", "survey = np.array([incomes, ages, years_of_education]).T\n", - "survey\n" + "survey" ] }, { @@ -78,7 +78,7 @@ ], "source": [ "mean_age = np.mean(survey[:, 1])\n", - "np.round(mean_age, 1)\n" + "np.round(mean_age, 1)" ] }, { @@ -140,7 +140,7 @@ ], "source": [ "mean_income = np.mean(survey[:, 0])\n", - "mean_income\n" + "mean_income" ] }, { @@ -160,7 +160,7 @@ } ], "source": [ - "np.round(mean_income, 1)\n" + "np.round(mean_income, 1)" ] }, { @@ -241,12 +241,12 @@ ], "source": [ "survey[survey[:, 0] < 20_000, 0] = survey[survey[:, 0] < 20_000, 0] + 1_500\n", - "survey\n" + "survey" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -255,13 +255,13 @@ "40214.3" ] }, - "execution_count": 27, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(np.mean(survey[:, 0]), 1)\n" + "np.round(np.mean(survey[:, 0]), 1)" ] }, { @@ -325,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -334,7 +334,7 @@ "(122603, 5)" ] }, - "execution_count": 11, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -346,28 +346,7 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "26.0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hourly = cps[:, 0] / cps[:, 1]\n", - "np.round(np.mean(hourly), 1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -376,19 +355,19 @@ "27.883329990399556" ] }, - "execution_count": 13, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "men = np.mean(hourly[cps[:, 2] == 1])\n", - "men\n" + "men" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -397,18 +376,18 @@ "27.9" ] }, - "execution_count": 14, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(men, 1)\n" + "np.round(men, 1)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -417,19 +396,19 @@ "23.803157653012704" ] }, - "execution_count": 15, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "women = np.mean(hourly[cps[:, 2] == 2])\n", - "women\n" + "women" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -438,18 +417,18 @@ "23.8" ] }, - "execution_count": 16, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(women, 1)\n" + "np.round(women, 1)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -458,18 +437,18 @@ "0.8536698328789393" ] }, - "execution_count": 17, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "women / men\n" + "women / men" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -478,13 +457,13 @@ "0.854" ] }, - "execution_count": 18, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(women / men, 3)\n" + "np.round(women / men, 3)" ] }, { @@ -518,7 +497,7 @@ ], "source": [ "black = np.mean(hourly[cps[:, 3] == 2])\n", - "black\n" + "black" ] }, { @@ -538,7 +517,7 @@ } ], "source": [ - "np.round(black, 1)\n" + "np.round(black, 1)" ] }, { @@ -559,7 +538,7 @@ ], "source": [ "white = np.mean(hourly[cps[:, 3] == 1])\n", - "white\n" + "white" ] }, { @@ -579,7 +558,7 @@ } ], "source": [ - "np.round(white, 1)\n" + "np.round(white, 1)" ] }, { @@ -599,7 +578,7 @@ } ], "source": [ - "black / white\n" + "black / white" ] }, { @@ -619,7 +598,7 @@ } ], "source": [ - "np.round(black / white, 3)\n" + "np.round(black / white, 3)" ] }, { @@ -651,7 +630,7 @@ "metadata": {}, "outputs": [], "source": [ - "true_average = np.sum((hourly * cps[:, 4]) / np.sum(cps[:, 4]))\n" + "true_average = np.sum((hourly * cps[:, 4]) / np.sum(cps[:, 4]))" ] }, { @@ -671,7 +650,7 @@ } ], "source": [ - "np.round(true_average, 2)\n" + "np.round(true_average, 2)" ] } ], @@ -691,7 +670,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb b/_build/jupyter_execute/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb index 2fed36bb..dcaec5c7 100644 --- a/_build/jupyter_execute/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb +++ b/_build/jupyter_execute/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb @@ -9,7 +9,7 @@ "\n", "**Note**: \n", "\n", - "> This exercise has been written out in something called a Jupyter Notebook. We'll discuss Jupyter Notebooks in more detail later in the course—they are a very powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don't need to *do* anything with the notebook except read its contents—just use write your Python code in a regular `.py` file.\n", + "> This exercise has been written out in something called a Jupyter Notebook. We'll discuss Jupyter Notebooks in more detail later in the course—they are very a powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don't need to *do* anything with the notebook except read its contents—just use write your Python code in a regular `.py` file.\n", "\n", "\n", "As previously discussed, we frequently use matrices and data science because they are a natural format for representing data generated by collecting the same type of information from numerous entities. For example, below is a toy dataset that you could imagine was created by collecting information about employees at a company—each column is a different type of information being collected (income, age, years of education), and each row is the information about a different employee.\n", @@ -47,7 +47,7 @@ "years_of_education = [12, 16, 11, 22, 12, 8, 12]\n", "\n", "survey = np.array([incomes, ages, years_of_education]).T\n", - "survey\n" + "survey" ] }, { @@ -56,9 +56,17 @@ "source": [ "## Part 1: Summarizing Data\n", "\n", - "1. What is the average (mean) age of all employees (rounded to 1 decimal place)?\n", - "2. What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?\n", - "3. What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?" + "### Question 1 \n", + "\n", + "What is the average (mean) age of all employees (rounded to 1 decimal place)?\n", + "\n", + "### Question 2\n", + "\n", + "What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?\n", + "\n", + "### Question 3\n", + "\n", + "What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?" ] }, { @@ -70,7 +78,9 @@ "\n", "The US government is thinking about offering a 1,500 tax credit to anyone making less than 20,000 a year. You can think of this tax credit as effectively an additional $1,500 of income to each person receiving the credit.\n", "\n", - "4. Using the data from `survey`, modify income values to calculate a new estimate of the employees' incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?\n", + "### Question 4 \n", + "\n", + "Using the data from `survey`, modify income values to calculate a new estimate of the employees' incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?\n", " - Do so by subsetting and editing values programmatically, *not* just typing values by hand. (Yes, writing out a new vector by hand is easy to do in this example, but you couldn't do it with a large, real dataset!)" ] }, @@ -82,7 +92,7 @@ "\n", "In this exercise, we'll be working with data from the [US Current Population Survey, provided by the National Bureau of Economic Research (NBER)](https://www.nber.org/research/data/current-population-survey-cps-merged-outgoing-rotation-group-earnings-data). This is a regular survey conducted by the US Bureau of Labor to calculate the US employment rate.\n", "\n", - "In this exercise, we'll use these data to study gender and racial wage inequality in the US.\n", + "In this exercise, we'll use this data to study gender and racial wage inequality in the US.\n", "\n", "Load data from the 2018 CPS survey with the following command:\n", "\n", @@ -90,16 +100,20 @@ "cps = np.loadtxt(\"data/cps.txt\")\n", "```\n", "\n", - "This data are a *subset* of the full CPS survey and contains only data on **employed respondents working at least 35 hours a week (e.g., full-time).**\n" + "This data is a *subset* of the full CPS survey and contains only data on **employed respondents working at least 35 hours a week (e.g., full-time).**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "5. How many rows does this matrix have?\n", + "### Question 5 \n", + "\n", + "How many rows does this matrix have?\n", + "\n", + "### Question 6 \n", "\n", - "6. The five columns of this matrix correspond to:\n", + "The five columns of this matrix correspond to:\n", " - Column 1: Weekly income in dollars.\n", " - Column 2: Usual hours respondent works per week.\n", " - Column 3: Gender. 2 is \"Female\", 1 is \"Male\"\n", @@ -115,7 +129,9 @@ "\n", "For the moment, let's ignore survey weights—they don't impact results here significantly.\n", "\n", - "**What is the *average hourly wage* for all workers in this dataset (round it to one decimal place)?**\n", + "**What is the *average hourly wage* for all workers in this data?** \n", + "\n", + "Please round your *final* answer to one decimal place, but *don't round any intermediate results!* (if you round at each stage of the calculation, those roundings may compound in odd ways).\n", "\n", "**Hint:** This will require more than just using `mean` on a single column!" ] @@ -124,11 +140,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "7. What is the average hourly wage of working men (rounded to one decimal place)? \n", + "### Question 7 \n", + "\n", + "What is the average hourly wage of working men? (Please round your *final* answer to one decimal place, but *don't round any intermediate results!*)\n", + "\n", + "### Question 8 \n", + "\n", + "What is the average hourly wage of working women? (Please round your *final* answer to one decimal place, but *don't round any intermediate results!*)\n", "\n", - "8. What is the average hourly wage of working women (rounded to one decimal place)?\n", + "### Question 9 \n", "\n", - "9. What share (e.g., a value between 0 and 1) of men's average hourly wage is women's average hourly wage? (rounded to three decimal places). In other words, what is women's average hourly wage divided by men's average hourly wage? *Don't round anything until you have your final answer!*\n", + "What share (e.g., a value between 0 and 1) of men's average hourly wage is women's average hourly wage? (Please round your *final* answer to three decimal place, but *don't round any intermediate results!*). In other words, what is women's average hourly wage divided by men's average hourly wage?\n", "\n", "Congratulations! You've just calculated the US gender wage gap, on your own, using real data! I mean... I guess \"congratulations\" is a weird thing to say after directly measuring one of the more egregious inequities in US society, but one of the reasons many of us study data science is so that we will have the ability to directly measure these types of phenomena in the hopes of being able to better understand and address them." ] @@ -137,11 +159,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "10. Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?\n", + "### Question 10 \n", "\n", - "11. What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?\n", + "Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?\n", "\n", - "12. What share (e.g., a value between 0 and 1) of respondents who identify as White's average hourly wage is respondents who identify as Black's average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black's average hourly wage divided by respondents who identify as White's average hourly wage? *Don't round anything until you have your final answer!*\n", + "### Question 11 \n", + "\n", + "What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?\n", + "\n", + "### Question 12 \n", + "\n", + "What share (e.g., a value between 0 and 1) of respondents who identify as White's average hourly wage is respondents who identify as Black's average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black's average hourly wage divided by respondents who identify as White's average hourly wage? *Don't round anything until you have your final answer!*\n", "\n", "Note that this will only be an approximation—one would normally also include all respondents of mixed-race into non-mutually exclusive categories like \"Any Part Black\" or \"Any Part White\", and we would also break out Hispanic and non-Hispanic respondents. But as most respondents only pick on racial category, this will still give us a reasonable approximation." ] @@ -154,7 +182,7 @@ "\n", "As noted above, the fifth column of our data contains something called \"sampling weights.\" That's because when the government conducted this survey, they didn't draw a random sample of respondents from the US population where everyone had the same probability of being interviewed. As a result, when we calculate the average hourly wage of the people in the survey, it isn't *quite* the best estimate we have for the average hourly wage for everyone working in the United States. To calculate this number as accurately as possible, we have to take into account the fact that some respondents in the data were more likely to be included than others, and thus can be thought of as standing in for a smaller group of people in the US.\n", "\n", - "Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than what we would get if we just took a random sample where everyone in the United States had the same probability of being included.\n", + "Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than when we would get if we just took a random sample where everyone in the United States had the same probability of being included.\n", "\n", "As I mentioned above, for the questions above, the sampling weights don't make a very big difference to our answers, but the way to get the *most accurate* estimates would be to take them into account. So let's give that a try!\n", "\n", @@ -162,9 +190,11 @@ "\n", "For a *weighted* average, we take the value for each observation $i$ and multiply it by\n", "\n", - "$$weight_i / \\sum_{j=1}^N weight_j$$\n", + "$$weight_i / \\sum_{j=1}^N weights_j$$\n", + "\n", + "where $weight_i$ is the observation's weight, and $\\sum_{j=1}^N weights_j$ is the total of all the weights in the population being averaged. Then we just add up those values!\n", "\n", - "where $weight_i$ is the observation's weight, and $\\sum_{j=1}^N weight_j$ is the total of all the weights in the population being averaged. Then we just add up those values!\n", + "### Question 13\n", "\n", "Given that, what is the average hourly wage of Americans working full-time jobs (i.e., the group in this survey) taking into account survey weights (rounded to **two** decimal places)?\n" ] @@ -172,7 +202,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.6.5 ('base')", + "display_name": "Python 3.10.6 ('base')", "language": "python", "name": "python3" }, @@ -186,11 +216,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.10.6" }, "vscode": { "interpreter": { - "hash": "f54e0b9f92ceb50229b3b7ea26b5d02f05ce8845dd8d997c014a0efede4c9c20" + "hash": "718fed28bf9f8c7851519acf2fb923cd655120b36de3b67253eeb0428bd33d2d" } } }, diff --git a/_build/jupyter_execute/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb b/_build/jupyter_execute/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb index 3cddd4a0..29bfd5e8 100644 --- a/_build/jupyter_execute/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb +++ b/_build/jupyter_execute/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb @@ -276,8 +276,8 @@ " 704 907 46 752 31 916 578 58 588 206 338 358 782 953 336 682 640 278\n", " 526 324 62 331 751 442 463 605 239 957 522 335 389 179 657 486 565 258\n", " 140 997 882 388 866 13 925 854 489 402]\n", - "894.7222222222222\n", - "894.0\n" + "425.078431372549\n", + "423.0\n" ] } ], @@ -291,11 +291,11 @@ "\n", "def remove_greater_than(array, threshold):\n", " '''remove entries in `array' greater than `threshold' '''\n", - " return array[array > threshold]\n", + " return array[array <= threshold]\n", "\n", "def remove_less_than(array, threshold):\n", " '''remove entries in `array' less than `threshold' ''' \n", - " return array[array < threshold]\n", + " return array[array >= threshold]\n", "\n", "def remove_if_equal(array, value_list):\n", " '''remove entries in `array' that equal any value in `value_list' '''\n", @@ -311,6 +311,7 @@ "data = remove_greater_than(data,800)\n", "even_integers_through_1000 = np.arange(2, 1001, 2)\n", "data = remove_if_equal(data,even_integers_through_1000)\n", + "data = remove_less_than(data,25)\n", "data = remove_duplicates(data)\n", "\n", "filtered_mean = np.mean(data)\n", @@ -322,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -330,21 +331,81 @@ "output_type": "stream", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 0 1 2 3\n", - " 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n", - "[11 12 13 14 15 16 17 18 19 11 12 13 14 15 16 17 18 19]\n", - "[0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]\n", - "[ 0 4 5 6 7 8 16 17 18 19 0 4 5 6 7 8 16 17 18 19]\n", - "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" + " 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" ] } ], "source": [ "test = np.concatenate((np.arange(0,20),np.arange(0,20)))\n", - "print(test)\n", - "print(remove_greater_than(test,10))\n", - "print(remove_less_than(test,10))\n", - "print(remove_if_equal(test,[1,2,3,9,10,11,12,13,14,15]))\n", - "print(remove_duplicates(test))\n" + "print(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10]\n" + ] + } + ], + "source": [ + "print(remove_greater_than(test,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_less_than(test,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 4 5 6 7 8 16 17 18 19 0 4 5 6 7 8 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_if_equal(test,[1,2,3,9,10,11,12,13,14,15]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_duplicates(test))" ] } ], @@ -364,7 +425,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.12.0" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_2/00_intro_to_pandas.ipynb b/_build/jupyter_execute/notebooks/class_3/week_2/00_intro_to_pandas.ipynb index e069ef7f..5159201e 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_2/00_intro_to_pandas.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_2/00_intro_to_pandas.ipynb @@ -2,17 +2,18 @@ "cells": [ { "cell_type": "markdown", - "id": "87502394", + "id": "8a82f2d8", "metadata": {}, "source": [ "# Working with tabular data\n", "\n", "In the last class, we discussed how to work with numerical data through arrays and matrices in cases when *all the data were of the same type*. You encounter this often, so `numpy` is a powerful tool for working with such homogeneous data (data of the same type) and in such cases is often the preferred tool if you want to do numeric computations because of its speed. However, there are several frequently encountered situations in which `numpy` tools may not be ideal and where instead the `pandas` library may be best. These include:\n", "\n", - "1. If you do not have purely numerical data (i.e. if your data are heterogeneous), and therefore have a mix of data types, `numpy` may not be appropriate, while `pandas` can easily handle and analyze mixed data types\n", - "2. If you have tabular data, `pandas` makes life much easier to **describe**, **summarize**, **query**, and **visualize** the data than the equivalent processes with `numpy`.\n", + "1. Your data includes not only numerical data, but also text, images, geometric objects, or any other type of data (i.e., your data are heterogeneous). In these situations, `numpy` may not be appropriate, while `pandas` can easily organize, manipulate, and analyze this kid of \"mixed data.\"\n", + "2. If you have tabular data — that is, data organized into rows and columns, as in a spreadsheet — it is often easier in `pandas` to **describe**, **summarize**, **query**, and **visualize** the data than the equivalent processes with `numpy`.\n", "\n", "## Mixed data types\n", + "\n", "Often our data are of mixed types (e.g. integers and strings). This happens all the time. Imagine that you are collecting basic medical information from a patient. You may ask for height and weight (numerical, floating point numbers), age (integer), and blood type (categorical, string). While numpy can store these together in an array, there's not much you're going to be able to do with it computationally. Consider the following example where `numpy` throws an error:" ] }, @@ -42,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "309f8fd3", + "id": "a1f3b4a8", "metadata": {}, "source": [ "`pandas`, on the other hand, doesn't have any problem with mixed data types. It will use the appropriate operation for each data type, adding the numbers and concatenating the strings:" @@ -90,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "9116ee0e", + "id": "4c0deeaa", "metadata": {}, "source": [ "## A hierarchy of data types: from lists to `numpy` arrays to `pandas` Series and DataFrames\n", @@ -102,7 +103,7 @@ }, { "cell_type": "markdown", - "id": "14130f0c", + "id": "61f1a77e", "metadata": {}, "source": [ "## Effectively working with tabular data\n", @@ -117,7 +118,7 @@ }, { "cell_type": "markdown", - "id": "418ae2c6", + "id": "cabe4f21", "metadata": {}, "source": [ "### Example 1: Quickly reading your data into a structured tabular form\n", @@ -222,7 +223,7 @@ }, { "cell_type": "markdown", - "id": "6c12747e", + "id": "0c2a15af", "metadata": {}, "source": [ "It really doesn't get much easier than that. We have text content (under `country` and `region`) and numerical content (`gdp_per_capita_2008``) and column headings are even included!" @@ -230,7 +231,7 @@ }, { "cell_type": "markdown", - "id": "d73ed6b9", + "id": "4366cc35", "metadata": {}, "source": [ "### Example 2: Quickly describe / summarize your data\n", @@ -328,7 +329,7 @@ }, { "cell_type": "markdown", - "id": "c9aa0edb", + "id": "d190502c", "metadata": {}, "source": [ "### Example 3 Quickly querying your data\n", @@ -402,7 +403,7 @@ }, { "cell_type": "markdown", - "id": "8667ff9a", + "id": "23c9b35f", "metadata": {}, "source": [ "### Example 4: Quickly plotting your data\n", @@ -419,7 +420,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -428,7 +429,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -478,7 +479,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_2/10_pandas_series.ipynb b/_build/jupyter_execute/notebooks/class_3/week_2/10_pandas_series.ipynb index 4fcc7aa9..14d74782 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_2/10_pandas_series.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_2/10_pandas_series.ipynb @@ -302,10 +302,11 @@ "\n", "Before we dive too far into Series manipulations, it's important to talk about datatypes. Every Series, as we will see, has a \"dtype\" (short for datatype). The `dtype` of a Series is important to understand because a Series' `dtype` determines what manipulations you can apply to that series. \n", "\n", - "There are, broadly, two types of Series: \n", + "There are, broadly, three types of Series: \n", "\n", "- Numeric: these hold numbers that `pandas` understands are numbers. Specific numeric datatypes include things like `int64`, and `int32` (integers), or `float64` and `float32` (floating point numbers).\n", - "- Object: these are Series that can hold any Python object, like strings, numbers, Sets, you name it. They have dtype `O` for \"objects\". They are flexible, but also very slow and actually harder to work with." + "- Object: these are Series that can hold any Python object, like strings, numbers, sets, you name it. They have dtype `O` for \"objects\". They are flexible, but also very slow and actually harder to work with.\n", + "- Categorical: this is a special data type (analogous to factors in R, if that means anything to you) that we'll discuss in a future reading. Don't worry about them for now." ] }, { diff --git a/_build/jupyter_execute/notebooks/class_3/week_2/15_manipulating_series.ipynb b/_build/jupyter_execute/notebooks/class_3/week_2/15_manipulating_series.ipynb index 36bba6de..7b2aabe4 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_2/15_manipulating_series.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_2/15_manipulating_series.ipynb @@ -495,7 +495,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb b/_build/jupyter_execute/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb index e11d8ab4..f2ec2332 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -152,7 +152,7 @@ "dtype: object" ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -173,7 +173,7 @@ "'dog'" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -209,7 +209,7 @@ "dtype: int64" ] }, - "execution_count": 16, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -220,16 +220,24 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_22245/1336667577.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " attendance[0]\n" + ] + }, { "data": { "text/plain": [ "132" ] }, - "execution_count": 17, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -245,6 +253,21 @@ "As a result, for someone reading your code to understand what `[]` is doing, they have to also be keeping track of the data type of the index of the pandas object being manipulated. And while you may think \"yeah, but my code is just for me, so who cares?\" let me let you in on a secret: that \"other person\" who may struggle to understand your code maybe you when you come back to your code in a week to fix something and no longer remember everything that feels obvious to you right now!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, as you may have noticed from the `FutureWarning` generated when we ran that code:\n", + "\n", + "```python\n", + "FutureWarning: Series.__getitem__ treating keys as positions is deprecated. \n", + "In a future version, integer keys will always be treated as labels (consistent \n", + "with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + "```\n", + "\n", + "the developers of pandas concur that this behavior is *very* weird, and at some point soon it will be depreciated (probably in `pandas 3.0`). But for now, it remains a \"gotcha\" to be aware of!" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -273,7 +296,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_2/20_Exercise_series.ipynb b/_build/jupyter_execute/notebooks/class_3/week_2/20_Exercise_series.ipynb index fa5c81f4..c795e19d 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_2/20_Exercise_series.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_2/20_Exercise_series.ipynb @@ -12,13 +12,22 @@ "\n", "```python\n", "import pandas as pd\n", - "gdppercap = pd.Series([34605, 34493, 12393, 44200, 10041, \n", - " 58138, 4709, 49284, 10109, 42536],\n", - " index=['Bahrain', 'Belgium', 'Bulgaria',\n", - " 'Ireland', 'Macedonia', 'Norway', \n", - " 'Paraguay', 'Singapore', \n", - " 'South Africa', 'Switzerland']\n", - " ) \n", + "\n", + "gdppercap = pd.Series(\n", + " [34605, 34493, 12393, 44200, 10041, 58138, 4709, 49284, 10109, 42536],\n", + " index=[\n", + " \"Bahrain\",\n", + " \"Belgium\",\n", + " \"Bulgaria\",\n", + " \"Ireland\",\n", + " \"Macedonia\",\n", + " \"Norway\",\n", + " \"Paraguay\",\n", + " \"Singapore\",\n", + " \"South Africa\",\n", + " \"Switzerland\",\n", + " ],\n", + ")\n", "```" ] }, @@ -112,15 +121,32 @@ "To test this hypothesis, let's do a small analysis of the dynamics of income inequality in our sample. Create the following Series in your Python session, which provides the average growth rate of GDP per capita for all the countries in our sample from 2000 to 2018. \n", "\n", "```python\n", - "avg_growth = pd.Series([-0.29768835, 0.980299584, 4.52991925, \n", - " 3.686556736, 2.621416804, 0.775132075, \n", - " 2.015489468, 3.345793635, 1.349993318, \n", - " 0.982775018],\n", - " index=['Bahrain', 'Belgium', 'Bulgaria', \n", - " 'Ireland', 'Macedonia', 'Norway', \n", - " 'Paraguay', 'Singapore', \n", - " 'South Africa', 'Switzerland']\n", - " )\n", + "avg_growth = pd.Series(\n", + " [\n", + " -0.29768835,\n", + " 0.980299584,\n", + " 4.52991925,\n", + " 3.686556736,\n", + " 2.621416804,\n", + " 0.775132075,\n", + " 2.015489468,\n", + " 3.345793635,\n", + " 1.349993318,\n", + " 0.982775018,\n", + " ],\n", + " index=[\n", + " \"Bahrain\",\n", + " \"Belgium\",\n", + " \"Bulgaria\",\n", + " \"Ireland\",\n", + " \"Macedonia\",\n", + " \"Norway\",\n", + " \"Paraguay\",\n", + " \"Singapore\",\n", + " \"South Africa\",\n", + " \"Switzerland\",\n", + " ],\n", + ")\n", "```" ] }, @@ -152,7 +178,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb b/_build/jupyter_execute/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb index d873ffea..6b9efa65 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb @@ -162,7 +162,7 @@ "source": [ "## `[]` Square brackets\n", "\n", - "As with Series, single square brackets in `pandas` change their behavior depending on the values you pass them. Again, it is worth emphasizing that there is *nothing* that one can do with square brackets that you can't do with `.loc` and `.iloc`, so if they seem to strange, you don't have to use them. \n", + "As with Series, single square brackets in `pandas` change their behavior depending on the values you pass them. Again, it is worth emphasizing that there is *nothing* that one can do with square brackets that you can't do with `.loc` and `.iloc`, so if they seem too strange, you don't have to use them. \n", "\n", "With that said, as summarized below, `[]` is actually much safer on DataFrames than on Series. \n", "\n", @@ -833,7 +833,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_14379/4041630850.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_22305/4041630850.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", " world.rank_doubled = range(0, 2 * len(world), 2)\n" ] }, @@ -963,7 +963,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/00_plaintext_files.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/00_plaintext_files.ipynb index 2bd5abb7..b20ddd2f 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/00_plaintext_files.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/00_plaintext_files.ipynb @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -146,13 +146,14 @@ "5 Ukraine C&E Europe 7271 16" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", + "\n", "pd.read_csv(\"data/world-very-small.csv\")" ] }, @@ -171,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -180,13 +181,12 @@ "{'pets': ['dogs', 'cats'], 'wild animals': ['zebras', 'penguins']}" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\n", "my_dict = {\"pets\": [\"dogs\", \"cats\"], \"wild animals\": [\"zebras\", \"penguins\"]}\n", "my_dict" ] @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -225,9 +225,9 @@ "\n", "# Print out what the json\n", "# representation of our dictionary would look like.\n", - "# Note that `indent=4` results in \n", + "# Note that `indent=4` results in\n", "# this nice looking indentation—without that option\n", - "# the contents all appear on one line. \n", + "# the contents all appear on one line.\n", "print(json.dumps(my_dict, indent=4))" ] }, @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -314,7 +314,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/05_binary_files.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/05_binary_files.ipynb index 2227fc75..f9296eca 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/05_binary_files.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/05_binary_files.ipynb @@ -27,6 +27,7 @@ "outputs": [], "source": [ "import pandas as pd\n", + "\n", "world = pd.read_csv(\"data/world-very-small.csv\")\n", "world.to_stata(\"data/world-very-small.dta\")" ] diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb index a70facd6..efa1f974 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb @@ -206,7 +206,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb index 9be0e8bd..badd096c 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb @@ -361,11 +361,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Adding pd.set_option(\"mode.copy_on_write\", True) Easily\n", + "## Adding pd.set_option(\"mode.copy_on_write\", True) Easily\n", "\n", - "The easiest way to consistently add the `pd.set_option(\"mode.copy_on_write\", True)` is probably to create a custom import snippet — basically, a short keyboard shortcut that automatically import pandas, numpy, *and* sets this setting.\n", + "The easiest way to consistently add the `pd.set_option(\"mode.copy_on_write\", True)` is probably to create a custom \"snippet\" — basically, a keyboard shortcut that your editor expands into a block of text you type regularly.\n", "\n", - "For example, I've set up so when I type `ipd` followed by `tab`, I get:\n", + "For example, I've set up my VS Code installation so that when I type `ipd` followed by pressing `tab`, I get:\n", "\n", "```python\n", "import pandas as pd\n", @@ -373,9 +373,10 @@ "\n", "pd.set_option(\"mode.copy_on_write\", True)\n", "```\n", + "\n", "(in my head, `ipd` is short for **i**mport pandas as **pd** --> `ipd`)\n", "\n", - "To set up your own, just look for documentation on adding a snippet to your preferred editor. Use VS Code? [Here's how to do it!](https://code.visualstudio.com/docs/editor/userdefinedsnippets#_create-your-own-snippets) My snippet is:\n", + "To set up your own, just look for documentation on adding a snippet to your preferred editor. Use VS Code? [Here's how to do it!](https://code.visualstudio.com/docs/editor/userdefinedsnippets#_create-your-own-snippets) My VS Code snippet is:\n", "\n", "```json\n", "\"import pandas, numpy, activate CoW\": {\n", @@ -390,7 +391,7 @@ "}\n", "```\n", "\n", - "(note the need to escape quotation marks)" + "(note the need to escape quotation marks by putting a backslash right before them)" ] }, { diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb index 4596de1c..54fdae56 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb @@ -379,6 +379,14 @@ "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/2520817220.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '3.14' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.iloc[1, 1] = 3.14\n" + ] + }, { "data": { "text/html": [ @@ -628,6 +636,14 @@ "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/673548005.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'a' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.iloc[0, 0] = \"a\"\n" + ] + }, { "data": { "text/html": [ @@ -771,7 +787,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_64429/164737205.py:1: SettingWithCopyWarning: \n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/164737205.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -812,237 +828,6 @@ "\n", "Which leads me to what I will admit is an infuriating piece of advice to have to offer: **if you take a subset for any purpose other than immediately analyzing, you should add .copy() to that subsetting.** Seriously. Just when in doubt, `.copy()`." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### An Aside: No, the problem doesn't *only* emerge when you change the data type of a column\n", - "\n", - "Some readers may have noticed a pattern in the illustrations I've presented, and from them developed an intuition that a column will only lose it's \"view-ness\" when one changes the datatype of that column. Though this will always cause problems, it is not the only place problems can arise. What follows isn't something you *need* to know, but may be useful if you're deeply interested. \n", - "\n", - "In the examples above, each column was it's own object, and so behaved independently. But this is not always the case in `pandas`. If a DataFrame is created from a single numpy matrix with multiple columns, `pandas` will try to be efficient by just keeping that matrix intact. \n", - "\n", - "But as a result, if you do something (like change the type) of *one* of the columns that is tied to that matrix, `pandas` will create new arrays to back *all* the columns that were once tied to the matrix. As a result, a view of a single column can stop being a view due to changes to a different column. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1],\n", - " [2, 3],\n", - " [4, 5]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "my_matrix = np.arange(6).reshape(3, 2)\n", - "my_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " a b\n", - "0 0 1\n", - "1 2 3\n", - "2 4 5" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(my_matrix, columns=[\"a\", \"b\"])\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 -42\n", - "1 2\n", - "2 4\n", - "Name: a, dtype: int64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Column_a starts of it's life as a view\n", - "column_a = df[\"a\"]\n", - "df.iloc[0, 0] = -42\n", - "column_a" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      \n", - "
      " - ], - "text/plain": [ - " a b\n", - "0 -42 new entry\n", - "1 2 3\n", - "2 4 5" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# But if I make a change to column b...\n", - "df.loc[0, \"b\"] = \"new entry\"\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 999999\n", - "1 2\n", - "2 4\n", - "Name: a, dtype: int64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Then the same type of change to column a of `df` will no longer\n", - "# be shared\n", - "\n", - "df.iloc[0, 0] = 999999\n", - "column_a" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, as noted before: it is best to never to try and infer whether a subset of a DataFrame if a view or a copy until you have *explicitly* made a copy with `.copy()`." - ] } ], "metadata": { @@ -1061,7 +846,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/20_cleaning_identifying.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/20_cleaning_identifying.ipynb index b1ceffb5..35329df4 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/20_cleaning_identifying.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/20_cleaning_identifying.ipynb @@ -15,7 +15,7 @@ "\n", "These parts of data science are not nearly as cool or widely discussed as new machine learning packages or the latest learning algorithm, but as anyone who has done data science in the real world will tell you, data cleaning and wrangling will take up a very large portion of your working life. Indeed, it is often said that \"data scientists spend 80% of their time cleaning data and only 20% doing analyses\" (or, in the version I prefer, \"data scientists spend 80% of their time cleaning data and the other 20% of their time complaining about cleaning data.\") That's probably a little high on average—a [recent survey by Anaconda](https://www.anaconda.com/state-of-data-science-2020) found on average data scientists reported data wrangling took up about 45% of their time—but what is undisputed is its importance to being a successful data scientist.\n", "\n", - "So in this reading we will discuss tools in pandas for identifying problems before we turn to tools for correcting problems in our next reading!" + "So in this reading, we will discuss tools in pandas for identifying problems before we turn to tools for correcting problems in our next reading!" ] }, { @@ -29,7 +29,7 @@ "\n", "### Your Challenge\n", "\n", - "Suppose you have been asked to calculate the overall US employment rate and average incomes for men and women, as well as the employment rate and average income of people in their 30s and 40s. To accomplish this task, you have been handed a 0.1% sample of the US American Community Survey—a representative survey of residents of the United States published by the US Census Bureau. This is real data, and so like all real data comes with lots of quirk and oddities we'll need to navigate.\n", + "Suppose you have been asked to calculate the overall US employment rate and average incomes for men and women, as well as the employment rate and average income of people in their 30s and 40s. To accomplish this task, you have been handed a 0.1% sample of the US American Community Survey—a representative survey of residents of the United States published by the US Census Bureau. This is real data, and so like all real data comes with lots of quirks and oddities we'll need to navigate.\n", "\n", "Moreover, you've even been told that the variable for gender in the dataset is `sex`, the variable for age is `age`, the variable for income is `inctot`, and the variable for whether someone is employed is `empstat`. Should be pretty easy, right? Let's do it!\n", "\n", @@ -43,6 +43,9 @@ "outputs": [], "source": [ "import pandas as pd\n", + "import numpy as np\n", + "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", "\n", "# Here we load the American Community Survey data.\n", "# Note that here we're using a data loading trick\n", @@ -55,12 +58,12 @@ ")\n", "\n", "# For this exercise, we'll focus on just these five variables,\n", - "# which from the official documentation provided by the US census \n", + "# which from the official documentation provided by the US census\n", "# bureau we know consist of the year the survey was conducted,\n", - "# the gender of the respondent, their age, their total income, \n", + "# the gender of the respondent, their age, their total income,\n", "# and their employment status.\n", "#\n", - "# You can find the documenation for this data here: \n", + "# You can find the documenation for this data here:\n", "\n", "acs = acs[[\"year\", \"sex\", \"age\", \"inctot\", \"empstat\"]]" ] @@ -74,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -83,15 +86,34 @@ "1723646.2703978634" ] }, - "execution_count": 2, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Simple output:\n", "acs[\"inctot\"].mean()" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The average income of Americans is $1,723,646.27\n" + ] + } + ], + "source": [ + "# Made pretty and better formatted:\n", + "print(f\"The average income of Americans is ${acs['inctot'].mean():,.2f}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -244,12 +266,12 @@ "import numpy.random as npr\n", "\n", "# The rows that `.sample()` picks will be random.\n", - "# Normally that's advantageous, but in \n", - "# this case it results in getting \n", + "# Normally that's advantageous, but in\n", + "# this case it results in getting\n", "# a slightly different set of rows every time\n", - "# I run this code, so I'm going to set a \n", - "# seed—something we talked about in Course 2 \n", - "# on numpy—so that the rows being picked \n", + "# I run this code, so I'm going to set a\n", + "# seed—something we talked about in Course 2\n", + "# on numpy—so that the rows being picked\n", "# stay the same every time I run this code.\n", "npr.seed(42)\n", "\n", @@ -264,7 +286,7 @@ "\n", "We can also begin to see the causes of some of our problems. For example, it's not clear to see why `acs[\"inctot\"].mean()` didn't get what we wanted. Why? Because in five random rows, we've already gotten three people who report making 9,999,999 dollars, and all three are less than 12 years old. So clearly the variable doesn't just contain actual income data! \n", "\n", - "(What are these `9999999` values? They're something called a \"Sentinel Value\"—a very old-school way of representing missing data, in which a special value that would never occur naturally was used to indicate when the field was \"missing.\" It was used back in the day when computers didn't use data types that had an actual special way of representing missing data, and some people still use it for backwards compatibility. We'll talk more about these and other ways of representing missing data in an upcoming reading.)\n", + "(What are these `9999999` values? They're something called a \"Sentinel Value\"—a very old-school way of representing missing data, in which a special value that would never occur naturally was used to indicate when the field was \"missing.\" It was used back in the day when computers didn't use data types that had an actual special way of representing missing data, and some people still use it for backward compatibility. We'll talk more about these and other ways of representing missing data in an upcoming reading.)\n", "\n", "Similarly, we can also see that while some observations of `empstat` make sense (`employed` or `not in labor force`), there are also values of `n/a` we need to figure out. `n/a` is a common shorthand for \"Not Applicable\", meaning it didn't make sense to ask the respondent that question. Since we see those appearing for respondents who are under 18, that probably means that `n/a` was filled in for all children, but that's something we'll have to investigate more. But it definitely tells us why we can't calculate the employment rate by running `(acs[\"empstat\"] == \"employed\").mean()`, since all the `n/a` values would pull down the share of respondents who are employed even though we don't consider children when calculating the employment rate!\n", "\n", @@ -287,11 +309,12 @@ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "n/a 57843\n", "unemployed 7727\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, "execution_count": 5, @@ -300,7 +323,7 @@ } ], "source": [ - "acs.empstat.value_counts()\n" + "acs.empstat.value_counts()" ] }, { @@ -318,11 +341,12 @@ { "data": { "text/plain": [ + "empstat\n", "employed 0.466320\n", "not in labor force 0.328134\n", "n/a 0.181324\n", "unemployed 0.024222\n", - "Name: empstat, dtype: float64" + "Name: proportion, dtype: float64" ] }, "execution_count": 6, @@ -340,7 +364,7 @@ "source": [ "From this, we can see that in addition to the `n/a` observations that appear to be children, we also have two categories of people who aren't employed: `unemployed` and `not in labor force`. A little googling will tell us that `not in the labor force` connotes someone who is not looking for work, and is thus not considered unemployed when calculating the employment (or unemployment) rate—it turns out that the unemployment rate is only meant to tell us about the share of people *who want to work* who haven't found jobs, not the proportion of the overall population with a job.\n", "\n", - "So now that we know that, we can calculate our unemployment rate by subsetting for people in those two categories, then calculating our average:" + "So now that we know that, we can calculate our unemployment rate by subsetting for people in those two categories, and then calculating our average:" ] }, { @@ -350,7 +374,9 @@ "outputs": [], "source": [ "# Employment Rate\n", - "emp_rate = (acs.loc[acs[\"empstat\"].isin([\"employed\", \"unemployed\"]), \"empstat\"] == \"employed\").mean()" + "emp_rate = (\n", + " acs.loc[acs[\"empstat\"].isin([\"employed\", \"unemployed\"]), \"empstat\"] == \"employed\"\n", + ").mean()" ] }, { @@ -388,18 +414,18 @@ "metadata": {}, "source": [ "Much better!\n", - "\n", - "Of course `.value_counts()` stops being useful when the number of unique values a variable takes on gets too big. For example, it isn't that useful to look at `.value_counts()` of total income, because, as we see below, it takes on over 8,000 unique values! Yes we can see there's a cluster at 0 and a cluster at our old friend `9999999`, but beyond that it isn't very helpful. " + "Of course, `.value_counts()` stops being useful when the number of unique values a variable takes on gets too big. For example, it isn't that useful to look at `.value_counts()` of total income, because, as we see below, it takes on over 8,000 unique values! Yes, we can see there's a cluster at 0 and a cluster at our old friend `9999999`, but beyond that it isn't very helpful. " ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "inctot\n", "9999999 53901\n", "0 33679\n", "30000 4778\n", @@ -411,10 +437,10 @@ "57760 1\n", "200310 1\n", "505400 1\n", - "Name: inctot, Length: 8471, dtype: int64" + "Name: count, Length: 8471, dtype: int64" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -429,7 +455,7 @@ "source": [ "**.describe()**\n", "\n", - "So for continuous numeric variables, we instead use `.describe()`, which provides summary statistics for a variable. Statistics is not required for this course, so not everything here will be familiar to all students, but just looking at the minimum value in the data (`min`), the maximum value in the data (`max`), and the the average value (`mean`) can tell you a lot:" + "For continuous numeric variables, we instead use `.describe()`, which provides summary statistics for a variable. Statistics is not required for this course, so not everything here will be familiar to all students, but just looking at the minimum value in the data (`min`), the maximum value in the data (`max`), and the average value (`mean`) can tell you a lot:" ] }, { @@ -457,7 +483,7 @@ } ], "source": [ - "acs[\"inctot\"].describe()\n" + "acs[\"inctot\"].describe()" ] }, { @@ -499,7 +525,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb index d81e7c23..a677dd30 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb @@ -18,7 +18,7 @@ "- You can use a general cleaning function, or \n", "- you can directly modify problematic values. \n", "\n", - "In this reading, I'll provide an overview of how to do make global edits before we learn to edit specific locations in the next reading.\n", + "In this reading, I'll provide an overview of how to make global edits before we learn to edit specific locations in the next reading.\n", "\n", "In these exercises, we'll work with our `world-very-small` toy dataset. We're doing so because working with a small dataset makes it easier to see everything the functions we use are doing. However, in some of the examples that follow we'll do things that seem a little silly given the size of the dataset (like print out all values of a variable with `.value_counts()` even though we can see all the values!), but we'll do they anyway since you would need them when working with any real data. " ] @@ -139,10 +139,10 @@ "import pandas as pd\n", "\n", "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", - "smallworld\n" + "smallworld" ] }, { @@ -255,7 +255,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Voila! Replace works for numbers too -- so suppose we learned that Polity had mis-coded all scores of 15 as 16, so we wanted to change all 16s to 15s:" + "Voila! Replace works for numbers too—suppose we learned that the Polity organization had mis-coded all scores of 15 as 16, so we wanted to change all 16s to 15s:" ] }, { @@ -470,7 +470,12 @@ } ], "source": [ - "changes = {\"Mexico\": \"México\", \"Russia\": \"Российская Федерация\", \"Ukraine\": \"Україна\", \"Brazil\": \"Brasil\"}\n", + "changes = {\n", + " \"Mexico\": \"México\",\n", + " \"Russia\": \"Российская Федерация\",\n", + " \"Ukraine\": \"Україна\",\n", + " \"Brazil\": \"Brasil\",\n", + "}\n", "\n", "smallworld[\"country\"] = smallworld[\"country\"].replace(changes)\n", "\n", @@ -483,9 +488,9 @@ "source": [ "**.replace() with Regular Expressions**\n", "\n", - "Finally, `.replace()` also works with regular expressions when one passes the `regex=True` keyword argument. We haven't covered regular expressions in this course, but basically they are a way of telling Python you want to look for *patterns* to replace instead of exact matches. If you don't know about regular expressions, feel free to skip this sub-section, but since some students may be familiar with them, here's a quick use of their overview:\n", + "Finally, `.replace()` also works with regular expressions when one passes the `regex=True` keyword argument. We haven't covered regular expressions in this course, but essentially they are a way of telling Python you want to look for *patterns* in text to replace instead of exact matches. If you don't know about regular expressions, feel free to skip this sub-section, but since some students may be familiar with them, here's a quick use of their overview:\n", "\n", - "Basically, when one passes `regex=True` to `.replace`, `.replace()` will treat the first argument as a regular expression rather than a literal value. Note that this will obviously only work when string values are provided.\n", + "When one passes `regex=True` to `.replace`, `.replace()` will treat the first argument as a regular expression rather than a literal value. This will only work when string values are provided.\n", "\n", "(`.replace(regex=True)` uses `re.sub` on each observation in a Series under the hood, and so supports all the same syntax as `re.sub`.)\n", "\n", @@ -817,7 +822,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb index 7c3345b9..1d0e65cc 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, when we write `my_list[2]` on the *left* side of the assignment operator (a single equals sign), then whatever we put on the right side of the assignment operator is being assigned *into the entry with index 2* of the list. \n", + "As we can see, when we write `my_list[2]` on the *left* side of the assignment operator (a single equals sign), whatever we put on the right side of the assignment operator is assigned _into_ the entry with index 2 of the list. \n", "\n", "As you may recall, this same logic can also be extended to two dimensions in `numpy` arrays. Consider the following: " ] @@ -208,6 +208,8 @@ "source": [ "import pandas as pd\n", "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", + "\n", "df = pd.DataFrame({\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8]})\n", "df" ] @@ -291,7 +293,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But this alone is only kinda useful. After all, our datasets are usually very large, and we rarely want to make modifications to cells whose row numbers we already know. But thankfully, in `pandas` we can pass boolean vectors to `.loc` to identify all rows that meet certain conditions and assign values to those specific cells. For example, suppose we wanted to set column `b` to 0 for all rows where column `a` is even. We could do:" + "But this alone is only kinda useful. After all, our datasets are usually very large, and we rarely want to make modifications to cells whose row numbers we already know. Thankfully, in `pandas` we can pass boolean vectors to `.loc` to identify all rows that meet certain conditions and assign values to those specific cells. For example, suppose we wanted to set column `b` to 0 for all rows where column `a` is even. We could do:" ] }, { @@ -374,14 +376,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "See how the boolean vector on the left subset for rows where `a` was even (the value of `a % 2` is zero), and the second entry (`b`) subset for the column `b`, then we assigned 0 into those cells? It's just a generalization of the kinds of assignments we did above with lists and numpy arrays, just using boolean vectors and column labels instead of indices!" + "See how the boolean vector on the left subset for rows where `a` was even (the value of `a % 2` is zero), and the second entry (`b`) subset for column `b`, then we assigned 0 into those cells? It's just a generalization of the kinds of assignments we did above with lists and numpy arrays, just using boolean vectors and column labels instead of indices!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Great! But now suppose we don't just want to set certain values to a constant, but instead we wanted to, say, double all the values in odd rows. We can do that to by assigning values that \"fit\" into the cells on the left of the assignment operator (i.e. by making sure the values we assign have the same dimensions as the cells into which we're trying to assign them):" + "Great! But now suppose we don't just want to set certain values to a constant, but instead we wanted to, say, double all the values in odd rows. We can do that too by assigning values that \"fit\" into the cells on the left of the assignment operator (i.e. by making sure the values we assign have the same dimensions as the cells into which we're trying to assign them):" ] }, { @@ -494,7 +496,8 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", + " polityIV\n", " \n", " \n", " \n", @@ -503,49 +506,55 @@ " Brazil\n", " S. America\n", " 10296\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", + " 16\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008\n", - "0 Brazil S. America 10296\n", - "1 Germany W. Europe 35613\n", - "2 Mexico N. America 14495\n", - "3 Mozambique Africa 855\n", - "4 Russia C&E Europe 16139\n", - "5 Ukraine C&E Europe 7271" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 16\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 9, @@ -554,10 +563,11 @@ } ], "source": [ - "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + "smallworld = smallworld = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", + "\n", "smallworld" ] }, @@ -589,7 +599,8 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", + " polityIV\n", " \n", " \n", " \n", @@ -598,49 +609,55 @@ " Brazil\n", " S. America\n", " 10296\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", + " 16\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008\n", - "0 Brazil S. America 10296\n", - "1 Germany W. Europe 35613\n", - "2 Mexico N. America 14495\n", - "3 Mozambique Africa 855\n", - "4 Russia C&E Europe 16139\n", - "5 Ukraine C&E Europe 7271" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 16\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 10, @@ -687,7 +704,7 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", " polityIV\n", " \n", " \n", @@ -697,55 +714,55 @@ " Brazil\n", " S. America\n", " 10296\n", - " NaN\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", - " NaN\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", - " NaN\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", - " 5.0\n", + " 5\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", - " NaN\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", - " NaN\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008 polityIV\n", - "0 Brazil S. America 10296 NaN\n", - "1 Germany W. Europe 35613 NaN\n", - "2 Mexico N. America 14495 NaN\n", - "3 Mozambique Africa 855 5.0\n", - "4 Russia C&E Europe 16139 NaN\n", - "5 Ukraine C&E Europe 7271 NaN" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 11, @@ -790,9 +807,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_37248/2079543889.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_27525/2079543889.py:1: ChainedAssignmentError: A value is trying to be set on a copy of a DataFrame or Series through chained assignment.\n", + "When using the Copy-on-Write mode, such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " smallworld[smallworld.country == \"Mozambique\"][\"polityIV\"] = 5\n" @@ -803,11 +821,126 @@ "smallworld[smallworld.country == \"Mozambique\"][\"polityIV\"] = 5" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smallworld" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You will get the `SettingWithCopyWarning` we discussed in our reading on views and copies. That's because it's possible that when you run `smallworld[smallworld.country == \"Mozambique\"]`, pandas **may** return an entirely new DataFrame, and the next part of the operation (changing the values of `polityIV`) will run against a completely new DataFrame, not `smallworld`, and in the end your original `smallworld` DataFrame won't end up being modified at all. This kind of chained assignment will SOMETIMES work, but not ALWAYS, which is why you get that warning. " + "Provided you have Copy-on-Write activated, this will result in a `ChainedAssignmentError`:\n", + "\n", + "```python\n", + "\n", + "ChainedAssignmentError: A value is trying to be set on a copy of a DataFrame \n", + "or Series through chained assignment.\n", + "When using the Copy-on-Write mode, such chained assignment never works to \n", + "update the original DataFrame or Series, because the intermediate object on \n", + "which we are setting values always behaves as a copy.\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the \n", + "assignment in a single step.\n", + "```\n", + "\n", + "That's because when you run `smallworld[smallworld.country == \"Mozambique\"]`, pandas returns an entirely new DataFrame, and the next part of the operation (changing the values of `polityIV`) is being excuted against that completely new DataFrame, not the one associated with the variable `smallworld`. As a result, `smallworld` DataFrame won't end up being modified at all.\n", + "\n", + "\n", + "(If you don't have Copy-on-Write activated, you will get a `SettingWithCopyWarning` warning instead. Whether the assignment will work in that case is basically unpredictable!)\n", + "\n" ] }, { @@ -823,13 +956,111 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "smallworld[\"region\"] = pd.Categorical(smallworld[\"region\"])" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smallworld" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -844,33 +1075,20 @@ "/Users/Nick/github/mids_coursera/class_3/week_3/32_cleaning_editing_specific_locations.ipynb Cell 29 in ()\n", "----> 1 smallworld.loc[smallworld[\"country\"] == \"Mexico\", \"region\"] = \"C America\"\n", "\n", - "File ~/opt/miniconda3/lib/python3.9/site-packages/pandas/core/indexing.py:716, in _LocationIndexer.__setitem__(self, key, value)\n", - " 713 self._has_valid_setitem_indexer(key)\n", - " 715 iloc = self if self.name == \"iloc\" else self.obj.iloc\n", - "--> 716 iloc._setitem_with_indexer(indexer, value, self.name)\n", - "\n", "[...]\n", "\n", - "File ~/opt/miniconda3/lib/python3.9/site-packages/pandas/core/arrays/categorical.py:1484, in Categorical._validate_scalar(self, fill_value)\n", - " 1482 fill_value = self._unbox_scalar(fill_value)\n", - " 1483 else:\n", - "-> 1484 raise TypeError(\n", - " 1485 \"Cannot setitem on a Categorical with a new \"\n", - " 1486 f\"category ({fill_value}), set the categories first\"\n", - " 1487 )\n", - " 1488 return fill_value\n", "\n", "TypeError: Cannot setitem on a Categorical with a new category (C America), set the categories first\n", "```\n", "\n", "So what do we do?\n", "\n", - "One option is to modify the allowable categories for the variable with `.cat.add_categories()`, then making the edit again:" + "One option is to modify the allowable categories for the variable with `.cat.add_categories()`, then make the edit again:" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -887,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb index 3ad79af4..904d316d 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb @@ -111,8 +111,10 @@ } ], "source": [ - "import numpy as np\n", "import pandas as pd\n", + "import numpy as np\n", + "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", "\n", "world = pd.read_csv(\"data/world-very-small-type-errors.csv\")\n", "world" @@ -194,7 +196,7 @@ "source": [ "Oops! Yup, there it is! Indeed, we can also see that `polityIV` is also an `object`, something we'll deal with later. \n", "\n", - "So this kind of phenomenon is such a common problem that pandas has a tool to deal with it: `.str.isnumeric()`. This method returns `True` is an observation can be converted to a number without issues, and `False` otherwise, so by using its logical negation, we can find all observations that aren't convertible (which we may then need to fix):" + "So this kind of phenomenon is such a common problem that pandas has a tool to deal with it: `.str.isnumeric()`. This method returns `True` if an observation can be converted to a number without issues, and `False` otherwise, so by using its logical negation, we can find all observations that aren't convertible (which we may then need to fix):" ] }, { @@ -225,7 +227,7 @@ "source": [ "(Recall we have to use `~` instead of `not` to invert `True` values to `False` in a numpy array or pandas Series)\n", "\n", - "There we are! One observation of `\"na\"` is the source of our problems. Note that you could also use `.unique()` instead of `.value_counts()` if you don't care about the number of observations are causing problems. \n", + "There we are! One observation of `\"na\"` is the source of our problems. Note that you could also use `.unique()` instead of `.value_counts()` if you don't care about the number of observations that are causing problems. \n", "\n", "OK, so how do we fix this? In this case, it seems like `na` is probably just short for `np.nan`, so we can replace it with `np.nan`, which *is* a number:" ] @@ -271,7 +273,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, there's one other option in these situations: if `na` is appearing in lots of places in your data as a representation for missing data, we can also communicate this fact to `read_csv`. Remember when we said that `read_csv` has more options than you could ever imagine? Well one is `na_values`, where you can specify how missing data is represented in a dataset. If we use that to tell `read_csv` that `\"na\"` means data is missing, it'll make this correction on the fly!" + "Now, there's one other option in these situations: if `na` is appearing in lots of places in your data as a representation for missing data, we can also communicate this fact to `read_csv`. Remember when we said that `read_csv` has more options than you could ever imagine? Well, one is `na_values`, where you can specify how missing data is represented in a dataset. If we use that to tell `read_csv` that `\"na\"` means data is missing, it'll make this correction on the fly!" ] }, { @@ -430,7 +432,7 @@ "source": [ "## Deliberate Non-Numerics\n", "\n", - "While in the example above `\"na\"` wasn't really meant to be interpreted as a string, sometimes non-numeric values are inserted into datasets to communicate something important to the user. For example, in this toy example `polityIV` has a non-numeric value:" + "While in the example above `\"na\"` wasn't really meant to be interpreted as a string, sometimes non-numeric values are inserted into datasets to communicate something important to the user. For example, in this toy example, `polityIV` has a non-numeric value:" ] }, { @@ -495,7 +497,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here, the value is to indicate that the value of policy could be 20 *or any value above 20*. Why might this occur? In this case I've made it up, but in many surveys there will be a maximum value allowed (e.g., some surveys have a top income value they can record). These are called \"top-codes\", and they are different from the case above because it's not immediately clear what value you should put into the field. You *could* put in 20, but... the value might not be 20, it could be 21!\n", + "Here, the value is to indicate that the value of `polityIV` could be 20 *or any value above 20*. Why might this occur? In this case, I've made it up, but in many surveys, there will be a maximum value allowed (e.g., some surveys have a top income value they can record). These are called \"top-codes\", and they are different from the case above because it's not immediately clear what value you should put into the field. You *could* put in 20, but... the value might not be 20, it could be 21!\n", "\n", "So when you find a top-code like this, you need to think carefully about what to do with it, as the answer will depend on the type of analysis you want to do. But the answer will almost always lie not in the data, but in the documentation for the data. " ] diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb index 02c4d92c..7c7f39cd 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ "False" ] }, - "execution_count": 10, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -143,7 +143,7 @@ "5 Ukraine C&E Europe 7271 16.0" ] }, - "execution_count": 11, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -151,9 +151,9 @@ "source": [ "import pandas as pd\n", "\n", - "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + "smallworld = smallworld = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", "smallworld.loc[smallworld.country == \"Mozambique\", \"polityIV\"] = np.nan\n", "smallworld" @@ -168,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -209,13 +209,13 @@ "Index: []" ] }, - "execution_count": 12, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[smallworld.polityIV == np.nan]\n" + "smallworld.loc[smallworld.polityIV == np.nan]" ] }, { @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -276,13 +276,13 @@ "3 Mozambique Africa 855 NaN" ] }, - "execution_count": 13, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[pd.isnull(smallworld.polityIV)]\n" + "smallworld.loc[pd.isnull(smallworld.polityIV)]" ] }, { @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -341,13 +341,13 @@ "3 Mozambique Africa 855 NaN" ] }, - "execution_count": 14, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[smallworld.polityIV.isnull()]\n" + "smallworld.loc[smallworld.polityIV.isnull()]" ] }, { @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -368,13 +368,13 @@ "True" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.isnull(None)\n" + "pd.isnull(None)" ] }, { @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -473,14 +473,14 @@ "5 Ukraine C&E Europe 7271 16.0" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "smallworld.loc[smallworld.polityIV.isnull(), \"polityIV\"] = 5\n", - "smallworld\n" + "smallworld" ] }, { @@ -494,20 +494,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "unemployed 7727\n", "n/a 0\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -531,21 +532,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "NaN 57843\n", "unemployed 7727\n", "n/a 0\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, - "execution_count": 21, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -615,7 +617,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb new file mode 100644 index 00000000..fd297b29 --- /dev/null +++ b/_build/jupyter_execute/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb @@ -0,0 +1,637 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Object and Categorical Data Types\n", + "\n", + "In our previous readings, we introduced the idea that not only can DataFrames and Series hold any of the numeric data types we've come to know and love from numpy — like `float64` or `int64` — but that they can also hold arbitrary Python objects in an `object`-type Series. Moreover, we also acknowledged (though didn't go into) the existence of another relatively unique pandas data type — the `Categorical` Series. \n", + "\n", + "In this reading, we will discuss the purpose of these two data types, as well as their advantages and disadvantages.\n", + "\n", + "## The object dtype\n", + "\n", + "The `object` type Series gives pandas _incredible_ flexibility as it allows any type of data to be stored in a table. The most common use of the `object` data type is to store text — for example, names, addresses, written answers, etc. — but the flexibility can also be used for applications like geospatial analysis, in which a single row of a DataFrame may represent a single country, the columns may represent features of the country (name, income, population), and the last column stores geometric Python objects that describe the shape and location of the country. \n", + "\n", + "But this flexibility also comes at a cost — performance and memory efficiency.\n", + "\n", + "### The object Performance Penalty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To understand why `object` Series are slow, it helps to first discuss why numeric Series (and numeric numpy arrays) are fast. \n", + "When you work with a numeric pandas Series or numpy array, all of the entries in those arrays live in the same place in memory (in your computer's RAM). This is possible because all those integers (or all those floating point numbers) are written with the same number of 0s and 1s. In an `int64` Series, for example, every integer is represented by 64 1s and 0s. This makes it easy for the computer to lay them out sequentially. Moreover, it makes it possible for the computer to find specific entries quickly, since it knows that the third integer in the Series will start 64 * 2 spots from the start of the Series and end 64 spots later.\n", + "\n", + "But an *object* Series is a little different. Python objects vary in size — some may only take up 128 0s and 1s, while others may require thousands — and so the actual data in an object Series can't be laid down in a nice regularly spaced sequence. Instead, every entry in an object Series gets put in a different location in your computer's memory (RAM), and only the *address* of that information is placed in a nice organized Series. These addresses are all the same size, and so the addresses can be organized in a regular manner, even if the actual content you want to store is irregular.\n", + "\n", + "The cost of this arrangement is that if you ask for the second entry of an object Series (e.g., `my_series.iloc[1]`), you're computer has first go to the second location in the array, read the address stored there, *then go to that address* to find the actual content you want. And those added steps waste time.\n", + "\n", + "The other problem with object Series is that because they can store anything, Python doesn't know before it looks up an entry whether it will find a string, an integer, or a Python set. As a result, when it sees code like: \n", + "\n", + "```python \n", + "my_array * 2\n", + "```\n", + "\n", + "Python can't be sure what is meant by `*` — it could mean \"do integer multiplication\" (if a given entry in `my_array` is an integer), but it might also mean \"double up the list you find\" (if the entry is a Python list)!\n", + "\n", + "Indeed we can see this if we make a numpy object array full of ints and compare it to a numpy integer array. The both have the same content, but they are organized in memory differently:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "object_numbers = pd.Series(np.arange(1000000), dtype=\"object\")\n", + "numbers = pd.Series(np.arange(1000000), dtype=\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15.9 ms ± 70.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%timeit object_numbers * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "771 µs ± 9.62 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit numbers * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See? Same operation (doubling each entry of arrays with the integers from 0 to 1,000,000), but the object array operation is ~20x slower. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So yes, `object` dtypes are absolutely wonderful and introduce unbelievable flexibility to pandas; but remember there is a cost to using them, so stick to numeric data types when possible!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Categoricals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `category` data type is a delightful little hack that allows us to avoid most of the problems with `object` Series *in certain circumstances*. \n", + "\n", + "To illustrate how `category` Series work, suppose we have a DataFrame with information on hundreds of thousands of customers in the United States, and that one column of that DataFrame contains the name of each customer's state of residence (substitute Province or any other sub-national administrative unit if states don't resonate for you). \n", + "\n", + "Because those state names are words, they are being stored in an `object` Series. That, in turn, means that Python has created hundreds of thousands of Python objects — each containing the name of a customer's state — *and* a vector containing addresses for each of those objects. \n", + "\n", + "But as there are only 50 states in the United States, this might strike you as absurd, since most of those hundreds of thousands of Python objects are holding the same text! Surely we can do something more efficient than that? \n", + "\n", + "Enter Categoricals. The idea of a `category` Series is to take a `object` Series that contains frequently repeated strings and:\n", + "\n", + "1. Replace each unique string with a number (for example, `\"Colorado\"` could become `1` and `\"Tennessee\"` could become `2`), and\n", + "2. Create a small \"lookup table\" that keeps track of what string is associated with each number. \n", + "\n", + "Now pandas doesn't need to create hundreds of thousands of Python strings to record each customer's state, it can just make a numeric Series with state names replaced by the numbers 1-50, and a new small vector with the fifty names of states.\n", + "\n", + "Moreover, in addition to saving memory, this also dramatically improves the *performance* of pandas. Suppose, for example, you want to subset for customers living in North Carolina. When these states were in an `object` Series, pandas would have to go to each entry, figure out where the associated Python object is stored, get that object, and check to see if it was the string `\"North Carolina\"`. \n", + "\n", + "But now, pandas can just go to the lookup table, see that `\"North Carolina\"` is the 33rd entry (and so is represented by the number 33 in our encoded Series of numbers), and look for values of 33 in that Series. Hooray!\n", + "\n", + "But the best part is that, in most cases, the fact your data has been split into a numeric vector and a lookup table is actually entirely hidden from you, the user. For *most* operations, using a Categorical Series is just like using an `object` Series, just faster. \n", + "\n", + "### Categorical Series in Practice\n", + "\n", + "To illustrate how one works with Categorical Series, let's make a toy version of this customer dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "1 Aditya Tennessee\n", + "2 Francisco Colorado\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "customers = pd.DataFrame(\n", + " {\n", + " \"customer\": [\"Bob\", \"Aditya\", \"Francisco\", \"Shufan\"],\n", + " \"state\": [\"Colorado\", \"Tennessee\", \"Colorado\", \"Virginia\"],\n", + " }\n", + ")\n", + "customers" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "customer object\n", + "state object\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, `state` begins its life as a standard `object` Series, but we can convert it to a Categorical with `.astype(\"category\")`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "1 Aditya Tennessee\n", + "2 Francisco Colorado\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"] = customers[\"state\"].astype(\"category\")\n", + "customers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, at first glance nothing about this column has changed. But if we pull it out, you can see it's dtype is `category` and that the Categories associated with the Series (the lookup table) contains three values: `\"Colorado\"`, `\"Tennessee\"` and \"`Virginia\"`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Colorado\n", + "1 Tennessee\n", + "2 Colorado\n", + "3 Virginia\n", + "Name: state, dtype: category\n", + "Categories (3, object): ['Colorado', 'Tennessee', 'Virginia']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And if you want to, you can see the two underlying pieces directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Colorado', 'Tennessee', 'Virginia'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"].cat.categories" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 0\n", + "3 2\n", + "dtype: int8" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"].cat.codes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But as we said, in most cases `category` Series will operate just like `object` Series. Subsetting, for example, will work just as it would with an `object` Series:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
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      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "2 Francisco Colorado" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.loc[customers[\"state\"] == \"Colorado\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only place that problems *may* arise is that one cannot make arbitrary edits to a `category` Series — if you try and set a cell to have a value that isn't in the current `Categories` table, you will get an error:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "\n", + "customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "\n", + "TypeError Traceback (most recent call last)\n", + "/Users/nce8/github/practicaldatascience_book/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb Cell 22 line 1\n", + "----> 1 customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "\n", + "[...]\n", + "\n", + "TypeError: Cannot setitem on a Categorical with a new category (Kansas), set the categories first\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You *can* add novel values, to be clear, you just have to add the category first:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Colorado\n", + "1 Tennessee\n", + "2 Colorado\n", + "3 Virginia\n", + "Name: state, dtype: category\n", + "Categories (4, object): ['Colorado', 'Tennessee', 'Virginia', 'Kansas']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"] = customers[\"state\"].cat.add_categories([\"Kansas\"])\n", + "customers[\"state\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
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      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Kansas\n", + "1 Aditya Tennessee\n", + "2 Francisco Kansas\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "customers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why Not Always Use Categoricals?\n", + "\n", + "Categoricals are great, but they are only useful when your `object` Series has a relatively small number of unique values. If you tried to convert an `object` Series with hundreds of thousands of addresses — and nearly all of them were unique — into a `category` Series, then pandas would have to create a lookup table that had... hundreds of thousands of unique entries (essentially, it would just be recreating your original `object` Series). And so there would be no real performance benefit." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/notebooks/class_3/week_3/40_EX_cleaning.ipynb b/_build/jupyter_execute/notebooks/class_3/week_3/40_EX_cleaning.ipynb index 3f04022b..c1268603 100644 --- a/_build/jupyter_execute/notebooks/class_3/week_3/40_EX_cleaning.ipynb +++ b/_build/jupyter_execute/notebooks/class_3/week_3/40_EX_cleaning.ipynb @@ -25,8 +25,11 @@ "outputs": [], "source": [ "import pandas as pd\n", - "acs = pd.read_stata(\"https://github.com/nickeubank/MIDS_Data/blob/master\"\n", - " \"/US_AmericanCommunitySurvey/US_ACS_2017_10pct_sample.dta?raw=true\")" + "\n", + "acs = pd.read_stata(\n", + " \"https://github.com/nickeubank/MIDS_Data/blob/master\"\n", + " \"/US_AmericanCommunitySurvey/US_ACS_2017_10pct_sample.dta?raw=true\"\n", + ")" ] }, { @@ -65,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -97,7 +100,7 @@ "Categories (97, object): ['less than 1 year old' < '1' < '2' < '3' ... '93' < '94' < '95' < '96']" ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -108,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -126,7 +129,7 @@ "41.30384885455982" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -134,7 +137,7 @@ "source": [ "acs.loc[acs[\"age\"] == \"less than 1 year old\", \"age\"] = 0\n", "acs.loc[acs[\"age\"] == \"90 (90+ in 1980 and 1990)\", \"age\"] = 90\n", - "acs[\"age\"] = acs[\"age\"].astype('float')\n", + "acs[\"age\"] = acs[\"age\"].astype(\"float\")\n", "acs[\"age\"].mean()" ] }, @@ -268,7 +271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/_build/jupyter_execute/notebooks/class_5/week_2/plotting_with_pandas.ipynb b/_build/jupyter_execute/notebooks/class_5/week_2/plotting_with_pandas.ipynb index f09f8f55..04f3440d 100644 --- a/_build/jupyter_execute/notebooks/class_5/week_2/plotting_with_pandas.ipynb +++ b/_build/jupyter_execute/notebooks/class_5/week_2/plotting_with_pandas.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "2b23e3ab", + "id": "44db75a7", "metadata": {}, "source": [ "# Plotting with Pandas\n", diff --git a/_toc.yml b/_toc.yml index 6b1aaf04..5e149a2b 100644 --- a/_toc.yml +++ b/_toc.yml @@ -90,6 +90,7 @@ parts: - file: notebooks/class_3/week_3/32_cleaning_editing_specific_locations - file: notebooks/class_3/week_3/33_cleaning_datatypes - file: notebooks/class_3/week_3/35_cleaning_missing_data + - file: notebooks/class_3/week_3/37_object_and_categorical_dtypes - file: notebooks/class_3/week_4/00_intro_to_querying_data title: Data Manipulations sections: diff --git a/docs/README.html b/docs/README.html index 4f9390bf..54c84665 100644 --- a/docs/README.html +++ b/docs/README.html @@ -225,7 +225,6 @@
    • Views and Copies in Pandas
      • @@ -238,6 +237,7 @@
    • Editing Specific Locations
    • Cleaning Data Types
    • Missing Data
      • +
    • The Object and Categorical Data Types
  • Data Manipulations
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b/docs/_sources/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb index dcf2a931..75bb85bc 100644 --- a/docs/_sources/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb +++ b/docs/_sources/notebooks/PDS_not_yet_in_coursera/30_big_data/70_dask.ipynb @@ -812,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1926,6 +1926,1089 @@ "execution_count": 9, "metadata": {}, "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-28 07:21:51,309 - tornado.application - ERROR - Exception in callback >\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/ioloop.py\", line 919, in _run\n", + " val = self.callback()\n", + " ^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/system_monitor.py\", line 168, in update\n", + " net_ioc = psutil.net_io_counters()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/psutil/__init__.py\", line 2119, in net_io_counters\n", + " rawdict = _psplatform.net_io_counters()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "OSError: [Errno 12] Cannot allocate memory\n", + "2023-11-29 12:24:43,023 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,086 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,125 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,130 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:24:43,280 - distributed.nanny - WARNING - Restarting worker\n", + "2023-11-29 12:32:25,398 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,405 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,753 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', 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self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:25,864 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:32:25,946 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:32:38,591 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:38,659 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,011 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,181 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:39,299 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 369): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 140): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1043): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 634): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 405): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1308): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:32:39,295 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:51,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,043 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,257 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,540 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:32:52,664 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:32:52,661 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:04,636 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:04,728 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:04,862 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:04,895 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:33:05,272 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:05,301 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:15,177 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:15,350 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:15,994 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:16,318 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:16,497 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', 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self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:29,499 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', 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last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:29,643 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:29,963 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:29,964 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:29,974 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:40,320 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:40,536 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,055 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,131 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:41,253 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 768): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 359): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 130): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1262): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1033): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 624): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1527): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1298): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 215): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 889): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1563): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 480): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 251): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1154): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 745): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 516): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:33:41,247 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,214 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:53,215 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,290 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:33:53,402 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:33:53,563 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 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distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:05,777 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:05,800 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 861, in _read_to_buffer\n", + " bytes_read = self.read_from_fd(buf)\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1116, in read_from_fd\n", + " return self.socket.recv_into(buf, len(buf))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:05,900 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:06,047 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:06,100 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,458 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,682 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : BrokenPipeError: [Errno 32] Broken pipe\n", + "2023-11-29 12:34:17,740 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n", + "2023-11-29 12:34:17,961 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', 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('assign-786d0153597eb09d7a8413baa8e8f555', 899): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 670): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1573): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 261): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1164): 'memory'}\n", + "2023-11-29 12:34:18,184 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 962, in _handle_write\n", + " num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py\", line 1124, in write_to_fd\n", + " return self.socket.send(data) # type: ignore\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + "ConnectionResetError: [Errno 54] Connection reset by peer\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py\", line 1783, in get_data\n", + " response = await comm.read(deserializers=serializers)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 237, in read\n", + " convert_stream_closed_error(self, e)\n", + " File \"/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 140, in convert_stream_closed_error\n", + " raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n", + "distributed.comm.core.CommClosedError: in : ConnectionResetError: [Errno 54] Connection reset by peer\n" + ] } ], "source": [ diff --git a/docs/_sources/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb b/docs/_sources/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb index 7354d5af..d8e69cd4 100644 --- a/docs/_sources/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb +++ b/docs/_sources/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb @@ -46,7 +46,7 @@ "years_of_education = [12, 16, 11, 22, 12, 8, 12]\n", "\n", "survey = np.array([incomes, ages, years_of_education]).T\n", - "survey\n" + "survey" ] }, { @@ -78,7 +78,7 @@ ], "source": [ "mean_age = np.mean(survey[:, 1])\n", - "np.round(mean_age, 1)\n" + "np.round(mean_age, 1)" ] }, { @@ -140,7 +140,7 @@ ], "source": [ "mean_income = np.mean(survey[:, 0])\n", - "mean_income\n" + "mean_income" ] }, { @@ -160,7 +160,7 @@ } ], "source": [ - "np.round(mean_income, 1)\n" + "np.round(mean_income, 1)" ] }, { @@ -241,12 +241,12 @@ ], "source": [ "survey[survey[:, 0] < 20_000, 0] = survey[survey[:, 0] < 20_000, 0] + 1_500\n", - "survey\n" + "survey" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -255,13 +255,13 @@ "40214.3" ] }, - "execution_count": 27, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(np.mean(survey[:, 0]), 1)\n" + "np.round(np.mean(survey[:, 0]), 1)" ] }, { @@ -325,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -334,7 +334,7 @@ "(122603, 5)" ] }, - "execution_count": 11, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -346,28 +346,7 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "26.0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hourly = cps[:, 0] / cps[:, 1]\n", - "np.round(np.mean(hourly), 1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -376,19 +355,19 @@ "27.883329990399556" ] }, - "execution_count": 13, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "men = np.mean(hourly[cps[:, 2] == 1])\n", - "men\n" + "men" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -397,18 +376,18 @@ "27.9" ] }, - "execution_count": 14, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(men, 1)\n" + "np.round(men, 1)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -417,19 +396,19 @@ "23.803157653012704" ] }, - "execution_count": 15, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "women = np.mean(hourly[cps[:, 2] == 2])\n", - "women\n" + "women" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -438,18 +417,18 @@ "23.8" ] }, - "execution_count": 16, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(women, 1)\n" + "np.round(women, 1)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -458,18 +437,18 @@ "0.8536698328789393" ] }, - "execution_count": 17, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "women / men\n" + "women / men" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -478,13 +457,13 @@ "0.854" ] }, - "execution_count": 18, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(women / men, 3)\n" + "np.round(women / men, 3)" ] }, { @@ -518,7 +497,7 @@ ], "source": [ "black = np.mean(hourly[cps[:, 3] == 2])\n", - "black\n" + "black" ] }, { @@ -538,7 +517,7 @@ } ], "source": [ - "np.round(black, 1)\n" + "np.round(black, 1)" ] }, { @@ -559,7 +538,7 @@ ], "source": [ "white = np.mean(hourly[cps[:, 3] == 1])\n", - "white\n" + "white" ] }, { @@ -579,7 +558,7 @@ } ], "source": [ - "np.round(white, 1)\n" + "np.round(white, 1)" ] }, { @@ -599,7 +578,7 @@ } ], "source": [ - "black / white\n" + "black / white" ] }, { @@ -619,7 +598,7 @@ } ], "source": [ - "np.round(black / white, 3)\n" + "np.round(black / white, 3)" ] }, { @@ -651,7 +630,7 @@ "metadata": {}, "outputs": [], "source": [ - "true_average = np.sum((hourly * cps[:, 4]) / np.sum(cps[:, 4]))\n" + "true_average = np.sum((hourly * cps[:, 4]) / np.sum(cps[:, 4]))" ] }, { @@ -671,7 +650,7 @@ } ], "source": [ - "np.round(true_average, 2)\n" + "np.round(true_average, 2)" ] } ], @@ -691,7 +670,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/docs/_sources/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb b/docs/_sources/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb index 0a589235..8d044ba4 100644 --- a/docs/_sources/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb +++ b/docs/_sources/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb @@ -9,7 +9,7 @@ "\n", "**Note**: \n", "\n", - "> This exercise has been written out in something called a Jupyter Notebook. We'll discuss Jupyter Notebooks in more detail later in the course—they are a very powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don't need to *do* anything with the notebook except read its contents—just use write your Python code in a regular `.py` file.\n", + "> This exercise has been written out in something called a Jupyter Notebook. We'll discuss Jupyter Notebooks in more detail later in the course—they are very a powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don't need to *do* anything with the notebook except read its contents—just use write your Python code in a regular `.py` file.\n", "\n", "\n", "As previously discussed, we frequently use matrices and data science because they are a natural format for representing data generated by collecting the same type of information from numerous entities. For example, below is a toy dataset that you could imagine was created by collecting information about employees at a company—each column is a different type of information being collected (income, age, years of education), and each row is the information about a different employee.\n", @@ -47,7 +47,7 @@ "years_of_education = [12, 16, 11, 22, 12, 8, 12]\n", "\n", "survey = np.array([incomes, ages, years_of_education]).T\n", - "survey\n" + "survey" ] }, { @@ -56,9 +56,17 @@ "source": [ "## Part 1: Summarizing Data\n", "\n", - "1. What is the average (mean) age of all employees (rounded to 1 decimal place)?\n", - "2. What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?\n", - "3. What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?" + "### Question 1 \n", + "\n", + "What is the average (mean) age of all employees (rounded to 1 decimal place)?\n", + "\n", + "### Question 2\n", + "\n", + "What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?\n", + "\n", + "### Question 3\n", + "\n", + "What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?" ] }, { @@ -70,7 +78,9 @@ "\n", "The US government is thinking about offering a 1,500 tax credit to anyone making less than 20,000 a year. You can think of this tax credit as effectively an additional $1,500 of income to each person receiving the credit.\n", "\n", - "4. Using the data from `survey`, modify income values to calculate a new estimate of the employees' incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?\n", + "### Question 4 \n", + "\n", + "Using the data from `survey`, modify income values to calculate a new estimate of the employees' incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?\n", " - Do so by subsetting and editing values programmatically, *not* just typing values by hand. (Yes, writing out a new vector by hand is easy to do in this example, but you couldn't do it with a large, real dataset!)" ] }, @@ -82,7 +92,7 @@ "\n", "In this exercise, we'll be working with data from the [US Current Population Survey, provided by the National Bureau of Economic Research (NBER)](https://www.nber.org/research/data/current-population-survey-cps-merged-outgoing-rotation-group-earnings-data). This is a regular survey conducted by the US Bureau of Labor to calculate the US employment rate.\n", "\n", - "In this exercise, we'll use these data to study gender and racial wage inequality in the US.\n", + "In this exercise, we'll use this data to study gender and racial wage inequality in the US.\n", "\n", "Load data from the 2018 CPS survey with the following command:\n", "\n", @@ -90,16 +100,20 @@ "cps = np.loadtxt(\"data/cps.txt\")\n", "```\n", "\n", - "This data are a *subset* of the full CPS survey and contains only data on **employed respondents working at least 35 hours a week (e.g., full-time).**\n" + "This data is a *subset* of the full CPS survey and contains only data on **employed respondents working at least 35 hours a week (e.g., full-time).**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "5. How many rows does this matrix have?\n", + "### Question 5 \n", + "\n", + "How many rows does this matrix have?\n", + "\n", + "### Question 6 \n", "\n", - "6. The five columns of this matrix correspond to:\n", + "The five columns of this matrix correspond to:\n", " - Column 1: Weekly income in dollars.\n", " - Column 2: Usual hours respondent works per week.\n", " - Column 3: Gender. 2 is \"Female\", 1 is \"Male\"\n", @@ -115,7 +129,9 @@ "\n", "For the moment, let's ignore survey weights—they don't impact results here significantly.\n", "\n", - "**What is the *average hourly wage* for all workers in this dataset (round it to one decimal place)?**\n", + "**What is the *average hourly wage* for all workers in this data?** \n", + "\n", + "Please round your *final* answer to one decimal place, but *don't round any intermediate results!* (if you round at each stage of the calculation, those roundings may compound in odd ways).\n", "\n", "**Hint:** This will require more than just using `mean` on a single column!" ] @@ -124,11 +140,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "7. What is the average hourly wage of working men (rounded to one decimal place)? \n", + "### Question 7 \n", + "\n", + "What is the average hourly wage of working men? (Please round your *final* answer to one decimal place, but *don't round any intermediate results!*)\n", + "\n", + "### Question 8 \n", + "\n", + "What is the average hourly wage of working women? (Please round your *final* answer to one decimal place, but *don't round any intermediate results!*)\n", "\n", - "8. What is the average hourly wage of working women (rounded to one decimal place)?\n", + "### Question 9 \n", "\n", - "9. What share (e.g., a value between 0 and 1) of men's average hourly wage is women's average hourly wage? (rounded to three decimal places). In other words, what is women's average hourly wage divided by men's average hourly wage? *Don't round anything until you have your final answer!*\n", + "What share (e.g., a value between 0 and 1) of men's average hourly wage is women's average hourly wage? (Please round your *final* answer to three decimal place, but *don't round any intermediate results!*). In other words, what is women's average hourly wage divided by men's average hourly wage?\n", "\n", "Congratulations! You've just calculated the US gender wage gap, on your own, using real data! I mean... I guess \"congratulations\" is a weird thing to say after directly measuring one of the more egregious inequities in US society, but one of the reasons many of us study data science is so that we will have the ability to directly measure these types of phenomena in the hopes of being able to better understand and address them." ] @@ -137,11 +159,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "10. Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?\n", + "### Question 10 \n", "\n", - "11. What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?\n", + "Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?\n", "\n", - "12. What share (e.g., a value between 0 and 1) of respondents who identify as White's average hourly wage is respondents who identify as Black's average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black's average hourly wage divided by respondents who identify as White's average hourly wage? *Don't round anything until you have your final answer!*\n", + "### Question 11 \n", + "\n", + "What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?\n", + "\n", + "### Question 12 \n", + "\n", + "What share (e.g., a value between 0 and 1) of respondents who identify as White's average hourly wage is respondents who identify as Black's average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black's average hourly wage divided by respondents who identify as White's average hourly wage? *Don't round anything until you have your final answer!*\n", "\n", "Note that this will only be an approximation—one would normally also include all respondents of mixed-race into non-mutually exclusive categories like \"Any Part Black\" or \"Any Part White\", and we would also break out Hispanic and non-Hispanic respondents. But as most respondents only pick on racial category, this will still give us a reasonable approximation." ] @@ -154,7 +182,7 @@ "\n", "As noted above, the fifth column of our data contains something called \"sampling weights.\" That's because when the government conducted this survey, they didn't draw a random sample of respondents from the US population where everyone had the same probability of being interviewed. As a result, when we calculate the average hourly wage of the people in the survey, it isn't *quite* the best estimate we have for the average hourly wage for everyone working in the United States. To calculate this number as accurately as possible, we have to take into account the fact that some respondents in the data were more likely to be included than others, and thus can be thought of as standing in for a smaller group of people in the US.\n", "\n", - "Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than what we would get if we just took a random sample where everyone in the United States had the same probability of being included.\n", + "Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than when we would get if we just took a random sample where everyone in the United States had the same probability of being included.\n", "\n", "As I mentioned above, for the questions above, the sampling weights don't make a very big difference to our answers, but the way to get the *most accurate* estimates would be to take them into account. So let's give that a try!\n", "\n", @@ -162,9 +190,11 @@ "\n", "For a *weighted* average, we take the value for each observation $i$ and multiply it by\n", "\n", - "$$weight_i / \\sum_{j=1}^N weight_j$$\n", + "$$weight_i / \\sum_{j=1}^N weights_j$$\n", + "\n", + "where $weight_i$ is the observation's weight, and $\\sum_{j=1}^N weights_j$ is the total of all the weights in the population being averaged. Then we just add up those values!\n", "\n", - "where $weight_i$ is the observation's weight, and $\\sum_{j=1}^N weight_j$ is the total of all the weights in the population being averaged. Then we just add up those values!\n", + "### Question 13\n", "\n", "Given that, what is the average hourly wage of Americans working full-time jobs (i.e., the group in this survey) taking into account survey weights (rounded to **two** decimal places)?\n" ] @@ -172,7 +202,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.6.5 ('base')", + "display_name": "Python 3.10.6 ('base')", "language": "python", "name": "python3" }, @@ -186,12 +216,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.10.6" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "f54e0b9f92ceb50229b3b7ea26b5d02f05ce8845dd8d997c014a0efede4c9c20" + "hash": "718fed28bf9f8c7851519acf2fb923cd655120b36de3b67253eeb0428bd33d2d" } } }, diff --git a/docs/_sources/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb b/docs/_sources/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb index 9a06eecf..327594d4 100644 --- a/docs/_sources/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb +++ b/docs/_sources/notebooks/class_2/week_4/45_EX_Week4_wrap_up_quiz_solutions.ipynb @@ -276,8 +276,8 @@ " 704 907 46 752 31 916 578 58 588 206 338 358 782 953 336 682 640 278\n", " 526 324 62 331 751 442 463 605 239 957 522 335 389 179 657 486 565 258\n", " 140 997 882 388 866 13 925 854 489 402]\n", - "894.7222222222222\n", - "894.0\n" + "425.078431372549\n", + "423.0\n" ] } ], @@ -291,11 +291,11 @@ "\n", "def remove_greater_than(array, threshold):\n", " '''remove entries in `array' greater than `threshold' '''\n", - " return array[array > threshold]\n", + " return array[array <= threshold]\n", "\n", "def remove_less_than(array, threshold):\n", " '''remove entries in `array' less than `threshold' ''' \n", - " return array[array < threshold]\n", + " return array[array >= threshold]\n", "\n", "def remove_if_equal(array, value_list):\n", " '''remove entries in `array' that equal any value in `value_list' '''\n", @@ -311,6 +311,7 @@ "data = remove_greater_than(data,800)\n", "even_integers_through_1000 = np.arange(2, 1001, 2)\n", "data = remove_if_equal(data,even_integers_through_1000)\n", + "data = remove_less_than(data,25)\n", "data = remove_duplicates(data)\n", "\n", "filtered_mean = np.mean(data)\n", @@ -322,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -330,21 +331,81 @@ "output_type": "stream", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 0 1 2 3\n", - " 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n", - "[11 12 13 14 15 16 17 18 19 11 12 13 14 15 16 17 18 19]\n", - "[0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]\n", - "[ 0 4 5 6 7 8 16 17 18 19 0 4 5 6 7 8 16 17 18 19]\n", - "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" + " 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" ] } ], "source": [ "test = np.concatenate((np.arange(0,20),np.arange(0,20)))\n", - "print(test)\n", - "print(remove_greater_than(test,10))\n", - "print(remove_less_than(test,10))\n", - "print(remove_if_equal(test,[1,2,3,9,10,11,12,13,14,15]))\n", - "print(remove_duplicates(test))\n" + "print(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10]\n" + ] + } + ], + "source": [ + "print(remove_greater_than(test,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_less_than(test,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 4 5 6 7 8 16 17 18 19 0 4 5 6 7 8 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_if_equal(test,[1,2,3,9,10,11,12,13,14,15]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n" + ] + } + ], + "source": [ + "print(remove_duplicates(test))" ] } ], @@ -364,7 +425,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.12.0" }, "orig_nbformat": 4, "vscode": { diff --git a/docs/_sources/notebooks/class_3/week_2/00_intro_to_pandas.ipynb b/docs/_sources/notebooks/class_3/week_2/00_intro_to_pandas.ipynb index fc7665f6..b6255028 100644 --- a/docs/_sources/notebooks/class_3/week_2/00_intro_to_pandas.ipynb +++ b/docs/_sources/notebooks/class_3/week_2/00_intro_to_pandas.ipynb @@ -8,10 +8,11 @@ "\n", "In the last class, we discussed how to work with numerical data through arrays and matrices in cases when *all the data were of the same type*. You encounter this often, so `numpy` is a powerful tool for working with such homogeneous data (data of the same type) and in such cases is often the preferred tool if you want to do numeric computations because of its speed. However, there are several frequently encountered situations in which `numpy` tools may not be ideal and where instead the `pandas` library may be best. These include:\n", "\n", - "1. If you do not have purely numerical data (i.e. if your data are heterogeneous), and therefore have a mix of data types, `numpy` may not be appropriate, while `pandas` can easily handle and analyze mixed data types\n", - "2. If you have tabular data, `pandas` makes life much easier to **describe**, **summarize**, **query**, and **visualize** the data than the equivalent processes with `numpy`.\n", + "1. Your data includes not only numerical data, but also text, images, geometric objects, or any other type of data (i.e., your data are heterogeneous). In these situations, `numpy` may not be appropriate, while `pandas` can easily organize, manipulate, and analyze this kid of \"mixed data.\"\n", + "2. If you have tabular data — that is, data organized into rows and columns, as in a spreadsheet — it is often easier in `pandas` to **describe**, **summarize**, **query**, and **visualize** the data than the equivalent processes with `numpy`.\n", "\n", "## Mixed data types\n", + "\n", "Often our data are of mixed types (e.g. integers and strings). This happens all the time. Imagine that you are collecting basic medical information from a patient. You may ask for height and weight (numerical, floating point numbers), age (integer), and blood type (categorical, string). While numpy can store these together in an array, there's not much you're going to be able to do with it computationally. Consider the following example where `numpy` throws an error:" ] }, @@ -410,7 +411,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -419,7 +420,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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yckJcXByGDRuGrVu3YuzYsdBoNFp1AwYMQOvWrfHll19i4cKF2Lhxo3iYqoqrqyvCwsIwa9YsTJgwAVevXsWePXu0aoyMjLBp06aHHuvTaDRa2y4qKoKjoyPUarXWMUlqHHh6OT0P/v77b1y+fBnOzs5iQNBVJSUlePHFF7F8+XKEhYXV6jUKhQI7duzA0KFD67y9K1euwNnZGWlpadV+UTd2vXv3xksvvSReJ4eqe9R/G0VFRVCpVLX6/V3nER0jIyNxMnLnzp1x/PhxfP7551izZk21Wnt7ezg5OYnHJutrUpOdnR2OHj2qtb6goADl5eXVRnrup1QqoVQq6/iJiYioJmlpaTh37hy6du0KtVqN+fPnAwCGDBkicWdE/++pr4wsCEK1EZoqt27dQk5OjjgsVV+Tmnx8fJCRkYHc3FyxJjExEUqlEl5eXk/7kYiIqJaWLVuGjh07on///igpKcFvv/0mHjZ6WgsXLtQ6Lfz+R0BAQL1soyaxsbEP3W7VGca64Lfffnvo5zA1NZW6vQZTp0NXH3zwAQICAuDo6Iji4mLExcVh8eLFSEhIgI+PD+bNm4fhw4fD3t4eV65cwQcffIDs7GxkZmbCzMwMAPDOO+/g559/xoYNG2BpaYno6GjcunVL6/TygIAAXL9+XRwlqrpa5P2nl7/00kuwtbXF0qVLcfv2bYSGhmLo0KF1Or28LkNf1PB46IqeB3I6dFXfbt++jdu3b9e4ztjYGC+++OIz2W5xcXGNRxaAf858etZX8q0vpaWl+PPPPx+6vuroTGMlyaGrGzduICQkBLm5uVCpVOjQoQMSEhIwYMAAlJaW4vTp09i0aRMKCwthb2+PPn364LvvvhNDDlA/k5r09fURHx+PiIgI9OjRA8bGxggODsayZcvq8nGIiKgRs7S01LpESUMxMzPT+r2lq4yNjRt9mGkIdZ6MLCcc0WncOKJDz4Oqv1qdnJye6Z2qiXTN3bt3cfXq1YafjExERPXHyMgIenp6uH79Opo1awYjI6OnviowkS4TBAFlZWW4efMm9PT0qt1hoa4YdIiIJKSnpwdnZ2fk5ubi+vXrUrdD1Gg0bdoULVq0eKrrHQEMOkREkjMyMkKLFi1w7969J75aL5Gc6Ovrw8DAoF5GNxl0iIgaAYVCAUNDQxgaGkrdCpGsPPV1dIiIiIgaKwYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIiki0GHSIiIpItBh0iIiKSLQYdIiIikq06BZ3Vq1ejQ4cOMDc3h7m5OXx8fLB7925xvSAImDdvHhwcHGBsbIzevXvjzJkzWu+h0WgQGRkJa2trmJiYICgoCNeuXdOqKSgoQEhICFQqFVQqFUJCQlBYWKhVk52djcGDB8PExATW1taYOnUqysrK6vjxiYiISM7qFHSaN2+OxYsX48SJEzhx4gT69u2LIUOGiGFmyZIlWLFiBVatWoXjx4/Dzs4OAwYMQHFxsfgeUVFR2LFjB+Li4pCcnIw7d+4gMDAQFRUVYk1wcDDS09ORkJCAhIQEpKenIyQkRFxfUVGBQYMGoaSkBMnJyYiLi8O2bdswffr0p90fREREJCMKQRCEp3kDS0tLLF26FOPGjYODgwOioqIwc+ZMAP+M3tja2uLTTz/FxIkToVar0axZM2zevBkjR44EAFy/fh2Ojo745Zdf4Ofnh8zMTLi7uyMlJQXdunUDAKSkpMDHxwfnzp2Dm5sbdu/ejcDAQOTk5MDBwQEAEBcXh9DQUOTn58Pc3LzGXjUaDTQajfi8qKgIjo6OUKvVD30NSaflrHipW6iVK4sHSd0CEdFzpaioCCqVqla/v594jk5FRQXi4uJQUlICHx8fXL58GXl5efD19RVrlEolevXqhcOHDwMAUlNTUV5erlXj4OAADw8PsebIkSNQqVRiyAEAb29vqFQqrRoPDw8x5ACAn58fNBoNUlNTH9rzokWLxMNhKpUKjo6OT/rxiYiISAfUOeicPn0apqamUCqVmDRpEnbs2AF3d3fk5eUBAGxtbbXqbW1txXV5eXkwMjKChYXFI2tsbGyqbdfGxkar5sHtWFhYwMjISKypyezZs6FWq8VHTk5OHT89ERER6RKDur7Azc0N6enpKCwsxLZt2zBmzBgcPHhQXK9QKLTqBUGotuxBD9bUVP8kNQ9SKpVQKpWP7IWIiIjko84jOkZGRnBxcUHnzp2xaNEidOzYEZ9//jns7OwAoNqISn5+vjj6Ymdnh7KyMhQUFDyy5saNG9W2e/PmTa2aB7dTUFCA8vLyaiM9RERE9Px66uvoCIIAjUYDZ2dn2NnZISkpSVxXVlaGgwcPonv37gAALy8vGBoaatXk5uYiIyNDrPHx8YFarcaxY8fEmqNHj0KtVmvVZGRkIDc3V6xJTEyEUqmEl5fX034kIiIikok6Hbr64IMPEBAQAEdHRxQXFyMuLg4HDhxAQkICFAoFoqKisHDhQri6usLV1RULFy5E06ZNERwcDABQqVQICwvD9OnTYWVlBUtLS0RHR8PT0xP9+/cHALRr1w7+/v4IDw/HmjVrAAATJkxAYGAg3NzcAAC+vr5wd3dHSEgIli5ditu3byM6Ohrh4eE8e4qIiIhEdQo6N27cQEhICHJzc6FSqdChQwckJCRgwIABAIAZM2agtLQUERERKCgoQLdu3ZCYmAgzMzPxPWJiYmBgYIARI0agtLQU/fr1w4YNG6Cvry/WxMbGYurUqeLZWUFBQVi1apW4Xl9fH/Hx8YiIiECPHj1gbGyM4OBgLFu27Kl2BhEREcnLU19HR5fV5Tx8ani8jg4REdWkQa6jQ0RERNTYMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbNUp6CxatAhdunSBmZkZbGxsMHToUGRlZWnVhIaGQqFQaD28vb21ajQaDSIjI2FtbQ0TExMEBQXh2rVrWjUFBQUICQmBSqWCSqVCSEgICgsLtWqys7MxePBgmJiYwNraGlOnTkVZWVldPhIRERHJWJ2CzsGDBzF58mSkpKQgKSkJ9+7dg6+vL0pKSrTq/P39kZubKz5++eUXrfVRUVHYsWMH4uLikJycjDt37iAwMBAVFRViTXBwMNLT05GQkICEhASkp6cjJCREXF9RUYFBgwahpKQEycnJiIuLw7Zt2zB9+vQn2Q9EREQkQwZ1KU5ISNB6vn79etjY2CA1NRU9e/YUlyuVStjZ2dX4Hmq1GuvWrcPmzZvRv39/AMCWLVvg6OiIvXv3ws/PD5mZmUhISEBKSgq6desGAFi7di18fHyQlZUFNzc3JCYm4uzZs8jJyYGDgwMAYPny5QgNDcUnn3wCc3Pzunw0IiIikqGnmqOjVqsBAJaWllrLDxw4ABsbG7Rp0wbh4eHIz88X16WmpqK8vBy+vr7iMgcHB3h4eODw4cMAgCNHjkClUokhBwC8vb2hUqm0ajw8PMSQAwB+fn7QaDRITU2tsV+NRoOioiKtBxEREcnXEwcdQRAwbdo0vPLKK/Dw8BCXBwQEIDY2Fvv27cPy5ctx/Phx9O3bFxqNBgCQl5cHIyMjWFhYaL2fra0t8vLyxBobG5tq27SxsdGqsbW11VpvYWEBIyMjseZBixYtEuf8qFQqODo6PunHJyIiIh1Qp0NX95syZQpOnTqF5ORkreUjR44U/+3h4YHOnTvDyckJ8fHxGDZs2EPfTxAEKBQK8fn9/36amvvNnj0b06ZNE58XFRUx7BAREcnYE43oREZGYufOndi/fz+aN2/+yFp7e3s4OTnhwoULAAA7OzuUlZWhoKBAqy4/P18cobGzs8ONGzeqvdfNmze1ah4cuSkoKEB5eXm1kZ4qSqUS5ubmWg8iIiKSrzoFHUEQMGXKFGzfvh379u2Ds7PzY19z69Yt5OTkwN7eHgDg5eUFQ0NDJCUliTW5ubnIyMhA9+7dAQA+Pj5Qq9U4duyYWHP06FGo1WqtmoyMDOTm5oo1iYmJUCqV8PLyqsvHIiIiIpmq06GryZMnY+vWrfjpp59gZmYmjqioVCoYGxvjzp07mDdvHoYPHw57e3tcuXIFH3zwAaytrfHaa6+JtWFhYZg+fTqsrKxgaWmJ6OhoeHp6imdhtWvXDv7+/ggPD8eaNWsAABMmTEBgYCDc3NwAAL6+vnB3d0dISAiWLl2K27dvIzo6GuHh4RypISIiIgB1HNFZvXo11Go1evfuDXt7e/Hx3XffAQD09fVx+vRpDBkyBG3atMGYMWPQpk0bHDlyBGZmZuL7xMTEYOjQoRgxYgR69OiBpk2bYteuXdDX1xdrYmNj4enpCV9fX/j6+qJDhw7YvHmzuF5fXx/x8fFo0qQJevTogREjRmDo0KFYtmzZ0+4TIiIikgmFIAiC1E1IpaioCCqVCmq1mqNAjVDLWfFSt1ArVxYPkroFIqLnSl1+f/NeV0RERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkW3UKOosWLUKXLl1gZmYGGxsbDB06FFlZWVo1giBg3rx5cHBwgLGxMXr37o0zZ85o1Wg0GkRGRsLa2homJiYICgrCtWvXtGoKCgoQEhIClUoFlUqFkJAQFBYWatVkZ2dj8ODBMDExgbW1NaZOnYqysrK6fCQiIiKSsToFnYMHD2Ly5MlISUlBUlIS7t27B19fX5SUlIg1S5YswYoVK7Bq1SocP34cdnZ2GDBgAIqLi8WaqKgo7NixA3FxcUhOTsadO3cQGBiIiooKsSY4OBjp6elISEhAQkIC0tPTERISIq6vqKjAoEGDUFJSguTkZMTFxWHbtm2YPn360+wPIiIikhGFIAjCk7745s2bsLGxwcGDB9GzZ08IggAHBwdERUVh5syZAP4ZvbG1tcWnn36KiRMnQq1Wo1mzZti8eTNGjhwJALh+/TocHR3xyy+/wM/PD5mZmXB3d0dKSgq6desGAEhJSYGPjw/OnTsHNzc37N69G4GBgcjJyYGDgwMAIC4uDqGhocjPz4e5uflj+y8qKoJKpYJara5VPTWslrPipW6hVq4sHiR1C0REz5W6/P5+qjk6arUaAGBpaQkAuHz5MvLy8uDr6yvWKJVK9OrVC4cPHwYApKamory8XKvGwcEBHh4eYs2RI0egUqnEkAMA3t7eUKlUWjUeHh5iyAEAPz8/aDQapKam1tivRqNBUVGR1oOIiIjk64mDjiAImDZtGl555RV4eHgAAPLy8gAAtra2WrW2trbiury8PBgZGcHCwuKRNTY2NtW2aWNjo1Xz4HYsLCxgZGQk1jxo0aJF4pwflUoFR0fHun5sIiIi0iFPHHSmTJmCU6dO4dtvv622TqFQaD0XBKHasgc9WFNT/ZPU3G/27NlQq9XiIycn55E9ERERkW57oqATGRmJnTt3Yv/+/WjevLm43M7ODgCqjajk5+eLoy92dnYoKytDQUHBI2tu3LhRbbs3b97UqnlwOwUFBSgvL6820lNFqVTC3Nxc60FERETyVaegIwgCpkyZgu3bt2Pfvn1wdnbWWu/s7Aw7OzskJSWJy8rKynDw4EF0794dAODl5QVDQ0OtmtzcXGRkZIg1Pj4+UKvVOHbsmFhz9OhRqNVqrZqMjAzk5uaKNYmJiVAqlfDy8qrLxyIiIiKZMqhL8eTJk7F161b89NNPMDMzE0dUVCoVjI2NoVAoEBUVhYULF8LV1RWurq5YuHAhmjZtiuDgYLE2LCwM06dPh5WVFSwtLREdHQ1PT0/0798fANCuXTv4+/sjPDwca9asAQBMmDABgYGBcHNzAwD4+vrC3d0dISEhWLp0KW7fvo3o6GiEh4dzpIaIiIgA1DHorF69GgDQu3dvreXr169HaGgoAGDGjBkoLS1FREQECgoK0K1bNyQmJsLMzEysj4mJgYGBAUaMGIHS0lL069cPGzZsgL6+vlgTGxuLqVOnimdnBQUFYdWqVeJ6fX19xMfHIyIiAj169ICxsTGCg4OxbNmyOu0AIiIikq+nuo6OruN1dBo3XkeHiIhq0mDX0SEiIiJqzBh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2GHSIiIhItgykboCIiJ4/LWfFS91CrVxZPEjqFugpcUSHiIiIZItBh4iIiGSLQYeIiIhki0GHiIiIZItBh4iIiGSLQYeIiIhki0GHiIiIZKvOQefQoUMYPHgwHBwcoFAo8OOPP2qtDw0NhUKh0Hp4e3tr1Wg0GkRGRsLa2homJiYICgrCtWvXtGoKCgoQEhIClUoFlUqFkJAQFBYWatVkZ2dj8ODBMDExgbW1NaZOnYqysrK6fiQiIiKSqToHnZKSEnTs2BGrVq16aI2/vz9yc3PFxy+//KK1PioqCjt27EBcXBySk5Nx584dBAYGoqKiQqwJDg5Geno6EhISkJCQgPT0dISEhIjrKyoqMGjQIJSUlCA5ORlxcXHYtm0bpk+fXtePRERERDJV5ysjBwQEICAg4JE1SqUSdnZ2Na5Tq9VYt24dNm/ejP79+wMAtmzZAkdHR+zduxd+fn7IzMxEQkICUlJS0K1bNwDA2rVr4ePjg6ysLLi5uSExMRFnz55FTk4OHBwcAADLly9HaGgoPvnkE5ibm9f1oxEREZHMPJM5OgcOHICNjQ3atGmD8PBw5Ofni+tSU1NRXl4OX19fcZmDgwM8PDxw+PBhAMCRI0egUqnEkAMA3t7eUKlUWjUeHh5iyAEAPz8/aDQapKam1tiXRqNBUVGR1oOIiIjkq96DTkBAAGJjY7Fv3z4sX74cx48fR9++faHRaAAAeXl5MDIygoWFhdbrbG1tkZeXJ9bY2NhUe28bGxutGltbW631FhYWMDIyEmsetGjRInHOj0qlgqOj41N/XiIiImq86v2mniNHjhT/7eHhgc6dO8PJyQnx8fEYNmzYQ18nCAIUCoX4/P5/P03N/WbPno1p06aJz4uKihh2iIiIZOyZn15ub28PJycnXLhwAQBgZ2eHsrIyFBQUaNXl5+eLIzR2dna4ceNGtfe6efOmVs2DIzcFBQUoLy+vNtJTRalUwtzcXOtBRERE8vXMg86tW7eQk5MDe3t7AICXlxcMDQ2RlJQk1uTm5iIjIwPdu3cHAPj4+ECtVuPYsWNizdGjR6FWq7VqMjIykJubK9YkJiZCqVTCy8vrWX8sIiIi0gF1PnR1584d/PHHH+Lzy5cvIz09HZaWlrC0tMS8efMwfPhw2Nvb48qVK/jggw9gbW2N1157DQCgUqkQFhaG6dOnw8rKCpaWloiOjoanp6d4Fla7du3g7++P8PBwrFmzBgAwYcIEBAYGws3NDQDg6+sLd3d3hISEYOnSpbh9+zaio6MRHh7OkRoiIiIC8ARB58SJE+jTp4/4vGrOy5gxY7B69WqcPn0amzZtQmFhIezt7dGnTx989913MDMzE18TExMDAwMDjBgxAqWlpejXrx82bNgAfX19sSY2NhZTp04Vz84KCgrSunaPvr4+4uPjERERgR49esDY2BjBwcFYtmxZ3fcCkcy1nBUvdQuPdWXxIKlbICIZUgiCIEjdhFSKioqgUqmgVqs5CtQI6cIvZ0A3fkHrwr7Uhf1I9UcXvpMAv5eNVV1+f/NeV0RERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkW3UOOocOHcLgwYPh4OAAhUKBH3/8UWu9IAiYN28eHBwcYGxsjN69e+PMmTNaNRqNBpGRkbC2toaJiQmCgoJw7do1rZqCggKEhIRApVJBpVIhJCQEhYWFWjXZ2dkYPHgwTExMYG1tjalTp6KsrKyuH4mIiIhkyqCuLygpKUHHjh0xduxYDB8+vNr6JUuWYMWKFdiwYQPatGmDBQsWYMCAAcjKyoKZmRkAICoqCrt27UJcXBysrKwwffp0BAYGIjU1Ffr6+gCA4OBgXLt2DQkJCQCACRMmICQkBLt27QIAVFRUYNCgQWjWrBmSk5Nx69YtjBkzBoIgYOXKlU+8Q+pDy1nxkm6/Nq4sHiR1C0RERM9cnYNOQEAAAgICalwnCAI+++wzfPjhhxg2bBgAYOPGjbC1tcXWrVsxceJEqNVqrFu3Dps3b0b//v0BAFu2bIGjoyP27t0LPz8/ZGZmIiEhASkpKejWrRsAYO3atfDx8UFWVhbc3NyQmJiIs2fPIicnBw4ODgCA5cuXIzQ0FJ988gnMzc2faIcQERGRfNTrHJ3Lly8jLy8Pvr6+4jKlUolevXrh8OHDAIDU1FSUl5dr1Tg4OMDDw0OsOXLkCFQqlRhyAMDb2xsqlUqrxsPDQww5AODn5weNRoPU1NQa+9NoNCgqKtJ6EBERkXzVa9DJy8sDANja2mott7W1Fdfl5eXByMgIFhYWj6yxsbGp9v42NjZaNQ9ux8LCAkZGRmLNgxYtWiTO+VGpVHB0dHyCT0lERES64pmcdaVQKLSeC4JQbdmDHqypqf5Jau43e/ZsqNVq8ZGTk/PInoiIiEi31WvQsbOzA4BqIyr5+fni6IudnR3KyspQUFDwyJobN25Ue/+bN29q1Ty4nYKCApSXl1cb6amiVCphbm6u9SAiIiL5qteg4+zsDDs7OyQlJYnLysrKcPDgQXTv3h0A4OXlBUNDQ62a3NxcZGRkiDU+Pj5Qq9U4duyYWHP06FGo1WqtmoyMDOTm5oo1iYmJUCqV8PLyqs+PRURERDqqzmdd3blzB3/88Yf4/PLly0hPT4elpSVatGiBqKgoLFy4EK6urnB1dcXChQvRtGlTBAcHAwBUKhXCwsIwffp0WFlZwdLSEtHR0fD09BTPwmrXrh38/f0RHh6ONWvWAPjn9PLAwEC4ubkBAHx9feHu7o6QkBAsXboUt2/fRnR0NMLDwzlSQ0RERACeIOicOHECffr0EZ9PmzYNADBmzBhs2LABM2bMQGlpKSIiIlBQUIBu3bohMTFRvIYOAMTExMDAwAAjRoxAaWkp+vXrhw0bNojX0AGA2NhYTJ06VTw7KygoCKtWrRLX6+vrIz4+HhEREejRoweMjY0RHByMZcuW1X0vEBERkSwpBEEQpG5CKkVFRVCpVFCr1fU6CsQLBtYPXdiPAPdlfdGF/Uj1Rxe+kwC/l41VXX5/815XREREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbDDpEREQkWww6REREJFsMOkRERCRbBlI3QERERE+u5ax4qVt4rCuLB0m2bY7oEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6BAREZFsMegQERGRbNV70Jk3bx4UCoXWw87OTlwvCALmzZsHBwcHGBsbo3fv3jhz5ozWe2g0GkRGRsLa2homJiYICgrCtWvXtGoKCgoQEhIClUoFlUqFkJAQFBYW1vfHISIiIh32TEZ02rdvj9zcXPFx+vRpcd2SJUuwYsUKrFq1CsePH4ednR0GDBiA4uJisSYqKgo7duxAXFwckpOTcefOHQQGBqKiokKsCQ4ORnp6OhISEpCQkID09HSEhIQ8i49DREREOsrgmbypgYHWKE4VQRDw2Wef4cMPP8SwYcMAABs3boStrS22bt2KiRMnQq1WY926ddi8eTP69+8PANiyZQscHR2xd+9e+Pn5ITMzEwkJCUhJSUG3bt0AAGvXroWPjw+ysrLg5uZWY18ajQYajUZ8XlRUVN8fnYiIiBqRZzKic+HCBTg4OMDZ2RlvvvkmLl26BAC4fPky8vLy4OvrK9YqlUr06tULhw8fBgCkpqaivLxcq8bBwQEeHh5izZEjR6BSqcSQAwDe3t5QqVRiTU0WLVokHupSqVRwdHSs189NREREjUu9B51u3bph06ZN2LNnD9auXYu8vDx0794dt27dQl5eHgDA1tZW6zW2trbiury8PBgZGcHCwuKRNTY2NtW2bWNjI9bUZPbs2VCr1eIjJyfnqT4rERERNW71fugqICBA/Lenpyd8fHzQunVrbNy4Ed7e3gAAhUKh9RpBEKote9CDNTXVP+59lEollEplrT4HERER6b5nfnq5iYkJPD09ceHCBXHezoOjLvn5+eIoj52dHcrKylBQUPDImhs3blTb1s2bN6uNFhEREdHz65kHHY1Gg8zMTNjb28PZ2Rl2dnZISkoS15eVleHgwYPo3r07AMDLywuGhoZaNbm5ucjIyBBrfHx8oFarcezYMbHm6NGjUKvVYg0RERFRvR+6io6OxuDBg9GiRQvk5+djwYIFKCoqwpgxY6BQKBAVFYWFCxfC1dUVrq6uWLhwIZo2bYrg4GAAgEqlQlhYGKZPnw4rKytYWloiOjoanp6e4llY7dq1g7+/P8LDw7FmzRoAwIQJExAYGPjQM66IiIjo+VPvQefatWsYNWoU/vrrLzRr1gze3t5ISUmBk5MTAGDGjBkoLS1FREQECgoK0K1bNyQmJsLMzEx8j5iYGBgYGGDEiBEoLS1Fv379sGHDBujr64s1sbGxmDp1qnh2VlBQEFatWlXfH4eIiIh0WL0Hnbi4uEeuVygUmDdvHubNm/fQmiZNmmDlypVYuXLlQ2ssLS2xZcuWJ22TiIiIngO81xURERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREckWgw4RERHJFoMOERERyRaDDhEREcmWgdQNEBHpkpaz4qVu4bGuLB4kdQtEjQZHdIiIiEi2GHSIiIhIthh0iIiISLYYdIiIiEi2dD7o/Pe//4WzszOaNGkCLy8v/Pbbb1K3RERERI2ETged7777DlFRUfjwww+RlpaGV199FQEBAcjOzpa6NSIiImoEdDrorFixAmFhYRg/fjzatWuHzz77DI6Ojli9erXUrREREVEjoLPX0SkrK0NqaipmzZqltdzX1xeHDx+u8TUajQYajUZ8rlarAQBFRUX12lul5m69vt+zUN+f+VnQhf0IcF/WF13YjwD3ZX3Rhf0IcF/Wl/rej1XvJwjCY2t1Nuj89ddfqKiogK2trdZyW1tb5OXl1fiaRYsW4eOPP6623NHR8Zn02JipPpO6A/ngvqwf3I/1h/uy/nBf1o9ntR+Li4uhUqkeWaOzQaeKQqHQei4IQrVlVWbPno1p06aJzysrK3H79m1YWVk99DVSKyoqgqOjI3JycmBubi51OzqN+7L+cF/WD+7H+sN9WX90YV8KgoDi4mI4ODg8tlZng461tTX09fWrjd7k5+dXG+WpolQqoVQqtZa98MILz6rFemVubt5ov3C6hvuy/nBf1g/ux/rDfVl/Gvu+fNxIThWdnYxsZGQELy8vJCUlaS1PSkpC9+7dJeqKiIiIGhOdHdEBgGnTpiEkJASdO3eGj48PvvrqK2RnZ2PSpElSt0ZERESNgE4HnZEjR+LWrVuYP38+cnNz4eHhgV9++QVOTk5St1ZvlEol5s6dW+2QG9Ud92X94b6sH9yP9Yf7sv7IbV8qhNqcm0VERESkg3R2jg4RERHR4zDoEBERkWwx6BAREZFsMegQERGRbDHoEBERkWwx6JCsHThwQOoWiIhIQgw6JGv+/v5o3bo1FixYgJycHKnbIdJSVlaGrKws3Lt3T+pWiADI8zvJ6+g0Ip06dar1zUVPnjz5jLuRh9u3b2PLli3YsGEDTp06hX79+iEsLAxDhw6FkZGR1O3pnMLCQqxbtw6ZmZlQKBRo164dwsLCan3PGfrH3bt3ERkZiY0bNwIAzp8/j1atWmHq1KlwcHDArFmzJO5Qt5w9exbZ2dkoKyvTWh4UFCRRR7pHzt9JBp1G5OOPP6517dy5c59hJ/KUnp6Ob775Bt9++y0qKyvx1ltvISwsDB07dpS6NZ1w4sQJ+Pn5wdjYGF27doUgCDhx4gRKS0uRmJiIl19+WeoWdca7776L33//HZ999hn8/f1x6tQptGrVCjt37sTcuXORlpYmdYs64dKlS3jttddw+vRpKBQKVP06q/qDsaKiQsr2dIqsv5MC0XPkzz//FObOnSsolUrBxMRE0NfXF1555RUhIyND6tYavVdeeUUIDQ0VysvLxWXl5eXCmDFjhFdffVXCznRPixYthCNHjgiCIAimpqbCxYsXBUEQhAsXLghmZmZStqZTAgMDhSFDhgj5+fmCqampcPbsWeG3334TunbtKhw6dEjq9nSKnL+TnKNDsldeXo4ffvgBAwcOhJOTE/bs2YNVq1bhxo0buHz5MhwdHfHGG29I3Wajd+LECcycORMGBv9/izwDAwPMmDEDJ06ckLAz3XPz5k3Y2NhUW15SUlLrw9cEHDlyBPPnz0ezZs2gp6cHPT09vPLKK1i0aBGmTp0qdXs6Rc7fSQadRsTS0hJ//fUXAMDCwgKWlpYPfVDtREZGwt7eHpMmTUKbNm2QlpaGI0eOYPz48TAxMYGjoyMWL16Mc+fOSd1qo2dubo7s7Oxqy3NycmBmZiZBR7qrS5cuiI+PF59X/SJZu3YtfHx8pGpL51RUVMDU1BQAYG1tjevXrwMAnJyckJWVJWVrOkfO30mdvnu53MTExIi/MGJiYnQ+RTcGZ8+excqVKzF8+PCHTj52cHDA/v37G7gz3TNy5EiEhYVh2bJl6N69OxQKBZKTk/H+++9j1KhRUrenUxYtWgR/f3+cPXsW9+7dw+eff44zZ87gyJEjOHjwoNTt6QwPDw9xLkm3bt2wZMkSGBkZ4auvvkKrVq2kbk+nyPk7ycnIRFQrZWVleP/99/Hll1+Kp54aGhrinXfeweLFi6FUKiXuULecPn0ay5YtQ2pqKiorK/Hyyy9j5syZ8PT0lLo1nbFnzx6UlJRg2LBhuHTpEgIDA3Hu3DlYWVnhu+++Q9++faVuUafI9TvJoNNI9e3bF7169ap2dlVBQQGGDx+Offv2SdSZ7jl//jwOHDiA/Px8VFZWaq376KOPJOpKd929excXL16EIAhwcXFB06ZNpW6JSHT79m1YWFhwRJxEDDqNlJ6eHqysrNCjRw/ExsbCxMQEAHDjxg04ODjwtMlaWrt2Ld555x1YW1vDzs5O64efQqHg9YjqQK1Wo6Kiotocsdu3b8PAwADm5uYSdaZ7aprrdL8WLVo0UCdE/6+yshJ//PFHjX8U9uzZU6Kunh6DTiOlp6eHtLQ0TJw4ESUlJdi1axdatmzJoFNHTk5OiIiIwMyZM6VuRecFBARg8ODBiIiI0Fr+5ZdfYufOnfjll18k6kz36OnpPXLEgf99P9ywYcOwYcMGmJubY9iwYY+s3b59ewN1pftSUlIQHByMq1ev4sFYoFAodPo7ycnIjZi9vT0OHjyIcePGoUuXLvj+++/Rrl07qdvSKQUFBTx1vJ4cPXoUK1asqLa8d+/e+PDDDyXoSHc9ePG18vJypKWlYcWKFfjkk08k6ko3qFQqMSTyitz1Z9KkSejcuTPi4+Nhb28vq0N/DDqNVNWXTKlUIjY2FgsWLIC/vz9HJurojTfeQGJiIiZNmiR1KzpPo9HUeP+b8vJylJaWStCR7qrpatydO3eGg4MDli5d+tiRiufZ+vXra/w3PZ0LFy7ghx9+gIuLi9St1DsGnUbqwaHDf/3rX2jXrh3GjBkjUUe6ycXFBXPmzEFKSgo8PT1haGiotZ4XFau9Ll264KuvvsLKlSu1ln/55Zfw8vKSqCt5adOmDY4fPy51GzqjtLQUgiCIE+KvXr2KHTt2wN3dHb6+vhJ3p1u6deuGP/74Q5ZBh3N0GqmrV6+iRYsW1YYPMzIykJqaysBTS87Ozg9dp1AocOnSpQbsRrf9/vvv6N+/P7p06YJ+/foBAH799VccP34ciYmJePXVVyXuUHcUFRVpPRcEAbm5uZg3bx7OnTuH9PR0aRrTMb6+vhg2bBgmTZqEwsJCuLm5wcjICH/99RdWrFiBd955R+oWdcaOHTvwr3/9C++//36NfxR26NBBos6eHoMOEdVaeno6li5divT0dBgbG6NDhw6YPXs2XF1dpW5Np9Q0GVkQBDg6OiIuLk7nr0TbUKytrXHw4EG0b98eX3/9NVauXIm0tDRs27YNH330ETIzM6VuUWfo6VW/UULVjVI5GZmemePHj+P7779HdnY2ysrKtNbxbAKSwksvvYTY2Fip29B5D16JW09PD82aNYOLi4vWvcTo0e7evSteTT4xMRHDhg2Dnp4evL29cfXqVYm70y2XL1+WuoVnhv9FNVJxcXEYPXo0fH19kZSUBF9fX1y4cAF5eXl47bXXpG5Pp1y7dg07d+6sMTDWdBYR/b+ioiLx+jgPHm55EK+jU3u9evWSugVZcHFxwY8//ojXXnsNe/bswXvvvQcAyM/P5/exjpycnKRu4Zlh0GmkFi5ciJiYGEyePBlmZmb4/PPP4ezsjIkTJ8Le3l7q9nTGr7/+iqCgIDg7OyMrKwseHh64cuUKBEHAyy+/LHV7jZ6FhQVyc3NhY2ODF154ocZTTuUwtN3Qdu7cWevaoKCgZ9iJbvvoo48QHByM9957D/369RMP+SUmJqJTp04Sd9f47dy5EwEBATA0NHzsd1KXv4eco9NImZiY4MyZM2jZsiWsra2xf/9+eHp6IjMzE3379kVubq7ULeqErl27wt/fH/Pnz4eZmRn+97//wcbGBm+99Rb8/f05WfExDh48iB49esDAwAAHDhx45LU1OEpRe1VzdGq6MNv9yxggHy8vLw+5ubno2LGjOM/k2LFjMDc3R9u2bSXurnHT09NDXl4ebGxsapyjU0XXv4cc0WmkLC0tUVxcDAB48cUXkZGRAU9PTxQWFuLu3bsSd6c7MjMz8e233wIADAwMUFpaClNTU8yfPx9Dhgxh0HmM+8NL7969pWtEZhITEzFz5kwsXLgQPj4+UCgUOHz4MP71r39h4cKFGDBggNQt6gw7OzvY2dkB+Ofw6r59++Dm5saQUwv33+bhwVs+yMnDIxxJ6tVXX0VSUhIAYMSIEXj33XcRHh6OUaNGiaf20uOZmJhAo9EAABwcHHDx4kVx3V9//SVVWzppzpw5Nf5Vp1arMWrUKAk60l1RUVH4/PPP4efnB3Nzc5iZmcHPzw8rVqzgtZ3qYMSIEVi1ahWAf66p07lzZ4wYMQIdOnTAtm3bJO6OGguO6DRSq1atwt9//w0AmD17NgwNDZGcnIxhw4Zhzpw5EnenO7y9vfH777/D3d0dgwYNwvTp03H69Gls374d3t7eUrenUzZt2oSkpCTExsaidevWAIADBw5g9OjRePHFFyXuTrdcvHixxtsXqFQqXLlypeEb0lGHDh0Sbz+yY8cOCIKAwsJCbNy4EQsWLMDw4cMl7lC3lJSU4ODBgzWeuKHLAZxzdBqhe/fuITY2Fn5+fuKQLD2ZS5cu4c6dO+jQoQPu3r2L6OhoJCcnw8XFBTExMbI+06C+qdVqTJw4EfHx8VixYgXOnz+Pzz//HLNmzcLcuXOhr68vdYs6o2fPnjA0NMSWLVvEkwvy8vIQEhKCsrIyHDx4UOIOdYOxsTHOnz8PR0dHjB49Gg4ODli8eDGys7Ph7u6OO3fuSN2izkhLS8PAgQNx9+5dlJSUwNLSEn/99ReaNm0KGxsb3b64qkCNkrGxsXDlyhWp2yCq5oMPPhAUCoVgaGgo7N27V+p2dNKFCxcEDw8PwdDQUGjdurXQunVrwdDQUGjfvr1w4cIFqdvTGa6ursJ3330n3LlzR2jWrJnw66+/CoIgCOnp6YKVlZXE3emWXr16CeHh4cK9e/cEU1NT4eLFi0J2drbQs2dPYdu2bVK391R46KqR6tatG9LS0jjiUI/u3LlTbcIdr7VRNytXrkRMTAxGjRqF1NRUTJ06FVu3bq3xJpX0cC4uLjh16hSSkpJw7tw5CIIAd3d39O/fX1Z3jX7WoqKi8NZbb8HU1BROTk7ihPlDhw7B09NT2uZ0THp6OtasWQN9fX3o6+tDo9GgVatWWLJkCcaMGaPTN5pl0GmkIiIiMH36dFy7dg1eXl4wMTHRWq/L9x1pSJcvX8aUKVNw4MABcc4TwGu/PImAgAAcP34cmzZtwuuvv47S0lJMmzYN3t7e+PjjjzFjxgypW9QpCoUCvr6+vPnkU4iIiEDXrl2Rk5ODAQMGiKdIt2rVCgsWLJC4O91iaGgohmxbW1tkZ2ejXbt2UKlUyM7Olri7p8M5Oo2UnK9p0JC6d+8OAHj33Xdha2tb7a9lXvul9gYMGICNGzfCwcFBa3l8fDzGjx/Pazs9xn/+8x9MmDABTZo0wX/+859H1uryxE/STb6+vggNDUVwcDAmTZqEtLQ0TJ06FZs3b0ZBQQGOHj0qdYtPjEGnkXrcfVp4SKt2TE1NkZqaCjc3N6lbkbW//voL1tbWUrfRqDk7O+PEiROwsrKCs7PzQ+sUCoVuT/xsQOPGjXvk+m+++aaBOtF9J06cQHFxMfr06YObN29izJgx4okb69ev1+nD0zx01UiZmprCysoKAJCTk4O1a9eitLQUQUFBePXVVyXuTnd06dIFOTk5DDr15LfffsOaNWtw8eJF/PDDD3jxxRexefNmODs745VXXpG6vUbt/psmyvkGig2poKBA63l5eTkyMjJQWFiIvn37StSV7hEEAc2aNUP79u0BAM2aNcMvv/wicVf1h0GnkTl9+jQGDx6MnJwcuLq6Ii4uDv7+/igpKYGenh5iYmLwww8/YOjQoVK3qhO+/vprTJo0CX/++Sc8PDxgaGiotZ5znWpv27ZtCAkJwVtvvYW0tDTxQozFxcVYuHChrH4wNqSqQXVOQq67HTt2VFtWWVmJiIgItGrVSoKOdJMgCHB1dcWZM2fg6uoqdTv1jldGbmRmzJgBT09PHDx4EL1790ZgYCAGDhwItVqNgoICTJw4EYsXL5a6TZ1x8+ZNXLx4EWPHjkWXLl3w0ksvoVOnTuL/Uu0tWLAAX375JdauXasVGLt3746TJ09K2JluWrduHTw8PNCkSRM0adIEHh4e+Prrr6VuS+fp6enhvffeQ0xMjNSt6Aw9PT24urri1q1bUrfyTHBEp5E5fvw49u3bhw4dOuCll17CV199hYiICHFycmRkJK/oWwfjxo1Dp06d8O2339Y4GZlqLysrCz179qy23NzcHIWFhQ3fkA6bM2cOYmJiEBkZKd5x+8iRI3jvvfdw5coVnjH0lC5evIh79+5J3YZOWbJkCd5//32sXr0aHh4eUrdTrxh0Gpnbt2+LV0M2NTWFiYkJLC0txfUWFhbizT7p8a5evYqdO3fCxcVF6lZ0nr29Pf744w+0bNlSa3lycjIPE9TR6tWrsXbtWq17hAUFBaFDhw6IjIxk0KmladOmaT0XBAG5ubn4+eefERoaKk1TOurtt9/G3bt30bFjRxgZGcHY2Fhr/e3btyXq7Okx6DRCD446cBTiyfXt2xf/+9//GHTqwcSJE/Huu+/im2++gUKhwPXr13HkyBFER0fjo48+kro9nVJRUYHOnTtXW+7l5cWRiDo4efKk1s9HPT09NGvWDCtWrMCgQYMk7Ez3xMTEyPZ3DU8vb2T09PQQEBAApVIJANi1axf69u0rXjBQo9EgISGB19Gppa+++goLFizAuHHj4OnpWW0yclBQkESd6aYPP/wQMTEx4sUXlUoloqOj8e9//1viznRLZGQkDA0NsWLFCq3l0dHRKC0txRdffCFRZ7ovLy8Pn3zyCb7++muUlpZK3Y4slJaWVhvh0SUMOo3M2LFja1W3fv36Z9yJPPDCi/Xv7t27OHv2LCorK+Hu7g5TU1OpW9IJ9x9muXfvHjZs2IAWLVqIc+5SUlKQk5OD0aNHY+XKlVK1qRMKCwsxefJkJCYmwsjICDNnzsSUKVPw8ccfY9myZXB3d8e0adO0Dg3So02ePLnGgF1SUoJBgwbhwIEDDd9UPWHQIaJHetxF2arw4myP1qdPn1rVKRQK7Nu37xl3o9siIiKwa9cujBw5Ert378a5c+fg5+eHv//+G3PnzuUVz5+Aq6srRo4cqTU/rKSkBP7+/gD+uYaWrmLQIdm6d+8emjRpgvT0dNmdRdCQ9PT04OTkhE6dOuFRPy5quqYJ0bPg5OSEdevWoX///rh06RJcXFwwdepUfPbZZ1K3prMuX76MV155BdHR0XjvvfdQXFwMPz8/GBgYYPfu3dXut6hLOBmZZMvAwABOTk48PPWUJk2ahLi4OFy6dAnjxo3D22+/rXUmIFFDu379Otzd3QH8cwPPJk2aYPz48RJ3pducnZ2xZ88e9O7dG3p6eoiLi4NSqUR8fLxOhxyAIzokc+vXr8f333+PLVu28JfzU9BoNNi+fTu++eYbHD58GIMGDUJYWBh8fX1le6bGs/T3339j5cqV2L9/P/Lz81FZWam1nhdgfDR9fX3k5eWhWbNmAAAzMzOcOnXqkfcQo9pJSUlB//790a1bN/z88886PQm5CoMOyVqnTp3wxx9/oLy8HE5OTtX+MuEvlLq7evUqNmzYgE2bNqG8vBxnz57lhOQ6Cg4ORlJSEl5//fUaL2Q5d+5ciTrTDY87O7XK9u3bpWhPZ3Tq1KnGP1SuXr0KGxsbrZCjyz8reeiKZI33BKt/CoUCCoUCgiBUG4mg2omPj8cvv/yCHj16SN2KThozZozW87fffluiTnTb8/LzkSM6RPRY9x+6Sk5ORmBgIMaOHQt/f/9HnsJPNXN3d0dcXBxvKkvUAPgTimSvsLAQX3/9NWbPni1exvzkyZP4888/Je5MN0RERMDe3h6ffvopAgMDce3aNXz//fcYOHAgQ84TWr58OWbOnImrV69K3Qo95/T09KCvr1/tYWFhAW9vb1kc/uOIDsnaqVOn0L9/f6hUKly5cgVZWVlo1aoV5syZg6tXr2LTpk1St9jo6enpoUWLFg89nl9FDj8QG8rNmzcxYsQIHDp0CE2bNq12xW5dvq8Q6ZYff/yxxv+uCwsLcezYMaxfvx4bN27EG2+8IUF39YNBh2Stf//+ePnll7FkyRKYmZnhf//7H1q1aoXDhw8jODgYV65ckbrFRi80NLRWZ1bxat21179/f2RnZyMsLKzGycgPzkEhksoXX3yBTZs24ejRo1K38sQYdEjWVCoVTp48idatW2sFnatXr8LNzU28ZxNRQ2ratCmOHDmCjh07St0K0SNduHABXbt2RUFBgdStPDEeYCdZa9KkCYqKiqotz8rKEq/BQdTQ2rZtyxtOkk4oLS1FkyZNpG7jqTDokKwNGTIE8+fPR3l5OYB/To3Ozs7GrFmzMHz4cIm7o+fV4sWLMX36dBw4cAC3bt1CUVGR1oOosVi7di06deokdRtPhYeuSNaKioowcOBAnDlzBsXFxXBwcEBeXh68vb11/v4tpLuqzlZ7cG6OIAhQKBS8bQk1mGnTptW4XK1W48SJE7h48SJ+++03nQ47DDr0XNi/fz9SU1NRWVmJl19+Gf3795e6JXqOHTx48JHrefdtaih9+vSpcbm5uTnatm2LiIgIODk5NXBX9YtBh2SptLQUv/76KwIDAwEAs2fPhkajEdcbGBhg/vz5On/smYiIHo23gCBZ2rRpE37++Wcx6KxatQrt27cX791y7tw52Nvb47333pOyTXrO3b17F9nZ2SgrK9NazismE9UfjuiQLPXs2RPvvfceXnvtNQDQOrUcALZs2YIvvvgCR44ckbJNek7dvHkTY8eOxe7du2tczzk6RPWHZ12RLJ0/fx5t2rQRnzdp0kTrdgVdu3bF2bNnpWiNCFFRUSgoKEBKSgqMjY2RkJCAjRs3wtXVFTt37pS6PSJZ4aErkiW1Wg0Dg///et+8eVNrfWVlpdacHaKGtG/fPvz000/o0qUL9PT04OTkhAEDBsDc3ByLFi3CoEGDpG6RSDY4okOy1Lx5c2RkZDx0/alTp9C8efMG7Ijo/5WUlMDGxgYAYGlpKQZxT09PnDx5UsrWiGSHQYdkaeDAgfjoo49qvMVDaWkpPv74Y/7VTJJxc3NDVlYWAOCll17CmjVr8Oeff+LLL7+Evb29xN0RyQsnI5Ms3bhxAy+99BKMjIwwZcoUtGnTBgqFAufOncOqVatw7949pKWlwdbWVupW6TkUGxuL8vJyhIaGIi0tDX5+frh16xaMjIywYcMGjBw5UuoWiWSDQYdk6/Lly3jnnXeQlJSEqq+5QqHAgAED8N///lc8A4tIanfv3sW5c+fQokULWFtbS90Okaww6JDs3b59G3/88QcAwMXFBZaWlhJ3RM+7Q4cOoW3btuI8nSrl5eU4cuQIevbsKVFnRPLDoENE1MD09PRga2uL7du3w8fHR1x+48YNODg48Do6RPWIk5GJiCTw5ptvol+/ftiwYYPWcv7tSVS/GHSIiBqYQqHA7NmzsWXLFkRGRmLatGla88iIqP4w6BARNbCqUDNs2DAcOnQIP/zwAwICAlBYWChtY0QyxKBDRCShTp064dixYygsLES/fv2kbodIdhh0iIga2JgxY2BsbCw+t7Ozw8GDB9GvXz+0aNFCws6I5IdnXREREZFs8aaeREQSKCwsxLp165CZmQmFQoF27dohLCwMKpVK6taIZIUjOkREDezEiRPw8/ODsbExunbtCkEQcOLECZSWlmLPnj3w8vKSukUi2WDQISJqYK+++ipcXFywdu1aGBj8M7B+7949jB8/HpcuXcKhQ4ck7pBIPhh0iIgamLGxMdLS0tC2bVut5WfPnkXnzp1x9+5diTojkh+edUVE1MDMzc2RnZ1dbXlOTg7MzMwk6IhIvhh0iIga2MiRIxEWFobvvvsOOTk5uHbtGuLi4jB+/HiMGjVK6vaIZIVnXRERNbBly5ZBoVBg9OjRuHfvHgRBgJGREd555x0sXrxY6vaIZIVzdIiIJHL37l1cvHgRgiDAxcUFTZs2lbolItnhiA4RUQMZN25creq++eabZ9wJ0fODIzpERA1ET08PTk5O6NSpEx71o3fHjh0N2BWRvDHoEBE1kIiICMTFxaFFixYYN24c3n77bVhaWkrdFpGsMegQETUgjUaD7du345tvvsHhw4cxaNAghIWFwdfXFwqFQur2iGSHQYeISCJXr17Fhg0bsGnTJpSXl+Ps2bMwNTWVui0iWeF1dIiIJKJQKKBQKCAIAiorK6Vuh0iWGHSIiBqQRqPBt99+iwEDBsDNzQ2nT5/GqlWrkJ2dzdEcomeAp5cTETWQ+ycjjx07FnFxcbCyspK6LSJZ4xwdIqIGoqenhxYtWqBTp06PnHi8ffv2BuyKSN44okNE1EBGjx7NM6uIGhhHdIiIiEi2OBmZiIiIZItBh4iIiGSLQYeIiIhki0GHiIiIZItBh4iIiGSLQYeInktXrlyBQqFAenq61K0Q0TPEoENE9AhlZWVSt0BET4FBh4gkUVlZiU8//RQuLi5QKpVo0aIFPvnkEwDA6dOn0bdvXxgbG8PKygoTJkzAnTt3xNf27t0bUVFRWu83dOhQhIaGis9btmyJhQsXYty4cTAzM0OLFi3w1VdfieudnZ0BQLxKce/evQEAoaGhGDp0KBYtWgQHBwe0adMG8+fPh6enZ7XP4OXlhY8++qie9ggRPQsMOkQkidmzZ+PTTz/FnDlzcPbsWWzduhW2tra4e/cu/P39YWFhgePHj+P777/H3r17MWXKlDpvY/ny5ejcuTPS0tIQERGBd955B+fOnQMAHDt2DACwd+9e5Obmat124ddff0VmZiaSkpLw888/Y9y4cTh79iyOHz8u1pw6dQppaWla4YqIGh/eAoKIGlxxcTE+//xzrFq1CmPGjAEAtG7dGq+88grWrl2L0tJSbNq0CSYmJgCAVatWYfDgwfj0009ha2tb6+0MHDgQERERAICZM2ciJiYGBw4cQNu2bdGsWTMAgJWVFezs7LReZ2Jigq+//hpGRkbiMj8/P6xfvx5dunQBAKxfvx69evVCq1atnnxHENEzxxEdImpwmZmZ0Gg06NevX43rOnbsKIYcAOjRowcqKyuRlZVVp+106NBB/LdCoYCdnR3y8/Mf+zpPT0+tkAMA4eHh+Pbbb/H333+jvLwcsbGxGDduXJ36IaKGxxEdImpwxsbGD10nCMJDb3xZtVxPTw8P3qavvLy8Wr2hoWG111dWVj62v/tDVpXBgwdDqVRix44dUCqV0Gg0GD58+GPfi4ikxREdImpwrq6uMDY2xq+//lptnbu7O9LT01FSUiIu+/3336Gnp4c2bdoAAJo1a4bc3FxxfUVFBTIyMurUQ9WITUVFRa3qDQwMMGbMGKxfvx7r16/Hm2++iaZNm9Zpm0TU8DiiQ0QNrkmTJpg5cyZmzJgBIyMj9OjRAzdv3sSZM2fw1ltvYe7cuRgzZgzmzZuHmzdvIjIyEiEhIeL8nL59+2LatGmIj49H69atERMTg8LCwjr1YGNjA2NjYyQkJKB58+Zo0qQJVCrVI18zfvx4tGvXDsA/4YuIGj+O6BCRJObMmYPp06fjo48+Qrt27TBy5Ejk5+ejadOm2LNnD27fvo0uXbrg9ddfR79+/bBq1SrxtePGjcOYMWMwevRo9OrVC87OzujTp0+dtm9gYID//Oc/WLNmDRwcHDBkyJDHvsbV1RXdu3eHm5sbunXrVufPTEQNTyE8eKCbiIhqJAgC2rZti4kTJ2LatGlSt0NEtcBDV0REtZCfn4/Nmzfjzz//xNixY6Vuh4hqiUGHiKgWbG1tYW1tja+++goWFhZSt0NEtcSgQ0RUCzzKT6SbOBmZiIiIZItBh4iIiGSLQYeIiIhki0GHiIiIZItBh4iIiGSLQYeIiIhki0GHiIiIZItBh4iIiGTr/wBcyytcPT1UKQAAAABJRU5ErkJggg==", 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      " ] @@ -469,7 +470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/docs/_sources/notebooks/class_3/week_2/10_pandas_series.ipynb b/docs/_sources/notebooks/class_3/week_2/10_pandas_series.ipynb index a8b7c855..b3184b0c 100644 --- a/docs/_sources/notebooks/class_3/week_2/10_pandas_series.ipynb +++ b/docs/_sources/notebooks/class_3/week_2/10_pandas_series.ipynb @@ -302,10 +302,11 @@ "\n", "Before we dive too far into Series manipulations, it's important to talk about datatypes. Every Series, as we will see, has a \"dtype\" (short for datatype). The `dtype` of a Series is important to understand because a Series' `dtype` determines what manipulations you can apply to that series. \n", "\n", - "There are, broadly, two types of Series: \n", + "There are, broadly, three types of Series: \n", "\n", "- Numeric: these hold numbers that `pandas` understands are numbers. Specific numeric datatypes include things like `int64`, and `int32` (integers), or `float64` and `float32` (floating point numbers).\n", - "- Object: these are Series that can hold any Python object, like strings, numbers, Sets, you name it. They have dtype `O` for \"objects\". They are flexible, but also very slow and actually harder to work with." + "- Object: these are Series that can hold any Python object, like strings, numbers, sets, you name it. They have dtype `O` for \"objects\". They are flexible, but also very slow and actually harder to work with.\n", + "- Categorical: this is a special data type (analogous to factors in R, if that means anything to you) that we'll discuss in a future reading. Don't worry about them for now." ] }, { diff --git a/docs/_sources/notebooks/class_3/week_2/15_manipulating_series.ipynb b/docs/_sources/notebooks/class_3/week_2/15_manipulating_series.ipynb index ed5e05c9..a0ec1323 100644 --- a/docs/_sources/notebooks/class_3/week_2/15_manipulating_series.ipynb +++ b/docs/_sources/notebooks/class_3/week_2/15_manipulating_series.ipynb @@ -495,7 +495,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb b/docs/_sources/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb index ab7ed7e0..b7936bba 100644 --- a/docs/_sources/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb +++ b/docs/_sources/notebooks/class_3/week_2/16_indexing_with_brackets.ipynb @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -152,7 +152,7 @@ "dtype: object" ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -173,7 +173,7 @@ "'dog'" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -209,7 +209,7 @@ "dtype: int64" ] }, - "execution_count": 16, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -220,16 +220,24 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_22245/1336667577.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " attendance[0]\n" + ] + }, { "data": { "text/plain": [ "132" ] }, - "execution_count": 17, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -245,6 +253,21 @@ "As a result, for someone reading your code to understand what `[]` is doing, they have to also be keeping track of the data type of the index of the pandas object being manipulated. And while you may think \"yeah, but my code is just for me, so who cares?\" let me let you in on a secret: that \"other person\" who may struggle to understand your code maybe you when you come back to your code in a week to fix something and no longer remember everything that feels obvious to you right now!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, as you may have noticed from the `FutureWarning` generated when we ran that code:\n", + "\n", + "```python\n", + "FutureWarning: Series.__getitem__ treating keys as positions is deprecated. \n", + "In a future version, integer keys will always be treated as labels (consistent \n", + "with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + "```\n", + "\n", + "the developers of pandas concur that this behavior is *very* weird, and at some point soon it will be depreciated (probably in `pandas 3.0`). But for now, it remains a \"gotcha\" to be aware of!" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -273,7 +296,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_2/20_Exercise_series.ipynb b/docs/_sources/notebooks/class_3/week_2/20_Exercise_series.ipynb index 0996b043..deddccf5 100644 --- a/docs/_sources/notebooks/class_3/week_2/20_Exercise_series.ipynb +++ b/docs/_sources/notebooks/class_3/week_2/20_Exercise_series.ipynb @@ -12,13 +12,22 @@ "\n", "```python\n", "import pandas as pd\n", - "gdppercap = pd.Series([34605, 34493, 12393, 44200, 10041, \n", - " 58138, 4709, 49284, 10109, 42536],\n", - " index=['Bahrain', 'Belgium', 'Bulgaria',\n", - " 'Ireland', 'Macedonia', 'Norway', \n", - " 'Paraguay', 'Singapore', \n", - " 'South Africa', 'Switzerland']\n", - " ) \n", + "\n", + "gdppercap = pd.Series(\n", + " [34605, 34493, 12393, 44200, 10041, 58138, 4709, 49284, 10109, 42536],\n", + " index=[\n", + " \"Bahrain\",\n", + " \"Belgium\",\n", + " \"Bulgaria\",\n", + " \"Ireland\",\n", + " \"Macedonia\",\n", + " \"Norway\",\n", + " \"Paraguay\",\n", + " \"Singapore\",\n", + " \"South Africa\",\n", + " \"Switzerland\",\n", + " ],\n", + ")\n", "```" ] }, @@ -112,15 +121,32 @@ "To test this hypothesis, let's do a small analysis of the dynamics of income inequality in our sample. Create the following Series in your Python session, which provides the average growth rate of GDP per capita for all the countries in our sample from 2000 to 2018. \n", "\n", "```python\n", - "avg_growth = pd.Series([-0.29768835, 0.980299584, 4.52991925, \n", - " 3.686556736, 2.621416804, 0.775132075, \n", - " 2.015489468, 3.345793635, 1.349993318, \n", - " 0.982775018],\n", - " index=['Bahrain', 'Belgium', 'Bulgaria', \n", - " 'Ireland', 'Macedonia', 'Norway', \n", - " 'Paraguay', 'Singapore', \n", - " 'South Africa', 'Switzerland']\n", - " )\n", + "avg_growth = pd.Series(\n", + " [\n", + " -0.29768835,\n", + " 0.980299584,\n", + " 4.52991925,\n", + " 3.686556736,\n", + " 2.621416804,\n", + " 0.775132075,\n", + " 2.015489468,\n", + " 3.345793635,\n", + " 1.349993318,\n", + " 0.982775018,\n", + " ],\n", + " index=[\n", + " \"Bahrain\",\n", + " \"Belgium\",\n", + " \"Bulgaria\",\n", + " \"Ireland\",\n", + " \"Macedonia\",\n", + " \"Norway\",\n", + " \"Paraguay\",\n", + " \"Singapore\",\n", + " \"South Africa\",\n", + " \"Switzerland\",\n", + " ],\n", + ")\n", "```" ] }, @@ -152,7 +178,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb b/docs/_sources/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb index 62c8f6cf..c2d96c63 100644 --- a/docs/_sources/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb +++ b/docs/_sources/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.ipynb @@ -162,7 +162,7 @@ "source": [ "## `[]` Square brackets\n", "\n", - "As with Series, single square brackets in `pandas` change their behavior depending on the values you pass them. Again, it is worth emphasizing that there is *nothing* that one can do with square brackets that you can't do with `.loc` and `.iloc`, so if they seem to strange, you don't have to use them. \n", + "As with Series, single square brackets in `pandas` change their behavior depending on the values you pass them. Again, it is worth emphasizing that there is *nothing* that one can do with square brackets that you can't do with `.loc` and `.iloc`, so if they seem too strange, you don't have to use them. \n", "\n", "With that said, as summarized below, `[]` is actually much safer on DataFrames than on Series. \n", "\n", @@ -833,7 +833,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_14379/4041630850.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_22305/4041630850.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", " world.rank_doubled = range(0, 2 * len(world), 2)\n" ] }, @@ -963,7 +963,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_3/00_plaintext_files.ipynb b/docs/_sources/notebooks/class_3/week_3/00_plaintext_files.ipynb index 08baadd3..57dbdfac 100644 --- a/docs/_sources/notebooks/class_3/week_3/00_plaintext_files.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/00_plaintext_files.ipynb @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -146,13 +146,14 @@ "5 Ukraine C&E Europe 7271 16" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", + "\n", "pd.read_csv(\"data/world-very-small.csv\")" ] }, @@ -171,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -180,13 +181,12 @@ "{'pets': ['dogs', 'cats'], 'wild animals': ['zebras', 'penguins']}" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\n", "my_dict = {\"pets\": [\"dogs\", \"cats\"], \"wild animals\": [\"zebras\", \"penguins\"]}\n", "my_dict" ] @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -225,9 +225,9 @@ "\n", "# Print out what the json\n", "# representation of our dictionary would look like.\n", - "# Note that `indent=4` results in \n", + "# Note that `indent=4` results in\n", "# this nice looking indentation—without that option\n", - "# the contents all appear on one line. \n", + "# the contents all appear on one line.\n", "print(json.dumps(my_dict, indent=4))" ] }, @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -314,7 +314,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/docs/_sources/notebooks/class_3/week_3/05_binary_files.ipynb b/docs/_sources/notebooks/class_3/week_3/05_binary_files.ipynb index 2ed888c3..a2e5aab1 100644 --- a/docs/_sources/notebooks/class_3/week_3/05_binary_files.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/05_binary_files.ipynb @@ -27,6 +27,7 @@ "outputs": [], "source": [ "import pandas as pd\n", + "\n", "world = pd.read_csv(\"data/world-very-small.csv\")\n", "world.to_stata(\"data/world-very-small.dta\")" ] diff --git a/docs/_sources/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb b/docs/_sources/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb index 98caa4f3..9ff0848d 100644 --- a/docs/_sources/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/10_views_and_copies_numpy_review.ipynb @@ -206,7 +206,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb b/docs/_sources/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb index a0159db2..4c773bf2 100644 --- a/docs/_sources/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/15_views_and_copies_in_pandas.ipynb @@ -361,11 +361,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Adding pd.set_option(\"mode.copy_on_write\", True) Easily\n", + "## Adding pd.set_option(\"mode.copy_on_write\", True) Easily\n", "\n", - "The easiest way to consistently add the `pd.set_option(\"mode.copy_on_write\", True)` is probably to create a custom import snippet — basically, a short keyboard shortcut that automatically import pandas, numpy, *and* sets this setting.\n", + "The easiest way to consistently add the `pd.set_option(\"mode.copy_on_write\", True)` is probably to create a custom \"snippet\" — basically, a keyboard shortcut that your editor expands into a block of text you type regularly.\n", "\n", - "For example, I've set up so when I type `ipd` followed by `tab`, I get:\n", + "For example, I've set up my VS Code installation so that when I type `ipd` followed by pressing `tab`, I get:\n", "\n", "```python\n", "import pandas as pd\n", @@ -373,9 +373,10 @@ "\n", "pd.set_option(\"mode.copy_on_write\", True)\n", "```\n", + "\n", "(in my head, `ipd` is short for **i**mport pandas as **pd** --> `ipd`)\n", "\n", - "To set up your own, just look for documentation on adding a snippet to your preferred editor. Use VS Code? [Here's how to do it!](https://code.visualstudio.com/docs/editor/userdefinedsnippets#_create-your-own-snippets) My snippet is:\n", + "To set up your own, just look for documentation on adding a snippet to your preferred editor. Use VS Code? [Here's how to do it!](https://code.visualstudio.com/docs/editor/userdefinedsnippets#_create-your-own-snippets) My VS Code snippet is:\n", "\n", "```json\n", "\"import pandas, numpy, activate CoW\": {\n", @@ -390,7 +391,7 @@ "}\n", "```\n", "\n", - "(note the need to escape quotation marks)" + "(note the need to escape quotation marks by putting a backslash right before them)" ] }, { diff --git a/docs/_sources/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb b/docs/_sources/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb index d263ecd3..041fdbeb 100644 --- a/docs/_sources/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/17_views_and_copies_in_pandas_wo_CoW.ipynb @@ -379,6 +379,14 @@ "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/2520817220.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '3.14' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.iloc[1, 1] = 3.14\n" + ] + }, { "data": { "text/html": [ @@ -628,6 +636,14 @@ "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/673548005.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'a' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.iloc[0, 0] = \"a\"\n" + ] + }, { "data": { "text/html": [ @@ -771,7 +787,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_64429/164737205.py:1: SettingWithCopyWarning: \n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_24865/164737205.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -812,237 +828,6 @@ "\n", "Which leads me to what I will admit is an infuriating piece of advice to have to offer: **if you take a subset for any purpose other than immediately analyzing, you should add .copy() to that subsetting.** Seriously. Just when in doubt, `.copy()`." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### An Aside: No, the problem doesn't *only* emerge when you change the data type of a column\n", - "\n", - "Some readers may have noticed a pattern in the illustrations I've presented, and from them developed an intuition that a column will only lose it's \"view-ness\" when one changes the datatype of that column. Though this will always cause problems, it is not the only place problems can arise. What follows isn't something you *need* to know, but may be useful if you're deeply interested. \n", - "\n", - "In the examples above, each column was it's own object, and so behaved independently. But this is not always the case in `pandas`. If a DataFrame is created from a single numpy matrix with multiple columns, `pandas` will try to be efficient by just keeping that matrix intact. \n", - "\n", - "But as a result, if you do something (like change the type) of *one* of the columns that is tied to that matrix, `pandas` will create new arrays to back *all* the columns that were once tied to the matrix. As a result, a view of a single column can stop being a view due to changes to a different column. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1],\n", - " [2, 3],\n", - " [4, 5]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "my_matrix = np.arange(6).reshape(3, 2)\n", - "my_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " a b\n", - "0 0 1\n", - "1 2 3\n", - "2 4 5" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame(my_matrix, columns=[\"a\", \"b\"])\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 -42\n", - "1 2\n", - "2 4\n", - "Name: a, dtype: int64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Column_a starts of it's life as a view\n", - "column_a = df[\"a\"]\n", - "df.iloc[0, 0] = -42\n", - "column_a" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
      \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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      " - ], - "text/plain": [ - " a b\n", - "0 -42 new entry\n", - "1 2 3\n", - "2 4 5" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# But if I make a change to column b...\n", - "df.loc[0, \"b\"] = \"new entry\"\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 999999\n", - "1 2\n", - "2 4\n", - "Name: a, dtype: int64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Then the same type of change to column a of `df` will no longer\n", - "# be shared\n", - "\n", - "df.iloc[0, 0] = 999999\n", - "column_a" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, as noted before: it is best to never to try and infer whether a subset of a DataFrame if a view or a copy until you have *explicitly* made a copy with `.copy()`." - ] } ], "metadata": { @@ -1061,7 +846,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_3/20_cleaning_identifying.ipynb b/docs/_sources/notebooks/class_3/week_3/20_cleaning_identifying.ipynb index 6cf1a269..9a4b21bc 100644 --- a/docs/_sources/notebooks/class_3/week_3/20_cleaning_identifying.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/20_cleaning_identifying.ipynb @@ -15,7 +15,7 @@ "\n", "These parts of data science are not nearly as cool or widely discussed as new machine learning packages or the latest learning algorithm, but as anyone who has done data science in the real world will tell you, data cleaning and wrangling will take up a very large portion of your working life. Indeed, it is often said that \"data scientists spend 80% of their time cleaning data and only 20% doing analyses\" (or, in the version I prefer, \"data scientists spend 80% of their time cleaning data and the other 20% of their time complaining about cleaning data.\") That's probably a little high on average—a [recent survey by Anaconda](https://www.anaconda.com/state-of-data-science-2020) found on average data scientists reported data wrangling took up about 45% of their time—but what is undisputed is its importance to being a successful data scientist.\n", "\n", - "So in this reading we will discuss tools in pandas for identifying problems before we turn to tools for correcting problems in our next reading!" + "So in this reading, we will discuss tools in pandas for identifying problems before we turn to tools for correcting problems in our next reading!" ] }, { @@ -29,7 +29,7 @@ "\n", "### Your Challenge\n", "\n", - "Suppose you have been asked to calculate the overall US employment rate and average incomes for men and women, as well as the employment rate and average income of people in their 30s and 40s. To accomplish this task, you have been handed a 0.1% sample of the US American Community Survey—a representative survey of residents of the United States published by the US Census Bureau. This is real data, and so like all real data comes with lots of quirk and oddities we'll need to navigate.\n", + "Suppose you have been asked to calculate the overall US employment rate and average incomes for men and women, as well as the employment rate and average income of people in their 30s and 40s. To accomplish this task, you have been handed a 0.1% sample of the US American Community Survey—a representative survey of residents of the United States published by the US Census Bureau. This is real data, and so like all real data comes with lots of quirks and oddities we'll need to navigate.\n", "\n", "Moreover, you've even been told that the variable for gender in the dataset is `sex`, the variable for age is `age`, the variable for income is `inctot`, and the variable for whether someone is employed is `empstat`. Should be pretty easy, right? Let's do it!\n", "\n", @@ -43,6 +43,9 @@ "outputs": [], "source": [ "import pandas as pd\n", + "import numpy as np\n", + "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", "\n", "# Here we load the American Community Survey data.\n", "# Note that here we're using a data loading trick\n", @@ -55,12 +58,12 @@ ")\n", "\n", "# For this exercise, we'll focus on just these five variables,\n", - "# which from the official documentation provided by the US census \n", + "# which from the official documentation provided by the US census\n", "# bureau we know consist of the year the survey was conducted,\n", - "# the gender of the respondent, their age, their total income, \n", + "# the gender of the respondent, their age, their total income,\n", "# and their employment status.\n", "#\n", - "# You can find the documenation for this data here: \n", + "# You can find the documenation for this data here:\n", "\n", "acs = acs[[\"year\", \"sex\", \"age\", \"inctot\", \"empstat\"]]" ] @@ -74,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -83,15 +86,34 @@ "1723646.2703978634" ] }, - "execution_count": 2, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Simple output:\n", "acs[\"inctot\"].mean()" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The average income of Americans is $1,723,646.27\n" + ] + } + ], + "source": [ + "# Made pretty and better formatted:\n", + "print(f\"The average income of Americans is ${acs['inctot'].mean():,.2f}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -244,12 +266,12 @@ "import numpy.random as npr\n", "\n", "# The rows that `.sample()` picks will be random.\n", - "# Normally that's advantageous, but in \n", - "# this case it results in getting \n", + "# Normally that's advantageous, but in\n", + "# this case it results in getting\n", "# a slightly different set of rows every time\n", - "# I run this code, so I'm going to set a \n", - "# seed—something we talked about in Course 2 \n", - "# on numpy—so that the rows being picked \n", + "# I run this code, so I'm going to set a\n", + "# seed—something we talked about in Course 2\n", + "# on numpy—so that the rows being picked\n", "# stay the same every time I run this code.\n", "npr.seed(42)\n", "\n", @@ -264,7 +286,7 @@ "\n", "We can also begin to see the causes of some of our problems. For example, it's not clear to see why `acs[\"inctot\"].mean()` didn't get what we wanted. Why? Because in five random rows, we've already gotten three people who report making 9,999,999 dollars, and all three are less than 12 years old. So clearly the variable doesn't just contain actual income data! \n", "\n", - "(What are these `9999999` values? They're something called a \"Sentinel Value\"—a very old-school way of representing missing data, in which a special value that would never occur naturally was used to indicate when the field was \"missing.\" It was used back in the day when computers didn't use data types that had an actual special way of representing missing data, and some people still use it for backwards compatibility. We'll talk more about these and other ways of representing missing data in an upcoming reading.)\n", + "(What are these `9999999` values? They're something called a \"Sentinel Value\"—a very old-school way of representing missing data, in which a special value that would never occur naturally was used to indicate when the field was \"missing.\" It was used back in the day when computers didn't use data types that had an actual special way of representing missing data, and some people still use it for backward compatibility. We'll talk more about these and other ways of representing missing data in an upcoming reading.)\n", "\n", "Similarly, we can also see that while some observations of `empstat` make sense (`employed` or `not in labor force`), there are also values of `n/a` we need to figure out. `n/a` is a common shorthand for \"Not Applicable\", meaning it didn't make sense to ask the respondent that question. Since we see those appearing for respondents who are under 18, that probably means that `n/a` was filled in for all children, but that's something we'll have to investigate more. But it definitely tells us why we can't calculate the employment rate by running `(acs[\"empstat\"] == \"employed\").mean()`, since all the `n/a` values would pull down the share of respondents who are employed even though we don't consider children when calculating the employment rate!\n", "\n", @@ -287,11 +309,12 @@ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "n/a 57843\n", "unemployed 7727\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, "execution_count": 5, @@ -300,7 +323,7 @@ } ], "source": [ - "acs.empstat.value_counts()\n" + "acs.empstat.value_counts()" ] }, { @@ -318,11 +341,12 @@ { "data": { "text/plain": [ + "empstat\n", "employed 0.466320\n", "not in labor force 0.328134\n", "n/a 0.181324\n", "unemployed 0.024222\n", - "Name: empstat, dtype: float64" + "Name: proportion, dtype: float64" ] }, "execution_count": 6, @@ -340,7 +364,7 @@ "source": [ "From this, we can see that in addition to the `n/a` observations that appear to be children, we also have two categories of people who aren't employed: `unemployed` and `not in labor force`. A little googling will tell us that `not in the labor force` connotes someone who is not looking for work, and is thus not considered unemployed when calculating the employment (or unemployment) rate—it turns out that the unemployment rate is only meant to tell us about the share of people *who want to work* who haven't found jobs, not the proportion of the overall population with a job.\n", "\n", - "So now that we know that, we can calculate our unemployment rate by subsetting for people in those two categories, then calculating our average:" + "So now that we know that, we can calculate our unemployment rate by subsetting for people in those two categories, and then calculating our average:" ] }, { @@ -350,7 +374,9 @@ "outputs": [], "source": [ "# Employment Rate\n", - "emp_rate = (acs.loc[acs[\"empstat\"].isin([\"employed\", \"unemployed\"]), \"empstat\"] == \"employed\").mean()" + "emp_rate = (\n", + " acs.loc[acs[\"empstat\"].isin([\"employed\", \"unemployed\"]), \"empstat\"] == \"employed\"\n", + ").mean()" ] }, { @@ -388,18 +414,18 @@ "metadata": {}, "source": [ "Much better!\n", - "\n", - "Of course `.value_counts()` stops being useful when the number of unique values a variable takes on gets too big. For example, it isn't that useful to look at `.value_counts()` of total income, because, as we see below, it takes on over 8,000 unique values! Yes we can see there's a cluster at 0 and a cluster at our old friend `9999999`, but beyond that it isn't very helpful. " + "Of course, `.value_counts()` stops being useful when the number of unique values a variable takes on gets too big. For example, it isn't that useful to look at `.value_counts()` of total income, because, as we see below, it takes on over 8,000 unique values! Yes, we can see there's a cluster at 0 and a cluster at our old friend `9999999`, but beyond that it isn't very helpful. " ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "inctot\n", "9999999 53901\n", "0 33679\n", "30000 4778\n", @@ -411,10 +437,10 @@ "57760 1\n", "200310 1\n", "505400 1\n", - "Name: inctot, Length: 8471, dtype: int64" + "Name: count, Length: 8471, dtype: int64" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -429,7 +455,7 @@ "source": [ "**.describe()**\n", "\n", - "So for continuous numeric variables, we instead use `.describe()`, which provides summary statistics for a variable. Statistics is not required for this course, so not everything here will be familiar to all students, but just looking at the minimum value in the data (`min`), the maximum value in the data (`max`), and the the average value (`mean`) can tell you a lot:" + "For continuous numeric variables, we instead use `.describe()`, which provides summary statistics for a variable. Statistics is not required for this course, so not everything here will be familiar to all students, but just looking at the minimum value in the data (`min`), the maximum value in the data (`max`), and the average value (`mean`) can tell you a lot:" ] }, { @@ -457,7 +483,7 @@ } ], "source": [ - "acs[\"inctot\"].describe()\n" + "acs[\"inctot\"].describe()" ] }, { @@ -499,7 +525,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb b/docs/_sources/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb index 6c9e0f00..ff922c07 100644 --- a/docs/_sources/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/30_cleaning_editing_globally.ipynb @@ -18,7 +18,7 @@ "- You can use a general cleaning function, or \n", "- you can directly modify problematic values. \n", "\n", - "In this reading, I'll provide an overview of how to do make global edits before we learn to edit specific locations in the next reading.\n", + "In this reading, I'll provide an overview of how to make global edits before we learn to edit specific locations in the next reading.\n", "\n", "In these exercises, we'll work with our `world-very-small` toy dataset. We're doing so because working with a small dataset makes it easier to see everything the functions we use are doing. However, in some of the examples that follow we'll do things that seem a little silly given the size of the dataset (like print out all values of a variable with `.value_counts()` even though we can see all the values!), but we'll do they anyway since you would need them when working with any real data. " ] @@ -139,10 +139,10 @@ "import pandas as pd\n", "\n", "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", - "smallworld\n" + "smallworld" ] }, { @@ -255,7 +255,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Voila! Replace works for numbers too -- so suppose we learned that Polity had mis-coded all scores of 15 as 16, so we wanted to change all 16s to 15s:" + "Voila! Replace works for numbers too—suppose we learned that the Polity organization had mis-coded all scores of 15 as 16, so we wanted to change all 16s to 15s:" ] }, { @@ -470,7 +470,12 @@ } ], "source": [ - "changes = {\"Mexico\": \"México\", \"Russia\": \"Российская Федерация\", \"Ukraine\": \"Україна\", \"Brazil\": \"Brasil\"}\n", + "changes = {\n", + " \"Mexico\": \"México\",\n", + " \"Russia\": \"Российская Федерация\",\n", + " \"Ukraine\": \"Україна\",\n", + " \"Brazil\": \"Brasil\",\n", + "}\n", "\n", "smallworld[\"country\"] = smallworld[\"country\"].replace(changes)\n", "\n", @@ -483,9 +488,9 @@ "source": [ "**.replace() with Regular Expressions**\n", "\n", - "Finally, `.replace()` also works with regular expressions when one passes the `regex=True` keyword argument. We haven't covered regular expressions in this course, but basically they are a way of telling Python you want to look for *patterns* to replace instead of exact matches. If you don't know about regular expressions, feel free to skip this sub-section, but since some students may be familiar with them, here's a quick use of their overview:\n", + "Finally, `.replace()` also works with regular expressions when one passes the `regex=True` keyword argument. We haven't covered regular expressions in this course, but essentially they are a way of telling Python you want to look for *patterns* in text to replace instead of exact matches. If you don't know about regular expressions, feel free to skip this sub-section, but since some students may be familiar with them, here's a quick use of their overview:\n", "\n", - "Basically, when one passes `regex=True` to `.replace`, `.replace()` will treat the first argument as a regular expression rather than a literal value. Note that this will obviously only work when string values are provided.\n", + "When one passes `regex=True` to `.replace`, `.replace()` will treat the first argument as a regular expression rather than a literal value. This will only work when string values are provided.\n", "\n", "(`.replace(regex=True)` uses `re.sub` on each observation in a Series under the hood, and so supports all the same syntax as `re.sub`.)\n", "\n", @@ -817,7 +822,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb b/docs/_sources/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb index 8685d509..5f853028 100644 --- a/docs/_sources/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/32_cleaning_editing_specific_locations.ipynb @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see, when we write `my_list[2]` on the *left* side of the assignment operator (a single equals sign), then whatever we put on the right side of the assignment operator is being assigned *into the entry with index 2* of the list. \n", + "As we can see, when we write `my_list[2]` on the *left* side of the assignment operator (a single equals sign), whatever we put on the right side of the assignment operator is assigned _into_ the entry with index 2 of the list. \n", "\n", "As you may recall, this same logic can also be extended to two dimensions in `numpy` arrays. Consider the following: " ] @@ -208,6 +208,8 @@ "source": [ "import pandas as pd\n", "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", + "\n", "df = pd.DataFrame({\"a\": [1, 2, 3, 4], \"b\": [5, 6, 7, 8]})\n", "df" ] @@ -291,7 +293,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But this alone is only kinda useful. After all, our datasets are usually very large, and we rarely want to make modifications to cells whose row numbers we already know. But thankfully, in `pandas` we can pass boolean vectors to `.loc` to identify all rows that meet certain conditions and assign values to those specific cells. For example, suppose we wanted to set column `b` to 0 for all rows where column `a` is even. We could do:" + "But this alone is only kinda useful. After all, our datasets are usually very large, and we rarely want to make modifications to cells whose row numbers we already know. Thankfully, in `pandas` we can pass boolean vectors to `.loc` to identify all rows that meet certain conditions and assign values to those specific cells. For example, suppose we wanted to set column `b` to 0 for all rows where column `a` is even. We could do:" ] }, { @@ -374,14 +376,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "See how the boolean vector on the left subset for rows where `a` was even (the value of `a % 2` is zero), and the second entry (`b`) subset for the column `b`, then we assigned 0 into those cells? It's just a generalization of the kinds of assignments we did above with lists and numpy arrays, just using boolean vectors and column labels instead of indices!" + "See how the boolean vector on the left subset for rows where `a` was even (the value of `a % 2` is zero), and the second entry (`b`) subset for column `b`, then we assigned 0 into those cells? It's just a generalization of the kinds of assignments we did above with lists and numpy arrays, just using boolean vectors and column labels instead of indices!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Great! But now suppose we don't just want to set certain values to a constant, but instead we wanted to, say, double all the values in odd rows. We can do that to by assigning values that \"fit\" into the cells on the left of the assignment operator (i.e. by making sure the values we assign have the same dimensions as the cells into which we're trying to assign them):" + "Great! But now suppose we don't just want to set certain values to a constant, but instead we wanted to, say, double all the values in odd rows. We can do that too by assigning values that \"fit\" into the cells on the left of the assignment operator (i.e. by making sure the values we assign have the same dimensions as the cells into which we're trying to assign them):" ] }, { @@ -494,7 +496,8 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", + " polityIV\n", " \n", " \n", " \n", @@ -503,49 +506,55 @@ " Brazil\n", " S. America\n", " 10296\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", + " 16\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008\n", - "0 Brazil S. America 10296\n", - "1 Germany W. Europe 35613\n", - "2 Mexico N. America 14495\n", - "3 Mozambique Africa 855\n", - "4 Russia C&E Europe 16139\n", - "5 Ukraine C&E Europe 7271" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 16\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 9, @@ -554,10 +563,11 @@ } ], "source": [ - "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + "smallworld = smallworld = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", + "\n", "smallworld" ] }, @@ -589,7 +599,8 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", + " polityIV\n", " \n", " \n", " \n", @@ -598,49 +609,55 @@ " Brazil\n", " S. America\n", " 10296\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", + " 16\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008\n", - "0 Brazil S. America 10296\n", - "1 Germany W. Europe 35613\n", - "2 Mexico N. America 14495\n", - "3 Mozambique Africa 855\n", - "4 Russia C&E Europe 16139\n", - "5 Ukraine C&E Europe 7271" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 16\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 10, @@ -687,7 +704,7 @@ " \n", " country\n", " region\n", - " gdp_per_capita_2008\n", + " gdppcap08\n", " polityIV\n", " \n", " \n", @@ -697,55 +714,55 @@ " Brazil\n", " S. America\n", " 10296\n", - " NaN\n", + " 18\n", " \n", " \n", " 1\n", " Germany\n", " W. Europe\n", " 35613\n", - " NaN\n", + " 20\n", " \n", " \n", " 2\n", " Mexico\n", " N. America\n", " 14495\n", - " NaN\n", + " 18\n", " \n", " \n", " 3\n", " Mozambique\n", " Africa\n", " 855\n", - " 5.0\n", + " 5\n", " \n", " \n", " 4\n", " Russia\n", " C&E Europe\n", " 16139\n", - " NaN\n", + " 17\n", " \n", " \n", " 5\n", " Ukraine\n", " C&E Europe\n", " 7271\n", - " NaN\n", + " 16\n", " \n", " \n", "\n", "" ], "text/plain": [ - " country region gdp_per_capita_2008 polityIV\n", - "0 Brazil S. America 10296 NaN\n", - "1 Germany W. Europe 35613 NaN\n", - "2 Mexico N. America 14495 NaN\n", - "3 Mozambique Africa 855 5.0\n", - "4 Russia C&E Europe 16139 NaN\n", - "5 Ukraine C&E Europe 7271 NaN" + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" ] }, "execution_count": 11, @@ -790,9 +807,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_37248/2079543889.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", + "/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_27525/2079543889.py:1: ChainedAssignmentError: A value is trying to be set on a copy of a DataFrame or Series through chained assignment.\n", + "When using the Copy-on-Write mode, such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " smallworld[smallworld.country == \"Mozambique\"][\"polityIV\"] = 5\n" @@ -803,11 +821,126 @@ "smallworld[smallworld.country == \"Mozambique\"][\"polityIV\"] = 5" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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      " + ], + "text/plain": [ + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smallworld" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You will get the `SettingWithCopyWarning` we discussed in our reading on views and copies. That's because it's possible that when you run `smallworld[smallworld.country == \"Mozambique\"]`, pandas **may** return an entirely new DataFrame, and the next part of the operation (changing the values of `polityIV`) will run against a completely new DataFrame, not `smallworld`, and in the end your original `smallworld` DataFrame won't end up being modified at all. This kind of chained assignment will SOMETIMES work, but not ALWAYS, which is why you get that warning. " + "Provided you have Copy-on-Write activated, this will result in a `ChainedAssignmentError`:\n", + "\n", + "```python\n", + "\n", + "ChainedAssignmentError: A value is trying to be set on a copy of a DataFrame \n", + "or Series through chained assignment.\n", + "When using the Copy-on-Write mode, such chained assignment never works to \n", + "update the original DataFrame or Series, because the intermediate object on \n", + "which we are setting values always behaves as a copy.\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the \n", + "assignment in a single step.\n", + "```\n", + "\n", + "That's because when you run `smallworld[smallworld.country == \"Mozambique\"]`, pandas returns an entirely new DataFrame, and the next part of the operation (changing the values of `polityIV`) is being excuted against that completely new DataFrame, not the one associated with the variable `smallworld`. As a result, `smallworld` DataFrame won't end up being modified at all.\n", + "\n", + "\n", + "(If you don't have Copy-on-Write activated, you will get a `SettingWithCopyWarning` warning instead. Whether the assignment will work in that case is basically unpredictable!)\n", + "\n" ] }, { @@ -823,13 +956,111 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "smallworld[\"region\"] = pd.Categorical(smallworld[\"region\"])" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " country region gdppcap08 polityIV\n", + "0 Brazil S. America 10296 18\n", + "1 Germany W. Europe 35613 20\n", + "2 Mexico N. America 14495 18\n", + "3 Mozambique Africa 855 5\n", + "4 Russia C&E Europe 16139 17\n", + "5 Ukraine C&E Europe 7271 16" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "smallworld" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -844,33 +1075,20 @@ "/Users/Nick/github/mids_coursera/class_3/week_3/32_cleaning_editing_specific_locations.ipynb Cell 29 in ()\n", "----> 1 smallworld.loc[smallworld[\"country\"] == \"Mexico\", \"region\"] = \"C America\"\n", "\n", - "File ~/opt/miniconda3/lib/python3.9/site-packages/pandas/core/indexing.py:716, in _LocationIndexer.__setitem__(self, key, value)\n", - " 713 self._has_valid_setitem_indexer(key)\n", - " 715 iloc = self if self.name == \"iloc\" else self.obj.iloc\n", - "--> 716 iloc._setitem_with_indexer(indexer, value, self.name)\n", - "\n", "[...]\n", "\n", - "File ~/opt/miniconda3/lib/python3.9/site-packages/pandas/core/arrays/categorical.py:1484, in Categorical._validate_scalar(self, fill_value)\n", - " 1482 fill_value = self._unbox_scalar(fill_value)\n", - " 1483 else:\n", - "-> 1484 raise TypeError(\n", - " 1485 \"Cannot setitem on a Categorical with a new \"\n", - " 1486 f\"category ({fill_value}), set the categories first\"\n", - " 1487 )\n", - " 1488 return fill_value\n", "\n", "TypeError: Cannot setitem on a Categorical with a new category (C America), set the categories first\n", "```\n", "\n", "So what do we do?\n", "\n", - "One option is to modify the allowable categories for the variable with `.cat.add_categories()`, then making the edit again:" + "One option is to modify the allowable categories for the variable with `.cat.add_categories()`, then make the edit again:" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -887,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ diff --git a/docs/_sources/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb b/docs/_sources/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb index 503b15bd..db153b76 100644 --- a/docs/_sources/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/33_cleaning_datatypes.ipynb @@ -111,8 +111,10 @@ } ], "source": [ - "import numpy as np\n", "import pandas as pd\n", + "import numpy as np\n", + "\n", + "pd.set_option(\"mode.copy_on_write\", True)\n", "\n", "world = pd.read_csv(\"data/world-very-small-type-errors.csv\")\n", "world" @@ -194,7 +196,7 @@ "source": [ "Oops! Yup, there it is! Indeed, we can also see that `polityIV` is also an `object`, something we'll deal with later. \n", "\n", - "So this kind of phenomenon is such a common problem that pandas has a tool to deal with it: `.str.isnumeric()`. This method returns `True` is an observation can be converted to a number without issues, and `False` otherwise, so by using its logical negation, we can find all observations that aren't convertible (which we may then need to fix):" + "So this kind of phenomenon is such a common problem that pandas has a tool to deal with it: `.str.isnumeric()`. This method returns `True` if an observation can be converted to a number without issues, and `False` otherwise, so by using its logical negation, we can find all observations that aren't convertible (which we may then need to fix):" ] }, { @@ -225,7 +227,7 @@ "source": [ "(Recall we have to use `~` instead of `not` to invert `True` values to `False` in a numpy array or pandas Series)\n", "\n", - "There we are! One observation of `\"na\"` is the source of our problems. Note that you could also use `.unique()` instead of `.value_counts()` if you don't care about the number of observations are causing problems. \n", + "There we are! One observation of `\"na\"` is the source of our problems. Note that you could also use `.unique()` instead of `.value_counts()` if you don't care about the number of observations that are causing problems. \n", "\n", "OK, so how do we fix this? In this case, it seems like `na` is probably just short for `np.nan`, so we can replace it with `np.nan`, which *is* a number:" ] @@ -271,7 +273,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, there's one other option in these situations: if `na` is appearing in lots of places in your data as a representation for missing data, we can also communicate this fact to `read_csv`. Remember when we said that `read_csv` has more options than you could ever imagine? Well one is `na_values`, where you can specify how missing data is represented in a dataset. If we use that to tell `read_csv` that `\"na\"` means data is missing, it'll make this correction on the fly!" + "Now, there's one other option in these situations: if `na` is appearing in lots of places in your data as a representation for missing data, we can also communicate this fact to `read_csv`. Remember when we said that `read_csv` has more options than you could ever imagine? Well, one is `na_values`, where you can specify how missing data is represented in a dataset. If we use that to tell `read_csv` that `\"na\"` means data is missing, it'll make this correction on the fly!" ] }, { @@ -430,7 +432,7 @@ "source": [ "## Deliberate Non-Numerics\n", "\n", - "While in the example above `\"na\"` wasn't really meant to be interpreted as a string, sometimes non-numeric values are inserted into datasets to communicate something important to the user. For example, in this toy example `polityIV` has a non-numeric value:" + "While in the example above `\"na\"` wasn't really meant to be interpreted as a string, sometimes non-numeric values are inserted into datasets to communicate something important to the user. For example, in this toy example, `polityIV` has a non-numeric value:" ] }, { @@ -495,7 +497,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here, the value is to indicate that the value of policy could be 20 *or any value above 20*. Why might this occur? In this case I've made it up, but in many surveys there will be a maximum value allowed (e.g., some surveys have a top income value they can record). These are called \"top-codes\", and they are different from the case above because it's not immediately clear what value you should put into the field. You *could* put in 20, but... the value might not be 20, it could be 21!\n", + "Here, the value is to indicate that the value of `polityIV` could be 20 *or any value above 20*. Why might this occur? In this case, I've made it up, but in many surveys, there will be a maximum value allowed (e.g., some surveys have a top income value they can record). These are called \"top-codes\", and they are different from the case above because it's not immediately clear what value you should put into the field. You *could* put in 20, but... the value might not be 20, it could be 21!\n", "\n", "So when you find a top-code like this, you need to think carefully about what to do with it, as the answer will depend on the type of analysis you want to do. But the answer will almost always lie not in the data, but in the documentation for the data. " ] diff --git a/docs/_sources/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb b/docs/_sources/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb index e437372e..0717c5ad 100644 --- a/docs/_sources/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/35_cleaning_missing_data.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -29,7 +29,7 @@ "False" ] }, - "execution_count": 10, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -143,7 +143,7 @@ "5 Ukraine C&E Europe 7271 16.0" ] }, - "execution_count": 11, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -151,9 +151,9 @@ "source": [ "import pandas as pd\n", "\n", - "smallworld = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/nickeubank/\"\n", - " \"practicaldatascience/master/Example_Data/world-very-small.csv\"\n", + "smallworld = smallworld = pd.read_csv(\n", + " \"https://raw.githubusercontent.com/nickeubank/practicaldatascience_book\"\n", + " \"/main/notebooks/class_3/week_3/data/world-very-small.csv\"\n", ")\n", "smallworld.loc[smallworld.country == \"Mozambique\", \"polityIV\"] = np.nan\n", "smallworld" @@ -168,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -209,13 +209,13 @@ "Index: []" ] }, - "execution_count": 12, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[smallworld.polityIV == np.nan]\n" + "smallworld.loc[smallworld.polityIV == np.nan]" ] }, { @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -276,13 +276,13 @@ "3 Mozambique Africa 855 NaN" ] }, - "execution_count": 13, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[pd.isnull(smallworld.polityIV)]\n" + "smallworld.loc[pd.isnull(smallworld.polityIV)]" ] }, { @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -341,13 +341,13 @@ "3 Mozambique Africa 855 NaN" ] }, - "execution_count": 14, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "smallworld.loc[smallworld.polityIV.isnull()]\n" + "smallworld.loc[smallworld.polityIV.isnull()]" ] }, { @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -368,13 +368,13 @@ "True" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.isnull(None)\n" + "pd.isnull(None)" ] }, { @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -473,14 +473,14 @@ "5 Ukraine C&E Europe 7271 16.0" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "smallworld.loc[smallworld.polityIV.isnull(), \"polityIV\"] = 5\n", - "smallworld\n" + "smallworld" ] }, { @@ -494,20 +494,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "unemployed 7727\n", "n/a 0\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -531,21 +532,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ + "empstat\n", "employed 148758\n", "not in labor force 104676\n", "NaN 57843\n", "unemployed 7727\n", "n/a 0\n", - "Name: empstat, dtype: int64" + "Name: count, dtype: int64" ] }, - "execution_count": 21, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -615,7 +617,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/_sources/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb b/docs/_sources/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb new file mode 100644 index 00000000..699f3b8f --- /dev/null +++ b/docs/_sources/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb @@ -0,0 +1,637 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Object and Categorical Data Types\n", + "\n", + "In our previous readings, we introduced the idea that not only can DataFrames and Series hold any of the numeric data types we've come to know and love from numpy — like `float64` or `int64` — but that they can also hold arbitrary Python objects in an `object`-type Series. Moreover, we also acknowledged (though didn't go into) the existence of another relatively unique pandas data type — the `Categorical` Series. \n", + "\n", + "In this reading, we will discuss the purpose of these two data types, as well as their advantages and disadvantages.\n", + "\n", + "## The object dtype\n", + "\n", + "The `object` type Series gives pandas _incredible_ flexibility as it allows any type of data to be stored in a table. The most common use of the `object` data type is to store text — for example, names, addresses, written answers, etc. — but the flexibility can also be used for applications like geospatial analysis, in which a single row of a DataFrame may represent a single country, the columns may represent features of the country (name, income, population), and the last column stores geometric Python objects that describe the shape and location of the country. \n", + "\n", + "But this flexibility also comes at a cost — performance and memory efficiency.\n", + "\n", + "### The object Performance Penalty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To understand why `object` Series are slow, it helps to first discuss why numeric Series (and numeric numpy arrays) are fast. \n", + "When you work with a numeric pandas Series or numpy array, all of the entries in those arrays live in the same place in memory (in your computer's RAM). This is possible because all those integers (or all those floating point numbers) are written with the same number of 0s and 1s. In an `int64` Series, for example, every integer is represented by 64 1s and 0s. This makes it easy for the computer to lay them out sequentially. Moreover, it makes it possible for the computer to find specific entries quickly, since it knows that the third integer in the Series will start 64 * 2 spots from the start of the Series and end 64 spots later.\n", + "\n", + "But an *object* Series is a little different. Python objects vary in size — some may only take up 128 0s and 1s, while others may require thousands — and so the actual data in an object Series can't be laid down in a nice regularly spaced sequence. Instead, every entry in an object Series gets put in a different location in your computer's memory (RAM), and only the *address* of that information is placed in a nice organized Series. These addresses are all the same size, and so the addresses can be organized in a regular manner, even if the actual content you want to store is irregular.\n", + "\n", + "The cost of this arrangement is that if you ask for the second entry of an object Series (e.g., `my_series.iloc[1]`), you're computer has first go to the second location in the array, read the address stored there, *then go to that address* to find the actual content you want. And those added steps waste time.\n", + "\n", + "The other problem with object Series is that because they can store anything, Python doesn't know before it looks up an entry whether it will find a string, an integer, or a Python set. As a result, when it sees code like: \n", + "\n", + "```python \n", + "my_array * 2\n", + "```\n", + "\n", + "Python can't be sure what is meant by `*` — it could mean \"do integer multiplication\" (if a given entry in `my_array` is an integer), but it might also mean \"double up the list you find\" (if the entry is a Python list)!\n", + "\n", + "Indeed we can see this if we make a numpy object array full of ints and compare it to a numpy integer array. The both have the same content, but they are organized in memory differently:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "object_numbers = pd.Series(np.arange(1000000), dtype=\"object\")\n", + "numbers = pd.Series(np.arange(1000000), dtype=\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15.9 ms ± 70.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%timeit object_numbers * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "771 µs ± 9.62 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit numbers * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See? Same operation (doubling each entry of arrays with the integers from 0 to 1,000,000), but the object array operation is ~20x slower. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So yes, `object` dtypes are absolutely wonderful and introduce unbelievable flexibility to pandas; but remember there is a cost to using them, so stick to numeric data types when possible!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Categoricals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `category` data type is a delightful little hack that allows us to avoid most of the problems with `object` Series *in certain circumstances*. \n", + "\n", + "To illustrate how `category` Series work, suppose we have a DataFrame with information on hundreds of thousands of customers in the United States, and that one column of that DataFrame contains the name of each customer's state of residence (substitute Province or any other sub-national administrative unit if states don't resonate for you). \n", + "\n", + "Because those state names are words, they are being stored in an `object` Series. That, in turn, means that Python has created hundreds of thousands of Python objects — each containing the name of a customer's state — *and* a vector containing addresses for each of those objects. \n", + "\n", + "But as there are only 50 states in the United States, this might strike you as absurd, since most of those hundreds of thousands of Python objects are holding the same text! Surely we can do something more efficient than that? \n", + "\n", + "Enter Categoricals. The idea of a `category` Series is to take a `object` Series that contains frequently repeated strings and:\n", + "\n", + "1. Replace each unique string with a number (for example, `\"Colorado\"` could become `1` and `\"Tennessee\"` could become `2`), and\n", + "2. Create a small \"lookup table\" that keeps track of what string is associated with each number. \n", + "\n", + "Now pandas doesn't need to create hundreds of thousands of Python strings to record each customer's state, it can just make a numeric Series with state names replaced by the numbers 1-50, and a new small vector with the fifty names of states.\n", + "\n", + "Moreover, in addition to saving memory, this also dramatically improves the *performance* of pandas. Suppose, for example, you want to subset for customers living in North Carolina. When these states were in an `object` Series, pandas would have to go to each entry, figure out where the associated Python object is stored, get that object, and check to see if it was the string `\"North Carolina\"`. \n", + "\n", + "But now, pandas can just go to the lookup table, see that `\"North Carolina\"` is the 33rd entry (and so is represented by the number 33 in our encoded Series of numbers), and look for values of 33 in that Series. Hooray!\n", + "\n", + "But the best part is that, in most cases, the fact your data has been split into a numeric vector and a lookup table is actually entirely hidden from you, the user. For *most* operations, using a Categorical Series is just like using an `object` Series, just faster. \n", + "\n", + "### Categorical Series in Practice\n", + "\n", + "To illustrate how one works with Categorical Series, let's make a toy version of this customer dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "1 Aditya Tennessee\n", + "2 Francisco Colorado\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "customers = pd.DataFrame(\n", + " {\n", + " \"customer\": [\"Bob\", \"Aditya\", \"Francisco\", \"Shufan\"],\n", + " \"state\": [\"Colorado\", \"Tennessee\", \"Colorado\", \"Virginia\"],\n", + " }\n", + ")\n", + "customers" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "customer object\n", + "state object\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, `state` begins its life as a standard `object` Series, but we can convert it to a Categorical with `.astype(\"category\")`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "1 Aditya Tennessee\n", + "2 Francisco Colorado\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"] = customers[\"state\"].astype(\"category\")\n", + "customers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, at first glance nothing about this column has changed. But if we pull it out, you can see it's dtype is `category` and that the Categories associated with the Series (the lookup table) contains three values: `\"Colorado\"`, `\"Tennessee\"` and \"`Virginia\"`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Colorado\n", + "1 Tennessee\n", + "2 Colorado\n", + "3 Virginia\n", + "Name: state, dtype: category\n", + "Categories (3, object): ['Colorado', 'Tennessee', 'Virginia']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And if you want to, you can see the two underlying pieces directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Colorado', 'Tennessee', 'Virginia'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"].cat.categories" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 0\n", + "3 2\n", + "dtype: int8" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"].cat.codes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But as we said, in most cases `category` Series will operate just like `object` Series. Subsetting, for example, will work just as it would with an `object` Series:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
      2FranciscoColorado
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "2 Francisco Colorado" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.loc[customers[\"state\"] == \"Colorado\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only place that problems *may* arise is that one cannot make arbitrary edits to a `category` Series — if you try and set a cell to have a value that isn't in the current `Categories` table, you will get an error:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "\n", + "customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "\n", + "TypeError Traceback (most recent call last)\n", + "/Users/nce8/github/practicaldatascience_book/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb Cell 22 line 1\n", + "----> 1 customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "\n", + "[...]\n", + "\n", + "TypeError: Cannot setitem on a Categorical with a new category (Kansas), set the categories first\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You *can* add novel values, to be clear, you just have to add the category first:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Colorado\n", + "1 Tennessee\n", + "2 Colorado\n", + "3 Virginia\n", + "Name: state, dtype: category\n", + "Categories (4, object): ['Colorado', 'Tennessee', 'Virginia', 'Kansas']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"] = customers[\"state\"].cat.add_categories([\"Kansas\"])\n", + "customers[\"state\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobKansas
      1AdityaTennessee
      2FranciscoKansas
      3ShufanVirginia
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Kansas\n", + "1 Aditya Tennessee\n", + "2 Francisco Kansas\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "customers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why Not Always Use Categoricals?\n", + "\n", + "Categoricals are great, but they are only useful when your `object` Series has a relatively small number of unique values. If you tried to convert an `object` Series with hundreds of thousands of addresses — and nearly all of them were unique — into a `category` Series, then pandas would have to create a lookup table that had... hundreds of thousands of unique entries (essentially, it would just be recreating your original `object` Series). And so there would be no real performance benefit." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/_sources/notebooks/class_3/week_3/40_EX_cleaning.ipynb b/docs/_sources/notebooks/class_3/week_3/40_EX_cleaning.ipynb index 1f7ed419..685ee402 100644 --- a/docs/_sources/notebooks/class_3/week_3/40_EX_cleaning.ipynb +++ b/docs/_sources/notebooks/class_3/week_3/40_EX_cleaning.ipynb @@ -25,8 +25,11 @@ "outputs": [], "source": [ "import pandas as pd\n", - "acs = pd.read_stata(\"https://github.com/nickeubank/MIDS_Data/blob/master\"\n", - " \"/US_AmericanCommunitySurvey/US_ACS_2017_10pct_sample.dta?raw=true\")" + "\n", + "acs = pd.read_stata(\n", + " \"https://github.com/nickeubank/MIDS_Data/blob/master\"\n", + " \"/US_AmericanCommunitySurvey/US_ACS_2017_10pct_sample.dta?raw=true\"\n", + ")" ] }, { @@ -65,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -97,7 +100,7 @@ "Categories (97, object): ['less than 1 year old' < '1' < '2' < '3' ... '93' < '94' < '95' < '96']" ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -108,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -126,7 +129,7 @@ "41.30384885455982" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -134,7 +137,7 @@ "source": [ "acs.loc[acs[\"age\"] == \"less than 1 year old\", \"age\"] = 0\n", "acs.loc[acs[\"age\"] == \"90 (90+ in 1980 and 1990)\", \"age\"] = 90\n", - "acs[\"age\"] = acs[\"age\"].astype('float')\n", + "acs[\"age\"] = acs[\"age\"].astype(\"float\")\n", "acs[\"age\"].mean()" ] }, @@ -268,7 +271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/docs/genindex.html b/docs/genindex.html index d9ca0e94..0ca750b8 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -224,7 +224,6 @@
    • Views and Copies in Pandas
      • @@ -237,6 +236,7 @@
    • Editing Specific Locations
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    • The Object and Categorical Data Types
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      @@ -2315,7 +2315,1086 @@

      Layer16: series-groupby-sum-agg

      - +
      2023-11-28 07:21:51,309 - tornado.application - ERROR - Exception in callback <bound method SystemMonitor.update of <SystemMonitor: cpu: 16 memory: 197 MB fds: 180>>
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/ioloop.py", line 919, in _run
+    val = self.callback()
+          ^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/system_monitor.py", line 168, in update
+    net_ioc = psutil.net_io_counters()
+              ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/psutil/__init__.py", line 2119, in net_io_counters
+    rawdict = _psplatform.net_io_counters()
+              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+OSError: [Errno 12] Cannot allocate memory
+2023-11-29 12:24:43,023 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:24:43,086 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:24:43,125 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:24:43,130 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:24:43,280 - distributed.nanny - WARNING - Restarting worker
+2023-11-29 12:32:25,398 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55482>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:25,405 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55483>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:25,753 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1406): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1177): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 94): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 768): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 359): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 130): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1262): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1033): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 624): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1527): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1298): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 215): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 889): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1563): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 480): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 251): 'memory', 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+2023-11-29 12:32:25,749 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55480>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:25,864 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55481>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:32:25,946 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55484>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:32:38,591 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55650>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:38,659 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55651>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:39,011 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55647>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:39,181 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55648>: ConnectionResetError: [Errno 54] Connection reset by peer
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+2023-11-29 12:32:39,295 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55649>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:51,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55658>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:52,043 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55659>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:52,257 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55656>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:52,540 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55655>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:32:52,664 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:32:52,661 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55657>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:04,636 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55667>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:04,728 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55663>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:04,862 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55666>: ConnectionResetError: [Errno 54] Connection reset by peer
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+2023-11-29 12:33:05,272 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55664>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:05,301 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55665>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:15,177 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55672>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:15,350 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55673>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:15,994 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55669>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:16,318 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55670>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:16,497 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:33:16,494 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55671>: ConnectionResetError: [Errno 54] Connection reset by peer
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+2023-11-29 12:33:29,522 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55678>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:29,643 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55679>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:29,963 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55676>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:29,964 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55675>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:29,974 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55677>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:40,320 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55687>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:40,536 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55686>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:41,055 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55684>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:41,131 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55683>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:41,253 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:33:41,247 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55685>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:53,214 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55695>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:53,215 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55691>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:53,290 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55694>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:33:53,402 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55692>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:33:53,563 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:33:53,845 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55693>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:05,777 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55722>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:05,800 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 861, in _read_to_buffer
+    bytes_read = self.read_from_fd(buf)
+                 ^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1116, in read_from_fd
+    return self.socket.recv_into(buf, len(buf))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55720>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:05,900 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55723>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:06,047 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55719>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:06,100 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:34:06,432 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55721>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:17,458 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55348 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55348 remote=tcp://127.0.0.1:55765>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:17,682 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55378 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+BrokenPipeError: [Errno 32] Broken pipe
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55378 remote=tcp://127.0.0.1:55764>: BrokenPipeError: [Errno 32] Broken pipe
+2023-11-29 12:34:17,740 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55381 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55381 remote=tcp://127.0.0.1:55761>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:17,860 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55333 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55333 remote=tcp://127.0.0.1:55762>: ConnectionResetError: [Errno 54] Connection reset by peer
+2023-11-29 12:34:17,961 - distributed.scheduler - ERROR - Couldn't gather keys: {('assign-786d0153597eb09d7a8413baa8e8f555', 755): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 526): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1429): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 117): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1020): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 791): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 382): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1285): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 876): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 647): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1550): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 238): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 1141): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 912): 'memory', ('assign-786d0153597eb09d7a8413baa8e8f555', 503): 'memory', 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+2023-11-29 12:34:18,184 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:55324 -> None
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 962, in _handle_write
+    num_bytes = self.write_to_fd(self._write_buffer.peek(size))
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/tornado/iostream.py", line 1124, in write_to_fd
+    return self.socket.send(data)  # type: ignore
+           ^^^^^^^^^^^^^^^^^^^^^^
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/worker.py", line 1783, in get_data
+    response = await comm.read(deserializers=serializers)
+               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 237, in read
+    convert_stream_closed_error(self, e)
+  File "/Users/nce8/opt/miniconda3/envs/dask_env/lib/python3.12/site-packages/distributed/comm/tcp.py", line 140, in convert_stream_closed_error
+    raise CommClosedError(f"in {obj}: {exc.__class__.__name__}: {exc}") from exc
+distributed.comm.core.CommClosedError: in <TCP (closed)  local=tcp://127.0.0.1:55324 remote=tcp://127.0.0.1:55763>: ConnectionResetError: [Errno 54] Connection reset by peer
+
      +
      +

      Or you can plot out a task graph. You can do this at a high level with .dask.visualize(), or in excruciating detail for a task this big with just .visualize(). Note visualize() does require you install additional libraries, so install ipycytoscape and python-graphviz with conda first. But these get so big I’m not gonna include one here—you can find examples here if you’d like to see more.

      diff --git a/docs/notebooks/PDS_not_yet_in_coursera/30_big_data/distributed_starting_dask_cluster.html b/docs/notebooks/PDS_not_yet_in_coursera/30_big_data/distributed_starting_dask_cluster.html index 1d01c3b7..756ec308 100644 --- a/docs/notebooks/PDS_not_yet_in_coursera/30_big_data/distributed_starting_dask_cluster.html +++ b/docs/notebooks/PDS_not_yet_in_coursera/30_big_data/distributed_starting_dask_cluster.html @@ -225,7 +225,6 @@
    • Views and Copies in Pandas
      • @@ -238,6 +237,7 @@
    • Editing Specific Locations
    • Cleaning Data Types
    • Missing Data
      • +
    • The Object and Categorical Data Types
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      @@ -495,10 +495,31 @@

      Contents

      @@ -514,7 +535,7 @@

      Contents

      Week 3 Quiz#

      Note:

      -

      This exercise has been written out in something called a Jupyter Notebook. We’ll discuss Jupyter Notebooks in more detail later in the course—they are a very powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don’t need to do anything with the notebook except read its contents—just use write your Python code in a regular .py file.

      +

      This exercise has been written out in something called a Jupyter Notebook. We’ll discuss Jupyter Notebooks in more detail later in the course—they are very a powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don’t need to do anything with the notebook except read its contents—just use write your Python code in a regular .py file.

      As previously discussed, we frequently use matrices and data science because they are a natural format for representing data generated by collecting the same type of information from numerous entities. For example, below is a toy dataset that you could imagine was created by collecting information about employees at a company—each column is a different type of information being collected (income, age, years of education), and each row is the information about a different employee.

      In the following questions, you will be asked to answer a number of questions about this toy dataset. As with other exercises in this class, you will find the directions for this graded exercise here, but please submit your actual answers in the graded quiz.

      @@ -545,81 +566,102 @@

      Week 3 Quiz

      Part 1: Summarizing Data#

      -
        -

      1. What is the average (mean) age of all employees (rounded to 1 decimal place)?

        2. -

      2. What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?

        3. -

      3. What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?

        4. -
      +
      +

      Question 1#

      +

      What is the average (mean) age of all employees (rounded to 1 decimal place)?

      +
      +
      +

      Question 2#

      +

      What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?

      +
      +
      +

      Question 3#

      +

      What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?

      +

      Part 2: Editing Data#

      The US government is thinking about offering a 1,500 tax credit to anyone making less than 20,000 a year. You can think of this tax credit as effectively an additional $1,500 of income to each person receiving the credit.

      -
        -

      1. Using the data from survey, modify income values to calculate a new estimate of the employees’ incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?

        -
          -

        • Do so by subsetting and editing values programmatically, not just typing values by hand. (Yes, writing out a new vector by hand is easy to do in this example, but you couldn’t do it with a large, real dataset!)

          • -
        -
        2. -
      +
      +

      Question 4#

      +

      Using the data from survey, modify income values to calculate a new estimate of the employees’ incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)? +- Do so by subsetting and editing values programmatically, not just typing values by hand. (Yes, writing out a new vector by hand is easy to do in this example, but you couldn’t do it with a large, real dataset!)

      +

      Part 3: Measuring Income Inequality (with Real Data!)#

      In this exercise, we’ll be working with data from the US Current Population Survey, provided by the National Bureau of Economic Research (NBER). This is a regular survey conducted by the US Bureau of Labor to calculate the US employment rate.

      -

      In this exercise, we’ll use these data to study gender and racial wage inequality in the US.

      +

      In this exercise, we’ll use this data to study gender and racial wage inequality in the US.

      Load data from the 2018 CPS survey with the following command:

      cps = np.loadtxt("data/cps.txt")
      -

      This data are a subset of the full CPS survey and contains only data on employed respondents working at least 35 hours a week (e.g., full-time).

      -
        -

      1. How many rows does this matrix have?

        2. -

      2. The five columns of this matrix correspond to:

        -
          -

        • Column 1: Weekly income in dollars.

          • -

        • Column 2: Usual hours respondent works per week.

          • -

        • Column 3: Gender. 2 is “Female”, 1 is “Male”

          • -

        • Column 4: Race. This can take on a lot of values for those who identify as mixed race, but for simplicity, in this exercise, we’ll just focus on a few values. For those interested, the full set of codes can be found on page 19 of the CPS codebook.

          -
            -

          • 1: White

            • -

          • 2: Black

            • -

          • 3: American Indian

            • -

          • 4: Asian only

            • -

          • 5: Hawaiian/Pacific Islander only

            • -
          -
          • -

        • Column 5: Survey weights.

          • -
        -
        3. -
      +

      This data is a subset of the full CPS survey and contains only data on employed respondents working at least 35 hours a week (e.g., full-time).

      +
      +

      Question 5#

      +

      How many rows does this matrix have?

      +
      +
      +

      Question 6#

      +

      The five columns of this matrix correspond to: +- Column 1: Weekly income in dollars. +- Column 2: Usual hours respondent works per week. +- Column 3: Gender. 2 is “Female”, 1 is “Male” +- Column 4: Race. This can take on a lot of values for those who identify as mixed race, but for simplicity, in this exercise, we’ll just focus on a few values. For those interested, the full set of codes can be found on page 19 of the CPS codebook. +- 1: White +- 2: Black +- 3: American Indian +- 4: Asian only +- 5: Hawaiian/Pacific Islander only +- Column 5: Survey weights.

      Note that race does not break out Hispanic / non-Hispanic identities. In US government surveys, Hispanic / non-Hispanic is usually recorded in a separate ethnicity variable, so many people who identify as Hispanic are identified as White or Black in the race variable.

      For the moment, let’s ignore survey weights—they don’t impact results here significantly.

      -

      What is the average hourly wage for all workers in this dataset (round it to one decimal place)?

      +

      What is the average hourly wage for all workers in this data?

      +

      Please round your final answer to one decimal place, but don’t round any intermediate results! (if you round at each stage of the calculation, those roundings may compound in odd ways).

      Hint: This will require more than just using mean on a single column!

      -
        -

      1. What is the average hourly wage of working men (rounded to one decimal place)?

        2. -

      2. What is the average hourly wage of working women (rounded to one decimal place)?

        3. -

      3. What share (e.g., a value between 0 and 1) of men’s average hourly wage is women’s average hourly wage? (rounded to three decimal places). In other words, what is women’s average hourly wage divided by men’s average hourly wage? Don’t round anything until you have your final answer!

        4. -
      +
      +
      +

      Question 7#

      +

      What is the average hourly wage of working men? (Please round your final answer to one decimal place, but don’t round any intermediate results!)

      +
      +
      +

      Question 8#

      +

      What is the average hourly wage of working women? (Please round your final answer to one decimal place, but don’t round any intermediate results!)

      +
      +
      +

      Question 9#

      +

      What share (e.g., a value between 0 and 1) of men’s average hourly wage is women’s average hourly wage? (Please round your final answer to three decimal place, but don’t round any intermediate results!). In other words, what is women’s average hourly wage divided by men’s average hourly wage?

      Congratulations! You’ve just calculated the US gender wage gap, on your own, using real data! I mean… I guess “congratulations” is a weird thing to say after directly measuring one of the more egregious inequities in US society, but one of the reasons many of us study data science is so that we will have the ability to directly measure these types of phenomena in the hopes of being able to better understand and address them.

      -
        -

      1. Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?

        2. -

      2. What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?

        3. -

      3. What share (e.g., a value between 0 and 1) of respondents who identify as White’s average hourly wage is respondents who identify as Black’s average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black’s average hourly wage divided by respondents who identify as White’s average hourly wage? Don’t round anything until you have your final answer!

        4. -
      +
      +
      +

      Question 10#

      +

      Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?

      +
      +
      +

      Question 11#

      +

      What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?

      +
      +
      +

      Question 12#

      +

      What share (e.g., a value between 0 and 1) of respondents who identify as White’s average hourly wage is respondents who identify as Black’s average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black’s average hourly wage divided by respondents who identify as White’s average hourly wage? Don’t round anything until you have your final answer!

      Note that this will only be an approximation—one would normally also include all respondents of mixed-race into non-mutually exclusive categories like “Any Part Black” or “Any Part White”, and we would also break out Hispanic and non-Hispanic respondents. But as most respondents only pick on racial category, this will still give us a reasonable approximation.

      +

      Bonus Question#

      As noted above, the fifth column of our data contains something called “sampling weights.” That’s because when the government conducted this survey, they didn’t draw a random sample of respondents from the US population where everyone had the same probability of being interviewed. As a result, when we calculate the average hourly wage of the people in the survey, it isn’t quite the best estimate we have for the average hourly wage for everyone working in the United States. To calculate this number as accurately as possible, we have to take into account the fact that some respondents in the data were more likely to be included than others, and thus can be thought of as standing in for a smaller group of people in the US.

      -

      Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than what we would get if we just took a random sample where everyone in the United States had the same probability of being included.

      +

      Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than when we would get if we just took a random sample where everyone in the United States had the same probability of being included.

      As I mentioned above, for the questions above, the sampling weights don’t make a very big difference to our answers, but the way to get the most accurate estimates would be to take them into account. So let’s give that a try!

      When we calculate the average of a variable, we do so by multiplying all the values of the variable by \(1/N\) (where \(N\) is the number of observations we have) and then adding up those multiplied values.

      For a weighted average, we take the value for each observation \(i\) and multiply it by

      -\[weight_i / \sum_{j=1}^N weight_j\]
      -

      where \(weight_i\) is the observation’s weight, and \(\sum_{j=1}^N weight_j\) is the total of all the weights in the population being averaged. Then we just add up those values!

      +\[weight_i / \sum_{j=1}^N weights_j\] +

      where \(weight_i\) is the observation’s weight, and \(\sum_{j=1}^N weights_j\) is the total of all the weights in the population being averaged. Then we just add up those values!

      +
      +

      Question 13#

      Given that, what is the average hourly wage of Americans working full-time jobs (i.e., the group in this survey) taking into account survey weights (rounded to two decimal places)?

      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
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      + + + Ctrl+K +
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      +

      The Object and Categorical Data Types

      + +
      + +
      +
      + + + + +
      + +
      +

      The Object and Categorical Data Types#

      +

      In our previous readings, we introduced the idea that not only can DataFrames and Series hold any of the numeric data types we’ve come to know and love from numpy — like float64 or int64 — but that they can also hold arbitrary Python objects in an object-type Series. Moreover, we also acknowledged (though didn’t go into) the existence of another relatively unique pandas data type — the Categorical Series.

      +

      In this reading, we will discuss the purpose of these two data types, as well as their advantages and disadvantages.

      +
      +

      The object dtype#

      +

      The object type Series gives pandas incredible flexibility as it allows any type of data to be stored in a table. The most common use of the object data type is to store text — for example, names, addresses, written answers, etc. — but the flexibility can also be used for applications like geospatial analysis, in which a single row of a DataFrame may represent a single country, the columns may represent features of the country (name, income, population), and the last column stores geometric Python objects that describe the shape and location of the country.

      +

      But this flexibility also comes at a cost — performance and memory efficiency.

      +
      +

      The object Performance Penalty#

      +

      To understand why object Series are slow, it helps to first discuss why numeric Series (and numeric numpy arrays) are fast. +When you work with a numeric pandas Series or numpy array, all of the entries in those arrays live in the same place in memory (in your computer’s RAM). This is possible because all those integers (or all those floating point numbers) are written with the same number of 0s and 1s. In an int64 Series, for example, every integer is represented by 64 1s and 0s. This makes it easy for the computer to lay them out sequentially. Moreover, it makes it possible for the computer to find specific entries quickly, since it knows that the third integer in the Series will start 64 * 2 spots from the start of the Series and end 64 spots later.

      +

      But an object Series is a little different. Python objects vary in size — some may only take up 128 0s and 1s, while others may require thousands — and so the actual data in an object Series can’t be laid down in a nice regularly spaced sequence. Instead, every entry in an object Series gets put in a different location in your computer’s memory (RAM), and only the address of that information is placed in a nice organized Series. These addresses are all the same size, and so the addresses can be organized in a regular manner, even if the actual content you want to store is irregular.

      +

      The cost of this arrangement is that if you ask for the second entry of an object Series (e.g., my_series.iloc[1]), you’re computer has first go to the second location in the array, read the address stored there, then go to that address to find the actual content you want. And those added steps waste time.

      +

      The other problem with object Series is that because they can store anything, Python doesn’t know before it looks up an entry whether it will find a string, an integer, or a Python set. As a result, when it sees code like:

      +
      my_array * 2
+
      +
      +

      Python can’t be sure what is meant by * — it could mean “do integer multiplication” (if a given entry in my_array is an integer), but it might also mean “double up the list you find” (if the entry is a Python list)!

      +

      Indeed we can see this if we make a numpy object array full of ints and compare it to a numpy integer array. The both have the same content, but they are organized in memory differently:

      +
      +
      +
      import pandas as pd
+import numpy as np
+
+object_numbers = pd.Series(np.arange(1000000), dtype="object")
+numbers = pd.Series(np.arange(1000000), dtype="int64")
+
      +
      +
      +
      +
      +
      +
      %timeit object_numbers * 2
+
      +
      +
      +
      +
      15.9 ms ± 70.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
      +
      +
      +
      +
      +
      +
      %timeit numbers * 2
+
      +
      +
      +
      +
      771 µs ± 9.62 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
+
      +
      +
      +
      +

      See? Same operation (doubling each entry of arrays with the integers from 0 to 1,000,000), but the object array operation is ~20x slower.

      +

      So yes, object dtypes are absolutely wonderful and introduce unbelievable flexibility to pandas; but remember there is a cost to using them, so stick to numeric data types when possible!

      +
      +
      +
      +

      Categoricals#

      +

      The category data type is a delightful little hack that allows us to avoid most of the problems with object Series in certain circumstances.

      +

      To illustrate how category Series work, suppose we have a DataFrame with information on hundreds of thousands of customers in the United States, and that one column of that DataFrame contains the name of each customer’s state of residence (substitute Province or any other sub-national administrative unit if states don’t resonate for you).

      +

      Because those state names are words, they are being stored in an object Series. That, in turn, means that Python has created hundreds of thousands of Python objects — each containing the name of a customer’s state — and a vector containing addresses for each of those objects.

      +

      But as there are only 50 states in the United States, this might strike you as absurd, since most of those hundreds of thousands of Python objects are holding the same text! Surely we can do something more efficient than that?

      +

      Enter Categoricals. The idea of a category Series is to take a object Series that contains frequently repeated strings and:

      +
        +

      1. Replace each unique string with a number (for example, "Colorado" could become 1 and "Tennessee" could become 2), and

        2. +

      2. Create a small “lookup table” that keeps track of what string is associated with each number.

        3. +
      +

      Now pandas doesn’t need to create hundreds of thousands of Python strings to record each customer’s state, it can just make a numeric Series with state names replaced by the numbers 1-50, and a new small vector with the fifty names of states.

      +

      Moreover, in addition to saving memory, this also dramatically improves the performance of pandas. Suppose, for example, you want to subset for customers living in North Carolina. When these states were in an object Series, pandas would have to go to each entry, figure out where the associated Python object is stored, get that object, and check to see if it was the string "North Carolina".

      +

      But now, pandas can just go to the lookup table, see that "North Carolina" is the 33rd entry (and so is represented by the number 33 in our encoded Series of numbers), and look for values of 33 in that Series. Hooray!

      +

      But the best part is that, in most cases, the fact your data has been split into a numeric vector and a lookup table is actually entirely hidden from you, the user. For most operations, using a Categorical Series is just like using an object Series, just faster.

      +
      +

      Categorical Series in Practice#

      +

      To illustrate how one works with Categorical Series, let’s make a toy version of this customer dataset:

      +
      +
      +
      import pandas as pd
+import numpy as np
+
+customers = pd.DataFrame(
+    {
+        "customer": ["Bob", "Aditya", "Francisco", "Shufan"],
+        "state": ["Colorado", "Tennessee", "Colorado", "Virginia"],
+    }
+)
+customers
+
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      +
      +
      +
      +
      +
      customers.dtypes
+
      +
      +
      +
      +
      customer    object
+state       object
+dtype: object
+
      +
      +
      +
      +

      As we can see, state begins its life as a standard object Series, but we can convert it to a Categorical with .astype("category"):

      +
      +
      +
      customers["state"] = customers["state"].astype("category")
+customers
+
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      +
      +
      +

      As you can see, at first glance nothing about this column has changed. But if we pull it out, you can see it’s dtype is category and that the Categories associated with the Series (the lookup table) contains three values: "Colorado", "Tennessee" and “Virginia":

      +
      +
      +
      customers["state"]
+
      +
      +
      +
      +
      0     Colorado
+1    Tennessee
+2     Colorado
+3     Virginia
+Name: state, dtype: category
+Categories (3, object): ['Colorado', 'Tennessee', 'Virginia']
+
      +
      +
      +
      +

      And if you want to, you can see the two underlying pieces directly:

      +
      +
      +
      customers["state"].cat.categories
+
      +
      +
      +
      +
      Index(['Colorado', 'Tennessee', 'Virginia'], dtype='object')
+
      +
      +
      +
      +
      +
      +
      customers["state"].cat.codes
+
      +
      +
      +
      +
      0    0
+1    1
+2    0
+3    2
+dtype: int8
+
      +
      +
      +
      +

      But as we said, in most cases category Series will operate just like object Series. Subsetting, for example, will work just as it would with an object Series:

      +
      +
      +
      customers.loc[customers["state"] == "Colorado"]
+
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + +
      customerstate
      0BobColorado
      2FranciscoColorado
      +
      +
      +

      The only place that problems may arise is that one cannot make arbitrary edits to a category Series — if you try and set a cell to have a value that isn’t in the current Categories table, you will get an error:

      +
      
+customers.loc[customers["state"] == "Colorado", "state"] = "Kansas"
+
+TypeError                                 Traceback (most recent call last)
+/Users/nce8/github/practicaldatascience_book/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb Cell 22 line 1
+----> 1 customers.loc[customers["state"] == "Colorado", "state"] = "Kansas"
+
+[...]
+
+TypeError: Cannot setitem on a Categorical with a new category (Kansas), set the categories first
+
      +
      +

      You can add novel values, to be clear, you just have to add the category first:

      +
      +
      +
      customers["state"] = customers["state"].cat.add_categories(["Kansas"])
+customers["state"]
+
      +
      +
      +
      +
      0     Colorado
+1    Tennessee
+2     Colorado
+3     Virginia
+Name: state, dtype: category
+Categories (4, object): ['Colorado', 'Tennessee', 'Virginia', 'Kansas']
+
      +
      +
      +
      +
      +
      +
      customers.loc[customers["state"] == "Colorado", "state"] = "Kansas"
+customers
+
      +
      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      customerstate
      0BobKansas
      1AdityaTennessee
      2FranciscoKansas
      3ShufanVirginia
      +
      +
      +
      +
      +

      Why Not Always Use Categoricals?#

      +

      Categoricals are great, but they are only useful when your object Series has a relatively small number of unique values. If you tried to convert an object Series with hundreds of thousands of addresses — and nearly all of them were unique — into a category Series, then pandas would have to create a lookup table that had… hundreds of thousands of unique entries (essentially, it would just be recreating your original object Series). And so there would be no real performance benefit.

      +
      +
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      + + + + +
      + + + + + + +
      + + + +
      + +
      +
      + Contents +
      +
      + +
      + + +
      +
      + +
      + +
      + +

      +By Kyle Bradbury & Nick Eubank +

      + +
      + +
      + +

      + + © Copyright 2023. +
      + +

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      + + \ No newline at end of file diff --git a/docs/notebooks/class_3/week_3/40_EX_cleaning.html b/docs/notebooks/class_3/week_3/40_EX_cleaning.html index 2f306bb7..fad871ec 100644 --- a/docs/notebooks/class_3/week_3/40_EX_cleaning.html +++ b/docs/notebooks/class_3/week_3/40_EX_cleaning.html @@ -227,7 +227,6 @@
    • Views and Copies in Pandas
      • @@ -240,6 +239,7 @@
    • Editing Specific Locations
    • Cleaning Data Types
    • Missing Data
      • +
    • The Object and Categorical Data Types

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  • Data Manipulations
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,15,17,18,20,23,25,26,27,28,31,32,33,34,35,36,40,42,43,44,46,47,48,49,55,58,59,61,62,64,68,70,71,75,77,79,81,89,90,93,94,95,97,98,101,102,103,104,105,107,108,109,112,113,114,115,117,118,121,123,124,126],"var":[17,22,23,32,42,48,68,89,94,101,104,118,119,126],"voil\u00e0":[43,62,101],"while":[2,6,7,8,9,10,12,13,14,16,17,18,19,20,23,25,26,27,30,31,32,34,35,36,39,40,41,44,46,47,48,49,54,55,56,57,60,62,65,67,73,74,75,76,77,79,88,89,90,91,92,93,96,97,99,100,102,103,105,106,107,108,109,110,111,114,118,120,123,126,127,133],A:[12,13,14,17,18,20,23,24,25,30,32,34,35,36,41,42,43,48,51,52,56,60,61,62,65,73,75,78,79,81,84,90,92,97,102,104,106,109,110,111,113,114,121,123,124,125,126,131,132],AND:[11,18,29,76,104,112,126,127],And:[6,8,11,12,13,14,15,16,17,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,40,41,42,43,48,49,54,55,60,62,63,64,67,68,74,76,90,91,93,94,96,97,98,99,101,102,104,105,106,109,110,111,112,117,118,120,121,124,125,126,127,128,133],As:[3,6,9,10,12,14,15,18,20,22,23,25,26,27,29,30,31,32,34,36,40,41,42,43,44,48,49,51,52,53,55,57,60,61,62,63,66,67,68,70,71,72,73,74,76,77,79,81,88,89,90,91,93,94,95,96,97,98,99,100,101,102,103,104,106,107,108,110,111,113,118,120,121,126,127,133],At:[27,29,32,34,35,54,76,97,100,102,123,126],BUT:[30,80],BUt:30,Be:[27,51,52,86,92,116],Being:[15,18,74,90,111],But:[2,6,7,8,9,11,12,13,15,17,18,19,20,22,23,24,26,27,29,30,31,32,34,35,36,37,39,40,42,43,44,48,49,50,53,54,55,61,62,67,68,70,71,75,76,89,90,91,92,93,95,96,97,98,99,100,101,102,104,105,106,107,109,111,112,113,117,118,120,121,123,124,126,127,128,129],By:[0,7,10,11,15,18,20,30,31,51,52,74,81,89,97,98,102,104,108,111,113,125],FOR:11,For:[0,8,9,10,11,12,13,14,15,16,17,18,20,22,23,24,25,27,29,30,31,32,36,37,38,40,41,42,43,44,46,47,48,49,51,52,54,60,61,62,63,64,66,68,70,71,73,75,76,77,80,81,82,83,89,92,93,94,95,97,99,100,101,102,103,104,105,106,107,108,110,111,113,115,118,121,124,126,127,128,129,133],IF:11,IN:[24,32,110],If:[3,6,7,8,9,10,11,12,13,14,15,16,17,18,20,23,24,25,27,29,30,31,33,34,35,36,39,41,42,43,45,46,47,48,49,51,52,55,58,59,60,61,63,65,67,68,70,71,72,75,76,77,78,79,82,83,88,89,90,91,92,93,94,95,97,103,104,105,107,108,109,111,113,117,118,120,126,127,133],In:[2,3,6,7,8,9,11,12,13,14,15,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,37,38,39,40,41,42,43,46,47,48,49,51,52,53,54,55,57,60,61,62,67,70,71,72,73,74,75,76,77,78,79,80,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,117,118,121,122,123,124,125,126,127,128,129,133],Is:[29,46,47,49,51,52,63,107,116,121],It:[3,4,8,9,10,11,12,13,14,15,16,17,19,20,21,22,23,28,30,31,32,33,34,35,38,42,51,52,54,74,75,77,81,88,94,97,98,99,100,102,104,107,111,113,118,120,121,122,126],Its:118,NOT:[9,13,55,82,93,123],Near:[98,127],No:[17,20,25,32,36,126],Not:[6,9,15,19,30,43,47,51,88,102,106,107,111,126,127,128,129],OF:[80,133],ON:[11,12],OR:110,Of:[18,35,41,43,55,60,67,89,94,97,102,103,118,123],On:[3,8,9,12,16,41,80,94,100,118],One:[9,10,12,14,24,25,27,30,34,42,43,47,61,66,68,77,89,92,95,104,105,106,109,110,118,121,124,126,127],Ones:41,Or:[12,17,27,32,35,41,43,51,52,63,64,67,68,93,95,97,102,103,106,120],Such:79,THAT:31,THE:[24,32,124],THEN:126,TO:[11,99,126,127],That:[3,9,10,11,12,13,17,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,40,43,44,55,61,70,71,75,76,77,90,91,95,97,98,102,104,107,117,118,123,125,126,128,129],The:[0,5,9,10,13,14,15,17,18,21,22,23,24,25,27,28,30,31,32,33,34,35,41,42,43,45,48,49,51,52,54,55,56,57,60,61,62,63,65,66,68,69,70,71,74,75,77,79,80,82,83,84,86,89,90,92,93,94,95,96,97,102,106,107,108,109,110,111,112,113,114,115,120,121,123,124,126,127,128,129,134],Their:117,Then:[3,6,7,8,9,10,12,16,17,19,20,23,27,28,29,30,31,32,33,41,42,43,46,47,48,49,51,52,55,62,68,70,71,75,76,80,91,93,100,101,105,107,111,114,118,121,123],There:[4,6,9,11,13,14,15,17,18,22,23,25,26,27,29,30,33,34,36,38,40,44,46,47,48,60,66,73,74,75,76,77,80,81,89,90,92,93,95,103,105,106,109,110,113,115,118,121,123,124,126,133],These:[6,12,13,14,15,17,20,25,31,32,46,47,51,52,55,72,73,75,88,89,92,93,95,97,99,102,103,105,115,118,121,123,126,133],To:[2,3,4,6,7,8,9,10,11,12,14,15,16,17,19,20,21,23,25,27,30,31,32,39,40,43,48,49,51,52,54,55,61,62,63,66,67,68,70,71,74,75,77,79,81,82,83,84,89,92,95,100,102,103,104,106,107,108,109,110,111,112,113,114,118,120,121,122,123,126,127,128,129,133],Will:30,With:[7,9,10,12,18,24,30,34,35,39,41,57,78,80,88,91,92,93,94,96,108,120,123,124,125,126,128,129,133],_____:80,______:80,_______:80,_________:95,__call__:[118,119],__name__:17,__nonzero__:17,__setitem__:104,__traceback__:30,_build:0,_compile_for_arg:30,_concatenate_chunk:126,_core:118,_decor:126,_engin:[94,126],_ensure_numer:105,_event_pip:30,_fit_predict:[118,119],_growth:92,_has_valid_setitem_index:104,_is_master_process:30,_lib:[94,126],_locationindex:104,_lsprof:30,_mean:107,_merg:[19,111],_of:107,_pandas_api:28,_rate:92,_read:126,_read_row:126,_reader:126,_schedule_flush:30,_setitem_with_index:104,_stat:[118,119],_sync_cluster_info:33,_thread:30,_tokenize_row:126,_unbox_scalar:104,_validate_scalar:104,_valu:92,_wait_for_tstate_lock:30,_x:107,a1:89,a52aba7de6d5:13,a93c:32,a9efa4ff5a9d6612f62c70f567177170:[],a_0:77,a_1:77,a_2:77,a_:[11,78],a_boolean_vector:41,a_d:132,a_fast_funct:30,a_float_vector:41,a_fold:12,a_folder_with_stuff:11,a_medium_funct:30,a_slow_funct:30,a_string_vector:41,a_toi:78,ab:62,abandon:15,abbrevi:[110,115],abcdefghiklmnopqrstuwx1:12,abcfghloprstuw:12,abil:[11,13,14,27,31,34,35,42,63,70,71,88,89,90,118,121,123,133],abl:[9,12,13,14,15,17,24,25,26,27,29,31,32,34,35,36,40,41,53,63,70,71,73,74,76,84,88,89,90,107,108,110,111,112,113,114,116,117,118,123,124,125,126,127,133],abort:17,about:[2,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29,30,31,35,36,39,40,41,42,43,44,46,47,48,49,51,52,53,54,55,61,62,63,66,67,68,69,70,71,74,75,76,77,79,80,81,82,87,88,89,91,93,94,95,97,98,99,100,101,102,103,104,105,106,109,111,112,113,115,117,118,120,121,122,123,126,127,129],abov:[3,13,15,17,18,25,27,30,31,32,33,34,36,40,41,42,43,44,46,47,48,49,51,52,54,55,60,61,62,63,66,67,70,71,73,74,75,79,80,82,89,90,93,96,97,99,100,101,102,104,105,107,108,109,110,111,112,118,120,121,124,126,127,133],absolut:[3,17,18,19,20,25,27,32,35,62,100,111],abstractli:15,abus:18,ac:[46,47,51,52,95,102,106,107],academ:[34,125],acceler:114,accelerationord:[],accent:103,accept:[6,12,30,32,40,98,103,111],access:[6,8,10,11,14,15,16,17,23,26,29,30,31,32,38,40,41,48,73,76,89,90,92,94,97,98,103,118,121],accid:19,accident:[17,29,55,76,105],acclaim:17,accommod:[49,110,121,126],accomplish:[13,15,17,18,20,24,39,48,49,53,62,82,83,84,92,102,108,109,110,112,114,123,124,128,129],accord:[111,112],accordingli:[68,73],account:[10,11,15,33,46,47,70,71,107,110],accru:[51,52],accumul:[30,32,95],accur:[23,40,46,47,70,71,95,102,107],accuraci:[70,71],accustom:[17,62,92],achiev:[13,14,15,18,25,30,34,114,115,118],acquir:[30,34],across:[17,20,26,27,30,31,32,35,42,46,47,48,51,52,61,73,74,77,78,92,97,98,103,107,108,109,110,111,112,115,121,123,124,127],acs_5yr_hh_incom:130,act:[34,42,94,124],action:[2,19,31,109,114,118,119,123,133,134],action_ind:32,activ:[3,8,10,12,14,16,18,27,30,31,34,39,58,59,76,77,100,123,125],actual:[2,3,6,7,8,9,10,12,13,15,17,20,21,22,23,24,25,26,27,28,29,30,31,32,33,35,36,40,43,46,47,48,51,52,54,55,57,61,62,64,67,68,70,71,76,78,87,89,92,93,94,95,98,99,100,101,102,106,107,113,117,118,120,121,122,128,129],acut:26,ad:[6,9,10,12,14,20,21,25,26,29,30,40,46,47,49,60,62,68,70,71,76,79,80,88,89,94,97,109,127,134],adapt:[78,92,123],add:[0,9,10,12,13,14,17,18,19,20,22,23,24,25,26,29,30,35,38,40,42,46,47,60,62,66,68,70,71,74,75,76,77,80,88,89,96,97,100,101,107,108,109,110,111,113,114,118,119,120,121,123,124,125,127,128,129,133],add_artist:123,add_categori:104,add_select:121,add_up_entri:13,addict:35,adding_tot:30,addit:[2,3,9,12,14,15,17,22,23,29,30,31,32,34,35,36,42,51,52,53,60,68,70,71,76,77,79,94,96,97,102,106,109,110,112,118,121,123,124,126],addition:[14,82,83,84,127],addr:33,address:[9,15,17,29,32,37,48,53,57,68,70,71,101,104,107,110,111,113,118,120,126,128],adeli:[118,119],adequ:34,adjust:[13,60,64,73,107,123,126,134],adjusted_incom:64,admin:[7,33],administr:127,admit:[35,96,101,118],admittedli:[62,93],admonit:0,adob:62,adopt:[17,124,133],adult:102,advanc:[9,11,29,30,34,36,76,97,123],advantag:[14,17,20,29,76,88,92,102,104,106,107,112],advic:[34,80,91,101],advis:[51,52],advoc:[18,24,92,101],ae:120,aesthet:[17,117,124],af:33,affect:[6,20,23,27,95],affili:32,afghanistan:[126,127,128,129],afraid:28,africa:[17,88,92,93,94,97,98,103,104,105,106,126,127,128,129],african:[93,127],after:[3,8,10,12,13,15,17,20,24,27,29,32,33,34,37,38,42,48,49,53,55,58,59,60,62,66,70,71,74,82,83,84,85,86,95,97,101,103,104,111,114,117,118,123,126,133],after_tax:42,after_tax_incom:66,afterward:12,ag:[15,41,43,49,60,63,64,66,70,71,88,89,95,102,107,118,121,124,133],again:[6,8,9,11,13,17,26,31,32,43,48,58,59,62,73,74,77,79,80,93,94,104,108,110,121,124,126,127],against:[8,12,16,17,20,40,104,113,124],agenc:[34,95,115],agent:32,agg:32,aggfunc:112,aggreg:[112,113,114],agnost:[96,121],ago:[19,106,120],agre:[9,10,15,32],ah:118,ahead:[6,7,15,32,55,62,92,102,110],ai:88,aid:98,aim:34,air:[35,109,110,118,121],airport:[6,7],ak:110,aka:36,al:110,alabama:110,alaina:[112,114],alameda:113,alaska:110,albania:[93,94,126,127,128,129],albeit:118,alert:[17,101],algebra:[31,40,60,77],algeria:[93,94,126,127],algorithm:[13,15,20,31,36,72,102],alia:40,alias:38,align:[86,90,91,93,94,109],aliv:118,all:[0,2,3,4,6,7,8,9,10,11,12,13,14,15,17,18,19,20,21,23,24,25,26,27,29,30,31,32,33,34,35,37,39,40,41,42,43,44,45,46,47,48,49,51,52,54,55,57,60,61,62,63,64,65,66,67,70,71,73,74,76,77,78,79,80,81,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,109,110,111,112,113,114,115,116,118,121,122,123,124,126,127,128,129,133,134],all_prescript:32,all_rows_w_even_a:28,alloc:[20,27,41,51,52,89,118],allow:[6,7,9,10,11,12,13,14,15,25,27,28,29,30,31,32,34,36,39,40,42,43,50,53,65,67,68,74,76,78,80,89,90,91,94,100,101,104,105,108,10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lifornia:[19,62,110,111,113],californian:113,call:[2,4,6,7,8,9,11,12,13,14,15,16,17,20,21,22,23,24,26,27,29,30,31,32,33,34,36,39,40,42,43,44,46,47,48,49,51,52,54,56,57,58,59,61,62,63,66,68,70,71,76,77,78,79,80,81,86,88,89,92,93,94,96,97,98,99,100,101,102,103,104,105,109,110,111,112,113,117,118,121,123,126,128,129],callabl:94,cambodia:127,came:[9,32,40,54,80,97,99,111],cameroon:127,campaign:41,can:[0,2,3,4,6,7,8,9,10,11,12,13,14,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,120,121,122,123,124,125,126,127,128,129,133,134],can_swim:93,canada:127,cancel:[13,51,52],cancellederror:33,cancer:[15,36],candid:[32,104,127],cannot:[19,30,31,32,36,46,47,61,93,104,123,127],canon:118,canva:123,cap:103,capabl:[8,16,31,36,123],capac:115,cape:127,capfac:115,capit:[51,52,103,111],capita:[17,88,92,93,103,110,121,125,126,127,128,129],captur:[77,78,95,123,133],car:[10,42,55,73,112,121],carbon:[13,125,127,128,129],card:79,cardin:[73,118],care:[12,17,20,25,27,31,73,79,89,91,92,104,105,109,110,111,118,123,124],career:[9,10,12,34,36,41,98],carefulli:[9,14,15,18,29,32,34,63,76,91,105,113,118],careless:17,carlo:[82,83],carolina:110,carpentri:11,carri:[17,111],carrol:32,cash:35,cast:[89,104,105],casted_kei:94,casual:12,cat:[12,48,80,91,93,97,104,109,110],categor:[27,28,36,86,88,105,118],categori:[19,28,70,71,102,104,107,113,118,119,124,127],categoricaldtyp:118,cathol:121,caught:17,caus:[9,10,14,17,18,19,29,30,33,39,49,76,94,101,102,105,106,111,118,119,124],causal:111,cautiou:36,caveat:[101,104],cbgufgtrki:28,cbserial:[107,130],cd:[0,6,7,11,12,116],cdef:30,cdot:[13,78],cede:10,celebr:80,cell:[13,14,25,39,41,49,61,67,74,88,89,94,104,109,121,123],censu:[15,41,43,46,47,51,52,95,102,107,110,113],census:[107,113],cent:[33,60],center:[43,67,74,110,123,124,126],centimet:125,central:[31,32,89,93,97,104,127],centric:9,ceo:49,cerebellum:67,certain:[9,17,23,30,58,59,67,82,83,84,97,104,105,106,111,113,117,118],certainli:[13,34,67,74,109,120,124,134],certif:[34,60,95],chad:[93,127],chain:[],challeng:[32,34,78,88,95,109,123],chanc:[9,17,18,34,53,79,93,97],chang:[0,2,7,10,11,12,13,14,17,18,19,20,21,22,23,24,25,27,29,31,34,36,41,43,49,51,52,53,54,55,56,58,59,61,62,64,66,76,78,79,82,83,84,88,89,90,93,94,96,97,99,100,103,104,111,113,114,116,118,119,124,126,128,129,133,134],change_contrast:62,channel:[8,9,74,121],channel_prior:9,chao:32,chapter:[20,82],charact:[41,44,48,97,103,126,127,133],character:[41,43,79,121],characterist:[15,73,79,89,106,112],charg:102,charl:20,chart:[122,124,127,129],chatgpt:88,chdir:[17,32],cheaper:35,cheatsheet:[0,25,32],check:[12,13,14,17,18,19,20,26,28,29,30,31,32,36,41,42,44,51,52,58,59,60,64,76,77,80,86,87,89,93,95,98,100,102,105,109,110,113,118,121,122,126],checklist:14,chem:110,chevel:[],chevroelt:[],chide:17,child:[14,80,118],childhood:118,children:[95,102,106,107,118],chile:[118,127],china:[17,127,128,129],chines:41,chip:[31,35],chl:118,choic:[6,7,11,17,18,28,40,79,93,118,120,124],cholesterol:108,choos:[9,13,14,73,123,124],chose:[10,41],chosen:[32,80,117,121,124],chunk:[18,24,27,32,116,126],chunksiz:[27,32,126],ci:[116,121],ci_high:[118,119,121],ci_low:[118,119,121],ciat:12,circl:[3,10,13,54,67,99,104,118,123,128,129],circumst:[55,77,85,104],cite:[46,47,51,52],citi:75,citizen:[15,103,118],civil:111,clarifi:[21,125],clariti:[76,77,124],class_2:[49,130],class_3:[88,104,109],classifi:[113,124],classroom:[15,34,41],clean:[9,10,13,15,17,19,24,28,34,51,52,87,102,104,106,108,109,110,118,124,127],cleanli:[24,102,103,105],clear:[14,15,17,24,25,26,30,31,34,36,49,55,63,77,87,89,91,101,102,105,107,110,111,118,126,128,129],clearer:[15,77,110],clearli:[13,15,17,19,36,40,67,76,79,100,101,102,109,112,114,118,124,126,128,129,134],clever:[44,62],click:[0,3,6,7,8,9,10,11,12,14,16,17,21,25,39,46,47,51,52,126],client:[3,12,32,33,110],climat:[41,55,67],clinic:36,clip:123,clip_on:123,close:[6,10,17,19,20,26,31,32,33,35,43,51,52,54,58,59,62,74,79,93,98,103,125,128,129],closer:[31,62,67],closest:127,cloth:17,cloud:[6,7,9,10,11,27,31,35,36,75],cloudcov:75,cloudi:75,clump:[128,129],clumsi:102,clunki:63,cluster:[11,27,31,32,102,107,130],clutter:[17,121],cmap:[62,67,74],cmd:[3,7,9,11,12],cmder:[3,11,12],co2:[118,121,125,126,127,128,129],co2_al:126,co2_emissions_tonnes_per_person:126,co:[10,17,110,123,125],coal:115,coauthorship:34,code:[2,6,8,9,13,16,17,18,20,23,24,26,27,28,29,31,32,33,35,36,37,38,40,41,46,47,48,49,51,52,55,57,58,59,60,63,64,66,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,91,92,94,95,97,98,100,101,102,103,105,107,109,110,111,113,114,118,120,121,123,124,126,133,134],codebook:[70,71,111,113,115],codec:113,coeffici:[15,51,52,92],coerc:[32,49],coercion:49,coher:[25,110],coil:32,coin:10,col1:104,col2:104,col:28,col_dict:126,col_index:104,colab:9,collabor:116,collaps:[27,32,111,113,121],colleagu:[2,82,83,97,98,112,124],collect:[15,23,29,30,32,34,37,40,41,42,43,45,46,47,51,52,53,55,60,67,70,71,73,75,81,82,83,85,86,88,89,98,107,109,111,113,123,128,129],colleg:[17,34,70,71,95],colloqui:[11,13],colombia:127,colon:[13,40,97,99,133],color:[10,17,18,41,60,67,72,97,113,118,120,121,123,128,129,134],color_list:[128,129],colorado:110,columbia:110,column:[13,14,15,25,27,28,32,40,41,42,60,61,64,65,66,68,70,71,73,74,75,78,86,88,95,96,97,104,105,107,108,109,110,111,112,113,118,121,123,126,127,128,129],column_a:101,columnar:[],com:[6,9,17,19,30,32,93,94,102,103,104,106,107,118,121,126],combin:[15,17,25,27,41,42,44,48,49,61,67,87,89,112,114,120,123,125,126,127,134],combined_labeler_nam:32,come:[3,6,9,10,11,12,13,15,17,20,21,22,23,26,27,28,29,30,31,32,35,36,39,40,41,43,46,47,51,52,53,61,62,63,64,67,74,76,80,90,91,92,95,96,97,98,99,100,101,102,103,104,107,108,110,117,118,120,122,133],comedi:62,comer:32,comfort:[9,10,12,18,36,41,62,118],comm:[32,33],comma:[10,25,63,67,68,93,97,126],command:[3,4,8,9,10,16,17,23,25,27,29,32,33,35,40,46,47,51,52,55,63,70,71,81,84,95,100,101,107,110,113,123,133],command_lin:[11,12],commandlin:[8,16],commandlinetool:3,commandnam:12,commensur:34,comment:[13,17,18,21,25,126],commerc:15,commit:[0,15,21,34,111,113],common:[4,8,12,13,14,15,16,21,22,23,32,38,40,41,54,67,72,73,77,79,87,89,93,94,97,99,102,103,105,106,109,110,111,112,114,115,116,120,123,126,134],commonli:[17,21,30,41,42,51,52,60,95,97,98,102,109,123,127,133],commun:[15,17,25,34,46,47,51,52,58,59,70,71,76,95,102,105,106,107,111,116,117,118,120,124,133],comoro:[126,127],compact:[38,114],compani:[2,11,15,25,27,32,33,35,36,49,55,70,71],compar:[12,15,17,28,29,30,31,32,37,51,52,55,60,62,68,73,77,78,97,110,123,124,125,127,133],comparison:[6,15,20,32,42,123,124,133],compartment:17,compat:[4,10,30,68,81,97,98,102,117],compel:[34,124,134],compens:34,compet:26,compil:[29,30,32,36,76,121],complain:102,complet:[3,6,10,15,29,31,32,34,36,40,46,47,51,52,58,59,63,76,77,82,83,84,85,95,104,107,110,111,116,118,123],complex:[14,23,35,36,41,75,85,87,105,108,111,114,128,129],complic:[10,13,14,16,17,31,41,48,62,81,88,94,97,98,99,121],complimentari:93,compon:[14,31,34,35,38,41,74,77,87,89,123,124],compos:[25,41,117,118,120,123],composit:[19,113],compound:92,comprehens:[85,110],compress:[12,27,28,32,97,98,126],compris:[],comput:[4,6,7,9,10,11,12,13,16,17,22,23,24,25,26,28,29,30,34,36,37,39,40,41,53,54,57,60,61,62,72,73,74,76,77,78,79,81,82,83,84,85,86,87,88,96,97,98,100,102,105,108,110,112,113],computation:[35,88,114],con:[97,98],concat:[28,109],concaten:[29,66,76,83,88,108,110,133],concatent:109,concept:[9,23,32,38,48,57,73,85,86,90,108,114,124],conceptu:[15,22,32,34],concern:[34,41,94,113,120,126],concert:27,concis:[30,40,42,53,57,77],conclud:[85,116],conclus:[2,36,98,111,124],concord:[],concret:[15,61,68],conda:[3,6,9,10,12,14,31,32,33,38,40,51,52],condabin:12,condit:[13,14,17,25,30,31,48,49,75,79,104,118,121],conduct:[17,41,43,60,70,71,95,102,110,111],conf_int:[118,119,121],confid:[17,36,82,84,127],config:[9,123,124,128],configur:[5,10,12,14,20,39],configure_legend:129,configure_mark:129,confirm:[10,54,60,105],conflict:[9,10,94],confound:15,confus:[11,13,17,31,49,89,93,123,124],congo:[93,94,127],congratul:[15,36,51,52,70,71,95],connect:[11,17,32,33,34,57,101,111,114,120,123],connect_timeout:33,connecticut:110,connector:33,connot:102,consecut:48,consequ:[18,54,61,118],conserv:17,consid:[12,13,15,20,27,29,30,31,32,34,43,48,49,55,67,88,102,104,109,110,112,113,121,124,127],consider:[49,97,127],consist:[10,25,31,34,43,44,65,67,77,79,91,100,101,102,111,124],consisten:124,consol:[14,39],consolid:34,constant:[18,31,104,107,118,121,123],constantli:[17,18,75,116],constitu:[89,118],constitut:[15,36,113],constrain:25,constraint:30,construct:[21,34,48,79,82,88,89,107,115,123],constructor:[85,89],construt:93,consult:[88,117],contact:[17,46,47,51,52],contain:[10,12,25,41,43,44,46,47,48,54,57,60,62,63,67,70,71,75,81,88,89,91,93,97,102,103,108,110,115,121,123,124,126,127],content:[0,3,6,12,14,17,25,29,37,40,46,47,51,52,57,58,59,68,70,71,73,85,88,89,93,95,97,108,110,112,117,124,125,126,127,128,129,133,134],context:[15,17,25,26,36,51,52],contextu:41,contin:[125,128,129],continu:[13,14,17,20,53,92,102,103,113],contour:123,contrast:[6,12,15,24,32,34,41,43,74,92,96,97,102,118],contribut:[18,34,62,74,111,125,128],contriv:19,control:[10,13,14,15,17,20,26,41,79,86,110,118,124,133],conveni:[20,46,47,51,52,58,59,70,71,74,76,88,133],convent:[22,27],converg:[10,92],convers:[89,116],convert:[13,17,27,29,30,40,44,56,60,61,62,72,76,96,97,100,103,105,118,125,126,127,133],convert_dtyp:96,convert_feet_and_inches_to_inch:17,convolut:94,cool:[19,38,42,49,61,89,102],coolor:124,coordin:[8,9,63,67,74,128,129],copi:[0,6,12,13,17,18,22,23,27,29,31,38,51,52,57,62,66,73,77,79,86,89,104,111,114,121,126,127],copilot:[36,88],copy_on_writ:[32,101,118,119],copyright:62,corbon:126,cord:67,core:[9,10,17,26,30,31,32,33,35,73,85,89,93,94,96,104,105,108,118],corner:[36,39,63],correct:[9,13,14,17,36,41,46,47,49,58,59,75,77,78,80,97,102,105,107,111,123,124,126,127],correction_no:32,correctli:[3,6,13,20,36,48,87,111],correctly_signed_and_squar:62,correl:[67,77,111,112,118],correspond:[12,41,43,57,62,63,70,71,74,75,79,80,81,89,91,95,109,110,114,123,126,127,133],corrupt:[19,24,31,55,94,98,100,111],cost:[20,27,31,34,35,42,46,47,49,66,89,117],costa:[93,127],cote:127,could:[8,9,11,12,14,15,17,18,20,24,25,26,29,30,32,34,37,40,41,42,43,44,46,47,48,49,51,52,54,55,60,61,63,64,67,68,70,71,73,74,75,76,82,89,90,93,97,101,102,103,104,105,106,108,109,110,111,112,113,114,118,121,124,126,127,128,129,133],couldn:[23,31,43,62,66,70,71,105,112],count:[12,17,22,38,43,73,74,79,86,88,89,92,93,94,95,96,102,105,106,107,109,110,111,112,118,121,126,130,133],count_nonzero:73,count_valu:75,count_values_in_each_row:75,counter:[13,25],counterpart:118,counti:[15,32,111,113],countri:[17,46,47,51,52,88,92,93,94,97,98,102,103,104,105,106,118,121,125,126,127,128,129],coup:[103,104],coupl:[8,9,12,18,24,25,27,30,32,34,40,43,48,55,60,62,94,95,107,111,115,118,121],cours:[6,7,9,10,11,12,15,17,18,20,21,26,32,35,36,39,40,41,42,43,46,47,49,51,52,53,54,55,58,59,60,63,67,70,71,73,77,82,83,84,88,89,95,97,99,102,103,108,110,114,116,118,121,123],coursera:[39,40,46,47],cousin:6,cover:[12,17,20,27,33,34,43,54,56,62,63,75,99,103,108,118,120],coverag:30,cow:100,cp:[0,12,70,71],cpu:[31,32,33],cpu_count:[31,32],cpython:30,craft:18,cram:49,crash:[27,32],crazi:18,creat:[2,3,4,9,11,12,13,14,17,18,20,22,23,24,27,31,32,33,36,39,40,42,43,44,45,46,47,48,49,51,52,54,55,56,57,60,61,62,63,64,65,66,67,68,70,71,73,74,75,77,78,79,80,81,82,83,84,85,86,87,88,90,92,93,94,95,96,97,98,99,100,101,109,110,111,114,116,117,118,119,120,121,123,124,125,126,127,128,129,133,134],creater:32,creation:[49,96],creatur:[25,74],credenti:[33,34],credibl:34,credit:[41,64,66,70,71,108,110],credit_a:51,credit_b:51,credit_cutoff_:51,credit_cutoff_d:51,cri:[],crime:[15,17,19,113],crimin:111,criteria:[15,82,83,84,108],critic:[18,35,36,104,108],croatia:127,crop:[67,101,102],cross:67,crowd:118,crucial:[2,118],crunch:33,crush:34,cryptic:42,crystal:[89,128,129],cs:34,csv:[12,17,19,27,28,32,86,88,93,94,96,97,98,103,104,105,106,110,111,113,118,121,126,130],ct:[67,74,110],cuba:127,cudf:32,culpa:18,cumbersom:[14,17,123],cumtim:30,cumul:92,cup:79,curiou:[25,32],curl:6,curli:40,currenc:[46,47,60],current:[6,7,8,10,12,14,15,16,18,24,29,31,36,39,40,41,58,59,60,62,64,70,71,77,92,95,96,104,105,107,117,118,120,121,133],cursor:14,curt:18,curv:[43,79,118,119,123],custom:[6,10,14,15,41,60,100,109,117,118,123,128,129,134],customiz:117,cut:[46,47,67],cute:118,cutoff:[17,46,47,121,127],cutoff_a:51,cutoff_b:51,cyber:20,cylind:[],cynthia:34,cypfoxeuok:28,cypru:127,cython_avg_numbers_up_to:30,cython_w_overflow_avg_numbers_up_to:30,czech:127,czechia:127,d010:[46,47,51,52],d3:[117,121],d5a6d15d0b1463d80fbfbb777dc826f7:[],d6ab8c8f87e7:30,d:[4,6,7,9,10,11,12,14,15,20,26,29,31,32,35,38,42,49,51,52,60,61,63,64,72,75,76,79,81,88,90,97,98,100,102,103,104,106,110,111,113,118,120,121,123,124,126,127,131,132,133],dai:[9,12,17,18,24,25,26,30,31,35,40,41,42,73,75,80,89,90,102,121],daili:[6,7,34,36,73],dakota:110,damag:111,danger:[0,9,18,20,111,118],dark:[6,12,41,62,67],darker:62,darn:[],darussalam:127,dash:12,dashboard:[32,33],dashboard_link:33,dask:[16,27,31,116],dask_env:16,data:[2,6,7,8,9,10,11,12,13,14,16,17,18,19,20,22,23,25,28,29,30,31,32,33,34,42,44,45,48,53,54,55,56,57,58,59,60,61,62,63,64,65,67,68,69,72,73,74,75,76,78,79,82,83,84,85,89,90,91,92,94,96,99,100,104,109,110,112,115,117,119,120,122,123,124,125,128,129,130,133,134],data_copi:114,data_serv:121,data_transform:121,databas:[27,93,108,115,118],datacamp:[11,12],dataclass:[118,119],datafram:[13,17,25,28,31,32,60,86,87,89,95,96,99,100,101,104,105,106,108,109,110,111,112,113,114,118,119,121,126,127,128,129,131,132],dataframe_typ:32,dataframeiolay:32,dataframetreereduct:32,datanum:107,datasauru:116,dataset:[15,17,20,25,26,27,28,30,31,32,33,35,46,47,48,51,52,56,60,66,70,71,75,87,88,90,92,93,95,98,102,103,104,105,106,108,111,113,114,115,118,119,123,124,125,127,128,129],datatyp:[20,27,30,49,96,101,106],date:[32,79,88,97,109],date_pars:126,datum:121,dayfirst:126,dbc2199fcf7e6302cdd1ea2955dc378605b52659:19,dbl:121,dc:110,dd:32,ddot:78,de:[2,30,32,110],deactiv:[8,14,16],dead:31,deal:[9,14,20,27,35,46,47,68,79,85,105,106,111,113,134],dealer:111,dealership:[42,112],death:110,debat:118,debt:[51,52,102],debug:[9,12,24,30],debugging_in_vscod:13,decad:[35,111],decemb:12,decid:[9,11,15,17,24,29,30,32,34,35,49,51,52,76,111,118],decim:[17,20,23,25,40,41,49,51,52,60,70,71,89,97,126,133],decis:[73,95,108],declar:[29,30,76,120],declin:[19,55,92,111,113],decod:113,decompos:[74,118],decomposit:118,decompress:[27,28,32],decor:30,decreas:[51,52,62,111,118,134],dedic:[18,30],deduc:30,deem:25,deep:[25,35,38,57,117,126],deeper:[13,73,85,87,123,124,126],deepli:[18,34,41,93,96,101],def:[13,17,23,30,31,62,75,77,78,80,82,83,93,118,119,121,123,126,127],defaultformatt:25,defen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13,15,18,19,20,21,22,25,26,27,30,31,32,33,34,35,36,39,40,41,42,43,46,47,48,49,51,52,54,55,58,59,60,62,63,67,70,71,77,79,82,83,89,90,91,93,94,95,97,98,100,102,103,104,105,106,110,111,112,113,116,118,121,123,124,126,127,134],done:[3,6,7,8,10,11,15,24,25,27,29,30,32,40,43,48,67,73,76,77,94,95,98,99,101,102,104,109,118,123,126,127,128,129],dont:[8,16,30],door:35,dos_str:32,dosage_unit:32,dot:[6,14,19,38,118,119,120,124],dotfil:12,doubl:[0,3,12,23,25,29,30,31,35,37,46,47,55,62,77,96,104,126],double_nonvector:77,double_vector:77,doublequot:126,doubt:101,dove:85,down:[9,11,17,20,27,28,30,31,36,38,43,46,47,51,52,55,61,73,81,95,102,110,111,112,113,128,129],download:[3,6,7,9,10,12,15,24,32,95,111,113,126],downstream:13,downward:[14,57],dozen:[2,9,17,18],dpi:124,dqcfywcfmx:[],dracula:6,draft:36,drag:121,dragon:80,dramat:53,drastic:77,draw:[15,48,70,71,79,80,123,124],drawn:62,dream:[96,123],drew:[38,80,86],drift:17,drive:[26,27,32,35,41,55],driven:[51,52,73,111],driver:125,drop:[17,27,32,34,46,47,48,51,52,89,93,95,97,106,107,109,110,118,119],drop_dupl:130,dropbox:[17,32,88],dropdown:121,dropna:[106,110,118,119],drug:[32,36,113],drug_cod:32,drug_nam:32,ds:126,ds_store:12,dt:32,dta:[28,93,95,98,102,106,107],dtype:[20,27,28,29,32,41,44,48,49,60,62,88,89,90,91,93,94,96,101,102,103,104,105,106,107,110,111,112,113,118,126,127,130],dtype_backend:96,dubiou:96,duct:13,due:[15,20,31,36,72,101,106,125],dug:19,duke:[9,10,11,12,25,34,35,36],dumb:31,dump:97,duplic:[12,31,77,80,82,83,84,113,127],durat:35,duration_with_copi:55,duration_with_view:55,dure:[8,9,12,15,16,17,19,25,27,30,33,34,42,46,47,51,52,79,126],dutch:[],dynam:[29,30,57,76,89,92],dz:126,dza:126,dzvsmydwbp:28,e:[2,3,4,7,8,9,11,12,13,14,16,17,20,24,25,26,27,29,30,31,32,34,38,40,41,42,43,46,47,48,49,51,52,56,62,63,65,67,68,70,71,76,79,80,81,88,89,90,92,93,94,96,97,98,99,102,103,104,105,106,107,108,109,110,113,116,118,121,122,127,132],each:[8,10,12,13,14,15,16,17,18,20,25,26,27,28,29,30,31,32,34,37,40,41,42,43,45,46,47,48,49,51,52,55,57,60,61,62,63,67,68,70,71,73,74,75,76,77,78,79,80,81,82,83,84,88,89,90,91,92,93,94,95,97,100,101,102,103,107,108,109,110,111,112,113,114,116,118,119,121,123,124,126,127,128,129,134],ear:80,earli:[2,8,12,15,17,19,20,34,36,40,96,126],earlier:[12,14,17,73,75,79,80,114,124,126],earn:[25,41,43,46,47,49,51,52,63,92,95,107],earner:[43,51,52],earth:[13,125],eas:[0,40,80,126],easi:[2,9,10,13,15,17,20,21,24,27,29,30,31,32,36,37,39,40,42,54,61,66,67,70,71,76,88,93,94,95,97,99,100,102,113,117,118,120,121,124,129],easier:[6,8,12,16,17,18,24,25,30,32,33,35,38,42,60,62,63,73,75,77,86,87,88,89,91,92,94,95,97,100,103,104,110,112,113,114,118,120,121,124,128,129,133],easiest:[12,33,89,93,95,100,133],easili:[2,9,10,12,13,14,17,18,24,25,27,28,35,36,40,41,42,45,49,60,63,67,74,79,87,88,89,97,103,108,109,111,118,120,121,124,126,133],east:[17,93,94],eastern:[113,126],echo:12,econ:110,econom:[17,70,71,92,95,102],economi:118,economist:[92,118],ecosystem:[2,3,38,88,97,116],ecuador:127,edg:[10,20,43,67,79,120,123,128,129],edgealpha:[],edgecolor:[120,123,128,129],edgestyl:[],edgewidth:[],edit:[2,10,12,13,14,17,38,49,54,62,65,99,100,103,106,116,118,126],editor:[2,3,9,10,13,25,97,98,100],edu:[46,47,51,52],educ:[9,34,36,60,63,64,66,70,71,118],edward:124,eerili:39,ef233c:124,effect:[9,13,14,15,17,20,27,32,36,39,43,51,52,62,70,71,77,93,95,100,111,112,118,125],effici:[13,15,18,27,30,31,32,38,40,45,55,60,75,77,85,87,96,101,108,112,114,118,123,124],efficiency_data:55,efficieni:124,effort:[2,17,34,51,52,70,71,91,93,98,110,111,118,124,126,127],effortlessli:96,eg:126,egi:126,egregi:[70,71],egrid2016:115,egrid:115,egweyn:[112,114],egypt:[17,126,127],eh:126,eight:[40,75],either:[9,10,11,12,13,14,15,17,27,31,34,35,39,42,48,49,54,56,60,73,89,90,93,95,99,100,101,102,105,109,110,112,118,121,124],el:127,elaps:77,elder:80,elect:41,electr:55,element:[29,38,42,48,49,50,54,61,63,64,68,73,77,78,89,90],eleph:[109,110],eleven:60,elid:30,elif:126,elimin:[79,127],elon:43,els:[13,18,24,27,30,34,48,96,97,104,123,124,126],elsewher:[18,121],emac:12,email:[6,7,18,34],embarass:17,embarassingli:31,embark:34,embodi:[10,120],embrac:42,emerg:[17,18,36],emir:[126,127,128,129],emiss:[118,121,127,128,129],emoji:41,emp_rat:102,emphas:[10,17,21,22,23,27,32,49,86,93,94,118,121],emphasi:124,empir:[36,80],emploi:[70,71,79,95,102,106,107],employ:[70,71,95,102,106],employe:[15,42,49,70,71,95,110,112,114],employees_by_year:112,empow:9,empstat:[95,102,106,107],empti:[16,17,27,48,75,77,126,133],en:[0,47],enabl:[10,25,39,77,89,96,100,101,104,116,121,124],enact:[51,52],enclos:[80,97],encod:[62,97,98,102,113,116,120,121,126,128,129],encoding_error:126,encount:[14,17,19,23,30,31,34,40,56,79,85,88,89,93,97,98,106,108,109,110,114,127,133],encourag:[13,27,82,118,123,127],end:[6,9,10,11,13,15,18,20,22,23,24,29,30,31,32,34,35,37,40,42,48,49,55,60,61,63,67,70,71,78,89,90,93,96,97,103,104,108,110,111,114,118,119,124,125,126],endless:106,endog:[118,119],endpoint:55,enemi:77,energi:[9,10,15,55,115,125],engag:[21,34,36],engin:[8,9,15,16,17,29,30,76,85,87,116,123,126],enhanc:[89,90,123],enjoi:35,enough:[9,11,14,20,24,25,27,29,32,35,36,40,51,52,63,70,71,74,81,93,97,107,120,121],enrol:34,ensu:32,ensur:[6,9,10,15,17,18,20,21,25,30,32,34,49,55,60,70,71,80,82,83,93,126],entail:[34,36],enter:[6,10,32,60,74,82,110],enterpris:32,entir:[2,6,9,17,18,23,24,29,30,31,36,41,48,62,63,67,76,87,99,104,120,123,124,127],entireti:93,entiti:[15,41,70,71],entri:[13,17,19,22,23,26,27,29,37,42,45,48,49,50,54,55,56,57,60,61,62,63,65,68,69,73,75,77,79,82,83,90,91,93,94,96,97,99,100,101,102,103,104,106,109,110,112,113,126,128,129],enumer:[13,123,124,128,129],env:126,environ:[3,4,5,6,7,9,12,13,14,33,38,81],environment:115,eon:80,epa:115,epoch:40,epvgfgzts:28,equal:[13,18,23,30,48,61,67,68,74,78,79,82,83,84,94,95,104,109,118,127],equatori:127,equit:118,equival:[11,32,38,60,67,77,88,115,133],eras:[12,17,27],erika:25,eritrea:[93,94,127],err:[36,94,105],erron:14,error:[3,12,14,17,18,19,25,27,30,32,33,36,42,48,49,61,68,82,83,87,88,89,93,94,98,101,102,104,105,111,113,116,118,121,126],error_bad_lin:126,error_rewrit:30,errorband:121,errorbar:123,escap:[12,100,103,133],escapechar:126,esh:126,esoter:54,especi:[3,11,12,17,20,25,27,31,34,35,36,40,41,57,60,64,112,113,118,123],esperanza:[112,114],essai:36,essenti:[10,26,88,89,100,101,109,110],establish:[34,35,49,55,104],estim:[17,26,32,40,41,42,46,47,51,52,60,70,71,72,82,83,92,107,108,118,119,123],estonia:127,estout:17,eswatini:127,etc:[0,2,3,6,7,9,11,12,13,15,17,24,25,28,29,30,32,34,35,36,39,40,41,42,51,52,60,61,68,75,76,81,82,83,84,85,86,89,97,98,100,101,103,105,108,110,113,115,116,118,120,123,133],ethiopia:127,ethnic:[70,71,107],etiquett:116,eubank:[1,25],euituavzq:28,europ:[88,93,94,97,98,103,104,105,106,126,127,128,129],eval:[114,126],evalu:[4,12,13,14,15,17,24,29,36,40,48,49,51,52,55,76,77,79,81,91,108],evang:121,even:[3,8,9,10,12,13,14,15,18,19,20,21,24,25,26,27,30,31,32,33,34,39,40,41,42,43,44,47,48,49,51,52,54,55,60,62,69,73,77,79,82,83,84,88,89,90,91,94,97,99,102,103,104,106,107,108,112,113,118,121,123,124,133,134],even_integers_through_1000:83,even_row:28,evenli:[51,52,92],event:34,eventu:[32,111,126],ever:[10,11,12,13,17,18,23,24,29,30,31,42,55,63,67,80,89,90,94,96,97,100,102,105,117,118,121],everi:[9,10,12,14,15,17,18,23,24,29,30,31,34,40,42,46,47,51,52,55,73,75,76,77,89,93,95,97,98,100,102,111,113,117,118,121,123,134],everyon:[9,10,15,17,25,31,32,34,41,46,47,49,51,52,60,63,70,71,89,94,107,118],everyth:[6,9,12,13,18,19,21,24,25,26,27,29,30,31,35,36,38,40,41,43,44,47,49,53,60,62,69,74,89,91,95,97,100,102,103,105,109,113,114,125,126,127],everywher:[8,12,17,33,49],evid:[39,51,52,68,95,105,111,134],evidenc:[14,34,68,121],evil:49,evolut:[53,67],evolutionari:118,evolv:[13,31,36,67,96,118],ewcskolhuv:[],ex10_gini_policy_d:51,ex11_gini_policy_:51,ex4_gini:51,ex4_share_below_poverti:51,ex6_gini_policy_a:51,ex6_gini_policy_b:51,ex7_gini_policy_c:51,ex8_revenue_rais:51,ex9_transf:51,ex:3,exact:[6,12,20,23,29,32,40,41,51,52,62,77,103,120,127],exactli:[3,13,15,17,18,29,30,32,33,37,48,49,77,93,94,97,102,110,111,113,117,123,125],exam:34,examin:[107,111,113,118],exampl:[2,8,9,10,11,12,13,14,15,19,20,21,22,23,24,26,27,28,29,32,34,35,37,38,40,41,42,43,44,46,47,48,49,51,52,54,55,58,59,60,61,62,63,64,66,67,68,70,71,72,73,74,77,78,79,80,81,82,85,86,89,90,93,94,96,97,98,99,101,102,103,104,105,106,107,109,111,112,113,114,115,117,118,121,122,123,124,126,127,128,129,133],example_csv:[11,12],example_data:[11,12,17,19,93,94,103,104,106,118,121],exc:33,exceed:[118,121],exceedingli:[24,56],excel:[15,18,26,40,60,76,82,83,84,93,97,98,124,127],except:[14,17,20,21,23,25,30,32,33,46,47,51,52,58,59,65,70,71,86,90,93,94,102,105,113,114,127],exception:79,exchang:[21,40,46,47],excit:[10,15],exclud:107,exclus:[70,71],excruci:32,exec:[30,127,128,129],execut:[3,4,10,12,13,14,17,25,26,29,30,31,32,33,39,76,77,78,79,81,82,83,90,100,101,110,114],exercis:[6,15,21,25,35,36,43,45,70,71,75,77,79,84,90,93,95,102,103,106,111,113,126,127],exhaust:[31,98],exhibit:[97,106],exist:[4,10,12,17,18,20,22,23,24,27,31,34,36,49,54,63,81,89,90,93,94,95,97,101,110,118,125],exit:[3,12,34],exog:[118,119],expand:[2,35],expect:[13,17,18,19,24,32,35,46,47,51,52,64,73,74,79,89,91,95,97,100,103,109,110,111,113,118,121,124,126,127],expens:[26,34,35],experi:[4,7,9,11,15,17,24,27,31,32,54,55,70,71,74,79,81,91,93,115,120,124],experienc:[18,42,104],experiment1:[51,52],experiment:[79,120],expert:[13,97],expertis:[36,112],explain:[36,40,41,43,48,49,100,101,118,123,124],explan:20,explanatori:126,explicit:[21,42,79,89,91,101,113],explicitli:[8,13,15,16,20,29,58,59,61,76,77,88,89,94,101,107,113,121,124],explor:[12,13,14,32,51,52,67,72,73,74,75,77,78,79,82,83,84,85,87,89,90,92,93,94,102,108,109,110,111,112,115,116,118,121,123,124,125,127,128],exploratori:[116,118,121,122],explos:17,expon:[62,124],exponenti:129,exported_statist:17,expos:[55,68,118,121],exposit:[93,116],express:[14,17,31,34,48,76,77,82,83,103,108,112,114],ext_modul:30,extend:[41,43,74,104],extens:[14,25,32,34,36,38,39,60,81,97,98,11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118,119,121,128,129],scale_x_continu:[],scaler:68,scan:[41,67,74],scandinavia:[93,94],scarc:118,scari:9,scatter:[111,116,118,119,120,121,122,123,128,129],scatterplot:[120,121,128,129],scenario:[79,82,83],scene:[32,39,49,55,62,96,99,100,120],schedul:[30,32,33],schema:121,scheme:60,school:[34,36,64,70,71,95,102,107,118],schouweil:[46,47,51,52],sci:[29,76],scienc:[2,6,7,8,9,11,12,15,16,17,18,20,24,25,26,27,30,31,38,41,42,43,45,46,47,48,51,52,53,54,58,59,60,67,70,71,72,73,75,79,82,83,84,85,87,88,89,90,93,102,108,111,112,115,117,118,120,123,127],scientif:[31,109],scientist:[6,7,8,9,10,12,15,17,18,20,24,27,29,30,31,32,34,35,36,40,60,62,67,74,76,77,85,87,89,95,97,98,102,106,116,117,120,121,134],scikit:[30,32,79,116],scipi:[4,18,81],scope:[14,34],score:[93,103,104,106,127],scrap:123,scratch:32,screen:[12,123,128,129],screw:[12,17],script:[11,12,13,30,86],scroll:[113,121],sd:[110,126],sdn:126,se:[32,121],sea:125,seaborn:[19,123],search:[9,10,12,14,18,25,36,39,80,87,95,126],second:[12,15,17,20,22,24,25,26,29,30,31,33,34,37,40,41,42,43,48,49,54,60,61,63,65,66,67,68,73,74,75,77,85,87,88,89,90,93,94,97,98,99,101,104,105,108,110,111,113,115,117,118,121,126,127],second_column:113,second_seri:90,second_subset:55,secondari:118,secret:[12,29,76,91],section:[3,11,12,17,18,20,30,31,62,67,76,79,88,89,101,103,104,108,110,114,115],secur:[20,31,36,113],see:[2,3,4,6,7,8,9,10,12,13,14,15,16,17,18,19,20,22,23,25,29,30,31,32,33,35,36,37,38,39,40,41,42,43,46,47,48,49,51,52,53,54,55,57,60,61,62,64,67,68,73,74,75,76,77,79,80,81,82,83,84,89,90,91,93,94,95,96,97,98,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,118,120,121,122,123,124,126,127,128,129,133],seed:[20,78,79,82,83,84,102,123],seek:[41,79,102],seem:[10,12,15,18,19,20,21,31,32,34,40,43,46,47,51,52,54,89,93,94,95,103,105,107,118,120,127],seen:[21,25,30,32,38,39,40,41,42,46,47,62,67,77,79,113,114],segment:57,select:[3,6,7,8,9,10,14,15,16,27,39,46,47,49,51,52,63,72,74,79,80,88,90,92,94,99,107,108,114,121,123,124,127],selection_interv:121,selection_singl:121,selector:[16,121],selectornam:121,self:[17,30,47,94,104,118,119,126,134],sell:[42,111],semest:[8,9,25],semi:[97,128,129],semicolon:126,semiolog:118,sen:[],send:[27,30,33,34,39,86],send_recv_from_rpc:33,seneg:127,senior:108,sens:[6,9,11,13,19,20,29,32,34,36,40,42,43,44,53,54,61,72,73,76,82,83,88,95,96,97,98,102,107,111,115,118],sensit:[12,35,74,118],sensor:[74,106],sent:98,sentec:15,sentenc:[15,80,133],sentinel:[102,106,107],sep:[32,97,126],separ:[2,3,12,31,46,47,51,52,55,63,67,70,71,81,82,83,90,93,97,99,104,107,109,110,120,123,124,126,128,129,133],seper:30,septemb:10,seqplt16:115,sequenc:[20,23,31,61,79,115,120],sequencematch:127,sequenti:[24,31,99],serbia:127,seri:[12,17,20,29,32,35,37,41,43,67,79,80,86,87,91,94,99,100,103,105,106,123,124,127],serial:[31,107,130],series_dtyp:32,series_w_numeric_index:91,seriou:[19,29,35,76,120],serious:[15,18,21,27,32,62,66,97,101,102],serv:[],server:[11,12,31],servic:[11,17,32,33,36,118],session:[3,6,7,8,10,12,14,16,24,31,32,33,39,40,58,59,92,100],set:[0,2,6,7,8,12,14,15,16,17,20,23,24,25,26,27,30,31,32,33,34,35,36,38,39,40,42,48,49,51,52,54,62,67,70,71,72,77,78,79,82,83,84,85,86,89,93,95,100,101,102,103,104,106,107,108,109,111,112,113,114,117,118,121,123,124,126,127,128,129],set_axisbelow:[128,129],set_bound:124,set_opt:[32,101,118,119],set_ticks_posit:124,set_titl:123,set_vis:[123,124],set_xlabel:[123,124],set_xlim:123,set_xtick:124,set_ylabel:[123,124],set_ylim:123,setitem:104,settingwithcopywarn:[101,104],settl:62,setup:[3,5,6,8,10,14,30,32,33],seven:32,sever:[6,7,11,12,17,19,25,26,30,36,40,41,48,49,55,60,88,111,113,117,118,120,121],sex:[102,118],sexist:15,seychel:127,sf:8,sfvmhgqeyn:[],sh:[6,7],shade:124,shall:80,shallow:38,shape:[15,18,35,36,49,60,61,62,65,67,68,69,70,74,78,82,83,93,118,123,126,130],share:[2,17,19,20,25,41,42,43,46,47,51,52,54,55,56,70,71,87,89,92,99,101,102,109,110,111,112,113,118,121,123,124,125,134],share_feloni:19,sharpli:19,she:[17,34,41,49,80],sheepskin:95,shelf:[22,23,57],shell:[6,10,11,12],shift:[3,10,55,62,94,113],shifted_and_scaled_back:62,shine:[74,112],shipment:32,shockingli:36,shoot:29,short_str:127,shortag:123,shortcom:31,shortcut:[6,7,13,100,108],shorter:[29,37],shortest:[42,89],shorthand:[102,126,127],shortli:0,shot:92,should:[3,4,6,8,10,12,13,15,16,18,24,25,26,27,28,29,30,32,34,35,36,39,40,43,49,51,52,55,57,58,59,61,62,65,73,76,78,80,81,89,91,93,95,97,100,101,102,105,106,107,111,113,116,117,118,119,121,123,124,126,127,133],shouldn:[61,67,113],shout:80,shove:32,show:[3,6,7,10,12,14,15,17,18,26,27,30,31,34,47,57,61,67,73,77,79,85,86,93,95,102,109,111,112,113,114,121,123,124,128,133],showcas:123,shower:79,shown:[14,17,20,31,36,43,57,79,89,102,109,110,112,114,123,125,128,129,133],shrink:73,shrunk:125,shuffl:[20,79,89,93],shy:67,si:126,sid:126,side:[10,29,33,36,37,41,43,48,49,55,60,67,89,92,94,104,133],sidewai:42,sierra:[93,127],sign:[12,13,17,46,47,62,94,104,108,110,124],signal:[19,41,95,123],signatur:[10,88],signific:[15,25,31,36,51,52,72,87,94,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,ty:100,type:[3,4,6,10,11,12,14,15,17,18,20,22,25,27,28,29,31,32,34,36,38,39,40,42,45,46,47,48,53,55,62,63,65,66,67,68,70,71,73,74,76,77,79,81,87,91,93,96,97,98,99,100,102,104,109,111,113,115,118,120,121,123,126,127,129,133,134],typeerror:[13,93,94,104,105],typeset:124,typic:[14,73,75,77,81,93,108,109,110,123,124,128,129],typingerror:30,typo:[13,111],typographi:124,u1:126,u21:44,u32:88,u4:48,u7:41,u:[41,46,47,51,52,110,115],uae:17,ubiquit:[6,7,108],ufunc:88,ufunctypeerror:88,uganda:127,ugh:[90,100,101],ugli:13,uh:[19,62,102],ui:10,uint16:20,uint32:20,uint64:20,uint:20,ukrain:[88,93,97,98,103,104,105,106,127],ultim:47,ultra:[13,35],umn:[46,47,51,52],un:[34,126],unabl:[32,114],unavail:[11,81,106],unawar:[34,111],unbeknownst:15,uncaught:14,uncertain:55,unchang:[73,101],uncheck:[10,14],unclear:94,uncomfort:36,uncommon:[31,54,106],uncompress:[27,28],uncov:118,undata:126,undefin:62,under:[10,27,32,36,46,47,51,52,57,88,93,102,103,118,121],underestim:[51,52,107],underflow:20,undergradu:[15,34],underli:[2,3,8,9,10,13,19,22,23,31,32,55,60,62,93,96,99,118,134],underscor:[12,22],understand:[2,6,12,13,14,15,18,19,20,21,23,25,27,29,31,32,34,35,36,38,42,53,54,55,57,58,59,61,70,71,75,76,77,86,87,88,89,91,96,97,100,101,102,107,112,113,114,116,117,118,124,126,129],understood:[128,129],undertak:[51,52],underwai:2,undesir:14,undisput:102,unemploi:[95,102,106],unemploy:102,unemployment_percentag:17,unequ:92,unexpect:[57,126],unfamiliar:123,unfold:17,unfortun:[18,20,23,29,76,100],ungrad:62,unhthmzuna:28,unicod:[41,97,98],unicodedecodeerror:113,unifi:[2,30],uniform:[18,41,43,51,52,79,80,123],uniformli:[20,79],unimagin:26,unimport:12,unintend:13,unintenti:100,union:8,uniqu:[17,23,31,36,42,74,83,89,90,100,102,104,105,106,107,110,113,121,123,124,126,127],unique_charact:126,unit:[17,25,31,32,38,41,43,46,47,51,52,60,70,71,93,94,95,102,104,107,110,113,124,126,127,128,129],united_nations_contin:126,univers:[11,15,25,33,34,47,97,98],unix:[20,40],unknown:34,unlabel:15,unless:[11,12,14,17,35,79,97,126],unlik:[4,9,12,17,23,25,28,29,30,32,36,40,49,51,52,58,59,61,63,65,74,76,79,90,93,97,102,106,120],unmatch:127,unnecessari:25,unneed:27,unnot:17,unpack:31,unpredict:100,unproduct:17,unread:[118,121],unreason:18,unsign:20,unsubscrib:18,unsuccess:61,unsupervis:116,unsupport:[13,30,93],unsupportederror:30,unsurprisingli:103,until:[10,12,14,17,23,29,32,46,47,54,70,71,74,76,100,101,118,120,127],unto:[31,98],unusu:[97,98],unwant:[18,110],unwittingli:13,unzip:7,up:[4,6,7,11,12,13,14,17,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,39,40,41,42,43,44,45,46,47,48,49,51,52,55,57,60,61,62,63,67,68,70,71,72,74,76,77,79,81,87,89,90,91,92,93,94,95,96,97,100,101,102,103,104,105,108,109,110,111,112,113,119,120,121,123,124,126,133],upcom:[42,51,52,79,102,110],updat:[0,4,14,17,21,25,32,49,51,52,64,66,81,86,88,113,118,126],updated_them:118,upfront:[17,27],upon:[8,14,123],upper:[17,20,23,67,103,123],uppercas:23,upsid:[17,40],upward:118,urban:[34,124],urg:80,uri:[],url:[102,121],uruguai:127,us:[0,2,3,4,6,7,9,10,11,13,14,15,17,18,20,21,22,23,26,27,31,33,34,35,41,42,43,45,48,49,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,68,70,71,72,73,74,75,77,78,79,80,81,82,83,84,85,86,87,88,91,92,93,95,97,100,102,103,104,105,106,107,109,110,111,112,113,114,115,116,117,118,119,120,121,122,125,126,127,128,129,133],us_acs_2017_10pct_sampl:[95,102,106,107],us_americancommunitysurvei:[102,106,107],us_household_incom:[43,46,47,51,52,97,130],usa:[46,47,51,52],usag:[12,93,96],usbempwyah:[],usd:[128,129],use_inf_as_na:118,usecol:[27,32,126],user:[2,6,7,9,10,11,12,13,15,17,18,20,23,25,28,29,30,31,32,33,35,36,39,46,47,49,51,52,54,88,89,91,96,97,99,100,101,104,105,109,111,117,118,120,121],user_guid:[101,104],usernam:18,userwarn:94,usr:3,usual:[4,11,12,15,18,20,21,24,27,29,30,32,33,41,53,55,60,67,70,71,76,81,90,93,97,98,104,105,117],ut:110,utah:110,utc:40,utf:113,util:[6,9,27,63,97,98,103,106,111,117,126],uzbekistan:127,v0_8:[19,118],v11:[46,47,51,52],v1:68,v2:[27,32,68],v5:121,v:[12,13,38,46,47,83,86,116,130],va:[110,123],vacat:62,vagu:15,valid:[12,13,19,79,111,118],validate_integ:126,valign:[],valu:[2,12,13,14,15,17,18,19,20,23,25,29,30,31,32,35,39,40,41,42,43,44,46,47,48,49,50,51,52,54,55,57,58,59,62,64,65,66,67,68,70,71,73,74,77,78,79,80,82,83,84,86,92,93,94,95,96,97,99,100,101,102,104,105,106,107,108,109,110,111,112,113,115,118,119,121,123,124,125,126,127,128,129],valuabl:[14,17,27,34,42,43,60,74,93,118],value_count:[89,95,102,103,105,107,111],value_list:[82,83],valueerror:[17,42,48,49,61,68,89,105],vanessa:21,vanilla:[9,13,20,30,45,48,53],vanish:118,vanuatu:127,vapor:125,var212:17,vari:[77,80,107,113,124],variabl:[6,7,9,13,14,15,17,21,24,27,29,30,32,40,46,47,48,51,52,54,55,64,70,71,76,77,79,81,85,89,92,93,94,95,99,102,103,104,105,107,109,110,111,113,114,115,118,121,133],varianc:[42,89],variant:80,variat:[15,51,52,118],variou:[6,7,10,15,24,32,41,73,88,97,111,118],vast:[43,125],vconcat:[],vct:[],vdot:78,ve:[2,3,6,9,10,11,12,14,15,17,18,19,22,23,24,25,26,28,30,31,32,34,36,38,39,4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Data Science","Practical Data Science in Python","The Jupyter Project","Running R Jupyter Notebooks in VS Code","Managing Python Packages","Set Up an Augmented Command Line","Improving Your Command Line Experience (macOS)","The Command Line on Windows / Installing Bash","Installing Geopandas","Installing Python and Miniconda","Setting Up Visual Studio Code","The Command Line, Basics","Advanced Command Line Tutorial","Principles and Tools for Debugging","Debugging Python in VS Code","Backwards Design","Managing Conda Environments","Defensive Programming","Getting Help Online","The Iceberg Principle","Numbers in Computers","Reviewing Code on Github","Python vs. R: Important Differences To Be Aware Of","Python: Variables vs. Objects","Workflow Management","Writing Good Jupyter Notebooks","What is Big Data?","Big Data Strategies","Parquet","Speed in Python","Solving Performance Issues","Parallel Computing","Distributed Computing with dask","Starting a Cloud Dask Cluster","So You\u2019re Thinking About a PhD","Advice on Buying Computers for Data Science Students","chatGPT and You","The Object dtype and Performance","Class Two: Representing Data: Objects, collections, and their uses for computation (Numpy)","Running Python in the VS Code Interactive Window","Numpy Arrays: The Workhorse of Data Science","Working with Vectors","Doing Math With Vectors","Visualizing Your Data with Histograms","Type Promotion in numpy","Vector Recap","(Ungraded) Vector Exercises","(Ungraded) Vector Exercises","Subsetting Vectors","Modifying Subsets of Vectors","Recap","Week 2 Numpy Re-cap Exercises","Week 2 Numpy Re-cap Exercises","Welcome to Matrices","Views and Copies","When Do I Get a View and When Do I Get a Copy?","Review of Views and Copies","Variables are pointers to objects","Views and Copies Exercises","Views and Copies Exercises","Intro to Matrices","Folding and Reshaping Matrices","Matrices as Images","Subsetting Matrices","Editing Subsets","Review of Matrices","Matrix Exercises","Arrays","Broadcasting","Review of ND-Arrays","Week 3 Quiz","Week 3 Quiz","Welcome to Week 4!","Summarizing Arrays","Color Images as Arrays","Practical Example: comparing weather by counting like-values","The Python Ease of Use / Speed Tradeoff","Vectorization in Python","Exercise: Speeding up matrix operations","Random numbers","Exercise 1: Calculating the mean and variance of common random variables","Understanding and Managing Python Packages","Week 4 Assessment","Week 4 Assessment Solutions (NOT FOR POSTING)","Assessment: filtering and querying a dataset","Course Wrap-up","Class Three: Loading and manipulating data","Programming with data","Working with tabular data","Pandas Series","Subsetting and Indexing Series","(OPTIONAL) Subsetting and Indexing with Single Square Brackets ([])","Series Exercises","Working with tabular data through Dataframes","Subsetting DataFrame Tricks and Gotchas","Estimating Labor Market Returns to Education","PyArrow: An Alternative to Numpy 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Python: customizing plots in matplotlib","Project: Global greenhouse gas emissions","Project: gathering, inspecting, and cleaning the data","Project: Data merging","Project: Plotting","Project: Plotting","<no title>","<no title>","CONCAT","Combining numbers and text into strings: f-strings","Why not just use Excel or 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0,53,54,55,61,62,67,68,70,71,75,76,89,90,91,92,93,95,96,97,98,99,100,101,102,104,105,106,107,108,110,112,113,114,118,119,121,122,124,125,127,128,129,130],By:[0,7,10,11,15,18,20,30,31,51,52,74,81,89,97,98,102,104,109,112,114,126],FOR:11,For:[0,8,9,10,11,12,13,14,15,16,17,18,20,22,23,24,25,27,29,30,31,32,36,37,38,40,41,42,43,44,46,47,48,49,51,52,54,60,61,62,63,64,66,68,70,71,73,75,76,77,80,81,82,83,89,92,93,94,95,97,99,100,102,103,104,105,106,107,108,109,111,112,114,116,119,122,125,127,128,129,130,134],IF:11,IN:[24,32,111],If:[3,6,7,8,9,10,11,12,13,14,15,16,17,18,20,23,24,25,27,29,30,31,33,34,35,36,39,41,42,43,45,46,47,48,49,51,52,55,58,59,60,61,63,65,67,68,70,71,72,75,76,77,78,79,82,83,88,89,90,91,92,93,94,95,97,103,104,105,107,108,109,110,112,114,118,119,121,127,128,134],In:[2,3,6,7,8,9,11,12,13,14,15,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,37,38,39,40,41,42,43,46,47,48,49,51,52,53,54,55,57,60,61,62,67,70,71,72,73,74,75,76,77,78,79,80,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,118,119,122,123,124,125,126,127,128,129,130,134],Is:[29,46,47,49,51,52,63,108,117,122],It:[3,4,8,9,10,11,12,13,14,15,16,17,19,20,21,22,23,28,30,31,32,33,34,35,38,42,51,52,54,74,75,77,81,88,94,97,98,99,100,102,104,108,112,114,119,121,122,123,127],Its:119,NOT:[9,13,55,82,93,124],Near:[98,128],No:[17,20,25,32,36,127],Not:[6,9,15,19,30,43,47,51,88,102,106,108,112,127,128,129,130],OF:[80,134],ON:[11,12],OR:111,Of:[18,35,41,43,55,60,67,89,94,97,102,103,119,124],On:[3,8,9,12,16,41,80,94,100,119],One:[9,10,12,14,24,25,27,30,34,42,43,47,61,66,68,77,89,92,95,104,105,106,110,111,119,122,125,127,128],Ones:41,Or:[12,17,27,32,35,41,43,51,52,63,64,67,68,93,95,97,102,103,106,121],Such:79,THAT:31,THE:[24,32,125],THEN:127,TO:[11,99,127,128],That:[3,9,10,11,12,13,17,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,40,43,44,55,61,70,71,75,76,77,90,91,95,97,98,102,104,107,108,118,119,124,126,127,129,130],The:[0,5,9,10,13,14,15,17,18,21,22,23,24,25,27,28,30,31,32,33,34,35,41,42,43,45,48,49,51,52,54,55,56,57,60,61,62,63,65,66,68,69,70,71,74,75,77,79,80,82,83,84,86,89,90,92,93,94,95,96,97,102,106,108,109,110,111,112,113,114,115,116,121,122,124,125,127,128,129,130,135],Their:118,Then:[3,6,7,8,9,10,12,16,17,19,20,23,27,28,29,30,31,32,33,41,42,43,46,47,48,49,51,52,55,62,68,70,71,75,76,80,91,93,100,105,108,112,115,119,122,124],There:[4,6,9,11,13,14,15,17,18,22,23,25,26,27,29,30,33,34,36,38,40,44,46,47,48,60,66,73,74,75,76,77,80,81,89,90,92,93,95,103,105,106,110,111,114,116,119,122,124,125,127,134],These:[6,12,13,14,15,17,20,25,31,32,46,47,51,52,55,72,73,75,88,89,92,93,95,97,99,102,103,105,107,116,119,122,124,127,134],To:[2,3,4,6,7,8,9,10,11,12,14,15,16,17,19,20,21,23,25,27,30,31,32,39,40,43,48,49,51,52,54,55,61,62,63,66,67,68,70,71,74,75,77,79,81,82,83,84,89,91,92,95,100,102,103,104,106,107,108,109,110,111,112,113,114,115,119,121,122,123,124,127,128,129,130,134],Will:30,With:[7,9,10,12,18,24,30,34,35,39,41,57,78,80,88,91,92,93,94,96,109,121,124,125,126,127,129,130,134],_____:80,______:80,_______:80,_________:95,__call__:[119,120],__class__:32,__getitem__:91,__init__:32,__name__:[17,32],__nonzero__:17,__setitem__:[],__traceback__:30,_build:0,_compile_for_arg:30,_concatenate_chunk:127,_core:119,_decor:127,_engin:[94,127],_ensure_numer:105,_event_pip:30,_fit_predict:[119,120],_growth:92,_handle_writ:32,_has_valid_setitem_index:[],_is_master_process:30,_lib:[94,127],_locationindex:[],_lsprof:30,_mean:108,_merg:[19,112],_of:108,_pandas_api:28,_psplatform:32,_rate:92,_read:127,_read_row:127,_read_to_buff:32,_reader:127,_run:32,_schedule_flush:30,_setitem_with_index:[],_stat:[119,120],_sync_cluster_info:33,_thread:30,_tokenize_row:127,_unbox_scalar:[],_validate_scalar:[],_valu:92,_wait_for_tstate_lock:30,_write_buff:32,_x:108,a1:89,a52aba7de6d5:13,a93c:32,a9efa4ff5a9d6612f62c70f567177170:[],a_0:77,a_1:77,a_2:77,a_:[11,78],a_boolean_vector:41,a_d:133,a_fast_funct:30,a_float_vector:41,a_fold:12,a_folder_with_stuff:11,a_medium_funct:30,a_slow_funct:30,a_string_vector:41,a_toi:78,ab:62,abandon:15,abbrevi:[111,116],abcdefghiklmnopqrstuwx1:12,abcfghloprstuw:12,abil:[11,13,14,27,31,34,35,42,63,70,71,88,89,90,119,122,124,134],abl:[9,12,13,14,15,17,24,25,26,27,29,31,32,34,35,36,40,41,53,63,70,71,73,74,76,84,88,89,90,108,109,111,112,113,114,115,117,118,119,124,125,126,127,128,134],abort:17,about:[2,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,24,25,26,27,28,29,30,31,35,36,39,40,41,42,43,44,46,47,48,49,51,52,53,54,55,61,62,63,66,67,68,69,70,71,74,75,76,77,79,80,81,82,87,88,89,91,93,94,95,97,98,99,100,101,102,103,104,105,106,107,110,112,113,114,116,118,119,121,122,123,124,127,128,130],abov:[3,13,15,17,18,25,27,30,31,32,33,34,36,40,41,42,43,44,46,47,48,49,51,52,54,55,60,61,62,63,66,67,70,71,73,74,75,79,80,82,89,90,93,96,97,99,100,102,104,105,108,109,110,111,112,113,119,121,122,125,127,128,134],absolut:[3,17,18,19,20,25,27,32,35,62,100,107,112],abstractli:15,absurd:107,abus:18,ac:[46,47,51,52,95,102,106,108],academ:[34,126],acceler:115,accelerationord:[],accent:103,accept:[6,12,30,32,40,98,103,112],access:[6,8,10,11,14,15,16,17,23,26,29,30,31,32,38,40,41,48,73,76,89,90,91,92,94,97,98,103,119,122],accid:19,accident:[17,29,55,76,105],acclaim:17,accommod:[49,111,122,127],accomplish:[13,15,17,18,20,24,39,48,49,53,62,82,83,84,92,102,109,110,111,113,115,124,125,129,130],accord:[112,113],accordingli:[68,73],account:[10,11,15,33,46,47,70,71,108,111],accru:[51,52],accumul:[30,32,95],accur:[23,40,46,47,70,71,95,102,108],accuraci:[70,71],accustom:[17,62,92],achiev:[13,14,15,18,25,30,34,115,116,119],acknowledg:107,acquir:[30,34],across:[17,20,26,27,30,31,32,35,42,46,47,48,51,52,61,73,74,77,78,92,97,98,103,108,109,110,111,112,113,116,122,124,125,128],acs_5yr_hh_incom:131,act:[34,42,94,125],action:[2,19,31,110,115,119,120,124,134,135],action_ind:32,activ:[3,8,10,12,14,16,18,27,30,31,34,39,58,59,76,77,100,104,124,126],actual:[2,3,6,7,8,9,10,12,13,15,17,20,21,22,23,24,25,26,27,28,29,30,31,32,33,35,36,40,43,46,47,48,51,52,54,55,57,61,62,64,67,68,70,71,76,78,87,89,92,93,94,95,98,99,100,101,102,106,107,108,114,118,119,121,122,123,129,130],acut:26,ad:[6,9,10,12,14,20,21,25,26,29,30,40,46,47,49,60,62,68,70,71,76,79,80,88,89,94,97,107,110,128,135],adapt:[78,92,124],add:[0,9,10,12,13,14,17,18,19,20,22,23,24,25,26,29,30,35,38,40,42,46,47,60,62,66,68,70,71,74,75,76,77,80,88,89,96,97,100,101,107,108,109,110,111,112,114,115,119,120,121,122,124,125,126,128,129,130,134],add_artist:124,add_categori:[104,107],add_select:122,add_up_entri:13,addict:35,adding_tot:30,addit:[2,3,9,12,14,15,17,22,23,29,30,31,32,34,35,36,42,51,52,53,60,68,70,71,76,77,79,94,96,97,102,106,107,110,111,113,119,122,124,125,127],addition:[14,82,83,84,128],addr:33,address:[9,15,17,29,32,37,48,53,57,68,70,71,101,104,107,108,111,112,114,119,121,127,129],adeli:[119,120],adequ:34,aditya:107,adjust:[13,60,64,73,108,124,127,135],adjusted_incom:64,admin:[7,33],administr:[107,128],admit:[35,96,101,119],admittedli:[62,93],admonit:0,adob:62,adopt:[17,125,134],adult:102,advanc:[9,11,29,30,34,36,76,97,124],advantag:[14,17,20,29,76,88,92,102,104,106,107,108,113],advic:[34,80,91,101],advis:[51,52],advoc:[18,24,92,101],ae:121,aesthet:[17,118,125],af:33,affect:[6,20,23,27,95],affili:32,afghanistan:[127,128,129,130],afraid:28,africa:[17,88,92,93,94,97,98,103,104,105,106,127,128,129,130],african:[93,128],after:[3,8,10,12,13,15,17,20,24,27,29,32,33,34,37,38,42,48,49,53,55,58,59,60,62,66,70,71,74,82,83,84,85,86,95,97,101,103,104,112,115,118,119,124,127,134],after_tax:42,after_tax_incom:66,afterward:12,ag:[15,41,43,49,60,63,64,66,70,71,88,89,95,102,108,119,122,125,134],again:[6,8,9,11,13,17,26,31,32,43,48,58,59,62,73,74,77,79,80,93,94,104,109,111,122,125,127,128],against:[8,12,16,17,20,40,104,114,125],agenc:[34,95,116],agent:32,agg:32,aggfunc:113,aggreg:[113,114,115],agnost:[96,122],ago:[19,106,121],agre:[9,10,15,32],ah:119,ahead:[6,7,15,32,55,62,92,102,111],ai:88,aid:98,aim:34,air:[35,110,111,119,122],airport:[6,7],ak:111,aka:36,al:111,alabama:111,alaina:[113,115],alameda:114,alaska:111,albania:[93,94,127,128,129,130],albeit:119,alert:[17,101],algebra:[31,40,60,77],algeria:[93,94,127,128],algorithm:[13,15,20,31,36,72,102],alia:40,alias:38,align:[86,90,91,93,94,110],aliv:119,all:[0,2,3,4,6,7,8,9,10,11,12,13,14,15,17,18,19,20,21,23,24,25,26,27,29,30,31,32,33,34,35,37,39,40,41,42,43,44,45,46,47,48,49,51,52,54,55,57,60,61,62,63,64,65,66,67,70,71,73,74,76,77,78,79,80,81,82,83,84,85,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,110,111,112,113,114,115,116,117,119,122,123,124,125,127,128,129,130,134,135],all_prescript:32,all_rows_w_even_a:28,alloc:[20,27,32,41,51,52,89,119],allow:[6,7,9,10,11,12,13,14,15,25,27,28,29,30,31,32,34,36,39,40,42,43,50,53,65,67,68,74,76,78,80,89,90,91,94,100,101,104,105,107,109,110,111,112,113,115,118,119,121,122,129,130],alma:47,almost:[6,7,11,12,17,18,21,23,27,29,31,34,36,43,55,63,70,71,74,76,89,95,97,105,121,122],alon:[8,32,104],along:[12,15,18,21,30,34,40,41,43,48,53,60,61,63,65,67,68,73,74,77,98,113,119,121,134],alongsid:43,alpaca:80,alpha2:127,alpha3:127,alpha:[119,120,121,122,129,130],alphabet:[93,115,127],alread:115,alreadi:[3,6,9,10,11,12,15,17,18,24,29,30,32,39,40,48,49,60,68,76,88,92,93,97,102,104,109,114,119,125],alright:125,also:[0,2,4,6,7,8,9,10,11,12,13,14,15,16,17,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,40,41,42,43,45,48,49,51,52,54,55,56,60,61,62,63,64,65,66,67,70,71,72,73,74,75,76,77,79,81,82,83,84,85,86,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,118,119,120,121,122,123,125,127,129,130],alt:[122,130],altair:[8,117,118,119],altair_data_serv:122,altair_figur:[],altair_sav:122,altern:[11,25,34,40,73,89,91,109,117],although:[9,14,15,51,52,62,89,119,124],altitud:41,altogeth:111,alwai:[4,8,9,11,12,13,15,16,17,18,19,20,21,22,23,25,27,28,30,31,32,34,35,36,38,43,44,48,49,54,55,57,60,61,63,65,73,77,78,86,89,90,91,93,94,97,98,99,100,103,104,105,106,109,110,111,11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n:128,bent:18,berin:119,berkelei:127,best:[9,10,12,13,14,15,17,18,22,23,25,28,31,32,34,35,36,38,41,57,70,71,73,77,80,87,88,96,107,113,118,119,121,127],bet:30,betrai:54,better:[5,6,9,11,13,17,18,19,21,26,27,31,32,34,35,36,43,49,51,52,70,71,75,81,89,95,102,105,112,119,125],between:[10,11,13,17,20,22,23,24,26,27,29,30,31,32,33,34,35,41,42,43,45,46,47,48,51,52,54,56,57,62,65,67,68,69,70,71,74,75,76,77,78,79,80,89,90,92,95,96,99,100,102,108,111,114,119,124,125,128,129],betwen:95,bewar:104,beyond:[89,102,119,135],bezo:43,bhutan:[17,128],bhvesyguml:28,bi:127,bia:15,bias:15,bibl:17,bidirect:34,big:[8,9,11,15,18,19,20,22,23,24,29,30,31,32,35,36,39,40,43,54,57,70,71,85,89,93,97,102,104,105,114,119,122,124,129,130],big_arrai:77,bigger:[19,20,27,34,60,104,112,119,129,130],biggest:[22,30,31,32,89,112],bill:119,bill_depth_mm:119,bill_length_mm:[119,120],billi:80,billion:[30,40,127,129,130],bin:[3,12,51,52,79,122,123],bin_edg:79,binari:[29,74,76,86,97],bind:122,binding_rang:122,biologist:119,bird:[93,110,111],birth:[15,110,119,122],birthdai:79,bisco:[],bissau:[93,94,128],bit:[8,9,10,13,15,18,20,26,27,29,30,37,41,48,54,57,74,76,88,89,97,98,99,113,116,122,124,127,128,129,130],bite:18,bitumin:116,bizarr:17,black:[2,25,41,62,67,70,71,74,108,114,129,130],blank:[48,80,110,116,129,130,134],blaze:[29,30,76],blend:[],blob:[102,106,108],block:[0,6,17,25,29,30,32,41,48,75,89,100,121],blockwis:32,blog:32,blood:[67,88,109,110],blow:18,blr:[],blue:[41,62,67,74,110,111,122,124],blueberri:80,blurri:125,bmatrix:[60,78],bntbtkxbdq:28,board:18,bob:107,bodi:[67,100,119],body_mass_g:[119,120],bokeh:118,bold:[6,124,125],bolivarian:128,bolivia:128,bolt:122,book:[22,23,57,100,119,124,125],bool:[17,23,41,48,49,90,93,111,114,128],bool_vector:49,bootcamp:9,bosnia:128,both:[8,9,10,11,13,15,17,19,20,22,23,25,27,29,30,31,32,34,37,40,49,51,52,54,56,63,64,65,67,68,76,79,89,90,91,95,97,99,100,102,103,104,106,107,108,109,110,111,112,114,118,119,124,127,129,130],bother:11,botswana:128,bottom:[3,6,7,8,10,13,16,17,20,27,35,43,63,93,97,118,119,124,125],bound:[32,94],boundari:[129,130],box:[0,10,14,17,30,48,62,122,123,124],boxplot:123,brace:40,bracket:[48,63,65,67,69,80,90,134],bradburi:1,brain:[41,67],brainer:32,branch:[6,17],brasil:103,brazil:[88,93,97,98,103,104,105,106,128],breakdown:[93,114],breakpoint:14,breath:17,breviti:104,brewer:125,brief:56,briefli:57,bright:[67,74],bring:[12,14,15,18,27,31,55,82,83,84,87,110,111,115,117,119,125,126,129,130,134],britain:128,british:[60,127],broad:15,broadcast:[42,49,69],broadli:[20,89,94,103,125],broken:[31,32,94],brokenpipeerror:32,brook:34,brought:17,brows:[17,46,47,51,52],browser:[0,10,122],brunei:128,brush:122,bsd:12,btw:[],bubbl:[4,81],buck:[27,35],buddhist:122,budget:35,buf:32,bug:[10,14,87],buggi:33,bui:[15,26,27,46,47,73,125],build:[9,15,21,30,31,32,34,41,60,66,75,77,85,88,89,90,109,119,121,122,126],built:[2,9,14,15,17,28,30,32,63,77,79,82,83,84,88,97,98,101,103,118,121,122,123,124,133],builtin:30,bulgaria:[92,128],bulk:103,bullet:[18,31],bunch:[15,18,29,31,76,93,119],bundl:[3,10,28,118],bureau:[15,19,41,43,46,47,51,52,70,71,95,102,108,111,114],burkina:[17,128],burn:[35,127],burundi:[127,128],busi:[15,17,32,36,41,109,112,119],busy_dai:90,button:[3,6,8,10,13,14,16,39,46,47,51,52,127],buyer_addl_co_info:32,buyer_address1:32,buyer_address2:32,buyer_bus_act:32,buyer_c:32,buyer_counti:32,buyer_dea_no:32,buyer_nam:32,buyer_st:32,buyer_zip:32,bytearrai:12,bytearray_inst:12,bytes_inst:12,bytes_read:32,c0:124,c1:[110,111,124,132,133],c2:[110,111,132,133],c3:[110,111,132,133],c4:124,c:[3,6,7,8,12,13,14,20,25,29,30,31,40,49,51,52,76,77,78,79,88,89,93,94,95,96,97,98,103,104,105,106,110,111,112,113,119,122,124,125,127,129,130,132,133],c_:78,c_d:133,c_parser_wrapp:127,ca:[19,32,111,114],ca_arrests_2009:112,ca_felonies_2001_2010:19,ca_misdomeaners_2001_2010:19,cabo:128,cach:122,cache_d:127,calc:111,calc_base_wt_in_gm:32,calcul:[13,17,20,26,32,40,41,42,43,46,47,49,51,52,60,63,70,71,73,74,78,86,92,102,105,108,109,111,112,113,114,115],california:[19,62,111,112,114],californian:114,call:[2,4,6,7,8,9,11,12,13,14,15,16,17,20,21,22,23,24,26,27,29,30,31,32,33,34,36,39,40,42,43,44,46,47,48,49,51,52,54,56,57,58,59,61,62,63,66,68,70,71,76,77,78,79,80,81,86,88,89,92,93,94,96,97,98,99,100,101,102,103,104,105,107,110,111,112,113,114,118,119,122,124,127,129,130],callabl:94,callback:32,cambodia:128,came:[9,32,40,54,80,97,99,112],cameroon:128,campaign:41,can:[0,2,3,4,6,7,8,9,10,11,12,13,14,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,121,122,123,124,125,126,127,128,129,130,134,135],can_swim:93,canada:128,cancel:[13,51,52],cancellederror:33,cancer:[15,36],candid:[32,104,128],cannot:[19,30,31,32,36,46,47,61,93,104,107,124,128],canon:119,canva:124,cap:103,capabl:[8,16,31,36,124],capac:116,cape:128,capfac:116,capit:[51,52,103,112],capita:[17,88,92,93,103,111,122,126,127,128,129,130],captur:[77,78,95,124,134],car:[10,42,55,73,113,122],carbon:[13,126,128,129,130],card:79,cardin:[73,119],care:[12,17,20,25,27,31,73,79,89,91,92,104,105,110,111,112,119,124,125],career:[9,10,12,34,36,41,98],carefulli:[9,14,15,18,29,32,34,63,76,91,105,114,119],careless:17,carlo:[82,83],carolina:[107,111],carpentri:11,carri:[17,112],carrol:32,cash:35,cast:[89,101,104,105],casted_kei:94,casual:12,cat:[12,48,80,91,93,97,104,107,110,111],categor:[27,28,36,86,88,89,105,119],categori:[19,28,70,71,102,104,107,108,114,119,120,125,128],categoricaldtyp:119,cathol:122,caught:17,caus:[9,10,14,17,18,19,29,30,32,33,39,49,76,94,101,102,105,106,112,119,120,125],causal:112,cautiou:36,caveat:[101,104],cbgufgtrki:28,cbserial:[108,131],cd:[0,6,7,11,12,117],cdef:30,cdot:[13,78],cede:10,celebr:80,cell:[13,14,25,39,41,49,61,67,74,88,89,94,104,107,110,122,124],censu:[15,41,43,46,47,51,52,95,102,108,111,114],census:[108,114],cent:[33,60],center:[43,67,74,111,124,125,127],centimet:126,central:[31,32,89,93,97,104,128],centric:9,ceo:49,cerebellum:67,certain:[9,17,23,30,58,59,67,82,83,84,97,104,105,106,107,112,114,118,119],certainli:[13,34,67,74,110,121,125,135],certif:[34,60,95],chad:[93,128],chain:[],chainedassignmenterror:104,challeng:[32,34,78,88,95,110,124],chanc:[9,17,18,34,53,79,93,97],chang:[0,2,7,10,11,12,13,14,17,18,19,20,21,22,23,24,25,27,29,31,34,36,41,43,49,51,52,53,54,55,56,58,59,61,62,64,66,76,78,79,82,83,84,88,89,90,93,94,96,97,99,100,101,103,104,107,112,114,115,117,119,120,125,127,129,130,134,135],change_contrast:62,channel:[8,9,74,122],channel_prior:9,chao:32,chapter:[20,82],charact:[41,44,48,97,103,127,128,134],character:[41,43,79,122],characterist:[15,73,79,89,106,113],charg:102,charl:20,chart:[123,125,128,130],chatgpt:88,chdir:[17,32],cheaper:35,cheatsheet:[0,25,32],check:[12,13,14,17,18,19,20,26,28,29,30,31,32,36,41,42,44,51,52,58,59,60,64,76,77,80,86,87,89,93,95,98,100,102,105,107,110,111,114,119,122,123,127],checklist:14,chem:111,chevel:[],chevroelt:[],chide:17,child:[14,80,119],childhood:119,children:[95,102,106,108,119],chile:[119,128],china:[17,128,129,130],chines:41,chip:[31,35],chl:119,choic:[6,7,11,17,18,28,40,79,93,119,121,125],cholesterol:109,choos:[9,13,14,73,124,125],chose:[10,41],chosen:[32,80,118,122,125],chunk:[18,24,27,32,117,127],chunksiz:[27,32,127],ci:[117,122],ci_high:[119,120,122],ci_low:[119,120,122],ciat:12,circl:[3,10,13,54,67,99,104,119,124,129,130],circumst:[55,77,85,104,107],cite:[46,47,51,52],citi:75,citizen:[15,103,119],civil:112,clarifi:[21,126],clariti:[76,77,125],class_2:[49,131],class_3:[88,103,104,106,107,110],classifi:[114,125],classroom:[15,34,41],clean:[9,10,13,15,17,19,24,28,34,51,52,87,102,104,106,109,110,111,119,125,128],cleanli:[24,102,103,105],clear:[14,15,17,24,25,26,30,31,34,36,49,55,63,77,87,89,91,101,102,105,107,108,111,112,119,127,129,130],clearer:[15,77,111],clearli:[13,15,17,19,36,40,67,76,79,100,101,102,110,113,115,119,125,127,129,130,135],clever:[44,62],click:[0,3,6,7,8,9,10,11,12,14,16,17,21,25,39,46,47,51,52,127],client:[3,12,32,33,111],climat:[41,55,67],clinic:36,clip:124,clip_on:124,close:[6,10,17,19,20,26,31,32,33,35,43,51,52,54,58,59,62,74,79,93,98,103,126,129,130],closer:[31,62,67],closest:128,cloth:17,cloud:[6,7,9,10,11,27,31,35,36,75],cloudcov:75,cloudi:75,clump:[129,130],clumsi:102,clunki:63,cluster:[11,27,31,32,102,108,131],clutter:[17,122],cmap:[62,67,74],cmd:[3,7,9,11,12],cmder:[3,11,12],co2:[119,122,126,127,128,129,130],co2_al:127,co2_emissions_tonnes_per_person:127,co:[10,17,111,124,126],coal:116,coauthorship:34,code:[2,6,8,9,13,16,17,18,20,23,24,26,27,28,29,31,32,33,35,36,37,38,40,41,46,47,48,49,51,52,55,57,58,59,60,63,64,66,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,91,92,94,95,97,98,100,101,102,103,105,107,108,110,111,112,114,115,119,121,122,124,125,127,134,135],codebook:[70,71,112,114,116],codec:114,coeffici:[15,51,52,92],coerc:[32,49],coercion:49,coher:[25,111],coil:32,coin:10,col1:104,col2:104,col:28,col_dict:127,col_index:104,colab:9,collabor:117,collaps:[27,32,112,114,122],colleagu:[2,82,83,97,98,113,125],collect:[15,23,29,30,32,34,37,40,41,42,43,45,46,47,51,52,53,55,60,67,70,71,73,75,81,82,83,85,86,88,89,98,108,110,112,114,124,129,130],colleg:[17,34,70,71,95],colloqui:[11,13],colombia:128,colon:[13,40,97,99,134],color:[10,17,18,41,60,67,72,97,114,119,121,122,124,129,130,135],color_list:[129,130],colorado:[107,111],columbia:111,column:[13,14,15,25,27,28,32,40,41,42,60,61,64,65,66,68,70,71,73,74,75,78,86,88,95,96,97,101,104,105,107,108,109,110,111,112,113,114,119,122,124,127,128,129,130],column_a:101,columnar:[],com:[6,9,17,19,30,32,93,94,102,103,104,106,108,119,122,127],combin:[15,17,25,27,41,42,44,48,49,61,67,87,89,113,115,121,124,126,127,128,135],combined_labeler_nam:32,come:[3,6,9,10,11,12,13,15,17,20,21,22,23,26,27,28,29,30,31,32,35,36,39,40,41,43,46,47,51,52,53,61,62,63,64,67,74,76,80,90,91,92,95,96,97,98,99,100,101,102,103,104,107,108,109,111,118,119,121,123,134],comedi:62,comer:32,comfort:[9,10,12,18,36,41,62,119],comm:[32,33],comma:[10,25,63,67,68,93,97,127],command:[3,4,8,9,10,16,17,23,25,27,29,32,33,35,40,46,47,51,52,55,63,70,71,81,84,95,100,101,108,111,114,124,134],command_lin:[11,12],commandlin:[8,16],commandlinetool:3,commandnam:12,commclosederror:32,commensur:34,comment:[13,17,18,21,25,127],commerc:15,commit:[0,15,21,34,112,114],common:[4,8,12,13,14,15,16,21,22,23,32,38,40,41,54,67,72,73,77,79,87,89,93,94,97,99,102,103,105,106,107,110,111,112,113,115,116,117,121,124,127,135],commonli:[17,21,30,41,42,51,52,60,95,97,98,102,110,124,128,134],commun:[15,17,25,34,46,47,51,52,58,59,70,71,76,95,102,105,106,108,112,117,118,119,121,125,134],comoro:[127,128],compact:[38,115],compani:[2,11,15,25,27,32,33,35,36,49,55,70,71],compar:[12,15,17,28,29,30,31,32,37,51,52,55,60,62,68,73,77,78,97,107,111,124,125,126,128,134],comparison:[6,15,20,32,42,124,125,134],compartment:17,compat:[4,10,30,68,81,97,98,101,102,118],compel:[34,125,135],compens:34,compet:26,compil:[29,30,32,36,76,122],complain:102,complet:[3,6,10,15,29,31,32,34,36,40,46,47,51,52,58,59,63,76,77,82,83,84,85,95,104,108,111,112,117,119,124],complex:[14,23,35,36,41,75,85,87,105,109,112,115,129,130],complic:[10,13,14,16,17,31,41,48,62,81,88,94,97,98,99,122],complimentari:93,compon:[14,31,34,35,38,41,74,77,87,89,124,125],compos:[25,41,118,119,121,124],composit:[19,114],compound:[71,92],comprehens:[85,111],compress:[12,27,28,32,97,98,127],compris:[],comput:[4,6,7,9,10,11,12,13,16,17,22,23,24,25,26,28,29,30,34,36,37,39,40,41,53,54,57,60,61,62,72,73,74,76,77,78,79,81,82,83,84,85,86,87,88,96,97,98,100,102,105,107,109,111,113,114],computation:[35,88,115],con:[97,98],concat:[28,110],concaten:[29,66,76,83,88,109,111,134],concatent:110,concept:[9,23,32,38,48,57,73,85,86,90,109,115,125],conceptu:[15,22,32,34],concern:[34,41,94,114,121,127],concert:27,concis:[30,40,42,53,57,77],conclud:[85,117],conclus:[2,36,98,112,125],concord:[],concret:[15,61,68],concur:91,conda:[3,6,9,10,12,14,31,32,33,38,40,51,52],condabin:12,condit:[13,14,17,25,30,31,48,49,75,79,104,119,122],conduct:[17,41,43,60,70,71,95,102,111,112],conf_int:[119,120,122],confid:[17,36,82,84,128],config:[9,124,125,129],configur:[5,10,12,14,20,39],configure_legend:130,configure_mark:130,confirm:[10,54,60,105],conflict:[9,10,94],confound:15,confus:[11,13,17,31,49,89,93,124,125],congo:[93,94,128],congratul:[15,36,51,52,70,71,95],connect:[11,17,32,33,34,57,101,112,115,121,124],connect_timeout:33,connecticut:111,connectionreseterror:32,connector:33,connot:102,consecut:48,consequ:[18,54,61,119],conserv:17,consid:[12,13,15,20,27,29,30,31,32,34,43,48,49,55,67,88,102,104,110,111,113,114,122,125,128],consider:[49,97,128],consist:[10,25,31,34,43,44,65,67,77,79,91,100,101,102,112,125],consisten:125,consol:[14,39],consolid:34,constant:[18,31,104,108,119,122,124],constantli:[17,18,75,117],constitu:[89,119],constitut:[15,36,114],constrain:25,constraint:30,construct:[21,34,48,79,82,88,89,108,116,124],constructor:[85,89],construt:93,consult:[88,118],contact:[17,46,47,51,52],contain:[10,12,25,41,43,44,46,47,48,54,57,60,62,63,67,70,71,75,81,88,89,91,93,97,102,103,107,109,111,116,122,124,125,127,128],content:[0,3,6,12,14,17,25,29,37,40,46,47,51,52,57,58,59,68,70,71,73,85,88,89,93,95,97,107,109,111,113,118,125,126,127,128,129,130,134,135],context:[15,17,25,26,36,51,52],contextu:41,contin:[126,129,130],continu:[13,14,17,20,53,92,102,103,114],contour:124,contrast:[6,12,15,24,32,34,41,43,74,92,96,97,102,119],contribut:[18,34,62,74,112,126,129],contriv:19,control:[10,13,14,15,17,20,26,41,79,86,111,119,125,134],conveni:[20,46,47,51,52,58,59,70,71,74,76,88,134],convent:[22,27],converg:[10,92],convers:[89,117],convert:[13,17,27,29,30,40,44,56,60,61,62,72,76,96,97,100,103,105,107,119,126,127,128,134],convert_dtyp:96,convert_feet_and_inches_to_inch:17,convert_stream_closed_error:32,convolut:94,cool:[19,38,42,49,61,89,102],coolor:125,coordin:[8,9,63,67,74,129,130],copi:[0,6,12,13,17,18,22,23,27,29,31,38,51,52,57,62,66,73,77,79,86,89,104,112,115,122,127,128],copilot:[36,88],copy_on_writ:[32,101,102,104,105,119,120],copyright:62,corbon:127,cord:67,core:[9,10,17,26,30,31,32,33,35,73,85,89,93,94,96,105,109,119],corner:[36,39,63],correct:[9,13,14,17,36,41,46,47,49,58,59,75,77,78,80,97,102,105,108,112,124,125,127,128],correction_no:32,correctli:[3,6,13,20,36,48,87,112],correctly_signed_and_squar:62,correl:[67,77,112,113,119],correspond:[12,41,43,57,62,63,70,71,74,75,79,80,81,89,91,95,110,111,115,124,127,128,134],corrupt:[19,24,31,55,94,98,100,112],cost:[20,27,31,34,35,42,46,47,49,66,89,107,118],costa:[93,128],cote:128,could:[8,9,11,12,14,15,17,18,20,24,25,26,29,30,32,34,37,40,41,42,43,44,46,47,48,49,51,52,54,55,60,61,63,64,67,68,70,71,73,74,75,76,82,89,90,93,97,101,102,103,104,105,106,107,109,110,111,112,113,114,115,119,122,125,127,128,129,130,134],couldn:[23,31,32,43,62,66,70,71,105,113],count:[12,17,22,38,43,73,74,79,86,88,89,92,93,94,95,96,102,105,106,108,110,111,112,113,119,122,127,131,134],count_nonzero:73,count_valu:75,count_values_in_each_row:75,counter:[13,25],counterpart:119,counti:[15,32,112,114],countri:[17,46,47,51,52,88,92,93,94,97,98,102,103,104,105,106,107,119,122,126,127,128,129,130],coup:[103,104],coupl:[8,9,12,18,24,25,27,30,32,34,40,43,48,55,60,62,94,95,108,112,116,119,122],cours:[6,7,9,10,11,12,15,17,18,20,21,26,32,35,36,39,40,41,42,43,46,47,49,51,52,53,54,55,58,59,60,63,67,70,71,73,77,82,83,84,88,89,95,97,99,102,103,109,111,115,117,119,122,124],coursera:[39,40,46,47],cousin:6,cover:[12,17,20,27,33,34,43,54,56,62,63,75,99,103,109,119,121],coverag:30,cow:100,cp:[0,12,70,71],cpu:[31,32,33],cpu_count:[31,32],cpython:30,craft:18,cram:49,crash:[27,32],crazi:18,creat:[2,3,4,9,11,12,13,14,17,18,20,22,23,24,27,31,32,33,36,39,40,42,43,44,45,46,47,48,49,51,52,54,55,56,57,60,61,62,63,64,65,66,67,68,70,71,73,74,75,77,78,79,80,81,82,83,84,85,86,87,88,90,92,93,94,95,96,97,98,99,100,101,107,110,111,112,115,117,118,119,120,121,122,124,125,126,127,128,129,130,134,135],creater:32,creation:[49,96],creatur:[25,74],credenti:[33,34],credibl:34,credit:[41,64,66,70,71,109,111],credit_a:51,credit_b:51,credit_cutoff_:51,credit_cutoff_d:51,cri:[],crime:[15,17,19,114],crimin:112,criteria:[15,82,83,84,109],critic:[18,35,36,104,109],croatia:128,crop:[67,101,102],cross:67,crowd:119,crucial:[2,119],crunch:33,crush:34,cryptic:42,crystal:[89,129,130],cs:34,csv:[12,17,19,27,28,32,86,88,93,94,96,97,98,103,104,105,106,111,112,114,119,122,127,131],ct:[67,74,111],cuba:128,cudf:32,culpa:18,cumbersom:[14,17,124],cumtim:30,cumul:92,cup:79,curiou:[25,32],curl:6,curli:40,currenc:[46,47,60],current:[6,7,8,10,12,14,15,16,18,24,29,31,36,39,40,41,58,59,60,62,64,70,71,77,92,95,96,104,105,107,108,118,119,121,122,134],cursor:14,curt:18,curv:[43,79,119,120,124],custom:[6,10,14,15,41,60,100,107,110,118,119,124,129,130,135],customiz:118,cut:[46,47,67],cute:119,cutoff:[17,46,47,122,128],cutoff_a:51,cutoff_b:51,cyber:20,cylind:[],cynthia:34,cypfoxeuok:28,cypru:128,cython_avg_numbers_up_to:30,cython_w_overflow_avg_numbers_up_to:30,czech:128,czechia:128,d010:[46,47,51,52],d3:[118,122],d5a6d15d0b1463d80fbfbb777dc826f7:[],d6ab8c8f87e7:30,d:[4,6,7,9,10,11,12,14,15,20,26,29,31,32,35,38,42,49,51,52,60,61,63,64,72,75,76,79,81,88,90,97,98,100,102,103,104,106,111,112,114,119,121,122,124,125,127,128,132,133,134],dai:[9,12,17,18,24,25,26,30,31,35,40,41,42,73,75,80,89,90,102,122],daili:[6,7,34,36,73],dakota:111,damag:112,danger:[0,9,18,20,112,119],dark:[6,12,41,62,67],darker:62,darn:[],darussalam:128,dash:12,dashboard:[32,33],dashboard_link:33,dask:[16,27,31,117],dask_env:[16,32],data:[2,6,7,8,9,10,11,12,13,14,16,17,18,19,20,22,23,25,28,29,30,31,32,33,34,42,44,45,48,53,54,55,56,57,58,59,60,61,62,63,64,65,67,68,69,72,73,74,75,76,78,79,82,83,84,85,89,90,91,92,94,96,99,100,101,104,110,111,113,116,118,120,121,123,124,125,126,129,130,131,134,135],data_copi:115,data_serv:122,data_transform:122,databas:[27,93,109,116,119],datacamp:[11,12],dataclass:[119,120],datafram:[13,17,25,28,31,32,60,86,87,89,91,95,96,99,100,101,104,105,106,107,109,110,111,112,113,114,115,119,120,122,127,128,129,130,132,133],dataframe_typ:32,dataframeiolay:32,dataframetreereduct:32,datanum:108,datasauru:117,dataset:[15,17,20,25,26,27,28,30,31,32,33,35,46,47,48,51,52,56,60,66,70,71,75,87,88,90,92,93,95,98,102,103,104,105,106,107,109,112,114,115,116,119,120,124,125,126,128,129,130],datatyp:[20,27,30,49,96,106],date:[32,79,88,97,110],date_pars:127,datum:122,dayfirst:127,dbc2199fcf7e6302cdd1ea2955dc378605b52659:19,dbl:122,dc:111,dd:32,ddot:78,de:[2,30,32,111],deactiv:[8,14,16],dead:31,deal:[9,14,20,27,35,46,47,68,79,85,105,106,112,114,135],dealer:112,dealership:[42,113],death:111,debat:119,debt:[51,52,102],debug:[9,12,24,30],debugging_in_vscod:13,decad:[35,112],decemb:12,decid:[9,11,15,17,24,29,30,32,34,35,49,51,52,76,112,119],decim:[17,20,23,25,40,41,49,51,52,60,70,71,89,97,127,134],decis:[73,95,109],declar:[29,30,76,121],declin:[19,55,92,112,114],decod:114,decompos:[74,119],decomposit:119,decompress:[27,28,32],decor:30,decreas:[51,52,62,112,119,135],dedic:[18,30],deduc:30,deem:25,deep:[25,35,38,57,118,127],deeper:[13,73,85,87,124,125,127],deepli:[18,34,41,93,96,101],def:[13,17,23,30,31,62,75,77,78,80,82,83,93,119,120,122,124,127,128],defaultformatt:25,defens:[],defer:62,defin:[6,7,13,23,30,34,46,47,73,92,110,119,125,134],definit:[12,25,26,30,34,35,43,51,52,54,81,95,98,102,104,108,127],deg:121,degre:[17,31,35,62,92,95,121,124],degredation_over_tim:55,dejavu:125,del:27,delai:[31,32,77],delawar:111,delet:[9,11,12,13,17,24,27,32],deliber:[10,42,70,71,106],delight:[99,107,124],delim_whitespac:127,delimit:[17,32,127],delin:97,deliv:[29,33,76],deliver:77,deliveri:119,dell:35,dem:128,demand:49,democraci:[93,103],democrat:128,demograph:[15,114],demonstr:[30,32,34,38,40,73,79,88,89,115,117,119,124,129,130,134],demystifi:[26,38],denmark:128,denot:[56,95,97,106,108],densiti:[121,122,123],depart:24,depend:[3,5,6,8,9,10,12,13,15,18,20,31,32,34,36,41,51,52,53,54,62,70,71,77,79,89,91,93,94,97,98,99,100,101,105,119,122],depict:23,deploi:[33,86,88],deprec:[28,91,101,119],depreci:91,depth:98,dequ:30,derefer:[29,76],dereferenc:29,deriv:[40,111,112,126],descend:97,describ:[11,14,15,27,38,41,47,73,76,86,89,93,102,107,113,115,119,126,127,131],descript:[10,12,18,24,86,100,116,127],deseri:32,design:[2,10,12,13,17,20,27,29,30,31,32,34,36,40,41,74,76,77,88,89,96,97,98,101,111,119,121,124,125],desir:[55,79,104,119],desit:127,desktop:[12,17,27,31,35],despit:[26,36,55,62,88,90,97],destruct:127,detach:10,detail:[11,12,14,20,21,32,33,42,43,46,47,51,52,54,58,59,60,61,66,67,70,71,79,89,91,99,102,109,116,119,122,124],detect:[13,19,74,102],determin:[10,15,29,34,43,48,51,52,55,75,76,80,89,92,93,108,111,112,116,124,125,127],determinist:[10,20,79],detriment:36,deutschland:103,dev:[20,29,30,37,107],devel:[119,120],develop:[3,13,15,17,18,30,31,32,34,36,40,54,73,81,85,90,91,92,111,115,125,127],deviat:[42,73,82,83,89],devot:[34,87,125],df1:[110,112],df1_var:112,df1f62c87a9d21a7c2e1959a33e7735a:[],df2:[28,110,112],df2_correct:112,df2_var:112,df3:110,df4:110,df:[17,28,32,93,94,96,97,98,100,101,104,105,114,123,131],df_ax0:110,df_ax1:110,df_concat:110,df_concat_axis1:110,df_concat_reindex:110,df_new:18,df_w_pyarrow_backend:96,dfa:111,dfb:111,di:32,diagnos:[12,14,15,109,112],diagnost:[15,112],diagon:63,diagram:99,dialect:127,dice:79,dict:[38,119,120],dictat:[30,31,98,127],dictionari:[23,40,85,93,97,103,119],did:[9,13,17,28,32,34,36,42,44,49,53,57,67,88,90,96,99,104,112,114,119,127],didn:[12,13,15,19,24,29,34,36,41,53,62,64,70,71,76,101,102,105,106,107,112],diff_nonvector:78,diff_vector:78,diffcar:108,differ:[2,3,4,6,8,9,10,11,12,13,14,15,16,17,20,23,25,26,27,29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,46,47,49,51,52,53,54,55,57,60,61,62,63,64,67,68,69,70,71,73,74,76,77,78,79,81,82,83,85,87,88,89,90,91,92,93,95,96,97,98,99,101,102,103,105,107,109,111,112,113,114,115,118,119,121,122,124,127,128,129,130,134],differenti:[25,34,67],diffey:108,diffhear:108,difficult:[51,52,91,119,125,129,130],difficulti:13,difflib:128,diffmob:108,diffphi:108,diffrem:108,diffsen:108,digit:[20,25,41,51,62,125,134],digitalocean:33,digress:[26,53],dimens:[40,41,48,53,60,63,65,66,67,68,69,73,74,79,88,93,104,110],dimension:[38,41,60,61,62,65,72,73,74,85,88,89,93,117,124],dioxid:[126,128,129,130],diploma:[70,71,95],direct:[3,6,8,9,11,12,16,18,32,33,34,35,44,61,62,70,71,119,127,128],directli:[8,10,11,12,13,14,15,17,24,26,28,30,32,33,34,46,47,51,52,57,60,70,71,77,87,96,102,103,104,105,107,111,118,123,126,127],directori:[6,7,10,11,12,32,112],dirti:[29,76],disabl:30,disable_max_row:122,disadvantag:107,disappear:[27,94,119],disappoint:122,disast:[86,91],disciplin:[31,98],discount:35,discov:[15,17,19,34,36,95,105,119],discret:[51,52,92,95,102],discuss:[3,6,9,10,12,13,14,17,18,20,21,22,27,28,29,31,32,34,35,38,42,43,46,47,48,49,51,52,58,59,60,62,70,71,72,73,76,77,79,80,82,83,84,85,88,89,91,93,96,99,100,102,103,107,108,109,110,111,112,114,115,118,119,121,124,127,134],diseas:111,disgust:18,disk:[17,96,122],dispatch:30,dispers:119,displac:111,displai:[2,3,6,12,17,39,43,62,74,97,118,124,125,129],disproportion:62,dissatisfi:34,dissemin:98,dissert:34,distant:121,distinct:[20,22,31,32,34,49,54,68,90,99,100,103,108,111,114,118],distinguish:25,distract:125,distribut:[16,20,28,29,30,33,41,43,51,52,75,76,79,80,82,83,92,95,98,102,108,112,119,122],district:111,distutil:30,dive:[15,22,31,34,40,42,54,73,87,88,89,93,104,118,119,122,124,125,126,127],divers:93,divid:[20,32,34,40,51,52,55,62,70,71,79,87,104,109,111,112,115,118,129,130],divis:[34,48],djibouti:128,dl:32,doc:[0,9,17,30,79,94,96,101,103,104],doctor:[27,110],documen:102,document:[2,6,12,17,23,25,30,34,42,46,47,66,88,95,97,99,100,101,102,104,105,110,116,118,119,121,122,123,124],doe:[7,10,11,12,13,15,17,20,22,24,27,29,30,31,32,35,38,40,43,46,47,48,51,52,55,62,63,64,67,70,71,74,77,81,82,83,90,91,93,94,95,97,100,101,105,106,108,109,110,112,114,119,121,122,127,135],doesn:[3,7,9,10,12,13,15,17,18,19,20,23,25,26,27,28,29,30,31,32,34,40,41,44,49,55,62,77,79,88,89,90,94,95,96,100,101,102,106,107,108,114,119,121,125,129,130],dog:[48,91,93,97,110,111],dogmat:25,doi:[46,47,51,52],doke:125,dollar:[12,13,27,35,41,42,43,46,47,49,51,52,60,64,70,71,102,108,111,115,127,129],domain:[0,15,27,36,130],domest:[92,119,127,129],domin:[30,129,130],dominica:128,dominican:128,don:[3,6,7,8,9,10,11,12,13,15,18,19,20,21,22,25,26,27,30,31,32,33,34,35,36,39,40,41,42,43,46,47,48,49,51,52,54,55,58,59,60,62,63,67,70,71,77,79,82,83,89,90,91,93,94,95,97,98,100,102,103,104,105,106,107,111,112,113,114,117,119,122,124,125,127,128,135],done:[3,6,7,8,10,11,15,24,25,27,29,30,32,40,43,48,67,73,76,77,94,95,98,99,101,102,104,110,119,124,127,128,129,130],dont:[8,16,30],door:35,dos_str:32,dosage_unit:32,dot:[6,14,19,38,119,120,121,125],dotfil:12,doubl:[0,3,12,23,25,29,30,31,35,37,46,47,55,62,77,96,104,107,127],double_nonvector:77,double_vector:77,doublequot:127,doubt:101,dove:85,down:[9,11,17,20,27,28,30,31,36,38,43,46,47,51,52,55,61,73,81,95,102,107,111,112,113,114,129,130],download:[3,6,7,9,10,12,15,24,32,95,112,114,127],downstream:13,downward:[14,57],dozen:[2,9,17,18],dpi:125,dqcfywcfmx:[],dracula:6,draft:36,drag:122,dragon:80,dramat:[53,107],drastic:77,draw:[15,48,70,71,79,80,124,125],drawn:62,dream:[96,124],drew:[38,80,86],drift:17,drive:[26,27,32,35,41,55],driven:[51,52,73,112],driver:126,drop:[17,27,32,34,46,47,48,51,52,89,93,95,97,106,108,110,111,119,120],drop_dupl:131,dropbox:[17,32,88],dropdown:122,dropna:[106,111,119,120],drug:[32,36,114],drug_cod:32,drug_nam:32,ds:127,ds_store:12,dt:32,dta:[28,93,95,98,102,106,108],dtype:[20,27,28,29,32,41,44,48,49,60,62,88,89,90,91,93,94,96,101,102,103,104,105,106,108,111,112,113,114,119,127,128,131],dtype_backend:96,dubiou:96,duct:13,due:[15,20,31,36,72,106,126],dug:19,duke:[9,10,11,12,25,34,35,36],dumb:31,dump:97,duplic:[12,31,77,80,82,83,84,114,128],durat:35,duration_with_copi:55,duration_with_view:55,dure:[8,9,12,15,16,17,19,25,27,30,32,33,34,42,46,47,51,52,79,127],dutch:[],dynam:[29,30,57,76,89,92],dz:127,dza:127,dzvsmydwbp:28,e:[2,3,4,7,8,9,11,12,13,14,16,17,20,24,25,26,27,29,30,31,32,34,38,40,41,42,43,46,47,48,49,51,52,56,62,63,65,67,68,70,71,76,79,80,81,88,89,90,92,93,94,96,97,98,99,102,103,104,105,106,107,108,109,110,111,114,117,119,122,123,128,133],each:[8,10,12,13,14,15,16,17,18,20,25,26,27,28,29,30,31,32,34,37,40,41,42,43,45,46,47,48,49,51,52,55,57,60,61,62,63,67,68,70,71,73,74,75,76,77,78,79,80,81,82,83,84,88,89,90,91,92,93,94,95,97,100,102,103,107,108,109,110,111,112,113,114,115,117,119,120,122,124,125,127,128,129,130,135],ear:80,earli:[2,8,12,15,17,19,20,34,36,40,96,127],earlier:[12,14,17,73,75,79,80,115,125,127],earn:[25,41,43,46,47,49,51,52,63,92,95,108],earner:[43,51,52],earth:[13,126],eas:[0,40,80,127],easi:[2,9,10,13,15,17,20,21,24,27,29,30,31,32,36,37,39,40,42,54,61,66,67,70,71,76,88,93,94,95,97,99,100,102,107,114,118,119,121,122,125,130],easier:[6,8,12,16,17,18,24,25,30,32,33,35,38,42,60,62,63,73,75,77,86,87,88,89,91,92,94,95,97,100,103,104,111,113,114,115,119,121,122,125,129,130,134],easiest:[12,33,89,93,95,100,134],easili:[2,9,10,12,13,14,17,18,24,25,27,28,35,36,40,41,42,45,49,60,63,67,74,79,87,88,89,97,103,109,110,112,119,121,122,125,127,134],east:[17,93,94],eastern:[114,127],echo:12,econ:111,econom:[17,70,71,92,95,102],economi:119,economist:[92,119],ecosystem:[2,3,38,88,97,117],ecuador:128,edg:[10,20,43,67,79,121,124,129,130],edgealpha:[],edgecolor:[121,124,129,130],edgestyl:[],edgewidth:[],edit:[2,10,12,13,14,17,38,49,54,62,65,99,100,103,106,107,117,119,127],editor:[2,3,9,10,13,25,97,98,100],edu:[46,47,51,52],educ:[9,34,36,60,63,64,66,70,71,119],edward:125,eerili:39,ef233c:125,effect:[9,13,14,15,17,20,27,32,36,39,43,51,52,62,70,71,77,93,95,100,112,113,119,126],effici:[13,15,18,27,30,31,32,38,40,45,55,60,75,77,85,87,96,107,109,113,115,119,124,125],efficiency_data:55,efficieni:125,effort:[2,17,34,51,52,70,71,91,93,98,111,112,119,125,127,128],effortlessli:96,eg:127,egi:127,egregi:[70,71],egrid2016:116,egrid:116,egweyn:[113,115],egypt:[17,127,128],eh:127,eight:[40,75],either:[9,10,11,12,13,14,15,17,27,31,34,35,39,42,48,49,54,56,60,73,89,90,93,95,99,100,101,102,105,110,111,113,119,122,125],el:128,elaps:77,elder:80,elect:41,electr:55,element:[29,38,42,48,49,50,54,61,63,64,68,73,77,78,89,90],eleph:[110,111],eleven:60,elid:30,elif:127,elimin:[79,128],elon:43,els:[13,18,24,27,30,34,48,96,97,124,125,127],elsewher:[18,122],emac:12,email:[6,7,18,34],embarass:17,embarassingli:31,embark:34,embodi:[10,121],embrac:42,emerg:[17,18,36],emir:[127,128,129,130],emiss:[119,122,128,129,130],emoji:41,emp_rat:102,emphas:[10,17,21,22,23,27,32,49,86,93,94,119,122],emphasi:125,empir:[36,80],emploi:[70,71,79,95,102,106,108],employ:[70,71,95,102,106],employe:[15,42,49,70,71,95,111,113,115],employees_by_year:113,empow:9,empstat:[95,102,106,108],empti:[16,17,27,48,75,77,127,134],en:[0,47],enabl:[10,25,39,77,89,96,100,101,104,117,122,125],enact:[51,52],enclos:[80,97],encod:[62,97,98,102,107,114,117,121,122,127,129,130],encoding_error:127,encount:[14,17,19,23,30,31,34,40,56,79,85,88,89,93,97,98,106,109,110,111,115,128,134],encourag:[13,27,82,119,124,128],end:[6,9,10,11,13,15,18,20,22,23,24,29,30,31,32,34,35,37,40,42,48,49,55,60,61,63,67,70,71,78,89,90,93,96,97,103,104,107,109,111,112,115,119,120,125,126,127],endless:106,endog:[119,120],endpoint:55,enemi:77,energi:[9,10,15,55,116,126],engag:[21,34,36],engin:[8,9,15,16,17,29,30,76,85,87,117,124,127],enhanc:[89,90,124],enjoi:35,enough:[9,11,14,20,24,25,27,29,32,35,36,40,51,52,63,70,71,74,81,93,97,108,121,122],enrol:34,ensu:32,ensur:[6,9,10,15,17,18,20,21,25,30,32,34,49,55,60,70,71,80,82,83,93,127],entail:[34,36],enter:[6,10,32,60,74,82,107,111],enterpris:32,entir:[2,6,9,17,18,23,24,29,30,31,36,41,48,62,63,67,76,87,99,104,107,121,124,125,128],entireti:93,entiti:[15,41,70,71],entri:[13,17,19,22,23,26,27,29,37,42,45,48,49,50,54,55,56,57,60,61,62,63,65,68,69,73,75,77,79,82,83,90,91,93,94,96,97,99,100,101,102,103,104,106,107,110,111,113,114,127,129,130],enumer:[13,124,125,129,130],env:[32,127],environ:[3,4,5,6,7,9,12,13,14,33,38,81],environment:116,eon:80,epa:116,epoch:40,epvgfgzts:28,equal:[13,18,23,30,48,61,67,68,74,78,79,82,83,84,94,95,104,110,119,128],equatori:128,equit:119,equival:[11,32,38,60,67,77,88,116,134],eras:[12,17,27],erika:25,eritrea:[93,94,128],err:[36,94,105],errno:32,erron:14,error:[3,12,14,17,18,19,25,27,30,32,33,36,42,48,49,61,68,82,83,87,88,89,93,94,98,101,102,104,105,107,112,114,117,119,122,127],error_bad_lin:127,error_rewrit:30,errorband:122,errorbar:124,escap:[12,100,103,134],escapechar:127,esh:127,esoter:54,especi:[3,11,12,17,20,25,27,31,34,35,36,40,41,57,60,64,113,114,119,124],esperanza:[113,115],essai:36,essenti:[10,26,88,89,100,101,103,107,110,111],establish:[34,35,49,55,104],estim:[17,26,32,40,41,42,46,47,51,52,60,70,71,72,82,83,92,108,109,119,120,124],estonia:128,estout:17,eswatini:128,etc:[0,2,3,6,7,9,11,12,13,15,17,24,25,28,29,30,32,34,35,36,39,40,41,42,51,52,60,61,68,75,76,81,82,83,84,85,86,89,97,98,100,101,103,105,107,109,111,114,116,117,119,121,124,134],ethiopia:128,ethnic:[70,71,108],etiquett:117,eubank:[1,25],euituavzq:28,europ:[88,93,94,97,98,103,104,105,106,127,128,129,130],eval:[115,127],evalu:[4,12,13,14,15,17,24,29,36,40,48,49,51,52,55,76,77,79,81,91,109],evang:122,even:[3,8,9,10,12,13,14,15,18,19,20,21,24,25,26,27,30,31,32,33,34,39,40,41,42,43,44,47,48,49,51,52,54,55,60,62,69,73,77,79,82,83,84,88,89,90,91,94,97,99,102,103,104,106,107,108,109,113,114,119,122,124,125,134,135],even_integers_through_1000:83,even_row:28,evenli:[51,52,92],event:34,eventu:[32,112,127],ever:[10,11,12,13,17,18,23,24,29,30,31,42,55,63,67,80,89,90,94,96,97,100,102,105,118,119,122],everi:[9,10,12,14,15,17,18,23,24,29,30,31,34,40,42,46,47,51,52,55,73,75,76,77,89,93,95,97,98,100,102,107,112,114,118,119,122,124,135],everyon:[9,10,15,17,25,31,32,34,41,46,47,49,51,52,60,63,70,71,89,94,108,119],everyth:[6,9,12,13,18,19,21,24,25,26,27,29,30,31,35,36,38,40,41,43,44,47,49,53,60,62,69,74,89,91,95,97,100,102,103,105,110,114,115,126,127,128],everywher:[8,12,17,33,49],evid:[39,51,52,68,95,105,112,135],evidenc:[14,34,68,122],evil:49,evolut:[53,67],evolutionari:119,evolv:[13,31,36,67,96,119],ewcskolhuv:[],ex10_gini_policy_d:51,ex11_gini_policy_:51,ex4_gini:51,ex4_share_below_poverti:51,ex6_gini_policy_a:51,ex6_gini_policy_b:51,ex7_gini_policy_c:51,ex8_revenue_rais:51,ex9_transf:51,ex:3,exact:[6,12,20,23,29,32,40,41,51,52,62,77,103,121,128],exactli:[3,13,15,17,18,29,30,32,33,37,48,49,77,93,94,97,102,111,112,114,118,124,126],exam:34,examin:[108,112,114,119],exampl:[2,8,9,10,11,12,13,14,15,19,20,21,22,23,24,26,27,28,29,32,34,35,37,38,40,41,42,43,44,46,47,48,49,51,52,54,55,58,59,60,61,62,63,64,66,67,68,70,71,72,73,74,77,78,79,80,81,82,85,86,89,90,93,94,96,97,98,99,101,102,103,104,105,106,107,108,110,112,113,114,115,116,118,119,122,123,124,125,127,128,129,130,134],example_csv:[11,12],example_data:[11,12,17,19,93,94,119,122],exc:[32,33],exceed:[119,122],exceedingli:[24,56],excel:[15,18,26,40,60,76,82,83,84,93,97,98,125,128],except:[14,17,20,21,23,25,30,32,33,46,47,51,52,58,59,65,70,71,86,90,93,94,102,105,114,115,128],exception:79,exchang:[21,40,46,47],excit:[10,15],exclud:108,exclus:[70,71],excruci:32,excut:104,exec:[30,128,129,130],execut:[3,4,10,12,13,14,17,25,26,29,30,31,32,33,39,76,77,78,79,81,82,83,90,100,101,111,115],exercis:[6,15,21,25,35,36,43,45,70,71,75,77,79,84,90,93,95,102,103,106,112,114,127,128],exhaust:[31,98],exhibit:[97,106],exist:[4,10,12,17,18,20,22,23,24,27,31,34,36,49,54,63,81,89,90,93,94,95,97,101,107,111,119,126],exit:[3,12,34],exog:[119,120],expand:[2,35,100],expect:[13,17,18,19,24,32,35,46,47,51,52,64,73,74,79,89,91,95,97,100,103,110,111,112,114,119,122,125,127,128],expens:[26,34,35],experi:[4,7,9,11,15,17,24,27,31,32,54,55,70,71,74,79,81,91,93,116,121,125],experienc:[18,42,104],experiment1:[51,52],experiment:[79,121],expert:[13,97],expertis:[36,113],explain:[36,40,41,43,48,49,100,101,119,124,125],explan:20,explanatori:127,explicit:[21,42,79,89,91,101,114],explicitli:[8,13,15,16,20,29,58,59,61,76,77,88,89,94,101,108,114,122,125],explor:[12,13,14,32,51,52,67,72,73,74,75,77,78,79,82,83,84,85,87,89,90,92,93,94,102,109,110,111,112,113,116,117,119,122,124,125,126,128,129],exploratori:[117,119,122,123],explos:17,expon:[62,125],exponenti:130,exported_statist:17,expos:[55,68,119,122],exposit:[93,117],express:[14,17,31,34,48,76,77,82,83,103,109,113,115],ext_modul:30,extend:[41,43,74,104],extens:[14,25,32,34,36,38,39,60,81,97,98,114],extent:122,extern:35,extra:[0,27,32,61,68,89,111,113,125],extract:[32,46,47,48,49,50,51,52,63,64,75,90,93,127],extran:111,extraordinari:80,extrapol:92,extrem:[8,9,12,14,16,31,34,36,41,43,48,51,52,62,79,88,89,90,94,95,105,108,118,119,134],ey:[13,17,19,74,80,95,114,119,122],ezlquomybn:28,f2777b5ca39c9fe965d9e5058695203d:32,f30d4b630726:114,f485e25f94f5664c192e73df6d42002d:[],f623161a15ec:32,f:[13,14,17,25,30,32,40,51,55,73,80,102,105,119,120,122,127,128,129,130,133],f_drugoff:112,face:[34,85,110],facebook:27,facecolor:12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Data Science","Practical Data Science in Python","The Jupyter Project","Running R Jupyter Notebooks in VS Code","Managing Python Packages","Set Up an Augmented Command Line","Improving Your Command Line Experience (macOS)","The Command Line on Windows / Installing Bash","Installing Geopandas","Installing Python and Miniconda","Setting Up Visual Studio Code","The Command Line, Basics","Advanced Command Line Tutorial","Principles and Tools for Debugging","Debugging Python in VS Code","Backwards Design","Managing Conda Environments","Defensive Programming","Getting Help Online","The Iceberg Principle","Numbers in Computers","Reviewing Code on Github","Python vs. R: Important Differences To Be Aware Of","Python: Variables vs. Objects","Workflow Management","Writing Good Jupyter Notebooks","What is Big Data?","Big Data Strategies","Parquet","Speed in Python","Solving Performance Issues","Parallel Computing","Distributed Computing with dask","Starting a Cloud Dask Cluster","So You\u2019re Thinking About a PhD","Advice on Buying Computers for Data Science Students","chatGPT and You","The Object dtype and Performance","Class Two: Representing Data: Objects, collections, and their uses for computation (Numpy)","Running Python in the VS Code Interactive Window","Numpy Arrays: The Workhorse of Data Science","Working with Vectors","Doing Math With Vectors","Visualizing Your Data with Histograms","Type Promotion in numpy","Vector Recap","(Ungraded) Vector Exercises","(Ungraded) Vector Exercises","Subsetting Vectors","Modifying Subsets of Vectors","Recap","Week 2 Numpy Re-cap Exercises","Week 2 Numpy Re-cap Exercises","Welcome to Matrices","Views and Copies","When Do I Get a View and When Do I Get a Copy?","Review of Views and Copies","Variables are pointers to objects","Views and Copies Exercises","Views and Copies Exercises","Intro to Matrices","Folding and Reshaping Matrices","Matrices as Images","Subsetting Matrices","Editing Subsets","Review of Matrices","Matrix Exercises","Arrays","Broadcasting","Review of ND-Arrays","Week 3 Quiz","Week 3 Quiz","Welcome to Week 4!","Summarizing Arrays","Color Images as Arrays","Practical Example: comparing weather by counting like-values","The Python Ease of Use / Speed Tradeoff","Vectorization in Python","Exercise: Speeding up matrix operations","Random numbers","Exercise 1: Calculating the mean and variance of common random variables","Understanding and Managing Python Packages","Week 4 Assessment","Week 4 Assessment Solutions (NOT FOR POSTING)","Assessment: filtering and querying a dataset","Course Wrap-up","Class Three: Loading and manipulating data","Programming with data","Working with tabular data","Pandas Series","Subsetting and Indexing Series","(OPTIONAL) Subsetting and Indexing with Single Square Brackets ([])","Series Exercises","Working with tabular data through Dataframes","Subsetting DataFrame Tricks and Gotchas","Estimating Labor Market Returns to Education","PyArrow: An Alternative to Numpy as Pandas Backend","Ways To Store and Read Data","Ways To Store and Read Data: Binary Files","Views and Copies in Pandas","Views and Copies in pandas","The View/Copy Headache in pandas without Copy on Write","Identifying Data Problems","Fixing Data Value Problems","Editing Specific Locations","Cleaning Data Types","Missing Data","The Object and Categorical Data Types","Exercise: Data Cleaning","Combining, grouping, and querying data","Combining datasets: concatenating","Combining datasets: merging","Merging Data to Understand the Relationship between Drug Legalization and Violent Crime","Grouping datasets","Groupby and Arrest Data","Queries reprised: asking your data questions","Exercise: queries into the state of electricity production and CO2 emissions in the United States","Class 5: Towards the practice of data science","Plotting in Python","seaborn and the Grammar of Graphics","seaborn.objects Recipes","matplotlib","Advanced Plotting with Altair","Plotting with Pandas","Plotting with matplotlib","Making pretty plots in Python: customizing plots in matplotlib","Project: Global greenhouse gas emissions","Project: gathering, inspecting, and cleaning the data","Project: Data merging","Project: Plotting","Project: Plotting","<no title>","<no title>","CONCAT","Combining numbers and text into strings: f-strings","Why not just use Excel or 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\ No newline at end of file diff --git a/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb b/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb index 7354d5af..d8e69cd4 100644 --- a/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb +++ b/notebooks/class_2/week_3/45_EX_nd_arrays_w_answers.ipynb @@ -46,7 +46,7 @@ "years_of_education = [12, 16, 11, 22, 12, 8, 12]\n", "\n", "survey = np.array([incomes, ages, years_of_education]).T\n", - "survey\n" + "survey" ] }, { @@ -78,7 +78,7 @@ ], "source": [ "mean_age = np.mean(survey[:, 1])\n", - "np.round(mean_age, 1)\n" + "np.round(mean_age, 1)" ] }, { @@ -140,7 +140,7 @@ ], "source": [ "mean_income = np.mean(survey[:, 0])\n", - "mean_income\n" + "mean_income" ] }, { @@ -160,7 +160,7 @@ } ], "source": [ - "np.round(mean_income, 1)\n" + "np.round(mean_income, 1)" ] }, { @@ -241,12 +241,12 @@ ], "source": [ "survey[survey[:, 0] < 20_000, 0] = survey[survey[:, 0] < 20_000, 0] + 1_500\n", - "survey\n" + "survey" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -255,13 +255,13 @@ "40214.3" ] }, - "execution_count": 27, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(np.mean(survey[:, 0]), 1)\n" + "np.round(np.mean(survey[:, 0]), 1)" ] }, { @@ -325,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -334,7 +334,7 @@ "(122603, 5)" ] }, - "execution_count": 11, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -346,28 +346,7 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "26.0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hourly = cps[:, 0] / cps[:, 1]\n", - "np.round(np.mean(hourly), 1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -376,19 +355,19 @@ "27.883329990399556" ] }, - "execution_count": 13, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "men = np.mean(hourly[cps[:, 2] == 1])\n", - "men\n" + "men" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -397,18 +376,18 @@ "27.9" ] }, - "execution_count": 14, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(men, 1)\n" + "np.round(men, 1)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -417,19 +396,19 @@ "23.803157653012704" ] }, - "execution_count": 15, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "women = np.mean(hourly[cps[:, 2] == 2])\n", - "women\n" + "women" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -438,18 +417,18 @@ "23.8" ] }, - "execution_count": 16, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(women, 1)\n" + "np.round(women, 1)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -458,18 +437,18 @@ "0.8536698328789393" ] }, - "execution_count": 17, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "women / men\n" + "women / men" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -478,13 +457,13 @@ "0.854" ] }, - "execution_count": 18, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.round(women / men, 3)\n" + "np.round(women / men, 3)" ] }, { @@ -518,7 +497,7 @@ ], "source": [ "black = np.mean(hourly[cps[:, 3] == 2])\n", - "black\n" + "black" ] }, { @@ -538,7 +517,7 @@ } ], "source": [ - "np.round(black, 1)\n" + "np.round(black, 1)" ] }, { @@ -559,7 +538,7 @@ ], "source": [ "white = np.mean(hourly[cps[:, 3] == 1])\n", - "white\n" + "white" ] }, { @@ -579,7 +558,7 @@ } ], "source": [ - "np.round(white, 1)\n" + "np.round(white, 1)" ] }, { @@ -599,7 +578,7 @@ } ], "source": [ - "black / white\n" + "black / white" ] }, { @@ -619,7 +598,7 @@ } ], "source": [ - "np.round(black / white, 3)\n" + "np.round(black / white, 3)" ] }, { @@ -651,7 +630,7 @@ "metadata": {}, "outputs": [], "source": [ - "true_average = np.sum((hourly * cps[:, 4]) / np.sum(cps[:, 4]))\n" + "true_average = np.sum((hourly * cps[:, 4]) / np.sum(cps[:, 4]))" ] }, { @@ -671,7 +650,7 @@ } ], "source": [ - "np.round(true_average, 2)\n" + "np.round(true_average, 2)" ] } ], @@ -691,7 +670,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": { diff --git a/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb b/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb index 0a589235..8d044ba4 100644 --- a/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb +++ b/notebooks/class_2/week_3/47_Week3_Numpy_Wrapup_Quiz.ipynb @@ -9,7 +9,7 @@ "\n", "**Note**: \n", "\n", - "> This exercise has been written out in something called a Jupyter Notebook. We'll discuss Jupyter Notebooks in more detail later in the course—they are a very powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don't need to *do* anything with the notebook except read its contents—just use write your Python code in a regular `.py` file.\n", + "> This exercise has been written out in something called a Jupyter Notebook. We'll discuss Jupyter Notebooks in more detail later in the course—they are very a powerful tool for data science communication!—but for the time being, the notebook is just a convenient way for us to write out the exercise. You don't need to *do* anything with the notebook except read its contents—just use write your Python code in a regular `.py` file.\n", "\n", "\n", "As previously discussed, we frequently use matrices and data science because they are a natural format for representing data generated by collecting the same type of information from numerous entities. For example, below is a toy dataset that you could imagine was created by collecting information about employees at a company—each column is a different type of information being collected (income, age, years of education), and each row is the information about a different employee.\n", @@ -47,7 +47,7 @@ "years_of_education = [12, 16, 11, 22, 12, 8, 12]\n", "\n", "survey = np.array([incomes, ages, years_of_education]).T\n", - "survey\n" + "survey" ] }, { @@ -56,9 +56,17 @@ "source": [ "## Part 1: Summarizing Data\n", "\n", - "1. What is the average (mean) age of all employees (rounded to 1 decimal place)?\n", - "2. What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?\n", - "3. What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?" + "### Question 1 \n", + "\n", + "What is the average (mean) age of all employees (rounded to 1 decimal place)?\n", + "\n", + "### Question 2\n", + "\n", + "What is the average (mean) income of employees over 30 (rounded to 1 decimal place)?\n", + "\n", + "### Question 3\n", + "\n", + "What is the average (mean) number of years of education for employees with incomes above the average income of all employees (rounded to 1 decimal place)?" ] }, { @@ -70,7 +78,9 @@ "\n", "The US government is thinking about offering a 1,500 tax credit to anyone making less than 20,000 a year. You can think of this tax credit as effectively an additional $1,500 of income to each person receiving the credit.\n", "\n", - "4. Using the data from `survey`, modify income values to calculate a new estimate of the employees' incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?\n", + "### Question 4 \n", + "\n", + "Using the data from `survey`, modify income values to calculate a new estimate of the employees' incomes after we take this credit into account. What will the average income be for all employees if those making less than 20,000 a year were to receive this credit (rounded to 1 decimal place)?\n", " - Do so by subsetting and editing values programmatically, *not* just typing values by hand. (Yes, writing out a new vector by hand is easy to do in this example, but you couldn't do it with a large, real dataset!)" ] }, @@ -82,7 +92,7 @@ "\n", "In this exercise, we'll be working with data from the [US Current Population Survey, provided by the National Bureau of Economic Research (NBER)](https://www.nber.org/research/data/current-population-survey-cps-merged-outgoing-rotation-group-earnings-data). This is a regular survey conducted by the US Bureau of Labor to calculate the US employment rate.\n", "\n", - "In this exercise, we'll use these data to study gender and racial wage inequality in the US.\n", + "In this exercise, we'll use this data to study gender and racial wage inequality in the US.\n", "\n", "Load data from the 2018 CPS survey with the following command:\n", "\n", @@ -90,16 +100,20 @@ "cps = np.loadtxt(\"data/cps.txt\")\n", "```\n", "\n", - "This data are a *subset* of the full CPS survey and contains only data on **employed respondents working at least 35 hours a week (e.g., full-time).**\n" + "This data is a *subset* of the full CPS survey and contains only data on **employed respondents working at least 35 hours a week (e.g., full-time).**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "5. How many rows does this matrix have?\n", + "### Question 5 \n", + "\n", + "How many rows does this matrix have?\n", + "\n", + "### Question 6 \n", "\n", - "6. The five columns of this matrix correspond to:\n", + "The five columns of this matrix correspond to:\n", " - Column 1: Weekly income in dollars.\n", " - Column 2: Usual hours respondent works per week.\n", " - Column 3: Gender. 2 is \"Female\", 1 is \"Male\"\n", @@ -115,7 +129,9 @@ "\n", "For the moment, let's ignore survey weights—they don't impact results here significantly.\n", "\n", - "**What is the *average hourly wage* for all workers in this dataset (round it to one decimal place)?**\n", + "**What is the *average hourly wage* for all workers in this data?** \n", + "\n", + "Please round your *final* answer to one decimal place, but *don't round any intermediate results!* (if you round at each stage of the calculation, those roundings may compound in odd ways).\n", "\n", "**Hint:** This will require more than just using `mean` on a single column!" ] @@ -124,11 +140,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "7. What is the average hourly wage of working men (rounded to one decimal place)? \n", + "### Question 7 \n", + "\n", + "What is the average hourly wage of working men? (Please round your *final* answer to one decimal place, but *don't round any intermediate results!*)\n", + "\n", + "### Question 8 \n", + "\n", + "What is the average hourly wage of working women? (Please round your *final* answer to one decimal place, but *don't round any intermediate results!*)\n", "\n", - "8. What is the average hourly wage of working women (rounded to one decimal place)?\n", + "### Question 9 \n", "\n", - "9. What share (e.g., a value between 0 and 1) of men's average hourly wage is women's average hourly wage? (rounded to three decimal places). In other words, what is women's average hourly wage divided by men's average hourly wage? *Don't round anything until you have your final answer!*\n", + "What share (e.g., a value between 0 and 1) of men's average hourly wage is women's average hourly wage? (Please round your *final* answer to three decimal place, but *don't round any intermediate results!*). In other words, what is women's average hourly wage divided by men's average hourly wage?\n", "\n", "Congratulations! You've just calculated the US gender wage gap, on your own, using real data! I mean... I guess \"congratulations\" is a weird thing to say after directly measuring one of the more egregious inequities in US society, but one of the reasons many of us study data science is so that we will have the ability to directly measure these types of phenomena in the hopes of being able to better understand and address them." ] @@ -137,11 +159,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "10. Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?\n", + "### Question 10 \n", "\n", - "11. What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?\n", + "Now, speaking of egregious inequities, what is the average hourly wage for respondents who identify as Black (rounded to one decimal place)?\n", "\n", - "12. What share (e.g., a value between 0 and 1) of respondents who identify as White's average hourly wage is respondents who identify as Black's average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black's average hourly wage divided by respondents who identify as White's average hourly wage? *Don't round anything until you have your final answer!*\n", + "### Question 11 \n", + "\n", + "What is the average hourly wage for respondents who identify as White (rounded to one decimal place)?\n", + "\n", + "### Question 12 \n", + "\n", + "What share (e.g., a value between 0 and 1) of respondents who identify as White's average hourly wage is respondents who identify as Black's average hourly wage? (rounded to three decimal places). In other words, what is the respondents who identify as Black's average hourly wage divided by respondents who identify as White's average hourly wage? *Don't round anything until you have your final answer!*\n", "\n", "Note that this will only be an approximation—one would normally also include all respondents of mixed-race into non-mutually exclusive categories like \"Any Part Black\" or \"Any Part White\", and we would also break out Hispanic and non-Hispanic respondents. But as most respondents only pick on racial category, this will still give us a reasonable approximation." ] @@ -154,7 +182,7 @@ "\n", "As noted above, the fifth column of our data contains something called \"sampling weights.\" That's because when the government conducted this survey, they didn't draw a random sample of respondents from the US population where everyone had the same probability of being interviewed. As a result, when we calculate the average hourly wage of the people in the survey, it isn't *quite* the best estimate we have for the average hourly wage for everyone working in the United States. To calculate this number as accurately as possible, we have to take into account the fact that some respondents in the data were more likely to be included than others, and thus can be thought of as standing in for a smaller group of people in the US.\n", "\n", - "Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than what we would get if we just took a random sample where everyone in the United States had the same probability of being included.\n", + "Why would the government do this? The main reason is that if we want to make statements about a group in a survey like this, the accuracy of those statements will depend on the number of individuals who have actually ended up taking the survey. If we are interested in a big group—like White men—we are almost guaranteed to have enough of them in any reasonably sized survey to be able to make accurate statements about that subpopulation. But if we were interested in the life experiences of, say, people in their twenties who have a high school diploma but never attended college, and who live in rural communities, we may have to make a deliberate effort to ensure that there are more of those people in our survey than when we would get if we just took a random sample where everyone in the United States had the same probability of being included.\n", "\n", "As I mentioned above, for the questions above, the sampling weights don't make a very big difference to our answers, but the way to get the *most accurate* estimates would be to take them into account. So let's give that a try!\n", "\n", @@ -162,9 +190,11 @@ "\n", "For a *weighted* average, we take the value for each observation $i$ and multiply it by\n", "\n", - "$$weight_i / \\sum_{j=1}^N weight_j$$\n", + "$$weight_i / \\sum_{j=1}^N weights_j$$\n", + "\n", + "where $weight_i$ is the observation's weight, and $\\sum_{j=1}^N weights_j$ is the total of all the weights in the population being averaged. Then we just add up those values!\n", "\n", - "where $weight_i$ is the observation's weight, and $\\sum_{j=1}^N weight_j$ is the total of all the weights in the population being averaged. Then we just add up those values!\n", + "### Question 13\n", "\n", "Given that, what is the average hourly wage of Americans working full-time jobs (i.e., the group in this survey) taking into account survey weights (rounded to **two** decimal places)?\n" ] @@ -172,7 +202,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.6.5 ('base')", + "display_name": "Python 3.10.6 ('base')", "language": "python", "name": "python3" }, @@ -186,12 +216,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.10.6" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "f54e0b9f92ceb50229b3b7ea26b5d02f05ce8845dd8d997c014a0efede4c9c20" + "hash": "718fed28bf9f8c7851519acf2fb923cd655120b36de3b67253eeb0428bd33d2d" } } }, diff --git a/notebooks/class_3/week_2/10_pandas_series.ipynb b/notebooks/class_3/week_2/10_pandas_series.ipynb index a8b7c855..b3184b0c 100644 --- a/notebooks/class_3/week_2/10_pandas_series.ipynb +++ b/notebooks/class_3/week_2/10_pandas_series.ipynb @@ -302,10 +302,11 @@ "\n", "Before we dive too far into Series manipulations, it's important to talk about datatypes. Every Series, as we will see, has a \"dtype\" (short for datatype). The `dtype` of a Series is important to understand because a Series' `dtype` determines what manipulations you can apply to that series. \n", "\n", - "There are, broadly, two types of Series: \n", + "There are, broadly, three types of Series: \n", "\n", "- Numeric: these hold numbers that `pandas` understands are numbers. Specific numeric datatypes include things like `int64`, and `int32` (integers), or `float64` and `float32` (floating point numbers).\n", - "- Object: these are Series that can hold any Python object, like strings, numbers, Sets, you name it. They have dtype `O` for \"objects\". They are flexible, but also very slow and actually harder to work with." + "- Object: these are Series that can hold any Python object, like strings, numbers, sets, you name it. They have dtype `O` for \"objects\". They are flexible, but also very slow and actually harder to work with.\n", + "- Categorical: this is a special data type (analogous to factors in R, if that means anything to you) that we'll discuss in a future reading. Don't worry about them for now." ] }, { diff --git a/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb b/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb deleted file mode 100644 index 69f1758f..00000000 --- a/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb +++ /dev/null @@ -1,141 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# The Object and Categorical Data Types\n", - "\n", - "In our previous readings, we introduced the idea that not only can DataFrames and Series hold any of the numeric data types we've come to know and love from numpy — like `float64` or `int64` — but that they can also hold arbitrary Python objects in an `object`-type Series. Moreover, we also acknowledged (though didn't go into) the existence of another relatively unique pandas data type — the `Categorical` Series. \n", - "\n", - "In this reading, we will discuss the purpose of these two data types, as well as their advantages and disadvantages.\n", - "\n", - "## The object dtype\n", - "\n", - "The `object` type Series gives pandas _incredible_ flexibility as it allows any type of data to be stored in a table. The most common use of the `object` data type is to store text — for example, names, addresses, written answers, etc. — but the flexibility can also be used for applications like geospatial analysis, in which a single row of a DataFrame may represent a single country, the columns may represent features of the country (name, income, population), and the last column stores geometric Python objects that describe the shape and location of the country. \n", - "\n", - "But this flexibility also comes at a cost — performance and memory efficiency.\n", - "\n", - "### The object Performance Penalty" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To understand why `object` Series are slow, it helps to first discuss why numeric Series (and numeric numpy arrays) are fast. \n", - "When you work with a numeric pandas Series or numpy array, all of the entries in those arrays live in the same place in memory (in your computer's RAM). This is possible because all those integers (or all those floating point numbers) are written with the same number of 0s and 1s. In an `int64` Series, for example, every integer is represented by 64 1s and 0s. This makes it easy for the computer to lay them out sequentially. Moreover, it makes it possible for the computer to find specific entries quickly, since it knows that the third integer in the Series will start 64 * 2 spots from the start of the Series and end 64 spots later.\n", - "\n", - "But an *object* Series is a little different. Python objects vary in size — some may only take up 128 0s and 1s, while others may require thousands — and so the actual data in an object Series can't be laid down in a nice regularly spaced sequence. Instead, every entry in an object Series gets put in a different location in your computer's memory (RAM), and only the *address* of that information is placed in a nice organized Series. These addresses are all the same size, and so the addresses can be organized in a regular manner, even if the actual content you want to store is irregular.\n", - "\n", - "The cost of this arrangement is that if you ask for the second entry of an object Series (e.g., `my_series.iloc[1]`), you're computer has first go to the second location in the array, read the address stored there, *then go to that address* to find the actual content you want. And those added steps waste time.\n", - "\n", - "The other problem with object Series is that because they can store anything, Python doesn't know before it looks up an entry whether it will find a string, an integer, or a Python set. As a result, when it sees code like: \n", - "\n", - "```python \n", - "my_array * 2\n", - "```\n", - "\n", - "Python can't be sure what is meant by `*` — it could mean \"do integer multiplication\" (if a given entry in `my_array` is an integer), but it might also mean \"double up the list you find\" (if the entry is a Python list)!\n", - "\n", - "Indeed we can see this if we make a numpy object array full of ints and compare it to a numpy integer array. The both have the same content, but they are organized in memory differently:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "object_numbers = pd.Series(np.arange(1000000), dtype=\"object\")\n", - "numbers = pd.Series(np.arange(1000000), dtype=\"int64\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "16.1 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" - ] - } - ], - "source": [ - "%timeit object_numbers * 2" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "743 µs ± 7.57 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" - ] - } - ], - "source": [ - "%timeit numbers * 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See? Same operation (doubling each entry of arrays with the integers from 0 to 1,000,000), but the object array operation is ~20x slower. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So yes, `object` dtypes are absolutely wonderful and introduce unbelievable flexibility to pandas; but remember there is a cost to using them, so stick to numeric data types when possible!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Categoricals" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Categoricals are a terrific little hack." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb b/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb new file mode 100644 index 00000000..699f3b8f --- /dev/null +++ b/notebooks/class_3/week_3/37_object_and_categorical_dtypes.ipynb @@ -0,0 +1,637 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Object and Categorical Data Types\n", + "\n", + "In our previous readings, we introduced the idea that not only can DataFrames and Series hold any of the numeric data types we've come to know and love from numpy — like `float64` or `int64` — but that they can also hold arbitrary Python objects in an `object`-type Series. Moreover, we also acknowledged (though didn't go into) the existence of another relatively unique pandas data type — the `Categorical` Series. \n", + "\n", + "In this reading, we will discuss the purpose of these two data types, as well as their advantages and disadvantages.\n", + "\n", + "## The object dtype\n", + "\n", + "The `object` type Series gives pandas _incredible_ flexibility as it allows any type of data to be stored in a table. The most common use of the `object` data type is to store text — for example, names, addresses, written answers, etc. — but the flexibility can also be used for applications like geospatial analysis, in which a single row of a DataFrame may represent a single country, the columns may represent features of the country (name, income, population), and the last column stores geometric Python objects that describe the shape and location of the country. \n", + "\n", + "But this flexibility also comes at a cost — performance and memory efficiency.\n", + "\n", + "### The object Performance Penalty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To understand why `object` Series are slow, it helps to first discuss why numeric Series (and numeric numpy arrays) are fast. \n", + "When you work with a numeric pandas Series or numpy array, all of the entries in those arrays live in the same place in memory (in your computer's RAM). This is possible because all those integers (or all those floating point numbers) are written with the same number of 0s and 1s. In an `int64` Series, for example, every integer is represented by 64 1s and 0s. This makes it easy for the computer to lay them out sequentially. Moreover, it makes it possible for the computer to find specific entries quickly, since it knows that the third integer in the Series will start 64 * 2 spots from the start of the Series and end 64 spots later.\n", + "\n", + "But an *object* Series is a little different. Python objects vary in size — some may only take up 128 0s and 1s, while others may require thousands — and so the actual data in an object Series can't be laid down in a nice regularly spaced sequence. Instead, every entry in an object Series gets put in a different location in your computer's memory (RAM), and only the *address* of that information is placed in a nice organized Series. These addresses are all the same size, and so the addresses can be organized in a regular manner, even if the actual content you want to store is irregular.\n", + "\n", + "The cost of this arrangement is that if you ask for the second entry of an object Series (e.g., `my_series.iloc[1]`), you're computer has first go to the second location in the array, read the address stored there, *then go to that address* to find the actual content you want. And those added steps waste time.\n", + "\n", + "The other problem with object Series is that because they can store anything, Python doesn't know before it looks up an entry whether it will find a string, an integer, or a Python set. As a result, when it sees code like: \n", + "\n", + "```python \n", + "my_array * 2\n", + "```\n", + "\n", + "Python can't be sure what is meant by `*` — it could mean \"do integer multiplication\" (if a given entry in `my_array` is an integer), but it might also mean \"double up the list you find\" (if the entry is a Python list)!\n", + "\n", + "Indeed we can see this if we make a numpy object array full of ints and compare it to a numpy integer array. The both have the same content, but they are organized in memory differently:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "object_numbers = pd.Series(np.arange(1000000), dtype=\"object\")\n", + "numbers = pd.Series(np.arange(1000000), dtype=\"int64\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15.9 ms ± 70.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%timeit object_numbers * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "771 µs ± 9.62 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit numbers * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See? Same operation (doubling each entry of arrays with the integers from 0 to 1,000,000), but the object array operation is ~20x slower. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So yes, `object` dtypes are absolutely wonderful and introduce unbelievable flexibility to pandas; but remember there is a cost to using them, so stick to numeric data types when possible!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Categoricals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `category` data type is a delightful little hack that allows us to avoid most of the problems with `object` Series *in certain circumstances*. \n", + "\n", + "To illustrate how `category` Series work, suppose we have a DataFrame with information on hundreds of thousands of customers in the United States, and that one column of that DataFrame contains the name of each customer's state of residence (substitute Province or any other sub-national administrative unit if states don't resonate for you). \n", + "\n", + "Because those state names are words, they are being stored in an `object` Series. That, in turn, means that Python has created hundreds of thousands of Python objects — each containing the name of a customer's state — *and* a vector containing addresses for each of those objects. \n", + "\n", + "But as there are only 50 states in the United States, this might strike you as absurd, since most of those hundreds of thousands of Python objects are holding the same text! Surely we can do something more efficient than that? \n", + "\n", + "Enter Categoricals. The idea of a `category` Series is to take a `object` Series that contains frequently repeated strings and:\n", + "\n", + "1. Replace each unique string with a number (for example, `\"Colorado\"` could become `1` and `\"Tennessee\"` could become `2`), and\n", + "2. Create a small \"lookup table\" that keeps track of what string is associated with each number. \n", + "\n", + "Now pandas doesn't need to create hundreds of thousands of Python strings to record each customer's state, it can just make a numeric Series with state names replaced by the numbers 1-50, and a new small vector with the fifty names of states.\n", + "\n", + "Moreover, in addition to saving memory, this also dramatically improves the *performance* of pandas. Suppose, for example, you want to subset for customers living in North Carolina. When these states were in an `object` Series, pandas would have to go to each entry, figure out where the associated Python object is stored, get that object, and check to see if it was the string `\"North Carolina\"`. \n", + "\n", + "But now, pandas can just go to the lookup table, see that `\"North Carolina\"` is the 33rd entry (and so is represented by the number 33 in our encoded Series of numbers), and look for values of 33 in that Series. Hooray!\n", + "\n", + "But the best part is that, in most cases, the fact your data has been split into a numeric vector and a lookup table is actually entirely hidden from you, the user. For *most* operations, using a Categorical Series is just like using an `object` Series, just faster. \n", + "\n", + "### Categorical Series in Practice\n", + "\n", + "To illustrate how one works with Categorical Series, let's make a toy version of this customer dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
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      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "1 Aditya Tennessee\n", + "2 Francisco Colorado\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "customers = pd.DataFrame(\n", + " {\n", + " \"customer\": [\"Bob\", \"Aditya\", \"Francisco\", \"Shufan\"],\n", + " \"state\": [\"Colorado\", \"Tennessee\", \"Colorado\", \"Virginia\"],\n", + " }\n", + ")\n", + "customers" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "customer object\n", + "state object\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, `state` begins its life as a standard `object` Series, but we can convert it to a Categorical with `.astype(\"category\")`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
      1AdityaTennessee
      2FranciscoColorado
      3ShufanVirginia
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "1 Aditya Tennessee\n", + "2 Francisco Colorado\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"] = customers[\"state\"].astype(\"category\")\n", + "customers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, at first glance nothing about this column has changed. But if we pull it out, you can see it's dtype is `category` and that the Categories associated with the Series (the lookup table) contains three values: `\"Colorado\"`, `\"Tennessee\"` and \"`Virginia\"`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Colorado\n", + "1 Tennessee\n", + "2 Colorado\n", + "3 Virginia\n", + "Name: state, dtype: category\n", + "Categories (3, object): ['Colorado', 'Tennessee', 'Virginia']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And if you want to, you can see the two underlying pieces directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Colorado', 'Tennessee', 'Virginia'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"].cat.categories" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 1\n", + "2 0\n", + "3 2\n", + "dtype: int8" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"].cat.codes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But as we said, in most cases `category` Series will operate just like `object` Series. Subsetting, for example, will work just as it would with an `object` Series:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobColorado
      2FranciscoColorado
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Colorado\n", + "2 Francisco Colorado" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.loc[customers[\"state\"] == \"Colorado\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only place that problems *may* arise is that one cannot make arbitrary edits to a `category` Series — if you try and set a cell to have a value that isn't in the current `Categories` table, you will get an error:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "\n", + "customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "\n", + "TypeError Traceback (most recent call last)\n", + "/Users/nce8/github/practicaldatascience_book/notebooks/class_3/week_2/37_object_and_categorical_dtypes.ipynb Cell 22 line 1\n", + "----> 1 customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "\n", + "[...]\n", + "\n", + "TypeError: Cannot setitem on a Categorical with a new category (Kansas), set the categories first\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You *can* add novel values, to be clear, you just have to add the category first:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Colorado\n", + "1 Tennessee\n", + "2 Colorado\n", + "3 Virginia\n", + "Name: state, dtype: category\n", + "Categories (4, object): ['Colorado', 'Tennessee', 'Virginia', 'Kansas']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers[\"state\"] = customers[\"state\"].cat.add_categories([\"Kansas\"])\n", + "customers[\"state\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      customerstate
      0BobKansas
      1AdityaTennessee
      2FranciscoKansas
      3ShufanVirginia
      \n", + "
      " + ], + "text/plain": [ + " customer state\n", + "0 Bob Kansas\n", + "1 Aditya Tennessee\n", + "2 Francisco Kansas\n", + "3 Shufan Virginia" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.loc[customers[\"state\"] == \"Colorado\", \"state\"] = \"Kansas\"\n", + "customers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why Not Always Use Categoricals?\n", + "\n", + "Categoricals are great, but they are only useful when your `object` Series has a relatively small number of unique values. If you tried to convert an `object` Series with hundreds of thousands of addresses — and nearly all of them were unique — into a `category` Series, then pandas would have to create a lookup table that had... hundreds of thousands of unique entries (essentially, it would just be recreating your original `object` Series). And so there would be no real performance benefit." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}

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