data_cleaning_solutions.ipynb

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  "metadata": {
    "colab": {
      "name": "[YCMI_CBDS Summer Course] data-cleaning-solutions",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxvkM7TcSr-i",
        "colab_type": "text"
      },
      "source": [
        "Let's go ahead and setup our usual libraries:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkscuS3gSsow",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import plotnine as p9"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3X9ZSINnnPI",
        "colab_type": "text"
      },
      "source": [
        "And the connection to Google Drive to get the course data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iW2VHPq0nqZF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "47dd38aa-070d-459e-dd8d-28f832362c35"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKsMHKQdQeoa",
        "colab_type": "text"
      },
      "source": [
        "### Tidy data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85Ss4Q6Xn2H3",
        "colab_type": "text"
      },
      "source": [
        "Read some of the early Connecticut daily coronavirus case report data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yoixxQV5QjvK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "coronavirus = pd.read_csv('/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/ct-coronavirus-counts.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWuLngbmoO45",
        "colab_type": "text"
      },
      "source": [
        "Preview the data by checking the `.head()`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-PtQVr2oRdM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0b20e7a7-0709-4a84-91d6-2b948fa57e21"
      },
      "source": [
        "coronavirus.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Date</th>\n",
              "      <th>New Haven</th>\n",
              "      <th>Hamden</th>\n",
              "      <th>West Haven</th>\n",
              "      <th>Fairfield</th>\n",
              "      <th>Stamford</th>\n",
              "      <th>Danbury</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21-Mar-20</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22-Mar-20</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23-Mar-20</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24-Mar-20</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>34</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25-Mar-20</td>\n",
              "      <td>29</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>48</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  New Haven  Hamden  West Haven  Fairfield  Stamford  Danbury\n",
              "0  21-Mar-20          6       0           6          4        19        4\n",
              "1  22-Mar-20          7       1           6          7        25        8\n",
              "2  23-Mar-20          8       1           6          8        30       15\n",
              "3  24-Mar-20         22       4          11          9        34       29\n",
              "4  25-Mar-20         29       6          12         13        48       63"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JI4tvcxqEZW",
        "colab_type": "text"
      },
      "source": [
        "**IS THIS TIDY DATA? WHY OR WHY NOT?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fv9eapzuovQ3",
        "colab_type": "text"
      },
      "source": [
        "The data here is based on the total counts as of the state's daily data releases. Changes in the number of reported cases do not necessarily correspond to the date people got sick or the date they were tested."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5oHfVo2vofqr",
        "colab_type": "text"
      },
      "source": [
        "Use `pd.to_datetime` to set the `Date` column to date objects."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjvvMxlEopVq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "coronavirus['Date'] = pd.to_datetime(coronavirus['Date'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7mxK0hppwV-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "651afd11-179f-4021-9f92-063705b91d41"
      },
      "source": [
        "coronavirus.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Date</th>\n",
              "      <th>New Haven</th>\n",
              "      <th>Hamden</th>\n",
              "      <th>West Haven</th>\n",
              "      <th>Fairfield</th>\n",
              "      <th>Stamford</th>\n",
              "      <th>Danbury</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>30</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>34</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-25</td>\n",
              "      <td>29</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>48</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  New Haven  Hamden  West Haven  Fairfield  Stamford  Danbury\n",
              "0 2020-03-21          6       0           6          4        19        4\n",
              "1 2020-03-22          7       1           6          7        25        8\n",
              "2 2020-03-23          8       1           6          8        30       15\n",
              "3 2020-03-24         22       4          11          9        34       29\n",
              "4 2020-03-25         29       6          12         13        48       63"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tFQaCzyuqLkk",
        "colab_type": "text"
      },
      "source": [
        "Plot the number of cases in New Haven over time. Hint: you can rotate the x-axis labels by adding e.g. `p9.theme(axis_text_x=p9.element_text(angle=20))`.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cDA8-KvqQd_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "afdcdf9f-ed01-43f5-b9e4-8385f0ab69ed"
      },
      "source": [
        "p9.ggplot(coronavirus, p9.aes(x='Date', y='New Haven')) + p9.geom_line() + p9.theme(axis_text_x=p9.element_text(angle=20))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363287042412775)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12BPHwFMtG1T",
        "colab_type": "text"
      },
      "source": [
        "Switch to a semi-log-y graph by adding a scale transformation of `log10` to the y-axis; i.e. add `p9.scale_y_continuous(trans='log10')`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYXh4pWYtSy5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "0a2ae95f-63e3-4eeb-d22a-e11b7d75f6e0"
      },
      "source": [
        "p9.ggplot(coronavirus, p9.aes(x='Date', y='New Haven')) + p9.geom_line() + p9.theme(axis_text_x=p9.element_text(angle=20)) + p9.scale_y_continuous(trans='log10')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363287042410422)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zh2GPSSntzc6",
        "colab_type": "text"
      },
      "source": [
        "The curve appears to be close to a straight line between April 5, 2020 and April 17, 2020. **What would a true straight line mean?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUc7yfrpvNLI",
        "colab_type": "text"
      },
      "source": [
        "**How hard would it be to add curves for all the other cities in our dataset? What makes it hard?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgbfMDZxuLSF",
        "colab_type": "text"
      },
      "source": [
        "Use `pd.melt` to turn the dataset into tidy data with three columns: `Date`, `City`, `Cases`. Verify that you did what you think you did.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymFYMpwxsfqx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ce71a653-008f-4b43-eba5-7441cd7223a3"
      },
      "source": [
        "tidy_coronavirus = pd.melt(\n",
        "    coronavirus,\n",
        "    id_vars=['Date'],\n",
        "    value_vars=['New Haven', 'West Haven', 'Hamden', 'Fairfield', 'Stamford', 'Danbury'],\n",
        "    var_name='City',\n",
        "    value_name='Cases')\n",
        "\n",
        "tidy_coronavirus"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Date</th>\n",
              "      <th>City</th>\n",
              "      <th>Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>New Haven</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>New Haven</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>New Haven</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>New Haven</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-25</td>\n",
              "      <td>New Haven</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>2020-04-13</td>\n",
              "      <td>Danbury</td>\n",
              "      <td>726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>2020-04-14</td>\n",
              "      <td>Danbury</td>\n",
              "      <td>740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>2020-04-15</td>\n",
              "      <td>Danbury</td>\n",
              "      <td>754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>2020-04-16</td>\n",
              "      <td>Danbury</td>\n",
              "      <td>776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>2020-04-17</td>\n",
              "      <td>Danbury</td>\n",
              "      <td>828</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>168 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date       City  Cases\n",
              "0   2020-03-21  New Haven      6\n",
              "1   2020-03-22  New Haven      7\n",
              "2   2020-03-23  New Haven      8\n",
              "3   2020-03-24  New Haven     22\n",
              "4   2020-03-25  New Haven     29\n",
              "..         ...        ...    ...\n",
              "163 2020-04-13    Danbury    726\n",
              "164 2020-04-14    Danbury    740\n",
              "165 2020-04-15    Danbury    754\n",
              "166 2020-04-16    Danbury    776\n",
              "167 2020-04-17    Danbury    828\n",
              "\n",
              "[168 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRXFnmZqvlsl",
        "colab_type": "text"
      },
      "source": [
        "Now using this tidy version of the dataset, plot all the cities on the same graph (with a semi-log-y axis). You can do this in a single line not much longer than the line for plotting just New Haven."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "0c4cec77-3e49-4512-99d3-7739cdabd48b",
        "id": "EmkhkmRdv6Zm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        }
      },
      "source": [
        "p9.ggplot(tidy_coronavirus, p9.aes(x='Date', y='Cases', color='City')) + p9.geom_line() + p9.theme(axis_text_x=p9.element_text(angle=20)) + p9.scale_y_continuous(trans='log10')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:679: RuntimeWarning: divide by zero encountered in log10\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8749812342648)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihNs7Audwm1X",
        "colab_type": "text"
      },
      "source": [
        "Finally, add markers in addition to the lines to highlight the actual data points."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3M2gHPOwybE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "8b195b42-feca-4017-ba10-db5e6416c46a"
      },
      "source": [
        "p9.ggplot(tidy_coronavirus, p9.aes(x='Date', y='Cases', color='City')) + p9.geom_line() + p9.geom_point() + p9.theme(axis_text_x=p9.element_text(angle=20)) + p9.scale_y_continuous(trans='log10')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:679: RuntimeWarning: divide by zero encountered in log10\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363287044090059)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOLcfV9nQkmF",
        "colab_type": "text"
      },
      "source": [
        "### Missing data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vcI29JsTKkO",
        "colab_type": "text"
      },
      "source": [
        "Let's load the \"Pima Indians Diabetes Database\" dataset from the web; this data comes from the National Institute of Diabetes and Digestive and Kidney Diseases circa 1990 and consists solely of women over age 21:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cq5pYJe-Qvc5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "diabetes = pd.read_csv('https://raw.githubusercontent.com/susanli2016/Machine-Learning-with-Python/master/diabetes.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awlwN8XnTRrC",
        "colab_type": "text"
      },
      "source": [
        "Examine the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfcWtE10TOOu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "7edc02fd-aef5-4427-baa8-c2e47dbf6f2e"
      },
      "source": [
        "diabetes"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  ...  Age  Outcome\n",
              "0              6      148  ...   50        1\n",
              "1              1       85  ...   31        0\n",
              "2              8      183  ...   32        1\n",
              "3              1       89  ...   21        0\n",
              "4              0      137  ...   33        1\n",
              "..           ...      ...  ...  ...      ...\n",
              "763           10      101  ...   63        0\n",
              "764            2      122  ...   27        0\n",
              "765            5      121  ...   30        0\n",
              "766            1      126  ...   47        1\n",
              "767            1       93  ...   23        0\n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GcqprginTVPr",
        "colab_type": "text"
      },
      "source": [
        "Verify that there are no columns that contain null values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q71A03jUTa2b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "94909caa-5d0c-4826-ded6-7587adeb0fca"
      },
      "source": [
        "diabetes.isnull().sum()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 0\n",
              "Glucose                     0\n",
              "BloodPressure               0\n",
              "SkinThickness               0\n",
              "Insulin                     0\n",
              "BMI                         0\n",
              "DiabetesPedigreeFunction    0\n",
              "Age                         0\n",
              "Outcome                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkHvJ6bWTb58",
        "colab_type": "text"
      },
      "source": [
        "In some but not all columns in this particular dataset, values of 0 indicate missing data. (Note: insulin measurements here are serum insulin levels and should never be 0.) Replace the zero values with `pd.NA` to indicate that the data is missing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aItEoBwLd7zL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "diabetes = diabetes.replace({\n",
        "    'Glucose': {0: pd.NA},\n",
        "    'BloodPressure': {0: pd.NA},\n",
        "    'SkinThickness': {0: pd.NA},\n",
        "    'Insulin': {0: pd.NA},\n",
        "    'BMI': {0: pd.NA},\n",
        "    'DiabetesPedigreeFunction': {0: pd.NA},\n",
        "    'Age': {0: pd.NA}\n",
        "})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6u0r2wydestJ",
        "colab_type": "text"
      },
      "source": [
        "How many missing values were found in each column?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zcY2tVveu4r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "5d02a9a4-2ddd-4a16-ea7e-fc449a973147"
      },
      "source": [
        "diabetes.isnull().sum()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   0\n",
              "Glucose                       5\n",
              "BloodPressure                35\n",
              "SkinThickness               227\n",
              "Insulin                     374\n",
              "BMI                          11\n",
              "DiabetesPedigreeFunction      0\n",
              "Age                           0\n",
              "Outcome                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rdcKkyxFg-nx",
        "colab_type": "text"
      },
      "source": [
        "What is the mean `Age`? How does the mean `Age` for participants with a `SkinThickness` (Triceps skin fold thickness in mm) value compare to those without a `SkinThickness` value?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCIScZbEh1QA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "816487e3-b235-4879-8046-35657c40684a"
      },
      "source": [
        "diabetes['Age'].mean()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33.240885416666664"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5b_44ixRfmGm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "81b03082-9350-4416-db36-ce1914dbdfa7"
      },
      "source": [
        "diabetes[diabetes['SkinThickness'].notnull()]['Age'].mean()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31.55822550831793"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOAayBwdh8-T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c454996e-0d3e-4d08-bb87-a6a608359a37"
      },
      "source": [
        "diabetes[diabetes['SkinThickness'].isnull()]['Age'].mean()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37.25110132158591"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNtY-SlwizkA",
        "colab_type": "text"
      },
      "source": [
        "Make a violin plot comparing the distribution of ages for people who do and do not have `SkinThickness` measurements. To make this easier, it may help to add a `hasThickness` column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVFq4cdnf-5s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "outputId": "a2ffa13a-43d9-41bc-a86d-7dd8cba77f5b"
      },
      "source": [
        "diabetes['hasThickness'] = diabetes['SkinThickness'].notnull()\n",
        "p9.ggplot(diabetes, p9.aes(y='Age', x='hasThickness')) + p9.geom_violin()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8749806405787)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EYRaOpXjWGn",
        "colab_type": "text"
      },
      "source": [
        "What fraction of people under age 25 have `SkinThickness` measurements? What about people over age 50?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOHuXkCNkEpa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0761e70e-692d-44e3-ffe7-c1f5b09f4943"
      },
      "source": [
        "population = diabetes[diabetes['Age'] < 25]\n",
        "(population['SkinThickness'].notnull().sum()) / len(population)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8356164383561644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbpmTCH_kX3u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "09447370-a8db-44a8-9c45-916949bcb3ca"
      },
      "source": [
        "population = diabetes[diabetes['Age'] > 50]\n",
        "(population['SkinThickness'].notnull().sum()) / len(population)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5061728395061729"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOlvOaZHj__x",
        "colab_type": "text"
      },
      "source": [
        "Without attempting any further statistical tests, do you think the the missing values of `Skinthickness` are MAR, MCAR, or MNAR?"
      ]
    }
  ]
}