Data_Science_With_Covid.ipynb

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      "name": "Data-Science-With-Covid.ipynb",
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      "machine_shape": "hm",
      "authorship_tag": "ABX9TyN83KSCrCTQjxDxLVkTlimd",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/paiml/practical-mlops-book/blob/main/Data_Science_With_Covid.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahIRZj6K-3-o"
      },
      "source": [
        "# Data Science With Covid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NS0R02LQwG_2"
      },
      "source": [
        "## Ingest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJntteRBwI46"
      },
      "source": [
        "*COVID-19 Data from [New York Times Github](https://github.com/nytimes/covid-19-data)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ABhk0OLwejq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "38b10029-6801-45bc-91b0-29162ac74db4"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\")\n",
        "df.head()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-21</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "         date       state  fips  cases  deaths\n",
              "0  2020-01-21  Washington    53      1       0\n",
              "1  2020-01-22  Washington    53      1       0\n",
              "2  2020-01-23  Washington    53      1       0\n",
              "3  2020-01-24    Illinois    17      1       0\n",
              "4  2020-01-24  Washington    53      1       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1iMW3XxnkUXF"
      },
      "source": [
        "Last five rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opOpFijSkXR6",
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          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "70ebee36-03fe-4a86-bc4e-7691f8481eb9"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18379</th>\n",
              "      <td>2021-01-30</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>51</td>\n",
              "      <td>502221</td>\n",
              "      <td>6449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18380</th>\n",
              "      <td>2021-01-30</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>315448</td>\n",
              "      <td>4368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18381</th>\n",
              "      <td>2021-01-30</td>\n",
              "      <td>West Virginia</td>\n",
              "      <td>54</td>\n",
              "      <td>120340</td>\n",
              "      <td>2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18382</th>\n",
              "      <td>2021-01-30</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>55</td>\n",
              "      <td>590997</td>\n",
              "      <td>6433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18383</th>\n",
              "      <td>2021-01-30</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>56</td>\n",
              "      <td>51704</td>\n",
              "      <td>596</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             date          state  fips   cases  deaths\n",
              "18379  2021-01-30       Virginia    51  502221    6449\n",
              "18380  2021-01-30     Washington    53  315448    4368\n",
              "18381  2021-01-30  West Virginia    54  120340    2015\n",
              "18382  2021-01-30      Wisconsin    55  590997    6433\n",
              "18383  2021-01-30        Wyoming    56   51704     596"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zr7wdjHswKfq"
      },
      "source": [
        "*What are the columns?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwAMxSNSwNWj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79f4633c-5075-42b1-b5ad-578336847d32"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'state', 'fips', 'cases', 'deaths'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3njEtIJwSCi"
      },
      "source": [
        "*What is the shape:  i.e. rows,columns?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyCznu1cwUv_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fe91fc4-609a-4ed2-d2c0-45dc1a349f91"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18384, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY_nFEZKwI7R"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNw9of3LiQjU"
      },
      "source": [
        "*What are general characteristics of the data?  A good way to find out is `df.describe`*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHuBZouXCYAZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "e39e325b-e400-4d86-b270-77582bbc8332"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>18384.000000</td>\n",
              "      <td>1.838400e+04</td>\n",
              "      <td>18384.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>31.915742</td>\n",
              "      <td>1.322358e+05</td>\n",
              "      <td>3165.143005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.642755</td>\n",
              "      <td>2.574721e+05</td>\n",
              "      <td>5822.014120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>5.096750e+03</td>\n",
              "      <td>110.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>3.793550e+04</td>\n",
              "      <td>840.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>46.000000</td>\n",
              "      <td>1.454778e+05</td>\n",
              "      <td>3566.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>78.000000</td>\n",
              "      <td>3.309463e+06</td>\n",
              "      <td>42996.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               fips         cases        deaths\n",
              "count  18384.000000  1.838400e+04  18384.000000\n",
              "mean      31.915742  1.322358e+05   3165.143005\n",
              "std       18.642755  2.574721e+05   5822.014120\n",
              "min        1.000000  1.000000e+00      0.000000\n",
              "25%       17.000000  5.096750e+03    110.000000\n",
              "50%       31.000000  3.793550e+04    840.000000\n",
              "75%       46.000000  1.454778e+05   3566.000000\n",
              "max       78.000000  3.309463e+06  42996.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YtAIL7HFjOw"
      },
      "source": [
        "*Cases and Deaths in the USA due to Covid-19*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9vXU4_fCj-8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "fe8f5491-0723-4978-cf53-ade88fea58e5"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.scatterplot(x=\"cases\", y=\"deaths\", \n",
        "                hue=\"deaths\",size=\"deaths\", data=df)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f24726fac18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_pRge294dFK"
      },
      "source": [
        "### Date-based EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSVSSgwev-Pp"
      },
      "source": [
        "*Dealing with dates by setting index*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOzYb89evmrr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f26a2688-803f-4e2c-b358-2d8fe27acf6f"
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\")\n",
        "df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
        "df = df.set_index(\"date\")\n",
        "print(f\"Columns: {df.columns}\")\n",
        "df.index"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Columns: Index(['state', 'fips', 'cases', 'deaths'], dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2020-01-21', '2020-01-22', '2020-01-23', '2020-01-24',\n",
              "               '2020-01-24', '2020-01-25', '2020-01-25', '2020-01-25',\n",
              "               '2020-01-26', '2020-01-26',\n",
              "               ...\n",
              "               '2021-01-30', '2021-01-30', '2021-01-30', '2021-01-30',\n",
              "               '2021-01-30', '2021-01-30', '2021-01-30', '2021-01-30',\n",
              "               '2021-01-30', '2021-01-30'],\n",
              "              dtype='datetime64[ns]', name='date', length=18384, freq=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULM1KdpWijcS"
      },
      "source": [
        "*Filter results by date range*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1ennZNsvlJx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab1b6682-86b9-43f8-e45b-f21d74373ada"
      },
      "source": [
        "from datetime import date, timedelta\n",
        "today = date.today()\n",
        "daybefore = today - timedelta(days = 2)\n",
        "print(f\"Today {today}\")\n",
        "print(f\"Two days ago {daybefore}\") "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Today 2021-01-31\n",
            "Two days ago 2021-01-29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zuHpxkm1sTA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "3060d008-0c8f-4537-a242-275852efd1be"
      },
      "source": [
        "df.loc[daybefore:today].head()"
      ],
      "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>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>1</td>\n",
              "      <td>455582</td>\n",
              "      <td>7566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>2</td>\n",
              "      <td>53705</td>\n",
              "      <td>253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>4</td>\n",
              "      <td>750787</td>\n",
              "      <td>13047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>Arkansas</td>\n",
              "      <td>5</td>\n",
              "      <td>292563</td>\n",
              "      <td>4831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>3292084</td>\n",
              "      <td>40192</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 state  fips    cases  deaths\n",
              "date                                         \n",
              "2021-01-29     Alabama     1   455582    7566\n",
              "2021-01-29      Alaska     2    53705     253\n",
              "2021-01-29     Arizona     4   750787   13047\n",
              "2021-01-29    Arkansas     5   292563    4831\n",
              "2021-01-29  California     6  3292084   40192"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ek_hurrf5FFi"
      },
      "source": [
        "*The distribution of the data by date*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHl20wNuiuMw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "outputId": "e54d7d01-8e4d-406c-a560-5afd930ba619"
      },
      "source": [
        "sns.kdeplot(df.loc[daybefore:today][\"deaths\"], shade=True)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f24718c3588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehV0NJINmR5S"
      },
      "source": [
        "*Sort DataFrame in place by states with highest deaths and cases and show first 10 results*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QcPkd6CnH6q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "0288aa94-059f-41fe-f025-02cad9f24088"
      },
      "source": [
        "current_df = df.loc[daybefore:today].sort_values(by=[\"deaths\", \"cases\"], ascending=False)\n",
        "current_df.head(10)"
      ],
      "execution_count": 11,
      "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>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-01-30</th>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>1408955</td>\n",
              "      <td>42996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>1396063</td>\n",
              "      <td>42825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-30</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>3309463</td>\n",
              "      <td>40694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>3292084</td>\n",
              "      <td>40192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-30</th>\n",
              "      <td>Texas</td>\n",
              "      <td>48</td>\n",
              "      <td>2362817</td>\n",
              "      <td>37096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>Texas</td>\n",
              "      <td>48</td>\n",
              "      <td>2349047</td>\n",
              "      <td>36804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-30</th>\n",
              "      <td>Florida</td>\n",
              "      <td>12</td>\n",
              "      <td>1713581</td>\n",
              "      <td>26359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>Florida</td>\n",
              "      <td>12</td>\n",
              "      <td>1698562</td>\n",
              "      <td>26253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-30</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>42</td>\n",
              "      <td>844519</td>\n",
              "      <td>21658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>42</td>\n",
              "      <td>839888</td>\n",
              "      <td>21524</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   state  fips    cases  deaths\n",
              "date                                           \n",
              "2021-01-30      New York    36  1408955   42996\n",
              "2021-01-29      New York    36  1396063   42825\n",
              "2021-01-30    California     6  3309463   40694\n",
              "2021-01-29    California     6  3292084   40192\n",
              "2021-01-30         Texas    48  2362817   37096\n",
              "2021-01-29         Texas    48  2349047   36804\n",
              "2021-01-30       Florida    12  1713581   26359\n",
              "2021-01-29       Florida    12  1698562   26253\n",
              "2021-01-30  Pennsylvania    42   844519   21658\n",
              "2021-01-29  Pennsylvania    42   839888   21524"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSNzoid28GEX"
      },
      "source": [
        "*There should be 50 states and District of Columbia*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ktah9SqV8I3i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4976060e-9991-46d9-d892-9833ef8cb323"
      },
      "source": [
        "current_df.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(110, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_M6NGQk8_2U"
      },
      "source": [
        "### State Based Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzGQ9z_oA6V7"
      },
      "source": [
        "*Get 10 states and subset*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeicLkAlA9y4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de2a62c9-2051-4b81-f047-26659c25a257"
      },
      "source": [
        "top_ten_states = list(current_df[\"state\"].head(10).values)\n",
        "top_ten_states"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['New York',\n",
              " 'New York',\n",
              " 'California',\n",
              " 'California',\n",
              " 'Texas',\n",
              " 'Texas',\n",
              " 'Florida',\n",
              " 'Florida',\n",
              " 'Pennsylvania',\n",
              " 'Pennsylvania']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzv8KJtx9bFO"
      },
      "source": [
        "top_states_df = df[df['state'].isin(top_ten_states)].drop(columns=\"fips\")"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkLverBTICn3"
      },
      "source": [
        "*Verify the unique states left is the same 10*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3bJzaxyHnD6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48cd1b25-a378-4c5f-8464-8f975c7cbcc6"
      },
      "source": [
        "set(top_states_df.state.values)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'California', 'Florida', 'New York', 'Pennsylvania', 'Texas'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ba-QT229CYE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "647ad1fc-5bff-456e-f3fa-d4a4d159d9f6"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "ax = sns.lmplot(x=\"cases\", y=\"deaths\",\n",
        "                    height=3.5,\n",
        "                    col=\"state\",\n",
        "                    col_wrap=5,\n",
        "                    hue=\"state\", \n",
        "                    palette=\"Set2\",\n",
        "                    data=top_states_df)\n",
        "ax.fig.subplots_adjust(wspace=.2)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1260x252 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "in-JRg0lOCrp"
      },
      "source": [
        "*Interactive plot of top states*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf7j4jUBaKMS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "38fec0bd-f55f-45ac-87be-923dcba21424"
      },
      "source": [
        "top_states_march_current_df = top_states_df.loc[\"2020-03-08\":today].sort_values(by=[\"deaths\", \"cases\"], ascending=True)\n",
        "top_states_march_current_df.head()"
      ],
      "execution_count": 17,
      "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>state</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-08</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-09</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-10</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-11</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   state  cases  deaths\n",
              "date                                   \n",
              "2020-03-08  Pennsylvania      6       0\n",
              "2020-03-09  Pennsylvania     10       0\n",
              "2020-03-10  Pennsylvania     12       0\n",
              "2020-03-11  Pennsylvania     16       0\n",
              "2020-03-12  Pennsylvania     22       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7yg3JizOHIv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "8b49176a-7e1a-4ba0-8e49-eb42d8bc6692"
      },
      "source": [
        "import plotly.io as pio\n",
        "pio.renderers.default = 'colab'\n",
        "import plotly.express as px\n",
        "fig = px.scatter(top_states_march_current_df, x=top_states_march_current_df.index, y=\"cases\", size=\"deaths\", color=\"state\", facet_col=\"state\")\n",
        "fig.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN0jv8_3mD-z"
      },
      "source": [
        "### Search for Features:  Political, Health and Finance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VLRtEYvBtoR"
      },
      "source": [
        "*Sugar Intake By State*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnLcHUUUBwzD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "0b90d536-8dc8-4fe1-8c6f-36c61159840d"
      },
      "source": [
        "cdc_2013 = pd.read_csv(\"https://raw.githubusercontent.com/noahgift/sugar/master/data/education_sugar_cdc_2003.csv\")\n",
        "cdc_2013.set_index(\"State\", inplace=True)\n",
        "for column in cdc_2013.columns:\n",
        "  cdc_2013[column]=cdc_2013[column].str.replace(r\"\\(.*\\)\",\"\")\n",
        "  cdc_2013[column]=pd.to_numeric(cdc_2013[column])\n",
        "  \n",
        "cdc_2013.reset_index(inplace=True)\n",
        "cdc_2013.rename(columns={\"State\": \"state\", \"Employed\": \"employed-sugar-intake-daily\"},inplace=True)\n",
        "cdc_2013.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>state</th>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <th>Not employed</th>\n",
              "      <th>Retired</th>\n",
              "      <th>&lt;High school</th>\n",
              "      <th>High school</th>\n",
              "      <th>Some college</th>\n",
              "      <th>College graduate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>26.2</td>\n",
              "      <td>32.1</td>\n",
              "      <td>16.0</td>\n",
              "      <td>47.1</td>\n",
              "      <td>34.9</td>\n",
              "      <td>24.2</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>33.0</td>\n",
              "      <td>28.7</td>\n",
              "      <td>13.8</td>\n",
              "      <td>40.4</td>\n",
              "      <td>36.5</td>\n",
              "      <td>24.4</td>\n",
              "      <td>14.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>California</td>\n",
              "      <td>22.9</td>\n",
              "      <td>30.2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>29.9</td>\n",
              "      <td>21.4</td>\n",
              "      <td>11.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Connecticut</td>\n",
              "      <td>18.9</td>\n",
              "      <td>24.3</td>\n",
              "      <td>15.0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>26.9</td>\n",
              "      <td>19.9</td>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>District of Columbia</td>\n",
              "      <td>18.5</td>\n",
              "      <td>34.6</td>\n",
              "      <td>18.5</td>\n",
              "      <td>45.6</td>\n",
              "      <td>39.0</td>\n",
              "      <td>28.9</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  state  ...  College graduate\n",
              "0                Alaska  ...              12.9\n",
              "1               Arizona  ...              14.6\n",
              "2            California  ...              11.5\n",
              "3           Connecticut  ...              10.2\n",
              "4  District of Columbia  ...               8.4\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BT7YQzUcCRSn"
      },
      "source": [
        "*Combine Sugar Data and Covid-19 Data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxZ76ZI8EDQ2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "fa4b25f2-b99b-4c79-9ace-84b337c38356"
      },
      "source": [
        "cdc_employed_df = cdc_2013[[\"employed-sugar-intake-daily\", \"state\"]]\n",
        "sugar_covid_df = df.merge(cdc_employed_df, how=\"inner\", on=\"state\")\n",
        "sugar_covid_df.head()"
      ],
      "execution_count": 20,
      "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state  fips  cases  deaths  employed-sugar-intake-daily\n",
              "0  California     6      1       0                         22.9\n",
              "1  California     6      2       0                         22.9\n",
              "2  California     6      2       0                         22.9\n",
              "3  California     6      2       0                         22.9\n",
              "4  California     6      2       0                         22.9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9syyMOxhTHMo"
      },
      "source": [
        "*What about data from the 2016 Election?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RZa-tDmTPXP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "80110a38-6440-44dd-9bba-9381daf4218a"
      },
      "source": [
        "election_df = pd.read_csv(\"https://raw.githubusercontent.com/paiml/minimal-python/master/data/2016-Electoral-Votes.csv\")\n",
        "election_df.rename(columns={\"State\": \"state\"},inplace=True)\n",
        "election_df.drop(columns=\"Votes\", inplace=True)\n",
        "election_df = pd.concat([election_df, pd.get_dummies(election_df[\"Winning Party\"])], axis=1);\n",
        "election_df.head()\n",
        "#election_df[\"Republican?\"] = pd.get_dummies(election_df, columns=[\"Winning Party\"])\n",
        "#election_df.head()"
      ],
      "execution_count": 21,
      "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",
              "\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>state</th>\n",
              "      <th>Winning Party</th>\n",
              "      <th>Democrats</th>\n",
              "      <th>Republicans</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>Republicans</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>Republicans</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>Republicans</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Arkansas</td>\n",
              "      <td>Republicans</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>California</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state Winning Party  Democrats  Republicans\n",
              "0     Alabama   Republicans          0            1\n",
              "1      Alaska   Republicans          0            1\n",
              "2     Arizona   Republicans          0            1\n",
              "3    Arkansas   Republicans          0            1\n",
              "4  California     Democrats          1            0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6CuViW_V2Hx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "1d7282f3-407c-4554-c0d1-dc58e08fbec6"
      },
      "source": [
        "sugar_covid_df = sugar_covid_df.merge(election_df, how=\"inner\", on=\"state\")\n",
        "sugar_covid_df.head()"
      ],
      "execution_count": 22,
      "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",
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              "\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>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <th>Winning Party</th>\n",
              "      <th>Democrats</th>\n",
              "      <th>Republicans</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state  fips  cases  ...  Winning Party  Democrats Republicans\n",
              "0  California     6      1  ...      Democrats          1           0\n",
              "1  California     6      2  ...      Democrats          1           0\n",
              "2  California     6      2  ...      Democrats          1           0\n",
              "3  California     6      2  ...      Democrats          1           0\n",
              "4  California     6      2  ...      Democrats          1           0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmBJqPnXE5wR"
      },
      "source": [
        "*Generate Heatmap*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cee_tCSBGdEM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "444db2e5-045a-4841-9577-106348146a39"
      },
      "source": [
        "sugar_covid_df.corr()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\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>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <th>Democrats</th>\n",
              "      <th>Republicans</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fips</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.101693</td>\n",
              "      <td>-0.028964</td>\n",
              "      <td>0.267035</td>\n",
              "      <td>-0.190805</td>\n",
              "      <td>0.190805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cases</th>\n",
              "      <td>-0.101693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.710424</td>\n",
              "      <td>-0.119311</td>\n",
              "      <td>0.199597</td>\n",
              "      <td>-0.199597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>deaths</th>\n",
              "      <td>-0.028964</td>\n",
              "      <td>0.710424</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.259075</td>\n",
              "      <td>0.407206</td>\n",
              "      <td>-0.407206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <td>0.267035</td>\n",
              "      <td>-0.119311</td>\n",
              "      <td>-0.259075</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.766834</td>\n",
              "      <td>0.766834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Democrats</th>\n",
              "      <td>-0.190805</td>\n",
              "      <td>0.199597</td>\n",
              "      <td>0.407206</td>\n",
              "      <td>-0.766834</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Republicans</th>\n",
              "      <td>0.190805</td>\n",
              "      <td>-0.199597</td>\n",
              "      <td>-0.407206</td>\n",
              "      <td>0.766834</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 fips     cases  ...  Democrats  Republicans\n",
              "fips                         1.000000 -0.101693  ...  -0.190805     0.190805\n",
              "cases                       -0.101693  1.000000  ...   0.199597    -0.199597\n",
              "deaths                      -0.028964  0.710424  ...   0.407206    -0.407206\n",
              "employed-sugar-intake-daily  0.267035 -0.119311  ...  -0.766834     0.766834\n",
              "Democrats                   -0.190805  0.199597  ...   1.000000    -1.000000\n",
              "Republicans                  0.190805 -0.199597  ...  -1.000000     1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6wVCIMIE74B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "f4d7744b-205e-4429-fd4e-9f16c3bc1cba"
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "fig = go.Figure(data=go.Heatmap(\n",
        "                   z=sugar_covid_df.corr(),\n",
        "                   x=['fips','cases', 'deaths', 'employed-sugar-intake-daily', 'Democrats', 'Republicans'],\n",
        "                   y=['fips','cases', 'deaths', 'employed-sugar-intake-daily', 'Democrats', 'Republicans'],\n",
        "                   hoverongaps = False))\n",
        "fig.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFaYmi8fBE6_"
      },
      "source": [
        "sugar_covid_df.to_csv(\"covid-eda.csv\")"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLqU5s5pH8PB"
      },
      "source": [
        "*What about Real-Estate Data?*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4W9aBagTHFC"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrRjhxe30C_q"
      },
      "source": [
        "## Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdsYYSuy0Exu"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFOuQGyn0E6w"
      },
      "source": [
        "## Conclusion\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmcf2OJ40mm4"
      },
      "source": [
        ""
      ]
    }
  ]
}