Chapter7_data_science.ipynb

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        "<a href=\"https://colab.research.google.com/github/paiml/minimal-python/blob/master/Chapter7_data_science.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      },
      "source": [
        "# Chapter 7:  Data Science"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hc7QdhV05VUF",
        "colab_type": "text"
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      "source": [
        "**This is a chapter in the book Minimal Python**\n",
        "\n",
        "* [Buy a copy on LeanPub](https://leanpub.com/minimalpython)\n",
        "* [Buy a copy from Amazon](https://www.amazon.com/Minimal-Python-efficient-programmer-onemillion2021-ebook/dp/B0855NSRR7)\n",
        "\n",
        "[*Licensed Under Attribution-NonCommercial-ShareAlike 4.0*](https://creativecommons.org/licenses/by-nc-sa/4.0/)\n",
        "\n",
        "![Minimal Python Book](https://d2sofvawe08yqg.cloudfront.net/minimalpython/hero?1582640276)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NS0R02LQwG_2",
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      },
      "source": [
        "## Ingest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJntteRBwI46",
        "colab_type": "text"
      },
      "source": [
        "*COVID-19 Data from [New York Times Github](https://github.com/nytimes/covid-19-data)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ABhk0OLwejq",
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        "outputId": "985ccf95-9dfc-40fe-8281-6484b9bc3377",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "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": 29,
      "outputs": [
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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>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": {
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          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1iMW3XxnkUXF",
        "colab_type": "text"
      },
      "source": [
        "Last five rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opOpFijSkXR6",
        "colab_type": "code",
        "outputId": "8425ed2f-b1b0-4516-a217-0f4efdf30d2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>1934</th>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>51</td>\n",
              "      <td>2878</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1935</th>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>8384</td>\n",
              "      <td>383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1936</th>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>West Virginia</td>\n",
              "      <td>54</td>\n",
              "      <td>345</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1937</th>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>55</td>\n",
              "      <td>2440</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1938</th>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>56</td>\n",
              "      <td>212</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            date          state  fips  cases  deaths\n",
              "1934  2020-04-06       Virginia    51   2878      54\n",
              "1935  2020-04-06     Washington    53   8384     383\n",
              "1936  2020-04-06  West Virginia    54    345       4\n",
              "1937  2020-04-06      Wisconsin    55   2440      83\n",
              "1938  2020-04-06        Wyoming    56    212       0"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 30
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zr7wdjHswKfq",
        "colab_type": "text"
      },
      "source": [
        "*What are the columns?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwAMxSNSwNWj",
        "colab_type": "code",
        "outputId": "ebd41475-85cc-4166-de5c-5595b0d0e222",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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",
        "colab_type": "text"
      },
      "source": [
        "*What is the shape:  i.e. rows,columns?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyCznu1cwUv_",
        "colab_type": "code",
        "outputId": "fb5ea532-c126-4225-ba7e-ca71249245ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1939, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY_nFEZKwI7R",
        "colab_type": "text"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNw9of3LiQjU",
        "colab_type": "text"
      },
      "source": [
        "*What are general characteristics of the data?  A good way to find out is `df.describe`*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHuBZouXCYAZ",
        "colab_type": "code",
        "outputId": "8e4826ed-e1d1-46c0-da39-86188dffc763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 32,
      "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>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1939.000000</td>\n",
              "      <td>1939.000000</td>\n",
              "      <td>1939.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>30.892728</td>\n",
              "      <td>1467.224342</td>\n",
              "      <td>34.104177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.062123</td>\n",
              "      <td>7403.847224</td>\n",
              "      <td>210.798255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>46.000000</td>\n",
              "      <td>565.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>78.000000</td>\n",
              "      <td>130703.000000</td>\n",
              "      <td>4758.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "              fips          cases       deaths\n",
              "count  1939.000000    1939.000000  1939.000000\n",
              "mean     30.892728    1467.224342    34.104177\n",
              "std      18.062123    7403.847224   210.798255\n",
              "min       1.000000       1.000000     0.000000\n",
              "25%      17.000000       7.000000     0.000000\n",
              "50%      30.000000      75.000000     1.000000\n",
              "75%      46.000000     565.000000    10.000000\n",
              "max      78.000000  130703.000000  4758.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YtAIL7HFjOw",
        "colab_type": "text"
      },
      "source": [
        "*Cases and Deaths in the USA due to Covid-19*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9vXU4_fCj-8",
        "colab_type": "code",
        "outputId": "eb684f08-960c-4847-a492-150e26343549",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "import seaborn as sns\n",
        "sns.scatterplot(x=\"cases\", y=\"deaths\", \n",
        "                hue=\"deaths\",size=\"deaths\", data=df)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdedc88c630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vr5/xvtesWUN+fj5z5sw5430opRInEgpz/NCJHtft3LCLQOvgNFUnIx3B3U1jfVOf65vq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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_pRge294dFK",
        "colab_type": "text"
      },
      "source": [
        "### Date-based EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSVSSgwev-Pp",
        "colab_type": "text"
      },
      "source": [
        "*Dealing with dates by setting index*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOzYb89evmrr",
        "colab_type": "code",
        "outputId": "327c031d-ee8b-4ae0-8683-5b1fa4e1a1d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "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": 34,
      "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",
              "               '2020-04-06', '2020-04-06', '2020-04-06', '2020-04-06',\n",
              "               '2020-04-06', '2020-04-06', '2020-04-06', '2020-04-06',\n",
              "               '2020-04-06', '2020-04-06'],\n",
              "              dtype='datetime64[ns]', name='date', length=1939, freq=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULM1KdpWijcS",
        "colab_type": "text"
      },
      "source": [
        "*Filter results by date range*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1ennZNsvlJx",
        "colab_type": "code",
        "outputId": "32bbc404-d549-4e6e-c578-05fca252fbe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "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": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Today 2020-04-08\n",
            "Two days ago 2020-04-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zuHpxkm1sTA",
        "colab_type": "code",
        "outputId": "ab87d37c-7186-479d-fc0d-83a69780a66d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "df.loc[daybefore:today].head()"
      ],
      "execution_count": 36,
      "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>2020-04-06</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>1</td>\n",
              "      <td>2006</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>2</td>\n",
              "      <td>189</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>4</td>\n",
              "      <td>2465</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Arkansas</td>\n",
              "      <td>5</td>\n",
              "      <td>927</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>16284</td>\n",
              "      <td>386</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 state  fips  cases  deaths\n",
              "date                                       \n",
              "2020-04-06     Alabama     1   2006      53\n",
              "2020-04-06      Alaska     2    189       4\n",
              "2020-04-06     Arizona     4   2465      67\n",
              "2020-04-06    Arkansas     5    927      16\n",
              "2020-04-06  California     6  16284     386"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ek_hurrf5FFi",
        "colab_type": "text"
      },
      "source": [
        "*The distribution of the data by date*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHl20wNuiuMw",
        "colab_type": "code",
        "outputId": "720f5839-d7ac-444f-9f53-f65c598ece8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "sns.kdeplot(df.loc[daybefore:today][\"deaths\"], shade=True)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdedc7fab00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4mmiMYVB1N+00t11dRiISimICYQUw1cxqzKyCaJC4rlOZOuCq+PFlwFKPbkZcB8yPZyHV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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehV0NJINmR5S",
        "colab_type": "text"
      },
      "source": [
        "*Sort DataFrame in place by states with highest deaths and cases and show first 10 results*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QcPkd6CnH6q",
        "colab_type": "code",
        "outputId": "0e30a007-7af4-4adb-e56b-c5e62e0e41e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        }
      },
      "source": [
        "current_df = df.loc[daybefore:today].sort_values(by=[\"deaths\", \"cases\"], ascending=False)\n",
        "current_df.head(10)"
      ],
      "execution_count": 38,
      "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>2020-04-06</th>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>130703</td>\n",
              "      <td>4758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>New Jersey</td>\n",
              "      <td>34</td>\n",
              "      <td>41090</td>\n",
              "      <td>1005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Michigan</td>\n",
              "      <td>26</td>\n",
              "      <td>17130</td>\n",
              "      <td>727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Louisiana</td>\n",
              "      <td>22</td>\n",
              "      <td>14867</td>\n",
              "      <td>512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>16284</td>\n",
              "      <td>386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>8384</td>\n",
              "      <td>383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>17</td>\n",
              "      <td>12262</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Georgia</td>\n",
              "      <td>13</td>\n",
              "      <td>7558</td>\n",
              "      <td>294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Massachusetts</td>\n",
              "      <td>25</td>\n",
              "      <td>13837</td>\n",
              "      <td>260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-06</th>\n",
              "      <td>Florida</td>\n",
              "      <td>12</td>\n",
              "      <td>13621</td>\n",
              "      <td>253</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    state  fips   cases  deaths\n",
              "date                                           \n",
              "2020-04-06       New York    36  130703    4758\n",
              "2020-04-06     New Jersey    34   41090    1005\n",
              "2020-04-06       Michigan    26   17130     727\n",
              "2020-04-06      Louisiana    22   14867     512\n",
              "2020-04-06     California     6   16284     386\n",
              "2020-04-06     Washington    53    8384     383\n",
              "2020-04-06       Illinois    17   12262     309\n",
              "2020-04-06        Georgia    13    7558     294\n",
              "2020-04-06  Massachusetts    25   13837     260\n",
              "2020-04-06        Florida    12   13621     253"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSNzoid28GEX",
        "colab_type": "text"
      },
      "source": [
        "*There should be 50 states and District of Columbia*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ktah9SqV8I3i",
        "colab_type": "code",
        "outputId": "2b0bb5eb-1186-405a-fb87-0cf89cffe8e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "current_df.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_M6NGQk8_2U",
        "colab_type": "text"
      },
      "source": [
        "### State Based Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzGQ9z_oA6V7",
        "colab_type": "text"
      },
      "source": [
        "*Get 10 states and subset*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeicLkAlA9y4",
        "colab_type": "code",
        "outputId": "319ec37e-6652-4684-fb48-1aa7245f0469",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "top_ten_states = list(current_df[\"state\"].head(10).values)\n",
        "top_ten_states"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['New York',\n",
              " 'New Jersey',\n",
              " 'Michigan',\n",
              " 'Louisiana',\n",
              " 'California',\n",
              " 'Washington',\n",
              " 'Illinois',\n",
              " 'Georgia',\n",
              " 'Massachusetts',\n",
              " 'Florida']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzv8KJtx9bFO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_states_df = df[df['state'].isin(top_ten_states)].drop(columns=\"fips\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkLverBTICn3",
        "colab_type": "text"
      },
      "source": [
        "*Verify the unique states left is the same 10*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3bJzaxyHnD6",
        "colab_type": "code",
        "outputId": "5f6d40b2-05e2-41b4-9506-a5431c3498c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "set(top_states_df.state.values)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'California',\n",
              " 'Florida',\n",
              " 'Georgia',\n",
              " 'Illinois',\n",
              " 'Louisiana',\n",
              " 'Massachusetts',\n",
              " 'Michigan',\n",
              " 'New Jersey',\n",
              " 'New York',\n",
              " 'Washington'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ba-QT229CYE",
        "colab_type": "code",
        "outputId": "cb08520e-7eba-4cf2-fa0d-7365b30518ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "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": 42,
      "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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JkiRJqhmTdpIkSZIkSVLNmLSTJEmSJEmSasaknSRJkiRJklQzJu0kSZIkSZKkmjFpJ0mS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\nSZIkSZIkSS3m/wIa03XIxgg9/AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1260x504 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "in-JRg0lOCrp",
        "colab_type": "text"
      },
      "source": [
        "*Interactive plot of top states*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf7j4jUBaKMS",
        "colab_type": "code",
        "outputId": "1ac26832-4bfe-4f23-cd50-be1d8d873f50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "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": 43,
      "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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              "</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>2020-03-09</th>\n",
              "      <td>Louisiana</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-10</th>\n",
              "      <td>Michigan</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-11</th>\n",
              "      <td>Michigan</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-08</th>\n",
              "      <td>New Jersey</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-10</th>\n",
              "      <td>Louisiana</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 state  cases  deaths\n",
              "date                                 \n",
              "2020-03-09   Louisiana      1       0\n",
              "2020-03-10    Michigan      2       0\n",
              "2020-03-11    Michigan      2       0\n",
              "2020-03-08  New Jersey      6       0\n",
              "2020-03-10   Louisiana      6       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7yg3JizOHIv",
        "colab_type": "code",
        "outputId": "c61a64f7-d477-4fb5-8a08-f1912449bc01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "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": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN0jv8_3mD-z",
        "colab_type": "text"
      },
      "source": [
        "### Search for Features:  Political, Health and Finance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VLRtEYvBtoR",
        "colab_type": "text"
      },
      "source": [
        "*Sugar Intake By State*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnLcHUUUBwzD",
        "colab_type": "code",
        "outputId": "0351e944-96b5-4ac5-cd76-81fb2752fbdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "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": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "      <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": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BT7YQzUcCRSn",
        "colab_type": "text"
      },
      "source": [
        "*Combine Sugar Data and Covid-19 Data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "0f04c9ce-bae4-43eb-c1a6-b3aef97e4bd1",
        "id": "mxZ76ZI8EDQ2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "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": 46,
      "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",
              "      <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": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9syyMOxhTHMo",
        "colab_type": "text"
      },
      "source": [
        "*What about data from the 2016 Election?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RZa-tDmTPXP",
        "colab_type": "code",
        "outputId": "e06bc642-feb3-480b-a3b4-0704e2ae8b03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "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": 47,
      "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>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": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6CuViW_V2Hx",
        "colab_type": "code",
        "outputId": "76ec7320-9f60-495c-9f7a-cce61c8e2d59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "sugar_covid_df = sugar_covid_df.merge(election_df, how=\"inner\", on=\"state\")\n",
        "sugar_covid_df.head()"
      ],
      "execution_count": 48,
      "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",
              "      <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": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmBJqPnXE5wR",
        "colab_type": "text"
      },
      "source": [
        "*Generate Heatmap*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cee_tCSBGdEM",
        "colab_type": "code",
        "outputId": "f74e0e33-66cb-4051-c208-699aa539517b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "sugar_covid_df.corr()"
      ],
      "execution_count": 49,
      "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>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>0.053269</td>\n",
              "      <td>0.042491</td>\n",
              "      <td>0.231301</td>\n",
              "      <td>-0.228550</td>\n",
              "      <td>0.228550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cases</th>\n",
              "      <td>0.053269</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.961783</td>\n",
              "      <td>-0.148097</td>\n",
              "      <td>0.221247</td>\n",
              "      <td>-0.221247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>deaths</th>\n",
              "      <td>0.042491</td>\n",
              "      <td>0.961783</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.119988</td>\n",
              "      <td>0.178478</td>\n",
              "      <td>-0.178478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <td>0.231301</td>\n",
              "      <td>-0.148097</td>\n",
              "      <td>-0.119988</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.761812</td>\n",
              "      <td>0.761812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Democrats</th>\n",
              "      <td>-0.228550</td>\n",
              "      <td>0.221247</td>\n",
              "      <td>0.178478</td>\n",
              "      <td>-0.761812</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Republicans</th>\n",
              "      <td>0.228550</td>\n",
              "      <td>-0.221247</td>\n",
              "      <td>-0.178478</td>\n",
              "      <td>0.761812</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  0.053269  ...  -0.228550     0.228550\n",
              "cases                        0.053269  1.000000  ...   0.221247    -0.221247\n",
              "deaths                       0.042491  0.961783  ...   0.178478    -0.178478\n",
              "employed-sugar-intake-daily  0.231301 -0.148097  ...  -0.761812     0.761812\n",
              "Democrats                   -0.228550  0.221247  ...   1.000000    -1.000000\n",
              "Republicans                  0.228550 -0.221247  ...  -1.000000     1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6wVCIMIE74B",
        "colab_type": "code",
        "outputId": "737a17b3-9160-40be-efd6-80ed03c9b12b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "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": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"65d1dd90-8f0b-4459-91a7-88d306485818\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFaYmi8fBE6_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sugar_covid_df.to_csv(\"covid-eda.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLqU5s5pH8PB",
        "colab_type": "text"
      },
      "source": [
        "*What about Real-Estate Data?*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4W9aBagTHFC",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrRjhxe30C_q",
        "colab_type": "text"
      },
      "source": [
        "## Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdsYYSuy0Exu",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFOuQGyn0E6w",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmcf2OJ40mm4",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}