c006_final_updated.ipynb

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      "name": "c006_final.ipynb",
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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/TashiNyangmi/MIT-Challenge-2020/blob/master/c006_final_updated.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBtl_lg5HjX5"
      },
      "source": [
        "# **MIT COVID-19 Datathon**\n",
        "\n",
        "#### **Team C-006**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylpjHQtbA1iW"
      },
      "source": [
        "# Environment Set-Up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvXeBjb0BC9X"
      },
      "source": [
        "## Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GW5LcskRHn2g",
        "outputId": "0855b9cd-0da1-4d92-9718-9ec57d78aff1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "# Import libraries\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "import matplotlib.path as path\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "!pip install -U -q pandasql\n",
        "import pandasql as psql\n",
        "from pandasql import sqldf\n",
        "\n",
        "# Below imports are used to print out pretty pandas dataframes\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "# Imports for accessing Datathon data using Google BigQuery.\n",
        "from google.colab import auth\n",
        "from google.cloud import bigquery\n",
        "\n",
        "# Import libraries for google drive\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# BigQuery Setup\n",
        "project_id='mit-covid-19-datathon'\n",
        "os.environ[\"GOOGLE_CLOUD_PROJECT\"]=project_id"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3FBdgk_yBiNm"
      },
      "source": [
        "## Authentication"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJqN73-XIVOZ"
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ao2lN3O2A-Ki"
      },
      "source": [
        "## Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcvWZDXkIejS"
      },
      "source": [
        "# Read data from BigQuery into pandas dataframes.\n",
        "def run_query(query):\n",
        "  return pd.io.gbq.read_gbq(query, project_id=project_id, verbose=False, configuration={'query':{'useLegacySql': False}})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skw8y_En5Wzu"
      },
      "source": [
        "def get_data_from_gdrive(link):\n",
        "  #extracting string following \"=\" in the link string\n",
        "  fluff, id = link.split('=')\n",
        "\n",
        "  # Store dataset in a new pd.DataFrame\n",
        "  downloaded = drive.CreateFile({'id':id}) \n",
        "  downloaded.GetContentFile('Filename.csv')  \n",
        "  df_original = pd.read_csv('Filename.csv')\n",
        "  return df_original"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uPtSpndI60P",
        "outputId": "6938408f-a1fd-48ca-ee52-e19612d6be31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "# BigQuery Sample\n",
        "df = run_query('''\n",
        "SELECT count(*)\n",
        "FROM `bigquery-public-data.census_utility.fips_codes_states`\n",
        "''')\n",
        "\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>0</th>\n",
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              "   f0_\n",
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          "metadata": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQfzwU_y_sGr"
      },
      "source": [
        "# Data Import\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VylnkAHFNAcF"
      },
      "source": [
        "## Google Mobility Dataset, Key Facts:\n",
        "\n",
        "- Changes for each day are **compared to a baseline value** for that day of the week\n",
        "- The **baseline** is the median value, for the **corresponding day of the week, during the 5-week period Jan 3–Feb 6**, 2020.\n",
        "- **Start date:** 15 February\n",
        "- **End date:** 7 May"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66aozgsLJBbK"
      },
      "source": [
        "### Mobility and County Populations by Race"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tsfo3cmbJMZC"
      },
      "source": [
        "# Old query\n",
        "\n",
        "# mobility_and_pop_by_county = run_query(\"\"\"\n",
        "# WITH mob AS (\n",
        "# SELECT \n",
        "# --   retail_and_recreation_percent_change_from_baseline retail_and_rec,\n",
        "# --   grocery_and_pharmacy_percent_change_from_baseline grocery_and_pharmacy,\n",
        "# --   parks_percent_change_from_baseline parks,\n",
        "# --   transit_stations_percent_change_from_baseline transit,\n",
        "# --   workplaces_percent_change_from_baseline work,\n",
        "# --   residential_percent_change_from_baseline residential,\n",
        "#   *\n",
        "# FROM `bigquery-public-data.covid19_google_mobility.mobility_report`\n",
        "# WHERE country_region_code = \"US\"\n",
        "#   AND sub_region_1 IS NOT NULL\n",
        "#   AND sub_region_2 IS NOT NULL\n",
        "# )\n",
        "# SELECT \n",
        "#   states.state_name state,\n",
        "#   fips.area_name county,\n",
        "#   acs.geo_id fips_code,\n",
        "#   mob.date,\n",
        "#   acs.total_pop,\n",
        "#   acs.white_pop,\n",
        "#   acs.black_pop,\n",
        "#   acs.asian_pop,\n",
        "#   acs.hispanic_pop,\n",
        "#   acs.amerindian_pop,\n",
        "#   acs.other_race_pop,\n",
        "#   acs.two_or_more_races_pop,\n",
        "#   acs.hispanic_any_race,\n",
        "#   acs.not_hispanic_pop,\n",
        "#   mob.retail_and_recreation_percent_change_from_baseline retail_and_rec,\n",
        "#   mob.grocery_and_pharmacy_percent_change_from_baseline grocery_and_pharmacy,\n",
        "#   mob.parks_percent_change_from_baseline parks,\n",
        "#   mob.transit_stations_percent_change_from_baseline transit,\n",
        "#   mob.workplaces_percent_change_from_baseline work,\n",
        "#   mob.residential_percent_change_from_baseline residential,\n",
        "# FROM `bigquery-public-data.census_bureau_acs.county_2018_5yr` acs\n",
        "# LEFT JOIN `bigquery-public-data.census_utility.fips_codes_states` states\n",
        "#   ON SUBSTR(acs.geo_id, 1, 2) = states.state_fips_code\n",
        "# LEFT JOIN `bigquery-public-data.census_utility.fips_codes_all` fips\n",
        "#   ON SUBSTR(acs.geo_id, 1, 2) = fips.state AND SUBSTR(acs.geo_id, 3, 3) = fips.county\n",
        "# INNER JOIN mob\n",
        "#   ON mob.sub_region_1 = states.state_name AND mob.sub_region_2 = fips.area_name\n",
        "# ORDER BY state, county, date\n",
        "# \"\"\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzqu_OUtUf9F"
      },
      "source": [
        "# Aggregated Query\n",
        "\n",
        "mobility_and_pop_by_county = run_query(\"\"\"\n",
        "WITH mob AS (\n",
        "SELECT\n",
        "   sub_region_1,\n",
        "   sub_region_2,\n",
        "   AVG(retail_and_recreation_percent_change_from_baseline) retail_and_rec,\n",
        "   AVG(grocery_and_pharmacy_percent_change_from_baseline) grocery_and_pharmacy,\n",
        "   AVG(workplaces_percent_change_from_baseline) work,\n",
        "FROM `bigquery-public-data.covid19_google_mobility.mobility_report`\n",
        "WHERE country_region_code = \"US\"\n",
        "  AND sub_region_1 IS NOT NULL\n",
        "  AND sub_region_2 IS NOT NULL\n",
        "  AND date >= \"2020-04-01\"\n",
        "GROUP BY sub_region_1, sub_region_2\n",
        "HAVING\n",
        "  retail_and_rec IS NOT NULL\n",
        "  AND grocery_and_pharmacy IS NOT NULL\n",
        "  AND work IS NOT NULL\n",
        ")\n",
        "SELECT \n",
        "  acs.geo_id fips_code,\n",
        "  states.state_name state,\n",
        "  fips.area_name county,\n",
        "  acs.total_pop,\n",
        "  acs.white_pop,\n",
        "  acs.black_pop,\n",
        "  acs.asian_pop,\n",
        "  acs.hispanic_pop,\n",
        "  acs.amerindian_pop,\n",
        "  acs.other_race_pop,\n",
        "  acs.two_or_more_races_pop,\n",
        "  acs.hispanic_any_race,\n",
        "  acs.not_hispanic_pop,\n",
        "  mob.retail_and_rec,\n",
        "  mob.grocery_and_pharmacy,\n",
        "  mob.work,\n",
        "FROM `bigquery-public-data.census_bureau_acs.county_2018_5yr` acs\n",
        "LEFT JOIN `bigquery-public-data.census_utility.fips_codes_states` states\n",
        "  ON SUBSTR(acs.geo_id, 1, 2) = states.state_fips_code\n",
        "LEFT JOIN `bigquery-public-data.census_utility.fips_codes_all` fips\n",
        "  ON SUBSTR(acs.geo_id, 1, 2) = fips.state AND SUBSTR(acs.geo_id, 3, 3) = fips.county\n",
        "INNER JOIN mob\n",
        "  ON mob.sub_region_1 = states.state_name AND mob.sub_region_2 = fips.area_name\n",
        "ORDER BY state, county\n",
        "\"\"\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alVXRfhdoGMj"
      },
      "source": [
        "## ACS Data Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-n50QxJRpxQ",
        "outputId": "0a1b373b-5d7e-4cc7-e9a8-6003db6d8c98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "# Sample Query using BigQuery in Colab\n",
        "# You can replicate this to run your own queries\n",
        "counties_squaremiles_df = run_query('''\n",
        "SELECT geo_id, state_fips_code, county_fips_code, area_land_meters\n",
        "FROM `bigquery-public-data.geo_us_boundaries.counties` \n",
        "''')\n",
        "\n",
        "counties_squaremiles_df"
      ],
      "execution_count": null,
      "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>geo_id</th>\n",
              "      <th>state_fips_code</th>\n",
              "      <th>county_fips_code</th>\n",
              "      <th>area_land_meters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01013</td>\n",
              "      <td>01</td>\n",
              "      <td>013</td>\n",
              "      <td>2012002531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01059</td>\n",
              "      <td>01</td>\n",
              "      <td>059</td>\n",
              "      <td>1641841404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01041</td>\n",
              "      <td>01</td>\n",
              "      <td>041</td>\n",
              "      <td>1576952799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01105</td>\n",
              "      <td>01</td>\n",
              "      <td>105</td>\n",
              "      <td>1863900621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01039</td>\n",
              "      <td>01</td>\n",
              "      <td>039</td>\n",
              "      <td>2669134207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3228</th>\n",
              "      <td>72059</td>\n",
              "      <td>72</td>\n",
              "      <td>059</td>\n",
              "      <td>109480385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3229</th>\n",
              "      <td>72111</td>\n",
              "      <td>72</td>\n",
              "      <td>111</td>\n",
              "      <td>115558557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3230</th>\n",
              "      <td>78020</td>\n",
              "      <td>78</td>\n",
              "      <td>020</td>\n",
              "      <td>50998479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3231</th>\n",
              "      <td>78010</td>\n",
              "      <td>78</td>\n",
              "      <td>010</td>\n",
              "      <td>215914992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3232</th>\n",
              "      <td>78030</td>\n",
              "      <td>78</td>\n",
              "      <td>030</td>\n",
              "      <td>81108425</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3233 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     geo_id state_fips_code county_fips_code  area_land_meters\n",
              "0     01013              01              013        2012002531\n",
              "1     01059              01              059        1641841404\n",
              "2     01041              01              041        1576952799\n",
              "3     01105              01              105        1863900621\n",
              "4     01039              01              039        2669134207\n",
              "...     ...             ...              ...               ...\n",
              "3228  72059              72              059         109480385\n",
              "3229  72111              72              111         115558557\n",
              "3230  78020              78              020          50998479\n",
              "3231  78010              78              010         215914992\n",
              "3232  78030              78              030          81108425\n",
              "\n",
              "[3233 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EmojxNFbabB2"
      },
      "source": [
        "# List of the files to be imported\n",
        "\n",
        "link_dict = {'Total_population ':'https://drive.google.com/open?id=1uAbDuOZ3_K4QcCCJFtdqnIE_S5yT4yw8', \\\n",
        "             'Overcrowding':'https://drive.google.com/open?id=1l4fBj1ZMqD6VoK7WJLum0fhdfhUD-gLV', \\\n",
        "             'Older_adults':'https://drive.google.com/open?id=1_TwxgrXK4FOFiuUoF2nFGHvWc-WX02AB', \\\n",
        "             'Occupation':'https://drive.google.com/open?id=1ppfuS7kQGWevOP5TFIMIkzYUuD5eqoR-', \\\n",
        "             'Low_income':'https://drive.google.com/open?id=11PG8moKvvgfTppouplzVwE8Ga-mryDme', \\\n",
        "             'Hispanic':'https://drive.google.com/open?id=1dCpouYjKM3qn-VAYz6GIMwmGtnHYQfZ7'}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xTQchwkacM0"
      },
      "source": [
        "df_list = []\n",
        "\n",
        "# This will apply the get_data_from_gdrive function to each file \n",
        "for i in link_dict.keys():\n",
        "  df = get_data_from_gdrive(link_dict[i])\n",
        "  df['table_name'] = i\n",
        "  df_list.append(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njCFI46mfzoZ"
      },
      "source": [
        "Total_population_df = df_list[0]\n",
        "Overcrowding_df = df_list[1]\n",
        "Older_adults_df = df_list[2]\n",
        "Occupation_df = df_list[3]\n",
        "Low_income_df = df_list[4]\n",
        "Hispanic_df = df_list[5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DIlHd-6NAH4P"
      },
      "source": [
        "# Data Processing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-rLgyWaDorPR"
      },
      "source": [
        "## ACS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9lACOgi6X0k"
      },
      "source": [
        "# For each dataframe, capturing the last 5 digits which correpsond to the fips code\n",
        "\n",
        "new_df_list = [Total_population_df, Overcrowding_df, Older_adults_df, Occupation_df, Low_income_df, Hispanic_df]\n",
        "\n",
        "for i in new_df_list:\n",
        "  # check before: print(i.head())\n",
        "  i['GEO_ID'] = i['GEO_ID'].astype(str).str[9:]\n",
        "  # check after: print(i.head())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPW6oN19DMZf"
      },
      "source": [
        "###  Pulling Square Area per County for Calculating Neighborhoods: Population Density\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nq7f6tqMOLMq"
      },
      "source": [
        "square_area_per_county = psql.sqldf('''\n",
        "SELECT counties_squaremiles_df.geo_id,\n",
        "  counties_squaremiles_df.area_land_meters/2.596 AS square_miles\n",
        "FROM counties_squaremiles_df''')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaoFEZgAs-pV",
        "outputId": "7adc7b86-9f1d-417a-ba07-5c5702a097ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "square_area_per_county.head()"
      ],
      "execution_count": null,
      "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>geo_id</th>\n",
              "      <th>square_miles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01013</td>\n",
              "      <td>7.750395e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01059</td>\n",
              "      <td>6.324505e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01041</td>\n",
              "      <td>6.074549e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01105</td>\n",
              "      <td>7.179895e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01039</td>\n",
              "      <td>1.028172e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  geo_id  square_miles\n",
              "0  01013  7.750395e+08\n",
              "1  01059  6.324505e+08\n",
              "2  01041  6.074549e+08\n",
              "3  01105  7.179895e+08\n",
              "4  01039  1.028172e+09"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jx_538RBoYVD"
      },
      "source": [
        "# Calculations: First Round"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpmH9webKGpN"
      },
      "source": [
        "## Mobility: Aggregating the Time Series Data over the Last 14 Days [24 April - 7 May]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_9PMozvJ67S",
        "outputId": "c3ebddff-1f53-44e9-cf63-e4a814a11b6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "mobility_and_pop_by_county.head()"
      ],
      "execution_count": null,
      "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_code</th>\n",
              "      <th>state</th>\n",
              "      <th>county</th>\n",
              "      <th>total_pop</th>\n",
              "      <th>white_pop</th>\n",
              "      <th>black_pop</th>\n",
              "      <th>asian_pop</th>\n",
              "      <th>hispanic_pop</th>\n",
              "      <th>amerindian_pop</th>\n",
              "      <th>other_race_pop</th>\n",
              "      <th>two_or_more_races_pop</th>\n",
              "      <th>hispanic_any_race</th>\n",
              "      <th>not_hispanic_pop</th>\n",
              "      <th>retail_and_rec</th>\n",
              "      <th>grocery_and_pharmacy</th>\n",
              "      <th>work</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01001</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga County</td>\n",
              "      <td>55200.0</td>\n",
              "      <td>41412.0</td>\n",
              "      <td>10475.0</td>\n",
              "      <td>568.0</td>\n",
              "      <td>1528.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>1528.0</td>\n",
              "      <td>53672.0</td>\n",
              "      <td>-21.307692</td>\n",
              "      <td>7.282051</td>\n",
              "      <td>-35.923077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01003</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin County</td>\n",
              "      <td>208107.0</td>\n",
              "      <td>172768.0</td>\n",
              "      <td>19529.0</td>\n",
              "      <td>1668.0</td>\n",
              "      <td>9353.0</td>\n",
              "      <td>1398.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>2972.0</td>\n",
              "      <td>9353.0</td>\n",
              "      <td>198754.0</td>\n",
              "      <td>-35.410256</td>\n",
              "      <td>-8.589744</td>\n",
              "      <td>-33.589744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01005</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour County</td>\n",
              "      <td>25782.0</td>\n",
              "      <td>11898.0</td>\n",
              "      <td>12199.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>344.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>24676.0</td>\n",
              "      <td>-15.750000</td>\n",
              "      <td>-3.200000</td>\n",
              "      <td>-25.102564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01007</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb County</td>\n",
              "      <td>22527.0</td>\n",
              "      <td>16801.0</td>\n",
              "      <td>4974.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>21980.0</td>\n",
              "      <td>-16.000000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>-29.717949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01009</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount County</td>\n",
              "      <td>57645.0</td>\n",
              "      <td>50232.0</td>\n",
              "      <td>820.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>818.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>52384.0</td>\n",
              "      <td>-17.809524</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>-32.794872</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  fips_code    state  ... grocery_and_pharmacy       work\n",
              "0     01001  Alabama  ...             7.282051 -35.923077\n",
              "1     01003  Alabama  ...            -8.589744 -33.589744\n",
              "2     01005  Alabama  ...            -3.200000 -25.102564\n",
              "3     01007  Alabama  ...             5.200000 -29.717949\n",
              "4     01009  Alabama  ...             5.083333 -32.794872\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlBfbA_LKLRE"
      },
      "source": [
        "# mobility_and_pop_by_county = mobility_and_pop_by_county.set_index(\"date\")\n",
        "\n",
        "# # for number of days to go back from the end_date\n",
        "# date_dict = {'07': '2020-05-01',\n",
        "#              '14': '2020-04-24',\n",
        "#              '21': '2020-04-17',\n",
        "#              '28': '2020-04-10'}\n",
        "# end_date = \"2020-05-07\"\n",
        "\n",
        "# # creates DataFrames sliced by date, \n",
        "# # and calculates mean for each variable on the county level.\n",
        "\n",
        "# df_result_list = []\n",
        "# for i in date_dict.keys():\n",
        "#   df = mobility_and_pop_by_county.loc[date_dict[i]:end_date].groupby(\"fips_code\").mean()\n",
        "#   df_result_list.append(df)\n",
        "\n",
        "# # Datasets filtered for different periods\n",
        "# mobility_and_pop_by_county_index_07 = df_result_list[0]\n",
        "# mobility_and_pop_by_county_index_14 = df_result_list[1]\n",
        "# mobility_and_pop_by_county_index_21 = df_result_list[2]\n",
        "# mobility_and_pop_by_county_index_28 = df_result_list[3]\n",
        "\n",
        "# # The final data set to use, filtered to include only the last 14 days\n",
        "# final_df = mobility_and_pop_by_county_index_14"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVxkWfmZKxPv",
        "outputId": "6fe8f7ee-90c0-4eb1-b72b-9a43e58b62ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "final_df = mobility_and_pop_by_county\n",
        "final_df.head()"
      ],
      "execution_count": null,
      "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_code</th>\n",
              "      <th>state</th>\n",
              "      <th>county</th>\n",
              "      <th>total_pop</th>\n",
              "      <th>white_pop</th>\n",
              "      <th>black_pop</th>\n",
              "      <th>asian_pop</th>\n",
              "      <th>hispanic_pop</th>\n",
              "      <th>amerindian_pop</th>\n",
              "      <th>other_race_pop</th>\n",
              "      <th>two_or_more_races_pop</th>\n",
              "      <th>hispanic_any_race</th>\n",
              "      <th>not_hispanic_pop</th>\n",
              "      <th>retail_and_rec</th>\n",
              "      <th>grocery_and_pharmacy</th>\n",
              "      <th>work</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01001</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga County</td>\n",
              "      <td>55200.0</td>\n",
              "      <td>41412.0</td>\n",
              "      <td>10475.0</td>\n",
              "      <td>568.0</td>\n",
              "      <td>1528.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>1528.0</td>\n",
              "      <td>53672.0</td>\n",
              "      <td>-21.307692</td>\n",
              "      <td>7.282051</td>\n",
              "      <td>-35.923077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01003</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin County</td>\n",
              "      <td>208107.0</td>\n",
              "      <td>172768.0</td>\n",
              "      <td>19529.0</td>\n",
              "      <td>1668.0</td>\n",
              "      <td>9353.0</td>\n",
              "      <td>1398.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>2972.0</td>\n",
              "      <td>9353.0</td>\n",
              "      <td>198754.0</td>\n",
              "      <td>-35.410256</td>\n",
              "      <td>-8.589744</td>\n",
              "      <td>-33.589744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01005</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour County</td>\n",
              "      <td>25782.0</td>\n",
              "      <td>11898.0</td>\n",
              "      <td>12199.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>344.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>24676.0</td>\n",
              "      <td>-15.750000</td>\n",
              "      <td>-3.200000</td>\n",
              "      <td>-25.102564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01007</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb County</td>\n",
              "      <td>22527.0</td>\n",
              "      <td>16801.0</td>\n",
              "      <td>4974.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>21980.0</td>\n",
              "      <td>-16.000000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>-29.717949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01009</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount County</td>\n",
              "      <td>57645.0</td>\n",
              "      <td>50232.0</td>\n",
              "      <td>820.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>818.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>52384.0</td>\n",
              "      <td>-17.809524</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>-32.794872</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  fips_code    state  ... grocery_and_pharmacy       work\n",
              "0     01001  Alabama  ...             7.282051 -35.923077\n",
              "1     01003  Alabama  ...            -8.589744 -33.589744\n",
              "2     01005  Alabama  ...            -3.200000 -25.102564\n",
              "3     01007  Alabama  ...             5.200000 -29.717949\n",
              "4     01009  Alabama  ...             5.083333 -32.794872\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCY3ya2kJ9qu",
        "outputId": "b503aaf7-920f-4636-b4b6-4c50dd6e6032",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(final_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2331"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmXw2dQ2C_SI"
      },
      "source": [
        "## Calculating Homes: Overcrowded Rooms\n",
        "\n",
        "[DONE] Need to include a total_rooms per county\n",
        "Then divide rooms_with_multiple_occupants with total number of rooms\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBsvx3DHrItt"
      },
      "source": [
        "#Taking only those columns into consideration where no. of occupants per room is more than one\n",
        "more_than_one_occupant_df = Overcrowding_df[['GEO_ID','NAME','B25014_001E','B25014_005E','B25014_006E','B25014_007E','B25014_011E','B25014_012E','B25014_013E']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ri32MrdPY4CR"
      },
      "source": [
        "# Total number of rooms having more than one occupant per room in each county\n",
        "overcrowded_calc_df = psql.sqldf('''select geo_id,name, sum(B25014_005E)+sum(B25014_006E)+sum(B25014_007E)+sum(B25014_011E)+sum(B25014_012E)+sum(B25014_013E) as rooms_with_multiple_occupants, B25014_001E as total_rooms, (sum(B25014_005E)+sum(B25014_006E)+sum(B25014_007E)+sum(B25014_011E)+sum(B25014_012E)+sum(B25014_013E))*1.0000/B25014_001E as percentage_overcrowded_rooms\n",
        "          from more_than_one_occupant_df\n",
        "          group by geo_id''',globals()).iloc[1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpHzBCKjmrCQ",
        "outputId": "20111604-532c-461d-dd92-9c3f5f622732",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "overcrowded_calc_df.head()"
      ],
      "execution_count": null,
      "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>GEO_ID</th>\n",
              "      <th>NAME</th>\n",
              "      <th>rooms_with_multiple_occupants</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>percentage_overcrowded_rooms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01001</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>299.0</td>\n",
              "      <td>21115</td>\n",
              "      <td>0.014161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01003</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>991.0</td>\n",
              "      <td>78622</td>\n",
              "      <td>0.012605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01005</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>313.0</td>\n",
              "      <td>9186</td>\n",
              "      <td>0.034074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01007</td>\n",
              "      <td>Bibb County, Alabama</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6840</td>\n",
              "      <td>0.007602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>01009</td>\n",
              "      <td>Blount County, Alabama</td>\n",
              "      <td>339.0</td>\n",
              "      <td>20600</td>\n",
              "      <td>0.016456</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  GEO_ID                     NAME  ...  total_rooms percentage_overcrowded_rooms\n",
              "1  01001  Autauga County, Alabama  ...        21115                     0.014161\n",
              "2  01003  Baldwin County, Alabama  ...        78622                     0.012605\n",
              "3  01005  Barbour County, Alabama  ...         9186                     0.034074\n",
              "4  01007     Bibb County, Alabama  ...         6840                     0.007602\n",
              "5  01009   Blount County, Alabama  ...        20600                     0.016456\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioTvxfadDUG9"
      },
      "source": [
        "## Calculating Workplaces: Essential Jobs \n",
        "\n",
        "Calculation for % Essential wokers by county (Essential_workers divided by total population of county) performed in Master DF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRIB9jwicrbN"
      },
      "source": [
        "# Keeping only those occupations from occupation_df which are considered essential by the colorado SDI\n",
        "essential_occupation_df = Occupation_df[['GEO_ID','NAME','C24050_002E','C24050_003E','C24050_004E','C24050_005E','C24050_006E','C24050_007E','C24050_011E','C24050_012E','C24050_013E','C24050_024E']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qu8ZDYmmaYT"
      },
      "source": [
        "# Sum of Essential Workers in each county\n",
        "\n",
        "total_essential_workers_df = psql.sqldf('''\n",
        "SELECT geo_id,\n",
        "  name,\n",
        "  sum(C24050_002E)+sum(C24050_003E)+sum(C24050_004E)+sum(C24050_005E)+(sum(C24050_006E)*0.6)+sum(C24050_007E)+sum(C24050_011E)+(sum(C24050_012E)*0.64)\n",
        "+(sum(C24050_013E)*0.4)+(sum(C24050_024E)*0.18) AS Essential_Workers\n",
        "FROM essential_occupation_df\n",
        "GROUP BY geo_id\n",
        "''').iloc[1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRz9HviOLTKU",
        "outputId": "13d63b23-52ed-44f1-97fb-d10b6ef5da57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "total_essential_workers_df.head()"
      ],
      "execution_count": null,
      "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>GEO_ID</th>\n",
              "      <th>NAME</th>\n",
              "      <th>Essential_Workers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01001</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>15300.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01003</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>60123.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01005</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>6564.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01007</td>\n",
              "      <td>Bibb County, Alabama</td>\n",
              "      <td>6041.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>01009</td>\n",
              "      <td>Blount County, Alabama</td>\n",
              "      <td>15216.38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  GEO_ID                     NAME  Essential_Workers\n",
              "1  01001  Autauga County, Alabama           15300.40\n",
              "2  01003  Baldwin County, Alabama           60123.62\n",
              "3  01005  Barbour County, Alabama            6564.50\n",
              "4  01007     Bibb County, Alabama            6041.06\n",
              "5  01009   Blount County, Alabama           15216.38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fznRmE6TiTOD"
      },
      "source": [
        "# Creating Master table\n",
        "[DONE] Join mobility_race dataset to acs datatsets via fips code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uc7e-_FXuEGV"
      },
      "source": [
        "Number of null rows in overcrowded = 0\n",
        "\n",
        "Number of null rows in square_miles = 0\n",
        "\n",
        "Number of null rows in essential workers = 1 (Rio Arriba)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsHtRTmNuB4S",
        "outputId": "bd0119b2-affe-44b6-e130-4393f83c07a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "print(f'Overcrowded rows: {len(overcrowded_calc_df)}')\n",
        "print(f'Square Miles rows: {len(square_area_per_county)}')\n",
        "print(f'Essential Workers rows: {len(total_essential_workers_df)}')\n",
        "print(f'Final_df Workers rows: {len(final_df)}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overcrowded rows: 3220\n",
            "Square Miles rows: 3233\n",
            "Essential Workers rows: 3220\n",
            "Final_df Workers rows: 2331\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8Ay5CzzwtIA",
        "outputId": "2e8155bd-c412-4779-9e7a-d6745a15a047",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "final_df.head()"
      ],
      "execution_count": null,
      "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_code</th>\n",
              "      <th>state</th>\n",
              "      <th>county</th>\n",
              "      <th>total_pop</th>\n",
              "      <th>white_pop</th>\n",
              "      <th>black_pop</th>\n",
              "      <th>asian_pop</th>\n",
              "      <th>hispanic_pop</th>\n",
              "      <th>amerindian_pop</th>\n",
              "      <th>other_race_pop</th>\n",
              "      <th>two_or_more_races_pop</th>\n",
              "      <th>hispanic_any_race</th>\n",
              "      <th>not_hispanic_pop</th>\n",
              "      <th>retail_and_rec</th>\n",
              "      <th>grocery_and_pharmacy</th>\n",
              "      <th>work</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01001</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga County</td>\n",
              "      <td>55200.0</td>\n",
              "      <td>41412.0</td>\n",
              "      <td>10475.0</td>\n",
              "      <td>568.0</td>\n",
              "      <td>1528.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>1528.0</td>\n",
              "      <td>53672.0</td>\n",
              "      <td>-21.307692</td>\n",
              "      <td>7.282051</td>\n",
              "      <td>-35.923077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01003</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin County</td>\n",
              "      <td>208107.0</td>\n",
              "      <td>172768.0</td>\n",
              "      <td>19529.0</td>\n",
              "      <td>1668.0</td>\n",
              "      <td>9353.0</td>\n",
              "      <td>1398.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>2972.0</td>\n",
              "      <td>9353.0</td>\n",
              "      <td>198754.0</td>\n",
              "      <td>-35.410256</td>\n",
              "      <td>-8.589744</td>\n",
              "      <td>-33.589744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01005</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour County</td>\n",
              "      <td>25782.0</td>\n",
              "      <td>11898.0</td>\n",
              "      <td>12199.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>344.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>24676.0</td>\n",
              "      <td>-15.750000</td>\n",
              "      <td>-3.200000</td>\n",
              "      <td>-25.102564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01007</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb County</td>\n",
              "      <td>22527.0</td>\n",
              "      <td>16801.0</td>\n",
              "      <td>4974.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>21980.0</td>\n",
              "      <td>-16.000000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>-29.717949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01009</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount County</td>\n",
              "      <td>57645.0</td>\n",
              "      <td>50232.0</td>\n",
              "      <td>820.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>818.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>52384.0</td>\n",
              "      <td>-17.809524</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>-32.794872</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  fips_code    state  ... grocery_and_pharmacy       work\n",
              "0     01001  Alabama  ...             7.282051 -35.923077\n",
              "1     01003  Alabama  ...            -8.589744 -33.589744\n",
              "2     01005  Alabama  ...            -3.200000 -25.102564\n",
              "3     01007  Alabama  ...             5.200000 -29.717949\n",
              "4     01009  Alabama  ...             5.083333 -32.794872\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zr6s-qP3wais",
        "outputId": "87013ccc-2ef0-4797-c753-99e610bb9c5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "first_join = pd.merge(final_df, overcrowded_calc_df, \n",
        "              how=\"inner\", \n",
        "              left_on=\"fips_code\", \n",
        "              right_on=\"GEO_ID\",\n",
        "              )\n",
        "len(first_join)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2331"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_uBJktfyVNU",
        "outputId": "ee513fa9-cae5-4ae6-b79b-e90e28f3c06a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "second_join = pd.merge(first_join, square_area_per_county, \n",
        "              how=\"inner\", \n",
        "              left_on=\"GEO_ID\", \n",
        "              right_on=\"geo_id\").drop(\"geo_id\", axis = 1)\n",
        "len(second_join)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2331"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "It0EjKT8yvAy",
        "outputId": "5c9138d4-d3aa-4837-d5d9-065309b2e835",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "master_df = pd.merge(second_join, total_essential_workers_df, \n",
        "              how=\"inner\", \n",
        "              left_on=\"GEO_ID\", \n",
        "              right_on=\"GEO_ID\")\n",
        "master_df = master_df.drop(\"NAME_y\", axis = 1)\n",
        "len(master_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2331"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R24ikGkz7HvY",
        "outputId": "ee6b6f7b-be88-4ab5-d5cc-cfee1be236c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "source": [
        "master_df.head()"
      ],
      "execution_count": null,
      "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_code</th>\n",
              "      <th>state</th>\n",
              "      <th>county</th>\n",
              "      <th>total_pop</th>\n",
              "      <th>white_pop</th>\n",
              "      <th>black_pop</th>\n",
              "      <th>asian_pop</th>\n",
              "      <th>hispanic_pop</th>\n",
              "      <th>amerindian_pop</th>\n",
              "      <th>other_race_pop</th>\n",
              "      <th>two_or_more_races_pop</th>\n",
              "      <th>hispanic_any_race</th>\n",
              "      <th>not_hispanic_pop</th>\n",
              "      <th>retail_and_rec</th>\n",
              "      <th>grocery_and_pharmacy</th>\n",
              "      <th>work</th>\n",
              "      <th>GEO_ID</th>\n",
              "      <th>NAME_x</th>\n",
              "      <th>rooms_with_multiple_occupants</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>percentage_overcrowded_rooms</th>\n",
              "      <th>square_miles</th>\n",
              "      <th>Essential_Workers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01001</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Autauga County</td>\n",
              "      <td>55200.0</td>\n",
              "      <td>41412.0</td>\n",
              "      <td>10475.0</td>\n",
              "      <td>568.0</td>\n",
              "      <td>1528.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>1528.0</td>\n",
              "      <td>53672.0</td>\n",
              "      <td>-21.307692</td>\n",
              "      <td>7.282051</td>\n",
              "      <td>-35.923077</td>\n",
              "      <td>01001</td>\n",
              "      <td>Autauga County, Alabama</td>\n",
              "      <td>299.0</td>\n",
              "      <td>21115</td>\n",
              "      <td>0.014161</td>\n",
              "      <td>5.930671e+08</td>\n",
              "      <td>15300.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01003</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Baldwin County</td>\n",
              "      <td>208107.0</td>\n",
              "      <td>172768.0</td>\n",
              "      <td>19529.0</td>\n",
              "      <td>1668.0</td>\n",
              "      <td>9353.0</td>\n",
              "      <td>1398.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>2972.0</td>\n",
              "      <td>9353.0</td>\n",
              "      <td>198754.0</td>\n",
              "      <td>-35.410256</td>\n",
              "      <td>-8.589744</td>\n",
              "      <td>-33.589744</td>\n",
              "      <td>01003</td>\n",
              "      <td>Baldwin County, Alabama</td>\n",
              "      <td>991.0</td>\n",
              "      <td>78622</td>\n",
              "      <td>0.012605</td>\n",
              "      <td>1.586141e+09</td>\n",
              "      <td>60123.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01005</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Barbour County</td>\n",
              "      <td>25782.0</td>\n",
              "      <td>11898.0</td>\n",
              "      <td>12199.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>344.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>24676.0</td>\n",
              "      <td>-15.750000</td>\n",
              "      <td>-3.200000</td>\n",
              "      <td>-25.102564</td>\n",
              "      <td>01005</td>\n",
              "      <td>Barbour County, Alabama</td>\n",
              "      <td>313.0</td>\n",
              "      <td>9186</td>\n",
              "      <td>0.034074</td>\n",
              "      <td>8.829585e+08</td>\n",
              "      <td>6564.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01007</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Bibb County</td>\n",
              "      <td>22527.0</td>\n",
              "      <td>16801.0</td>\n",
              "      <td>4974.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>547.0</td>\n",
              "      <td>21980.0</td>\n",
              "      <td>-16.000000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>-29.717949</td>\n",
              "      <td>01007</td>\n",
              "      <td>Bibb County, Alabama</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6840</td>\n",
              "      <td>0.007602</td>\n",
              "      <td>6.210198e+08</td>\n",
              "      <td>6041.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01009</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>Blount County</td>\n",
              "      <td>57645.0</td>\n",
              "      <td>50232.0</td>\n",
              "      <td>820.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>818.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>52384.0</td>\n",
              "      <td>-17.809524</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>-32.794872</td>\n",
              "      <td>01009</td>\n",
              "      <td>Blount County, Alabama</td>\n",
              "      <td>339.0</td>\n",
              "      <td>20600</td>\n",
              "      <td>0.016456</td>\n",
              "      <td>6.433374e+08</td>\n",
              "      <td>15216.38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  fips_code    state  ...  square_miles  Essential_Workers\n",
              "0     01001  Alabama  ...  5.930671e+08           15300.40\n",
              "1     01003  Alabama  ...  1.586141e+09           60123.62\n",
              "2     01005  Alabama  ...  8.829585e+08            6564.50\n",
              "3     01007  Alabama  ...  6.210198e+08            6041.06\n",
              "4     01009  Alabama  ...  6.433374e+08           15216.38\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g46lwMkw0kCa"
      },
      "source": [
        "# Calculations: Second Round\n",
        "\n",
        "- Population density: total_pop / square_miles\n",
        "- % Essential workers: Essential_workers / total_pop\n",
        "- Black, White, Asian, etc.: {black/white/asian/etc}_pop/ total_pop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s54nkCm9KDA1"
      },
      "source": [
        "## Calculating Population Density"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1c0OXBAcFCTi",
        "outputId": "cd7d47e4-2374-4ebb-eede-9004499a0b34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "# Calculating population density\n",
        "\n",
        "master_df['pop_density'] = master_df['total_pop']/ master_df['square_miles']\n",
        "\n",
        "print(master_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     fips_code    state  ... Essential_Workers  pop_density\n",
            "0        01001  Alabama  ...          15300.40     0.000093\n",
            "1        01003  Alabama  ...          60123.62     0.000131\n",
            "2        01005  Alabama  ...           6564.50     0.000029\n",
            "3        01007  Alabama  ...           6041.06     0.000036\n",
            "4        01009  Alabama  ...          15216.38     0.000090\n",
            "...        ...      ...  ...               ...          ...\n",
            "2326     56035  Wyoming  ...           4055.62     0.000002\n",
            "2327     56037  Wyoming  ...          16966.26     0.000004\n",
            "2328     56039  Wyoming  ...           8952.00     0.000006\n",
            "2329     56041  Wyoming  ...           7342.78     0.000010\n",
            "2330     56043  Wyoming  ...           2834.02     0.000004\n",
            "\n",
            "[2331 rows x 24 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6150GSj2KP51"
      },
      "source": [
        "## Calculating % Essential workers by County"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4hgbmO9Gi4W"
      },
      "source": [
        "# Calculating % of essential workers\n",
        "\n",
        "master_df['percent_essential_workers'] = master_df['Essential_Workers']/master_df['total_pop']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A0pdZgdqKmVV"
      },
      "source": [
        "## Calculating % Race by County"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VljJk7mHm88",
        "outputId": "1bb605c6-e3ce-4fa6-81d4-c6888546cbab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "# Calculating %'s of races within counties\n",
        "\n",
        "\n",
        "race_list = ['white_pop','black_pop','asian_pop','hispanic_pop',\t'amerindian_pop',\t'other_race_pop',\t'two_or_more_races_pop',\t'hispanic_any_race',\t'not_hispanic_pop']\n",
        "\n",
        "for i in race_list:\n",
        "  column_name = i + '_percent'\n",
        "  master_df[column_name] = master_df[i]/master_df['total_pop']\n",
        "\n",
        "print(master_df)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     fips_code    state  ... hispanic_any_race_percent  not_hispanic_pop_percent\n",
            "0        01001  Alabama  ...                  0.027681                  0.972319\n",
            "1        01003  Alabama  ...                  0.044943                  0.955057\n",
            "2        01005  Alabama  ...                  0.042898                  0.957102\n",
            "3        01007  Alabama  ...                  0.024282                  0.975718\n",
            "4        01009  Alabama  ...                  0.091266                  0.908734\n",
            "...        ...      ...  ...                       ...                       ...\n",
            "2326     56035  Wyoming  ...                  0.073460                  0.926540\n",
            "2327     56037  Wyoming  ...                  0.159621                  0.840379\n",
            "2328     56039  Wyoming  ...                  0.148879                  0.851121\n",
            "2329     56041  Wyoming  ...                  0.091416                  0.908584\n",
            "2330     56043  Wyoming  ...                  0.141838                  0.858162\n",
            "\n",
            "[2331 rows x 34 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tyPyISk_ikko"
      },
      "source": [
        "# Calcuating final SDI\n",
        "[TODO] Calculate final score\n",
        "\n",
        "The Final Social Distance Index values represent:\n",
        "* Maximum capacity for Social Distancing (Index Value = 0)\n",
        "* Maximum capacity for Social Distancing (Index value = Max)\n",
        "\n",
        "i.e. as the overall Social Distance Index value increase, so to does a county's capacity for observing Social Distancing\n",
        "\n",
        "**Change in Travel(s):** \n",
        "Ranked on a 5 point scale normalized to that specific type of travel's distribution, where 5=max, 1=min\n",
        "\n",
        "**Overcrowded Rooms: ** \n",
        "Ranked on a 10 point scale, where 10= min, 0=max (see Colorado Social Index) - i.e. the more crowded rooms there are, the less capacity for Social Distancing (thus the inverse ranking)\n",
        "\n",
        "\n",
        "**Pop Density: ** \n",
        "Ranked on a 10 point scale, where 10= min, 0=max (see Colorado Social Index)- i.e. the higher the density, the less capacity for Social Distancing (thus the inverse ranking) \n",
        "\n",
        "\n",
        "**Percent Essential Workers: ** \n",
        "Ranked on a 10 point scale, where 10= min, 0=max (see Colorado Social Index) - i.e. the higher proportion of essential workers, the less capacity for Social Distancing (thus the inverse ranking) as they will potentially be in greater direct & indirect contact with others\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oULQkCtQ0K5c"
      },
      "source": [
        "### Starter functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6Mqe53bzbWP"
      },
      "source": [
        "# Function that will allos us to get the values corresponding \n",
        "# to the 10 bins of a distribution of values\n",
        "\n",
        "def get_thresholds_10(x):\n",
        "  # This gives us the thresholds for a series\n",
        "  th10 = np.nanpercentile(x,10)\n",
        "  th20 = np.nanpercentile(x,20)\n",
        "  th30 = np.nanpercentile(x,30)\n",
        "  th30 = np.nanpercentile(x,30)\n",
        "  th40 = np.nanpercentile(x,40)\n",
        "  th50 = np.nanpercentile(x,50)\n",
        "  th60 = np.nanpercentile(x,60)\n",
        "  th70 = np.nanpercentile(x,70)\n",
        "  th80 = np.nanpercentile(x,80)\n",
        "  th90 = np.nanpercentile(x,90)\n",
        "\n",
        "  return [th10, th20, th30, th40, th50, th60, th70, th80, th90]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8hozN67AzY4V"
      },
      "source": [
        "# Function that will be used to assign points\n",
        "# based on the percentile a county's value falls\n",
        "# More points, indicates a positive contribution to Social Distancing\n",
        "# Less points, correspond to a negative contribution to SOcial Distancing \n",
        "\n",
        "def assign_points_10(x, thresholds_list):\n",
        "  # Assign points to thresholds\n",
        "  if x <= thresholds_list[0]: \n",
        "    return 10\n",
        "  elif thresholds_list[0] < x and x <= thresholds_list[1]:\n",
        "    return 9\n",
        "  elif thresholds_list[1] < x and x <= thresholds_list[2]:\n",
        "    return 8\n",
        "  elif thresholds_list[2] < x and x <= thresholds_list[3]:\n",
        "    return 7\n",
        "  elif thresholds_list[3] < x and x <= thresholds_list[4]:\n",
        "    return 6\n",
        "  elif thresholds_list[4] < x and x <= thresholds_list[5]:\n",
        "    return 5\n",
        "  elif thresholds_list[5] < x and x <= thresholds_list[6]:\n",
        "    return 4\n",
        "  elif thresholds_list[6] < x and x <= thresholds_list[7]:\n",
        "    return 3\n",
        "  elif thresholds_list[7] < x and x <= thresholds_list[8]:\n",
        "    return 2\n",
        "  elif thresholds_list[8] < x:\n",
        "    return 1\n",
        "  else:\n",
        "    return np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cB9pd3c82r3E"
      },
      "source": [
        "def assign_points_5_up(x, thresholds_list):\n",
        "  # Assign points to thresholds\n",
        "  if x <= thresholds_list[1]: \n",
        "    return 5\n",
        "  elif thresholds_list[1] < x and x <= thresholds_list[3]:\n",
        "    return 4\n",
        "  elif thresholds_list[3] < x and x <= thresholds_list[5]:\n",
        "    return 3\n",
        "  elif thresholds_list[5] < x and x <= thresholds_list[7]:\n",
        "    return 2\n",
        "  elif thresholds_list[7] < x:\n",
        "    return 1\n",
        "  else:\n",
        "    return np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pod21XXv2txL"
      },
      "source": [
        "def assign_points_5_down(x, thresholds_list):\n",
        "  # Assign points to thresholds\n",
        "  if x <= thresholds_list[1]: \n",
        "    return 1\n",
        "  elif thresholds_list[1] < x and x <= thresholds_list[3]:\n",
        "    return 2\n",
        "  elif thresholds_list[3] < x and x <= thresholds_list[5]:\n",
        "    return 3\n",
        "  elif thresholds_list[5] < x and x <= thresholds_list[7]:\n",
        "    return 4\n",
        "  elif thresholds_list[7] < x:\n",
        "    return 5\n",
        "  else:\n",
        "    return np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMRRo4amzEVB"
      },
      "source": [
        "## Step 1: Calculating the variables corresponding to the Coloado Social Index \n",
        "\n",
        "- Overcrowded Rooms\n",
        "- Pop Density\n",
        "- Percent Essential Workers\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmFKAdxW0VB9"
      },
      "source": [
        "master_df_10pts = master_df[['GEO_ID','percent_essential_workers','pop_density','percentage_overcrowded_rooms']].copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsvmP8830OM9"
      },
      "source": [
        "### Assigning points to 'percent_essential_workers' by % percentile rank"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgGkPd870Ioc"
      },
      "source": [
        "# Assigning points to 'percent_essential_workers' by % percentile rank\n",
        "\n",
        "essential_workers_thesholds = master_df_10pts[['percent_essential_workers']].apply(get_thresholds_10)\n",
        "master_df_10pts['percent_essential_workers_withPoints'] = master_df_10pts['percent_essential_workers'].apply(assign_points_10, thresholds_list=essential_workers_thesholds[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5Q7IcSv0e5Y"
      },
      "source": [
        "### Assigning points to 'pop_density' by % percentile rank"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fsnITyRI0j_c"
      },
      "source": [
        "pop_density_thesholds = master_df_10pts[['pop_density']].apply(get_thresholds_10)\n",
        "master_df_10pts['pop_densitys_withPoints'] = master_df_10pts['pop_density'].apply(assign_points_10, thresholds_list=pop_density_thesholds[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oziltjCj0mfH"
      },
      "source": [
        "### Assigning points to 'percentage_overcrowded_rooms' by % percentile rank"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgsZMCn-0p9K"
      },
      "source": [
        "percentage_overcrowded_rooms_thesholds = master_df_10pts[['percentage_overcrowded_rooms']].apply(get_thresholds_10)\n",
        "master_df_10pts['percentage_overcrowded_rooms_withPoints'] = master_df_10pts['percentage_overcrowded_rooms'].apply(assign_points_10, thresholds_list=percentage_overcrowded_rooms_thesholds[0])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vH-KlD4Y0xI7"
      },
      "source": [
        "### Check\n",
        "\n",
        "We need to confirm that the points were correctly assigned by the %tile by matching against the dataframe %tiles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2qoyRqs01vP",
        "outputId": "96772514-23b9-4dea-f951-e352f0c88d87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "master_df_10pts.describe()"
      ],
      "execution_count": null,
      "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>percent_essential_workers</th>\n",
              "      <th>pop_density</th>\n",
              "      <th>percentage_overcrowded_rooms</th>\n",
              "      <th>percent_essential_workers_withPoints</th>\n",
              "      <th>pop_densitys_withPoints</th>\n",
              "      <th>percentage_overcrowded_rooms_withPoints</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2330.000000</td>\n",
              "      <td>2.331000e+03</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2330.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.312661</td>\n",
              "      <td>3.103692e-04</td>\n",
              "      <td>0.024176</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>5.501931</td>\n",
              "      <td>5.501931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.045465</td>\n",
              "      <td>2.036831e-03</td>\n",
              "      <td>0.017435</td>\n",
              "      <td>2.872898</td>\n",
              "      <td>2.873793</td>\n",
              "      <td>2.873793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.107733</td>\n",
              "      <td>7.148673e-07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.283870</td>\n",
              "      <td>3.211496e-05</td>\n",
              "      <td>0.013348</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.313111</td>\n",
              "      <td>6.679775e-05</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.340877</td>\n",
              "      <td>1.631648e-04</td>\n",
              "      <td>0.029070</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.459795</td>\n",
              "      <td>7.220791e-02</td>\n",
              "      <td>0.181158</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       percent_essential_workers  ...  percentage_overcrowded_rooms_withPoints\n",
              "count                2330.000000  ...                              2331.000000\n",
              "mean                    0.312661  ...                                 5.501931\n",
              "std                     0.045465  ...                                 2.873793\n",
              "min                     0.107733  ...                                 1.000000\n",
              "25%                     0.283870  ...                                 3.000000\n",
              "50%                     0.313111  ...                                 6.000000\n",
              "75%                     0.340877  ...                                 8.000000\n",
              "max                     0.459795  ...                                10.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOAXpuoW00Hd",
        "outputId": "4b3b8bb7-a184-47a8-ec9c-440830c2f0d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "master_df_10pts.head(10)"
      ],
      "execution_count": null,
      "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>GEO_ID</th>\n",
              "      <th>percent_essential_workers</th>\n",
              "      <th>pop_density</th>\n",
              "      <th>percentage_overcrowded_rooms</th>\n",
              "      <th>percent_essential_workers_withPoints</th>\n",
              "      <th>pop_densitys_withPoints</th>\n",
              "      <th>percentage_overcrowded_rooms_withPoints</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01001</td>\n",
              "      <td>0.277181</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.014161</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01003</td>\n",
              "      <td>0.288907</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.012605</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01005</td>\n",
              "      <td>0.254616</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.034074</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01007</td>\n",
              "      <td>0.268170</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.007602</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01009</td>\n",
              "      <td>0.263967</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>0.016456</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>01011</td>\n",
              "      <td>0.298196</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>01013</td>\n",
              "      <td>0.294486</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.017740</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>01015</td>\n",
              "      <td>0.289382</td>\n",
              "      <td>0.000190</td>\n",
              "      <td>0.017143</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>01017</td>\n",
              "      <td>0.315131</td>\n",
              "      <td>0.000057</td>\n",
              "      <td>0.029891</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>01019</td>\n",
              "      <td>0.300466</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>0.014614</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  GEO_ID  ...  percentage_overcrowded_rooms_withPoints\n",
              "0  01001  ...                                        8\n",
              "1  01003  ...                                        8\n",
              "2  01005  ...                                        2\n",
              "3  01007  ...                                       10\n",
              "4  01009  ...                                        7\n",
              "5  01011  ...                                       10\n",
              "6  01013  ...                                        6\n",
              "7  01015  ...                                        6\n",
              "8  01017  ...                                        3\n",
              "9  01019  ...                                        7\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QlPMliRj2-PY"
      },
      "source": [
        "## Step 2: Calculating variables corresponding to Travel types"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHlPD-qQ51e8"
      },
      "source": [
        "master_df_5pts = master_df[['GEO_ID','retail_and_rec', 'grocery_and_pharmacy', 'work',]].copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVDCxPwI59N1"
      },
      "source": [
        "### Assigning points to 'retail_and_rec' by % percentile rank"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOkoIeho4O9U"
      },
      "source": [
        "# Assigning points to 'retail_and_rec' by % percentile rank\n",
        "\n",
        "retail_and_rec_thesholds = master_df_5pts[['retail_and_rec']].apply(get_thresholds_10)\n",
        "master_df_5pts['retail_and_rec_withPoints'] = master_df_5pts['retail_and_rec'].apply(assign_points_10, thresholds_list=retail_and_rec_thesholds[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4tHZkq6W6GuE"
      },
      "source": [
        "### Assigning points to 'grocery_and_pharmacy' by % percentile rank"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQ-oHtWR6OPh"
      },
      "source": [
        "# Assigning points to 'grocery_and_pharmacy' by % percentile rank\n",
        "\n",
        "grocery_and_pharmacy_thesholds = master_df_5pts[['grocery_and_pharmacy']].apply(get_thresholds_10)\n",
        "master_df_5pts['grocery_and_pharmacy_withPoints'] = master_df_5pts['grocery_and_pharmacy'].apply(assign_points_10, thresholds_list=grocery_and_pharmacy_thesholds[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zaT5-Et6hI6"
      },
      "source": [
        "### Assigning points to 'work' by % percentile rank"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIuZ3oDZ625e"
      },
      "source": [
        "work_thesholds = master_df_5pts[['work']].apply(get_thresholds_10)\n",
        "master_df_5pts['work_withPoints'] = master_df_5pts['work'].apply(assign_points_10, thresholds_list=work_thesholds[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Px9P1tTu68fo"
      },
      "source": [
        "### Check Results\n",
        "\n",
        "We need to confirm that the points were correctly assigned by the %tile by matching against the dataframe %tiles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TC04rYb7PaF",
        "outputId": "1910bbed-3373-4fda-e46d-41424f11efc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "master_df_5pts.describe()"
      ],
      "execution_count": null,
      "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>retail_and_rec</th>\n",
              "      <th>grocery_and_pharmacy</th>\n",
              "      <th>work</th>\n",
              "      <th>retail_and_rec_withPoints</th>\n",
              "      <th>grocery_and_pharmacy_withPoints</th>\n",
              "      <th>work_withPoints</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-31.842732</td>\n",
              "      <td>-4.460146</td>\n",
              "      <td>-35.916110</td>\n",
              "      <td>5.507937</td>\n",
              "      <td>5.506650</td>\n",
              "      <td>5.501931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.151460</td>\n",
              "      <td>13.316269</td>\n",
              "      <td>8.145993</td>\n",
              "      <td>2.871691</td>\n",
              "      <td>2.870648</td>\n",
              "      <td>2.873793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-91.933333</td>\n",
              "      <td>-64.000000</td>\n",
              "      <td>-69.589744</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-39.294872</td>\n",
              "      <td>-12.025641</td>\n",
              "      <td>-40.717949</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-30.400000</td>\n",
              "      <td>-4.800000</td>\n",
              "      <td>-34.871795</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-22.724736</td>\n",
              "      <td>2.440171</td>\n",
              "      <td>-29.846154</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>16.600000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>-17.358974</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       retail_and_rec  ...  work_withPoints\n",
              "count     2331.000000  ...      2331.000000\n",
              "mean       -31.842732  ...         5.501931\n",
              "std         13.151460  ...         2.873793\n",
              "min        -91.933333  ...         1.000000\n",
              "25%        -39.294872  ...         3.000000\n",
              "50%        -30.400000  ...         6.000000\n",
              "75%        -22.724736  ...         8.000000\n",
              "max         16.600000  ...        10.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IlZqsFrd7RFH",
        "outputId": "5c7830b3-561c-4814-ec19-c666f604cb7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "master_df_5pts.head(10)"
      ],
      "execution_count": null,
      "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>GEO_ID</th>\n",
              "      <th>retail_and_rec</th>\n",
              "      <th>grocery_and_pharmacy</th>\n",
              "      <th>work</th>\n",
              "      <th>retail_and_rec_withPoints</th>\n",
              "      <th>grocery_and_pharmacy_withPoints</th>\n",
              "      <th>work_withPoints</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01001</td>\n",
              "      <td>-21.307692</td>\n",
              "      <td>7.282051</td>\n",
              "      <td>-35.923077</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01003</td>\n",
              "      <td>-35.410256</td>\n",
              "      <td>-8.589744</td>\n",
              "      <td>-33.589744</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01005</td>\n",
              "      <td>-15.750000</td>\n",
              "      <td>-3.200000</td>\n",
              "      <td>-25.102564</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01007</td>\n",
              "      <td>-16.000000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>-29.717949</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01009</td>\n",
              "      <td>-17.809524</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>-32.794872</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>01011</td>\n",
              "      <td>-22.000000</td>\n",
              "      <td>-4.750000</td>\n",
              "      <td>-29.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>01013</td>\n",
              "      <td>-28.600000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>-34.102564</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>01015</td>\n",
              "      <td>-23.743590</td>\n",
              "      <td>-4.820513</td>\n",
              "      <td>-36.256410</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>01017</td>\n",
              "      <td>-16.692308</td>\n",
              "      <td>2.777778</td>\n",
              "      <td>-40.666667</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>01019</td>\n",
              "      <td>-21.600000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>-26.333333</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  GEO_ID  retail_and_rec  ...  grocery_and_pharmacy_withPoints  work_withPoints\n",
              "0  01001      -21.307692  ...                                2                6\n",
              "1  01003      -35.410256  ...                                7                5\n",
              "2  01005      -15.750000  ...                                5                1\n",
              "3  01007      -16.000000  ...                                2                3\n",
              "4  01009      -17.809524  ...                                2                4\n",
              "5  01011      -22.000000  ...                                5                3\n",
              "6  01013      -28.600000  ...                                2                5\n",
              "7  01015      -23.743590  ...                                6                6\n",
              "8  01017      -16.692308  ...                                3                8\n",
              "9  01019      -21.600000  ...                                2                1\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XFUB3Jt3Ncm"
      },
      "source": [
        "## Step 3: Combining the variables & points to calculate a total Social Distance Index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MddFZL178HuR"
      },
      "source": [
        "### Join the calculated variables with points"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tczO0M6l8GPb"
      },
      "source": [
        "sdi_df = master_df_5pts.merge(master_df_10pts, left_on='GEO_ID', right_on=\"GEO_ID\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GahAyA-n_grh",
        "outputId": "1c2afbdf-5598-4271-a74c-8c3594730181",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "print(sdi_df.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['GEO_ID', 'retail_and_rec', 'grocery_and_pharmacy', 'work',\n",
            "       'retail_and_rec_withPoints', 'grocery_and_pharmacy_withPoints',\n",
            "       'work_withPoints', 'percent_essential_workers', 'pop_density',\n",
            "       'percentage_overcrowded_rooms', 'percent_essential_workers_withPoints',\n",
            "       'pop_densitys_withPoints', 'percentage_overcrowded_rooms_withPoints'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jKHGAGwGWxt"
      },
      "source": [
        "### Calculate the Total Score for the Social Distance Index\n",
        "\n",
        "The Total Score is a weighted average of inidividual the individual contributions of mobility types, % essential workers, pop density, and % overcrowded rooms by county."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mpqErF9-2f_"
      },
      "source": [
        "sdi_df['retail_and_rec_withPoints_final']= sdi_df['retail_and_rec_withPoints']/10.0\n",
        "sdi_df['grocery_and_pharmacy_withPoints_final']= sdi_df['grocery_and_pharmacy_withPoints']/10.0\n",
        "sdi_df['work_withPoints_final']= sdi_df['work_withPoints']/10.0\n",
        "sdi_df['percent_essential_workers_withPoints_final']= sdi_df['percent_essential_workers_withPoints']/10.0\n",
        "sdi_df['pop_densitys_withPoints_final']= sdi_df['pop_densitys_withPoints']/10.0\n",
        "sdi_df['percentage_overcrowded_rooms_withPoints_final']= sdi_df['percentage_overcrowded_rooms_withPoints']/10.0\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_reN0aicDDyE"
      },
      "source": [
        "sdi_df['Total_SDI_Score'] = sdi_df[['retail_and_rec_withPoints_final','grocery_and_pharmacy_withPoints_final','work_withPoints_final','percent_essential_workers_withPoints_final','pop_densitys_withPoints_final','percentage_overcrowded_rooms_withPoints_final']].mean(axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tm0qUJErTbKH"
      },
      "source": [
        "master_df_select = master_df[['GEO_ID', 'NAME_x', 'white_pop_percent',\n",
        "       'black_pop_percent', 'asian_pop_percent', 'hispanic_pop_percent',\n",
        "       'amerindian_pop_percent', 'other_race_pop_percent',\n",
        "       'two_or_more_races_pop_percent', 'hispanic_any_race_percent',\n",
        "       'not_hispanic_pop_percent']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5wpPEVaSVHO"
      },
      "source": [
        "sdi_df_select = sdi_df[[\"GEO_ID\", \"Total_SDI_Score\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNnKFXUIT_Hq"
      },
      "source": [
        "regression_df = pd.merge(master_df_select, sdi_df_select, how=\"outer\", on=\"GEO_ID\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8eATxlzUSdT",
        "outputId": "89468768-f582-451a-e431-96894f680969",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "regression_df.describe()"
      ],
      "execution_count": null,
      "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>white_pop_percent</th>\n",
              "      <th>black_pop_percent</th>\n",
              "      <th>asian_pop_percent</th>\n",
              "      <th>hispanic_pop_percent</th>\n",
              "      <th>amerindian_pop_percent</th>\n",
              "      <th>other_race_pop_percent</th>\n",
              "      <th>two_or_more_races_pop_percent</th>\n",
              "      <th>hispanic_any_race_percent</th>\n",
              "      <th>not_hispanic_pop_percent</th>\n",
              "      <th>Total_SDI_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "      <td>2331.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.757116</td>\n",
              "      <td>0.095848</td>\n",
              "      <td>0.015432</td>\n",
              "      <td>0.097738</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.001218</td>\n",
              "      <td>0.019953</td>\n",
              "      <td>0.097738</td>\n",
              "      <td>0.902262</td>\n",
              "      <td>0.550346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.193011</td>\n",
              "      <td>0.139561</td>\n",
              "      <td>0.028639</td>\n",
              "      <td>0.139395</td>\n",
              "      <td>0.042658</td>\n",
              "      <td>0.002021</td>\n",
              "      <td>0.016202</td>\n",
              "      <td>0.139395</td>\n",
              "      <td>0.139395</td>\n",
              "      <td>0.118669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.007278</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009312</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.639607</td>\n",
              "      <td>0.009277</td>\n",
              "      <td>0.003874</td>\n",
              "      <td>0.023510</td>\n",
              "      <td>0.001356</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.011813</td>\n",
              "      <td>0.023510</td>\n",
              "      <td>0.897037</td>\n",
              "      <td>0.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.818640</td>\n",
              "      <td>0.031461</td>\n",
              "      <td>0.007123</td>\n",
              "      <td>0.045256</td>\n",
              "      <td>0.002729</td>\n",
              "      <td>0.000660</td>\n",
              "      <td>0.016878</td>\n",
              "      <td>0.045256</td>\n",
              "      <td>0.954744</td>\n",
              "      <td>0.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.914632</td>\n",
              "      <td>0.116694</td>\n",
              "      <td>0.014767</td>\n",
              "      <td>0.102963</td>\n",
              "      <td>0.005844</td>\n",
              "      <td>0.001609</td>\n",
              "      <td>0.023287</td>\n",
              "      <td>0.102963</td>\n",
              "      <td>0.976490</td>\n",
              "      <td>0.633333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.993958</td>\n",
              "      <td>0.837123</td>\n",
              "      <td>0.414846</td>\n",
              "      <td>0.990688</td>\n",
              "      <td>0.736578</td>\n",
              "      <td>0.030995</td>\n",
              "      <td>0.233014</td>\n",
              "      <td>0.990688</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       white_pop_percent  ...  Total_SDI_Score\n",
              "count        2331.000000  ...      2331.000000\n",
              "mean            0.757116  ...         0.550346\n",
              "std             0.193011  ...         0.118669\n",
              "min             0.007278  ...         0.200000\n",
              "25%             0.639607  ...         0.466667\n",
              "50%             0.818640  ...         0.550000\n",
              "75%             0.914632  ...         0.633333\n",
              "max             0.993958  ...         1.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3d1fRAPFiehx"
      },
      "source": [
        "# Regression Analysis of SDI vs. Racial Distribution by County"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpv7iTMVbOrM"
      },
      "source": [
        "features = ['white_pop_percent',\n",
        "                   'black_pop_percent',\n",
        "                   'asian_pop_percent', \n",
        "                   'hispanic_pop_percent',\n",
        "            \"amerindian_pop_percent\",\n",
        "            \"other_race_pop_percent\",\n",
        "            ]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUsttlKCVk9C"
      },
      "source": [
        "lr = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVyusVXNVqls",
        "outputId": "7c0733e0-7927-4796-bf88-32432a04c34b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X = regression_df[features]\n",
        "y = regression_df[\"Total_SDI_Score\"]\n",
        "lr.fit(X, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TS0rMZjgX5WT",
        "outputId": "cc7f5c97-5f3b-4a87-ec36-1aa08de46af7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "print('intercept:', lr.intercept_)\n",
        "coefs = lr.coef_\n",
        "for x, a in zip(features, coefs):\n",
        "  print(f\"{x}: {a}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "intercept: -0.08785805637080535\n",
            "white_pop_percent: 0.6439725678551933\n",
            "black_pop_percent: 0.5291851581998621\n",
            "asian_pop_percent: 1.5798083521836057\n",
            "hispanic_pop_percent: 0.6212874607244375\n",
            "amerindian_pop_percent: 0.8222141469454463\n",
            "other_race_pop_percent: 4.166148837028793\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUwkeIR3hU7l",
        "outputId": "e982cb61-5f99-4bc8-c7be-b1663aad1884",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "plt.bar(features[:-1], coefs[:-1])\n",
        "plt.xlabel('Coefficient')  \n",
        "plt.ylabel('race')\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "v0zQ82EZKlxD"
      },
      "source": [
        "# Visual Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTMWrqSecdej",
        "outputId": "5549e3df-596e-440f-940b-93600d6c287f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(regression_df[features + [\"Total_SDI_Score\"] ].corr(), annot = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f65a30a2d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZPl5kygceKQ",
        "outputId": "49590484-ba67-4998-ed77-1fbee6019ff3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "race_list = [master_df[\"white_pop\"], master_df[\"black_pop\"], master_df[\"asian_pop\"], master_df[\"hispanic_pop\"]]\n",
        "race_label = [\"white_pop\", \"black_pop\", \"asian_pop\", \"hispanic_pop\"]\n",
        "for i in range(len(race_list)):\n",
        "  X =race_list[i]/master_df[\"total_pop\"]\n",
        "  y= sdi_df[\"Total_SDI_Score\"]\n",
        "  plt.figure(figsize=(10,5))\n",
        "  plt.plot(X,y, 'o')\n",
        "  plt.ylabel(\"SDI Score\")\n",
        "  m, b = np.polyfit(X,y, 1)\n",
        "  plt.plot(X, m*X + b)\n",
        "  plt.xlabel(race_label[i])\n",
        "  print(f\"The intercept for proportion of \", race_label[i], \"is\", b)\n",
        "  print(f\"The slope for proportion of \", race_label[i], \"is\", m, \"\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The intercept for proportion of  white_pop is 0.5400959440382791\n",
            "The slope for proportion of  white_pop is 0.013538374646164298 \n",
            "\n",
            "The intercept for proportion of  black_pop is 0.560227922974997\n",
            "The slope for proportion of  black_pop is -0.10309929781207458 \n",
            "\n",
            "The intercept for proportion of  asian_pop is 0.5380909826134733\n",
            "The slope for proportion of  asian_pop is 0.7941572616743567 \n",
            "\n",
            "The intercept for proportion of  hispanic_pop is 0.547278719396223\n",
            "The slope for proportion of  hispanic_pop is 0.03138326325797388 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z78EvnpweIp6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}