02_Data_Analysis_with_Pandas.ipynb

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        "<a href=\"https://colab.research.google.com/github/JoseEliasDuranRoa/Bio-bio-Coal-mining-municipality-MAP/blob/main/02_Data_Analysis_with_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\n",
        "# 1 Introduction\n",
        "\n",
        "----\n",
        "- **Pandas** is a high performance package to work with table-type data structures (like in a spreadsheet).\n",
        "\n",
        "    + It is used for data analysis and visualization.\n",
        "\n",
        "- It is built on top of `NumPy` and `MatPlotLib`.\n",
        "- Documentation <https://pandas.pydata.org/pandas-docs/stable/>\n",
        "- Quick Start Tutorial <https://pandas.pydata.org/pandas-docs/stable/10min.html>\n",
        "- Summary of the main functionalities <https://towardsdatascience.com/a-complete-pandas-glossary-for-data-science-a3bdd37febeb>   \n",
        "\n",
        "<!--\n",
        "-  (https://dataprep.ai/ collect, EDA, Clean)\n",
        "-->\n",
        "\n",
        "---"
      ]
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    {
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      "source": [
        "# Uncomment to update Pandas\n",
        "# !pip install pandas -U -q\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "pd.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "'2.1.4'"
            ],
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    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "# 2 Data frames\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgteOiNyzbxG"
      },
      "source": [
        "- Data frames are rectangular data structures where\n",
        "    + each column is a variable\n",
        "    + each row is a measument or observation\n",
        "    \n",
        "- \"Similar\" to a 2D Numpy array with names in the rows and columns,\n",
        "- BUT it can contain *different types of data* with each column\n",
        "\n",
        "- They can be build manually build it manually from a dictionary or an array, but it will be more often imported from files (text, spreadsheets, etc.)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## DataFrame from a dictionary.\n",
        "\n"
      ]
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      "source": [
        "Name    = np.array(['Juan', 'Pedro', 'Ana', 'Isabel'])\n",
        "Morning = [True, False, False, True]\n",
        "Grade   = [8, 5, 9, 5.5]\n",
        "\n",
        "print(type(Morning))\n",
        "\n",
        "course_dict = {'Name':Name, 'Grade':Grade, 'Group':Morning}\n",
        "\n",
        "#pd.DataFrame combines different types of Python objects\n",
        "course = pd.DataFrame(course_dict, columns=['Name', 'Grade', 'Group'])\n",
        "print(course)\n",
        "print(type(course))"
      ],
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      "outputs": [
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          "text": [
            "<class 'list'>\n",
            "     Name  Grade  Group\n",
            "0    Juan    8.0   True\n",
            "1   Pedro    5.0  False\n",
            "2     Ana    9.0  False\n",
            "3  Isabel    5.5   True\n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "- Here it was unnecesary to use the explicit `columns` option since they were taken from the dictionary\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "\n",
        "- But we may want to give name to the rows"
      ]
    },
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      "source": [
        "# Definition of row_labels\n",
        "row_labels = ['01', '02', '03', '04']\n",
        "\n",
        "# Specify row labels of course\n",
        "course.index = row_labels\n",
        "print(course)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Name  Grade  Group\n",
            "01    Juan    8.0   True\n",
            "02   Pedro    5.0  False\n",
            "03     Ana    9.0  False\n",
            "04  Isabel    5.5   True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Importing data"
      ]
    },
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      "source": [
        "* Data can be imported with functions `read_csv()`  `read_table()`,`read_excel()`, `read_stata()`, etc.\n",
        "\n",
        "* Further information on all the functions available and its arguments: https://pandas.pydata.org/pandas-docs/stable/reference/io.html"
      ]
    },
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      "source": [
        "unemployment = pd.read_csv('https://www.dropbox.com/s/r0owh4kjx6zg8f9/country_total.csv?dl=1')\n",
        "unemployment"
      ],
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              "      country seasonality    month  unemployment  unemployment_rate\n",
              "0          at         nsa  1993.01        171000                4.5\n",
              "1          at         nsa  1993.02        175000                4.6\n",
              "2          at         nsa  1993.03        166000                4.4\n",
              "3          at         nsa  1993.04        157000                4.1\n",
              "4          at         nsa  1993.05        147000                3.9\n",
              "...       ...         ...      ...           ...                ...\n",
              "20791      uk       trend  2010.06       2429000                7.7\n",
              "20792      uk       trend  2010.07       2422000                7.7\n",
              "20793      uk       trend  2010.08       2429000                7.7\n",
              "20794      uk       trend  2010.09       2447000                7.8\n",
              "20795      uk       trend  2010.10       2455000                7.8\n",
              "\n",
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              "  </script>\n",
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              "\n",
              "  <div id=\"id_aca84944-45bc-402e-870a-16dab427dec5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('unemployment')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('unemployment');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "unemployment",
              "summary": "{\n  \"name\": \"unemployment\",\n  \"rows\": 20796,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"sk\",\n          \"it\",\n          \"pt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seasonality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"nsa\",\n          \"sa\",\n          \"trend\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.483750816970295,\n        \"min\": 1983.01,\n        \"max\": 2010.12,\n        \"num_unique_values\": 336,\n        \"samples\": [\n          1999.01,\n          2002.03,\n          1989.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1015280,\n        \"min\": 2000,\n        \"max\": 4773000,\n        \"num_unique_values\": 2632,\n        \"samples\": [\n          340000,\n          649000,\n          2524000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.922533374669806,\n        \"min\": 1.1,\n        \"max\": 20.9,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          15.8,\n          13.8,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bF_DFgo3xnTe"
      },
      "source": [
        "# 3 Using data files in Colab\n",
        "\n",
        "\n",
        "* Your cloud storage for Google Colab can be accessed using a folder icon on the left vertical bar\n",
        "\n",
        "\n",
        "* NOTE: Google Colab storage is lost when your virtual machine is closed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krgR1hoqQ4d6"
      },
      "source": [
        "## 3.1 Data directly uploaded from your local hard drive\n",
        "\n",
        "* Example:\n",
        "\n",
        "  + Download [this file](https://www.dropbox.com/scl/fi/09vg6szyjxzkj89oz7n2u/brics.csv?rlkey=v9nlhvvnxht7m4wkzmm20ue7v&st=o9duhoxm&dl=0) as a .csv to your hard drive\n",
        "  \n",
        "  + Use the upload icon to have in your Colab folder\n",
        "\n",
        "  + You can now read it\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wZNnIr2MSZg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f96f4201-f2ab-4764-a4b9-26f39e0d3a74"
      },
      "source": [
        "# Argumente index_col = 0: use first column as row names, not a column variable\n",
        "brics = pd.read_csv('/content/brics.csv', index_col = 0)\n",
        "brics"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         country    capital    area  population\n",
              "BR        Brazil   Brasilia   8.516      200.40\n",
              "RU        Russia     Moscow  17.100      143.50\n",
              "IN         India  New Delhi   3.286     1252.00\n",
              "CH         China    Beijing   9.597     1357.00\n",
              "SA  South Africa   Pretoria   1.221       52.98"
            ],
            "text/html": [
              "\n",
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              "      <th>BR</th>\n",
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              "      <td>8.516</td>\n",
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              "      <th>IN</th>\n",
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              "      <td>1357.00</td>\n",
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              "      <th>SA</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>Pretoria</td>\n",
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              "      <td>52.98</td>\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
              "  <div id=\"id_23d9ad25-f440-4616-ba87-2da2878bb2e5\">\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('brics')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('brics');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "brics",
              "summary": "{\n  \"name\": \"brics\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Russia\",\n          \"South Africa\",\n          \"India\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Moscow\",\n          \"Pretoria\",\n          \"New Delhi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.200556870152875,\n        \"min\": 1.221,\n        \"max\": 17.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          17.1,\n          1.221,\n          3.286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 645.2614538929162,\n        \"min\": 52.98,\n        \"max\": 1357.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          143.5,\n          52.98,\n          1252.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPCEBecqMxEi"
      },
      "source": [
        "## 3.2 Data downloaded from internet to Colab\n",
        "\n",
        "* We need to use terminal commands"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bh3nxwFgxu9Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89a14eca-260f-4bf2-c6b9-482df0c83b60"
      },
      "source": [
        "# Zipped data in a shared Drobpox folder (or any other internet link)\n",
        "!wget https://www.dropbox.com/s/g39gepnkfcsdos1/basic_data.zip?dl=1 -O basic_data.zip\n",
        "!unzip -o basic_data.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-09-16 13:11:48--  https://www.dropbox.com/s/g39gepnkfcsdos1/basic_data.zip?dl=1\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6016:18::a27d:112\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://www.dropbox.com/scl/fi/xwridjq0hfi72rjniezgj/basic_data.zip?rlkey=f1oo2u2klroh1skm5hvyk5pha&dl=1 [following]\n",
            "--2024-09-16 13:11:48--  https://www.dropbox.com/scl/fi/xwridjq0hfi72rjniezgj/basic_data.zip?rlkey=f1oo2u2klroh1skm5hvyk5pha&dl=1\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc7d7f159a36fc9e5107f29ff465.dl.dropboxusercontent.com/cd/0/inline/Car0LltJNBWqpXJMcDR8a7ynAdfG4wbF8KdVSOnTmSUjVQt9WdT1Szf7GSCoxWUF7kBfjt3HpCa9IbeWcMXYKzoUoxadfI6WeugALitA8Y4EAIKmBZ7OPSVSWY2YES6GQ_A/file?dl=1# [following]\n",
            "--2024-09-16 13:11:48--  https://uc7d7f159a36fc9e5107f29ff465.dl.dropboxusercontent.com/cd/0/inline/Car0LltJNBWqpXJMcDR8a7ynAdfG4wbF8KdVSOnTmSUjVQt9WdT1Szf7GSCoxWUF7kBfjt3HpCa9IbeWcMXYKzoUoxadfI6WeugALitA8Y4EAIKmBZ7OPSVSWY2YES6GQ_A/file?dl=1\n",
            "Resolving uc7d7f159a36fc9e5107f29ff465.dl.dropboxusercontent.com (uc7d7f159a36fc9e5107f29ff465.dl.dropboxusercontent.com)... 162.125.1.15, 2620:100:6016:15::a27d:10f\n",
            "Connecting to uc7d7f159a36fc9e5107f29ff465.dl.dropboxusercontent.com (uc7d7f159a36fc9e5107f29ff465.dl.dropboxusercontent.com)|162.125.1.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/CaoVhGWG52X5hjPYm9qbusMKqm0EF1jY_G-gmgQvo4RDaFKaneGlk-EuiB_X7GmYS2ienKCpFFhe_uCJGMnAmIsla_uXRKNdKdwnZtbOKvkl0iIyBykUvAyc-rXiCE_d5PjZZ2p8KvLyrMix8FNhaa5rIEXlBecqoV55w6qvMbvDgTcJYCxm4ReFd0OgCYeGacZtzrTFxnhgn_-ExnorFsApN88g7DVjcfP5gqjsW37IKaeB2dpYZlf00h5yh1UOC3CY56BfKbqNDsyHFUSr5RRCfl3gwHjv1FWaRQfpVp-ugUJZ1k9qI_TknBqzHxd4QRsg7YrqSUi6evM8QCZUx3XpRz3jVo7BajvadlUXX5zbaw/file?dl=1 [following]\n",
            "--2024-09-16 13:11:49--  https://uc7d7f159a36fc9e5107f29ff465.dl.dropboxusercontent.com/cd/0/inline2/CaoVhGWG52X5hjPYm9qbusMKqm0EF1jY_G-gmgQvo4RDaFKaneGlk-EuiB_X7GmYS2ienKCpFFhe_uCJGMnAmIsla_uXRKNdKdwnZtbOKvkl0iIyBykUvAyc-rXiCE_d5PjZZ2p8KvLyrMix8FNhaa5rIEXlBecqoV55w6qvMbvDgTcJYCxm4ReFd0OgCYeGacZtzrTFxnhgn_-ExnorFsApN88g7DVjcfP5gqjsW37IKaeB2dpYZlf00h5yh1UOC3CY56BfKbqNDsyHFUSr5RRCfl3gwHjv1FWaRQfpVp-ugUJZ1k9qI_TknBqzHxd4QRsg7YrqSUi6evM8QCZUx3XpRz3jVo7BajvadlUXX5zbaw/file?dl=1\n",
            "Reusing existing connection to uc7d7f159a36fc9e5107f29ff465.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 261702 (256K) [application/binary]\n",
            "Saving to: ‘basic_data.zip’\n",
            "\n",
            "basic_data.zip      100%[===================>] 255.57K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2024-09-16 13:11:49 (10.5 MB/s) - ‘basic_data.zip’ saved [261702/261702]\n",
            "\n",
            "Archive:  basic_data.zip\n",
            "  inflating: basic_data/.DS_Store    \n",
            "  inflating: basic_data/airline.csv  \n",
            "  inflating: basic_data/breast-cancer.csv  \n",
            "  inflating: basic_data/cpu.csv      \n",
            "  inflating: basic_data/credit-g.csv  \n",
            "  inflating: basic_data/diabetes.csv  \n",
            "  inflating: basic_data/diabetes_01.csv  \n",
            "  inflating: basic_data/glass.csv    \n",
            "  inflating: basic_data/hypothyroid.csv  \n",
            "  inflating: basic_data/ionosphere.csv  \n",
            "  inflating: basic_data/iris.csv     \n",
            "  inflating: basic_data/labor.csv    \n",
            "  inflating: basic_data/soybean.csv  \n",
            "  inflating: basic_data/supermarket.csv  \n",
            "  inflating: basic_data/unbalanced.csv  \n",
            "  inflating: basic_data/vote.csv     \n",
            "  inflating: basic_data/weather.nominal.csv  \n",
            "  inflating: basic_data/weather.numeric.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-5AZQJpTzmG"
      },
      "source": [
        "## 3.3 Mounting Google Drive\n",
        "\n",
        "* If you click on the \"Mount Drive\" icon, a code cell with the following code appears\n",
        "\n",
        "```\n",
        "   from google.colab import drive\n",
        "   drive.mount('/content/drive')\n",
        "```\n",
        "\n",
        "\n",
        "* Run that cell; you will be asked for an authorization code\n",
        "\n",
        "* You can now access to your Google Drive files and folders"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tO16RwbjUO-I"
      },
      "source": [
        "* Note: if you are familiar on shell commands, you can use the typical commands to list files/subfolders in a folder or to see the content of a text file"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "   from google.colab import drive\n"
      ],
      "metadata": {
        "id": "9oVi-eF5Z4Wy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "   drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRQf5LraZ4PB",
        "outputId": "d140a1aa-a5bf-484f-d231-ea68ab3ad35f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlkxGFt2wYES",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a80a6784-3739-4122-dd2a-753b70e6ff73"
      },
      "source": [
        "!ls '/content/drive'\n",
        "!cat '/content/brics.csv'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MyDrive  Shareddrives\n",
            ",country,capital,area,population\n",
            "BR,Brazil,Brasilia,8.516,200.4\n",
            "RU,Russia,Moscow,17.1,143.5\n",
            "IN,India,New Delhi,3.286,1252\n",
            "CH,China,Beijing,9.597,1357\n",
            "SA,South Africa,Pretoria,1.221,52.98"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rTJmW29un2My"
      },
      "source": [
        "# 4 Some data frames methods\n",
        "\n",
        "## head()\n",
        "\n",
        "*  This shows the header (column names) and the first *five* rows\n",
        "\n",
        "  + you can provide an integer, $n$, to .head() to return that number of rows.\n",
        "\n",
        "* To see the last $n$ rows, use **.tail()**.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvnYom5sZ4lN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "b81d6dae-8fd5-4b7e-aab2-90ca4c329798"
      },
      "source": [
        "print(type(brics))\n",
        "\n",
        "brics.head(n=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   country   capital    area  population\n",
              "BR  Brazil  Brasilia   8.516       200.4\n",
              "RU  Russia    Moscow  17.100       143.5"
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <td>Russia</td>\n",
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              "      <td>17.100</td>\n",
              "      <td>143.5</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8df94fa8-91a4-4b77-985e-3230bed824bb')\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-8df94fa8-91a4-4b77-985e-3230bed824bb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8df94fa8-91a4-4b77-985e-3230bed824bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
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              "\n",
              "\n",
              "<div id=\"df-c5fd291f-ecb4-47a3-bbd1-3f733248b61b\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "</div>\n",
              "\n",
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            ],
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              "type": "dataframe",
              "variable_name": "brics",
              "summary": "{\n  \"name\": \"brics\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Russia\",\n          \"South Africa\",\n          \"India\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Moscow\",\n          \"Pretoria\",\n          \"New Delhi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.200556870152875,\n        \"min\": 1.221,\n        \"max\": 17.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          17.1,\n          1.221,\n          3.286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 645.2614538929162,\n        \"min\": 52.98,\n        \"max\": 1357.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          143.5,\n          52.98,\n          1252.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* In the left vertical bar, you have also a Variable inspector `{x}` to get visual information about your data."
      ],
      "metadata": {
        "id": "EZO12EMq2t4-"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6s57JMJTaKcs"
      },
      "source": [
        "## shape\n",
        "\n",
        "* The shape attribute returns the number of rows and colums\n",
        "\n",
        "* The function `len()` gives only the number of rows.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ab378dXuaoHa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50a6585c-afce-47be-b48f-c14a8067fae4"
      },
      "source": [
        "brics.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1f-vE_78a5iM"
      },
      "source": [
        "## describe\n",
        "\n",
        "* It is a useful method to generate various summary statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZLbtJg6bJPB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "b004031a-735c-423e-997f-017b2e6abcf5"
      },
      "source": [
        "brics.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            area   population\n",
              "count   5.000000     5.000000\n",
              "mean    7.944000   601.176000\n",
              "std     6.200557   645.261454\n",
              "min     1.221000    52.980000\n",
              "25%     3.286000   143.500000\n",
              "50%     8.516000   200.400000\n",
              "75%     9.597000  1252.000000\n",
              "max    17.100000  1357.000000"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"brics\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.821793332065619,\n        \"min\": 1.221,\n        \"max\": 17.1,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.944,\n          8.516,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 532.4764554624984,\n        \"min\": 5.0,\n        \"max\": 1357.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          601.176,\n          200.4,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wCWeCjsbZbS"
      },
      "source": [
        "* Summary statistics are based on non-missing values: note the `count` in the `unemployment` data frame\n",
        "\n",
        "* We will see that summary statistics depend on the type of data, that is, quantatitive information (numerical variables) or qualitative information (categorical variables)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VVFT6XsybTqU",
        "outputId": "95f34617-6739-4b4d-a2ab-f36828757726"
      },
      "source": [
        "unemployment.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              month  unemployment  unemployment_rate\n",
              "count  20796.000000  2.079600e+04       19851.000000\n",
              "mean    1999.401290  7.900818e+05           8.179764\n",
              "std        7.483751  1.015280e+06           3.922533\n",
              "min     1983.010000  2.000000e+03           1.100000\n",
              "25%     1994.090000  1.400000e+05           5.200000\n",
              "50%     2001.010000  3.100000e+05           7.600000\n",
              "75%     2006.010000  1.262250e+06          10.000000\n",
              "max     2010.120000  4.773000e+06          20.900000"
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>20796.000000</td>\n",
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              "      <td>19851.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1999.401290</td>\n",
              "      <td>7.900818e+05</td>\n",
              "      <td>8.179764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2001.010000</td>\n",
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              "      <td>7.600000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>4.773000e+06</td>\n",
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              "        document.querySelector('#df-be287443-57b7-4bb4-bafb-b1f81c014e94 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-be287443-57b7-4bb4-bafb-b1f81c014e94');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  .colab-df-spinner {\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"unemployment\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6782.248618913606,\n        \"min\": 7.483750816970295,\n        \"max\": 20796.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1999.4012896710906,\n          2001.01,\n          20796.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1581449.1369318203,\n        \"min\": 2000.0,\n        \"max\": 4773000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          790081.7945758799,\n          310000.0,\n          20796.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7015.516826276408,\n        \"min\": 1.1,\n        \"max\": 19851.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.179764243614931,\n          7.6,\n          19851.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBXRoIMIc7YZ"
      },
      "source": [
        "#5 Index and select data\n",
        "\n",
        "## Column access with square brackets\n",
        "\n",
        "* You can select a column from a dataframe using the **column label(s)** inside square brackets\n",
        "\n",
        "* **Single** square brackets returns an type of object named Pandas Series: kind of a 1-dimensional array with a label\n",
        "\n",
        "* **Double** square brackets creates a DataFrame.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vc2FlL_YeVdQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5712bde-2156-4cbe-a204-74308152e250"
      },
      "source": [
        "# Print out column as Pandas Series\n",
        "print(unemployment['unemployment'], type(unemployment['unemployment']))\n",
        "unemp1 = unemployment['unemployment']\n",
        "\n",
        "\n",
        "# Print out column as Pandas DataFrame\n",
        "print(unemployment[['unemployment']], type(unemployment[['unemployment']]))\n",
        "unemp2 = unemployment[['unemployment']]\n",
        "\n",
        "# Print out DataFrame several columns\n",
        "print(unemployment[['month','unemployment']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0         171000\n",
            "1         175000\n",
            "2         166000\n",
            "3         157000\n",
            "4         147000\n",
            "          ...   \n",
            "20791    2429000\n",
            "20792    2422000\n",
            "20793    2429000\n",
            "20794    2447000\n",
            "20795    2455000\n",
            "Name: unemployment, Length: 20796, dtype: int64 <class 'pandas.core.series.Series'>\n",
            "       unemployment\n",
            "0            171000\n",
            "1            175000\n",
            "2            166000\n",
            "3            157000\n",
            "4            147000\n",
            "...             ...\n",
            "20791       2429000\n",
            "20792       2422000\n",
            "20793       2429000\n",
            "20794       2447000\n",
            "20795       2455000\n",
            "\n",
            "[20796 rows x 1 columns] <class 'pandas.core.frame.DataFrame'>\n",
            "         month  unemployment\n",
            "0      1993.01        171000\n",
            "1      1993.02        175000\n",
            "2      1993.03        166000\n",
            "3      1993.04        157000\n",
            "4      1993.05        147000\n",
            "...        ...           ...\n",
            "20791  2010.06       2429000\n",
            "20792  2010.07       2422000\n",
            "20793  2010.08       2429000\n",
            "20794  2010.09       2447000\n",
            "20795  2010.10       2455000\n",
            "\n",
            "[20796 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unem1 = unemployment['unemployment']\n",
        "unem2 = unemployment[['unemployment']]\n",
        "\n",
        "print(type(unem1))\n",
        "print(type(unem2))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSWCKg5Le1Pn",
        "outputId": "078c14de-7744-44f7-ffda-113f850a843a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lXJSVmDfbWF"
      },
      "source": [
        "## Row access with square brackets\n",
        "\n",
        "* You can  get rows, or observations, from a DataFrame by using **integer index(es)** and/or **slicing** inside square brackets\n",
        "\n",
        "* The result is another DataFrame containing only the rows you specified.\n",
        "\n",
        "* Note: you must use integers, *not* row labels\n",
        "\n",
        "* Remember: the index starts at zero and the end of the slice is exclusive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PKRcW8LiA2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fed8f54-3da7-490a-a1ad-d586d9e24e4a"
      },
      "source": [
        "# Print out first 3 observations\n",
        "print(unemployment[0:3])\n",
        "\n",
        "# Print out fourth, fifth and sixth observation\n",
        "print(unemployment[3:6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  country seasonality    month  unemployment  unemployment_rate\n",
            "0      at         nsa  1993.01        171000                4.5\n",
            "1      at         nsa  1993.02        175000                4.6\n",
            "2      at         nsa  1993.03        166000                4.4\n",
            "  country seasonality    month  unemployment  unemployment_rate\n",
            "3      at         nsa  1993.04        157000                4.1\n",
            "4      at         nsa  1993.05        147000                3.9\n",
            "5      at         nsa  1993.06        134000                3.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* You can slice both *contiguous* rows (by index number) and columns (by column name)"
      ],
      "metadata": {
        "id": "yMgzZztU6y9y"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGGsJ24wWJBS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "43afdcaf-7fc0-4bbd-f47f-d4f1a055b0a8"
      },
      "source": [
        "unemployment[0:1][['month','unemployment']]\n",
        "\n",
        "# print(type(unemployment[0:1][['unemployment']]))\n",
        "# print(type(unemployment['unemployment'][0:1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     month  unemployment\n",
              "0  1993.01        171000"
            ],
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"# print(type(unemployment['unemployment'][0:1]))\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1993.01,\n        \"max\": 1993.01,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1993.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 171000,\n        \"max\": 171000,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          171000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0NSJGuliZh7"
      },
      "source": [
        "## `loc()` and `iloc()`\n",
        "\n",
        "* Any data selection operation on DataFrames can be done with `loc` and `iloc`\n",
        "\n",
        "* `loc` is label-based, for both rows and columns based on their row and column labels\n",
        "\n",
        "* `iloc` is based on integer index"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcMAqls-jm5_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62c7618d-65bc-4211-e7b5-406215e7a20d"
      },
      "source": [
        "print(brics)\n",
        "\n",
        "# Print out observation for China\n",
        "print(brics.loc['CH'])   # label based\n",
        "print(brics.iloc[3])\n",
        "\n",
        "# Print out observations for Brasil and South Africa\n",
        "print(brics.iloc[[1,-1]]) # integer index based"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         country    capital    area  population\n",
            "BR        Brazil   Brasilia   8.516      200.40\n",
            "RU        Russia     Moscow  17.100      143.50\n",
            "IN         India  New Delhi   3.286     1252.00\n",
            "CH         China    Beijing   9.597     1357.00\n",
            "SA  South Africa   Pretoria   1.221       52.98\n",
            "country         China\n",
            "capital       Beijing\n",
            "area            9.597\n",
            "population     1357.0\n",
            "Name: CH, dtype: object\n",
            "country         China\n",
            "capital       Beijing\n",
            "area            9.597\n",
            "population     1357.0\n",
            "Name: CH, dtype: object\n",
            "         country   capital    area  population\n",
            "RU        Russia    Moscow  17.100      143.50\n",
            "SA  South Africa  Pretoria   1.221       52.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrvTFZGRkLKv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4aece72-a415-4e0f-c1ed-6ef31a1d83bd"
      },
      "source": [
        "print(brics)\n",
        "# Print out population value of Russia\n",
        "print(brics.loc['RU','population'])\n",
        "print(brics.iloc[1,3])\n",
        "\n",
        "# Print sub-DataFrame\n",
        "print(brics.iloc[[1, 3],[2,3]])\n",
        "print(brics.loc[['RU','CH'],['area', 'population']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         country    capital    area  population\n",
            "BR        Brazil   Brasilia   8.516      200.40\n",
            "RU        Russia     Moscow  17.100      143.50\n",
            "IN         India  New Delhi   3.286     1252.00\n",
            "CH         China    Beijing   9.597     1357.00\n",
            "SA  South Africa   Pretoria   1.221       52.98\n",
            "143.5\n",
            "143.5\n",
            "      area  population\n",
            "RU  17.100       143.5\n",
            "CH   9.597      1357.0\n",
            "      area  population\n",
            "RU  17.100       143.5\n",
            "CH   9.597      1357.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCoNeuREmkgI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbf347be-402a-4eca-91c7-a89989d8debb"
      },
      "source": [
        "# Print out population column as Series\n",
        "print(brics.loc[:, 'population'])\n",
        "\n",
        "# Print out drives_right column as DataFrame\n",
        "print(brics[['population']])\n",
        "\n",
        "# Print out cars_per_cap and drives_right as DataFrame\n",
        "print(brics[['population','area']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BR     200.40\n",
            "RU     143.50\n",
            "IN    1252.00\n",
            "CH    1357.00\n",
            "SA      52.98\n",
            "Name: population, dtype: float64\n",
            "    population\n",
            "BR      200.40\n",
            "RU      143.50\n",
            "IN     1252.00\n",
            "CH     1357.00\n",
            "SA       52.98\n",
            "    population    area\n",
            "BR      200.40   8.516\n",
            "RU      143.50  17.100\n",
            "IN     1252.00   3.286\n",
            "CH     1357.00   9.597\n",
            "SA       52.98   1.221\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Ko2Llwmivzy"
      },
      "source": [
        "# 6 Manipulating Columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCjwdvS5iv7f"
      },
      "source": [
        "## Rename\n",
        "\n",
        "* The .rename() method allows you to modify index labels and/or column names.\n",
        "\n",
        "* You pass a dictionary: the original name as the key and the new name as the value.\n",
        "\n",
        "* Also set the `inplace` parameter to `True`, which modifies the DataFrame, not a copy of it.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ANUsVDDoxW6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ca574fd-d5b8-4203-c7bb-5aebcb33f29b"
      },
      "source": [
        "print(unemployment.head())\n",
        "unemployment.rename(columns={'month' : 'year_month'}, inplace=True)\n",
        "print(unemployment.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  country seasonality    month  unemployment  unemployment_rate\n",
            "0      at         nsa  1993.01        171000                4.5\n",
            "1      at         nsa  1993.02        175000                4.6\n",
            "2      at         nsa  1993.03        166000                4.4\n",
            "3      at         nsa  1993.04        157000                4.1\n",
            "4      at         nsa  1993.05        147000                3.9\n",
            "  country seasonality  year_month  unemployment  unemployment_rate\n",
            "0      at         nsa     1993.01        171000                4.5\n",
            "1      at         nsa     1993.02        175000                4.6\n",
            "2      at         nsa     1993.03        166000                4.4\n",
            "3      at         nsa     1993.04        157000                4.1\n",
            "4      at         nsa     1993.05        147000                3.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvsNklsJpL_4"
      },
      "source": [
        "## Create\n",
        "\n",
        "* Writing the name of the data frame followed by the name of the new column within square brackets adds a new variable/column\n",
        "\n",
        "* For example, you can get the year from the year_month variable by converting it from float64 to an integer\n",
        "\n",
        "* For more information on pandas dtypes, check the documentation [here.](http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrTaCGoGpLk-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "74ab0dd2-4abe-4753-afc7-93d3dba05fa2"
      },
      "source": [
        "#import math\n",
        "#unemployment['log_unemployment'] = math.log(unemployment['unemployment'])\n",
        "unemployment['log_unemployment'] = np.log(unemployment['unemployment'])\n",
        "\n",
        "unemployment['year'] = unemployment['year_month'].astype(int)\n",
        "\n",
        "unemployment['month'] = ((unemployment['year_month'] - unemployment['year']) * 100).round(0).astype(int)\n",
        "\n",
        "unemployment.head(12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   country seasonality  year_month  unemployment  unemployment_rate  \\\n",
              "0       at         nsa     1993.01        171000                4.5   \n",
              "1       at         nsa     1993.02        175000                4.6   \n",
              "2       at         nsa     1993.03        166000                4.4   \n",
              "3       at         nsa     1993.04        157000                4.1   \n",
              "4       at         nsa     1993.05        147000                3.9   \n",
              "5       at         nsa     1993.06        134000                3.5   \n",
              "6       at         nsa     1993.07        128000                3.4   \n",
              "7       at         nsa     1993.08        130000                3.4   \n",
              "8       at         nsa     1993.09        132000                3.5   \n",
              "9       at         nsa     1993.10        141000                3.7   \n",
              "10      at         nsa     1993.11        156000                4.1   \n",
              "11      at         nsa     1993.12        169000                4.4   \n",
              "\n",
              "    log_unemployment  year  month  \n",
              "0          12.049419  1993      1  \n",
              "1          12.072541  1993      2  \n",
              "2          12.019743  1993      3  \n",
              "3          11.964001  1993      4  \n",
              "4          11.898188  1993      5  \n",
              "5          11.805595  1993      6  \n",
              "6          11.759786  1993      7  \n",
              "7          11.775290  1993      8  \n",
              "8          11.790557  1993      9  \n",
              "9          11.856515  1993     10  \n",
              "10         11.957611  1993     11  \n",
              "11         12.037654  1993     12  "
            ],
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              "      <th>seasonality</th>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2f080024-924e-4177-9df4-7132359eb7d7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "unemployment",
              "summary": "{\n  \"name\": \"unemployment\",\n  \"rows\": 20796,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"sk\",\n          \"it\",\n          \"pt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seasonality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"nsa\",\n          \"sa\",\n          \"trend\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.483750816970295,\n        \"min\": 1983.01,\n        \"max\": 2010.12,\n        \"num_unique_values\": 336,\n        \"samples\": [\n          1999.01,\n          2002.03,\n          1989.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1015280,\n        \"min\": 2000,\n        \"max\": 4773000,\n        \"num_unique_values\": 2632,\n        \"samples\": [\n          340000,\n          649000,\n          2524000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.922533374669806,\n        \"min\": 1.1,\n        \"max\": 20.9,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          15.8,\n          13.8,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log_unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6454483653272822,\n        \"min\": 7.600902459542082,\n        \"max\": 15.378485595988034,\n        \"num_unique_values\": 2632,\n        \"samples\": [\n          12.736700896592344,\n          13.383187995686226,\n          14.741355502643241\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1983,\n        \"max\": 2010,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2002,\n          1990,\n          2001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIZo4tzzrJZn"
      },
      "source": [
        "## Reorder\n",
        "\n",
        "* Use bracket notation passing in a list of column names in the desired order\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rud0_JwbrV8s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "73d1004d-635a-45d9-c70e-df55390c0325"
      },
      "source": [
        "unemployment = unemployment[['country', 'seasonality',\n",
        "                             'year_month', 'year', 'month',\n",
        "                             'unemployment', 'unemployment_rate']]\n",
        "\n",
        "unemployment.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  country seasonality  year_month  year  month  unemployment  \\\n",
              "0      at         nsa     1993.01  1993      1        171000   \n",
              "1      at         nsa     1993.02  1993      2        175000   \n",
              "2      at         nsa     1993.03  1993      3        166000   \n",
              "3      at         nsa     1993.04  1993      4        157000   \n",
              "4      at         nsa     1993.05  1993      5        147000   \n",
              "\n",
              "   unemployment_rate  \n",
              "0                4.5  \n",
              "1                4.6  \n",
              "2                4.4  \n",
              "3                4.1  \n",
              "4                3.9  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>seasonality</th>\n",
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              "      <td>1993</td>\n",
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              "      <td>4.4</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>at</td>\n",
              "      <td>nsa</td>\n",
              "      <td>1993.04</td>\n",
              "      <td>1993</td>\n",
              "      <td>4</td>\n",
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              "      <td>4.1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>at</td>\n",
              "      <td>nsa</td>\n",
              "      <td>1993.05</td>\n",
              "      <td>1993</td>\n",
              "      <td>5</td>\n",
              "      <td>147000</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-5db29663-3d72-4b04-b66e-d8ba1c0401c4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5db29663-3d72-4b04-b66e-d8ba1c0401c4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "unemployment",
              "summary": "{\n  \"name\": \"unemployment\",\n  \"rows\": 20796,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"sk\",\n          \"it\",\n          \"pt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seasonality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"nsa\",\n          \"sa\",\n          \"trend\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.483750816970295,\n        \"min\": 1983.01,\n        \"max\": 2010.12,\n        \"num_unique_values\": 336,\n        \"samples\": [\n          1999.01,\n          2002.03,\n          1989.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1983,\n        \"max\": 2010,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2002,\n          1990,\n          2001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1015280,\n        \"min\": 2000,\n        \"max\": 4773000,\n        \"num_unique_values\": 2632,\n        \"samples\": [\n          340000,\n          649000,\n          2524000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.922533374669806,\n        \"min\": 1.1,\n        \"max\": 20.9,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          15.8,\n          13.8,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1v3oX6ufrtU-"
      },
      "source": [
        "#7 Merging\n",
        "\n",
        "* Joining data frames allows to combine two sources of information\n",
        "\n",
        "* For example, you need the name of the country for each country code above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Bni34xjlseMr",
        "outputId": "1b3b6f0d-db32-4397-ec48-c88af10ff81c"
      },
      "source": [
        "countries_url = 'https://www.dropbox.com/s/mqym60yymir28ro/countries.csv?dl=1'\n",
        "countries = pd.read_csv(countries_url)\n",
        "\n",
        "countries.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  country google_country_code country_group   name_en   name_fr     name_de  \\\n",
              "0      at                  AT            eu   Austria  Autriche  Österreich   \n",
              "1      be                  BE            eu   Belgium  Belgique     Belgien   \n",
              "2      bg                  BG            eu  Bulgaria  Bulgarie   Bulgarien   \n",
              "3      hr                  HR        non-eu   Croatia   Croatie    Kroatien   \n",
              "4      cy                  CY            eu    Cyprus    Chypre      Zypern   \n",
              "\n",
              "    latitude  longitude  \n",
              "0  47.696554  13.345980  \n",
              "1  50.501045   4.476674  \n",
              "2  42.725674  25.482322  \n",
              "3  44.746643  15.340844  \n",
              "4  35.129141  33.428682  "
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              "      <td>Cyprus</td>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "countries",
              "summary": "{\n  \"name\": \"countries\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"se\",\n          \"lv\",\n          \"ro\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"google_country_code\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"SE\",\n          \"LV\",\n          \"RO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"non-eu\",\n          \"eu\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name_en\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"Sweden\",\n          \"Latvia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name_fr\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"Su\\u00e8de\",\n          \"Lettonie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name_de\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"Schweden\",\n          \"Lettland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.956624412076442,\n        \"min\": 35.129141,\n        \"max\": 64.95015875,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          62.1984675,\n          56.880117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.257009578841792,\n        \"min\": -8.2391222,\n        \"max\": 35.43979471,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          14.89630657,\n          24.60655505\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pKNfHvntskXV"
      },
      "source": [
        "\n",
        "* Select the relevant information\n",
        "\n",
        "* Use `merge()` function to join the two DataFrames on country code.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrScsOr2tp-t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "0ae77e52-7e67-4170-882b-a688a408f73d"
      },
      "source": [
        "country_names = countries[['country', 'country_group', 'name_en']]\n",
        "print(unemployment.shape, country_names.shape)\n",
        "\n",
        "\n",
        "unemployment = pd.merge(unemployment, country_names, on='country')\n",
        "unemployment.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20796, 7) (30, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  country seasonality  year_month  year  month  unemployment  \\\n",
              "0      at         nsa     1993.01  1993      1        171000   \n",
              "1      at         nsa     1993.02  1993      2        175000   \n",
              "2      at         nsa     1993.03  1993      3        166000   \n",
              "3      at         nsa     1993.04  1993      4        157000   \n",
              "4      at         nsa     1993.05  1993      5        147000   \n",
              "\n",
              "   unemployment_rate country_group  name_en  \n",
              "0                4.5            eu  Austria  \n",
              "1                4.6            eu  Austria  \n",
              "2                4.4            eu  Austria  \n",
              "3                4.1            eu  Austria  \n",
              "4                3.9            eu  Austria  "
            ],
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              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>seasonality</th>\n",
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              "      <th>unemployment</th>\n",
              "      <th>unemployment_rate</th>\n",
              "      <th>country_group</th>\n",
              "      <th>name_en</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>at</td>\n",
              "      <td>nsa</td>\n",
              "      <td>1993.01</td>\n",
              "      <td>1993</td>\n",
              "      <td>1</td>\n",
              "      <td>171000</td>\n",
              "      <td>4.5</td>\n",
              "      <td>eu</td>\n",
              "      <td>Austria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>at</td>\n",
              "      <td>nsa</td>\n",
              "      <td>1993.02</td>\n",
              "      <td>1993</td>\n",
              "      <td>2</td>\n",
              "      <td>175000</td>\n",
              "      <td>4.6</td>\n",
              "      <td>eu</td>\n",
              "      <td>Austria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>at</td>\n",
              "      <td>nsa</td>\n",
              "      <td>1993.03</td>\n",
              "      <td>1993</td>\n",
              "      <td>3</td>\n",
              "      <td>166000</td>\n",
              "      <td>4.4</td>\n",
              "      <td>eu</td>\n",
              "      <td>Austria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>at</td>\n",
              "      <td>nsa</td>\n",
              "      <td>1993.04</td>\n",
              "      <td>1993</td>\n",
              "      <td>4</td>\n",
              "      <td>157000</td>\n",
              "      <td>4.1</td>\n",
              "      <td>eu</td>\n",
              "      <td>Austria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>at</td>\n",
              "      <td>nsa</td>\n",
              "      <td>1993.05</td>\n",
              "      <td>1993</td>\n",
              "      <td>5</td>\n",
              "      <td>147000</td>\n",
              "      <td>3.9</td>\n",
              "      <td>eu</td>\n",
              "      <td>Austria</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ff64548a-7c04-408a-9591-9a862e1c457e')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "unemployment",
              "summary": "{\n  \"name\": \"unemployment\",\n  \"rows\": 20796,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"sk\",\n          \"it\",\n          \"pt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seasonality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"nsa\",\n          \"sa\",\n          \"trend\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.483750816970295,\n        \"min\": 1983.01,\n        \"max\": 2010.12,\n        \"num_unique_values\": 336,\n        \"samples\": [\n          1999.01,\n          2002.03,\n          1989.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1983,\n        \"max\": 2010,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2002,\n          1990,\n          2001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1015280,\n        \"min\": 2000,\n        \"max\": 4773000,\n        \"num_unique_values\": 2632,\n        \"samples\": [\n          340000,\n          649000,\n          2524000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unemployment_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.922533374669806,\n        \"min\": 1.1,\n        \"max\": 20.9,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          15.8,\n          13.8,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"non-eu\",\n          \"eu\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name_en\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"Slovakia\",\n          \"Italy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLJ8u9Jcto9t"
      },
      "source": [
        "* To merge on multiple columns, you can pass a list of column names to the on parameter.\n",
        "\n",
        "  `pd.merge(first, second, on=['name', 'id'])`\n",
        "\n",
        "* Sometimes columns have different names:  you need to use the `left_on` and `right_on` parameters, for the variables in the first and in the second dataframes (respectively)\n",
        "\n",
        "  `pd.merge(one, two, left_on='city', right_on='city_name')`\n",
        "\n",
        "* For more information on merging, check [the documentation](http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging).\n",
        "\n",
        "* pandas also provides a .merge() method that can act on a DataFrame; more information [here](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdfkklBeDyrk"
      },
      "source": [
        "# 8 Unique Values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KuUEriqz0jnb"
      },
      "source": [
        "\n",
        "* The **Series** object includes a .unique() method.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9aOrucn09G5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a6d0b9c-9357-415e-b568-8eb6122e4d15"
      },
      "source": [
        "unemployment.head()\n",
        "\n",
        "unemployment['name_en'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Austria', 'Belgium', 'Bulgaria', 'Cyprus', 'Czech Republic',\n",
              "       'Germany (including  former GDR from 1991)', 'Denmark', 'Estonia',\n",
              "       'Spain', 'Finland', 'France', 'Greece', 'Croatia', 'Hungary',\n",
              "       'Ireland', 'Italy', 'Lithuania', 'Luxembourg', 'Latvia', 'Malta',\n",
              "       'Netherlands', 'Norway', 'Poland', 'Portugal', 'Romania', 'Sweden',\n",
              "       'Slovenia', 'Slovakia', 'Turkey', 'United Kingdom'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1n3tvzl09nZ"
      },
      "source": [
        "* To get a count of the number of unique values, you can use the `Series.nunique()` method\n",
        "\n",
        "  + you could also use `len()` to get the number of items in the array, or we\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4s1hmJq3D__",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d81aa4a8-8cae-4cec-d941-f6e23137c09c"
      },
      "source": [
        "unemployment['name_en'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eilk_iAh3DTI"
      },
      "source": [
        "* A method called .value_counts() returns the counts for the unique values in the Series, that is, a frequency table\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7A__0j6K6gwk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bbe127ef-2552-41d9-e88f-3fb3f401b463"
      },
      "source": [
        "unemployment['name_en'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name_en\n",
              "France                                       1008\n",
              "Sweden                                       1008\n",
              "Portugal                                     1008\n",
              "Netherlands                                  1008\n",
              "Luxembourg                                   1008\n",
              "Denmark                                      1008\n",
              "Belgium                                      1008\n",
              "Spain                                        1008\n",
              "Ireland                                      1008\n",
              "United Kingdom                               1002\n",
              "Italy                                         924\n",
              "Finland                                       828\n",
              "Norway                                        786\n",
              "Austria                                       648\n",
              "Hungary                                       576\n",
              "Slovakia                                      576\n",
              "Slovenia                                      576\n",
              "Bulgaria                                      576\n",
              "Malta                                         576\n",
              "Poland                                        576\n",
              "Germany (including  former GDR from 1991)     504\n",
              "Czech Republic                                468\n",
              "Latvia                                        459\n",
              "Lithuania                                     459\n",
              "Greece                                        450\n",
              "Romania                                       423\n",
              "Cyprus                                        396\n",
              "Estonia                                       387\n",
              "Croatia                                       324\n",
              "Turkey                                        210\n",
              "Name: count, dtype: int64"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Slovakia</th>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Slovenia</th>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bulgaria</th>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malta</th>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Poland</th>\n",
              "      <td>576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany (including  former GDR from 1991)</th>\n",
              "      <td>504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Czech Republic</th>\n",
              "      <td>468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latvia</th>\n",
              "      <td>459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lithuania</th>\n",
              "      <td>459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greece</th>\n",
              "      <td>450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romania</th>\n",
              "      <td>423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cyprus</th>\n",
              "      <td>396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estonia</th>\n",
              "      <td>387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Croatia</th>\n",
              "      <td>324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nqpHtJZz6flI"
      },
      "source": [
        "* It can be sorted by index-label name with `.sort_index()` method.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KMNhsnb6lIx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "db20bf3f-2802-4488-cdf1-3e39110be342"
      },
      "source": [
        "unemployment['name_en'].value_counts().sort_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name_en\n",
              "Austria                                       648\n",
              "Belgium                                      1008\n",
              "Bulgaria                                      576\n",
              "Croatia                                       324\n",
              "Cyprus                                        396\n",
              "Czech Republic                                468\n",
              "Denmark                                      1008\n",
              "Estonia                                       387\n",
              "Finland                                       828\n",
              "France                                       1008\n",
              "Germany (including  former GDR from 1991)     504\n",
              "Greece                                        450\n",
              "Hungary                                       576\n",
              "Ireland                                      1008\n",
              "Italy                                         924\n",
              "Latvia                                        459\n",
              "Lithuania                                     459\n",
              "Luxembourg                                   1008\n",
              "Malta                                         576\n",
              "Netherlands                                  1008\n",
              "Norway                                        786\n",
              "Poland                                        576\n",
              "Portugal                                     1008\n",
              "Romania                                       423\n",
              "Slovakia                                      576\n",
              "Slovenia                                      576\n",
              "Spain                                        1008\n",
              "Sweden                                       1008\n",
              "Turkey                                        210\n",
              "United Kingdom                               1002\n",
              "Name: count, dtype: int64"
            ],
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYxB4Amr7PGN"
      },
      "source": [
        "# 9 Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQaws4q45cCw"
      },
      "source": [
        "## A few statistical functions\n",
        "\n",
        "<hr>\n",
        "<center>\n",
        "<table >\n",
        "   <tr>\n",
        "     <th>Function</th>\n",
        "     <th>Description</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>max(); min()</td>\n",
        "    <td>Calculates the maximum or the minimum<td></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>mean(); std()</td>\n",
        "    <td>Calculates the means and the standard deviation<td>\n",
        "    </td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>median()</td>\n",
        "    <td>Calculates the medians<td>\n",
        "    </td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>count()</td>\n",
        "    <td>Count non-missing values<td>\n",
        "    </td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>skew(); kurt()</td>\n",
        "    <td>Calculates the skewness and the kurtosis\n",
        "    <td>\n",
        "    </td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>corr(); var(); cov()</td>\n",
        "    <td>Calculates correlation, variance and covariance.<td>\n",
        "    </td>\n",
        "  </tr>\n",
        "  </table>\n",
        "  </center>\n",
        "  <hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08NrD5wHJ50F"
      },
      "source": [
        "* The axis parameter can be passed to these functions\n",
        "\n",
        "* `axis=0` to obtain a result by columns (default)\n",
        "* `axis=1` to obtain it by rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8M2MhTLdy1Se",
        "outputId": "d2a18f68-3961-4465-f951-c8c5782e053a"
      },
      "source": [
        "# Data on course grades\n",
        "data = pd.read_csv('https://www.dropbox.com/s/7h4710rz3s5g6mz/asignatura2.csv?dl=1')\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Curso     Género   Grupo    P1    P2   P3   P4\n",
              "0   2013   Femenino  Mañana   8.5   8.3  7.0  8.0\n",
              "1   2013   Femenino  Mañana  10.0   9.8  8.0  8.8\n",
              "2   2013  Masculino  Mañana   5.0  10.0  7.4  8.4\n",
              "3   2013   Femenino  Mañana   7.5   9.2  8.6  8.3\n",
              "4   2013  Masculino  Mañana   7.0   7.5  7.0  7.7"
            ],
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2797feae-d64a-475b-be10-8d3aa46a9810 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 599,\n  \"fields\": [\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2013,\n        \"max\": 2015,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2013,\n          2014,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Masculino\",\n          \"Femenino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grupo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tarde\",\n          \"Ma\\u00f1ana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5492458661061461,\n        \"min\": 2.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          6.5,\n          8.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6919277146168337,\n        \"min\": 0.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          10.8,\n          6.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1925015248952695,\n        \"min\": 1.2,\n        \"max\": 11.0,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          7.5,\n          5.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.060704599272574,\n        \"min\": 0.5,\n        \"max\": 9.6,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          8.9,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jT7CJbexJo7Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "fbabed04-ff70-4798-a078-437be1c0663b"
      },
      "source": [
        "# Mean by columns\n",
        "data[['P1','P2','P3','P4']].mean().round(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "P1    8.8\n",
              "P2    8.5\n",
              "P3    8.6\n",
              "P4    7.8\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>P1</th>\n",
              "      <td>8.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P2</th>\n",
              "      <td>8.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>8.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>7.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Mean by rows\n",
        "data[['P1','P2','P3','P4']].mean(axis=1).round(1).head()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "JYUxwliXi5M3",
        "outputId": "550d10ea-8d0c-485d-8c14-e1d6c9788bf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    8.0\n",
              "1    9.2\n",
              "2    7.7\n",
              "3    8.4\n",
              "4    7.3\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Skewness\n",
        "data[['P1','P2','P3','P4']].skew().round(1)"
      ],
      "metadata": {
        "id": "Ln8tfM0Vi6pG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "9aa6cd6e-cd36-4a10-b2ae-2b1e18b1c49f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "P1   -1.5\n",
              "P2   -1.5\n",
              "P3   -1.4\n",
              "P4   -1.4\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>P1</th>\n",
              "      <td>-1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P2</th>\n",
              "      <td>-1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>-1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>-1.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNqaIJk3z04W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "9799b294-bcd9-4a73-fae8-7cdadcd519e5"
      },
      "source": [
        "# Histogram\n",
        "data[['P1','P2','P3','P4']].hist(figsize=(7,7))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'P1'}>, <Axes: title={'center': 'P2'}>],\n",
              "       [<Axes: title={'center': 'P3'}>, <Axes: title={'center': 'P4'}>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 161
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSQj8VghAaUk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "2c219d88-642c-4cb3-ea67-ff9837de5770"
      },
      "source": [
        "correlation = data[['P1','P2','P3','P4']].corr().round(2)\n",
        "correlation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      P1    P2    P3    P4\n",
              "P1  1.00  0.11  0.19  0.41\n",
              "P2  0.11  1.00  0.18  0.52\n",
              "P3  0.19  0.18  1.00  0.49\n",
              "P4  0.41  0.52  0.49  1.00"
            ],
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5d30d49c-4567-4198-9932-77e057cb0429 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_022d9e4c-8a13-41f2-9abb-6efaa6e4782d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('correlation')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_022d9e4c-8a13-41f2-9abb-6efaa6e4782d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('correlation');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "correlation",
              "summary": "{\n  \"name\": \"correlation\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4021919110739714,\n        \"min\": 0.11,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.11,\n          0.41,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.406560778564124,\n        \"min\": 0.11,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.52,\n          0.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3845776904605882,\n        \"min\": 0.18,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.18,\n          0.49,\n          0.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2673948391424188,\n        \"min\": 0.41,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.52,\n          1.0,\n          0.41\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNrXo_A0z_AV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "ba689282-4f9d-4244-841e-7b4378687a39"
      },
      "source": [
        "# Non-missing values\n",
        "data.count()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Curso     599\n",
              "Género    599\n",
              "Grupo     599\n",
              "P1        574\n",
              "P2        577\n",
              "P3        564\n",
              "P4        580\n",
              "dtype: int64"
            ],
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              "      <th>P2</th>\n",
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              "      <th>P3</th>\n",
              "      <td>564</td>\n",
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              "      <th>P4</th>\n",
              "      <td>580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Missing values\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "xU0kF1vzjVRp",
        "outputId": "b033aa10-6897-4aa1-8f2c-44e9078223df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Curso      0\n",
              "Género     0\n",
              "Grupo      0\n",
              "P1        25\n",
              "P2        22\n",
              "P3        35\n",
              "P4        19\n",
              "dtype: int64"
            ],
            "text/html": [
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              "      <td>0</td>\n",
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              "      <th>Género</th>\n",
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              "    <tr>\n",
              "      <th>Grupo</th>\n",
              "      <td>0</td>\n",
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              "      <th>P1</th>\n",
              "      <td>25</td>\n",
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              "      <th>P2</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CWZTirRQ0eFZ"
      },
      "source": [
        "## How to deal with missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jt4jtnqe1B0p"
      },
      "source": [
        "* Let's see observation with missing values by filtering rows using a condition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fATK0Nn_0cxQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "58386e9a-8897-4d5a-fc31-209bd90947ae"
      },
      "source": [
        "data[data.isnull().any(axis=1)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Curso     Género   Grupo   P1   P2   P3   P4\n",
              "18    2013  Masculino  Mañana  7.0  4.0  NaN  3.2\n",
              "65    2013  Masculino  Mañana  5.7  NaN  NaN  0.5\n",
              "67    2013  Masculino  Mañana  NaN  7.8  6.0  6.4\n",
              "92    2013  Masculino  Mañana  NaN  7.8  6.8  5.6\n",
              "101   2013   Femenino  Mañana  NaN  NaN  NaN  NaN\n",
              "114   2013  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "129   2013   Femenino   Tarde  NaN  NaN  NaN  NaN\n",
              "131   2013  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "143   2013   Femenino   Tarde  4.0  6.5  NaN  5.5\n",
              "145   2013   Femenino   Tarde  NaN  NaN  NaN  NaN\n",
              "148   2013   Femenino   Tarde  4.0  9.3  NaN  5.6\n",
              "150   2013  Masculino   Tarde  6.5  6.5  NaN  4.9\n",
              "154   2013   Femenino   Tarde  9.4  9.3  NaN  6.1\n",
              "163   2013  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "166   2013  Masculino   Tarde  7.5  9.0  NaN  5.7\n",
              "167   2013   Femenino   Tarde  9.8  7.2  NaN  5.0\n",
              "168   2013   Femenino   Tarde  9.6  6.2  NaN  4.9\n",
              "170   2013  Masculino   Tarde  7.5  6.4  NaN  5.1\n",
              "173   2013   Femenino   Tarde  9.6  6.2  NaN  4.5\n",
              "175   2013  Masculino   Tarde  8.7  9.2  NaN  5.3\n",
              "176   2013  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "186   2013   Femenino   Tarde  4.0  6.5  NaN  4.6\n",
              "219   2014   Femenino  Mañana  NaN  9.4  8.3  7.6\n",
              "305   2014   Femenino   Tarde  6.0  NaN  NaN  5.6\n",
              "314   2014   Femenino   Tarde  NaN  NaN  NaN  NaN\n",
              "323   2014   Femenino   Tarde  NaN  NaN  NaN  5.1\n",
              "334   2014  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "336   2014  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "354   2014   Femenino   Tarde  NaN  NaN  NaN  NaN\n",
              "373   2014   Femenino   Tarde  NaN  NaN  NaN  NaN\n",
              "386   2014   Femenino   Tarde  NaN  9.7  9.1  6.3\n",
              "464   2015   Femenino  Mañana  NaN  NaN  NaN  NaN\n",
              "506   2015  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "512   2015  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "526   2015   Femenino   Tarde  NaN  NaN  NaN  NaN\n",
              "532   2015   Femenino   Tarde  NaN  NaN  NaN  NaN\n",
              "537   2015  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "545   2015  Masculino   Tarde  NaN  NaN  NaN  NaN\n",
              "573   2015   Femenino   Tarde  NaN  5.7  NaN  3.7"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-59bee90f-22b3-472b-863f-57c6edf31796\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>Curso</th>\n",
              "      <th>Género</th>\n",
              "      <th>Grupo</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
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              "      <th>P4</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>5.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.8</td>\n",
              "      <td>6.8</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>6.5</td>\n",
              "      <td>6.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>9.4</td>\n",
              "      <td>9.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>7.5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>9.8</td>\n",
              "      <td>7.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>9.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>7.5</td>\n",
              "      <td>6.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>9.6</td>\n",
              "      <td>6.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>8.7</td>\n",
              "      <td>9.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.4</td>\n",
              "      <td>8.3</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>323</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334</th>\n",
              "      <td>2014</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>2014</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>373</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.1</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>464</th>\n",
              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>2015</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>2015</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>532</th>\n",
              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>537</th>\n",
              "      <td>2015</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>545</th>\n",
              "      <td>2015</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59bee90f-22b3-472b-863f-57c6edf31796')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-59bee90f-22b3-472b-863f-57c6edf31796 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-59bee90f-22b3-472b-863f-57c6edf31796');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
              "<div id=\"df-870da048-3275-4294-8e25-18f9c886cafe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-870da048-3275-4294-8e25-18f9c886cafe')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-870da048-3275-4294-8e25-18f9c886cafe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[data\",\n  \"rows\": 39,\n  \"fields\": [\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2013,\n        \"max\": 2015,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2013,\n          2014,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Femenino\",\n          \"Masculino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grupo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tarde\",\n          \"Ma\\u00f1ana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1542317158825526,\n        \"min\": 4.0,\n        \"max\": 9.8,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8.7,\n          5.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6466320350410804,\n        \"min\": 4.0,\n        \"max\": 9.7,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          9.7,\n          9.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4059397805975427,\n        \"min\": 6.0,\n        \"max\": 9.1,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6.8,\n          9.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4343162684341053,\n        \"min\": 0.5,\n        \"max\": 7.6,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          3.2,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Yw3Qj6C1HWa"
      },
      "source": [
        "### Solution?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOVKNTLlS7gH"
      },
      "source": [
        "* Drop observations with missing values in at least one variable or in all the relevant variables\n",
        "\n",
        "* Imputation\n",
        "\n",
        "* Both \"solutions\" may hide/disregard an important problem in empirical Economics: **sample selection**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0X4Ri1p1OSz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "e8a6df05-a1e1-412c-ec33-949187cec730"
      },
      "source": [
        "# new data frame without missing\n",
        "data.dropna(axis=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Curso     Género   Grupo    P1    P2   P3   P4\n",
              "0     2013   Femenino  Mañana   8.5   8.3  7.0  8.0\n",
              "1     2013   Femenino  Mañana  10.0   9.8  8.0  8.8\n",
              "2     2013  Masculino  Mañana   5.0  10.0  7.4  8.4\n",
              "3     2013   Femenino  Mañana   7.5   9.2  8.6  8.3\n",
              "4     2013  Masculino  Mañana   7.0   7.5  7.0  7.7\n",
              "..     ...        ...     ...   ...   ...  ...  ...\n",
              "594   2015   Femenino   Tarde  10.0  10.0  6.2  7.6\n",
              "595   2015  Masculino   Tarde  10.0   3.0  9.2  7.1\n",
              "596   2015   Femenino   Tarde  10.0   7.1  7.9  7.4\n",
              "597   2015   Femenino   Tarde  10.0  10.0  9.9  8.1\n",
              "598   2015   Femenino   Tarde  10.0   9.1  8.6  8.2\n",
              "\n",
              "[560 rows x 7 columns]"
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              "      <th>Curso</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
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              "      <td>9.8</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.8</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Mañana</td>\n",
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              "      <td>10.0</td>\n",
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              "      <td>8.4</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>7.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>8.3</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.5</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>2015</td>\n",
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              "      <td>9.2</td>\n",
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              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.9</td>\n",
              "      <td>7.4</td>\n",
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              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
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              "      <td>8.1</td>\n",
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              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Tarde</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.1</td>\n",
              "      <td>8.6</td>\n",
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              "    </tr>\n",
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              "<p>560 rows × 7 columns</p>\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    async function quickchart(key) {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 560,\n  \"fields\": [\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2013,\n        \"max\": 2015,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2013,\n          2014,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Masculino\",\n          \"Femenino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grupo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tarde\",\n          \"Ma\\u00f1ana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5105320262410942,\n        \"min\": 2.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          9.9,\n          9.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6844335396056225,\n        \"min\": 0.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          6.3,\n          6.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1889880954113463,\n        \"min\": 1.2,\n        \"max\": 11.0,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          7.5,\n          5.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9039063752632254,\n        \"min\": 4.5,\n        \"max\": 9.6,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          6.9,\n          8.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVL0qJPwn2M0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "05b4001e-6827-4d1b-a75d-e3f140daa6de"
      },
      "source": [
        "# Imputation with mean\n",
        "data[['P1','P2','P3','P4']].fillna(data[['P1','P2','P3','P4']].mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       P1    P2   P3   P4\n",
              "0     8.5   8.3  7.0  8.0\n",
              "1    10.0   9.8  8.0  8.8\n",
              "2     5.0  10.0  7.4  8.4\n",
              "3     7.5   9.2  8.6  8.3\n",
              "4     7.0   7.5  7.0  7.7\n",
              "..    ...   ...  ...  ...\n",
              "594  10.0  10.0  6.2  7.6\n",
              "595  10.0   3.0  9.2  7.1\n",
              "596  10.0   7.1  7.9  7.4\n",
              "597  10.0  10.0  9.9  8.1\n",
              "598  10.0   9.1  8.6  8.2\n",
              "\n",
              "[599 rows x 4 columns]"
            ],
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              "      <th>0</th>\n",
              "      <td>8.5</td>\n",
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              "      <td>9.8</td>\n",
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              "      <td>8.6</td>\n",
              "      <td>8.3</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>10.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.9</td>\n",
              "      <td>7.4</td>\n",
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              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.9</td>\n",
              "      <td>8.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.1</td>\n",
              "      <td>8.6</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
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              "<p>599 rows × 4 columns</p>\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-bc2c2d20-5d0b-4f02-b04d-47ca5e722a73 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bc2c2d20-5d0b-4f02-b04d-47ca5e722a73');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8624d481-b6b7-416e-980d-0882cfc15ddf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[['P1','P2','P3','P4']]\",\n  \"rows\": 599,\n  \"fields\": [\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5165162384241773,\n        \"min\": 2.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          7.7,\n          8.2,\n          5.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.660513665891081,\n        \"min\": 0.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          10.8,\n          5.5,\n          8.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1570777662039717,\n        \"min\": 1.2,\n        \"max\": 11.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          9.2,\n          10.1,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0437179241986347,\n        \"min\": 0.5,\n        \"max\": 9.6,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          8.9,\n          4.9,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeieHOfcSic4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "37b51e21-7cde-41bd-8a0f-a47bed848dbd"
      },
      "source": [
        "# Missing in this case most likely means 0\n",
        "data_filtered = data.fillna(0)\n",
        "data_filtered.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Curso     0\n",
              "Género    0\n",
              "Grupo     0\n",
              "P1        0\n",
              "P2        0\n",
              "P3        0\n",
              "P4        0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Curso</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Género</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grupo</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUdxjqryukZ0"
      },
      "source": [
        "## Categorical Variables\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PPcI_TyFruE"
      },
      "source": [
        "* Function `map` change the labels of categories in variables with qualitative information\n",
        "\n",
        "* You can use a dictionary with key and value as the original and the new values, respectively."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tK58-oa4ur5D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "ccc5c52b-866e-4010-d546-db9774ce1c58"
      },
      "source": [
        "print(data_filtered.head())\n",
        "\n",
        "data_filtered['Género'].map({'Masculino':1, 'Femenino':2}).head()\n",
        "\n",
        "data_filtered['Grupo'].map({\"Mañana\":1, \"Tarde\": 2}).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Curso     Género   Grupo    P1    P2   P3   P4\n",
            "0   2013   Femenino  Mañana   8.5   8.3  7.0  8.0\n",
            "1   2013   Femenino  Mañana  10.0   9.8  8.0  8.8\n",
            "2   2013  Masculino  Mañana   5.0  10.0  7.4  8.4\n",
            "3   2013   Femenino  Mañana   7.5   9.2  8.6  8.3\n",
            "4   2013  Masculino  Mañana   7.0   7.5  7.0  7.7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: Grupo, dtype: int64"
            ],
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djWAocP8vShb"
      },
      "source": [
        "* The function `get_dummies()` generates dummies variables for each class of a categorical variable\n",
        "\n",
        "En `Pandas` existe un función que nos realiza esta operación en un solo paso."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjW6upyBD2m7",
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          "height": 206
        },
        "outputId": "150cde7a-5068-41ec-e9cf-d3dec91eeb46"
      },
      "source": [
        "pd.get_dummies(data_filtered).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Curso    P1    P2   P3   P4  Género_Femenino  Género_Masculino  \\\n",
              "0   2013   8.5   8.3  7.0  8.0             True             False   \n",
              "1   2013  10.0   9.8  8.0  8.8             True             False   \n",
              "2   2013   5.0  10.0  7.4  8.4            False              True   \n",
              "3   2013   7.5   9.2  8.6  8.3             True             False   \n",
              "4   2013   7.0   7.5  7.0  7.7            False              True   \n",
              "\n",
              "   Grupo_Mañana  Grupo_Tarde  \n",
              "0          True        False  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2013,\n        \"max\": 2013,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8506755523321747,\n        \"min\": 5.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.05023806824929,\n        \"min\": 7.5,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6928203230275508,\n        \"min\": 7.0,\n        \"max\": 8.6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4159326868617087,\n        \"min\": 7.7,\n        \"max\": 8.8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero_Femenino\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero_Masculino\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grupo_Ma\\u00f1ana\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grupo_Tarde\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtzN6mUMEHiH"
      },
      "source": [
        "* Question:  Why  doesn't `Curso` change its values to columns? In our case it contains independent values according to the academic year of the beginning of the course."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcoP8m6Q4AFe"
      },
      "source": [
        "## Data types"
      ]
    },
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        "outputId": "0932f354-9ad5-43fb-c040-f29a09422ab9"
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      "source": [
        "data_filtered.dtypes"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Curso       int64\n",
              "Género     object\n",
              "Grupo      object\n",
              "P1        float64\n",
              "P2        float64\n",
              "P3        float64\n",
              "P4        float64\n",
              "dtype: object"
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              "      <td>float64</td>\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
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          },
          "metadata": {},
          "execution_count": 171
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dgxktUC9uvI"
      },
      "source": [
        "* Changing types of variables one by one"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1jEkJVp4JiG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "04b86d64-2f12-4ebb-ad0a-302b0f4a43e1"
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      "source": [
        "data_filtered['Curso'] = data_filtered['Curso'].astype('category')\n",
        "data_filtered['Género'] = data_filtered['Género'].astype('category')\n",
        "data_filtered['Grupo'] = data_filtered['Grupo'].astype('category')\n",
        "\n",
        "data_filtered.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Curso     category\n",
              "Género    category\n",
              "Grupo     category\n",
              "P1         float64\n",
              "P2         float64\n",
              "P3         float64\n",
              "P4         float64\n",
              "dtype: object"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Curso</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Género</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grupo</th>\n",
              "      <td>category</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P1</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P2</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n5rc3ssj-Aoj"
      },
      "source": [
        "* Changing types in one line"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72kY3kFZ-Aol",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6c10685d-d7aa-4eb6-f8ea-f360f6fcb29a"
      },
      "source": [
        "data_filtered = data_filtered.astype(\n",
        "    {'Curso':'category','Género':'category','Grupo':'category'})\n",
        "\n",
        "data_filtered.dtypes\n",
        "data_filtered.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Curso     Género   Grupo    P1    P2   P3   P4\n",
              "0  2013   Femenino  Mañana   8.5   8.3  7.0  8.0\n",
              "1  2013   Femenino  Mañana  10.0   9.8  8.0  8.8\n",
              "2  2013  Masculino  Mañana   5.0  10.0  7.4  8.4\n",
              "3  2013   Femenino  Mañana   7.5   9.2  8.6  8.3\n",
              "4  2013  Masculino  Mañana   7.0   7.5  7.0  7.7"
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-4fa5dc08-d798-4215-95f1-4afcc324f933');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
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              "  }\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_filtered",
              "summary": "{\n  \"name\": \"data_filtered\",\n  \"rows\": 599,\n  \"fields\": [\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2013,\n          2014,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Masculino\",\n          \"Femenino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grupo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tarde\",\n          \"Ma\\u00f1ana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3169437210323016,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          7.7,\n          8.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3068082646429127,\n        \"min\": 0.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          10.2,\n          6.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3267974938541696,\n        \"min\": 0.0,\n        \"max\": 11.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          9.2,\n          10.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.720897761028654,\n        \"min\": 0.0,\n        \"max\": 9.6,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          8.9,\n          4.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uoJqhRTt_BTb"
      },
      "source": [
        "* You can set data type from the beginning when importing data\n",
        "\n",
        "* This is more efficient and use lower amount of RAM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9F2rY93_7zS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "681093ed-889d-470a-e307-0639c511fd89"
      },
      "source": [
        "dtype = {'Curso':'category','Género':'category','Grupo':'category'}\n",
        "data_mem = pd.read_csv('https://www.dropbox.com/s/7h4710rz3s5g6mz/asignatura2.csv?dl=1', dtype = dtype)\n",
        "\n",
        "print(data_mem.shape)\n",
        "print(data_mem.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(599, 7)\n",
            "Curso     category\n",
            "Género    category\n",
            "Grupo     category\n",
            "P1         float64\n",
            "P2         float64\n",
            "P3         float64\n",
            "P4         float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNMgVo4FZ9T3"
      },
      "source": [
        "## Aggregate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfqZY2Z_8gzd"
      },
      "source": [
        "* You can get a new data frame with aggregates (summary stastitics)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ig_ey7nN_NKd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "1038a4a9-534a-410d-e285-0015ad56c7fe"
      },
      "source": [
        "data_filtered.head()\n",
        "df = data_filtered[['P1','P2','P3','P4']].aggregate(['mean','std','count']).round(1)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          P1     P2     P3     P4\n",
              "mean     8.4    8.2    8.1    7.6\n",
              "std      2.3    2.3    2.3    1.7\n",
              "count  599.0  599.0  599.0  599.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f7a35f0-e4f9-47c5-bf15-5f074559c6db\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.4</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>599.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>599.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f7a35f0-e4f9-47c5-bf15-5f074559c6db')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5f7a35f0-e4f9-47c5-bf15-5f074559c6db button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5f7a35f0-e4f9-47c5-bf15-5f074559c6db');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8dcbb5d0-a683-447d-8474-057d83212765\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8dcbb5d0-a683-447d-8474-057d83212765')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8dcbb5d0-a683-447d-8474-057d83212765 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d1b82c73-016f-4492-bdcf-8fc2319add6b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d1b82c73-016f-4492-bdcf-8fc2319add6b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 342.7575576604159,\n        \"min\": 2.3,\n        \"max\": 599.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.4,\n          2.3,\n          599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 342.8144152939508,\n        \"min\": 2.3,\n        \"max\": 599.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.2,\n          2.3,\n          599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 342.84285515864747,\n        \"min\": 2.3,\n        \"max\": 599.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.1,\n          2.3,\n          599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 343.16081264231406,\n        \"min\": 1.7,\n        \"max\": 599.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7.6,\n          1.7,\n          599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raGaFUDi8Zzh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "7ab80cfd-ccaf-4b1d-898f-69156f3ab323"
      },
      "source": [
        "\n",
        "# remember transpose\n",
        "df = data_filtered[['P1','P2','P3','P4']].aggregate(['mean','std','count']).T.round(1)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    mean  std  count\n",
              "P1   8.4  2.3  599.0\n",
              "P2   8.2  2.3  599.0\n",
              "P3   8.1  2.3  599.0\n",
              "P4   7.6  1.7  599.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f2a83abd-59ce-4500-bd24-02e8911962cd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>P1</th>\n",
              "      <td>8.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>599.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P2</th>\n",
              "      <td>8.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>599.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>8.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>599.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>7.6</td>\n",
              "      <td>1.7</td>\n",
              "      <td>599.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2a83abd-59ce-4500-bd24-02e8911962cd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f2a83abd-59ce-4500-bd24-02e8911962cd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f2a83abd-59ce-4500-bd24-02e8911962cd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d9b6ceb1-5e96-408f-874f-4af211c06405\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34034296427770244,\n        \"min\": 7.6,\n        \"max\": 8.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8.2,\n          7.6,\n          8.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29999999999999993,\n        \"min\": 1.7,\n        \"max\": 2.3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.7,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 599.0,\n        \"max\": 599.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMK_oN9Y9IjS"
      },
      "source": [
        "## The .groupby() method\n",
        "\n",
        "* Allows to compute aggregates within a group defined by several variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJcX05hV9V0_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "873d125a-4800-4f61-9a2b-9447f7ee59dd"
      },
      "source": [
        "data_filtered[['Curso','P1','P2','P3','P4']].groupby('Curso').aggregate(['mean','std','count']).round(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-177-aec944e2c333>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  data_filtered[['Curso','P1','P2','P3','P4']].groupby('Curso').aggregate(['mean','std','count']).round(1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        P1              P2              P3              P4           \n",
              "      mean  std count mean  std count mean  std count mean  std count\n",
              "Curso                                                                \n",
              "2013   8.0  2.3   191  8.2  2.1   191  7.4  2.7   191  7.5  1.9   191\n",
              "2014   8.3  2.2   214  8.0  2.5   214  8.3  1.9   214  7.6  1.6   214\n",
              "2015   8.8  2.4   194  8.4  2.2   194  8.6  2.1   194  7.6  1.7   194"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">P1</th>\n",
              "      <th colspan=\"3\" halign=\"left\">P2</th>\n",
              "      <th colspan=\"3\" halign=\"left\">P3</th>\n",
              "      <th colspan=\"3\" halign=\"left\">P4</th>\n",
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              "      <th></th>\n",
              "      <th>mean</th>\n",
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              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>count</th>\n",
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              "      <th>Curso</th>\n",
              "      <th></th>\n",
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              "      <td>191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>8.3</td>\n",
              "      <td>2.2</td>\n",
              "      <td>214</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>214</td>\n",
              "      <td>8.3</td>\n",
              "      <td>1.9</td>\n",
              "      <td>214</td>\n",
              "      <td>7.6</td>\n",
              "      <td>1.6</td>\n",
              "      <td>214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>8.8</td>\n",
              "      <td>2.4</td>\n",
              "      <td>194</td>\n",
              "      <td>8.4</td>\n",
              "      <td>2.2</td>\n",
              "      <td>194</td>\n",
              "      <td>8.6</td>\n",
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              "      <td>194</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    })();\n",
              "  </script>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_filtered[['Curso','P1','P2','P3','P4']]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": [\n        \"Curso\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2013,\n          2014,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P1\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4041451884327384,\n        \"min\": 8.0,\n        \"max\": 8.8,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.0,\n          8.3,\n          8.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P1\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09999999999999987,\n        \"min\": 2.2,\n        \"max\": 2.4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.3,\n          2.2,\n          2.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P1\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 191,\n        \"max\": 214,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          191,\n          214,\n          194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P2\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20000000000000018,\n        \"min\": 8.0,\n        \"max\": 8.4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.2,\n          8.0,\n          8.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P2\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2081665999466132,\n        \"min\": 2.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.1,\n          2.5,\n          2.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P2\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 191,\n        \"max\": 214,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          191,\n          214,\n          194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P3\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6244997998398396,\n        \"min\": 7.4,\n        \"max\": 8.6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7.4,\n          8.3,\n          8.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P3\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4163331998932267,\n        \"min\": 1.9,\n        \"max\": 2.7,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.7,\n          1.9,\n          2.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P3\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 191,\n        \"max\": 214,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          191,\n          214,\n          194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P4\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05773502691896237,\n        \"min\": 7.5,\n        \"max\": 7.6,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7.6,\n          7.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P4\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15275252316519458,\n        \"min\": 1.6,\n        \"max\": 1.9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.9,\n          1.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P4\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 191,\n        \"max\": 214,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          191,\n          214\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "fDBG2eGZP3Am",
        "outputId": "09d8ad04-74d1-42d4-e59b-57e799ffab8c"
      },
      "source": [
        "data_filtered[['Curso','Género','P1','P2','P3','P4']].groupby(by=[\"Curso\",\"Género\"]).aggregate(['mean','std','count']).round(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-178-e74d53cb3d51>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  data_filtered[['Curso','Género','P1','P2','P3','P4']].groupby(by=[\"Curso\",\"Género\"]).aggregate(['mean','std','count']).round(1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  P1              P2              P3              P4       \\\n",
              "                mean  std count mean  std count mean  std count mean  std   \n",
              "Curso Género                                                                \n",
              "2013  Femenino   8.4  2.1   110  8.6  1.8   110  7.7  2.6   110  7.8  1.7   \n",
              "      Masculino  7.6  2.6    81  7.7  2.4    81  6.9  2.8    81  7.1  2.1   \n",
              "2014  Femenino   8.2  2.3   141  8.3  2.5   141  8.5  1.8   141  7.7  1.5   \n",
              "      Masculino  8.6  2.0    73  7.4  2.6    73  8.1  2.0    73  7.2  1.6   \n",
              "2015  Femenino   8.9  2.3   134  8.7  1.9   134  8.8  1.8   134  7.8  1.4   \n",
              "      Masculino  8.7  2.6    60  7.8  2.7    60  8.1  2.7    60  7.1  2.1   \n",
              "\n",
              "                       \n",
              "                count  \n",
              "Curso Género           \n",
              "2013  Femenino    110  \n",
              "      Masculino    81  \n",
              "2014  Femenino    141  \n",
              "      Masculino    73  \n",
              "2015  Femenino    134  \n",
              "      Masculino    60  "
            ],
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-01875bec-0ea1-4f4f-8b67-dd700d7333e5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_filtered[['Curso','G\\u00e9nero','P1','P2','P3','P4']]\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": [\n        \"P1\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4604345773288537,\n        \"min\": 7.6,\n        \"max\": 8.9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8.4,\n          7.6,\n          8.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P1\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24832774042918904,\n        \"min\": 2.0,\n        \"max\": 2.6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.6,\n          2.0,\n          2.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P1\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 60,\n        \"max\": 141,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          110,\n          81,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P2\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5269408568963564,\n        \"min\": 7.4,\n        \"max\": 8.7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8.6,\n          7.7,\n          7.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P2\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37638632635454056,\n        \"min\": 1.8,\n        \"max\": 2.7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.8,\n          2.4,\n          2.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P2\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 60,\n        \"max\": 141,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          110,\n          81,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P3\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6645800679125629,\n        \"min\": 6.9,\n        \"max\": 8.8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.9,\n          8.8,\n          8.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P3\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4665476038590988,\n        \"min\": 1.8,\n        \"max\": 2.8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.8,\n          2.7,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P3\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 60,\n        \"max\": 141,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          110,\n          81,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P4\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3507135583350037,\n        \"min\": 7.1,\n        \"max\": 7.8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7.1,\n          7.2,\n          7.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P4\",\n        \"std\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30110906108363245,\n        \"min\": 1.4,\n        \"max\": 2.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.1,\n          1.4,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"P4\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 60,\n        \"max\": 141,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          110,\n          81,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1S17V2ujC9dx"
      },
      "source": [
        "* Note the difference in the output of `describe()`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-aNOKQgC9OM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "2e3c77bc-b836-423c-8dfb-9a586ce512bc"
      },
      "source": [
        "# Only results for quantitative variables\n",
        "data_filtered.describe().round(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          P1     P2     P3     P4\n",
              "count  599.0  599.0  599.0  599.0\n",
              "mean     8.4    8.2    8.1    7.6\n",
              "std      2.3    2.3    2.3    1.7\n",
              "min      0.0    0.0    0.0    0.0\n",
              "25%      7.5    7.5    7.8    7.2\n",
              "50%      9.4    9.2    8.8    8.0\n",
              "75%     10.0    9.7    9.4    8.5\n",
              "max     10.0   11.0   11.0    9.6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a6ba348-5d40-421d-9d4d-4695149bc323\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>599.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>599.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.4</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.8</td>\n",
              "      <td>7.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.4</td>\n",
              "      <td>9.2</td>\n",
              "      <td>8.8</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.4</td>\n",
              "      <td>8.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a6ba348-5d40-421d-9d4d-4695149bc323')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9a6ba348-5d40-421d-9d4d-4695149bc323 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9a6ba348-5d40-421d-9d4d-4695149bc323');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-40d0281a-c071-467d-a093-76698c94726f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-40d0281a-c071-467d-a093-76698c94726f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-40d0281a-c071-467d-a093-76698c94726f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_filtered\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 209.407332045193,\n        \"min\": 0.0,\n        \"max\": 599.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          599.0,\n          8.4,\n          9.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 209.3935320156762,\n        \"min\": 0.0,\n        \"max\": 599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.2,\n          9.2,\n          599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 209.41770289475122,\n        \"min\": 0.0,\n        \"max\": 599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.1,\n          8.8,\n          599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 209.65467525705895,\n        \"min\": 0.0,\n        \"max\": 599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.6,\n          8.0,\n          599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "A_NSsmBiD2ee",
        "outputId": "c8005b50-c8f5-424b-e321-a471a2845aca"
      },
      "source": [
        "# Different output only categorical variables\n",
        "data_filtered[['Curso', 'Género']].describe().round(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Curso    Género\n",
              "count     599       599\n",
              "unique      3         2\n",
              "top      2014  Femenino\n",
              "freq      214       385"
            ],
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              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-828a43a1-1e29-4030-9fc9-cddaad56390a');\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3058ed02-2db3-43b4-b422-26956901b8f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_filtered[['Curso', 'G\\u00e9nero']]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 905,\n        \"min\": 3,\n        \"max\": 2014,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          214,\n          599\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"385\",\n          \"599\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trkCvNHDEhGG"
      },
      "source": [
        "## Sorting Values\n",
        "\n",
        "* The DataFrame.sort_values() method to sort the data.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oQa3SyQoFXD7",
        "outputId": "e4b91627-2ee1-45f2-c43b-7c1fcd296dd1"
      },
      "source": [
        "data_filtered.sort_values('P4', ascending=False).head()\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Curso     Género   Grupo    P1    P2   P3   P4\n",
              "234  2014   Femenino  Mañana  10.0  10.0  9.8  9.6\n",
              "10   2013   Femenino  Mañana  10.0  10.0  9.6  9.6\n",
              "192  2014  Masculino  Mañana  10.0  10.0  9.8  9.6\n",
              "54   2013   Femenino  Mañana   9.7   9.8  9.6  9.5\n",
              "500  2015   Femenino  Mañana  10.0  10.0  9.7  9.5"
            ],
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              "\n",
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              "    <tr>\n",
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              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
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              "      <td>9.7</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
              "<style>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5148781d-c143-4221-9fad-525cb183b9fb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_filtered\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2014,\n          2013,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Masculino\",\n          \"Femenino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grupo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Ma\\u00f1ana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1341640786499877,\n        \"min\": 9.7,\n        \"max\": 10.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08944271909999127,\n        \"min\": 9.8,\n        \"max\": 10.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10000000000000053,\n        \"min\": 9.6,\n        \"max\": 9.8,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05477225575051641,\n        \"min\": 9.5,\n        \"max\": 9.6,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3LlD3ww1RpLK",
        "outputId": "4e582218-13a4-4e92-a6cf-8e26a8ecdb38"
      },
      "source": [
        "data_filtered.sort_values(['P4','P3'], ascending=False).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Curso     Género   Grupo    P1    P2    P3   P4\n",
              "192  2014  Masculino  Mañana  10.0  10.0   9.8  9.6\n",
              "234  2014   Femenino  Mañana  10.0  10.0   9.8  9.6\n",
              "10   2013   Femenino  Mañana  10.0  10.0   9.6  9.6\n",
              "208  2014   Femenino  Mañana  10.0   9.8  10.0  9.5\n",
              "500  2015   Femenino  Mañana  10.0  10.0   9.7  9.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-111d5724-aceb-4514-a572-78854af14aff\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Curso</th>\n",
              "      <th>Género</th>\n",
              "      <th>Grupo</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>2014</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.8</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.8</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2013</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.6</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>2014</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>2015</td>\n",
              "      <td>Femenino</td>\n",
              "      <td>Mañana</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.7</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-111d5724-aceb-4514-a572-78854af14aff')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-111d5724-aceb-4514-a572-78854af14aff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-111d5724-aceb-4514-a572-78854af14aff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d5bf0ef6-9f21-4875-ac2f-5fdcacebabb0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d5bf0ef6-9f21-4875-ac2f-5fdcacebabb0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d5bf0ef6-9f21-4875-ac2f-5fdcacebabb0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_filtered\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Curso\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2014,\n          2013,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"G\\u00e9nero\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Femenino\",\n          \"Masculino\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grupo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Ma\\u00f1ana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 10.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08944271909999127,\n        \"min\": 9.8,\n        \"max\": 10.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1483239697419135,\n        \"min\": 9.6,\n        \"max\": 10.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05477225575051641,\n        \"min\": 9.5,\n        \"max\": 9.6,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJKyqXqqFOIH"
      },
      "source": [
        "\n",
        "## Exporting a DataFrame\n",
        "\n",
        "* Data can be exported with several methods, depending on the desired format\n",
        "\n",
        "  + data_filtered.to_csv()\n",
        "\n",
        "  + data_filtered.to_excel()\n"
      ]
    }
  ]
}