From e0d98655875bf9baa05c36bffc8cafb63fc2510f Mon Sep 17 00:00:00 2001 From: Nick Eubank Date: Tue, 8 Oct 2024 14:12:16 -0400 Subject: [PATCH] update sword --- _build/.doctrees/environment.pickle | Bin 1077899 -> 1092403 bytes .../exercises/Solutions_cleaning.doctree | Bin 0 -> 80148 bytes .../exercises/Solutions_missing.doctree | Bin 0 -> 61034 bytes .../ids720_specific/solutions.doctree | Bin 5204 -> 6303 bytes .../_sources/ids720_specific/solutions.ipynb | 4 +- .../exercises/Solutions_cleaning.html | 1561 +++++++++++++++++ .../exercises/Solutions_missing.html | 1165 ++++++++++++ _build/html/ids720_specific/solutions.html | 7 + _build/html/objects.inv | Bin 7227 -> 7248 bytes _build/html/searchindex.js | 2 +- .../00_setup_env/setup_conda_envs.ipynb | 4 +- docs/_sources/ids720_specific/solutions.ipynb | 4 +- .../exercises/Solutions_cleaning.html | 325 ++-- .../exercises/Solutions_missing.html | 204 ++- docs/ids720_specific/solutions.html | 7 + docs/objects.inv | Bin 7227 -> 7248 bytes 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zl~)MP&nYd*%+D*HlEEH3B}1$SO~K@aOd_mVJd6wslQ~$8S+h7nEL&D*M(xRstSWG$ z^O+?lKVVnoRrAY`&Ctn^&SC|ryU4785Mcs|IC5w(s!i5m(Gqloxzsr)H8BtBRE}gA zXC{k=A=Ifb1(V?@N<<1k3Y_Qj+sa^GbAcGxJJI zQj4c#-JbkKP>JykP^rXZeIW(LTayEXOr>rDHDOi$dh&cBP1RzcbXFdaDDL6ROUz9z asVo4RRXZgMB+*%%p$KH=ZGJ11&IkZt-={19 delta 133 zcmbPlctwM?fpw~<$VS##T$^9 + + + + + + + + + Cleaning Data Exercises — Practical Data Science with Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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

Cleaning Data Exercises

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

Cleaning Data Exercises#

+

In this exercise, we’ll be returning to the American Community Survey data we used previously to measuring racial income inequality in the United States. In today’s exercise, we’ll be using it to measure the returns to education and how those returns vary by race and gender.

+
+

Gradescope Autograding#

+

Please follow all standard guidance for submitting this assignment to the Gradescope autograder, including storing your solutions in a dictionary called results and ensuring your notebook runs from the start to completion without any errors.

+

For this assignment, please name your file exercise_cleaning.ipynb before uploading.

+

You can check that you have answers for all questions in your results dictionary with this code:

+
assert set(results.keys()) == {
+    "ex5_age_young",
+    "ex5_age_old",
+    "ex7_avg_age",
+    "ex8_avg_age",
+    "ex9_num_college",
+    "ex11_share_male_w_degrees",
+    "ex11_share_female_w_degrees",
+    "ex12_comparing",
+}
+
+
+
+

Submission Limits#

+

Please remember that you are only allowed three submissions to the autograder. Your last submission (if you submit 3 or fewer times), or your third submission (if you submit more than 3 times) will determine your grade Submissions that error out will not count against this total.

+
+
+
+

Exercises#

+
+

Exercise 1#

+

For these cleaning exercises, we’ll return to the ACS data we’ve used before one last time. We’ll be working with US_ACS_2017_10pct_sample.dta. Import the data (please use url for the autograder).

+
+
+
import pandas as pd
+import numpy as np
+
+pd.set_option("mode.copy_on_write", True)
+
+# Download the data
+acs = pd.read_stata(
+    "https://github.com/nickeubank/MIDS_Data/blob/master"
+    "/US_AmericanCommunitySurvey/US_ACS_2017_10pct_sample.dta?raw=True"
+)
+
+
+
+
+
+
+

Exercise 2#

+

For our exercises today, we’ll focus on age, sex, educ (education), and inctot (total income). Subset your data to those variables, and quickly look at a sample of 10 rows.

+
+
+
acs = acs[["age", "sex", "educ", "inctot"]].copy()
+
+
+
+
+
+
+
acs.sample(10)
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
agesexeducinctot
4540043femalegrade 128100
26110774femalegrade 128400
2837446malenursery school to grade 49999999
22195318malegrade 125000
23587745male5+ years of college841000
30876728male4 years of college50000
25711624female4 years of college20000
13498256malegrade 1260200
1731449malenursery school to grade 49999999
11398658female4 years of college20000
+
+
+
+
+

Exercise 3#

+

As before, all the values of 9999999 have the potential to cause us real problems, so replace all the values of inctot that are 9999999 with np.nan.

+
+
+
acs["inctot"] = acs["inctot"].replace(9999999, np.nan)
+
+
+
+
+
+
+

Exercise 4#

+

Attempt to calculate the average age of people in our data. What do you get? Why are you getting that error?

+

You should get an error in trying to answer this question, but PLEASE LEAVE THE CODE THAT GENERATES THIS ERROR COMMENTED OUT SO YOUR NOTEBOOK WILL RUN IN THE AUTOGRADER.

+

Then talk about the error in a markdown cell.

+
+
+
# Code I'd run, but which I'm commenting out so this notebook runs:
+
+# acs["age"].mean()
+
+
+
+
+
+

I get a TypeError. Namely: TypeError: 'Categorical' with dtype category does not support reduction 'mean' It appears I’m getting the error because age is being stored as a Categorical rather than as a numeric type, and mean only works for numeric data.

+
+
+
+

Exercise 5#

+

We want to be able to calculate things using age, so we need it to be a numeric type. Check the current type of age, and look at all the values of age to figure out why it’s categorical and not numeric. You should find two problematic categories. Store the values of these categories in "ex5_age_young" and "ex5_age_old" (once you find them, it should be clear which is which).

+
+
+
results = dict()
+
+# One way to find problems:
+
+# Make string so can use `str.isnumeric`
+acs["age"] = acs["age"].astype("str")
+
+# us `str.isnumeric`
+problems = acs.loc[~acs["age"].str.isnumeric(), "age"].unique()
+problems
+
+
+
+
+
array(['less than 1 year old', '90 (90+ in 1980 and 1990)'], dtype=object)
+
+
+
+
+
+
+
# Or just poke around!
+
+# I could run this code, but I'm commenting it out so this notebook isn't awful to look at:
+
+# for i in acs.age.value_counts().index:
+#     print(i)
+
+
+
+
+
+
+
results["ex5_age_young"] = problems[0]
+results["ex5_age_old"] = problems[1]
+print(
+    f"The problematic values are '{results['ex5_age_young']}' and '{results['ex5_age_old']}'"
+)
+
+
+
+
+
The problematic values are 'less than 1 year old' and '90 (90+ in 1980 and 1990)'
+
+
+
+
+
+
+

Exercise 6#

+

In order to convert age into a numeric variable, we need to replace those problematic entries with values that pandas can later convert into numbers. Pick appropriate substitutions for the existing values and replace the current values.

+

Hint 1: Categorical variables act like strings, so you might want to use string methods!

+

Hint 2: Remember that characters like parentheses, pluses, asterisks, etc. are special in Python strings, and you have to escape them if you want them to be interpreted literally!

+

Hint 3: Because the US Census has been conducted regularly for hundreds of years but exactly how the census has been conducted has occasionally changed, variables are sometimes coded in a way that might be interpreted in different ways for different census years. For example, hypothetically, one might write 90 (90+ in 1980 and 1990) if the Censuses conducted in 1980 and 1990 used to top-code age at 90 (any values over 90 were just coded as 90), but more recent Censuses no longer top-coded age and recorded ages over 90 as the respondent’s actual age. We’re only working with more recent data, so anyone with a value of 90 (90+ in 1980 and 1990) can safely be assumed to be 90 years old. People with an age less than 1 year old can be treated as being of age 0.

+
+
+
acs["age"] = acs["age"].str.replace("less than 1 year old", "0", regex=True)
+acs["age"] = acs["age"].str.replace("90 \(90\+ in 1980 and 1990\)", "90", regex=True)
+
+
+
+
+
<>:2: SyntaxWarning: invalid escape sequence '\('
+<>:2: SyntaxWarning: invalid escape sequence '\('
+/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_77720/3726935054.py:2: SyntaxWarning: invalid escape sequence '\('
+  acs["age"] = acs["age"].str.replace("90 \(90\+ in 1980 and 1990\)", "90", regex=True)
+
+
+
+
+
+
+
# Check that worked
+acs.loc[~acs["age"].str.isnumeric(), "age"].unique()
+
+
+
+
+
array([], dtype=object)
+
+
+
+
+
+
+
assert acs["age"].str.isnumeric().all()
+
+
+
+
+
+
+

Exercise 7#

+

Now convert age from a categorical to numeric. Calculate the average age amoung this group, and store it in "ex7_avg_age".

+
+
+
acs["age"] = acs["age"].astype("int")
+results["ex7_avg_age"] = acs["age"].mean()
+print(f"The average age is {results['ex7_avg_age']:.2f}")
+
+
+
+
+
The average age is 41.30
+
+
+
+
+
+
+

Exercise 8#

+

Let’s now filter out anyone in our data whose age is less than 18. Note that before made age a numeric variable, we couldn’t do this! Again, calculate the average age and this time store it in "ex8_avg_age".

+

Use this sample of people 18 and over for all subsequent exercises.

+
+
+
acs = acs[acs["age"] >= 18].copy()
+results["ex8_avg_age"] = acs["age"].mean()
+print(f"The average age of those above 18 is {results['ex8_avg_age']:.2f}")
+
+
+
+
+
The average age of those above 18 is 49.76
+
+
+
+
+
+
+

Exercise 9#

+

Create an indicator variable for whether each person has at least a college Bachelor’s degree called college_degree. Use this variable to calculate the number of people in the dataset with a college degree. You may assume that to get a college degree you need to complete at least 4 years of college. Save the result as "ex9_num_college".

+
+
+
acs["educ"].value_counts(dropna=False)
+
+
+
+
+
educ
+grade 12                     92576
+4 years of college           47212
+1 year of college            38746
+5+ years of college          29801
+2 years of college           20753
+grade 5, 6, 7, or 8           5975
+grade 11                      5816
+grade 10                      4078
+n/a or no schooling           3644
+grade 9                       3145
+nursery school to grade 4     1288
+Name: count, dtype: int64
+
+
+
+
+
+
+
# Could do with two conditions and |
+acs["college_degree"] = (acs["educ"] == "4 years of college") | (
+    acs["educ"] == "5+ years of college"
+)
+acs["college_degree"].value_counts(dropna=False)
+
+
+
+
+
college_degree
+False    176021
+True      77013
+Name: count, dtype: int64
+
+
+
+
+
+
+
# Or you could do with `.isin`
+acs["college_degree"] = acs["educ"].isin(["4 years of college", "5+ years of college"])
+acs["college_degree"].value_counts(dropna=False)
+
+
+
+
+
college_degree
+False    176021
+True      77013
+Name: count, dtype: int64
+
+
+
+
+
+
+
# Check it worked
+acs.loc[acs["college_degree"] == 1, "educ"].value_counts()
+
+
+
+
+
educ
+4 years of college           47212
+5+ years of college          29801
+n/a or no schooling              0
+nursery school to grade 4        0
+grade 5, 6, 7, or 8              0
+grade 9                          0
+grade 10                         0
+grade 11                         0
+grade 12                         0
+1 year of college                0
+2 years of college               0
+Name: count, dtype: int64
+
+
+
+
+
+
+
# Remember booleans are just special cases of 0 and 1, so
+# the count that's true we can get with `sum`
+results["ex9_num_college"] = acs["college_degree"].sum()
+print(
+    f"There are {results['ex9_num_college']:,.0f} people in our data with a college degree."
+)
+
+
+
+
+
There are 77,013 people in our data with a college degree.
+
+
+
+
+
+
+

Exercise 10#

+

Let’s examine how the educational gender gap. Use pd.crosstab to create a cross-tabulation of sex and college_degree. pd.crosstab will give you the number of people who have each combination of sex and college_degree (so in this case, it will give us a 2x2 table with Male and Female as rows, and college_degree True and False as columns, or vice versa.

+
+
+
pd.crosstab(acs["college_degree"], acs["sex"])
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
sexmalefemale
college_degree
False8582190200
True3618140832
+
+
+
+
+

Exercise 11#

+

Counts are kind of hard to interpret. pd.crosstab can also normalize values to give percentages. Look at the pd.crosstab help file to figure out how to normalize the values in the table. Normalize them so that you get the share of men with and without college degree, and the share of women with and without college degrees.

+

Store the share (between 0 and 1) of men with college degrees in "ex11_share_male_w_degrees", and the share of women with degrees in "ex11_share_female_w_degrees".

+
+
+
tab = pd.crosstab(acs["college_degree"], acs["sex"], normalize="columns")
+tab
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
sexmalefemale
college_degree
False0.7034390.688381
True0.2965610.311619
+
+
+
+
+
for g in ["male", "female"]:
+    results[f"ex11_share_{g}_w_degrees"] = tab.loc[True, g]
+    print(
+        f"The share of {g}s with college degrees is "
+        f"{results[f'ex11_share_{g}_w_degrees']:.4f}"
+    )
+
+
+
+
+
The share of males with college degrees is 0.2966
+The share of females with college degrees is 0.3116
+
+
+
+
+
+
+

Exercise 12#

+

Now, let’s recreate that table for people who are 40 and over and people under 40. Over time, what does this suggest about the absolute difference in the share of men and women earning college degrees? Has it gotten larger, stayed the same, or gotten smaller? Store your answer (either "the absolute difference has increased" or "the absolute difference has decreased") in "ex12_comparing".

+
+
+
older = acs[40 <= acs["age"]]
+older_table = pd.crosstab(older["college_degree"], older["sex"], normalize="columns")
+older_table
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
sexmalefemale
college_degree
False0.6821230.699144
True0.3178770.300856
+
+
+
+
+
older_gap = older_table.loc[True, "male"] - tab.loc[True, "female"]
+print(
+    f"the gap between men and women's college attainment"
+    f" for those OVER 40 is {older_gap:.3f}"
+)
+
+
+
+
+
the gap between men and women's college attainment for those OVER 40 is 0.006
+
+
+
+
+
+
+
younger = acs[acs["age"] < 40]
+younger_table = pd.crosstab(
+    younger["college_degree"], younger["sex"], normalize="columns"
+)
+tab
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
sexmalefemale
college_degree
False0.7034390.688381
True0.2965610.311619
+
+
+
+
+
younger_gap = tab.loc[True, "male"] - tab.loc[True, "female"]
+print(
+    f"the gap between men and women's college attainment"
+    f" for those UNDER 40 is {younger_gap:.3f}"
+)
+
+
+
+
+
the gap between men and women's college attainment for those UNDER 40 is -0.015
+
+
+
+
+
+
+
print(
+    f"The male-female gap has gone from {older_gap:.3f} to {younger_gap:.3f}. "
+    f"Thus the absolute change from older to younger cohorts is {np.abs(younger_gap) - np.abs(older_gap):.3f}."
+)
+
+
+
+
+
The male-female gap has gone from 0.006 to -0.015. Thus the absolute change from older to younger cohorts is 0.009.
+
+
+
+
+
+
+
results["ex12_comparing"] = "the absolute difference has increased"
+
+
+
+
+
+
+

Exercise 13#

+

In words, what is causing the change noted in Exercise 12 (i.e., looking at the tables above, tell me a story about Men and Women’s College attainment).

+
+

While a larger proportion of men than women have a college degree in the older cohort, that relationship is flipped for younger Americans; among those under 40, a larger share of adult women have college degrees then men.

+

Interestingly, this appears to be the result of both an increasing share of women getting college degrees and a decreasing share of men getting college degrees:

+
+
+
+
print(
+    f"Among older Americans, {older_table.loc[True, 'male']:.1%} of \n"
+    f"men have college degrees; among younger men, this number has \n"
+    f"fallen to {younger_table.loc[True, 'male']:.1%}."
+)
+
+print(
+    f"By contrast, among older Americans {older_table.loc[True, 'female']:.1%} of \n"
+    f"women have college degrees; among younger women, this number has \n"
+    f"risen to {younger_table.loc[True, 'female']:.1%}."
+)
+
+
+
+
+
Among older Americans, 31.8% of 
+men have college degrees; among younger men, this number has 
+fallen to 25.7%.
+By contrast, among older Americans 30.1% of 
+women have college degrees; among younger women, this number has 
+risen to 33.4%.
+
+
+
+
+
+
+
+

Want More Practice?#

+

Calculate the educational racial gap in the United States for White Americans, Black Americans, Hispanic Americans, and other groups.

+

Note that to do these calculations, you’ll have to deal with the fact that unlike most Americans, the American Census Bureau treats “Hispanic” not as a racial category, but a linguistic one. As a result, the racial category “White” in race actually includes most Hispanic Americans. For this analysis, we wish to work with the mutually exclusive categories of “White, non-Hispanic”, “White, Hispanic”, “Black (Hispanic or non-Hispanic)”, and a category for everyone else.

+
+
+
assert set(results.keys()) == {
+    "ex5_age_young",
+    "ex5_age_old",
+    "ex7_avg_age",
+    "ex8_avg_age",
+    "ex9_num_college",
+    "ex11_share_male_w_degrees",
+    "ex11_share_female_w_degrees",
+    "ex12_comparing",
+}
+
+
+
+
+
+
+
sorted(list(results.keys()))
+
+
+
+
+
['ex11_share_female_w_degrees',
+ 'ex11_share_male_w_degrees',
+ 'ex12_comparing',
+ 'ex5_age_old',
+ 'ex5_age_young',
+ 'ex7_avg_age',
+ 'ex8_avg_age',
+ 'ex9_num_college']
+
+
+
+
+
+
+
len(results.keys())
+
+
+
+
+
8
+
+
+
+
+
+
+
import json
+
+for k in results.keys():
+    if not isinstance(results[k], str):
+        results[k] = float(results[k])
+
+with open("autograder/solutions.json", "w") as outfile:
+    json.dump(results, outfile)
+
+
+
+
+
+
+
len(results.keys())
+
+
+
+
+
8
+
+
+
+
+
+
+ + + + +
+ + + + + + +
+ +
+
+
+ +
+ + + +
+ + +
+
+ Contents +
+
+ +
+ + +
+
+ +
+ +
+ +

+By Kyle Bradbury & Nick Eubank +

+ +
+ +
+ + +

+ + © Copyright 2024. +
+ +

+ +
+ +
+ +
+ +
+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/_build/html/ids720_specific/exercises/Solutions_missing.html b/_build/html/ids720_specific/exercises/Solutions_missing.html new file mode 100644 index 00000000..c14b8153 --- /dev/null +++ b/_build/html/ids720_specific/exercises/Solutions_missing.html @@ -0,0 +1,1165 @@ + + + + + + + + + + + Missing Values Exercises — Practical Data Science with Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + +
+
+
+
+ + + Ctrl+K +
+
+ +
+ +
+ + +
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+ + +
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+ + + + + +
+ + + +
+ + + + +
+ +
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+ + + + + +
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+ + + + + + +
+ + +
+ + + + + + + + + + + + + +
+ +
+ +
+
+ + + +
+

Missing Values Exercises

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

Missing Values Exercises#

+
+

Gradescope Autograding#

+

Please follow all standard guidance for submitting this assignment to the Gradescope autograder, including storing your solutions in a dictionary called results and ensuring your notebook runs from the start to completion without any errors.

+

For this assignment, please name your file exercise_missing.ipynb before uploading.

+

You can check that you have answers for all questions in your results dictionary with this code:

+
assert set(results.keys()) == {
+    "ex2_avg_income",
+    "ex3_share_making_9999999",
+    "ex3_share_making_zero",
+    "ex5_avg_income",
+    "ex8_avg_income_black",
+    "ex8_avg_income_white",
+    "ex8_racial_difference",
+    "ex9_avg_income_black",
+    "ex9_avg_income_white",
+    "ex10_wage_gap",
+}
+
+
+
+

Submission Limits#

+

Please remember that you are only allowed three submissions to the autograder. Your last submission (if you submit 3 or fewer times), or your third submission (if you submit more than 3 times) will determine your grade Submissions that error out will not count against this total.

+
+
+
+

Exercises#

+
+

Exercise 1#

+

Today, we will be using the ACS data we used during out first pandas exercise to examine the US income distribution, and how it varies by race. Note that because the US income distribution has a very small number of people with extremely high incomes, and the ACS is just a sample of Americans, the far right tail of the distribution will not be very well estimated. However, this data should suffice for helping to understand wealth inequality in the United States.

+

To begin, load the ACS Data we used in our first pandas exercise. That data can be found here. We’ll be working with US_ACS_2017_10pct_sample.dta.

+
+
+
import pandas as pd
+import numpy as np
+
+pd.set_option("mode.copy_on_write", True)
+
+
+# Download the data
+acs = pd.read_stata(
+    "https://github.com/nickeubank/MIDS_Data/raw/master/"
+    "US_AmericanCommunitySurvey/US_ACS_2017_10pct_sample.dta"
+)
+
+
+
+
+
+
+
# Too much output to be useful in a
+# solution, but here's the code:
+# for c in acs.columns:
+#     print(c)
+
+
+
+
+
+
+

Exercise 2#

+

Let’s begin by calculating the mean US incomes from this data (recall that income is stored in the inctot variable). Store the answer in results under the key "ex2_avg_income".

+
+
+
results = dict()
+results["ex2_avg_income"] = acs.inctot.mean()
+print(
+    f"The average value of `inctot` in the full ACS data is ${results['ex2_avg_income']:,.2f}"
+)
+
+
+
+
+
The average value of `inctot` in the full ACS data is $1,723,646.27
+
+
+
+
+
+
+

Exercise 3#

+

Hmmm… That doesn’t look right. The average American is definitely not earning that much a year! Let’s look at the values of inctot using value_counts(). Do you see a problem?

+

Now use value_counts() with the argument normalize=True to see proportions of the sample that report each value instead of the count of people in each category. What percentage of our sample has an income of 9,999,999? Store that proportion (between 0 and 1) as "ex3_share_making_9999999". What percentage has an income of 0? Store that proportion as "ex3_share_making_zero".

+

(Recall .value_counts() returns a Series, so you can pull values out with our usual pandas tools.)

+
+
+
shares = acs.inctot.value_counts(normalize=True)
+results["ex3_share_making_9999999"] = shares.loc[9_999_999]
+results["ex3_share_making_zero"] = shares.loc[0]
+print(f"Share making 9,999,999 is: {results['ex3_share_making_9999999']:.3f}")
+print(f"Share making zero income is: {results['ex3_share_making_zero']:.3f}")
+
+
+
+
+
Share making 9,999,999 is: 0.169
+Share making zero income is: 0.106
+
+
+
+
+
+
+

Exercise 4#

+

As we discussed before, the ACS uses a value of 9999999 to denote that income information is not available for someone. The problem with using this kind of “sentinel value” is that pandas doesn’t understand that this is supposed to denote missing data, and so when it averages the variable, it doesn’t know to ignore 9999999.

+

To help out pandas, use the replace command to replace all values of 9999999 with np.nan.

+
+
+
acs["inctot"] = acs["inctot"].replace(9999999, np.nan)
+
+
+
+
+
+
+

Exercise 5#

+

Now that we’ve properly labeled our missing data as np.nan, let’s calculate the average US income once more. Store the answer in results under the key "ex5_avg_income".

+
+
+
results["ex5_avg_income"] = acs.inctot.mean()
+print(
+    f"The average value of `inctot` in the ACS data after "
+    f"replacing 9,999,999 with np.nan is {results['ex5_avg_income']:,.2f}"
+)
+
+
+
+
+
The average value of `inctot` in the ACS data after replacing 9,999,999 with np.nan is 40,890.18
+
+
+
+
+
+
+

Exercise 6#

+

OK, now we’ve been able to get a reasonable average income number. As we can see, a major advantage of using np.nan is that pandas knows that np.nan observations should just be ignored when we are calculating means.

+

But it’s not enough to just get rid of the people who had inctot values of 9999999. We also need to know why those values were missing. Suppose, for example, that the value of 9999999 was used for anyone who made more than 100,000 dollars: if we just dropped those people, then our estimate of average income wouldn’t mean much, would it?

+

So let’s make sure we understand why data is missing for some people. If you recall from our last exercise, it seemed to be the case that most of the people who had incomes of 9999999 were children. Let’s make sure that’s true by looking at the distribution of the variable age for people for whom inctot is missing (i.e. subset the data to people with inctot missing, then look at the values of age with value_counts()).

+

Then do the opposite: look at the distribution of the age variable for people who whom inctot is not missing.

+

Can you determine when 9999999 was being used? Is it ok we’re excluding those people from our analysis?

+

Note: In this data, Python doesn’t understand age is a number; it thinks it is a string because the original data has categories like “90 (90+ in 1980 and 1990)” and “less than 1 year old”. So you can’t just use min() or max(). We’ll discuss converting string variables into numbers in a future class.

+
+
+
acs.loc[pd.isnull(acs.inctot), "age"].value_counts()
+
+
+
+
+
age
+10    3997
+9     3977
+14    3847
+12    3845
+13    3800
+      ... 
+39       0
+38       0
+37       0
+36       0
+96       0
+Name: count, Length: 97, dtype: int64
+
+
+
+
+
+
+
acs.loc[pd.notnull(acs.inctot), "age"].value_counts()
+
+
+
+
+
age
+60                      4950
+54                      4821
+59                      4776
+56                      4776
+58                      4734
+                        ... 
+5                          0
+4                          0
+3                          0
+2                          0
+less than 1 year old       0
+Name: count, Length: 97, dtype: int64
+
+
+
+
+
+

Looks like the folks who are missing income are all very young, and everyone older has incomes. So must be that only people above a certain age were eligible to be asked their income.

+
+
+
+

Exercise 7#

+

Great, so now we know why those people had missing data, and we’re ok with excluding them.

+

But as we previously noted, there are also a lot of observations of zero income in our data, and it’s not clear that we want everyone with a zero-income should be included in this average, since those may be people who are retired, or in school.

+

Let’s limit our attention to people who are currently working by subsetting to only employed respondents. We can do this using empstat. Remember you can use value_counts() to see what values of empstat are in the data!

+
+
+
# Check values taken by var
+acs.empstat.value_counts()
+
+
+
+
+
empstat
+employed              148758
+not in labor force    104676
+n/a                    57843
+unemployed              7727
+Name: count, dtype: int64
+
+
+
+
+
+
+
# now subset
+employed = acs[acs.empstat == "employed"]
+
+
+
+
+
+
+

Exercise 8#

+

Now let’s estimate the racial income gap in the United States. What is the average salary for employed Black Americans, and what is the average salary for employed White Americans? In percentage terms, how much more does the average White American make than the average Black American?

+

Note: these values are not quite accurate estimates. As we’ll discuss in later lessons, to get completely accurate estimates from the ACS we have to take into account how people were selected to be interviewed. But you get pretty good estimates in most cases even without weights—your estimate of the racial wage gap without weights is within 5% of the corrected value.

+

Note: This is actually an underestimate of the wage gap. The US Census treats Hispanic respondents as a sub-category of “White.” While all ethnic distinctions are socially constructed, and so on some level these distinctions are all deeply problematic, this coding is inconsistent with what most Americans think of when they hear the term “White,” a term most Americans think of as a category that is mutually exclusive of being Hispanic or Latino (categories which are also usually conflated in American popular discussion). With that in mind, most researchers working with US Census data split “White” into “White, Hispanic” and “White, Non-Hispanic” using race and hispan. But for the moment, just identify “White” respondents using the value in race.

+

Store your results in results under the keys "ex8_avg_income_black", "ex8_avg_income_white", and the percentage difference as ex8_racial_difference. Please note the wording above when calculating the percentage difference to ensure you get the reference category correct, and interpret your result as well.

+
+
+
# Check values of `race`:
+employed["race"].value_counts()
+
+
+
+
+
race
+white                               116017
+black/african american/negro         13175
+other asian or pacific islander       6424
+other race, nec                       5755
+two major races                       3135
+chinese                               2149
+american indian or alaska native      1290
+three or more major races              426
+japanese                               387
+Name: count, dtype: int64
+
+
+
+
+
+
+
# life is easier with simple race label
+employed["race"] = employed.race.replace("black/african american/negro", "black")
+
+
+
+
+
+
+
# Avg Income
+for r in ["white", "black"]:
+    results[f"ex8_avg_income_{r}"] = employed.loc[
+        (employed["race"] == r), "inctot"
+    ].mean()
+
+    print(
+        f"Average income for employed {r.capitalize()} respondents "
+        f"is ${results[f'ex8_avg_income_{r}']:,.2f}"
+    )
+
+
+
+
+
Average income for employed White respondents is $60,473.15
+Average income for employed Black respondents is $41,747.95
+
+
+
+
+
+
+
# Percent Whites make more than Blacks
+results["ex8_racial_difference"] = (
+    (results["ex8_avg_income_white"] - results["ex8_avg_income_black"])
+    * 100
+    / results["ex8_avg_income_black"]
+)
+print(
+    f"Employed White Americans earns an average of {results['ex8_racial_difference']:.1f}% "
+    "more than employed Black respondents."
+)
+
+
+
+
+
Employed White Americans earns an average of 44.9% more than employed Black respondents.
+
+
+
+
+
+
+

Exercise 9#

+

As noted above, these estimates are not actually quite correct because we aren’t taking into account the fact that when the US Census decided who to survey, not all Americans had the same likelihood of being asked. The US American Community Survey is an example of a weighted survey (essentially, people from smaller subpopulations have a higher likelihood of being included to ensure enough individuals in the final survey to constitute a representative sample that can be used statistically).

+

To calculate a weighted average that takes into account these survey weights (in other words, a more accurate estimate of US incomes), you need to use the following formula:

+
+\[weighted\_mean\_of\_x = \frac{\sum_i x_i * weight_i}{\sum_i weight_i}\]
+

(As you can see, when \(weight_i\) is constant for all observations, this just simplifies to our normal formula for mean values. It is only when weights vary across individuals that weights must be explicitly addressed).

+

In this data, weights are stored in the variable perwt, which is the number of people for which each observation is a stand-in (the inverse of that observation’s sampling probability).

+

Using the formula, re-calculate the weighted average income for both populations and store them as ex9_avg_income_white and ex9_avg_income_black.

+
+
+
# Now the exact estimates taking into account sampling weights
+
+for r in ["white", "black"]:
+    group_employed = employed[employed["race"] == r]
+    results[f"ex9_avg_income_{r}"] = (
+        group_employed["inctot"]
+        * group_employed["perwt"]
+        / group_employed["perwt"].sum()
+    ).sum()
+
+    print(
+        f"Weighted avg income for employed {r.capitalize()} Americans "
+        f"is ${results[f'ex9_avg_income_{r}']:,.2f}"
+    )
+
+
+
+
+
Weighted avg income for employed White Americans is $58,361.48
+Weighted avg income for employed Black Americans is $40,430.95
+
+
+
+
+
+
+

Exercise 10#

+

Now calculate the weighted average income gap between non-Hispanic White Americans and Black Americans. What percentage more do employed White non-Hispanic Americans earn than employed Black Americans? Store as "ex10_wage_gap".

+
+
+
white_nonhisp = employed.loc[
+    (employed["race"] == "white") & (employed["hispan"] == "not hispanic"),
+    ["inctot", "perwt"],
+]
+
+
+
+
+
+
+
# White non-hispanic income
+white_nonhisp_weighted = (
+    white_nonhisp["inctot"] * white_nonhisp["perwt"]
+).sum() / white_nonhisp["perwt"].sum()
+white_nonhisp_weighted
+
+
+
+
+
61669.28927599297
+
+
+
+
+
+
+
results["ex10_wage_gap"] = (
+    (white_nonhisp_weighted - results[f"ex9_avg_income_black"])
+    * 100
+    / results[f"ex9_avg_income_black"]
+)
+
+
+
+
+
+
+
print(
+    f"Employed non-Hispanic White Americans earns an average of "
+    f"{results['ex10_wage_gap']:.1f}% "
+    "more than employed Black respondents."
+)
+
+
+
+
+
Employed non-Hispanic White Americans earns an average of 52.5% more than employed Black respondents.
+
+
+
+
+
+
+

Exercise 11#

+

Is that greater or less than the difference you found in Exercise 8? Why do you think that’s the case?

+
+

The gap is greater, which makes sense as White Hispanic Americans tend to work in less well-paid industries as compared to non-Hispanic White Americans. Thus their exclusion drives up the average calculated White wage, increasing the gap.

+
+
+
+
sorted(list(results.keys()))
+
+
+
+
+
['ex10_wage_gap',
+ 'ex2_avg_income',
+ 'ex3_share_making_9999999',
+ 'ex3_share_making_zero',
+ 'ex5_avg_income',
+ 'ex8_avg_income_black',
+ 'ex8_avg_income_white',
+ 'ex8_racial_difference',
+ 'ex9_avg_income_black',
+ 'ex9_avg_income_white']
+
+
+
+
+
+
+
len(results.keys())
+
+
+
+
+
10
+
+
+
+
+
+
+
+ + + + +
+ + + + + + +
+ +
+
+
+ +
+ + + +
+ + +
+
+ Contents +
+
+ +
+ + +
+
+ +
+ +
+ +

+By Kyle Bradbury & Nick Eubank +

+ +
+ +
+ + +

+ + © Copyright 2024. +
+ +

+ +
+ +
+ +
+ +
+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/_build/html/ids720_specific/solutions.html b/_build/html/ids720_specific/solutions.html index 77b614c1..8df14bc6 100644 --- a/_build/html/ids720_specific/solutions.html +++ b/_build/html/ids720_specific/solutions.html @@ -276,6 +276,11 @@
  • Data Manipulations
    @@ -573,6 +578,8 @@

    SolutionsNumpy View/Copy Solutions

  • Pandas Series Solutions

  • Pandas DataFrames Solutions

    • +

  • Pandas Missing Solutions

    • +

  • Pandas Cleaning Solutions

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When logical errors cause syntax errors": [[57, "when-logical-errors-cause-syntax-errors"]], "4 and 5. Ticks and tick labels": [[194, "and-5-ticks-and-tick-labels"]], "4) What Data Do You Need?": [[59, "what-data-do-you-need"]], "4. Keep it simple: use as little \u201cink\u201d as possible to accurately create your plot": [[190, "keep-it-simple-use-as-little-ink-as-possible-to-accurately-create-your-plot"]], "4. Make the legend be effortless to read": [[220, "make-the-legend-be-effortless-to-read"]], "5) Where Can You Get That Data?": [[59, "where-can-you-get-that-data"]], "5. Size fonts for the reader": [[220, "size-fonts-for-the-reader"]], "5. Use color to your advantage rather than to your detriment.": [[190, "use-color-to-your-advantage-rather-than-to-your-detriment"]], "6. Axis limits": [[194, "axis-limits"]], "6. Make your figure crystal clear by saving at a high resolution": [[220, "make-your-figure-crystal-clear-by-saving-at-a-high-resolution"]], "7. 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Title": [[194, "title"]], "A Common Gotcha: Narrow Matrices v. 1-Dimensional Vectors": [[114, "a-common-gotcha-narrow-matrices-v-1-dimensional-vectors"]], "A Digression on Supervised Machine Learning": [[59, "a-digression-on-supervised-machine-learning"]], "A Distinction In Purpose": [[246, "a-distinction-in-purpose"]], "A Good Chair": [[80, "a-good-chair"]], "A Line of Best Fit in Higher Dimensions": [[244, "a-line-of-best-fit-in-higher-dimensions"]], "A Little History": [[86, "a-little-history"]], "A Minimalist Strategy for Working with Index Labels": [[149, "a-minimalist-strategy-for-working-with-index-labels"]], "A Note On Object Vectors": [[73, "a-note-on-object-vectors"]], "A Note on Databases": [[148, "a-note-on-databases"]], "A Note on Jupyter Notebooks": [[15, "a-note-on-jupyter-notebooks"]], "A Note on Software Versioning": [[77, "a-note-on-software-versioning"]], "A Real Image": [[120, "a-real-image"]], "A Reminder on Combining Logical Tests": [[139, "a-reminder-on-combining-logical-tests"]], "A Reminder on When numpy Returns Views": [[150, "a-reminder-on-when-numpy-returns-views"]], "A Review of Views in numpy": [[150, "a-review-of-views-in-numpy"]], "A figure in 10 pieces": [[194, null]], "A hierarchy of data types: from lists to numpy arrays to pandas Series and DataFrames": [[133, "a-hierarchy-of-data-types-from-lists-to-numpy-arrays-to-pandas-series-and-dataframes"]], "A table": [[214, "a-table"]], "A true grayscale image": [[120, "a-true-grayscale-image"]], "Absolutely positively need the solutions?": [[29, "absolutely-positively-need-the-solutions"], [31, "absolutely-positively-need-the-solutions"]], "Accessing Results": [[247, "accessing-results"]], "Adding More Channels": [[225, "adding-more-channels"]], "Adding Tests": [[61, "adding-tests"]], "Adding Titles Labelling Axes": [[225, "adding-titles-labelling-axes"]], "Adding grouped aggregates to the original dataset (transform)": [[174, "adding-grouped-aggregates-to-the-original-dataset-transform"]], "Adding in 1D histograms": [[210, "adding-in-1d-histograms"]], "Adding labels and titles to subplots": [[199, "adding-labels-and-titles-to-subplots"]], "Adding pd.set_option(\u201cmode.copy_on_write\u201d, True) Easily": [[151, "adding-pd-set-option-mode-copy-on-write-true-easily"]], "Adjusting Contrast": [[108, "adjusting-contrast"]], "Adjusting the width and height distribution of subplots": [[199, "adjusting-the-width-and-height-distribution-of-subplots"]], "Advanced Command Line Exercises": [[5, null]], "Advanced Command Line Tutorial": [[55, null]], "Advanced Plotting with Altair": [[187, null]], "Advice on Buying Computers for Data Science Students": [[79, null]], "Aggregators": [[187, "aggregators"]], "Algorithm pseudo-code": [[231, "algorithm-pseudo-code"]], "Altair": [[186, "altair"]], "Altair Quirks & Convenience Features": [[187, "altair-quirks-convenience-features"]], "Alternative Standard Error Calculations": [[247, "alternative-standard-error-calculations"]], "Amdahl\u2019s Law": [[75, "amdahl-s-law"]], "An Example": [[151, "an-example"]], "An Example: Parallel Processing with Joblib": [[75, "an-example-parallel-processing-with-joblib"]], "An IMPORTANT Note on Pyenv": [[52, "an-important-note-on-pyenv"]], "And that\u2019s it!": [[52, "and-that-s-it"]], "Another way to set properties": [[194, "another-way-to-set-properties"]], "Arrays": [[113, null]], "Aside on as_index = False": [[174, "aside-on-as-index-false"]], "Axes transforms for plotting text with respect to the data, Axes, or Figure": [[195, "axes-transforms-for-plotting-text-with-respect-to-the-data-axes-or-figure"]], "Background": [[222, "background"], [224, "background"]], "Backwards Design": [[59, null]], "Bar plot": [[218, "bar-plot"]], "Bars plots": [[196, null]], "Basic Descriptive Plotting": [[225, "basic-descriptive-plotting"]], "Basic Plotting": [[191, "basic-plotting"]], "Basic subplots": [[199, "basic-subplots"]], "Basic 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[[29, "comparing-models"], [32, "comparing-models"]], "Comparing with 2018 Arrests": [[21, "comparing-with-2018-arrests"]], "Complete Pipeline": [[261, null]], "Computation with Series": [[134, "computation-with-series"]], "Computation with columns": [[178, "computation-with-columns"]], "Computations with dask.dataframe": [[10, "computations-with-dask-dataframe"]], "Computer Vision": [[82, "computer-vision"]], "Concatenating along different axes": [[165, "concatenating-along-different-axes"]], "Concatenating with mismatched datasets": [[165, "concatenating-with-mismatched-datasets"]], "Conceptual Questions": [[128, "conceptual-questions"]], "Conceptual questions": [[129, "conceptual-questions"]], "Configure Cmder": [[50, "configure-cmder"]], "Configuring ZSh": [[48, "configuring-zsh"]], "Confused?": [[67, "confused"]], "Congratulations!": [[14, "congratulations"]], "Considerations when making histograms": [[208, "considerations-when-making-histograms"]], "Constructing DataFrames": [[139, "constructing-dataframes"]], "Constructing Matrices": [[106, "constructing-matrices"]], "Converting Scheduled Departure Time (CRSDepTime) to a timestamp": [[10, "converting-scheduled-departure-time-crsdeptime-to-a-timestamp"]], "Converting and Editing a Seaborn Objects Plot as Matplotlib Axes": [[225, "converting-and-editing-a-seaborn-objects-plot-as-matplotlib-axes"]], "Converting datatypes": [[134, "converting-datatypes"]], "Copy on Write": [[151, "copy-on-write"]], "Course Wrap-up": [[130, null]], "Create Configuration File": [[49, "create-configuration-file"], [50, "create-configuration-file"]], "Create a New Environment": [[51, "create-a-new-environment"], [60, "create-a-new-environment"]], "Create our 2D histogram": [[210, "create-our-2d-histogram"]], "Creating a Merge Conflict": [[15, "creating-a-merge-conflict"]], "Creating a Vector": [[87, "creating-a-vector"]], "Creating a series": [[134, "creating-a-series"]], "Custom functions for combining the data (apply)": [[174, "custom-functions-for-combining-the-data-apply"]], "Custom legend elements": [[211, "custom-legend-elements"]], "Customizing styles": [[217, null]], "Customizing your own styles": [[217, "customizing-your-own-styles"]], "Cython Advantages": [[74, "cython-advantages"]], "Cython limitations": [[74, "cython-limitations"]], "Dask DataFrames": [[10, null]], "Data": [[202, "data"]], "Data Citation": [[23, "data-citation"], [36, "data-citation"], [92, "data-citation"], [93, "data-citation"], [97, "data-citation"], [98, "data-citation"], [141, "data-citation"], [144, "data-citation"], [160, "data-citation"], [163, "data-citation"]], "Data Exploration": [[252, "data-exploration"]], "Data Manipulation": [[131, "data-manipulation"]], "Data Sources": [[202, "data-sources"], [252, "data-sources"]], "Data structures for computation (numpy)": [[84, "data-structures-for-computation-numpy"]], "DataFrames: Collections of Series": [[139, "dataframes-collections-of-series"]], "Dealing with Views If You Don\u2019t Want To Use CoW": [[152, "dealing-with-views-if-you-don-t-want-to-use-cow"]], "Debugging Python in VS Code": [[58, null]], "Debugging Summary": [[58, "debugging-summary"]], "Debugging Tools": [[57, "debugging-tools"]], "Defensive Programming": [[61, null]], "Defining a Good Question": [[59, "defining-a-good-question"]], "Deliberate Non-Numerics": [[158, "deliberate-non-numerics"]], "Did the type of merge you were expecting happen? 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181], "abcdefghiklmnopqrstuwx1": 55, "abcfghloprstuw": 55, "abil": [23, 24, 34, 36, 37, 54, 56, 57, 58, 71, 75, 78, 79, 80, 88, 109, 116, 117, 133, 134, 135, 138, 145, 146, 187, 188, 191, 200, 225, 244, 261, 266], "abl": [0, 5, 7, 9, 14, 15, 16, 21, 22, 23, 26, 28, 29, 32, 33, 36, 42, 52, 55, 57, 58, 59, 61, 68, 69, 70, 71, 73, 75, 76, 78, 79, 81, 82, 86, 87, 99, 109, 116, 117, 119, 120, 122, 133, 134, 135, 160, 161, 162, 163, 164, 167, 170, 171, 172, 175, 176, 177, 178, 184, 189, 190, 200, 203, 206, 207, 212, 216, 217, 220, 222, 225, 230, 235, 236, 247, 248, 251, 252, 253, 254, 255, 256, 257, 258, 260, 266], "abnorm": 246, "abort": [15, 61], "about": [3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 43, 44, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 79, 80, 81, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 101, 107, 108, 109, 112, 113, 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163, 249], "anyth": [4, 9, 10, 14, 15, 17, 21, 24, 26, 28, 29, 31, 32, 37, 46, 51, 52, 53, 55, 57, 60, 61, 63, 64, 67, 68, 73, 76, 80, 81, 83, 86, 87, 88, 92, 93, 94, 95, 97, 98, 104, 105, 108, 116, 117, 127, 134, 137, 138, 141, 142, 143, 144, 145, 147, 148, 153, 160, 161, 162, 163, 170, 171, 175, 187, 198, 225, 247, 254, 256, 260, 265], "anytim": [58, 87], "anywai": [10, 148, 156], "anywher": [40, 147], "aosabook": 216, "ap": 13, "apach": [71, 72, 76, 143, 148], "apex": 134, "api": [9, 10, 26, 29, 32, 76, 81, 185, 186, 187, 191, 225, 244, 245, 247, 248, 249, 250], "apologi": [23, 36, 92, 93, 97, 98], "app": [45, 81, 184], "appar": [48, 64, 74, 151], "appeal": [86, 189, 217], "appear": [8, 13, 14, 15, 16, 19, 20, 23, 36, 38, 49, 55, 58, 61, 68, 77, 97, 98, 103, 106, 120, 125, 126, 138, 147, 149, 150, 155, 158, 159, 161, 173, 190, 191, 192, 193, 194, 206, 207, 208, 211, 217, 222, 237, 238, 239, 240, 241, 244, 248], "append": [3, 10, 34, 37, 55, 66, 67, 72, 74, 75, 76, 88, 121, 123, 165, 169, 170, 171, 208, 210, 223, 232, 235, 236, 238, 240, 255, 258, 260, 265, 266], "append_42": 67, "appendectomi": 56, "appl": [45, 54, 62, 79, 80, 135, 225, 266], "apple_selector": 135, "appli": [2, 4, 6, 9, 10, 15, 23, 26, 30, 31, 36, 38, 42, 43, 55, 56, 58, 61, 71, 73, 74, 75, 76, 77, 78, 82, 88, 94, 97, 98, 110, 114, 119, 121, 124, 128, 129, 130, 134, 135, 138, 139, 145, 152, 165, 173, 179, 180, 183, 185, 187, 188, 189, 191, 195, 198, 199, 203, 204, 205, 208, 210, 211, 212, 215, 216, 217, 218, 219, 220, 225, 229, 234, 235, 236, 238, 239, 247, 248, 252, 254, 255, 257, 260, 266], "applic": [9, 24, 29, 31, 32, 37, 43, 45, 48, 52, 53, 55, 61, 71, 76, 78, 82, 106, 113, 119, 121, 125, 126, 137, 155, 164, 165, 173, 179, 180, 183, 191, 195, 216, 220, 251, 261], "appreci": [23, 36, 97, 98, 148], "approach": [9, 14, 28, 43, 52, 56, 57, 59, 78, 81, 82, 87, 99, 119, 121, 123, 128, 129, 136, 149, 165, 179, 180, 183, 184, 191, 195, 196, 197, 199, 200, 211, 221, 225, 238, 239, 257, 258, 259, 260, 261, 265, 266], "appropri": [7, 16, 20, 26, 29, 32, 40, 47, 52, 69, 82, 133, 161, 162, 164, 167, 178, 179, 180, 202, 220, 257, 258, 260, 265], "approv": [76, 81], "approx": 194, "approxim": [69, 75, 82, 113, 116, 117, 125, 214, 239], "aquarium": 108, "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 104, 105, 106, 107, 108, 109, 111, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 165, 166, 167, 168, 169, 170, 171, 173, 174, 175, 176, 177, 178, 179, 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40, 48, 53, 55, 57, 59, 61, 67, 68, 69, 71, 74, 75, 76, 78, 80, 81, 87, 88, 92, 93, 101, 104, 105, 114, 125, 136, 139, 140, 141, 144, 147, 148, 154, 155, 157, 158, 160, 163, 175, 187, 190, 210, 217, 222, 225, 229, 244, 245, 246, 248], "arestrong": [244, 245, 250], "arg": [55, 74], "argentina": [139, 140, 149, 203, 221, 253, 254, 255, 258], "argmin": 240, "argtyp": 74, "argu": [21, 30, 31, 38, 79, 138, 145, 170, 171, 186, 225], "arguabl": [21, 170, 171, 214], "argument": [9, 10, 16, 17, 21, 22, 33, 55, 64, 74, 75, 81, 87, 94, 95, 108, 123, 124, 125, 134, 139, 143, 147, 149, 153, 156, 159, 160, 163, 170, 171, 174, 175, 187, 191, 194, 195, 198, 200, 203, 204, 211, 212, 215, 225, 226, 248, 249, 252, 266], "argument1": 124, "argument2": 124, "arial": [217, 220, 221, 222, 223, 224], "aris": [43, 53, 64, 82, 127, 140, 142, 149, 179, 180], "arithmet": [10, 134], "arizona": 167, "arkansa": 167, "arm": [13, 225], "arm64": 52, "armchair": 225, "armenia": [139, 140, 225, 253, 254, 255, 258], 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154], "arrival_pr": [17, 18, 153, 154], "arrow": [20, 34, 48, 49, 53, 58, 103, 134, 143, 195, 216], "art": [78, 190], "art3d": 244, "articl": [92, 93, 202], "articul": 59, "artist": [36, 89, 97, 173, 190, 204, 211, 216], "artists_to_remov": 216, "arwen": 266, "as_": 187, "as_a_float": 87, "as_index": [16, 176, 177, 179], "ascend": [38, 138, 179, 203, 211, 221], "ascii": [4, 147, 148], "ascii_uppercas": [17, 72, 153], "asia": [61, 139, 140, 197, 221, 222, 247, 249, 256, 257, 258, 259, 260, 265], "asian": [29, 32, 33, 116, 117, 160, 250], "asid": [82, 108, 119, 120, 190], "asizeof": 86, "ask": [1, 4, 9, 10, 11, 13, 14, 16, 20, 21, 22, 23, 26, 28, 30, 36, 38, 46, 48, 52, 53, 54, 55, 58, 59, 61, 64, 65, 67, 68, 69, 70, 73, 74, 76, 77, 78, 80, 81, 82, 88, 89, 90, 92, 93, 97, 98, 100, 101, 113, 114, 116, 117, 119, 122, 126, 127, 132, 133, 135, 137, 138, 139, 141, 144, 145, 147, 148, 155, 160, 161, 162, 163, 164, 170, 171, 180, 182, 187, 190, 217, 222, 225, 229, 230, 240, 244, 246, 251, 255], 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187], "carbon": [57, 195, 251, 252, 254, 255, 256, 257, 259, 260, 265], "card": [8, 125, 206, 207], "cardin": [119, 225, 245], "care": [1, 4, 10, 21, 24, 26, 28, 29, 30, 31, 32, 37, 38, 49, 55, 61, 64, 69, 71, 75, 119, 125, 134, 136, 138, 145, 149, 157, 158, 165, 167, 170, 171, 188, 194, 208, 214, 220, 225, 230, 240, 246], "career": [52, 53, 55, 78, 80, 81, 87, 148], "carefulli": [1, 12, 16, 23, 35, 36, 52, 58, 59, 62, 73, 76, 78, 109, 122, 136, 158, 190, 225], "careless": [55, 61], "caretak": 190, "caribbean": [247, 249], "carlo": [82, 128, 129], "carolina": [142, 167], "carpentri": 54, "carri": [19, 20, 21, 61, 82, 168], "carrol": 76, "cartographi": 82, "case": [4, 7, 9, 10, 12, 13, 14, 15, 16, 21, 22, 24, 25, 28, 29, 31, 32, 33, 34, 35, 37, 48, 54, 55, 56, 57, 58, 59, 61, 62, 69, 71, 73, 74, 75, 76, 78, 81, 87, 88, 89, 90, 99, 101, 108, 113, 114, 115, 119, 120, 122, 124, 125, 127, 128, 129, 133, 134, 135, 136, 139, 140, 141, 142, 143, 144, 149, 152, 155, 156, 157, 158, 160, 161, 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220, 245, 246], "causal": [21, 28, 170, 171], "caution": 217, "cautionari": 195, "cautiou": [81, 190], "cavali": [29, 32], "caveat": [152, 157, 176], "cbar": [210, 237], "cbgufgtrki": 72, "cbook": 216, "cbserial": [35, 141, 262], "cc79a7": 217, "cd": [0, 4, 5, 48, 49, 50, 52, 54, 55], "cdef": 74, "cdot": [57, 124], "ce": [4, 5], "cede": 53, "celebr": [126, 235, 236], "cell": [1, 7, 8, 11, 15, 16, 24, 37, 57, 58, 69, 85, 87, 95, 107, 113, 120, 133, 134, 140, 142, 157, 187, 205, 209], "censu": [7, 12, 22, 23, 35, 36, 59, 87, 89, 92, 93, 97, 98, 141, 144, 155, 160, 161, 162, 163, 167], "census": [7, 161, 162], "cent": [77, 106], "center": [15, 89, 113, 120, 167, 189, 196, 199, 202, 205, 211, 213, 214, 216, 217, 218, 220, 221, 237, 246, 252], "centimet": 251, "central": [56, 75, 76, 78, 82, 134, 139, 147, 157, 204, 243, 247, 249, 258], "centric": 52, "centuri": [202, 224], "ceo": 95, "cerebellum": 113, "certain": [1, 15, 16, 21, 52, 61, 67, 74, 104, 105, 113, 128, 129, 138, 141, 142, 144, 145, 147, 157, 158, 159, 160, 161, 162, 163, 170, 171, 175, 176, 177, 184, 217, 225, 229], "certainli": [9, 28, 56, 57, 78, 113, 120, 165, 173, 186, 192, 193, 195, 209, 217, 220, 267], "certainti": 204, "certif": [12, 35, 78, 82, 106, 141, 144], "chad": [139, 258], "chain": [24, 37, 82], "chainedassignmenterror": 157, "challeng": [6, 12, 34, 35, 76, 78, 82, 124, 133, 141, 144, 165, 190, 194, 202, 210, 213, 214, 217, 222, 224, 229, 231, 235, 236, 239, 251], "chanc": [15, 52, 61, 62, 78, 99, 125, 139, 147, 221, 241, 245], "chang": [0, 4, 6, 7, 11, 15, 16, 18, 20, 21, 23, 24, 26, 28, 32, 33, 34, 36, 37, 42, 44, 49, 50, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 73, 75, 78, 80, 81, 82, 87, 89, 95, 97, 98, 99, 100, 101, 102, 104, 105, 107, 108, 110, 112, 122, 124, 125, 128, 129, 133, 134, 135, 139, 140, 141, 142, 143, 144, 147, 150, 151, 152, 154, 156, 157, 161, 162, 173, 175, 178, 181, 185, 189, 190, 191, 194, 195, 197, 198, 199, 201, 202, 203, 204, 209, 212, 214, 215, 216, 217, 218, 220, 221, 222, 224, 225, 237, 240, 245, 246, 248, 249, 252, 260, 265, 266, 267], "change_contrast": 108, "change_high": 170, "change_low": 170, "channel": [51, 52, 120, 187], "chao": 76, "chapter": [2, 33, 64, 128, 216, 250], "charact": [6, 7, 87, 90, 94, 147, 156, 161, 162, 255, 257, 258, 266], "character": [33, 87, 89, 125, 187], "characterist": [33, 59, 119, 125, 134, 159, 172, 173, 174, 182, 199, 203, 208, 230, 231, 239, 250, 259], "charg": 155, "charl": 64, "charlott": 190, "chart": [20, 189, 215, 220, 258, 265], "chartjunk": 190, "chatgpt": [2, 133], "chdir": [61, 76], "cheaper": 79, "cheat": [9, 29, 31], "cheatsheet": [0, 56, 69, 76], "check": [4, 6, 7, 8, 11, 12, 14, 15, 16, 21, 22, 23, 24, 25, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 43, 52, 55, 57, 58, 61, 62, 63, 64, 70, 72, 73, 74, 75, 76, 80, 81, 82, 87, 88, 90, 97, 98, 104, 105, 106, 110, 122, 123, 126, 131, 132, 134, 139, 141, 142, 144, 148, 151, 155, 158, 160, 161, 162, 165, 167, 168, 170, 171, 176, 177, 179, 180, 187, 190, 191, 215, 225, 234, 235, 236, 239, 244, 247, 249, 252, 254, 255, 258, 261], "checklist": 58, "checkout": [14, 49], "chem": 169, "chen": 82, "cheshir": 190, "chi": [192, 193], "chide": 61, "child": [29, 32, 33, 58, 126, 201, 202, 203, 216, 225, 230, 250], "child_mort": 201, "childhood": 225, "children": [12, 22, 29, 32, 33, 35, 141, 144, 155, 159, 160, 163, 201, 202, 216, 225], "children_list": 216, "chile": 258, "china": [61, 203, 258, 260, 265], "chines": [87, 160], "chip": [52, 75, 79], "choic": [9, 17, 29, 32, 48, 49, 50, 54, 61, 62, 72, 86, 125, 139, 153, 186, 188, 190, 208, 209, 210, 217, 220, 225, 236, 237, 260], "cholera": [189, 208], "choleski": 82, "cholesterol": 164, "choos": [19, 20, 29, 32, 49, 52, 57, 58, 82, 119, 189, 190, 199, 204, 206, 207, 214, 217, 220, 230, 261], "chose": [53, 87, 188], "chosen": [76, 126, 184, 187, 194, 220], "chpt": 2, "chrome": 4, "chunk": [6, 11, 62, 68, 71, 76], "chunksiz": [71, 76], "ci": [26, 187], "ci_high": [185, 187], "ci_low": [185, 187], "ciat": 55, "circl": [45, 53, 57, 100, 113, 150, 157, 203, 204, 211, 225, 237, 260, 265], "circumst": [101, 123, 130, 142, 157, 246], "cite": [23, 36, 92, 93, 97, 98, 141, 144, 160, 163], "citi": [4, 5, 6, 10, 121, 245], "citizen": [4, 59, 156, 225], "cividi": 190, "civil": [21, 170, 171], "claim": 210, "clarifi": [65, 229, 251], "clariti": [122, 123, 195, 199, 214, 217, 218, 220], "class": [1, 6, 8, 9, 12, 22, 27, 29, 32, 35, 40, 48, 51, 52, 54, 55, 56, 59, 65, 67, 69, 71, 74, 75, 76, 78, 79, 81, 82, 89, 108, 113, 116, 117, 119, 127, 130, 133, 134, 139, 141, 143, 144, 147, 160, 163, 169, 185, 186, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 243, 244, 247, 249, 250, 260, 265], "class_2": [95, 262], "class_3": [133, 142, 156, 157, 159], "class_5": [], "class_estim": 236, "class_label": [234, 237], "classic": [231, 234], "classif": [29, 32, 82, 188, 232, 233, 234, 235, 236, 237, 238, 240, 241, 245], "classifi": [29, 32, 183, 220, 230, 231, 234, 238], 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97, 98, 252], "clickier": 80, "client": [10, 45, 55, 76, 77, 169], "climat": [6, 87, 101, 113], "climatologi": 6, "clinic": [81, 246], "clip": 240, "clip_box": 216, "clip_on": 216, "clip_path": 216, "clock": 8, "clone": [14, 15, 80], "close": [5, 6, 8, 9, 14, 20, 21, 23, 24, 36, 37, 48, 49, 53, 61, 63, 64, 70, 75, 76, 77, 79, 82, 89, 97, 98, 100, 104, 105, 108, 120, 125, 139, 148, 156, 195, 196, 217, 222, 237, 239, 240, 244, 246, 248, 251, 257, 260, 265], "closer": [1, 75, 108, 113, 210, 237], "closest": [239, 257, 258], "cloth": 61, "cloud": [1, 10, 48, 49, 50, 52, 53, 54, 55, 56, 71, 75, 79, 81, 82, 121], "cloudcov": 121, "cloudi": 121, "club": [206, 207], "clue": [170, 171], "clump": [195, 260, 265], "clumsi": [155, 226], "clunki": 109, "cluster": [8, 11, 35, 54, 71, 75, 76, 82, 141, 155, 210, 241, 247, 262], "clutter": [61, 187, 217], "cm": [234, 237], "cmap": [108, 113, 120, 209, 237], "cmd": [45, 49, 50, 52, 54, 55], "cmder": [45, 49, 54, 55], "co": [53, 56, 61, 82, 167, 251], "co2": [181, 187, 195, 225, 251, 252, 253, 254, 255, 256, 257, 258, 260, 261, 265], "co2_al": 253, "co2_emissions_tonnes_per_person": [252, 253, 261], "co2_fil": [253, 258, 261], "co2emitt": 179, "coal": [43, 179, 180, 181], "coal_plant": 179, "coal_plants_group": 179, "coal_plants_grouped_sort": 179, "coal_stat": 179, "coauthor": 202, "coauthorship": 78, "cod": 195, "code": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 42, 43, 44, 48, 51, 52, 56, 57, 60, 61, 62, 64, 67, 68, 70, 71, 72, 73, 75, 76, 77, 79, 80, 81, 82, 83, 84, 86, 87, 92, 93, 94, 95, 97, 98, 101, 103, 104, 105, 106, 109, 110, 112, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 140, 141, 142, 144, 145, 147, 148, 149, 151, 152, 153, 154, 155, 156, 158, 160, 161, 162, 163, 165, 167, 170, 171, 175, 178, 179, 180, 186, 187, 189, 191, 194, 195, 208, 210, 212, 213, 215, 216, 217, 218, 220, 222, 224, 225, 228, 230, 233, 234, 237, 238, 239, 244, 245, 249, 250, 251, 252, 256, 257, 258, 259, 260, 261, 266, 267], "codebook": [21, 43, 116, 117, 170, 171, 179, 180], "coef": [244, 245, 247, 249, 250], "coef_": [29, 32, 247], "coeffic": [9, 26], "coeffici": [9, 23, 28, 29, 32, 33, 36, 59, 97, 98, 192, 193, 244, 245, 246, 247, 248, 250], "coerc": [76, 95], "coercion": 95, "cog": [43, 179, 180], "coher": [69, 167, 203, 251, 259], "cohort": [161, 162], "coil": 76, "coin": [53, 190], "col": [72, 200], "col1": [16, 157], "col2": 157, "col_index": 157, "colab": 52, "collabor": [14, 15, 42, 56, 82, 261], "collaps": [16, 21, 71, 76, 170, 171, 176, 177, 187], "colleagu": [13, 42, 44, 56, 128, 129, 147, 148, 172, 190, 220, 244], "collect": [10, 23, 33, 34, 36, 59, 67, 73, 74, 76, 78, 82, 83, 86, 87, 88, 89, 91, 92, 93, 97, 98, 99, 101, 106, 113, 116, 117, 119, 121, 127, 128, 129, 130, 131, 133, 134, 148, 165, 194, 196, 202, 203, 204, 214, 216, 230, 235, 236, 237, 238, 239, 247, 250, 254, 255, 257, 258, 260, 261, 265], "colleg": [7, 12, 35, 61, 78, 116, 117, 141, 144, 161, 162, 245], "college_degre": [7, 161, 162], "colloqui": [54, 57, 230], "colombia": 258, "colon": [4, 57, 86, 147, 150, 217, 226, 266], "color": [2, 15, 26, 53, 61, 62, 80, 87, 106, 113, 118, 147, 186, 187, 191, 194, 195, 198, 199, 203, 204, 205, 209, 210, 211, 212, 214, 216, 217, 218, 219, 221, 222, 223, 224, 225, 231, 234, 237, 239, 244, 259, 260, 265, 267], "color_list": [260, 265], "colorado": [142, 167], "colorbar": [203, 205, 209, 210, 211, 237], "colorblind": 190, "colormap": [190, 209, 237], "columbia": 167, "column": [5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 24, 25, 28, 29, 32, 33, 35, 37, 57, 58, 59, 69, 71, 72, 76, 80, 86, 87, 88, 106, 107, 110, 111, 112, 114, 116, 117, 119, 120, 121, 124, 131, 133, 137, 141, 142, 143, 144, 147, 149, 152, 153, 154, 157, 158, 160, 161, 162, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 179, 180, 187, 192, 193, 195, 196, 197, 199, 203, 204, 205, 206, 209, 211, 214, 215, 221, 225, 233, 234, 237, 239, 242, 245, 247, 248, 252, 253, 254, 255, 256, 257, 258, 260, 265], "column_a": 152, "column_count": [196, 214, 218], "com": [14, 23, 25, 35, 36, 42, 48, 52, 61, 63, 74, 76, 139, 140, 155, 156, 157, 159, 161, 187, 195, 225, 256], "combin": [2, 6, 7, 9, 10, 15, 16, 18, 42, 59, 61, 69, 71, 80, 87, 88, 90, 94, 95, 107, 113, 132, 134, 154, 161, 162, 167, 173, 178, 182, 186, 190, 191, 210, 211, 220, 221, 244, 251, 253, 258, 267], "combined_labeler_nam": 76, "come": [4, 6, 10, 12, 14, 15, 16, 21, 23, 28, 29, 30, 31, 32, 34, 35, 36, 38, 40, 42, 45, 48, 52, 53, 54, 55, 56, 57, 59, 61, 64, 65, 66, 67, 70, 71, 72, 73, 74, 75, 76, 79, 80, 81, 85, 86, 87, 89, 92, 93, 97, 98, 99, 107, 108, 109, 110, 113, 120, 122, 126, 135, 136, 137, 138, 141, 142, 143, 144, 145, 146, 147, 148, 150, 151, 152, 155, 156, 157, 164, 166, 170, 171, 184, 186, 190, 197, 215, 217, 225, 244, 245, 247, 248, 266], "comedi": 108, "comer": 76, "comfort": [10, 21, 29, 32, 52, 53, 55, 56, 62, 81, 87, 108, 138, 145, 149, 225], "comm": [76, 77], "comma": [4, 6, 25, 53, 69, 109, 113, 114, 139, 147, 179, 180, 181, 211, 248, 256], "command": [1, 2, 3, 11, 12, 13, 14, 15, 16, 19, 20, 22, 35, 42, 45, 46, 51, 52, 53, 56, 60, 61, 67, 69, 71, 73, 76, 77, 79, 80, 86, 92, 93, 97, 98, 101, 109, 116, 117, 127, 138, 141, 144, 145, 151, 152, 160, 163, 166, 175, 191, 194, 195, 196, 199, 200, 209, 210, 212, 234, 266], "command_lin": [54, 55], "commandlin": [51, 60], "commandlinetool": 45, "commandnam": 55, "commandtolaunchyoureditor": 14, "commclosederror": 76, "commensur": 78, "comment": [1, 7, 14, 21, 28, 56, 57, 61, 62, 65, 69, 82, 161], "commerc": [59, 189], "commit": [0, 14, 15, 16, 21, 49, 56, 59, 65, 78, 170, 171, 176, 177], "common": [10, 13, 14, 15, 21, 23, 24, 26, 29, 32, 33, 36, 37, 43, 46, 51, 55, 57, 58, 59, 60, 65, 66, 67, 76, 78, 82, 84, 86, 87, 100, 113, 118, 119, 123, 125, 132, 134, 137, 139, 140, 147, 149, 150, 155, 156, 158, 159, 165, 166, 167, 169, 170, 171, 174, 178, 179, 180, 183, 186, 190, 191, 195, 196, 197, 199, 201, 204, 210, 213, 217, 219, 230, 235, 236, 237, 239, 244, 245, 246, 249, 250, 252, 253, 257, 267], "commonli": [4, 12, 23, 35, 36, 56, 61, 65, 74, 82, 87, 88, 97, 98, 106, 141, 144, 147, 148, 155, 165, 246, 249, 258, 266], "commun": [5, 7, 10, 12, 21, 22, 23, 25, 27, 33, 35, 36, 56, 59, 61, 69, 78, 80, 82, 92, 93, 97, 98, 104, 105, 116, 117, 122, 138, 141, 144, 145, 153, 155, 158, 159, 160, 161, 162, 163, 170, 171, 183, 184, 186, 190, 204, 205, 209, 211, 217, 219, 220, 225, 259, 266], "comoro": [256, 258], "compact": [84, 178, 194, 210], "compani": [5, 11, 14, 29, 32, 44, 54, 56, 59, 69, 71, 76, 77, 79, 81, 82, 95, 101, 116, 117, 190, 217, 246], "compar": [6, 12, 13, 15, 16, 20, 23, 24, 28, 33, 35, 36, 37, 43, 55, 59, 61, 72, 73, 74, 75, 76, 80, 82, 83, 97, 98, 101, 106, 108, 114, 119, 123, 124, 137, 141, 144, 147, 153, 160, 169, 176, 177, 179, 180, 191, 192, 193, 194, 195, 196, 206, 207, 210, 212, 219, 221, 222, 224, 229, 230, 232, 233, 235, 236, 238, 240, 245, 246, 250, 251, 252, 257, 258, 266], "comparison": [15, 21, 29, 31, 32, 48, 59, 64, 76, 82, 88, 206, 207, 219, 220, 235, 236, 266], "compartment": 61, "compat": [4, 46, 53, 74, 114, 127, 147, 148, 152, 155, 184, 235, 236], "compel": [78, 190, 219, 220, 260, 267], "compens": [78, 80], "compet": [70, 82], "compil": [73, 74, 76, 81, 82, 122, 187, 203], "complain": 155, "complaint": [4, 5], "complement": [10, 188, 192, 193], "complet": [1, 7, 12, 14, 15, 16, 19, 20, 21, 22, 23, 24, 28, 30, 32, 33, 34, 35, 36, 37, 38, 40, 45, 48, 53, 59, 73, 75, 76, 78, 81, 82, 86, 92, 93, 97, 98, 104, 105, 109, 122, 123, 128, 129, 130, 141, 144, 157, 160, 161, 162, 163, 167, 170, 171, 176, 177, 181, 191, 206, 207, 211, 225, 229, 235, 236, 238, 245, 246, 253, 254, 258], "complete_the_inputs_her": 235, "complex": [26, 58, 67, 79, 81, 87, 121, 130, 132, 158, 164, 170, 171, 173, 178, 194, 211, 260, 265], "complic": [4, 53, 57, 58, 60, 61, 75, 80, 87, 94, 108, 127, 133, 140, 147, 148, 149, 150, 187, 246], "complimentari": 139, "compon": [29, 32, 49, 58, 75, 78, 79, 82, 84, 87, 120, 123, 132, 134, 173, 189, 191, 194, 196, 197, 206, 207, 208, 215, 216, 220, 228, 232, 235, 236, 251, 259, 261], "compos": [69, 87, 181, 184, 186, 194, 217, 225], "composit": [28, 63, 176, 177], "compositegenerictran": 216, "compositegenerictransform": 216, "compound": [30, 31, 38, 117, 138, 141, 144, 145, 160, 161, 162, 163], "comprehens": [33, 82, 130, 167, 250], "compress": [11, 15, 28, 55, 71, 72, 76, 147, 148], "compris": 216, "compromis": 1, "compsci": 82, "comput": [1, 2, 4, 6, 8, 11, 14, 15, 24, 29, 32, 37, 42, 46, 48, 49, 50, 52, 53, 54, 55, 57, 60, 61, 66, 67, 68, 69, 70, 72, 73, 74, 78, 81, 83, 85, 86, 87, 99, 100, 103, 106, 107, 108, 118, 119, 120, 122, 123, 124, 125, 127, 128, 129, 130, 131, 132, 133, 137, 138, 141, 142, 143, 144, 145, 147, 148, 151, 155, 158, 160, 161, 162, 163, 164, 169, 170, 171, 174, 176, 177, 190, 192, 193, 216, 217, 233, 235, 236, 237, 238, 240, 241, 247, 248, 260], "computation": [79, 133, 178], "con": [80, 147, 148], "concat": [72, 165, 221, 245, 247], "concaten": [2, 5, 28, 73, 112, 122, 129, 133, 164, 169, 173, 182, 208, 210, 223, 266], "concatent": 165, "conceiv": 82, "concept": [2, 40, 52, 67, 76, 82, 84, 94, 103, 119, 130, 131, 135, 149, 164, 178, 188, 194, 199, 208, 214, 216, 220, 230, 231, 243, 245, 246, 247, 248], "conceptu": [59, 66, 76, 78], "concern": [29, 31, 32, 78, 87, 140, 186, 217, 253, 254], "concert": 71, "concis": [74, 86, 88, 99, 103, 123], "conclud": [21, 25, 26, 130, 244], "conclus": [26, 33, 44, 56, 81, 148, 170, 171, 220, 246], "concret": [59, 107, 114, 248], "concur": 136, "concurr": 82, "cond": [244, 245, 247, 249, 250], "conda": [0, 2, 10, 11, 19, 20, 23, 36, 42, 45, 48, 53, 55, 58, 75, 76, 77, 84, 86, 97, 98], "condabin": 55, "condens": 209, "condit": [57, 58, 61, 69, 74, 75, 82, 94, 95, 121, 125, 157, 161, 187, 202, 225, 235, 236, 239, 244, 245, 246, 250], "conduct": [7, 12, 21, 35, 61, 82, 87, 89, 106, 116, 117, 141, 144, 155, 161, 162, 167, 168, 183], "conf_int": [185, 187], "confer": [192, 193], "confess": 19, "confid": [26, 29, 32, 61, 81, 128, 204, 247, 257, 258], "config": [14, 190, 191, 192, 194, 195, 196, 197, 198, 199, 200, 201, 203, 204, 205, 206, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 223, 234, 237, 240, 244, 260], "configur": [2, 47, 53, 55, 58, 64, 80, 85, 217], "configure_legend": 265, "configure_mark": 265, "confirm": [5, 33, 53, 55, 100, 106, 158], "conflat": [22, 160, 163], "conflic": 56, "conflict": [52, 53, 56, 140], "conflicted_fil": 15, "conform": 173, "confound": 59, "confus": [18, 54, 57, 61, 75, 95, 134, 139, 149, 154, 190, 213, 216, 220], "confusingli": [18, 154], "congo": [139, 140, 195, 203, 258], "congratul": [4, 12, 15, 23, 29, 32, 35, 36, 49, 59, 81, 97, 98, 116, 117, 141, 144, 235, 236, 259], "conjug": 82, "conn": 190, "connect": [6, 8, 10, 14, 27, 54, 61, 76, 77, 78, 82, 103, 152, 168, 178, 186, 194, 216, 235, 236], "connect_timeout": 77, "connecticut": 167, "connectionreseterror": 76, "connector": 77, "connot": 155, "consecut": 94, "consent": 82, "consequ": [26, 40, 62, 100, 107, 225], "conserv": 61, "consid": [15, 16, 17, 29, 32, 33, 34, 55, 57, 59, 64, 71, 73, 74, 75, 76, 78, 80, 89, 94, 95, 101, 113, 133, 153, 155, 157, 165, 166, 172, 173, 174, 175, 179, 180, 187, 190, 192, 193, 195, 200, 204, 208, 211, 212, 214, 216, 217, 220, 235, 236, 244, 245, 246, 251, 257, 258, 260], "consider": [95, 147, 189, 190, 197, 217, 234, 257, 258], "consist": [9, 21, 26, 29, 32, 53, 69, 75, 78, 89, 90, 111, 113, 123, 125, 136, 151, 152, 155, 168, 170, 171, 173, 203, 204, 206, 207, 208, 216, 233, 237], "consol": [14, 19, 20, 58, 85], "consolid": 78, "constain": 6, "constant": [9, 14, 15, 22, 28, 62, 75, 157, 160, 163, 187, 195, 219, 225, 244, 245], "constantli": [61, 62, 78, 82, 121], "constitu": [134, 225, 237], "constitut": [22, 29, 32, 59, 81, 141, 144, 160, 163, 175], "constrain": 69, "constraint": [74, 80, 247, 249], "construct": [22, 65, 78, 94, 125, 128, 133, 134, 160, 163, 179, 180, 191, 206, 207, 216, 235, 236, 239], "constructor": [130, 134], "construt": 139, "consult": [133, 184], "consum": [29, 32, 221, 222], "consumpt": [217, 221, 222], "contact": [23, 36, 61, 92, 93, 97, 98, 141, 144, 160, 163], "contain": [3, 4, 5, 6, 10, 11, 15, 23, 25, 28, 34, 36, 43, 53, 55, 69, 87, 89, 90, 92, 93, 94, 100, 103, 106, 108, 109, 113, 116, 117, 121, 127, 133, 134, 136, 139, 142, 143, 147, 155, 156, 164, 167, 169, 173, 179, 180, 187, 190, 191, 192, 193, 194, 199, 206, 207, 209, 211, 215, 216, 217, 220, 221, 230, 247, 248, 252, 253, 255, 258, 261], "container": 82, "contamin": 189, "content": [0, 4, 5, 12, 13, 15, 20, 35, 42, 45, 48, 55, 58, 61, 69, 73, 83, 86, 92, 93, 97, 98, 103, 104, 105, 114, 116, 117, 119, 130, 133, 134, 137, 138, 139, 141, 142, 144, 145, 147, 153, 160, 161, 162, 163, 164, 166, 169, 174, 175, 182, 184, 190, 194, 195, 199, 206, 207, 211, 212, 216, 217, 220, 230, 234, 248, 251, 252, 253, 254, 257, 258, 260, 265, 266, 267], "context": [11, 23, 29, 32, 36, 59, 61, 69, 70, 81, 82, 97, 98, 170, 171, 214, 229, 230, 243, 244, 246], "contextu": [87, 240], "contin": [221, 222, 251, 252, 253, 254, 255, 257, 259, 260, 261, 265], "continent_color": 221, "continent_fil": 258, "continents_al": 256, "continents_fil": [256, 258, 261], "continu": [1, 5, 11, 14, 15, 17, 20, 29, 30, 31, 32, 33, 38, 42, 57, 58, 61, 64, 80, 82, 99, 138, 145, 155, 156, 175, 217, 229, 230], "contour": [191, 237], "contract": 246, "contrast": [4, 15, 29, 30, 31, 32, 38, 48, 55, 59, 68, 76, 78, 82, 87, 89, 120, 138, 142, 143, 145, 147, 155, 225, 247, 249], "contrastresult": [247, 249], "contribut": [14, 21, 56, 62, 78, 108, 120, 170, 171, 174, 197, 244, 251, 259], "contriv": 63, "control": [4, 53, 56, 57, 58, 59, 61, 64, 70, 82, 87, 125, 131, 167, 195, 199, 211, 225, 237, 245, 246, 266], "convei": [190, 220, 222, 224], "conveni": [33, 64, 92, 93, 97, 98, 104, 105, 116, 117, 120, 122, 133, 138, 141, 144, 145, 153, 160, 161, 162, 163, 174, 199, 200, 247, 248, 266], "convent": [66, 71, 191, 199], "converg": [30, 31, 33, 38, 53, 138, 145], "convers": [31, 49, 56, 134, 208], "convert": [6, 7, 9, 11, 17, 22, 29, 32, 57, 61, 71, 73, 74, 86, 90, 102, 106, 107, 108, 118, 122, 142, 143, 147, 151, 153, 156, 158, 160, 161, 162, 163, 183, 197, 199, 204, 214, 218, 245, 251, 252, 253, 254, 255, 256, 257, 266], "convert_dtyp": 143, "convert_feet_and_inches_to_inch": 61, "convert_stream_closed_error": 76, "convinc": 189, "convolut": [140, 246], "cool": [10, 33, 63, 84, 88, 95, 107, 134, 155], "cooler": 190, "coolor": 220, "coop": 179, "coordin": [5, 26, 51, 52, 78, 109, 113, 120, 195, 211, 260, 265], "copi": [0, 2, 14, 16, 21, 23, 24, 36, 37, 41, 48, 52, 55, 56, 57, 61, 62, 66, 67, 71, 73, 75, 84, 97, 98, 103, 108, 112, 119, 123, 125, 131, 134, 146, 149, 157, 161, 168, 178, 187, 222, 224, 254, 255, 258], "copilot": [80, 81, 133], "copy_on_writ": [21, 25, 35, 76, 152, 155, 157, 158, 160, 161, 165, 166, 167, 168, 169, 170, 172, 173, 174, 175, 176, 178, 185, 225, 226, 244, 245, 247, 248, 249, 250], "copyright": 108, "corbon": 252, "cord": 113, "core": [10, 11, 14, 52, 53, 61, 70, 74, 75, 76, 77, 79, 82, 119, 130, 134, 139, 140, 143, 158, 164, 173, 189, 194, 196, 199, 225, 228, 230, 241], "corequisit": 82, "corner": [29, 32, 81, 85, 109, 195, 200, 206, 240], "corr": 192, "corrcoef": [192, 193], "correct": [9, 15, 16, 21, 22, 24, 31, 34, 37, 38, 52, 57, 58, 61, 81, 82, 87, 92, 93, 95, 104, 105, 121, 123, 124, 126, 138, 147, 155, 158, 160, 163, 168, 170, 171, 213, 220, 233, 235, 236, 237, 240, 252, 254, 257, 258], "correct_output": [235, 236, 238], "correction_no": 76, "correctli": [21, 45, 48, 57, 64, 80, 81, 94, 132, 168, 175, 206, 207, 217, 233, 237, 244, 245, 247, 249, 250], "correctly_signed_and_squar": 108, "correl": [15, 21, 25, 28, 29, 32, 113, 123, 170, 171, 172, 192, 193, 225, 247, 249], "correspond": [12, 35, 55, 87, 89, 103, 108, 109, 116, 117, 120, 121, 125, 126, 127, 134, 136, 141, 144, 165, 169, 176, 177, 178, 206, 208, 209, 210, 211, 213, 216, 219, 222, 230, 231, 232, 233, 235, 236, 237, 238, 253, 255, 257, 258, 259, 260, 261, 266], "corrupt": [21, 63, 68, 75, 101, 140, 148, 151, 168, 247, 249], "corruption_model": [247, 249], "cost": [6, 8, 64, 71, 75, 78, 88, 92, 93, 95, 112, 134, 137, 142, 184, 224, 230, 240, 245], "costa": [139, 258], "cote": [257, 258], "could": [15, 21, 23, 27, 29, 32, 34, 36, 51, 54, 55, 58, 59, 61, 62, 64, 68, 69, 70, 73, 74, 76, 78, 80, 83, 86, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 100, 101, 106, 107, 109, 110, 113, 114, 116, 117, 119, 120, 121, 122, 128, 134, 135, 137, 139, 142, 147, 149, 152, 155, 156, 157, 158, 159, 161, 164, 165, 168, 169, 170, 171, 173, 174, 178, 182, 187, 195, 196, 197, 199, 203, 204, 206, 207, 210, 211, 214, 217, 218, 219, 220, 222, 224, 225, 229, 230, 231, 232, 234, 235, 236, 237, 240, 244, 245, 246, 248, 252, 257, 260, 265, 266], "couldn": [7, 67, 75, 76, 89, 108, 112, 116, 117, 158, 161, 162, 174], "count": [1, 5, 6, 7, 10, 12, 16, 21, 22, 23, 24, 26, 28, 30, 31, 32, 33, 35, 36, 37, 38, 55, 61, 66, 78, 84, 89, 119, 120, 125, 131, 133, 134, 138, 139, 140, 141, 143, 144, 145, 149, 155, 158, 159, 160, 161, 162, 163, 165, 167, 168, 170, 171, 174, 179, 180, 187, 196, 199, 206, 207, 208, 209, 210, 214, 218, 225, 234, 240, 253, 262, 266], "count_nonzero": 119, "count_seri": 206, "count_valu": 121, "count_values_in_each_row": 121, "counter": [4, 57, 69], "counterpart": 225, "counti": [8, 11, 16, 21, 33, 59, 76, 170, 171, 176, 177, 182, 239, 240, 244], "countless": 189, "countri": [6, 14, 23, 25, 26, 28, 30, 31, 36, 38, 61, 92, 93, 97, 98, 133, 137, 138, 139, 140, 145, 147, 148, 149, 155, 156, 157, 158, 159, 187, 195, 202, 203, 204, 211, 221, 222, 225, 247, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 265], "country_nam": [247, 249], "coup": [156, 157], "coupl": [8, 11, 12, 21, 27, 35, 40, 49, 51, 52, 55, 56, 62, 68, 69, 71, 74, 76, 78, 80, 86, 89, 94, 101, 106, 108, 140, 141, 144, 168, 187, 206, 207, 216, 225], "cours": [9, 12, 23, 26, 29, 32, 33, 35, 36, 40, 42, 48, 49, 50, 52, 53, 54, 55, 59, 61, 62, 64, 65, 70, 76, 79, 81, 82, 85, 86, 87, 88, 89, 92, 93, 95, 97, 98, 99, 100, 101, 104, 105, 106, 109, 113, 116, 117, 119, 123, 128, 129, 133, 134, 141, 144, 147, 149, 150, 155, 156, 164, 169, 170, 171, 178, 182, 183, 187, 189, 191, 197, 200, 203, 205, 208, 210, 211, 222, 224, 225, 229, 234, 241, 243, 244, 245, 246, 247, 248, 249, 251, 252, 257], "coursera": [40, 42, 85, 86, 92, 93, 170, 171], "cousin": 48, "cov": 210, "cov_typ": [247, 249], "covari": [9, 210, 244, 245, 247, 249, 250], "cover": [0, 10, 40, 55, 61, 64, 71, 77, 78, 82, 89, 100, 102, 108, 109, 121, 149, 150, 156, 164, 170, 171, 186, 188, 190, 191, 194, 195, 214, 225, 241, 243, 244, 245, 247], "coverag": [74, 82], "coverg": [30, 31, 38], "covid": 214, "cow": [2, 151], "cp": [0, 55, 116, 117], "cpia": [247, 249], "cpia_public_sector_r": [247, 248, 249], "cplace": 221, "cpu": [8, 10, 75, 76, 77], "cpu_count": [75, 76], "cpython": 74, "craft": 62, "cram": 95, "crash": [10, 11, 71, 76], "crazi": [15, 62], "creat": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 14, 17, 18, 19, 21, 23, 25, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 52, 54, 55, 56, 57, 58, 61, 62, 64, 66, 67, 68, 71, 75, 76, 77, 81, 82, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 97, 98, 100, 101, 102, 103, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 119, 120, 121, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 135, 138, 139, 140, 141, 142, 143, 144, 145, 147, 148, 150, 151, 152, 153, 154, 161, 162, 165, 166, 167, 169, 170, 171, 173, 174, 176, 177, 178, 179, 180, 183, 184, 185, 186, 187, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 203, 206, 207, 208, 209, 211, 212, 213, 214, 216, 217, 218, 219, 220, 225, 226, 232, 233, 235, 236, 237, 238, 239, 240, 245, 247, 248, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 265, 266, 267], "create_boundari": 237, "creater": 76, "creation": [15, 95, 143, 189, 252], "creativ": [216, 217, 243], "creator": 146, "creatur": [69, 120], "credenc": 190, "credenti": [49, 77, 78], "credibl": 190, "credibli": 78, "credit": [8, 82, 87, 110, 112, 116, 117, 164, 169, 189, 246], "credit_a": [36, 97], "credit_b": [36, 97], "credit_cutoff_": [36, 97], "credit_cutoff_d": [36, 97], "creek": 179, "crime": [16, 33, 59, 61, 63, 176, 177, 181], "crimin": [21, 170, 171], "criteria": [59, 128, 129, 164], "critic": [14, 21, 62, 79, 81, 82, 157, 164, 170, 171, 204, 248, 251], "crn": 6, "croatia": [195, 258], "crop": [15, 113, 152, 155, 196, 198], "crop_nam": 196, "cross": [5, 7, 29, 32, 38, 113, 138, 161, 162, 244], "crosstab": [7, 161, 162], "crowd": 225, "crs_dep_tim": 10, "crselapsedtim": 10, "crucial": [29, 32, 44, 56, 225, 244], "crude": 31, "crunch": 77, "crush": 78, "cry": 10, "cryptic": 88, "crystal": [134, 260, 265], "csv": [4, 5, 6, 8, 10, 14, 20, 21, 25, 29, 32, 33, 55, 61, 63, 71, 72, 76, 131, 133, 139, 140, 143, 147, 148, 149, 156, 157, 158, 159, 167, 170, 171, 176, 177, 179, 180, 187, 192, 193, 195, 201, 203, 204, 206, 207, 209, 211, 214, 218, 221, 222, 223, 224, 225, 234, 235, 236, 237, 239, 242, 244, 245, 247, 249, 250, 252, 253, 254, 255, 256, 258, 260, 261, 262], "ct": [82, 113, 120, 167], "ctrl": [4, 49], "cuba": 258, "cudf": 76, "culpa": 62, "cum_data": 223, "cumbersom": [58, 61, 213], "cumsum": 223, "cumtim": 74, "cumul": [30, 31, 38, 138, 145, 196], "cup": 125, "cure": 21, "curiou": [69, 76, 82, 247], "curl": 48, "curli": 86, "currenc": [31, 92, 93, 106], "current": [4, 5, 7, 12, 14, 15, 19, 20, 22, 30, 31, 35, 38, 43, 48, 49, 50, 51, 52, 53, 55, 58, 59, 60, 62, 68, 73, 75, 80, 81, 82, 85, 86, 87, 104, 105, 106, 108, 110, 116, 117, 123, 138, 141, 142, 144, 145, 157, 158, 160, 161, 162, 163, 179, 180, 184, 186, 187, 196, 206, 207, 214, 217, 218, 224, 225, 261, 266], "curriculum": 82, "cursor": [19, 58], "cursorless": 80, "curt": 62, "curv": [31, 80, 89, 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52, 55, 61, 62, 67, 68, 69, 72, 73, 74, 75, 82, 134, 142, 143, 149, 151, 154, 160, 184, 186, 190, 220, 225, 240, 243, 244, 245, 246, 247, 249, 250, 260, 265], "r1": 78, "race": [7, 22, 29, 32, 33, 53, 75, 77, 116, 117, 160, 161, 162, 163, 176, 177, 181, 187, 245, 250], "race_percent_properti": [176, 177], "race_percent_viol": [176, 177], "race_recod": [29, 32], "racial": [7, 22, 29, 32, 33, 59, 116, 117, 160, 161, 162, 163, 176, 177, 245, 246], "racist": 59, "radar": 82, "radian": 194, "radic": [26, 246, 247], "radiologist": 246, "radiu": 198, "rage": 75, "rahbar": 82, "rai": [59, 120], "rain": 121, "rainbow": 147, "raindrop": 125, "raini": 121, "rais": [16, 23, 36, 42, 57, 61, 69, 74, 76, 77, 78, 81, 95, 97, 98, 108, 114, 134, 139, 140, 148, 152, 158, 175], "raise_parser_error": 256, "ram": [6, 24, 37, 70, 71, 72, 75, 76, 79, 86, 137, 157], "ramp": 5, "ran": [3, 10, 13, 26, 29, 32, 33, 34, 52, 59, 71, 73, 74, 76, 86, 122, 136, 151, 244, 250], "rand": [64, 87, 124, 125, 126, 127, 129, 178, 194, 208, 212, 242], "rand_matrix": 178, "randint": [17, 24, 37, 72, 126, 128, 129, 153], "randn": [125, 126, 194, 200, 204, 208, 211, 212], "random": [12, 17, 23, 24, 32, 35, 36, 37, 62, 72, 75, 82, 84, 86, 87, 89, 92, 93, 97, 98, 101, 116, 117, 118, 124, 127, 130, 139, 141, 144, 153, 155, 172, 178, 187, 194, 195, 200, 203, 204, 208, 210, 211, 212, 220, 234, 236, 241, 242], "random_arrai": 125, "random_indic": 126, "random_integ": [24, 37, 128, 129], "random_st": [29, 32], "randomli": [26, 29, 30, 32, 38, 59, 89, 125, 138, 145, 210, 234, 235, 236, 246], "randomuser123": 62, "rang": [17, 23, 31, 33, 36, 38, 42, 57, 59, 64, 72, 73, 74, 75, 78, 86, 87, 88, 89, 91, 94, 97, 98, 101, 102, 109, 111, 122, 123, 124, 125, 128, 129, 134, 135, 138, 139, 140, 141, 144, 146, 148, 150, 153, 155, 156, 167, 187, 192, 194, 195, 202, 205, 206, 207, 208, 211, 224, 225, 237, 239, 240, 247, 265, 266], "range_": 265, "rangeindex": [139, 143], "rank": [23, 30, 31, 36, 38, 78, 97, 98, 138, 140, 145, 173, 190, 206, 207, 247, 249], "rank_doubl": 140, "rapid": [1, 76, 82], "rapidli": 81, "rare": [65, 78, 125, 149, 157, 165, 187], "raster": [51, 216], "rasterio": 51, "rasterization_zord": 216, "rate": [14, 15, 30, 31, 33, 38, 70, 80, 87, 88, 92, 93, 116, 117, 138, 145, 155, 164, 167, 176, 177, 181, 187, 203, 204, 225, 244, 245, 247, 249], "rather": [4, 6, 20, 21, 24, 25, 31, 37, 38, 53, 55, 56, 57, 58, 61, 62, 66, 67, 69, 73, 74, 76, 81, 82, 85, 86, 99, 100, 101, 103, 113, 118, 122, 123, 124, 130, 133, 134, 138, 140, 148, 150, 152, 156, 161, 165, 167, 170, 171, 187, 195, 211, 220, 225, 240, 243, 245, 246, 247, 254], "ratio": [23, 31, 36, 80, 97, 98, 190, 219, 220, 247, 258], "raw": [4, 5, 6, 15, 16, 19, 23, 25, 28, 29, 32, 35, 36, 48, 61, 63, 68, 71, 131, 139, 140, 155, 156, 157, 159, 161, 176, 177, 187, 195, 206, 207, 212, 225, 244, 251, 252, 260, 265], "raw_co2_fil": [253, 261], "raw_continents_fil": [256, 261], "raw_data": 234, "raw_income_fil": [254, 261], "raw_population_fil": [255, 261], "rawdict": 76, "rayleigh": 82, "raza": 69, "rb": 240, "rc": [43, 179, 180], "rcparam": [194, 217, 220, 221, 223], "rdd": 76, "re": [1, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 91, 94, 95, 100, 101, 103, 107, 108, 109, 110, 114, 119, 121, 122, 123, 124, 125, 127, 128, 129, 133, 134, 135, 137, 138, 139, 140, 141, 143, 144, 145, 148, 150, 152, 153, 154, 155, 156, 157, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 184, 186, 187, 189, 190, 192, 193, 194, 195, 196, 199, 202, 204, 205, 206, 207, 208, 209, 212, 213, 214, 216, 217, 220, 225, 226, 229, 230, 232, 235, 236, 237, 239, 240, 243, 244, 245, 247, 248, 251, 253, 254, 255, 256, 257, 258, 260, 265, 266], "re_center_and_re_scal": 108, "reach": [44, 53, 58, 59, 62, 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254, 255, 256, 258, 260, 262], "read_excel": [139, 148], "read_from_fd": 76, "read_hdf": 139, "read_hdf5": 148, "read_json": [10, 147], "read_orc": 10, "read_parquet": [10, 72, 148], "read_pickl": [148, 265], "read_spss": 148, "read_sql": 139, "read_stata": [12, 35, 139, 141, 144, 148, 155, 159, 160, 161], "readabl": [55, 61, 109, 121, 123, 147, 148, 178, 187, 189, 190, 195, 199, 217, 222, 225, 254, 255, 259, 260, 265, 266], "reader": [4, 26, 27, 29, 32, 38, 69, 78, 138, 189, 190, 196, 204, 219, 243, 249, 260, 265], "readi": [45, 52, 58, 82, 119, 125, 139, 167, 209, 220, 234, 238, 239, 252, 253, 254, 255, 256, 257, 258, 259], "readili": [9, 119, 123, 260, 265], "reading_valu": 173, "readm": [4, 5, 6, 14, 15], "real": [7, 11, 12, 17, 23, 28, 29, 30, 31, 32, 35, 36, 38, 42, 45, 58, 63, 65, 69, 71, 73, 75, 76, 78, 80, 81, 82, 87, 89, 97, 98, 112, 113, 122, 125, 131, 138, 139, 141, 142, 144, 145, 153, 155, 156, 158, 161, 162, 183, 203, 204, 229, 235, 236, 244, 246], "real_sum": 174, 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26, 29, 32, 59, 61, 63, 82, 106, 123, 186, 188, 225, 234, 237, 241, 246, 248, 250], "regressionresult": 247, "regressor": [33, 244], "regret": 79, "regular": [1, 4, 11, 14, 17, 24, 37, 40, 48, 51, 55, 60, 64, 74, 75, 76, 80, 82, 86, 92, 93, 97, 98, 107, 113, 114, 116, 117, 134, 137, 138, 141, 142, 144, 145, 149, 153, 156, 160, 161, 162, 163, 196], "regularli": [7, 74, 78, 81, 133, 137, 151, 161, 162, 189], "regulatori": [21, 170, 171], "reilli": [195, 211], "reinsert": 37, "reinvent": 76, "reject": 38, "rejoic": 126, "rel": [4, 8, 12, 23, 35, 36, 48, 64, 69, 75, 79, 80, 86, 88, 108, 115, 120, 125, 141, 142, 144, 149, 151, 188, 191, 195, 197, 198, 199, 208, 217, 222, 225, 240, 245], "relabel": 210, "relat": [16, 21, 28, 32, 33, 43, 51, 60, 62, 75, 79, 80, 81, 82, 100, 103, 107, 111, 114, 118, 124, 149, 164, 170, 171, 173, 179, 180, 182, 190, 214, 229, 248], "relatedli": 62, "relationship": [12, 16, 26, 28, 33, 35, 78, 102, 103, 141, 144, 165, 176, 177, 183, 195, 199, 210, 216, 225, 229, 241, 244, 245, 246, 250, 251, 257, 259, 260], "relatively_democrat": 139, "releas": [21, 74, 133, 134, 148, 151, 225], "relev": [29, 32, 53, 55, 56, 59, 62, 75, 87, 95, 128, 129, 206, 225, 253], "reli": [28, 30, 31, 38, 46, 55, 58, 64, 74, 119, 127, 138, 145, 184, 239, 267], "reliabl": [24, 37, 61, 73, 83, 92, 93], "religion": 187, "reload": [60, 72, 237], "remain": [21, 86, 128, 129, 136, 147, 165, 170, 171, 196, 204, 206, 207, 215, 217, 224, 240, 243, 244, 252, 257, 258, 266], "remaind": [94, 157, 170, 171], "remak": 218, "remark": [79, 81], "remedi": 199, "rememb": [4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 49, 55, 56, 57, 64, 67, 71, 72, 73, 74, 75, 78, 80, 84, 86, 88, 90, 94, 99, 100, 107, 109, 119, 122, 123, 135, 136, 137, 138, 141, 144, 145, 149, 150, 158, 160, 161, 162, 163, 167, 199, 219, 245, 250], "remind": [9, 16, 28, 76, 80, 84, 94, 142, 170, 171, 174, 258], "remiss": 74, "remot": [14, 15, 23, 36, 54, 55, 76, 77, 82, 225], "remov": [3, 5, 15, 23, 36, 46, 52, 55, 57, 58, 61, 71, 72, 82, 127, 128, 129, 147, 156, 158, 167, 189, 195, 206, 207, 208, 211, 213, 216, 217, 220, 221, 224, 254, 257, 258, 260, 265], "remove_dupl": [128, 129], "remove_greater_than": [128, 129], "remove_if_equ": [128, 129], "remove_less_than": [128, 129], "renam": [4, 54, 55, 149, 167, 170, 171, 237, 245, 253, 254, 255, 256], "render": [15, 24, 37, 44, 187, 191, 208], "renew": [43, 82, 104, 105, 179, 180], "renov": 239, "rent": 71, "reorder": 197, "reorgan": [125, 187], "rep": [203, 247, 258], "repair": 244, "repeat": [10, 21, 29, 32, 64, 73, 88, 91, 113, 120, 121, 122, 142, 157, 176, 177, 189, 192, 193, 199, 213, 219, 231, 232, 237, 240, 257, 258, 266], "repeatedli": [78, 114], "repetit": 16, "repit": 175, "replac": [7, 22, 43, 44, 54, 80, 85, 95, 110, 119, 125, 126, 128, 131, 142, 152, 157, 158, 159, 160, 161, 162, 163, 170, 179, 180, 210, 237, 246, 255, 257, 258, 266], "replacement_dict": 179, "replai": 15, "replic": [29, 32, 58, 59, 61, 62, 120, 125, 222, 224, 246], "repo": [0, 15, 29, 32, 33, 76], "report": [2, 4, 5, 11, 15, 21, 22, 33, 51, 55, 56, 60, 62, 74, 76, 81, 88, 92, 93, 106, 114, 150, 155, 159, 160, 163, 164, 167, 170, 171, 174, 189, 190, 203, 212, 220, 245, 247, 250, 259], "reporter_addl_co_info": 76, "reporter_address1": 76, "reporter_address2": 76, "reporter_bus_act": 76, "reporter_c": 76, "reporter_counti": 76, "reporter_dea_no": 76, "reporter_famili": 76, "reporter_nam": 76, "reporter_st": 76, "reporter_zip": 76, "repositori": [14, 15, 21, 23, 36, 48, 49, 52, 54, 55, 56, 62, 68, 97, 98], "repositorii": 56, "repres": [4, 12, 22, 23, 26, 35, 36, 59, 61, 64, 87, 90, 92, 93, 97, 98, 103, 106, 108, 109, 113, 116, 117, 119, 120, 121, 126, 132, 134, 137, 139, 141, 142, 144, 147, 148, 155, 158, 159, 160, 163, 167, 169, 172, 173, 175, 187, 194, 195, 197, 198, 203, 204, 205, 206, 207, 208, 209, 210, 211, 214, 216, 217, 224, 225, 230, 231, 232, 235, 236, 237, 238, 239, 240, 245, 254, 255, 258, 260, 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122, 127, 133, 143, 149, 156, 178, 186, 187, 194, 217, 225, 228, 244, 245, 249], "syntaxerror": 57, "syria": 258, "syrian": 258, "system": [1, 4, 8, 10, 14, 20, 21, 29, 32, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 64, 69, 70, 71, 75, 76, 82, 86, 101, 107, 152, 169, 170, 171, 184, 190, 192, 193, 212, 225], "system2": 45, "system76": 79, "system_monitor": 76, "systemat": [30, 31, 32, 38, 61, 138, 145, 225, 246], "systemmonitor": 76, "systol": 248, "systolic_bp": 173, "sytax": 178, "sytem": 55, "t": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 40, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 77, 78, 79, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 116, 117, 120, 123, 125, 127, 128, 129, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 147, 148, 149, 151, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 167, 168, 170, 171, 172, 173, 174, 175, 184, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 197, 199, 201, 203, 204, 205, 206, 207, 210, 211, 212, 214, 216, 217, 218, 220, 221, 222, 223, 225, 229, 231, 234, 235, 236, 237, 238, 239, 241, 243, 244, 245, 246, 247, 249, 250, 252, 254, 257, 258, 259, 260, 261, 265, 267], "t0": [123, 124], "t1": [123, 124], "t_test": [247, 249], "ta": [1, 23, 36, 65, 69], "tab": [6, 8, 10, 14, 19, 25, 48, 49, 61, 65, 71, 76, 147, 151, 161, 234, 237], "tabl": [7, 12, 20, 23, 28, 35, 36, 59, 61, 62, 63, 68, 70, 76, 82, 97, 98, 106, 121, 131, 134, 137, 139, 142, 147, 148, 161, 162, 164, 165, 166, 173, 178, 189, 194, 202, 205, 222, 230, 233, 238, 239, 244, 245], "tableau": 189, "tabul": [7, 134, 161, 162, 225], "tabular": [2, 4, 42, 72, 76, 106, 113, 119, 130, 132, 134, 146, 149, 205], "tack": 74, "tada": [139, 155], "tag": [16, 62, 175, 212, 225], "tail": [5, 10, 12, 22, 23, 35, 36, 89, 97, 98, 139, 141, 144, 155, 160, 163, 240], "tailnum": 10, "tailor": [8, 40, 59], "taiwan": [221, 258], "tajikistan": 258, "take": [2, 4, 5, 6, 8, 9, 10, 11, 14, 15, 16, 18, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 40, 52, 54, 55, 57, 58, 62, 64, 65, 67, 68, 70, 71, 74, 75, 76, 78, 80, 81, 82, 86, 87, 88, 92, 93, 94, 95, 97, 98, 100, 103, 107, 108, 112, 113, 116, 117, 118, 119, 120, 123, 125, 126, 130, 134, 135, 137, 138, 139, 142, 145, 147, 149, 151, 152, 154, 155, 157, 158, 159, 160, 163, 165, 167, 169, 173, 176, 177, 178, 184, 186, 187, 188, 189, 191, 194, 195, 198, 199, 202, 203, 204, 206, 207, 208, 209, 210, 212, 213, 214, 216, 217, 219, 220, 223, 225, 231, 232, 234, 235, 236, 237, 238, 240, 243, 244, 245, 246, 247, 248, 250, 251, 252, 253, 254, 255, 256, 257, 258, 265, 267], "takeawai": [102, 220, 251], "taken": [10, 23, 36, 52, 68, 81, 82, 87, 88, 89, 97, 98, 101, 106, 125, 160, 165, 167, 188, 208, 235, 236, 246, 247, 248, 250, 257], "talk": [6, 7, 15, 16, 19, 21, 23, 36, 44, 51, 53, 54, 55, 57, 58, 61, 62, 66, 69, 72, 73, 74, 75, 76, 78, 81, 85, 86, 87, 88, 89, 97, 98, 99, 100, 101, 106, 107, 113, 114, 120, 122, 125, 127, 132, 133, 134, 143, 147, 148, 150, 151, 152, 155, 165, 172, 195, 216, 220, 225], "tall": [194, 206, 207, 222, 224, 230], "taller": [89, 211], "tallest": [88, 134], "talon": 80, "tank": 78, "tanzania": 258, "tape": 57, "tar": 6, "target": [133, 190, 225, 230, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241], "tarokh": 82, "task": [5, 9, 10, 11, 30, 31, 33, 38, 50, 71, 75, 77, 78, 81, 86, 94, 118, 135, 138, 145, 155, 156, 164, 191, 208, 229, 241, 246, 249], "tast": 57, "taught": [40, 42, 64, 155, 186], "tax": [23, 36, 87, 88, 92, 93, 97, 98, 106, 110, 112, 116, 117], "tax_": [36, 97], "tax_c": [36, 97], "tax_cutoff_": [36, 97], "tax_cutoff_c": [36, 97], "tax_cutoff_d": [36, 97], "tax_d": [36, 97], "taxi": 5, "taylor": 76, "tb": 74, "tbxygkmyip": 72, "tca": 225, "tcp": [76, 77], "tcpclient": 77, "te": 44, "teach": [20, 52, 61, 74, 76, 78, 81, 82, 188, 245], "team": [11, 14, 15, 56, 61, 63, 65, 76, 78, 82, 165, 169], "teammat": 15, "tech": [56, 82], "technic": [2, 43, 56, 78, 82, 106, 151, 179, 180, 204], "techniqu": [82, 84, 119, 121, 123, 125, 126, 139, 157, 165, 178, 188, 189, 195, 204, 208, 220, 221, 222, 224, 232, 239, 241, 243], "technolog": 82, "technologi": [30, 31, 38, 74, 78, 79, 82, 138, 145], "teenag": 125, "teeth": 113, "teh": 34, "tell": [4, 5, 7, 10, 12, 14, 15, 16, 21, 26, 27, 32, 34, 35, 48, 51, 55, 57, 58, 60, 63, 66, 67, 69, 71, 73, 74, 75, 78, 83, 86, 87, 89, 94, 95, 103, 106, 108, 120, 122, 125, 134, 140, 141, 143, 144, 147, 153, 155, 156, 158, 168, 170, 171, 176, 177, 192, 193, 195, 196, 200, 203, 208, 213, 214, 220, 225, 244, 245, 248, 250, 251, 260, 265, 266, 267], "telltal": 220, "temp": [6, 75, 173, 204], "temp_at_t": 75, "temp_matrix": 204, "temp_max": 204, "temp_mean": 204, "temp_min": 204, "temperatur": [6, 75, 87, 88, 113, 119, 164, 204], "templat": [34, 59], "temporarili": 58, "tempt": 136, "temptat": 81, "ten": [12, 26, 35, 57, 68, 74, 95, 121, 141, 144, 187, 190, 202, 217], "tend": [9, 12, 21, 24, 28, 29, 32, 33, 35, 37, 52, 62, 66, 78, 86, 100, 109, 135, 141, 144, 147, 150, 160, 170, 171, 225, 239, 240, 245, 250], "tendenc": [75, 78, 246], "tennesse": [142, 167], "tensor": 87, "tensorflow": [82, 241], "tent": [42, 80], "term": [4, 6, 9, 12, 16, 21, 22, 32, 35, 48, 54, 59, 61, 62, 64, 65, 67, 69, 70, 75, 76, 82, 86, 100, 103, 108, 113, 114, 119, 125, 127, 134, 141, 144, 147, 149, 150, 160, 163, 170, 171, 186, 190, 199, 202, 205, 206, 207, 210, 216, 225, 230, 232, 235, 236, 239, 244, 246, 247, 250, 252], "termin": [1, 4, 14, 44, 48, 50, 51, 52, 54, 55, 57, 58, 60, 73, 75, 85, 122, 191], "terminologi": [29, 32], "terribl": [15, 78, 190], "terrif": [75, 243, 244], "territori": [21, 170, 171, 251, 252, 253, 254, 255, 256, 257, 258, 260, 265], "test": [1, 9, 13, 14, 21, 24, 29, 30, 31, 32, 33, 37, 38, 53, 57, 58, 59, 64, 67, 68, 69, 72, 81, 82, 94, 95, 101, 102, 104, 105, 109, 119, 121, 122, 124, 126, 127, 128, 129, 131, 136, 138, 140, 145, 148, 157, 168, 170, 171, 175, 176, 177, 183, 192, 193, 206, 207, 222, 224, 232, 233, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 250, 253, 254, 255, 256, 257, 258, 261], "test_fil": 61, "test_input": 121, "test_siz": [29, 32], "tex": [61, 220], "texa": 167, "text": [4, 8, 11, 14, 15, 16, 19, 20, 26, 44, 45, 48, 49, 50, 52, 53, 54, 55, 56, 61, 65, 69, 71, 80, 82, 126, 133, 137, 142, 143, 147, 148, 151, 156, 187, 189, 191, 194, 196, 198, 199, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 234, 237, 238, 240, 244, 252, 260, 261, 265, 267], "text_color": 217, "text_fil": 61, "text_label": 198, "text_perc": 198, "textbook": 42, "textfil": 14, "textprop": [214, 218], "textual": [131, 164, 254, 255], "th": 134, "thailand": 258, "than": [1, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33, 35, 36, 38, 40, 43, 44, 45, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103, 108, 109, 112, 113, 114, 115, 116, 117, 118, 120, 122, 123, 124, 128, 129, 130, 133, 134, 135, 138, 139, 140, 141, 142, 143, 144, 145, 149, 150, 151, 152, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 165, 167, 170, 171, 174, 176, 177, 178, 179, 180, 181, 186, 187, 195, 198, 201, 202, 208, 211, 217, 220, 224, 225, 230, 231, 232, 234, 237, 239, 240, 245, 246, 247, 248, 250, 254, 258, 260, 261, 265, 266], "thank": 62, "thankfulli": [6, 18, 29, 32, 52, 54, 55, 57, 74, 88, 139, 151, 154, 157, 187, 198, 225, 245], "the_list": 57, "the_name_of_your_commit": 15, "thei": [1, 3, 4, 5, 6, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 28, 29, 32, 33, 34, 35, 37, 40, 43, 44, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 64, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 92, 93, 94, 97, 98, 100, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 116, 117, 119, 122, 123, 125, 126, 128, 129, 130, 134, 135, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146, 147, 148, 150, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 169, 170, 171, 176, 177, 179, 180, 184, 186, 187, 188, 189, 190, 191, 192, 193, 194, 196, 199, 204, 205, 206, 207, 211, 212, 216, 217, 220, 225, 230, 235, 236, 237, 239, 240, 243, 244, 245, 246, 247, 248, 250, 252, 257, 258, 259, 266, 267], "them": [4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 21, 22, 23, 24, 26, 28, 29, 32, 33, 35, 36, 37, 40, 44, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 64, 66, 68, 69, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 83, 85, 86, 88, 89, 91, 94, 95, 97, 98, 103, 108, 109, 112, 113, 116, 117, 120, 123, 125, 126, 127, 128, 129, 131, 134, 137, 139, 140, 141, 142, 144, 147, 148, 149, 151, 154, 155, 156, 157, 160, 161, 162, 163, 164, 165, 167, 168, 169, 170, 171, 173, 178, 179, 180, 187, 188, 190, 191, 194, 196, 197, 198, 199, 200, 204, 205, 206, 207, 210, 211, 214, 216, 217, 222, 224, 229, 230, 232, 234, 235, 236, 237, 239, 243, 245, 246, 248, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 265, 266], "theme": [48, 49, 53, 63, 84, 131, 173, 183, 202, 210], "themselv": [10, 42, 62, 74, 75, 82, 120, 133, 148, 169, 191, 194, 198, 206, 207, 216, 221], "theorem": 82, "theoret": [82, 139], "theori": [16, 21, 29, 32, 33, 59, 61, 73, 82, 100, 120, 136, 147, 170, 171, 188, 190, 220, 243], "ther": 52, "therefor": [26, 57, 78, 121, 166, 173, 190, 213, 238], "thereof": 257, "thi": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146, 147, 148, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 184, 186, 187, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 224, 225, 226, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 243, 244, 245, 246, 247, 249, 250, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 265, 266, 267], "thin": [11, 28, 71, 73, 76, 122, 204, 217], "thing": [0, 3, 4, 6, 7, 11, 13, 14, 15, 16, 18, 21, 26, 27, 28, 29, 32, 33, 34, 40, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 100, 101, 103, 106, 109, 110, 113, 116, 117, 122, 123, 127, 131, 134, 136, 139, 143, 148, 149, 154, 155, 156, 158, 159, 161, 162, 168, 170, 171, 175, 187, 190, 191, 194, 195, 197, 199, 202, 203, 210, 213, 215, 216, 217, 224, 225, 229, 234, 235, 236, 237, 239, 243, 247, 248, 249, 252, 253], "think": [1, 4, 5, 9, 10, 12, 13, 15, 16, 21, 22, 23, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 44, 45, 52, 53, 54, 55, 56, 59, 61, 62, 63, 64, 66, 67, 69, 71, 72, 73, 74, 75, 76, 79, 80, 81, 87, 88, 89, 90, 92, 93, 94, 97, 98, 100, 101, 103, 106, 107, 108, 112, 113, 114, 116, 117, 119, 120, 122, 123, 132, 133, 135, 136, 138, 139, 140, 141, 144, 145, 146, 148, 149, 150, 152, 158, 160, 163, 165, 168, 170, 171, 174, 175, 176, 177, 183, 186, 187, 190, 194, 202, 216, 220, 225, 226, 230, 244, 245, 248, 252, 257, 258, 265], "thinner": 221, "third": [1, 7, 12, 16, 17, 21, 22, 23, 24, 28, 30, 32, 33, 35, 36, 37, 38, 61, 73, 78, 83, 86, 88, 94, 95, 100, 101, 106, 109, 110, 112, 113, 120, 137, 140, 153, 170, 171, 173, 187, 199, 217, 235, 236, 248], "third_column": 16, "thirti": 106, "this_file_is_invis": 55, "this_is_gonna_be_a_problem": 15, "those": [4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 21, 22, 23, 25, 29, 32, 33, 35, 36, 40, 43, 44, 46, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 64, 66, 67, 68, 69, 70, 71, 73, 75, 76, 78, 80, 81, 82, 83, 86, 87, 88, 89, 95, 97, 98, 99, 100, 101, 103, 106, 107, 109, 116, 117, 118, 119, 120, 121, 122, 125, 126, 127, 130, 132, 134, 137, 139, 140, 141, 142, 144, 148, 149, 151, 152, 153, 155, 156, 157, 159, 160, 161, 162, 163, 164, 165, 167, 169, 170, 171, 176, 177, 179, 180, 181, 188, 189, 190, 191, 192, 193, 194, 195, 196, 198, 199, 200, 202, 204, 210, 211, 214, 217, 218, 219, 222, 225, 229, 230, 231, 235, 236, 237, 238, 239, 241, 243, 244, 245, 246, 247, 248, 249, 250, 251, 253, 254, 256, 257, 258, 259, 260, 261, 265], "though": [1, 4, 8, 12, 14, 15, 21, 25, 29, 32, 34, 35, 45, 46, 47, 48, 49, 51, 52, 53, 54, 56, 59, 61, 64, 71, 75, 76, 79, 80, 87, 89, 90, 95, 106, 108, 131, 134, 135, 136, 139, 141, 143, 144, 147, 148, 149, 155, 156, 157, 167, 170, 171, 174, 187, 203, 204, 210, 214, 225, 244, 245, 248, 260, 265], "thought": [11, 17, 21, 25, 37, 55, 57, 59, 61, 78, 92, 93, 107, 113, 116, 117, 153, 158, 168, 225, 248, 250, 266], "thousand": [6, 68, 71, 75, 86, 95, 106, 137, 139, 142, 187, 214, 252, 254, 255], "thread": [10, 74, 76, 82], "three": [1, 3, 6, 7, 10, 12, 13, 16, 20, 21, 22, 23, 28, 30, 32, 33, 34, 35, 36, 38, 40, 50, 53, 54, 57, 59, 61, 68, 69, 75, 78, 79, 86, 87, 89, 93, 94, 95, 106, 107, 108, 114, 116, 117, 120, 121, 126, 133, 134, 135, 139, 142, 146, 149, 155, 160, 161, 162, 163, 166, 169, 170, 171, 173, 174, 187, 191, 195, 196, 198, 204, 206, 207, 208, 210, 211, 217, 219, 220, 222, 224, 225, 230, 231, 232, 233, 234, 235, 236, 237, 239, 244, 245, 251, 252, 257], "threshold": [108, 128, 129, 223, 257, 258], "through": [2, 4, 6, 8, 9, 11, 15, 19, 20, 23, 26, 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71, 73, 74, 76, 77, 87, 99, 101, 106, 113, 116, 117, 122, 127, 135, 139, 147, 148, 149, 157, 158, 160, 163, 184, 190, 217, 240, 246], "ut": 167, "utah": 167, "utc": 86, "util": [21, 48, 52, 71, 82, 109, 146, 147, 148, 156, 159, 168, 184], "utterli": 13, "uzbekistan": [221, 258], "v": [2, 6, 14, 24, 37, 42, 44, 48, 51, 52, 55, 57, 60, 84, 92, 93, 129, 131, 147, 148, 151, 164, 170, 171, 192, 193, 195, 202, 203, 211, 228, 236, 239, 260, 262], "v0_8": [63, 217, 225], "v1": 114, "v11": [23, 36, 92, 93, 97, 98, 141, 144, 160, 163, 202], "v14": 202, "v2": [71, 76, 80, 114, 216], "v5": 187, "v8_0": 217, "va": 167, "vacat": 108, "vagu": 59, "val": 76, "valid": [1, 2, 16, 29, 32, 55, 57, 63, 125, 170, 171, 225], "valu": [1, 4, 6, 7, 9, 10, 12, 16, 18, 21, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 43, 44, 55, 56, 57, 58, 59, 61, 62, 63, 64, 67, 69, 73, 74, 75, 76, 79, 82, 85, 86, 87, 88, 89, 90, 92, 93, 94, 95, 96, 97, 98, 100, 101, 103, 104, 105, 108, 110, 111, 112, 113, 114, 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170, 171, 188, 202, 203, 225, 254], "vast": [89, 246, 251], "vastli": 195, "vct": 195, "vdot": 124, "ve": [4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 20, 22, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 38, 44, 45, 48, 52, 53, 54, 55, 58, 59, 61, 62, 63, 66, 67, 68, 69, 70, 72, 74, 75, 76, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 94, 95, 101, 103, 106, 107, 108, 109, 113, 114, 116, 117, 118, 120, 123, 128, 129, 133, 134, 135, 137, 138, 139, 140, 141, 142, 143, 144, 145, 147, 148, 151, 152, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 170, 171, 172, 173, 174, 178, 182, 184, 186, 188, 189, 190, 191, 192, 193, 196, 200, 202, 203, 210, 211, 215, 216, 225, 226, 231, 234, 235, 236, 237, 238, 239, 241, 244, 245, 246, 251, 253, 254, 255, 256, 257, 259], "vect1": 88, "vect2": 88, "vector": [2, 9, 23, 29, 30, 31, 32, 33, 36, 38, 41, 51, 61, 66, 70, 74, 75, 82, 84, 86, 89, 90, 96, 97, 98, 99, 100, 101, 106, 107, 109, 110, 111, 112, 113, 115, 116, 117, 118, 119, 121, 124, 128, 129, 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"virtualenv": 84, "viru": 10, "visibl": [14, 15, 19, 23, 36, 48, 49, 50, 58, 62, 67, 97, 98, 196, 210, 211, 216, 221], "vision": [106, 120, 220], "visit": [59, 62, 74, 246], "visual": [2, 10, 15, 23, 30, 31, 36, 38, 49, 52, 61, 67, 69, 82, 85, 87, 94, 95, 113, 120, 133, 138, 139, 145, 150, 184, 186, 187, 192, 193, 194, 198, 200, 202, 205, 206, 207, 208, 210, 216, 217, 220, 225, 234, 244, 248, 251, 257, 259, 265], "vital": [164, 173, 189, 217, 220], "vm": [8, 52], "vm_size": 8, "vmax": 108, "vmin": 108, "vocabulari": 2, "voila": [76, 113, 156, 157, 266], "voil\u00e0": [89, 108, 152], "volum": [113, 190, 216], "volumetr": 113, "volunt": 78, "vote": 61, "voter": 190, "vstack": 112, "vt": [167, 179], "vulner": 81, "vx": [195, 203, 211], "vy": [195, 203, 211], "w": [61, 133, 138, 139, 140, 147, 148, 149, 152, 156, 157, 158, 159, 190, 205, 217], "wa": [3, 4, 8, 10, 11, 12, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 32, 33, 35, 36, 38, 43, 44, 48, 51, 52, 53, 55, 60, 61, 63, 64, 66, 67, 69, 72, 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Make a professional-quality plot (Level 2 - OPTIONAL)": [[223, "iii-make-a-professional-quality-plot-level-2-optional"], [224, "iii-make-a-professional-quality-plot-level-2-optional"], [225, "iii-make-a-professional-quality-plot-level-2-optional"], [226, "iii-make-a-professional-quality-plot-level-2-optional"]], "Identifying Data Problems": [[157, null]], "Identifying Data Problems in Pandas": [[157, "identifying-data-problems-in-pandas"]], "Identifying merge issues: the indicator keyword": [[170, "identifying-merge-issues-the-indicator-keyword"]], "If you really want to get into dask\u2026": [[78, "if-you-really-want-to-get-into-dask"]], "If you want to make a chart from a large dataset\u2026": [[189, "if-you-want-to-make-a-chart-from-a-large-dataset"]], "If you\u2019ve done some Python": [[42, "if-you-ve-done-some-python"]], "Image Manipulation": [[110, "image-manipulation"]], "Implicit vs explicit syntax": [[215, null]], "Import and Exporting Plaintext Tabular Data": [[149, 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"Making pretty plots in Python: customizing plots in matplotlib": [[222, null]], "Making the plot interactive through altair": [[267, "making-the-plot-interactive-through-altair"]], "Making this easier to read and more professional": [[212, "making-this-easier-to-read-and-more-professional"]], "Managing Conda Environments": [[62, null]], "Managing Python Packages": [[48, null]], "Managing Resolution": [[193, "managing-resolution"]], "Managing Settings/Preferences in VS Code": [[55, "managing-settings-preferences-in-vs-code"]], "Maternal Smoking and Birth Weight": [[33, null], [252, null]], "Math with Equal-Length Vectors": [[90, "math-with-equal-length-vectors"]], "Math with Matrices": [[108, "math-with-matrices"]], "Math with Scalars": [[90, "math-with-scalars"]], "Math, Probability, and Statistics": [[84, "math-probability-and-statistics"]], "Matrices as Images": [[110, null]], "Matrix Basics": [[113, "matrix-basics"]], "Matrix Exercises": [[114, null]], "Measuring Income Equality 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25, 35, 36, 38, 40, 57, 63, 73, 78, 81, 89, 91, 99, 100, 111, 112, 115, 116, 140, 141, 143, 149, 157, 158, 161, 163, 178, 179, 188, 206, 250], "Such": 127, "THAT": [7, 35, 77, 240], "THE": [4, 7, 14, 15, 28, 35, 70, 78, 220, 222, 240, 254, 259], "THEN": 256, "THERE": 14, "TO": [4, 15, 28, 33, 56, 152, 254, 259], "That": [1, 3, 4, 6, 9, 11, 12, 14, 15, 18, 22, 26, 32, 33, 36, 37, 47, 51, 54, 55, 56, 57, 59, 63, 65, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 88, 91, 92, 103, 109, 118, 119, 123, 124, 125, 137, 138, 143, 144, 146, 149, 150, 151, 156, 157, 159, 162, 165, 186, 193, 201, 219, 227, 231, 235, 242, 247, 250, 253, 254, 256, 260, 262, 267], "The": [0, 1, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 44, 45, 49, 54, 55, 58, 59, 60, 61, 63, 64, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 79, 80, 81, 82, 84, 89, 90, 91, 93, 96, 97, 99, 100, 102, 103, 104, 105, 108, 109, 110, 111, 113, 114, 116, 117, 118, 119, 122, 123, 125, 127, 128, 130, 131, 133, 136, 137, 140, 141, 142, 143, 145, 146, 147, 149, 151, 155, 157, 161, 162, 163, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 180, 181, 182, 183, 185, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 204, 205, 206, 207, 208, 209, 210, 211, 212, 214, 215, 216, 218, 219, 220, 221, 222, 223, 224, 226, 228, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 248, 249, 252, 255, 256, 260, 262, 263, 267, 269], "Their": 186, "Then": [4, 5, 6, 7, 9, 11, 14, 15, 17, 19, 20, 21, 22, 23, 28, 29, 32, 34, 35, 37, 38, 47, 50, 51, 52, 53, 55, 57, 62, 63, 65, 66, 69, 73, 74, 75, 76, 77, 78, 79, 89, 90, 91, 94, 95, 96, 97, 99, 100, 103, 110, 116, 118, 119, 123, 124, 128, 138, 141, 153, 155, 160, 162, 165, 172, 173, 180, 189, 193, 199, 201, 213, 221, 227, 239, 240, 245, 247], "There": [4, 5, 6, 9, 10, 12, 14, 15, 26, 28, 30, 31, 33, 35, 36, 40, 42, 48, 50, 54, 56, 58, 59, 60, 61, 63, 64, 68, 69, 71, 72, 73, 75, 76, 79, 80, 83, 86, 88, 92, 94, 95, 96, 108, 114, 121, 122, 123, 124, 125, 128, 129, 136, 137, 140, 141, 143, 146, 147, 158, 160, 161, 163, 167, 169, 171, 175, 189, 191, 192, 193, 196, 197, 198, 200, 202, 204, 206, 207, 211, 214, 218, 219, 222, 223, 224, 226, 227, 232, 234, 236, 237, 238, 240, 241, 246, 249, 252, 254, 258, 262, 263, 268], "These": [1, 4, 10, 12, 15, 23, 30, 31, 33, 36, 38, 40, 42, 50, 57, 59, 60, 61, 63, 66, 71, 77, 78, 82, 94, 95, 99, 100, 103, 120, 121, 123, 135, 136, 139, 140, 141, 143, 146, 147, 149, 151, 152, 157, 158, 160, 162, 165, 178, 179, 184, 189, 190, 194, 195, 196, 204, 205, 217, 218, 224, 227, 232, 233, 237, 238, 243, 246, 247, 252, 254, 257, 268], "To": [0, 2, 5, 6, 8, 9, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 42, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 65, 66, 67, 69, 71, 73, 76, 77, 78, 87, 88, 91, 96, 97, 99, 100, 102, 103, 109, 110, 111, 114, 115, 116, 118, 119, 122, 123, 125, 127, 129, 130, 131, 136, 138, 139, 140, 143, 144, 146, 147, 151, 153, 155, 156, 157, 158, 159, 161, 162, 165, 166, 167, 169, 170, 171, 172, 173, 174, 176, 178, 179, 180, 188, 189, 192, 196, 197, 198, 200, 202, 204, 205, 206, 207, 208, 209, 210, 211, 213, 215, 216, 217, 219, 220, 227, 228, 233, 236, 237, 238, 239, 242, 246, 247, 248, 249, 250, 251, 252, 253, 258, 262, 267, 268], "WILL": [7, 35], "WITH": 81, "Will": [76, 84, 232], "With": [9, 10, 11, 16, 22, 29, 30, 31, 32, 37, 40, 52, 54, 55, 57, 64, 70, 76, 80, 87, 89, 105, 126, 128, 135, 138, 140, 141, 142, 145, 147, 162, 165, 166, 188, 191, 211, 222, 227, 237, 238, 242, 245, 246, 253, 255, 256, 257, 259, 261, 262, 263, 267, 268], "_": [36, 143], "_1": 234, "_2": 234, "_____": [128, 232], "______": [128, 232], "_______": [128, 234], "_________": [12, 36], "__call__": 187, "__class__": 78, "__getitem__": 138, "__init__": [9, 78, 234, 237, 238, 240, 241], "__main__": 13, "__name__": [13, 63, 78], "__nonzero__": 63, "__traceback__": 76, "_build": 0, "_child0": 218, "_compile_for_arg": 76, "_engin": 142, "_ensure_numer": 160, "_event_pip": 76, "_fit_predict": 187, "_growth": [30, 31, 40, 140, 147], "_handle_writ": 78, "_i": 242, "_is_master_process": 76, "_lib": [142, 258], "_lsprof": 76, "_matrix": 39, "_mean": [22, 37, 162, 165], "_merg": [21, 65, 170, 172, 173], "_of": [22, 37, 162, 165], "_pandas_api": 74, "_psplatform": 78, "_rate": [30, 31, 40, 140, 147], "_read_to_buff": 78, "_run": 78, "_schedule_flush": 76, "_stat": 187, "_sync_cluster_info": 79, "_thread": 76, "_valu": [30, 31, 40, 140, 147], "_w_degre": [35, 163], "_wait_for_tstate_lock": 76, "_write_buff": 78, "_x": [22, 25, 37, 162, 165, 172, 173], "_y": [25, 172, 173], "a1": 136, "a52aba7de6d5": 59, "a93c": 78, "a_": [56, 126], "a_0": 125, "a_1": 125, "a_2": 125, "a_boolean_vector": 89, "a_d": 266, "a_fast_funct": 76, "a_float_vector": 89, "a_fold": 57, "a_folder_with_stuff": 56, "a_list": 39, "a_medium_funct": 76, "a_slow_funct": 76, "a_string_vector": 89, "a_toi": 126, "aa": 218, "ab": [35, 110, 163, 172, 242], "abandon": [61, 80], "abbrevi": [45, 169, 181, 182, 183], "abcdefghiklmnopqrstuwx1": 57, "abcfghloprstuw": 57, "abil": [23, 24, 34, 38, 39, 56, 58, 59, 60, 73, 77, 80, 81, 82, 90, 111, 118, 119, 135, 136, 137, 140, 147, 148, 189, 190, 193, 202, 227, 246, 263, 268], "abl": [0, 5, 7, 9, 14, 15, 16, 21, 22, 23, 26, 28, 29, 32, 33, 35, 37, 38, 44, 54, 57, 59, 60, 61, 63, 70, 71, 72, 73, 75, 77, 78, 80, 81, 83, 84, 88, 89, 101, 111, 118, 119, 121, 122, 124, 135, 136, 137, 162, 163, 164, 165, 166, 169, 172, 173, 174, 177, 178, 179, 180, 186, 191, 192, 202, 205, 208, 209, 214, 218, 219, 222, 224, 227, 232, 237, 238, 249, 250, 253, 254, 255, 256, 257, 258, 259, 260, 262, 268], "abnorm": 248, "abort": [15, 63], "about": [3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 42, 45, 46, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 81, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 100, 101, 102, 103, 109, 110, 111, 114, 115, 116, 117, 118, 119, 122, 123, 124, 125, 127, 128, 129, 130, 134, 135, 136, 138, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 156, 157, 158, 159, 160, 161, 163, 164, 167, 170, 172, 173, 174, 175, 181, 182, 186, 188, 189, 190, 191, 192, 196, 197, 201, 204, 206, 208, 212, 217, 218, 219, 224, 226, 227, 228, 231, 232, 239, 240, 242, 246, 247, 248, 249, 250, 252, 254, 258, 259, 260, 261, 267], "abov": [7, 10, 12, 15, 16, 20, 21, 22, 23, 24, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 47, 51, 59, 61, 63, 64, 71, 73, 76, 77, 78, 79, 80, 82, 83, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 100, 102, 103, 108, 109, 110, 111, 114, 115, 118, 119, 121, 122, 123, 127, 128, 130, 136, 137, 140, 141, 145, 147, 149, 151, 152, 153, 157, 159, 160, 162, 163, 165, 166, 167, 168, 169, 171, 172, 173, 174, 176, 178, 179, 188, 189, 192, 193, 196, 197, 201, 205, 206, 208, 209, 210, 211, 212, 213, 214, 218, 219, 222, 223, 227, 232, 233, 234, 236, 237, 238, 240, 241, 242, 246, 250, 252, 254, 255, 256, 258, 259, 260, 261, 262, 263, 268], "absent": 21, "absolut": [4, 7, 21, 32, 35, 47, 63, 64, 65, 66, 71, 73, 78, 81, 110, 139, 153, 163, 172, 173, 242], "absolute_percent_error": 242, "abstract": [46, 59, 75, 84, 102, 110, 115, 124, 134, 151, 227], "abstractli": 61, "absurd": 144, "abus": [64, 82], "ac": [7, 12, 22, 23, 27, 35, 36, 37, 38, 94, 95, 99, 100, 143, 146, 157, 161, 162, 163, 164, 165, 208, 209], "academ": [58, 80, 84, 253], "acceler": [84, 180], "accent": 158, "accept": [10, 21, 50, 54, 57, 76, 78, 88, 150, 158, 170, 219, 221], "access": [1, 8, 14, 24, 30, 31, 33, 39, 40, 42, 50, 53, 55, 56, 58, 60, 61, 62, 63, 69, 72, 75, 76, 77, 78, 84, 86, 88, 89, 96, 121, 124, 136, 137, 138, 140, 142, 147, 149, 150, 151, 158, 189, 191, 192, 201, 211, 218, 226, 227, 243, 251, 252, 261, 263], "accesscomput": 82, "accessor": [10, 218], "accid": 65, "accident": [63, 75, 103, 124, 160], "acclaim": 63, "accommod": [97, 151, 169, 189, 190, 196, 197, 202, 204, 205, 213, 216, 247, 257, 258], "accomplish": [21, 29, 30, 31, 32, 33, 40, 59, 61, 63, 64, 66, 70, 87, 96, 97, 101, 110, 130, 131, 140, 147, 157, 166, 167, 168, 172, 173, 174, 175, 176, 180, 192, 193, 202, 213, 218, 222, 224, 226, 234, 236, 239, 248, 251, 262, 267], "accord": [21, 172, 173, 174, 176, 197, 216], "accordingli": [116, 121, 196, 205, 213], "account": [2, 8, 15, 22, 29, 32, 33, 37, 42, 55, 56, 61, 79, 94, 95, 118, 119, 162, 165, 171, 178, 183, 198, 246, 247, 249, 251], "account_nam": 8, "accru": [23, 28, 38, 99, 100], "accumul": [12, 36, 76, 78, 143, 146], "accur": [9, 12, 21, 22, 26, 36, 37, 69, 88, 94, 95, 118, 119, 143, 146, 157, 162, 165, 184, 206, 221, 232, 233, 235], "accuraci": [6, 29, 32, 84, 118, 119, 219, 235, 237, 238, 248], "accustom": [3, 30, 40, 63, 110, 140, 147], "achiev": [17, 45, 59, 60, 61, 64, 71, 76, 80, 155, 180, 181, 182, 202, 204, 210, 218, 227, 236, 248], "acknowledg": 247, "acquir": [76, 80, 84], "across": [4, 6, 10, 11, 21, 22, 23, 25, 26, 28, 29, 30, 31, 32, 37, 38, 40, 63, 66, 72, 73, 76, 77, 78, 81, 90, 94, 95, 96, 99, 100, 109, 121, 122, 125, 126, 140, 147, 149, 150, 158, 162, 165, 166, 167, 169, 170, 172, 173, 174, 176, 178, 179, 181, 182, 184, 189, 197, 198, 199, 200, 201, 202, 206, 207, 208, 209, 210, 211, 216, 221, 222, 223, 224, 226, 234, 239, 241, 254, 259, 260], "acs_5yr_hh_incom": 264, "act": [7, 35, 80, 90, 142, 163, 164, 222], "action": [5, 18, 46, 65, 77, 84, 156, 167, 168, 180, 187, 191, 192, 193, 196, 205, 206, 216, 221, 227, 228, 239, 248, 268, 269], "action_ind": 78, "activ": [6, 11, 14, 15, 19, 20, 47, 53, 55, 57, 60, 62, 64, 73, 76, 77, 80, 84, 87, 106, 107, 124, 125, 153, 159, 215, 219, 248], "actual": [1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 16, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 44, 46, 47, 50, 51, 52, 53, 54, 55, 57, 58, 59, 61, 63, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 83, 88, 91, 94, 95, 96, 99, 100, 102, 103, 105, 109, 110, 112, 115, 116, 118, 119, 124, 126, 134, 136, 139, 140, 141, 142, 143, 144, 146, 147, 150, 151, 152, 153, 154, 157, 161, 162, 163, 164, 165, 172, 173, 178, 179, 186, 188, 189, 197, 198, 202, 210, 213, 217, 219, 227, 234, 239, 241, 242, 246, 247, 248, 250, 262, 267], "acut": 72, "ad": [8, 10, 14, 15, 17, 18, 21, 31, 33, 40, 50, 54, 55, 57, 58, 60, 66, 67, 71, 72, 75, 76, 84, 88, 94, 95, 97, 108, 110, 116, 118, 119, 124, 127, 128, 135, 136, 139, 140, 142, 149, 151, 155, 156, 167, 193, 196, 197, 198, 202, 205, 210, 213, 216, 218, 219, 250, 252, 260, 262, 269], "adapt": [30, 31, 40, 126, 140, 147, 192, 224, 226, 240], "add": [0, 2, 5, 9, 10, 12, 14, 15, 16, 18, 21, 25, 26, 28, 34, 36, 51, 54, 55, 57, 58, 59, 60, 63, 64, 65, 66, 68, 69, 70, 71, 72, 75, 76, 81, 86, 88, 90, 94, 95, 108, 110, 114, 116, 118, 119, 122, 123, 124, 125, 128, 135, 136, 143, 144, 145, 146, 149, 151, 153, 154, 156, 166, 167, 169, 170, 176, 177, 178, 179, 180, 187, 188, 189, 192, 193, 196, 197, 198, 200, 201, 202, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 217, 218, 219, 221, 222, 223, 225, 226, 227, 228, 234, 235, 236, 239, 242, 246, 247, 250, 252, 253, 260, 261, 262, 267, 268], "add_a_4": 39, "add_artist": 213, "add_ax": 202, "add_categori": [144, 159], "add_select": 189, "add_subplot": 246, "add_up_entri": 59, "addict": 81, "adding_tot": 76, "addit": [6, 9, 10, 15, 23, 38, 46, 47, 54, 57, 58, 60, 61, 63, 68, 69, 75, 76, 77, 78, 80, 81, 83, 84, 90, 99, 100, 101, 108, 116, 118, 119, 124, 125, 127, 142, 143, 144, 145, 146, 149, 151, 157, 161, 167, 169, 175, 176, 177, 189, 193, 196, 197, 202, 205, 212, 214, 216, 218, 221, 222, 227, 228, 243, 248, 249, 254], "addition": [10, 60, 130, 131, 191, 233, 259, 260], "addr": 79, "address": [4, 5, 14, 15, 21, 22, 33, 37, 42, 54, 61, 63, 75, 78, 85, 96, 101, 105, 116, 118, 119, 139, 144, 154, 159, 162, 165, 171, 172, 173, 188, 227, 228, 232, 247, 252, 258, 261], "adeli": [187, 227], "adequ": 80, "aditya": 144, "adj": [246, 247, 249, 251, 252], "adjac": [207, 212], "adjust": [25, 28, 33, 36, 51, 59, 82, 108, 112, 121, 143, 172, 173, 193, 196, 197, 198, 199, 200, 205, 206, 208, 209, 210, 213, 218, 219, 224, 225, 226, 239, 242, 254, 257, 258, 260, 261, 262, 269], "adjusted_incom": 112, "admin": [52, 79], "administr": [33, 57, 84, 144, 260], "admit": [20, 81, 145, 154, 227], "admittedli": [110, 141, 151], "admonit": 0, "adob": [4, 110], "adopt": [63, 84, 151, 201, 268], "ador": 192, "adult": [35, 157], "advanc": [2, 14, 21, 27, 44, 54, 56, 75, 76, 80, 83, 84, 124, 149, 191, 193, 230, 245, 251], "advantag": [4, 5, 11, 22, 30, 31, 33, 37, 40, 60, 63, 66, 75, 82, 124, 135, 140, 147, 157, 159, 161, 162, 165, 174, 205, 208, 209, 246], "adventur": 14, "advertis": 80, "advic": [24, 39, 80, 82, 84, 128, 138, 151, 154], "advis": [23, 38, 99, 100], "advoc": [30, 31, 40, 64, 70, 82, 140, 147, 154], "ae": 188, "aesthet": [63, 186, 192, 219, 222, 262], "af": 79, "affect": [12, 15, 29, 32, 36, 50, 66, 69, 73, 143, 146, 208, 209], "affili": [78, 82, 192], "afghanistan": [205, 255, 256, 257, 258, 260, 262, 267], "afraid": [8, 74], "africa": [30, 31, 40, 63, 135, 140, 141, 142, 147, 149, 150, 151, 158, 159, 160, 161, 199, 205, 223, 224, 249, 251, 255, 256, 257, 258, 259, 260, 261, 262, 267], "african": [37, 141, 162, 260], "after": [5, 6, 12, 14, 15, 17, 21, 23, 28, 29, 30, 31, 32, 33, 36, 37, 38, 40, 47, 53, 54, 55, 57, 59, 61, 63, 66, 70, 73, 75, 78, 79, 80, 85, 86, 90, 96, 97, 101, 103, 106, 107, 108, 110, 114, 118, 119, 122, 130, 131, 132, 133, 140, 143, 146, 147, 149, 154, 155, 158, 159, 162, 163, 164, 170, 172, 173, 180, 186, 194, 195, 208, 209, 213, 214, 221, 227, 228, 248, 250, 253, 254, 256, 268], "after_tax": 90, "after_tax_incom": 114, "afterward": 57, "ag": [1, 7, 12, 22, 29, 32, 35, 36, 37, 61, 89, 91, 97, 108, 111, 112, 114, 118, 119, 135, 136, 143, 146, 157, 162, 163, 164, 165, 189, 192, 203, 204, 222, 227, 231, 232, 241, 248, 250, 268], "again": [6, 7, 8, 10, 13, 14, 16, 17, 18, 20, 24, 29, 32, 33, 34, 35, 39, 50, 53, 55, 56, 57, 58, 59, 63, 72, 77, 78, 82, 84, 91, 96, 106, 107, 110, 121, 122, 125, 127, 128, 141, 142, 151, 155, 156, 159, 166, 171, 172, 173, 178, 179, 189, 193, 196, 210, 215, 219, 228, 239, 240, 246, 247, 249, 252, 256, 257, 258, 260], "against": [1, 7, 9, 12, 14, 16, 21, 22, 23, 28, 29, 30, 32, 33, 35, 36, 37, 38, 40, 53, 57, 62, 63, 66, 88, 159, 192, 213, 237, 238], "age_group": 178, "agenc": [5, 12, 36, 45, 80, 143, 146, 181, 182, 204], "agent": [2, 78], "agg": 78, "agg_filt": 218, "aggfunc": 174, "aggreg": [10, 16, 28, 80, 174, 180, 184], "agnost": [145, 189], "ago": [33, 58, 65, 161, 188, 252], "agre": [29, 32, 54, 55, 61, 78, 151], "agreement": 54, "agrium": 181, "ah": [208, 209, 227], "ahead": [9, 11, 12, 30, 31, 36, 40, 44, 50, 51, 52, 61, 78, 103, 110, 140, 147, 157, 168, 172, 173, 217, 236, 241, 253], "ahv": 10, "ai": [82, 135], "aic": [33, 246, 247, 249, 251, 252], "aid": [21, 150, 198], "aim": [80, 84], "air": [81, 167, 171, 189, 227], "airport": [10, 50, 51, 52], "ajani": [192, 219], "ak": [169, 181], "aka": 83, "al": [169, 181, 183, 192, 204], "alabama": [169, 172], "alaina": [174, 176, 180], "alakanuk": 181, "alameda": [16, 172, 178, 179], "alaska": [37, 162, 169], "albania": [141, 142, 205, 255, 256, 257, 258, 260, 262, 267], "alert": [63, 154, 177], "algebra": [9, 77, 84, 88, 108, 125, 246, 250], "algeria": [141, 142, 223, 258, 260], "algorithm": [29, 32, 59, 61, 66, 77, 83, 84, 120, 157, 190, 231, 232, 234, 235, 236, 237, 238, 239, 241, 242, 243, 248], "alia": [14, 88], "alias": 86, "align": [133, 137, 138, 141, 142, 167, 196, 197, 210, 213, 216, 219, 226, 239, 260], "aliv": 227, "all": [0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 75, 76, 77, 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57, 63, 73, 78, 80, 84, 108, 115, 121, 123, 127, 128, 139, 157, 166, 167, 175, 181, 182, 185, 193, 197, 218, 222, 253, 263], "appreci": [23, 38, 99, 100, 150], "approach": [9, 14, 28, 45, 54, 58, 59, 61, 80, 83, 84, 89, 101, 121, 123, 125, 130, 131, 138, 151, 167, 181, 182, 185, 186, 193, 197, 198, 199, 201, 202, 213, 223, 227, 240, 241, 259, 260, 261, 262, 263, 267, 268], "appropri": [7, 16, 20, 26, 29, 32, 35, 42, 49, 54, 71, 84, 135, 163, 164, 166, 169, 180, 181, 182, 204, 222, 259, 260, 262, 267], "approv": [78, 83], "approx": 196, "approxim": [71, 77, 84, 115, 118, 119, 127, 216, 241], "aquarium": 110, "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 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115, 139, 144, 151, 159, 246], "arbitrarili": 109, "arcgi": 64, "architectur": [66, 72, 77, 218], "archiv": 58, "arco": [8, 78], "arcos_": 8, "arcos_all_washpost_2006_2012": 78, "arctic": 253, "area": [10, 30, 31, 40, 42, 60, 77, 115, 121, 140, 147, 201, 204, 211, 212, 217, 218, 221, 222, 241, 248, 258], "aren": [1, 6, 9, 10, 12, 13, 15, 16, 18, 22, 24, 33, 34, 36, 37, 39, 42, 50, 55, 57, 59, 61, 63, 69, 70, 71, 73, 76, 77, 78, 80, 82, 83, 89, 90, 94, 95, 103, 106, 107, 116, 127, 138, 141, 142, 143, 146, 149, 150, 156, 157, 159, 160, 162, 165, 177, 189, 192, 212, 219, 224, 227, 231, 246, 247, 248, 250], "arestrong": [246, 247, 252], "arg": [57, 76], "argentina": [141, 142, 151, 205, 223, 255, 256, 257, 260], "argmin": 242, "argtyp": 76, "argu": [21, 30, 31, 40, 81, 140, 147, 172, 173, 188, 227], "arguabl": [21, 172, 173, 216], "argument": [9, 10, 16, 17, 21, 22, 33, 37, 57, 66, 76, 77, 83, 89, 96, 97, 110, 125, 126, 127, 136, 141, 145, 149, 151, 155, 158, 161, 162, 165, 172, 173, 176, 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"children": [12, 22, 29, 32, 33, 36, 37, 143, 146, 157, 161, 162, 165, 203, 204, 218, 227], "children_list": 218, "chile": 260, "china": [63, 205, 260, 262, 267], "chines": [37, 89, 162], "chip": [54, 77, 81], "choic": [9, 17, 29, 32, 50, 51, 52, 56, 63, 64, 74, 88, 127, 141, 155, 188, 190, 192, 210, 211, 212, 219, 222, 227, 238, 239, 262], "cholera": [191, 210], "choleski": 84, "cholesterol": 166, "choos": [19, 20, 29, 32, 51, 54, 59, 60, 84, 121, 191, 192, 201, 206, 208, 209, 216, 219, 222, 232, 263], "chose": [55, 89, 190], "chosen": [78, 128, 186, 189, 196, 222], "chpt": 2, "chrome": 4, "chunk": [6, 11, 64, 70, 73, 78], "chunksiz": [73, 78], "ci": [26, 189], "ci_high": [187, 189], "ci_low": [187, 189], "ciat": 57, "circl": [47, 55, 59, 102, 115, 152, 159, 205, 206, 213, 227, 239, 262, 267], "circumst": [103, 125, 132, 144, 159, 248], "cite": [23, 38, 94, 95, 99, 100, 143, 146, 162, 165], "citi": [4, 5, 6, 10, 123, 247], "citizen": [4, 61, 158, 227], "cividi": 192, "civil": [21, 172, 173], "claim": 212, "clarifi": [67, 231, 253], "clariti": [124, 125, 197, 201, 216, 219, 220, 222], "class": [1, 6, 8, 9, 12, 22, 27, 29, 32, 36, 37, 42, 50, 53, 54, 56, 57, 58, 61, 67, 69, 71, 73, 76, 77, 78, 80, 81, 83, 84, 91, 110, 115, 118, 119, 121, 129, 132, 135, 136, 141, 143, 145, 146, 149, 162, 165, 171, 187, 188, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 245, 246, 249, 251, 252, 262, 267], "class_2": [97, 264], "class_3": [135, 144, 158, 159, 161], "class_5": [], "class_estim": 238, "class_label": [236, 239], "classic": [233, 236], "classif": [29, 32, 84, 190, 234, 235, 236, 237, 238, 239, 240, 242, 243, 247], "classifi": [29, 32, 185, 222, 232, 233, 236, 240], "classroom": [42, 44, 61, 80, 89], "clean": [2, 11, 23, 28, 33, 38, 43, 44, 54, 55, 59, 61, 63, 65, 70, 74, 80, 99, 100, 134, 148, 157, 159, 161, 163, 164, 166, 167, 168, 169, 171, 184, 190, 192, 213, 219, 222, 227, 239, 260], "cleaner": 239, "cleanest": [23, 38], "cleanli": [11, 44, 70, 157, 158, 160, 214], "clear": [7, 14, 21, 22, 26, 29, 32, 33, 35, 37, 60, 61, 63, 70, 71, 72, 76, 77, 80, 83, 84, 97, 103, 111, 125, 134, 136, 138, 144, 151, 154, 157, 160, 162, 165, 169, 172, 173, 192, 194, 195, 196, 199, 201, 206, 212, 221, 227, 236, 242, 247, 256, 257, 259, 262, 267], "clearer": [61, 125, 169, 192, 206, 207, 211, 232], "clearli": [1, 59, 61, 63, 65, 83, 88, 115, 124, 127, 153, 154, 157, 167, 176, 180, 191, 199, 201, 205, 206, 207, 208, 209, 210, 211, 212, 213, 221, 222, 224, 226, 227, 239, 247, 256, 261, 262, 267, 269], "cleveland": 192, "clever": [92, 110], "cleverli": 10, "click": [0, 4, 8, 10, 11, 14, 15, 19, 20, 23, 38, 47, 50, 51, 52, 53, 54, 55, 56, 57, 60, 62, 63, 67, 71, 82, 87, 94, 95, 99, 100, 254], "clickier": 82, "client": [10, 47, 57, 78, 79, 171], "climat": [6, 89, 103, 115], "climatologi": 6, "clinic": [83, 248], "clip": 242, "clip_box": 218, "clip_on": 218, "clip_path": 218, "clock": 8, "clone": [14, 15, 82], "close": [5, 6, 8, 9, 14, 20, 21, 23, 24, 38, 39, 50, 51, 55, 63, 65, 66, 72, 77, 78, 79, 81, 84, 91, 99, 100, 102, 106, 107, 110, 122, 127, 141, 150, 158, 197, 198, 219, 224, 239, 241, 242, 246, 248, 250, 253, 259, 262, 267], "closer": [1, 77, 110, 115, 212, 239], "closest": [241, 259, 260], "cloth": 63, "cloud": [1, 10, 50, 51, 52, 54, 55, 56, 57, 58, 73, 77, 81, 83, 84, 123], "cloudcov": 123, "cloudi": 123, "club": [208, 209], "clue": [172, 173], "clump": [197, 262, 267], "clumsi": [157, 228], "clunki": 111, "cluster": [8, 11, 36, 56, 73, 77, 78, 84, 143, 157, 212, 243, 249, 264], "clutter": [63, 189, 219], "cm": [236, 239], "cmap": [110, 115, 122, 211, 239], "cmd": [47, 51, 52, 54, 56, 57], "cmder": [47, 51, 56, 57], "co": [55, 58, 63, 84, 169, 253], "co2": [183, 189, 197, 227, 253, 254, 255, 256, 257, 258, 259, 260, 262, 263, 267], "co2_al": 255, "co2_emissions_tonnes_per_person": [254, 255, 263], "co2_fil": [255, 260, 263], "co2emitt": 181, "coal": [45, 181, 182, 183], "coal_plant": 181, "coal_plants_group": 181, "coal_plants_grouped_sort": 181, "coal_stat": 181, "coauthor": 204, "coauthorship": 80, "cod": 197, "code": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 44, 45, 46, 50, 53, 54, 58, 59, 62, 63, 64, 66, 69, 70, 72, 73, 74, 75, 77, 78, 79, 81, 82, 83, 84, 85, 86, 88, 89, 94, 95, 96, 97, 99, 100, 103, 105, 106, 107, 108, 111, 112, 114, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 138, 139, 140, 142, 143, 144, 146, 147, 149, 150, 151, 153, 154, 155, 156, 157, 158, 160, 162, 163, 164, 165, 167, 169, 172, 173, 177, 180, 181, 182, 188, 189, 191, 193, 196, 197, 210, 212, 214, 215, 217, 218, 219, 220, 222, 224, 226, 227, 230, 232, 235, 236, 239, 240, 241, 246, 247, 251, 252, 253, 254, 258, 259, 260, 261, 262, 263, 268, 269], "codebook": [21, 45, 118, 119, 172, 173, 181, 182], "coef": [246, 247, 249, 251, 252], "coef_": [29, 32, 249], "coeffic": [9, 26], "coeffici": [9, 23, 28, 29, 32, 33, 38, 61, 99, 100, 194, 195, 246, 247, 248, 249, 250, 252], "coerc": [78, 97], "coercion": 97, "cog": [45, 181, 182], "coher": [71, 169, 205, 253, 261], "cohort": [35, 163, 164], "coil": 78, "coin": [55, 192], "col": [74, 202], "col1": [16, 159], "col2": 159, "col_index": 159, "colab": 54, "collabor": [14, 15, 44, 58, 84, 263], "collaps": [16, 21, 73, 78, 172, 173, 178, 179, 189], "colleagu": [13, 44, 46, 58, 130, 131, 149, 150, 174, 192, 222, 246], "collect": [10, 23, 33, 34, 38, 61, 69, 75, 76, 78, 80, 84, 85, 88, 89, 90, 91, 93, 94, 95, 99, 100, 101, 103, 108, 115, 118, 119, 121, 123, 129, 130, 131, 132, 133, 135, 136, 150, 167, 196, 198, 204, 205, 206, 216, 218, 232, 237, 238, 239, 240, 241, 249, 252, 256, 257, 259, 260, 262, 263, 267], "colleg": [7, 12, 35, 36, 63, 80, 118, 119, 143, 146, 163, 164, 247], "college_degre": [7, 35, 163, 164], "colloqui": [56, 59, 232], "colombia": 260, "colon": [4, 59, 88, 149, 152, 219, 228, 268], "color": [2, 15, 26, 55, 63, 64, 82, 89, 108, 115, 120, 149, 188, 189, 193, 196, 197, 200, 201, 205, 206, 207, 211, 212, 213, 214, 216, 218, 219, 220, 221, 223, 224, 225, 226, 227, 233, 236, 239, 241, 246, 261, 262, 267, 269], "color_list": [262, 267], "colorado": [144, 169], "colorbar": [205, 207, 211, 212, 213, 239], "colorblind": 192, "colormap": [192, 211, 239], "columbia": 169, "column": [5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 24, 25, 28, 29, 32, 33, 35, 36, 37, 39, 59, 60, 61, 71, 73, 74, 78, 82, 88, 89, 90, 108, 109, 112, 113, 114, 116, 118, 119, 121, 122, 123, 126, 133, 135, 139, 143, 144, 145, 146, 149, 151, 154, 155, 156, 159, 160, 162, 163, 164, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 189, 194, 195, 197, 198, 199, 201, 205, 206, 207, 208, 211, 213, 216, 217, 223, 227, 235, 236, 239, 241, 244, 247, 249, 250, 254, 255, 256, 257, 258, 259, 260, 262, 267], "column_a": 154, "column_count": [198, 216, 220], "com": [14, 23, 25, 35, 36, 37, 38, 44, 50, 54, 63, 65, 76, 78, 141, 142, 157, 158, 159, 161, 163, 189, 197, 227, 258], "combin": [2, 6, 7, 9, 10, 15, 16, 18, 35, 44, 61, 63, 71, 73, 82, 89, 90, 92, 96, 97, 109, 115, 134, 136, 156, 163, 164, 169, 175, 180, 184, 188, 192, 193, 212, 213, 222, 223, 246, 253, 255, 260, 269], "combined_labeler_nam": 78, "come": [4, 6, 10, 12, 14, 15, 16, 21, 23, 28, 29, 30, 31, 32, 34, 36, 38, 40, 42, 44, 47, 50, 54, 55, 56, 57, 58, 59, 61, 63, 66, 67, 68, 69, 72, 73, 74, 75, 76, 77, 78, 81, 82, 83, 87, 88, 89, 91, 94, 95, 99, 100, 101, 109, 110, 111, 112, 115, 122, 124, 128, 137, 138, 139, 140, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 157, 158, 159, 166, 168, 172, 173, 186, 188, 192, 199, 217, 219, 227, 246, 247, 249, 250, 268], "comedi": 110, "comer": 78, "comfort": [10, 21, 29, 32, 54, 55, 57, 58, 64, 83, 89, 110, 140, 147, 151, 227], "comm": [78, 79], "comma": [4, 6, 25, 55, 71, 111, 115, 116, 141, 149, 181, 182, 183, 213, 250, 258], "command": [1, 2, 3, 11, 12, 13, 14, 15, 16, 19, 20, 22, 36, 37, 44, 47, 48, 53, 54, 55, 58, 62, 63, 69, 71, 73, 75, 78, 79, 81, 82, 88, 94, 95, 99, 100, 103, 111, 118, 119, 129, 140, 143, 146, 147, 153, 154, 162, 165, 168, 177, 193, 196, 197, 198, 201, 202, 211, 212, 214, 236, 268], "command_lin": [56, 57], "commandlin": [53, 62], "commandlinetool": 47, "commandnam": 57, "commandtolaunchyoureditor": 14, "commclosederror": 78, "commensur": 80, "comment": [1, 7, 14, 21, 28, 35, 58, 59, 63, 64, 67, 71, 84, 163], "commerc": [61, 191], "commit": [0, 14, 15, 16, 21, 51, 58, 61, 67, 80, 172, 173, 178, 179], "common": [10, 13, 14, 15, 21, 23, 24, 26, 29, 32, 33, 38, 39, 45, 48, 53, 57, 59, 60, 61, 62, 67, 68, 69, 78, 80, 84, 86, 88, 89, 102, 115, 120, 121, 125, 127, 134, 136, 139, 141, 142, 149, 151, 152, 157, 158, 160, 161, 167, 168, 169, 171, 172, 173, 176, 180, 181, 182, 185, 188, 192, 193, 197, 198, 199, 201, 203, 206, 212, 215, 219, 221, 232, 237, 238, 239, 241, 246, 247, 248, 251, 252, 254, 255, 259, 269], "commonli": [4, 12, 23, 36, 38, 58, 63, 67, 76, 84, 89, 90, 99, 100, 108, 143, 146, 149, 150, 157, 167, 248, 251, 260, 268], "commun": [5, 7, 10, 12, 21, 22, 23, 25, 27, 33, 35, 36, 37, 38, 58, 61, 63, 71, 80, 82, 84, 94, 95, 99, 100, 106, 107, 118, 119, 124, 140, 143, 146, 147, 155, 157, 160, 161, 162, 163, 164, 165, 172, 173, 185, 186, 188, 192, 206, 207, 211, 213, 219, 221, 222, 227, 261, 268], "comoro": [258, 260], "compact": [86, 180, 196, 212], "compani": [5, 11, 14, 29, 32, 46, 56, 58, 61, 71, 73, 78, 79, 81, 83, 84, 97, 103, 118, 119, 192, 219, 248], "compar": [6, 12, 13, 15, 16, 20, 23, 24, 28, 33, 36, 37, 38, 39, 45, 57, 61, 63, 74, 75, 76, 77, 78, 82, 84, 85, 99, 100, 103, 108, 110, 116, 121, 125, 126, 139, 143, 146, 149, 155, 162, 171, 178, 179, 181, 182, 193, 194, 195, 196, 197, 198, 208, 209, 212, 214, 221, 223, 224, 226, 231, 232, 234, 235, 237, 238, 240, 242, 247, 248, 252, 253, 254, 259, 260, 268], "comparison": [15, 21, 29, 31, 32, 50, 61, 66, 78, 84, 90, 208, 209, 221, 222, 237, 238, 268], "compartment": 63, "compat": [4, 48, 55, 76, 116, 129, 149, 150, 154, 157, 186, 237, 238], "compel": [80, 192, 221, 222, 262, 269], "compens": [80, 82], "compet": [72, 84], "compil": [75, 76, 78, 83, 84, 124, 189, 205], "complain": 157, "complaint": [4, 5], "complement": [10, 190, 194, 195], "complet": [1, 7, 12, 14, 15, 16, 19, 20, 21, 22, 23, 24, 28, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 47, 50, 55, 61, 75, 77, 78, 80, 83, 84, 88, 94, 95, 99, 100, 106, 107, 111, 124, 125, 130, 131, 132, 143, 146, 159, 162, 163, 164, 165, 169, 172, 173, 178, 179, 183, 193, 208, 209, 213, 227, 231, 237, 238, 240, 247, 248, 255, 256, 260], "complete_the_inputs_her": 237, "complex": [26, 60, 69, 81, 83, 89, 123, 132, 134, 160, 166, 172, 173, 175, 180, 196, 213, 262, 267], "complic": [4, 55, 59, 60, 62, 63, 77, 82, 89, 96, 110, 129, 135, 142, 149, 150, 151, 152, 189, 248], "complimentari": 141, "compon": [29, 32, 51, 60, 77, 80, 81, 84, 86, 89, 122, 125, 134, 136, 175, 191, 193, 196, 198, 199, 208, 209, 210, 217, 218, 222, 230, 234, 237, 238, 253, 261, 263], "compos": [71, 89, 183, 186, 188, 196, 219, 227], "composit": [28, 65, 178, 179], "compositegenerictran": 218, "compositegenerictransform": 218, "compound": [30, 31, 40, 119, 140, 143, 146, 147, 162, 163, 164, 165], "comprehens": [33, 84, 132, 169, 252], "compress": [11, 15, 28, 57, 73, 74, 78, 149, 150], "compris": 218, "compromis": 1, "compsci": 84, "comput": [1, 2, 4, 6, 8, 11, 14, 15, 24, 29, 32, 39, 44, 48, 50, 51, 52, 54, 55, 56, 57, 59, 62, 63, 68, 69, 70, 71, 72, 74, 75, 76, 80, 83, 85, 87, 88, 89, 101, 102, 105, 108, 109, 110, 120, 121, 122, 124, 125, 126, 127, 129, 130, 131, 132, 133, 134, 135, 139, 140, 143, 144, 145, 146, 147, 149, 150, 153, 157, 160, 162, 163, 164, 165, 166, 171, 172, 173, 176, 178, 179, 192, 194, 195, 218, 219, 235, 237, 238, 239, 240, 242, 243, 249, 250, 262], "computation": [81, 135, 180], "con": [82, 149, 150], "concat": [74, 167, 223, 247, 249], "concaten": [2, 5, 28, 75, 114, 124, 131, 135, 166, 171, 175, 184, 210, 212, 225, 268], "concatent": 167, "conceiv": 84, "concept": [2, 42, 54, 69, 78, 84, 86, 96, 105, 121, 132, 133, 137, 151, 166, 180, 190, 196, 201, 210, 216, 218, 222, 232, 233, 245, 247, 248, 249, 250], "conceptu": [61, 68, 78, 80], "concern": [29, 31, 32, 80, 89, 142, 188, 219, 255, 256], "concert": 73, "concis": [76, 88, 90, 101, 105, 125], "conclud": [21, 25, 26, 132, 246], "conclus": [26, 33, 46, 58, 83, 150, 172, 173, 222, 248], "concret": [61, 109, 116, 250], "concur": 138, "concurr": 84, "cond": [246, 247, 249, 251, 252], "conda": [0, 2, 10, 11, 19, 20, 23, 38, 44, 47, 50, 55, 57, 60, 77, 78, 79, 86, 88, 99, 100], "condabin": 57, "condens": 211, "condit": [35, 59, 60, 63, 71, 76, 77, 84, 96, 97, 123, 127, 159, 163, 189, 204, 227, 237, 238, 241, 246, 247, 248, 252], "conduct": [7, 12, 21, 35, 36, 63, 84, 89, 91, 108, 118, 119, 143, 146, 157, 163, 164, 169, 170, 185], "conf_int": [187, 189], "confer": [194, 195], "confess": 19, "confid": [26, 29, 32, 63, 83, 130, 206, 249, 259, 260], "config": [14, 192, 193, 194, 196, 197, 198, 199, 200, 201, 202, 203, 205, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 225, 236, 239, 242, 246, 262], "configur": [2, 49, 55, 57, 60, 66, 82, 87, 219], "configure_legend": 267, "configure_mark": 267, "confirm": [5, 33, 55, 57, 102, 108, 160], "conflat": [22, 37, 162, 165], "conflic": 58, "conflict": [54, 55, 58, 142], "conflicted_fil": 15, "conform": 175, "confound": 61, "confus": [18, 56, 59, 63, 77, 97, 136, 141, 151, 156, 192, 215, 218, 222], "confusingli": [18, 156], "congo": [141, 142, 197, 205, 260], "congratul": [4, 12, 15, 23, 29, 32, 36, 38, 51, 61, 83, 99, 100, 118, 119, 143, 146, 237, 238, 261], "conjug": 84, "conn": 192, "connect": [6, 8, 10, 14, 27, 56, 63, 78, 79, 80, 84, 105, 154, 170, 180, 188, 196, 218, 237, 238], "connect_timeout": 79, "connecticut": 169, "connectionreseterror": 78, "connector": 79, "connot": 157, "consecut": 96, "consent": 84, "consequ": [26, 42, 64, 102, 109, 227], "conserv": 63, "consid": [15, 16, 17, 29, 32, 33, 34, 57, 59, 61, 66, 73, 75, 76, 77, 78, 80, 82, 91, 96, 97, 103, 115, 135, 155, 157, 159, 167, 168, 174, 175, 176, 177, 181, 182, 189, 192, 194, 195, 197, 202, 206, 210, 213, 214, 216, 218, 219, 222, 237, 238, 246, 247, 248, 253, 259, 260, 262], "consider": [97, 149, 191, 192, 199, 219, 236, 259, 260], "consist": [9, 21, 26, 29, 32, 55, 71, 77, 80, 91, 92, 113, 115, 125, 127, 138, 153, 154, 157, 170, 172, 173, 175, 205, 206, 208, 209, 210, 218, 235, 239], "consol": [14, 19, 20, 60, 87], "consolid": 80, "constain": 6, "constant": [9, 14, 15, 22, 28, 37, 64, 77, 159, 162, 165, 189, 197, 221, 227, 246, 247], "constantli": [63, 64, 80, 84, 123], "constitu": [136, 227, 239], "constitut": [22, 29, 32, 37, 61, 83, 143, 146, 162, 165, 177], "constrain": 71, "constraint": [76, 82, 249, 251], "construct": [22, 37, 67, 80, 96, 127, 130, 135, 136, 162, 165, 181, 182, 193, 208, 209, 218, 237, 238, 241], "constructor": [132, 136], "construt": 141, "consult": [135, 186], "consum": [29, 32, 223, 224], "consumpt": [219, 223, 224], "contact": [23, 38, 63, 94, 95, 99, 100, 143, 146, 162, 165], "contain": [3, 4, 5, 6, 10, 11, 15, 23, 25, 28, 34, 38, 45, 55, 57, 71, 89, 91, 92, 94, 95, 96, 102, 105, 108, 110, 111, 115, 118, 119, 123, 129, 135, 136, 138, 141, 144, 145, 149, 157, 158, 166, 169, 171, 175, 181, 182, 189, 192, 193, 194, 195, 196, 201, 208, 209, 211, 213, 217, 218, 219, 222, 223, 232, 249, 250, 254, 255, 257, 260, 263], "container": 84, "contamin": 191, "content": [0, 4, 5, 12, 13, 15, 20, 36, 44, 47, 50, 57, 60, 63, 71, 75, 85, 88, 94, 95, 99, 100, 105, 106, 107, 116, 118, 119, 121, 132, 135, 136, 139, 140, 141, 143, 144, 146, 147, 149, 155, 162, 163, 164, 165, 166, 168, 171, 176, 177, 184, 186, 192, 196, 197, 201, 208, 209, 213, 214, 218, 219, 222, 232, 236, 250, 253, 254, 255, 256, 259, 260, 262, 267, 268, 269], "context": [11, 23, 29, 32, 38, 61, 63, 71, 72, 83, 84, 99, 100, 172, 173, 216, 231, 232, 245, 246, 248], "contextu": [89, 242], "contin": [223, 224, 253, 254, 255, 256, 257, 259, 261, 262, 263, 267], "continent_color": 223, "continent_fil": 260, "continents_al": 258, "continents_fil": [258, 260, 263], "continu": [1, 5, 11, 14, 15, 17, 20, 29, 30, 31, 32, 33, 40, 44, 59, 60, 63, 66, 82, 84, 101, 140, 147, 157, 158, 177, 219, 231, 232], "contour": [193, 239], "contract": 248, "contrast": [4, 15, 29, 30, 31, 32, 35, 40, 50, 57, 61, 70, 78, 80, 84, 89, 91, 122, 140, 144, 145, 147, 149, 157, 227, 249, 251], "contrastresult": [249, 251], "contribut": [14, 21, 58, 64, 80, 110, 122, 172, 173, 176, 199, 246, 253, 261], "contriv": 65, "control": [4, 55, 58, 59, 60, 61, 63, 66, 72, 84, 89, 127, 133, 169, 197, 201, 213, 227, 239, 247, 248, 268], "convei": [192, 222, 224, 226], "conveni": [33, 66, 94, 95, 99, 100, 106, 107, 118, 119, 122, 124, 135, 140, 143, 146, 147, 155, 162, 163, 164, 165, 176, 201, 202, 249, 250, 268], "convent": [68, 73, 193, 201], "converg": [30, 31, 33, 40, 55, 140, 147], "convers": [31, 51, 58, 136, 210], "convert": [6, 7, 9, 11, 17, 22, 29, 32, 35, 37, 59, 63, 73, 75, 76, 88, 92, 104, 108, 109, 110, 120, 124, 144, 145, 149, 153, 155, 158, 160, 162, 163, 164, 165, 185, 199, 201, 206, 216, 220, 247, 253, 254, 255, 256, 257, 258, 259, 268], "convert_dtyp": 145, "convert_feet_and_inches_to_inch": 63, "convert_stream_closed_error": 78, "convinc": 191, "convolut": [142, 248], "cool": [10, 33, 65, 86, 90, 97, 109, 136, 157], "cooler": 192, "coolor": 222, "coop": 181, "coordin": [5, 26, 53, 54, 80, 111, 115, 122, 197, 213, 262, 267], "copi": [0, 2, 14, 16, 21, 23, 24, 35, 38, 39, 43, 50, 54, 57, 58, 59, 63, 64, 68, 69, 73, 75, 77, 86, 99, 100, 105, 110, 114, 121, 125, 127, 133, 136, 148, 151, 159, 163, 170, 180, 189, 224, 226, 256, 257, 260], "copilot": [82, 83, 135], "copy_on_writ": [21, 25, 35, 36, 37, 78, 154, 157, 159, 160, 162, 163, 167, 168, 169, 170, 171, 172, 174, 175, 176, 177, 178, 180, 187, 227, 228, 246, 247, 249, 250, 251, 252], "copyright": 110, "corbon": 254, "cord": 115, "core": [10, 11, 14, 54, 55, 63, 72, 76, 77, 78, 79, 81, 84, 121, 132, 136, 141, 142, 145, 160, 166, 175, 191, 196, 198, 201, 227, 230, 232, 243], "corequisit": 84, "corner": [29, 32, 83, 87, 111, 197, 202, 208, 242], "corr": 194, "corrcoef": [194, 195], "correct": [9, 15, 16, 21, 22, 24, 31, 34, 37, 39, 40, 54, 59, 60, 63, 83, 84, 89, 94, 95, 97, 106, 107, 123, 125, 126, 128, 140, 149, 157, 160, 162, 165, 170, 172, 173, 215, 222, 235, 237, 238, 239, 242, 254, 256, 259, 260], "correct_output": [237, 238, 240], "correction_no": 78, "correctli": [21, 47, 50, 59, 66, 82, 83, 96, 134, 170, 177, 208, 209, 219, 235, 239, 246, 247, 249, 251, 252], "correctly_signed_and_squar": 110, "correl": [15, 21, 25, 28, 29, 32, 115, 125, 172, 173, 174, 194, 195, 227, 249, 251], "correspond": [12, 36, 57, 89, 91, 105, 110, 111, 118, 119, 122, 123, 127, 128, 129, 136, 138, 143, 146, 167, 171, 178, 179, 180, 208, 210, 211, 212, 213, 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88, 94, 95, 96, 99, 100, 101, 105, 109, 111, 115, 121, 124, 127, 135, 136, 138, 141, 144, 145, 149, 150, 151, 152, 153, 156, 158, 162, 165, 168, 171, 175, 176, 188, 192, 193, 196, 197, 198, 201, 204, 205, 206, 210, 211, 213, 214, 215, 221, 227, 236, 239, 241, 242, 246, 249, 251, 253, 258, 260, 262, 267, 268, 269], "hrv": 197, "hspace": [208, 212], "hstack": 114, "hte": [262, 267], "html": [0, 20, 24, 39, 76, 78, 127, 142, 154, 159, 187, 189, 218], "http": [0, 14, 23, 25, 35, 36, 37, 38, 50, 63, 65, 76, 78, 79, 94, 95, 99, 100, 127, 140, 141, 142, 143, 146, 147, 154, 157, 158, 159, 161, 162, 163, 165, 187, 189, 197, 218, 227, 266], "huge": [10, 12, 16, 24, 36, 39, 66, 68, 72, 73, 75, 76, 77, 78, 79, 82, 90, 124, 143, 146, 148, 189, 250], "huh": [59, 65, 76, 249], "hullman": [192, 219], "human": [29, 32, 55, 57, 61, 63, 71, 72, 111, 122, 149, 150, 186, 191, 194, 195, 204, 227, 248, 253, 259, 268, 269], "hummingbird": 128, "hunch": 82, "hundr": [7, 35, 56, 58, 70, 73, 76, 77, 80, 81, 88, 141, 144, 163, 164], "hungari": 260, "hunger": 226, "hunter": 218, "hurt": [21, 24, 39, 192], "hybrid": 52, "hydro": 181, "hydro_group": 181, "hydroelectr": [181, 182, 183], "hygein": 4, "hygen": [262, 267], "hyper": 84, "hyperbol": [29, 32], "hyperparamet": 2, "hyperthread": 77, "hypothes": [33, 251], "hypothesi": [30, 31, 40, 61, 84, 140, 147], "hypothet": [7, 33, 35, 163, 164], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 59, 60, 62, 64, 65, 66, 67, 68, 69, 70, 71, 74, 75, 76, 79, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, 142, 143, 144, 146, 147, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 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154, 155, 158, 159, 161, 168, 171, 188, 189, 227, 246, 247, 248, 249, 250, 251], "iloc": [40, 133, 136, 137, 138, 139, 140, 142, 151, 153, 154, 159, 172, 181, 205, 208, 213, 225, 236, 238, 241, 244, 249], "im": [211, 212], "imag": [2, 4, 20, 77, 84, 87, 89, 108, 115, 120, 135, 150, 186, 188, 189, 192, 193, 207, 211, 214, 218, 222, 227, 232], "imageri": [86, 122, 132], "imagin": [15, 21, 30, 31, 40, 60, 61, 64, 80, 89, 114, 118, 119, 121, 135, 140, 147, 160, 166, 189, 193, 206, 207, 219, 227, 228, 231, 233, 235, 237, 238, 240, 246, 250, 258], "imbalanc": 236, "imessag": 81, "img": [110, 122, 214, 221, 222, 223, 225, 227, 262, 263], "immedi": [10, 12, 15, 19, 21, 36, 57, 59, 60, 61, 63, 78, 108, 123, 125, 143, 146, 154, 160, 170, 192, 201, 206, 227, 246, 247, 254], "immens": [194, 195, 227], "immut": [3, 34, 39], "impact": [12, 14, 15, 21, 24, 29, 32, 33, 36, 39, 68, 69, 82, 89, 102, 104, 116, 118, 119, 140, 143, 146, 147, 152, 172, 173, 191, 192, 210, 216, 241, 248, 250, 252, 253, 261], 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"incarcer": 61, "inch": [63, 82, 196, 201, 204, 208, 209, 214, 222, 224, 226, 261, 262, 267], "incid": [5, 210], "inclin": 57, "includ": [1, 2, 4, 5, 6, 7, 9, 10, 12, 16, 21, 22, 23, 26, 28, 29, 30, 32, 33, 35, 36, 37, 38, 40, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 64, 71, 73, 78, 82, 84, 87, 88, 91, 94, 95, 97, 99, 100, 103, 105, 110, 111, 115, 116, 117, 118, 119, 122, 127, 129, 132, 134, 135, 136, 137, 141, 143, 146, 149, 151, 157, 162, 165, 169, 175, 178, 179, 181, 182, 186, 187, 189, 190, 191, 192, 193, 194, 195, 196, 198, 201, 206, 213, 214, 216, 218, 219, 222, 224, 226, 227, 232, 236, 237, 238, 239, 241, 243, 245, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 259, 260, 262, 263, 268], "inclus": [28, 267], "incom": [7, 12, 22, 28, 29, 32, 35, 36, 37, 42, 63, 78, 80, 89, 91, 108, 111, 112, 114, 123, 139, 141, 143, 146, 149, 157, 160, 161, 162, 163, 164, 165, 166, 189, 205, 213, 223, 226, 227, 253, 254, 255, 258, 259, 260, 261, 262, 263, 267], "income_2017": [63, 256], "income_2018": 63, "income_2019": 63, "income_al": 256, "income_column": 112, "income_fil": [256, 260, 263], "income_level": 225, "income_per_person_gdppercapita_ppp_inflation_adjust": [254, 256, 263], "income_threshold": 63, "income_vector": [23, 38, 99, 100], "incomes_under_20_000": 114, "incompat": 154, "incomplet": [90, 168, 171, 188], "inconsist": [22, 37, 162, 165, 172, 173, 253, 254], "incorpor": [60, 180, 221, 247, 258], "incorrect": [10, 59, 60, 140, 147, 172, 173, 239, 260], "incorrectli": [11, 154], "increas": [7, 8, 10, 21, 23, 26, 35, 37, 38, 65, 75, 91, 97, 99, 100, 101, 109, 110, 114, 124, 125, 132, 143, 146, 162, 163, 172, 173, 187, 191, 193, 199, 206, 222, 224, 226, 239, 246, 247, 250, 262, 267], "increasingli": [63, 149, 150, 161, 245, 247], "incred": [80, 139, 148, 192], "incredibli": [46, 59, 60, 64, 73, 88, 91, 108, 189, 227], "increment": [109, 123, 167, 262, 267], "inctot": [7, 12, 22, 35, 36, 37, 143, 146, 157, 161, 162, 163, 164, 165], "inde": [1, 4, 15, 18, 26, 29, 32, 33, 46, 58, 59, 63, 72, 75, 76, 80, 83, 85, 89, 91, 92, 96, 102, 109, 116, 123, 139, 145, 148, 149, 150, 153, 154, 156, 157, 160, 189, 216, 217, 227, 246, 247, 248, 250], "indefinit": 21, "indent": [59, 63, 149], "independ": [14, 15, 34, 63, 77, 78, 80, 84, 176, 194, 195, 232], "index": [0, 2, 3, 10, 15, 16, 28, 30, 31, 32, 35, 36, 39, 40, 45, 77, 86, 97, 98, 103, 104, 112, 113, 122, 123, 128, 133, 140, 142, 143, 144, 147, 148, 150, 152, 153, 154, 159, 163, 167, 174, 175, 176, 178, 179, 181, 182, 187, 198, 199, 200, 201, 208, 217, 218, 223, 236, 238, 239, 254, 255, 256, 257, 258, 260, 264, 266], "index_col": 151, "indexengin": 142, "india": [205, 260], "indian": [37, 108, 118, 119, 162, 258], "indiana": 169, "indic": [2, 4, 7, 14, 16, 17, 21, 25, 26, 28, 29, 33, 35, 50, 51, 55, 60, 61, 65, 80, 89, 96, 103, 104, 105, 109, 111, 123, 128, 133, 137, 141, 152, 155, 157, 159, 160, 161, 163, 164, 167, 172, 173, 178, 179, 197, 206, 211, 212, 213, 222, 226, 227, 236, 245, 246, 248, 249, 252, 255, 268], "individu": [5, 10, 22, 23, 27, 28, 32, 33, 37, 38, 55, 57, 63, 67, 73, 77, 86, 88, 89, 90, 91, 92, 94, 95, 99, 100, 108, 118, 119, 122, 125, 136, 141, 143, 146, 150, 159, 162, 165, 175, 196, 201, 206, 212, 219, 242, 248, 259, 262, 263, 267], "indivu": 263, "indonesia": [205, 260], "induc": [64, 82, 253], "industri": [37, 80, 84, 161, 162, 171, 216, 220, 231, 253], "ineffici": [73, 80, 125, 149], "ineqpi": [23, 38, 99, 100], "inequ": [7, 22, 28, 30, 31, 35, 37, 40, 89, 140, 147, 162, 163, 164, 165], "inescap": 108, "inevit": [55, 80, 83], "inexpens": 82, "inf": 133, "infant": [14, 15, 29, 32, 33, 227, 249, 251, 252], "infer": [10, 12, 21, 36, 76, 84, 89, 143, 146, 190, 191, 243, 246, 249], "infer_str": 145, "inferior": 88, "infil": [255, 256, 257, 258, 262], "infinit": [24, 39, 77, 84, 186, 219, 222], "infinitesim": 84, "inflat": [28, 108, 112, 205, 226, 254], "influenc": [31, 33, 241], "influenti": 222, "info": [15, 76, 78, 79, 141, 145], "inform": [4, 10, 12, 15, 22, 26, 28, 29, 32, 33, 36, 37, 45, 46, 50, 51, 52, 54, 57, 60, 61, 64, 71, 74, 76, 79, 84, 86, 90, 97, 108, 109, 113, 115, 118, 119, 121, 127, 132, 135, 139, 141, 143, 144, 146, 149, 150, 162, 165, 166, 167, 169, 178, 179, 181, 182, 184, 188, 189, 192, 194, 195, 204, 206, 207, 211, 213, 224, 226, 227, 231, 239, 241, 252, 253, 254, 258, 261, 262], "infrar": 122, "infrastructur": [10, 78], "infuri": 154, "ingest": [10, 76, 134], "ingredient_nam": 78, "inher": [86, 115, 188, 206, 227], "inherit": [61, 186], "init": [14, 51, 189], "initi": [9, 11, 50, 51, 54, 58, 60, 66, 76, 78, 80, 88, 91, 125, 126, 127, 136, 137, 141, 154, 188, 219, 234, 237, 238, 239, 240, 241, 242], "initial_data": 103, "initial_node_count": 8, "injo": 172, "ink": [221, 222], "inlin": 133, "inlinebackend": [192, 193, 194, 196, 197, 198, 199, 200, 201, 202, 203, 205, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 225, 236, 239, 242, 246, 262], "inner": 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"instanti": [29, 32], "instantli": [57, 81, 191, 227, 240], "instead": [1, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 18, 21, 22, 23, 28, 30, 31, 36, 37, 38, 40, 50, 51, 52, 54, 55, 56, 57, 58, 59, 63, 66, 73, 74, 75, 76, 77, 78, 80, 84, 85, 88, 89, 96, 97, 99, 100, 101, 102, 103, 105, 108, 109, 110, 111, 112, 113, 115, 116, 120, 122, 124, 125, 135, 136, 139, 140, 141, 143, 145, 146, 147, 149, 150, 152, 153, 154, 156, 157, 158, 159, 160, 161, 162, 165, 167, 169, 170, 176, 180, 188, 189, 192, 193, 196, 197, 198, 202, 206, 208, 209, 211, 213, 214, 217, 219, 224, 226, 227, 231, 239, 240, 246, 247, 248, 250, 256, 260, 262, 263, 267, 268], "instinct": 121, "institut": [26, 80, 192, 219, 224, 227, 263], "instruct": [1, 42, 46, 47, 52, 53, 55, 57, 62, 76, 78], "instructor": [54, 58, 61, 84], "insuffici": [224, 226], "insul": 61, "insult": 56, "insur": [24, 29, 32, 39], "int": [10, 35, 59, 66, 69, 73, 75, 76, 85, 89, 97, 128, 136, 139, 140, 141, 163, 187, 239, 242, 247, 249], "int16": 66, "int32": [66, 73, 78, 136], "int64": [10, 18, 21, 35, 36, 37, 40, 66, 73, 74, 76, 78, 89, 108, 136, 137, 138, 139, 140, 141, 143, 144, 145, 151, 154, 156, 157, 160, 161, 162, 163, 170, 172, 176, 198, 236], "int8": 144, "int_vector": 97, "integ": [10, 29, 32, 59, 75, 76, 85, 88, 89, 92, 96, 97, 105, 108, 109, 123, 124, 125, 128, 130, 131, 135, 136, 137, 139, 142, 144, 154, 239, 247, 249, 250, 256, 268], "integr": [1, 15, 42, 45, 46, 51, 58, 63, 67, 76, 78, 81, 83, 84, 86, 128, 169, 181, 182, 191], "intel": [54, 77, 81, 103], "intellectu": 80, "intend": [23, 38, 80, 84, 94, 95, 99, 100, 143, 146, 162, 165, 192, 249], "intens": [81, 84, 90, 115, 122, 211, 224, 263], "intent": 56, "inter": [67, 78, 204], "interact": [4, 15, 27, 32, 33, 46, 50, 51, 52, 56, 57, 60, 63, 75, 79, 84, 88, 116, 124, 150, 151, 188, 193, 227, 252, 253], "intercept": [32, 188, 246, 247, 249, 250, 251, 252], "interchang": [56, 77], "interdisciplinari": [80, 84], "interest": [12, 23, 26, 31, 33, 34, 36, 38, 44, 55, 59, 60, 61, 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238, 240, 241, 246, 247, 248, 249, 261, 262, 267], "property_tot": [178, 179], "property_x": 172, "propon": [21, 172, 173], "proport": [16, 22, 23, 31, 35, 37, 38, 71, 77, 99, 100, 122, 136, 157, 162, 165, 178, 179, 205, 226, 250, 252], "proportion": [16, 21, 172, 173], "propos": [21, 23, 38, 63, 99, 100, 172, 173], "proposit": 227, "proprietari": [150, 218, 269], "prosector": 61, "prosecutor": 61, "prospect": [199, 204], "protect": [15, 45, 57, 66, 78, 82, 128, 181, 182], "protest": [94, 95], "protocol": [10, 79], "prototyp": 11, "proud": [9, 128], "prove": [57, 83, 84], "provid": [1, 6, 8, 9, 12, 14, 15, 16, 17, 20, 21, 29, 30, 32, 36, 40, 42, 45, 46, 50, 54, 55, 60, 61, 63, 64, 67, 70, 73, 76, 78, 79, 80, 83, 84, 88, 89, 90, 102, 103, 110, 111, 118, 119, 121, 124, 125, 127, 129, 130, 131, 136, 140, 143, 146, 147, 152, 154, 155, 157, 158, 159, 161, 170, 171, 172, 173, 176, 178, 179, 181, 182, 186, 188, 189, 190, 191, 192, 194, 195, 197, 199, 200, 202, 204, 206, 207, 208, 209, 213, 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242, 246, 262, 267], "pyramid": 192, "pyspark": 77, "pysupport": 76, "python": [1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 19, 20, 22, 24, 28, 30, 31, 33, 34, 35, 36, 37, 39, 40, 46, 47, 50, 51, 52, 53, 56, 57, 58, 59, 62, 63, 64, 67, 70, 71, 73, 74, 76, 77, 78, 79, 84, 86, 88, 89, 90, 92, 93, 94, 95, 96, 99, 100, 101, 103, 105, 106, 107, 108, 110, 118, 119, 120, 127, 130, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 153, 155, 158, 161, 162, 163, 164, 165, 169, 185, 189, 190, 191, 193, 197, 213, 216, 227, 228, 230, 234, 243, 246, 247, 248, 250, 252, 263, 269], "python3": [63, 74, 76, 78, 79, 136, 141, 142, 160, 249, 251], "python_tot": 88, "pythondebug": 57, "pythondontwritebytecod": 57, "pythoninspect": 57, "pythonpath": 57, "pytorch": [84, 243], "pyx": [76, 142, 258], "q": [4, 54, 90, 189, 208, 209], "q_99xm5c": 78, "qat": 197, "qatar": [26, 142, 197, 223, 260], "qeexgjddhx": 74, "qgi": 53, "qr": 84, "qth": 90, "quad": 108, "quadrant": 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238, 239, 250], "yourself": [81, 82, 168], "zone": 82, "zoo": [216, 220], "zoom": 189, "zsh": 50}}) \ No newline at end of file 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 4e8c8db3..50b95844 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": "63873995", + "id": "c1a19aa1", "metadata": {}, "source": [ "# Installing Geopandas" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "192bea65", + "id": "f1a7515e", "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/docs/_sources/ids720_specific/solutions.ipynb b/docs/_sources/ids720_specific/solutions.ipynb index d3d44b04..f0f670c0 100644 --- a/docs/_sources/ids720_specific/solutions.ipynb +++ b/docs/_sources/ids720_specific/solutions.ipynb @@ -14,7 +14,9 @@ "- [Numpy Vector Solutions](./exercises/Solutions_numpy_vectors.ipynb) \n", "- [Numpy View/Copy Solutions](./exercises/Solutions_numpy_viewcopies.ipynb) \n", "- [Pandas Series Solutions](./exercises/Solutions_series.ipynb)\n", - "- [Pandas DataFrames Solutions](./exercises/Solutions_dataframes.ipynb)\n" + "- [Pandas DataFrames Solutions](./exercises/Solutions_dataframes.ipynb)\n", + "- [Pandas Missing Solutions](./exercises/Solutions_missing.ipynb)\n", + "- [Pandas Cleaning Solutions](./exercises/Solutions_cleaning.ipynb)\n" ] } ], diff --git a/docs/ids720_specific/exercises/Solutions_cleaning.html b/docs/ids720_specific/exercises/Solutions_cleaning.html index c248b6a8..2049216e 100644 --- a/docs/ids720_specific/exercises/Solutions_cleaning.html +++ b/docs/ids720_specific/exercises/Solutions_cleaning.html @@ -14,45 +14,45 @@ - - - + + + - - - - + + + + - + - + - - - + + + - + - + - + @@ -70,27 +70,23 @@ - +
    + Back to top - + id="pst-primary-sidebar-checkbox"/> + - + id="pst-secondary-sidebar-checkbox"/> +
    @@ -112,8 +108,13 @@ Ctrl+K
    + +
    + +
    + -
    +
    @@ -139,6 +140,8 @@
    + +

    age into a numeric variable, we need to replace those problematic entries with values that pandas can later convert into numbers. Pick appropriate substitutions for the existing values and replace the current values.

    Hint 1: Categorical variables act like strings, so you might want to use string methods!

    Hint 2: Remember that characters like parentheses, pluses, asterisks, etc. are special in Python strings, and you have to escape them if you want them to be interpreted literally!

    -

    Hint 3: Because the US Census has been conducted regularly for hundreds of years but exactly how the census has been conducted have occasionally changed, variables are sometimes coded in a way that might be interpreted in different ways for different census years. For example, hypothetically, one might write 90 (90+ in 1980 and 1990) if the Censuses conducted in 1980 and 1990 used to top-code age at 90 (any values over 90 were just coded as 90), but more recent Censuses no longer top-coded age and recorded ages over 90 as the respondents actual age.

    +

    Hint 3: Because the US Census has been conducted regularly for hundreds of years but exactly how the census has been conducted has occasionally changed, variables are sometimes coded in a way that might be interpreted in different ways for different census years. For example, hypothetically, one might write 90 (90+ in 1980 and 1990) if the Censuses conducted in 1980 and 1990 used to top-code age at 90 (any values over 90 were just coded as 90), but more recent Censuses no longer top-coded age and recorded ages over 90 as the respondent’s actual age. We’re only working with more recent data, so anyone with a value of 90 (90+ in 1980 and 1990) can safely be assumed to be 90 years old. People with an age less than 1 year old can be treated as being of age 0.

    acs["age"] = acs["age"].str.replace("less than 1 year old", "0", regex=True)
@@ -811,6 +860,14 @@ 
    Exercise 6
+
    <>:2: SyntaxWarning: invalid escape sequence '\('
+<>:2: SyntaxWarning: invalid escape sequence '\('
+/var/folders/fs/h_8_rwsn5hvg9mhp0txgc_s9v6191b/T/ipykernel_77720/3726935054.py:2: SyntaxWarning: invalid escape sequence '\('
+  acs["age"] = acs["age"].str.replace("90 \(90\+ in 1980 and 1990\)", "90", regex=True)
+
    
+
    
+
    @@ -1089,14 +1146,14 @@

    Exercise 11results[f"ex11_share_{g}_w_degrees"] = tab.loc[True, g] print( f"The share of {g}s with college degrees is " - f"{results[f'ex11_share_{g}_w_degrees']:.3f}" + f"{results[f'ex11_share_{g}_w_degrees']:.4f}" )

    -
    The share of males with college degrees is 0.297
-The share of females with college degrees is 0.312
+
    The share of males with college degrees is 0.2966
+The share of females with college degrees is 0.3116
    @@ -1288,8 +1345,12 @@

    Exercise 13 -
    Among older Americans, 31.8% of men have college degrees; among younger men, this number has fallen to 25.7%.
-By contrast, among older Americans 30.1% of women have college degrees; among younger women, this number has risen to 33.4%.
+
    Among older Americans, 31.8% of 
+men have college degrees; among younger men, this number has 
+fallen to 25.7%.
+By contrast, among older Americans 30.1% of 
+women have college degrees; among younger women, this number has 
+risen to 33.4%.
    @@ -1322,6 +1383,18 @@

    Want More Practice? +
    ['ex11_share_female_w_degrees',
+ 'ex11_share_male_w_degrees',
+ 'ex12_comparing',
+ 'ex5_age_old',
+ 'ex5_age_young',
+ 'ex7_avg_age',
+ 'ex8_avg_age',
+ 'ex9_num_college']
+
    +
    +

    @@ -1391,7 +1464,7 @@

    Want More Practice? +

    + +
    + +
    + -
    +
    @@ -141,6 +142,8 @@
    + +

    Exercise 9#

    -

    As noted above, these estimates are not actually quite correct because we aren’t using survey weights. To calculate a weighted average that takes into account survey weights, you need to use the following formula:

    +

    As noted above, these estimates are not actually quite correct because we aren’t taking into account the fact that when the US Census decided who to survey, not all Americans had the same likelihood of being asked. The US American Community Survey is an example of a weighted survey (essentially, people from smaller subpopulations have a higher likelihood of being included to ensure enough individuals in the final survey to constitute a representative sample that can be used statistically).

    +

    To calculate a weighted average that takes into account these survey weights (in other words, a more accurate estimate of US incomes), you need to use the following formula:

    \[weighted\_mean\_of\_x = \frac{\sum_i x_i * weight_i}{\sum_i weight_i}\]

    (As you can see, when \(weight_i\) is constant for all observations, this just simplifies to our normal formula for mean values. It is only when weights vary across individuals that weights must be explicitly addressed).

    -

    In this data, weights are stored in the variable perwt, which is the number of people for which each observation is a stand-in (the inverse of that observations sampling probability).

    +

    In this data, weights are stored in the variable perwt, which is the number of people for which each observation is a stand-in (the inverse of that observation’s sampling probability).

    Using the formula, re-calculate the weighted average income for both populations and store them as ex9_avg_income_white and ex9_avg_income_black.

    @@ -1021,7 +1071,7 @@

    Exercise 11 +