notebooks/city-demographic-factors-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze ZIP code–level COVID-19 case / demographic correlations\n",
    "\n",
    "This notebook examines ZIP code–level COVID-19 case data for the following five cities:\n",
    "\n",
    "- New York City\n",
    "- Chicago\n",
    "- Detroit\n",
    "- Philadelphia\n",
    "- Baltimore\n",
    "\n",
    "For each, the notebook:\n",
    "\n",
    "1. Loads the data \n",
    "2. Analyzes correlation \n",
    "3. Outputs data for charts and maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gp\n",
    "import matplotlib.pyplot as plt\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Census Data\n",
    "\n",
    "The data loaded below has been pre-processed from ACS 5-year estimates for 2018, to cut down on the data file size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "census_data = pd.read_csv(\n",
    "    \"../data/processed/census/zip_census_data.csv\",\n",
    "    dtype={\n",
    "        \"zip_code\": str, \n",
    "        \"median_age\": float, \n",
    "        \"pct_below_poverty_level\": float, \n",
    "        \"average_household_size\": float\n",
    "    },\n",
    "    na_values=\"-\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Median income exists in the range of 2,500 to 250,000 and we need to clean up both sides\n",
    "# for the regression model\n",
    "def fix_median_income(median_income):\n",
    "    if pd.isnull(median_income):\n",
    "        return None\n",
    "    elif median_income == \"250,000+\":\n",
    "        return 250000\n",
    "    elif median_income == \"2,500-\":\n",
    "        return 2500\n",
    "    else:\n",
    "        return int(median_income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "census_data = census_data.assign(\n",
    "    household_median_income = lambda df: df[\"household_median_income\"].apply(lambda x: fix_median_income(x)),\n",
    "    pct_over_age_65 = lambda df: df[\"age_65_and_over\"] / df[\"total_population\"],\n",
    "    pct_white = lambda df: df[\"total_white_nonhispanic\"] / df[\"total_population\"],\n",
    "    pct_minority = lambda df: 1 - df[\"pct_white\"],\n",
    "    pct_black = lambda df: df[\"total_black_nonhispanic\"] / df[\"total_population\"],\n",
    "    pct_hispanic = lambda df: df[\"total_hispanic\"] / df[\"total_population\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>zip_code</th>\n",
       "      <th>06390</th>\n",
       "      <th>10001</th>\n",
       "      <th>10002</th>\n",
       "      <th>10003</th>\n",
       "      <th>10004</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_population</th>\n",
       "      <td>183</td>\n",
       "      <td>22924</td>\n",
       "      <td>74993</td>\n",
       "      <td>54682</td>\n",
       "      <td>3028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_non_hispanic</th>\n",
       "      <td>179</td>\n",
       "      <td>19678</td>\n",
       "      <td>54603</td>\n",
       "      <td>50176</td>\n",
       "      <td>2916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_white_nonhispanic</th>\n",
       "      <td>175</td>\n",
       "      <td>12182</td>\n",
       "      <td>16472</td>\n",
       "      <td>38521</td>\n",
       "      <td>1834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_black_nonhispanic</th>\n",
       "      <td>0</td>\n",
       "      <td>1289</td>\n",
       "      <td>5249</td>\n",
       "      <td>2961</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_asian_alone</th>\n",
       "      <td>0</td>\n",
       "      <td>5731</td>\n",
       "      <td>30847</td>\n",
       "      <td>7366</td>\n",
       "      <td>899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_black</th>\n",
       "      <td>4</td>\n",
       "      <td>1687</td>\n",
       "      <td>7645</td>\n",
       "      <td>3491</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_hispanic</th>\n",
       "      <td>4</td>\n",
       "      <td>3246</td>\n",
       "      <td>20390</td>\n",
       "      <td>4506</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_household_size</th>\n",
       "      <td>2.39562</td>\n",
       "      <td>1.75738</td>\n",
       "      <td>2.18401</td>\n",
       "      <td>1.70247</td>\n",
       "      <td>1.82545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_more_than_one_occupant_per_room</th>\n",
       "      <td>0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_male</th>\n",
       "      <td>110</td>\n",
       "      <td>11671</td>\n",
       "      <td>36230</td>\n",
       "      <td>27503</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_female</th>\n",
       "      <td>73</td>\n",
       "      <td>11253</td>\n",
       "      <td>38763</td>\n",
       "      <td>27179</td>\n",
       "      <td>1761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_60_and_over</th>\n",
       "      <td>66</td>\n",
       "      <td>4825</td>\n",
       "      <td>21188</td>\n",
       "      <td>9253</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_65_and_over</th>\n",
       "      <td>35</td>\n",
       "      <td>3400</td>\n",
       "      <td>15810</td>\n",
       "      <td>6433</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_75_and_over</th>\n",
       "      <td>10</td>\n",
       "      <td>1807</td>\n",
       "      <td>8099</td>\n",
       "      <td>2993</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>58.9</td>\n",
       "      <td>36.7</td>\n",
       "      <td>43.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>below_poverty_level</th>\n",
       "      <td>3</td>\n",
       "      <td>2912</td>\n",
       "      <td>20565</td>\n",
       "      <td>3812</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <td>1.7</td>\n",
       "      <td>13.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_median_income</th>\n",
       "      <td>NaN</td>\n",
       "      <td>88526</td>\n",
       "      <td>35859</td>\n",
       "      <td>112131</td>\n",
       "      <td>157645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_mean_income</th>\n",
       "      <td>114860</td>\n",
       "      <td>196505</td>\n",
       "      <td>71841</td>\n",
       "      <td>306152</td>\n",
       "      <td>305243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_services</th>\n",
       "      <td>0.246269</td>\n",
       "      <td>0.128273</td>\n",
       "      <td>0.275438</td>\n",
       "      <td>0.0662777</td>\n",
       "      <td>0.0163776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <td>0.191257</td>\n",
       "      <td>0.148316</td>\n",
       "      <td>0.21082</td>\n",
       "      <td>0.117644</td>\n",
       "      <td>0.0752972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white</th>\n",
       "      <td>0.956284</td>\n",
       "      <td>0.531408</td>\n",
       "      <td>0.219647</td>\n",
       "      <td>0.704455</td>\n",
       "      <td>0.60568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_minority</th>\n",
       "      <td>0.0437158</td>\n",
       "      <td>0.468592</td>\n",
       "      <td>0.780353</td>\n",
       "      <td>0.295545</td>\n",
       "      <td>0.39432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0562293</td>\n",
       "      <td>0.0699932</td>\n",
       "      <td>0.0541494</td>\n",
       "      <td>0.0155218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic</th>\n",
       "      <td>0.0218579</td>\n",
       "      <td>0.141598</td>\n",
       "      <td>0.271892</td>\n",
       "      <td>0.0824037</td>\n",
       "      <td>0.0369881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "zip_code                                 06390      10001      10002  \\\n",
       "total_population                           183      22924      74993   \n",
       "total_non_hispanic                         179      19678      54603   \n",
       "total_white_nonhispanic                    175      12182      16472   \n",
       "total_black_nonhispanic                      0       1289       5249   \n",
       "total_asian_alone                            0       5731      30847   \n",
       "total_black                                  4       1687       7645   \n",
       "total_hispanic                               4       3246      20390   \n",
       "average_household_size                 2.39562    1.75738    2.18401   \n",
       "pct_more_than_one_occupant_per_room          0        3.1        8.3   \n",
       "total_male                                 110      11671      36230   \n",
       "total_female                                73      11253      38763   \n",
       "age_60_and_over                             66       4825      21188   \n",
       "age_65_and_over                             35       3400      15810   \n",
       "age_75_and_over                             10       1807       8099   \n",
       "median_age                                58.9       36.7       43.6   \n",
       "below_poverty_level                          3       2912      20565   \n",
       "pct_below_poverty_level                    1.7       13.3       27.7   \n",
       "household_median_income                    NaN      88526      35859   \n",
       "household_mean_income                   114860     196505      71841   \n",
       "pct_services                          0.246269   0.128273   0.275438   \n",
       "pct_over_age_65                       0.191257   0.148316    0.21082   \n",
       "pct_white                             0.956284   0.531408   0.219647   \n",
       "pct_minority                         0.0437158   0.468592   0.780353   \n",
       "pct_black                                    0  0.0562293  0.0699932   \n",
       "pct_hispanic                         0.0218579   0.141598   0.271892   \n",
       "\n",
       "zip_code                                 10003      10004  \n",
       "total_population                         54682       3028  \n",
       "total_non_hispanic                       50176       2916  \n",
       "total_white_nonhispanic                  38521       1834  \n",
       "total_black_nonhispanic                   2961         47  \n",
       "total_asian_alone                         7366        899  \n",
       "total_black                               3491         67  \n",
       "total_hispanic                            4506        112  \n",
       "average_household_size                 1.70247    1.82545  \n",
       "pct_more_than_one_occupant_per_room        3.5        3.8  \n",
       "total_male                               27503       1267  \n",
       "total_female                             27179       1761  \n",
       "age_60_and_over                           9253        415  \n",
       "age_65_and_over                           6433        228  \n",
       "age_75_and_over                           2993         61  \n",
       "median_age                                32.3       37.1  \n",
       "below_poverty_level                       3812         66  \n",
       "pct_below_poverty_level                    8.5        2.2  \n",
       "household_median_income                 112131     157645  \n",
       "household_mean_income                   306152     305243  \n",
       "pct_services                         0.0662777  0.0163776  \n",
       "pct_over_age_65                       0.117644  0.0752972  \n",
       "pct_white                             0.704455    0.60568  \n",
       "pct_minority                          0.295545    0.39432  \n",
       "pct_black                            0.0541494  0.0155218  \n",
       "pct_hispanic                         0.0824037  0.0369881  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_data.set_index(\"zip_code\").head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load City Data\n",
    "\n",
    "By the end of this section, we will have one dataframe for each city:\n",
    "\n",
    "- nyc_df\n",
    "- chi_df\n",
    "- balt_df\n",
    "- phi_df\n",
    "- det_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New York City\n",
    "\n",
    "*Note:* There are more than 1,800 cases without an assigned ZIP code in NYC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MODZCTA</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Total</th>\n",
       "      <th>zcta_cum.perc_pos</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>total_population</th>\n",
       "      <th>total_non_hispanic</th>\n",
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       "      <th>total_black_nonhispanic</th>\n",
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       "      <th>...</th>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <th>household_median_income</th>\n",
       "      <th>household_mean_income</th>\n",
       "      <th>pct_services</th>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <th>pct_white</th>\n",
       "      <th>pct_minority</th>\n",
       "      <th>pct_black</th>\n",
       "      <th>pct_hispanic</th>\n",
       "      <th>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>311</td>\n",
       "      <td>878</td>\n",
       "      <td>35.42</td>\n",
       "      <td>10001</td>\n",
       "      <td>22924.0</td>\n",
       "      <td>19678.0</td>\n",
       "      <td>12182.0</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>5731.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.3</td>\n",
       "      <td>88526.0</td>\n",
       "      <td>196505</td>\n",
       "      <td>0.128273</td>\n",
       "      <td>0.148316</td>\n",
       "      <td>0.531408</td>\n",
       "      <td>0.468592</td>\n",
       "      <td>0.056229</td>\n",
       "      <td>0.141598</td>\n",
       "      <td>13.566568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>878</td>\n",
       "      <td>2069</td>\n",
       "      <td>42.44</td>\n",
       "      <td>10002</td>\n",
       "      <td>74993.0</td>\n",
       "      <td>54603.0</td>\n",
       "      <td>16472.0</td>\n",
       "      <td>5249.0</td>\n",
       "      <td>30847.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.7</td>\n",
       "      <td>35859.0</td>\n",
       "      <td>71841</td>\n",
       "      <td>0.275438</td>\n",
       "      <td>0.210820</td>\n",
       "      <td>0.219647</td>\n",
       "      <td>0.780353</td>\n",
       "      <td>0.069993</td>\n",
       "      <td>0.271892</td>\n",
       "      <td>11.707759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>399</td>\n",
       "      <td>1251</td>\n",
       "      <td>31.89</td>\n",
       "      <td>10003</td>\n",
       "      <td>54682.0</td>\n",
       "      <td>50176.0</td>\n",
       "      <td>38521.0</td>\n",
       "      <td>2961.0</td>\n",
       "      <td>7366.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.5</td>\n",
       "      <td>112131.0</td>\n",
       "      <td>306152</td>\n",
       "      <td>0.066278</td>\n",
       "      <td>0.117644</td>\n",
       "      <td>0.704455</td>\n",
       "      <td>0.295545</td>\n",
       "      <td>0.054149</td>\n",
       "      <td>0.082404</td>\n",
       "      <td>7.296734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>28</td>\n",
       "      <td>87</td>\n",
       "      <td>32.18</td>\n",
       "      <td>10004</td>\n",
       "      <td>3028.0</td>\n",
       "      <td>2916.0</td>\n",
       "      <td>1834.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2</td>\n",
       "      <td>157645.0</td>\n",
       "      <td>305243</td>\n",
       "      <td>0.016378</td>\n",
       "      <td>0.075297</td>\n",
       "      <td>0.605680</td>\n",
       "      <td>0.394320</td>\n",
       "      <td>0.015522</td>\n",
       "      <td>0.036988</td>\n",
       "      <td>9.247028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10005</td>\n",
       "      <td>54</td>\n",
       "      <td>214</td>\n",
       "      <td>25.23</td>\n",
       "      <td>10005</td>\n",
       "      <td>8831.0</td>\n",
       "      <td>8223.0</td>\n",
       "      <td>6132.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.5</td>\n",
       "      <td>173333.0</td>\n",
       "      <td>292722</td>\n",
       "      <td>0.023957</td>\n",
       "      <td>0.012683</td>\n",
       "      <td>0.694372</td>\n",
       "      <td>0.305628</td>\n",
       "      <td>0.024799</td>\n",
       "      <td>0.068848</td>\n",
       "      <td>6.114823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  MODZCTA  Positive  Total  zcta_cum.perc_pos zip_code  total_population  \\\n",
       "1   10001       311    878              35.42    10001           22924.0   \n",
       "2   10002       878   2069              42.44    10002           74993.0   \n",
       "3   10003       399   1251              31.89    10003           54682.0   \n",
       "4   10004        28     87              32.18    10004            3028.0   \n",
       "5   10005        54    214              25.23    10005            8831.0   \n",
       "\n",
       "   total_non_hispanic  total_white_nonhispanic  total_black_nonhispanic  \\\n",
       "1             19678.0                  12182.0                   1289.0   \n",
       "2             54603.0                  16472.0                   5249.0   \n",
       "3             50176.0                  38521.0                   2961.0   \n",
       "4              2916.0                   1834.0                     47.0   \n",
       "5              8223.0                   6132.0                    219.0   \n",
       "\n",
       "   total_asian_alone  ...  pct_below_poverty_level  household_median_income  \\\n",
       "1             5731.0  ...                     13.3                  88526.0   \n",
       "2            30847.0  ...                     27.7                  35859.0   \n",
       "3             7366.0  ...                      8.5                 112131.0   \n",
       "4              899.0  ...                      2.2                 157645.0   \n",
       "5             1497.0  ...                      9.5                 173333.0   \n",
       "\n",
       "   household_mean_income  pct_services  pct_over_age_65  pct_white  \\\n",
       "1                 196505      0.128273         0.148316   0.531408   \n",
       "2                  71841      0.275438         0.210820   0.219647   \n",
       "3                 306152      0.066278         0.117644   0.704455   \n",
       "4                 305243      0.016378         0.075297   0.605680   \n",
       "5                 292722      0.023957         0.012683   0.694372   \n",
       "\n",
       "   pct_minority  pct_black  pct_hispanic  cases_per_1000  \n",
       "1      0.468592   0.056229      0.141598       13.566568  \n",
       "2      0.780353   0.069993      0.271892       11.707759  \n",
       "3      0.295545   0.054149      0.082404        7.296734  \n",
       "4      0.394320   0.015522      0.036988        9.247028  \n",
       "5      0.305628   0.024799      0.068848        6.114823  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df = (\n",
    "    pd.read_csv(\n",
    "        \"../data/raw/nyc-health/tests-by-zcta-2020-05-04.csv\",\n",
    "        dtype = { \"MODZCTA\": str }\n",
    "    )\n",
    "    .merge(\n",
    "        census_data,\n",
    "        how = \"left\",\n",
    "        left_on = \"MODZCTA\",\n",
    "        right_on = \"zip_code\"\n",
    "    )\n",
    "    .assign(\n",
    "        cases_per_1000 = lambda df: df[\"Positive\"] / df[\"total_population\"] * 1000,\n",
    "    )\n",
    "    .dropna(\n",
    "        subset = [ \"MODZCTA\" ]\n",
    "    )\n",
    ")\n",
    "\n",
    "nyc_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chicago"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>zip_code</th>\n",
       "      <th>confirmed_cases</th>\n",
       "      <th>total_tested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46324</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46394</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53142</td>\n",
       "      <td>13</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54302</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60002</td>\n",
       "      <td>56</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zip_code  confirmed_cases  total_tested\n",
       "0    46324                7            75\n",
       "1    46394                6            36\n",
       "2    53142               13            87\n",
       "3    54302                7            32\n",
       "4    60002               56           391"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_illinois():\n",
    "    zip_dicts = []\n",
    "    with open(\"../data/raw/idph/illinois-zip-codes-2020-05-04.json\", \"r\") as f:\n",
    "        data = json.loads(f.read())\n",
    "        for zc in data[\"zip_values\"]:\n",
    "            zip_dict = {\n",
    "                \"zip_code\": zc[\"zip\"],\n",
    "                \"confirmed_cases\": zc[\"confirmed_cases\"],\n",
    "                \"total_tested\": zc[\"total_tested\"]\n",
    "            }\n",
    "            zip_dicts.append(zip_dict)\n",
    "    return pd.DataFrame(zip_dicts)\n",
    "\n",
    "ill_df = load_illinois()\n",
    "ill_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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>objectid</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_len</th>\n",
       "      <th>zip</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.0</td>\n",
       "      <td>1.060523e+08</td>\n",
       "      <td>42720.044406</td>\n",
       "      <td>60647</td>\n",
       "      <td>POLYGON ((-87.67762 41.91776, -87.67761 41.917...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</td>\n",
       "      <td>1.274761e+08</td>\n",
       "      <td>48103.782721</td>\n",
       "      <td>60639</td>\n",
       "      <td>POLYGON ((-87.72683 41.92265, -87.72693 41.922...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.0</td>\n",
       "      <td>4.506904e+07</td>\n",
       "      <td>27288.609612</td>\n",
       "      <td>60707</td>\n",
       "      <td>POLYGON ((-87.78500 41.90915, -87.78531 41.909...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>7.085383e+07</td>\n",
       "      <td>42527.989679</td>\n",
       "      <td>60622</td>\n",
       "      <td>POLYGON ((-87.66707 41.88885, -87.66707 41.888...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.0</td>\n",
       "      <td>9.903962e+07</td>\n",
       "      <td>47970.140153</td>\n",
       "      <td>60651</td>\n",
       "      <td>POLYGON ((-87.70656 41.89555, -87.70672 41.895...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   objectid    shape_area     shape_len    zip  \\\n",
       "0      33.0  1.060523e+08  42720.044406  60647   \n",
       "1      34.0  1.274761e+08  48103.782721  60639   \n",
       "2      35.0  4.506904e+07  27288.609612  60707   \n",
       "3      36.0  7.085383e+07  42527.989679  60622   \n",
       "4      37.0  9.903962e+07  47970.140153  60651   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-87.67762 41.91776, -87.67761 41.917...  \n",
       "1  POLYGON ((-87.72683 41.92265, -87.72693 41.922...  \n",
       "2  POLYGON ((-87.78500 41.90915, -87.78531 41.909...  \n",
       "3  POLYGON ((-87.66707 41.88885, -87.66707 41.888...  \n",
       "4  POLYGON ((-87.70656 41.89555, -87.70672 41.895...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_zips = gp.read_file(\"../data/raw/chi-shapefiles/geo_export_eb16a741-d078-4408-8a62-9eb56bb6cba3.shp\")\n",
    "chi_zips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>confirmed_cases</th>\n",
       "      <th>total_tested</th>\n",
       "      <th>total_population</th>\n",
       "      <th>total_non_hispanic</th>\n",
       "      <th>total_white_nonhispanic</th>\n",
       "      <th>total_black_nonhispanic</th>\n",
       "      <th>total_asian_alone</th>\n",
       "      <th>total_black</th>\n",
       "      <th>total_hispanic</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <th>household_median_income</th>\n",
       "      <th>household_mean_income</th>\n",
       "      <th>pct_services</th>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <th>pct_white</th>\n",
       "      <th>pct_minority</th>\n",
       "      <th>pct_black</th>\n",
       "      <th>pct_hispanic</th>\n",
       "      <th>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60601</td>\n",
       "      <td>57</td>\n",
       "      <td>379</td>\n",
       "      <td>14675</td>\n",
       "      <td>13401</td>\n",
       "      <td>9677</td>\n",
       "      <td>767</td>\n",
       "      <td>2641</td>\n",
       "      <td>818</td>\n",
       "      <td>1274</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4</td>\n",
       "      <td>103243.0</td>\n",
       "      <td>186892</td>\n",
       "      <td>0.055037</td>\n",
       "      <td>0.141397</td>\n",
       "      <td>0.659421</td>\n",
       "      <td>0.340579</td>\n",
       "      <td>0.052266</td>\n",
       "      <td>0.086814</td>\n",
       "      <td>3.884157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60602</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>1244</td>\n",
       "      <td>1163</td>\n",
       "      <td>788</td>\n",
       "      <td>47</td>\n",
       "      <td>242</td>\n",
       "      <td>67</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>157125.0</td>\n",
       "      <td>253316</td>\n",
       "      <td>0.035484</td>\n",
       "      <td>0.004019</td>\n",
       "      <td>0.633441</td>\n",
       "      <td>0.366559</td>\n",
       "      <td>0.037781</td>\n",
       "      <td>0.065113</td>\n",
       "      <td>5.627010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60604</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>782</td>\n",
       "      <td>748</td>\n",
       "      <td>479</td>\n",
       "      <td>37</td>\n",
       "      <td>232</td>\n",
       "      <td>54</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>22.3</td>\n",
       "      <td>116250.0</td>\n",
       "      <td>376124</td>\n",
       "      <td>0.011952</td>\n",
       "      <td>0.118926</td>\n",
       "      <td>0.612532</td>\n",
       "      <td>0.387468</td>\n",
       "      <td>0.047315</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>12.787724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60605</td>\n",
       "      <td>131</td>\n",
       "      <td>850</td>\n",
       "      <td>27519</td>\n",
       "      <td>25911</td>\n",
       "      <td>15781</td>\n",
       "      <td>4550</td>\n",
       "      <td>4431</td>\n",
       "      <td>5165</td>\n",
       "      <td>1608</td>\n",
       "      <td>...</td>\n",
       "      <td>9.6</td>\n",
       "      <td>111164.0</td>\n",
       "      <td>240969</td>\n",
       "      <td>0.076846</td>\n",
       "      <td>0.093354</td>\n",
       "      <td>0.573458</td>\n",
       "      <td>0.426542</td>\n",
       "      <td>0.165340</td>\n",
       "      <td>0.058432</td>\n",
       "      <td>4.760347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60606</td>\n",
       "      <td>31</td>\n",
       "      <td>236</td>\n",
       "      <td>3101</td>\n",
       "      <td>2906</td>\n",
       "      <td>2129</td>\n",
       "      <td>73</td>\n",
       "      <td>561</td>\n",
       "      <td>115</td>\n",
       "      <td>195</td>\n",
       "      <td>...</td>\n",
       "      <td>7.6</td>\n",
       "      <td>131477.0</td>\n",
       "      <td>278453</td>\n",
       "      <td>0.039231</td>\n",
       "      <td>0.138987</td>\n",
       "      <td>0.686553</td>\n",
       "      <td>0.313447</td>\n",
       "      <td>0.023541</td>\n",
       "      <td>0.062883</td>\n",
       "      <td>9.996775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  zip_code  confirmed_cases  total_tested  total_population  \\\n",
       "0    60601               57           379             14675   \n",
       "1    60602                7            56              1244   \n",
       "2    60604               10            47               782   \n",
       "3    60605              131           850             27519   \n",
       "4    60606               31           236              3101   \n",
       "\n",
       "   total_non_hispanic  total_white_nonhispanic  total_black_nonhispanic  \\\n",
       "0               13401                     9677                      767   \n",
       "1                1163                      788                       47   \n",
       "2                 748                      479                       37   \n",
       "3               25911                    15781                     4550   \n",
       "4                2906                     2129                       73   \n",
       "\n",
       "   total_asian_alone  total_black  total_hispanic  ...  \\\n",
       "0               2641          818            1274  ...   \n",
       "1                242           67              81  ...   \n",
       "2                232           54              34  ...   \n",
       "3               4431         5165            1608  ...   \n",
       "4                561          115             195  ...   \n",
       "\n",
       "   pct_below_poverty_level  household_median_income  household_mean_income  \\\n",
       "0                      8.4                 103243.0                 186892   \n",
       "1                      2.4                 157125.0                 253316   \n",
       "2                     22.3                 116250.0                 376124   \n",
       "3                      9.6                 111164.0                 240969   \n",
       "4                      7.6                 131477.0                 278453   \n",
       "\n",
       "   pct_services  pct_over_age_65  pct_white  pct_minority  pct_black  \\\n",
       "0      0.055037         0.141397   0.659421      0.340579   0.052266   \n",
       "1      0.035484         0.004019   0.633441      0.366559   0.037781   \n",
       "2      0.011952         0.118926   0.612532      0.387468   0.047315   \n",
       "3      0.076846         0.093354   0.573458      0.426542   0.165340   \n",
       "4      0.039231         0.138987   0.686553      0.313447   0.023541   \n",
       "\n",
       "   pct_hispanic  cases_per_1000  \n",
       "0      0.086814        3.884157  \n",
       "1      0.065113        5.627010  \n",
       "2      0.043478       12.787724  \n",
       "3      0.058432        4.760347  \n",
       "4      0.062883        9.996775  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_df = (\n",
    "    ill_df\n",
    "    .loc[lambda x: x[\"zip_code\"].isin(chi_zips[\"zip\"])]\n",
    "    .merge(\n",
    "        census_data,\n",
    "        how = \"left\",\n",
    "        on = \"zip_code\",\n",
    "    ).assign(\n",
    "        cases_per_1000 = lambda df: df[\"confirmed_cases\"] / df[\"total_population\"] * 1000,\n",
    "    )\n",
    ")\n",
    "\n",
    "chi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert chi_df[\"confirmed_cases\"].isnull().sum() == 0\n",
    "assert chi_df[\"total_population\"].isnull().sum() == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Detroit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>zip_code</th>\n",
       "      <th>confirmed_cases</th>\n",
       "      <th>total_population</th>\n",
       "      <th>total_non_hispanic</th>\n",
       "      <th>total_white_nonhispanic</th>\n",
       "      <th>total_black_nonhispanic</th>\n",
       "      <th>total_asian_alone</th>\n",
       "      <th>total_black</th>\n",
       "      <th>total_hispanic</th>\n",
       "      <th>average_household_size</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <th>household_median_income</th>\n",
       "      <th>household_mean_income</th>\n",
       "      <th>pct_services</th>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <th>pct_white</th>\n",
       "      <th>pct_minority</th>\n",
       "      <th>pct_black</th>\n",
       "      <th>pct_hispanic</th>\n",
       "      <th>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48219</td>\n",
       "      <td>772</td>\n",
       "      <td>48184</td>\n",
       "      <td>47754</td>\n",
       "      <td>2848</td>\n",
       "      <td>43882</td>\n",
       "      <td>236</td>\n",
       "      <td>44587</td>\n",
       "      <td>430</td>\n",
       "      <td>2.478443</td>\n",
       "      <td>...</td>\n",
       "      <td>32.9</td>\n",
       "      <td>33029.0</td>\n",
       "      <td>51314</td>\n",
       "      <td>0.421581</td>\n",
       "      <td>0.153101</td>\n",
       "      <td>0.059107</td>\n",
       "      <td>0.940893</td>\n",
       "      <td>0.910717</td>\n",
       "      <td>0.008924</td>\n",
       "      <td>16.021916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48223</td>\n",
       "      <td>297</td>\n",
       "      <td>24632</td>\n",
       "      <td>24296</td>\n",
       "      <td>2066</td>\n",
       "      <td>21446</td>\n",
       "      <td>111</td>\n",
       "      <td>22005</td>\n",
       "      <td>336</td>\n",
       "      <td>2.495243</td>\n",
       "      <td>...</td>\n",
       "      <td>30.7</td>\n",
       "      <td>35562.0</td>\n",
       "      <td>53956</td>\n",
       "      <td>0.449888</td>\n",
       "      <td>0.142254</td>\n",
       "      <td>0.083875</td>\n",
       "      <td>0.916125</td>\n",
       "      <td>0.870656</td>\n",
       "      <td>0.013641</td>\n",
       "      <td>12.057486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48228</td>\n",
       "      <td>627</td>\n",
       "      <td>55737</td>\n",
       "      <td>52594</td>\n",
       "      <td>8890</td>\n",
       "      <td>41902</td>\n",
       "      <td>37</td>\n",
       "      <td>42456</td>\n",
       "      <td>3143</td>\n",
       "      <td>2.880687</td>\n",
       "      <td>...</td>\n",
       "      <td>41.6</td>\n",
       "      <td>26269.0</td>\n",
       "      <td>39710</td>\n",
       "      <td>0.472901</td>\n",
       "      <td>0.100849</td>\n",
       "      <td>0.159499</td>\n",
       "      <td>0.840501</td>\n",
       "      <td>0.751781</td>\n",
       "      <td>0.056390</td>\n",
       "      <td>11.249260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48235</td>\n",
       "      <td>915</td>\n",
       "      <td>44753</td>\n",
       "      <td>44320</td>\n",
       "      <td>586</td>\n",
       "      <td>43129</td>\n",
       "      <td>45</td>\n",
       "      <td>43457</td>\n",
       "      <td>433</td>\n",
       "      <td>2.486242</td>\n",
       "      <td>...</td>\n",
       "      <td>31.1</td>\n",
       "      <td>31759.0</td>\n",
       "      <td>49848</td>\n",
       "      <td>0.429984</td>\n",
       "      <td>0.177575</td>\n",
       "      <td>0.013094</td>\n",
       "      <td>0.986906</td>\n",
       "      <td>0.963712</td>\n",
       "      <td>0.009675</td>\n",
       "      <td>20.445557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48227</td>\n",
       "      <td>584</td>\n",
       "      <td>41777</td>\n",
       "      <td>41582</td>\n",
       "      <td>669</td>\n",
       "      <td>39967</td>\n",
       "      <td>25</td>\n",
       "      <td>40662</td>\n",
       "      <td>195</td>\n",
       "      <td>2.704630</td>\n",
       "      <td>...</td>\n",
       "      <td>35.1</td>\n",
       "      <td>29250.0</td>\n",
       "      <td>43053</td>\n",
       "      <td>0.506074</td>\n",
       "      <td>0.141920</td>\n",
       "      <td>0.016014</td>\n",
       "      <td>0.983986</td>\n",
       "      <td>0.956675</td>\n",
       "      <td>0.004668</td>\n",
       "      <td>13.978984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  zip_code  confirmed_cases  total_population  total_non_hispanic  \\\n",
       "0    48219              772             48184               47754   \n",
       "1    48223              297             24632               24296   \n",
       "2    48228              627             55737               52594   \n",
       "3    48235              915             44753               44320   \n",
       "4    48227              584             41777               41582   \n",
       "\n",
       "   total_white_nonhispanic  total_black_nonhispanic  total_asian_alone  \\\n",
       "0                     2848                    43882                236   \n",
       "1                     2066                    21446                111   \n",
       "2                     8890                    41902                 37   \n",
       "3                      586                    43129                 45   \n",
       "4                      669                    39967                 25   \n",
       "\n",
       "   total_black  total_hispanic  average_household_size  ...  \\\n",
       "0        44587             430                2.478443  ...   \n",
       "1        22005             336                2.495243  ...   \n",
       "2        42456            3143                2.880687  ...   \n",
       "3        43457             433                2.486242  ...   \n",
       "4        40662             195                2.704630  ...   \n",
       "\n",
       "   pct_below_poverty_level  household_median_income  household_mean_income  \\\n",
       "0                     32.9                  33029.0                  51314   \n",
       "1                     30.7                  35562.0                  53956   \n",
       "2                     41.6                  26269.0                  39710   \n",
       "3                     31.1                  31759.0                  49848   \n",
       "4                     35.1                  29250.0                  43053   \n",
       "\n",
       "   pct_services  pct_over_age_65  pct_white  pct_minority  pct_black  \\\n",
       "0      0.421581         0.153101   0.059107      0.940893   0.910717   \n",
       "1      0.449888         0.142254   0.083875      0.916125   0.870656   \n",
       "2      0.472901         0.100849   0.159499      0.840501   0.751781   \n",
       "3      0.429984         0.177575   0.013094      0.986906   0.963712   \n",
       "4      0.506074         0.141920   0.016014      0.983986   0.956675   \n",
       "\n",
       "   pct_hispanic  cases_per_1000  \n",
       "0      0.008924       16.021916  \n",
       "1      0.013641       12.057486  \n",
       "2      0.056390       11.249260  \n",
       "3      0.009675       20.445557  \n",
       "4      0.004668       13.978984  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "det_df = (\n",
    "    pd.read_csv(\n",
    "        \"../data/raw/detroit-health/detroit-cases-2020-05-07.csv\",\n",
    "        dtype = { \"zip_code\": str }\n",
    "    )\n",
    "    .dropna(subset = [ \"zip_code\" ])\n",
    "    .merge(\n",
    "        census_data,\n",
    "        how = \"left\",\n",
    "        on = \"zip_code\",\n",
    "    )\n",
    "    .assign(\n",
    "        cases_per_1000 = lambda df: df[\"confirmed_cases\"] / df[\"total_population\"] * 1000,\n",
    "    )\n",
    ")\n",
    "\n",
    "det_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Philadelphia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_df = gp.read_file(\"../data/raw/pa-health/zip-cases-2020-05-04.geojson\")\n",
    "phi_shp = gp.read_file(\"../data/raw/phi-shapefile/Zipcodes_Poly.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>OBJECTID</th>\n",
       "      <th>ZIP_CODE</th>\n",
       "      <th>PO_NAME</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Probable</th>\n",
       "      <th>Additional_Tracking</th>\n",
       "      <th>Positive_Redacted</th>\n",
       "      <th>Negative_Redacted</th>\n",
       "      <th>Shape__Area</th>\n",
       "      <th>Shape__Length</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>227</td>\n",
       "      <td>15521</td>\n",
       "      <td>Alum Bank</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.009600</td>\n",
       "      <td>0.540144</td>\n",
       "      <td>POLYGON ((-78.69882 40.16141, -78.69321 40.174...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>228</td>\n",
       "      <td>15522</td>\n",
       "      <td>Bedford</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.051744</td>\n",
       "      <td>1.719056</td>\n",
       "      <td>POLYGON ((-78.73625 39.72304, -78.73601 39.723...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>229</td>\n",
       "      <td>15530</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.024916</td>\n",
       "      <td>1.187654</td>\n",
       "      <td>POLYGON ((-78.76807 40.00961, -78.76774 40.009...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>230</td>\n",
       "      <td>15531</td>\n",
       "      <td>Boswell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.016014</td>\n",
       "      <td>1.060947</td>\n",
       "      <td>MULTIPOLYGON (((-79.01568 40.28520, -79.01565 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>231</td>\n",
       "      <td>15533</td>\n",
       "      <td>Breezewood</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.009377</td>\n",
       "      <td>1.000603</td>\n",
       "      <td>POLYGON ((-78.21323 40.08838, -78.21233 40.088...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID ZIP_CODE     PO_NAME  Positive  Negative  Probable  \\\n",
       "0       227    15521   Alum Bank      -1.0       8.0       0.0   \n",
       "1       228    15522     Bedford      -1.0      51.0       0.0   \n",
       "2       229    15530      Berlin      -1.0      43.0      -1.0   \n",
       "3       230    15531     Boswell       0.0      22.0       0.0   \n",
       "4       231    15533  Breezewood       0.0       8.0       0.0   \n",
       "\n",
       "  Additional_Tracking Positive_Redacted Negative_Redacted  Shape__Area  \\\n",
       "0                None              None              None     0.009600   \n",
       "1                None              None              None     0.051744   \n",
       "2                None              None              None     0.024916   \n",
       "3                None              None              None     0.016014   \n",
       "4                None              None              None     0.009377   \n",
       "\n",
       "   Shape__Length                                           geometry  \n",
       "0       0.540144  POLYGON ((-78.69882 40.16141, -78.69321 40.174...  \n",
       "1       1.719056  POLYGON ((-78.73625 39.72304, -78.73601 39.723...  \n",
       "2       1.187654  POLYGON ((-78.76807 40.00961, -78.76774 40.009...  \n",
       "3       1.060947  MULTIPOLYGON (((-79.01568 40.28520, -79.01565 ...  \n",
       "4       1.000603  POLYGON ((-78.21323 40.08838, -78.21233 40.088...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>CODE</th>\n",
       "      <th>COD</th>\n",
       "      <th>Shape__Are</th>\n",
       "      <th>Shape__Len</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19120</td>\n",
       "      <td>20</td>\n",
       "      <td>9.177970e+07</td>\n",
       "      <td>49921.544063</td>\n",
       "      <td>POLYGON ((-75.11107 40.04682, -75.10943 40.045...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19121</td>\n",
       "      <td>21</td>\n",
       "      <td>6.959879e+07</td>\n",
       "      <td>39534.887217</td>\n",
       "      <td>POLYGON ((-75.19227 39.99463, -75.19205 39.994...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>19122</td>\n",
       "      <td>22</td>\n",
       "      <td>3.591632e+07</td>\n",
       "      <td>24124.645221</td>\n",
       "      <td>POLYGON ((-75.15406 39.98601, -75.15328 39.985...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>19123</td>\n",
       "      <td>23</td>\n",
       "      <td>3.585175e+07</td>\n",
       "      <td>26421.728982</td>\n",
       "      <td>POLYGON ((-75.15190 39.97056, -75.15150 39.970...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>19124</td>\n",
       "      <td>24</td>\n",
       "      <td>1.448080e+08</td>\n",
       "      <td>63658.770420</td>\n",
       "      <td>POLYGON ((-75.09660 40.04249, -75.09281 40.039...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID   CODE  COD    Shape__Are    Shape__Len  \\\n",
       "0         1  19120   20  9.177970e+07  49921.544063   \n",
       "1         2  19121   21  6.959879e+07  39534.887217   \n",
       "2         3  19122   22  3.591632e+07  24124.645221   \n",
       "3         4  19123   23  3.585175e+07  26421.728982   \n",
       "4         5  19124   24  1.448080e+08  63658.770420   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-75.11107 40.04682, -75.10943 40.045...  \n",
       "1  POLYGON ((-75.19227 39.99463, -75.19205 39.994...  \n",
       "2  POLYGON ((-75.15406 39.98601, -75.15328 39.985...  \n",
       "3  POLYGON ((-75.15190 39.97056, -75.15150 39.970...  \n",
       "4  POLYGON ((-75.09660 40.04249, -75.09281 40.039...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phi_shp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>OBJECTID</th>\n",
       "      <th>ZIP_CODE</th>\n",
       "      <th>PO_NAME</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Probable</th>\n",
       "      <th>Additional_Tracking</th>\n",
       "      <th>Positive_Redacted</th>\n",
       "      <th>Negative_Redacted</th>\n",
       "      <th>Shape__Area</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <th>household_median_income</th>\n",
       "      <th>household_mean_income</th>\n",
       "      <th>pct_services</th>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <th>pct_white</th>\n",
       "      <th>pct_minority</th>\n",
       "      <th>pct_black</th>\n",
       "      <th>pct_hispanic</th>\n",
       "      <th>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1384</td>\n",
       "      <td>19102</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>36.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>...</td>\n",
       "      <td>14.3</td>\n",
       "      <td>90750.0</td>\n",
       "      <td>223116</td>\n",
       "      <td>0.046250</td>\n",
       "      <td>0.183715</td>\n",
       "      <td>0.725744</td>\n",
       "      <td>0.274256</td>\n",
       "      <td>0.044561</td>\n",
       "      <td>0.052461</td>\n",
       "      <td>7.291878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1385</td>\n",
       "      <td>19103</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>150.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>73611.0</td>\n",
       "      <td>219188</td>\n",
       "      <td>0.058501</td>\n",
       "      <td>0.215987</td>\n",
       "      <td>0.721252</td>\n",
       "      <td>0.278748</td>\n",
       "      <td>0.061894</td>\n",
       "      <td>0.056856</td>\n",
       "      <td>6.193484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1386</td>\n",
       "      <td>19104</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>197.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>...</td>\n",
       "      <td>45.3</td>\n",
       "      <td>25865.0</td>\n",
       "      <td>57260</td>\n",
       "      <td>0.230878</td>\n",
       "      <td>0.070207</td>\n",
       "      <td>0.345823</td>\n",
       "      <td>0.654177</td>\n",
       "      <td>0.418810</td>\n",
       "      <td>0.050929</td>\n",
       "      <td>3.627258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1387</td>\n",
       "      <td>19106</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>34.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5</td>\n",
       "      <td>109393.0</td>\n",
       "      <td>228289</td>\n",
       "      <td>0.058237</td>\n",
       "      <td>0.176162</td>\n",
       "      <td>0.767273</td>\n",
       "      <td>0.232727</td>\n",
       "      <td>0.081051</td>\n",
       "      <td>0.064404</td>\n",
       "      <td>2.747475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1388</td>\n",
       "      <td>19107</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>119.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>...</td>\n",
       "      <td>23.3</td>\n",
       "      <td>53534.0</td>\n",
       "      <td>134247</td>\n",
       "      <td>0.131706</td>\n",
       "      <td>0.110616</td>\n",
       "      <td>0.554907</td>\n",
       "      <td>0.445093</td>\n",
       "      <td>0.079877</td>\n",
       "      <td>0.051329</td>\n",
       "      <td>8.688668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID ZIP_CODE       PO_NAME  Positive  Negative  Probable  \\\n",
       "0      1384    19102  Philadelphia      36.0     147.0       0.0   \n",
       "1      1385    19103  Philadelphia     150.0     421.0       0.0   \n",
       "2      1386    19104  Philadelphia     197.0     649.0       0.0   \n",
       "3      1387    19106  Philadelphia      34.0     230.0       0.0   \n",
       "4      1388    19107  Philadelphia     119.0     396.0       0.0   \n",
       "\n",
       "  Additional_Tracking Positive_Redacted Negative_Redacted  Shape__Area  ...  \\\n",
       "0                None              None              None     0.000047  ...   \n",
       "1                None              None              None     0.000194  ...   \n",
       "2                None              None              None     0.000860  ...   \n",
       "3                None              None              None     0.000275  ...   \n",
       "4                None              None              None     0.000145  ...   \n",
       "\n",
       "   pct_below_poverty_level household_median_income household_mean_income  \\\n",
       "0                     14.3                 90750.0                223116   \n",
       "1                      8.8                 73611.0                219188   \n",
       "2                     45.3                 25865.0                 57260   \n",
       "3                      7.5                109393.0                228289   \n",
       "4                     23.3                 53534.0                134247   \n",
       "\n",
       "   pct_services  pct_over_age_65  pct_white  pct_minority  pct_black  \\\n",
       "0      0.046250         0.183715   0.725744      0.274256   0.044561   \n",
       "1      0.058501         0.215987   0.721252      0.278748   0.061894   \n",
       "2      0.230878         0.070207   0.345823      0.654177   0.418810   \n",
       "3      0.058237         0.176162   0.767273      0.232727   0.081051   \n",
       "4      0.131706         0.110616   0.554907      0.445093   0.079877   \n",
       "\n",
       "   pct_hispanic  cases_per_1000  \n",
       "0      0.052461        7.291878  \n",
       "1      0.056856        6.193484  \n",
       "2      0.050929        3.627258  \n",
       "3      0.064404        2.747475  \n",
       "4      0.051329        8.688668  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phi_df = (\n",
    "    pa_df.loc[lambda x: x[\"ZIP_CODE\"].isin(phi_shp[\"CODE\"])]\n",
    "    .merge(\n",
    "        census_data,\n",
    "        how = \"left\",\n",
    "        left_on = \"ZIP_CODE\",\n",
    "        right_on = \"zip_code\",\n",
    "    )\n",
    "    .assign(\n",
    "        cases_per_1000 = lambda df: df[\"Positive\"] / df[\"total_population\"] * 1000,\n",
    "    )\n",
    ")\n",
    "\n",
    "phi_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*There are no ZIP codes with either redacted data for positives or missing total_population:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert (\n",
    "    phi_df\n",
    "    [[\n",
    "        \"Positive\",\n",
    "        \"total_population\",\n",
    "        \"ZIP_CODE\"\n",
    "    ]]\n",
    "    .isnull()\n",
    "    .sum().sum()\n",
    ") == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baltimore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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>zip_code</th>\n",
       "      <th>zip_name</th>\n",
       "      <th>case_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21230</td>\n",
       "      <td>Morrell Park</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20626</td>\n",
       "      <td>Coltons Point</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21703</td>\n",
       "      <td>Frederick</td>\n",
       "      <td>266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21207</td>\n",
       "      <td>Gwynn Oak</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20675</td>\n",
       "      <td>Pomfret</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zip_code       zip_name  case_count\n",
       "0    21230   Morrell Park       133.0\n",
       "1    20626  Coltons Point         0.0\n",
       "2    21703      Frederick       266.0\n",
       "3    21207      Gwynn Oak       273.0\n",
       "4    20675        Pomfret         0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_maryland(): \n",
    "    zip_dicts = []\n",
    "    with open(\"../data/raw/maryland/maryland-case-count-2020-05-04.json\", \"r\") as f:\n",
    "        data = json.loads(f.read())\n",
    "        for zf in data[\"features\"]:\n",
    "            attrs = zf[\"attributes\"]\n",
    "            zip_dict = {\n",
    "                \"zip_code\": attrs[\"ZIPCODE1\"],\n",
    "                \"zip_name\": attrs[\"ZIPName\"],\n",
    "                \"case_count\": attrs[\"ProtectedCount\"]\n",
    "            }\n",
    "            zip_dicts.append(zip_dict)\n",
    "    return pd.DataFrame(zip_dicts)\n",
    "\n",
    "md_df = (\n",
    "    load_maryland()\n",
    "    .assign(case_count = lambda df: df[\"case_count\"].fillna(0))\n",
    ")\n",
    "\n",
    "md_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>OBJECTID</th>\n",
       "      <th>ZIPCODE1</th>\n",
       "      <th>ZIPCODE2</th>\n",
       "      <th>ZIPNAME</th>\n",
       "      <th>zipcode_SH</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>21213</td>\n",
       "      <td>02451021213</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>9.411134e+07</td>\n",
       "      <td>POLYGON ((1432141.370 607325.721, 1432279.780 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "      <td>21205</td>\n",
       "      <td>02451021205</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>5.682149e+07</td>\n",
       "      <td>POLYGON ((1439929.248 600706.435, 1440023.469 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>21227</td>\n",
       "      <td>02451021227</td>\n",
       "      <td>Halethorpe</td>\n",
       "      <td>3.044209e+04</td>\n",
       "      <td>POLYGON ((1402910.236 582020.031, 1402976.835 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>21231</td>\n",
       "      <td>02451021231</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>2.283470e+07</td>\n",
       "      <td>POLYGON ((1429913.376 593788.900, 1429926.602 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51</td>\n",
       "      <td>21229</td>\n",
       "      <td>02451021229</td>\n",
       "      <td>Carroll</td>\n",
       "      <td>1.462700e+08</td>\n",
       "      <td>POLYGON ((1406852.864 584336.370, 1406269.539 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID ZIPCODE1     ZIPCODE2     ZIPNAME    zipcode_SH  \\\n",
       "0        60    21213  02451021213     Clifton  9.411134e+07   \n",
       "1        70    21205  02451021205   Baltimore  5.682149e+07   \n",
       "2        52    21227  02451021227  Halethorpe  3.044209e+04   \n",
       "3        41    21231  02451021231   Baltimore  2.283470e+07   \n",
       "4        51    21229  02451021229     Carroll  1.462700e+08   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((1432141.370 607325.721, 1432279.780 ...  \n",
       "1  POLYGON ((1439929.248 600706.435, 1440023.469 ...  \n",
       "2  POLYGON ((1402910.236 582020.031, 1402976.835 ...  \n",
       "3  POLYGON ((1429913.376 593788.900, 1429926.602 ...  \n",
       "4  POLYGON ((1406852.864 584336.370, 1406269.539 ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balt_zips = gp.read_file(\"../data/raw/baltimore-shapefiles/ZIP_Codes.shp\")\n",
    "balt_zips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip_code</th>\n",
       "      <th>zip_name</th>\n",
       "      <th>case_count</th>\n",
       "      <th>total_population</th>\n",
       "      <th>total_non_hispanic</th>\n",
       "      <th>total_white_nonhispanic</th>\n",
       "      <th>total_black_nonhispanic</th>\n",
       "      <th>total_asian_alone</th>\n",
       "      <th>total_black</th>\n",
       "      <th>total_hispanic</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <th>household_median_income</th>\n",
       "      <th>household_mean_income</th>\n",
       "      <th>pct_services</th>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <th>pct_white</th>\n",
       "      <th>pct_minority</th>\n",
       "      <th>pct_black</th>\n",
       "      <th>pct_hispanic</th>\n",
       "      <th>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21230</td>\n",
       "      <td>Morrell Park</td>\n",
       "      <td>133.0</td>\n",
       "      <td>34947</td>\n",
       "      <td>32528</td>\n",
       "      <td>20830</td>\n",
       "      <td>9512</td>\n",
       "      <td>1207</td>\n",
       "      <td>9982</td>\n",
       "      <td>2419</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>81446.0</td>\n",
       "      <td>124801</td>\n",
       "      <td>0.148223</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.596045</td>\n",
       "      <td>0.403955</td>\n",
       "      <td>0.272184</td>\n",
       "      <td>0.069219</td>\n",
       "      <td>3.805763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21207</td>\n",
       "      <td>Gwynn Oak</td>\n",
       "      <td>273.0</td>\n",
       "      <td>47687</td>\n",
       "      <td>46043</td>\n",
       "      <td>4348</td>\n",
       "      <td>39740</td>\n",
       "      <td>725</td>\n",
       "      <td>40581</td>\n",
       "      <td>1644</td>\n",
       "      <td>...</td>\n",
       "      <td>13.8</td>\n",
       "      <td>56719.0</td>\n",
       "      <td>82310</td>\n",
       "      <td>0.272607</td>\n",
       "      <td>0.191876</td>\n",
       "      <td>0.091178</td>\n",
       "      <td>0.908822</td>\n",
       "      <td>0.833351</td>\n",
       "      <td>0.034475</td>\n",
       "      <td>5.724831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21213</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>128.0</td>\n",
       "      <td>31701</td>\n",
       "      <td>31178</td>\n",
       "      <td>2312</td>\n",
       "      <td>28051</td>\n",
       "      <td>79</td>\n",
       "      <td>28629</td>\n",
       "      <td>523</td>\n",
       "      <td>...</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36136.0</td>\n",
       "      <td>55649</td>\n",
       "      <td>0.392523</td>\n",
       "      <td>0.111984</td>\n",
       "      <td>0.072931</td>\n",
       "      <td>0.927069</td>\n",
       "      <td>0.884862</td>\n",
       "      <td>0.016498</td>\n",
       "      <td>4.037728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21227</td>\n",
       "      <td>Halethorpe</td>\n",
       "      <td>163.0</td>\n",
       "      <td>33822</td>\n",
       "      <td>30203</td>\n",
       "      <td>22172</td>\n",
       "      <td>5048</td>\n",
       "      <td>1638</td>\n",
       "      <td>6005</td>\n",
       "      <td>3619</td>\n",
       "      <td>...</td>\n",
       "      <td>16.5</td>\n",
       "      <td>65395.0</td>\n",
       "      <td>84909</td>\n",
       "      <td>0.294882</td>\n",
       "      <td>0.126929</td>\n",
       "      <td>0.655550</td>\n",
       "      <td>0.344450</td>\n",
       "      <td>0.149252</td>\n",
       "      <td>0.107001</td>\n",
       "      <td>4.819348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21224</td>\n",
       "      <td>Highlandtown</td>\n",
       "      <td>354.0</td>\n",
       "      <td>50433</td>\n",
       "      <td>40671</td>\n",
       "      <td>28918</td>\n",
       "      <td>8098</td>\n",
       "      <td>1543</td>\n",
       "      <td>9255</td>\n",
       "      <td>9762</td>\n",
       "      <td>...</td>\n",
       "      <td>18.1</td>\n",
       "      <td>69615.0</td>\n",
       "      <td>97281</td>\n",
       "      <td>0.214084</td>\n",
       "      <td>0.101362</td>\n",
       "      <td>0.573394</td>\n",
       "      <td>0.426606</td>\n",
       "      <td>0.160569</td>\n",
       "      <td>0.193564</td>\n",
       "      <td>7.019214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  zip_code      zip_name  case_count  total_population  total_non_hispanic  \\\n",
       "0    21230  Morrell Park       133.0             34947               32528   \n",
       "1    21207     Gwynn Oak       273.0             47687               46043   \n",
       "2    21213       Clifton       128.0             31701               31178   \n",
       "3    21227    Halethorpe       163.0             33822               30203   \n",
       "4    21224  Highlandtown       354.0             50433               40671   \n",
       "\n",
       "   total_white_nonhispanic  total_black_nonhispanic  total_asian_alone  \\\n",
       "0                    20830                     9512               1207   \n",
       "1                     4348                    39740                725   \n",
       "2                     2312                    28051                 79   \n",
       "3                    22172                     5048               1638   \n",
       "4                    28918                     8098               1543   \n",
       "\n",
       "   total_black  total_hispanic  ...  pct_below_poverty_level  \\\n",
       "0         9982            2419  ...                     14.0   \n",
       "1        40581            1644  ...                     13.8   \n",
       "2        28629             523  ...                     30.7   \n",
       "3         6005            3619  ...                     16.5   \n",
       "4         9255            9762  ...                     18.1   \n",
       "\n",
       "   household_median_income  household_mean_income  pct_services  \\\n",
       "0                  81446.0                 124801      0.148223   \n",
       "1                  56719.0                  82310      0.272607   \n",
       "2                  36136.0                  55649      0.392523   \n",
       "3                  65395.0                  84909      0.294882   \n",
       "4                  69615.0                  97281      0.214084   \n",
       "\n",
       "   pct_over_age_65  pct_white  pct_minority  pct_black  pct_hispanic  \\\n",
       "0         0.090909   0.596045      0.403955   0.272184      0.069219   \n",
       "1         0.191876   0.091178      0.908822   0.833351      0.034475   \n",
       "2         0.111984   0.072931      0.927069   0.884862      0.016498   \n",
       "3         0.126929   0.655550      0.344450   0.149252      0.107001   \n",
       "4         0.101362   0.573394      0.426606   0.160569      0.193564   \n",
       "\n",
       "   cases_per_1000  \n",
       "0        3.805763  \n",
       "1        5.724831  \n",
       "2        4.037728  \n",
       "3        4.819348  \n",
       "4        7.019214  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balt_df = (\n",
    "    md_df.loc[lambda x: (\n",
    "        x[\"zip_code\"].isin(balt_zips[\"ZIPCODE1\"]) & \n",
    "        ~x[\"zip_code\"].isin([\n",
    "            \"21287\", # Johns Hopkins University, no Census population\n",
    "            \"21251\", # Morgan State University, closed\n",
    "        ])\n",
    "    )]\n",
    "    .merge(\n",
    "        census_data,\n",
    "        how = \"left\",\n",
    "        on = \"zip_code\",\n",
    "    )\n",
    "    .assign(\n",
    "        cases_per_1000 = lambda df: df[\"case_count\"] / df[\"total_population\"] * 1000,\n",
    "    )\n",
    ")\n",
    "\n",
    "balt_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine correlations for each city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlate_census(df):\n",
    "    return (\n",
    "        df.corr()\n",
    "        .loc[lambda df: df.index.str.contains(\"pct|median|average\")]\n",
    "        [[\"cases_per_1000\"]]\n",
    "        .assign(abs = lambda df: df[\"cases_per_1000\"].abs())\n",
    "        .sort_values(\"abs\", ascending = False)\n",
    "        .drop(columns = \"abs\")\n",
    "        .round(3)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New York City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pct_services</th>\n",
       "      <td>0.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_household_size</th>\n",
       "      <td>0.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_median_income</th>\n",
       "      <td>-0.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white</th>\n",
       "      <td>-0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_minority</th>\n",
       "      <td>0.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic</th>\n",
       "      <td>0.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black</th>\n",
       "      <td>0.343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_more_than_one_occupant_per_room</th>\n",
       "      <td>0.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <td>0.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     cases_per_1000\n",
       "pct_services                                  0.639\n",
       "average_household_size                        0.624\n",
       "household_median_income                      -0.519\n",
       "pct_white                                    -0.518\n",
       "pct_minority                                  0.518\n",
       "pct_hispanic                                  0.411\n",
       "pct_black                                     0.343\n",
       "pct_more_than_one_occupant_per_room           0.247\n",
       "pct_below_poverty_level                       0.228\n",
       "pct_over_age_65                               0.097\n",
       "median_age                                    0.077"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlate_census(nyc_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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>MODZCTA</th>\n",
       "      <th>cases_per_1000</th>\n",
       "      <th>average_household_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>11369</td>\n",
       "      <td>39.861657</td>\n",
       "      <td>3.217181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>11370</td>\n",
       "      <td>37.491542</td>\n",
       "      <td>3.171097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10475</td>\n",
       "      <td>35.410109</td>\n",
       "      <td>2.225844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>10469</td>\n",
       "      <td>35.034520</td>\n",
       "      <td>3.174385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>11372</td>\n",
       "      <td>34.651704</td>\n",
       "      <td>2.580632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>10461</td>\n",
       "      <td>33.566378</td>\n",
       "      <td>2.602317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>11368</td>\n",
       "      <td>33.302201</td>\n",
       "      <td>3.916339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>11004</td>\n",
       "      <td>33.280819</td>\n",
       "      <td>2.767258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>11239</td>\n",
       "      <td>33.071580</td>\n",
       "      <td>1.946211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>11428</td>\n",
       "      <td>31.750225</td>\n",
       "      <td>3.402933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>10302</td>\n",
       "      <td>30.982202</td>\n",
       "      <td>2.938748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>10465</td>\n",
       "      <td>30.743129</td>\n",
       "      <td>2.643925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>11429</td>\n",
       "      <td>30.471116</td>\n",
       "      <td>3.713188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>11412</td>\n",
       "      <td>30.087031</td>\n",
       "      <td>3.386992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>10304</td>\n",
       "      <td>30.026300</td>\n",
       "      <td>2.819038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MODZCTA  cases_per_1000  average_household_size\n",
       "141   11369       39.861657                3.217181\n",
       "142   11370       37.491542                3.171097\n",
       "81    10475       35.410109                2.225844\n",
       "75    10469       35.034520                3.174385\n",
       "143   11372       34.651704                2.580632\n",
       "67    10461       33.566378                2.602317\n",
       "140   11368       33.302201                3.916339\n",
       "82    11004       33.280819                2.767258\n",
       "126   11239       33.071580                1.946211\n",
       "166   11428       31.750225                3.402933\n",
       "46    10302       30.982202                2.938748\n",
       "71    10465       30.743129                2.643925\n",
       "167   11429       30.471116                3.713188\n",
       "152   11412       30.087031                3.386992\n",
       "48    10304       30.026300                2.819038"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    nyc_df\n",
    "    .sort_values(\"cases_per_1000\", ascending=False)\n",
    "    .head(15)\n",
    "    [[\"MODZCTA\", \"cases_per_1000\", \"average_household_size\"]]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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>MODZCTA</th>\n",
       "      <th>cases_per_1000</th>\n",
       "      <th>household_median_income</th>\n",
       "      <th>pct_white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>10280</td>\n",
       "      <td>4.049446</td>\n",
       "      <td>169844.0</td>\n",
       "      <td>0.704604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10006</td>\n",
       "      <td>6.079907</td>\n",
       "      <td>176250.0</td>\n",
       "      <td>0.632021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10005</td>\n",
       "      <td>6.114823</td>\n",
       "      <td>173333.0</td>\n",
       "      <td>0.694372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10012</td>\n",
       "      <td>6.193670</td>\n",
       "      <td>109037.0</td>\n",
       "      <td>0.697621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10010</td>\n",
       "      <td>6.937444</td>\n",
       "      <td>117923.0</td>\n",
       "      <td>0.639490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10007</td>\n",
       "      <td>6.977075</td>\n",
       "      <td>246813.0</td>\n",
       "      <td>0.719920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>7.296734</td>\n",
       "      <td>112131.0</td>\n",
       "      <td>0.704455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10013</td>\n",
       "      <td>7.558605</td>\n",
       "      <td>105442.0</td>\n",
       "      <td>0.523947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>11215</td>\n",
       "      <td>7.611608</td>\n",
       "      <td>123583.0</td>\n",
       "      <td>0.669380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>11109</td>\n",
       "      <td>7.691022</td>\n",
       "      <td>148590.0</td>\n",
       "      <td>0.621802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MODZCTA  cases_per_1000  household_median_income  pct_white\n",
       "43    10280        4.049446                 169844.0   0.704604\n",
       "6     10006        6.079907                 176250.0   0.632021\n",
       "5     10005        6.114823                 173333.0   0.694372\n",
       "11    10012        6.193670                 109037.0   0.697621\n",
       "9     10010        6.937444                 117923.0   0.639490\n",
       "7     10007        6.977075                 246813.0   0.719920\n",
       "3     10003        7.296734                 112131.0   0.704455\n",
       "12    10013        7.558605                 105442.0   0.523947\n",
       "103   11215        7.611608                 123583.0   0.669380\n",
       "89    11109        7.691022                 148590.0   0.621802"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    nyc_df\n",
    "    .sort_values(\"cases_per_1000\", ascending=True)\n",
    "    .head(10)\n",
    "    [[\"MODZCTA\", \"cases_per_1000\", \"household_median_income\", \"pct_white\"]]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    nyc_df[\"average_household_size\"],\n",
    "    nyc_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Average Household Size vs. Cases Per 1,000 in NYC\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Average Household Size\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    nyc_df[\"pct_services\"],\n",
    "    nyc_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Percent Services vs. Cases Per 1,000 in NYC\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Percent Frontline Service Workers\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    nyc_df[\"household_median_income\"],\n",
    "    nyc_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Household Median Income vs. Cases Per 1,000 in NYC\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Household Median Income\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chicago"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pct_services</th>\n",
       "      <td>0.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_household_size</th>\n",
       "      <td>0.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white</th>\n",
       "      <td>-0.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_minority</th>\n",
       "      <td>0.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_more_than_one_occupant_per_room</th>\n",
       "      <td>0.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_median_income</th>\n",
       "      <td>-0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <td>0.583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic</th>\n",
       "      <td>0.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black</th>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>-0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     cases_per_1000\n",
       "pct_services                                  0.738\n",
       "average_household_size                        0.693\n",
       "pct_white                                    -0.672\n",
       "pct_minority                                  0.672\n",
       "pct_more_than_one_occupant_per_room           0.633\n",
       "household_median_income                      -0.589\n",
       "pct_below_poverty_level                       0.583\n",
       "pct_hispanic                                  0.486\n",
       "pct_black                                     0.357\n",
       "median_age                                   -0.069\n",
       "pct_over_age_65                               0.053"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlate_census(chi_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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>zip_code</th>\n",
       "      <th>cases_per_1000</th>\n",
       "      <th>pct_black</th>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>60623</td>\n",
       "      <td>17.748520</td>\n",
       "      <td>0.303539</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>60639</td>\n",
       "      <td>16.615663</td>\n",
       "      <td>0.129114</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>60632</td>\n",
       "      <td>14.367469</td>\n",
       "      <td>0.011039</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>60644</td>\n",
       "      <td>14.315057</td>\n",
       "      <td>0.895184</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>60633</td>\n",
       "      <td>13.984927</td>\n",
       "      <td>0.232072</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>60645</td>\n",
       "      <td>13.973854</td>\n",
       "      <td>0.170452</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>60624</td>\n",
       "      <td>13.136789</td>\n",
       "      <td>0.903728</td>\n",
       "      <td>44.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60609</td>\n",
       "      <td>12.976665</td>\n",
       "      <td>0.243288</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>60629</td>\n",
       "      <td>12.901207</td>\n",
       "      <td>0.195485</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60604</td>\n",
       "      <td>12.787724</td>\n",
       "      <td>0.047315</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code  cases_per_1000  pct_black  pct_below_poverty_level\n",
       "21    60623       17.748520   0.303539                     33.4\n",
       "35    60639       16.615663   0.129114                     21.4\n",
       "29    60632       14.367469   0.011039                     19.9\n",
       "40    60644       14.315057   0.895184                     34.5\n",
       "30    60633       13.984927   0.232072                     21.4\n",
       "41    60645       13.973854   0.170452                     21.1\n",
       "22    60624       13.136789   0.903728                     44.2\n",
       "7     60609       12.976665   0.243288                     27.9\n",
       "26    60629       12.901207   0.195485                     18.3\n",
       "2     60604       12.787724   0.047315                     22.3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    chi_df\n",
    "    .sort_values(\"cases_per_1000\", ascending=False)\n",
    "    [[\"zip_code\", \"cases_per_1000\", \"pct_black\", \"pct_below_poverty_level\"]]\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4tkrSGcBpZrYN4ZkcZ2S4fOeccznKsi+yaYS+lfoNZll316sS+k9yzjnXgZrdiux4YKqkswjJ7QPlJpQ0nvCwKEaNGlVuMueca1uTZ/Vx5tQHmbdwMesMH8aEPcYwbtuyj1dpO82u5D8WOMHM1gNOIDwhrSQzm2RmvWbW29OTWzNu55zLxORZfZx0zWz6Fi7GgL6FiznpmtlMnpXmMVftodkJ5nCWdQ99Jf2fNeCcc13jzKkPsnjJ0n7DFi9ZyplTH8wpooHX7AQzj/C8C4BdCM98cc65rjNv4eKahrejzOpgJF0GjAVGSJpLeEjPp4EfxyfPvUqsY3HOuW6zzvBh9JVIJusMH5ZDNNnILMGY2SFlRr07q2U651y7mLDHGE66Zna/YrJhQwczYY9yD8htP23RF5lzznWaQmuxTm5F5gnGOedyMm7bkR2VUIp5X2TOOecy4QnGOedcJjzBOOecy4QnGOecc5nwBOOccy4TnmCcc85lwhOMc865TPh9MM4514BO73K/EZ5gnHOuToUu9wvdvRS63Ac8yeBFZM45V7du6HK/EZ5gnHOuTt3Q5X4jPME451ydynWt30ld7jfCE4xzztVpwh5jGDZ0cL9hndblfiO8kt855+rUDV3uN8ITjHPONaDTu9xvRGZFZJLOlzRf0j1Fw4+T9ICkeyWdkdXynXPO5SvLOpgLgD2TAyTtDOwHbG1mmwNnZbh855xzOcoswZjZNOC5osHHAhPN7LU4zfyslu+ccy5fzW5Ftgmwk6TbJP1N0nvKTShpvKQZkmYsWLCgiSE655wbCM1OMEOA1YHtgQnAFZJUakIzm2RmvWbW29PT08wYnXPODYBmJ5i5wDUW3A68CYxocgzOOeeaoNkJZjKwM4CkTYB3AM80OQbnnHNNkNl9MJIuA8YCIyTNBU4BzgfOj02XXwcONzPLKgbnnHP5ySzBmNkhZUYdltUynXPOtQ7vi8w551wmPME455zLhCcY55xzmfAE45xzLhOeYJxzzmXCE4xzzrlMeIJxzjmXCU8wzjnnMuEJxjnnXCY8wTjnnMuEJxjnnHOZ8ATjnHMuE5l1dumcc51i8qw+zpz6IPMWLmad4cOYsMcYxm07Mu+wWp4nGOecq2DyrD5OumY2i5csBaBv4WJOumY2QMNJptMTV9UiMgWHSfpmfD9K0nuzD8055/J35tQH30ouBYuXLOXMqQ82NN9C4upbuBhjWeKaPKuvofm2kjR1MOcA7wcKz3dZBPwss4icc66FzFu4uKbhaWWVuFpJmgTzPjP7HPAqgJk9T3jUsXPOdbx1hg+raXhaWSWuVpImwSyRNBgwAEk9wJvVPiTpfEnz4+ORi8d9WZJJGlFzxM65iibP6mOHiTeywYnXssPEGzuqyCUPE/YYw7Chg/sNGzZ0MBP2GNPQfLNKXK0kTYI5G/gdsKak7wK3At9L8bkLgD2LB0paD9gdeDx9mM5lrxMOzN1Qrt9s47Ydyen7b8nI4cMQMHL4ME7ff8uGK+OzSlytpGorMjP7jaSZwK6AgHFmdn+Kz02TNLrEqB8CXwV+X1uozmUny5ZCzVSpXL+d1qPVjNt25IBvv8L8OrkVWdkEI2n1xNv5wGXJcWb2XK0Lk7Qf0Gdmd0mqNu14YDzAqFGjal2UczXplANzN5Trd5IsElcrqXQFM5NQ71IqExiwYS0LkrQC8HVC8VhVZjYJmATQ29trtSzLuVp1yoF5neHD6CsRcyeV67v2UbYOxsw2MLMN4//iv5qSS7QRsAFwl6Q5wLrAHZL+q77QnRs4nVLh2g3l+q59pOqLTNL+kn4g6f8kjatnQWY228zWNLPRZjYamAtsZ2ZP1TM/5wZSpxyYs6qQdq4eVSv5JZ0DbMyyOphjJO0W742p9LnLgLHACElzgVPM7LwG43UuE51U4drp5fqufciscvWGpAeAd1mcUNIg4F4ze1cT4gNCHcyMGTOatTjnnOsIkmaaWW9ey09TRPYfINmMa704zDnnnCsrTW/KKwP3S7o9vn8PMEPSFAAz2zer4JxzzrWvNAnmm5lH4ZxzXajTu+tPcyf/3yStRbhyAbjdzOZnG5ZzznW2Tuk9opI0z4M5CLgd+BhwEHCbpAOzDsw55zpZN3TXn6aI7BvAewpXLbE35RuAq7IMzDnnatFuxU2d0ntEJWkSzKCiIrFnSXmDpnPONUOzipsGMol1Q7c+aRLFdZKmSjpC0hHAtcCfsw3L5a0Tuq533aMZxU0D/SiETuk9opI0lfwTJO0P7BgHTTKz32UblstTN1Q+us7SjOKmge5xu5N6jygnTREZhJ6VXzSzGyStIGllM1uUZWAuP53Sdb3rHs0obsoiiXV6tz5pWpF9mlCh/8s4aCQwOcugXL66ofLRdZZmFDc12uN2rcXOnVBMnaYO5nPADsCLAGb2b2DNLINy+eqUrutd92hGL9KNJLFa62865dHXaYrIXjOz1wtPoJQ0hPDAMdehJuwxpl8dDHRe5aPrPFkXNzVSZ1JrsXOnFFOnSTB/k/QNYJik3YDPAn/INiyXp26ofHSuHvUmsVqLnTulmDpNgjkROAqYDXwG+BNwbpZBufx1euWjc81UayOETrlHpmwdjKQ1Jf0ImAJsCBxlZgea2a+s2kNknHPOvaXW+ptOuUem0hXMRYTmyT8B9gbOBo5oQkzOuQ7Ubl25DKRai507pZi67BMtJd1lZlsn3t9hZtulnrF0PiExzTezLeKwM4F9gNeBh4EjzWxhtXn5Ey2da2/FN+9COCMf6JZerr+WfqKlpNUkrS5pdWBw0ftqLgD2LBp2PbCFmW0FPAScVE/Qzrn20g09B7u3q1REtiqhiEyJYXfE/0aolynLzKZJGl007C+Jt9MB7/bfuS7QKa2iXG3KJhgzG53xsj8F/LbcSEnjgfEAo0aNyjgU51yWOqVVlKtNLt3ux/tq3gB+U24aM5tkZr1m1tvT09O84JxzA65TWkW52qTt7HLAxC7/9wZ29ebOznWHTmkV5WrT1AQjaU/gq8AHzeyVZi7bOZcvv3m3+1RMMAodkL2X0IMyQB9we5orD0mXAWOBEZLmAqcQWo0tB1wf+zabbmbH1B29cwOkm+/RcC4rZROMpN2Bc4B/ExILwLrAxpI+W9Qi7G3M7JASg8+rN1DnsuIPWHMuG5WuYH4MfMjM5iQHStqA0B/ZuzKMy7mm6ZSea51rNZVakQ0B5pYY3gcMzSYc55rP79FwLhuVrmDOB/4l6XLgiThsPeBgvKjLdRC/R8O1mk6pEyx7BWNmpwOHEu7kf3/8E3BoHOdcR/B7NFwr6ZSnWUKVVmRmdh9wX6HvMTN7rilROddEfo9Ga+iUs/ZGdVKdYKVWZKOAM4BdgBfCIK0C3AicWFz571w783s08uUt+ZbppDrBSpX8vwV+B6xtZu80s42BtYHJwOXNCM451x28t+VlKj3lst1UKiIbYWb9OqM0s6XA5ZK+nW1Yzrlu0kln7bUoVSy486Y9XDL98bdNu/Om7dcnY6UEM1PSOcCF9G9FdjgwK+vAnHPdoxtb8pUrFlxuSOmCpZseWNDM8AZEpSKyTwKzgdOAqfHvNOAe4BPZh+ac6xbd2JKvXLHgwsVLSk7fjldzlZ4H8zrw8/jnnHMDqrh46IB3j+SmBxZ0TSuyWhNGO17NVWpFNgQ4ChhH/84ufw+cZ2al06xzzlVRqnjo6pl9nL7/lh2dVJLKFQuutsJQXl3yZr+rm3a9mqtURHYxsA2hWGyv+HcasDVwSfahOec6lbcaK18seMo+m3P6/lsycvgwBIwcPqxtE2+lSv53m9kmRcPmAtMlPZRhTM61DL/5Lxvd2mosqdoNvp2wn1VKMM9J+hhwtZm9CSBpEPAx4PlmBOdcnvzmv+x0Y6uxUjr9Bt9KRWQHAwcCT0t6KF61PAXsH8c519G8GCc73dhqrBtVakU2B/g4gKQ14rBnmxOWc/nzYpzseP9v3aFiZ5cFycQiqReYZ2bzKn1G0vnA3sB8M9siDlud0AXNaGAOcJCZeXGba0mNFON43U11nV48VK9O2ndSJZgixwFbSXrIzD5eYboLgJ8CFyWGnQj81cwmSjoxvv9aHTF0nU7a6WqR53pP2GNMvzoYSFeM43U37Sur/S3NfCfP6uO0P9zL868suwOk3fedSnUwJZnZ4Wa2LXB0lemmAcXd++9H6HqG+H9crcvvRp30fIha5L3e47YdWVdzUa+7aU9Z7W9p5luYJplcCtp536nnCgZJm5rZA3V8dC0zezK+fgpYq8IyxgPjAUaNGlXHojpHJz0fohatsN71FON43U17ymp/SzPfUtMkteu+U/MVTPSXRhdsZgZYhfGTzKzXzHp7etqvF9GB1K0HrHZd707qbr2bZLW/pZlvtWW0675TqauYs8uNAobXubynJa1tZk9KWhuYX+d8ukq33jPQrutdb92Ny1dW+1ua+ZabBtp736l0BXMkoefkmUV/M4DX61zeFEJ3/8T/v69zPl2l0+8ZmDyrjx0m3sgGJ17LDhNvfKtsutH1LjffrNVbd+PyldXvLM18S00DMHzY0LbedyrVwfwLuMfM/lE8QtKp1WYs6TJgLDBC0lzgFGAicIWko4DHgIPqiLnrdPI9A2laXNWz3nm35PImuO0nq99Zmvl26m9coSqkxIhwz8qrZvZKc0N6u97eXpsxY0beYbgM7DDxxpJFAyOHD+PvJ+7ScvN1rp1ImmlmvXktv9Kd/MVNjJ0bcHlWrDrnslVvKzLnBkRWLa68JZdz+fME43KVZ8Wqcy5bFW+0lDQY+L6ZfaVJ8bguk2fFakG3dsPjXNbKVvK/NYE03cy2b1I8JXklv8tKcWszCFc6zW4a6knOZaFlK/kTZkmaAlwJvFwYaGbXZBaVc03SCt3R5N2k2rmspKmDWR54FtgF2Cf+7Z1lUM41Syu0NvPOMV2nqnoFY2ZHNiMQ5/LQCt3RtEKScy4LVa9gJG0i6a+S7onvt5J0cvahOZe9Vmht5k2qXadKU0T2K+AkYAmAmd0NHJxlUM41Syv0G9YKSc65LKSp5F/BzG6XlBz2RkbxONd0efcb1qn9UDmXJsE8I2kj4rNbJB0IPFn5I865WuSd5JzLQpoE8zlgErCppD7gUeDQTKNyzjnX9qrdyb8NsDFwHPA4MMjMFjUjMOecc+2tbCW/pG8CVwAHANcC/+PJxTnnXFqVrmA+DmxjZq9IWgO4jtCizDnnnKuqUjPl1woPGzOzZ6tM65xzzvVT6Qpmw9gHGYCAjRLvMbN9612opBOAowkt02YDR5rZq/XOzznnXOuplGD2K3p/1kAsUNJI4AvAZma2WNIVhBs3LxiI+TvnnGsNlR6Z/LeMlztM0hJgBWBehstyzjmXg6bXq5hZH+Fq6HHCDZsvmNlfmh2Hc865bDU9wUhajVD8tgGwDrCipMNKTDde0gxJMxYsWNDsMJ1zzjWopgQjaZCkVRpc5oeAR81sgZktAa4BPlA8kZlNMrNeM+vt6elpcJHOOeeaLU13/ZdKWkXSisA9wH2SJjSwzMeB7SWtoNCD5q7A/Q3MzznnXAtKcwWzmZm9CIwD/kwo2vpEvQs0s9uAq4A7CE2UBxH6OusKk2f1scPEG9ngxGvZYeKNTJ7Vl3dIzjmXiTSdXQ6VNJSQYH5qZkskWSMLNbNTgFMamUc78mevO5e9ybP6/NEHLSJNgvklMAe4C5gmaX3gxSyD6lSVnr3uPwDnGteOJ3GdnBCrFpGZ2dlmNtLM9rLgMWDnJsTWcfzZ685lq9JJXCsqJMS+hYsxliXETik6T1PJv5ak8yT9Ob7fDDg888g6kD973blstdtJXLslxFqlqeS/AJhKuGcF4CHg+KwC6mT+7PX24A0x2le7ncS1W0KsVZoEM8LMrgDeBDCzN4CllT/iShm37UhO339LRg4fhoCRw4dx+v5bdkx5ayfo9CKLTpfnSVw9JybtlhBrlaaS/+X4PBgDkLQ98EKmUXWwbn/2eqtXaHpDjPZW+I6avY/V27hgwh5j+n0OOqtUI02C+RIwhdBd/9+BHuDATKNyHaWQVPoWLkbEMxVas4VPpxdZdIM8TuLqPTHJKyE2S9UEY2Z3SPogMIbwXJgHYxcvrios+HwAABGwSURBVE3kedVQfGZXfANVq10drDN8GH0lkkmnFFm4bDRyYtLJpRppWpF9DBhmZvcSbrb8raTtMo/MDYi86xRKndkVa6WrA2+I4erR6XUp9UpTyf+/ZrZI0o6EfsPOA36ebVhuoOTdDDJN8milH6E3xHD18BOT0tLUwRSOTh8BfmVm10r6ToYxuQGUd51CuSKnglb8EXZykYXLRqfXpdQrTYLpk/RLYDfg+5KWI4fnyLj65F2nUKqVTKGif6T/CF0H8ROTt0uTYA4C9gTOMrOFktYGGumu3zVR3s0g/czOue4ls3QdI0taE1i+8N7MHs8qqGK9vb02Y8aMZi2u47T6vSfOuWxImmlmvXktv+oVjKR9gf8jdBUzHxgFPABsnm1ojfGD6jLdeOnu379z+UtTl/JtYHvgITPbgPDI4+mZRtWgvJvmunz59+9ca0iTYJaY2bPAIEmDzOwmILdLrjTybprr8uXfv3OtIU0l/0JJKwHTgN9Img+8nG1Yjcm7aa7Ll3//zrWGNFcw+wGvACcA1wEPA/s0slBJwyVdJekBSfdLen8j8yvmd9V2N//+B44/usA1omyCkbSxpB3M7GUze9PM3jCzC4E7gOENLvfHwHVmtimwNXB/g/Prx++q7W7+/Q8Mr8tyjap0BfMj4MUSw1+I4+oiaVXgvwldzmBmr5vZwnrnV4p399Hd/PsfGF6X5RpVqQ5mLTObXTzQzGZLGt3AMjcAFgC/lrQ1MBP4opn1q9eRNB4YDzBq1KiaF9KNTXPdMv79N87rslyjKl3BVCoGa6QwewiwHfBzM9uW0GDgxOKJzGySmfWaWW9PT08Di3OdwOsCms/rslyjKiWYGZI+XTxQ0tGEq456zQXmmtlt8f1VhITjmqidDtheF5APr8tyjapURHY88DtJh7IsofQC7wA+Wu8CzewpSU9IGmNmDxIeAXBfvfNztav38a558ccY58P7kXONKptgzOxp4AOSdga2iIOvNbMbB2C5xxHuqXkH8Ahw5ADMs6nauSuSdjtge11AfrwuyzUizSOTbwJuGsiFmtmdtHhvAJW02xVAsXY7YOf9yAHnXH38uS51aPfmm+1Weet1Ac61J08wdWi3K4Bi7XbA9vtanGtPafoic0XqKbJppTqbdqy89boA59qPJ5g61PqUyFass/EDtnMua55g6lDrFUC7tdpyLq1WujJ3rccTTJ3SXAEUfnylitOgfepsnCulFa/MXWvp+AST1xlW8Y+vlFZtteVcGn5l7qrp6AST5xlWqR9fUiu32mqEF5l0j3ZvTemy19HNlPO8X6XSj6xTm9l6n2Hdpd3up3LN19EJJs8zrHI/spHDh/H3E3fpuOQC7X8DqqtNu91P5ZqvoxNMnmdYnfbjS9P7sheZdBe/AdZV09F1MLXerzKQ2vFmxnLS1mV5n2Hdx++ncpV0dILJ+yDfKT++tK2F8kzozrnW09EJBmq7X6XdrzSykrboK++E7pxrLR2fYKrxm8Wqq6Xoq1Ou2pxzjevoSv40vOVTdZ3WYME51xxdfwXjLZ+q86Iv51w9ckswkgYDM4A+M9s7rzi85VM6XvTlnKtVnlcwXwTuB1bJMYa2afmUtiGCN1hwzrWKXOpgJK0LfAQ4N4/lJ7XDzWJpu2Dxrlqcc60kryuYHwFfBVYuN4Gk8cB4gFGjRmUaTKsX/6S9D8V7t3XOtZKmX8FI2huYb2YzK01nZpPMrNfMent6epoUXWtK2xDBGyw451pJHkVkOwD7SpoDXA7sIumSHOJoG2n7VPPebZ1zraTpCcbMTjKzdc1sNHAwcKOZHdbsONpJ2vtQ/H4V51wr6fr7YNpB2vtQ/H4V51wrkZnlHUNVvb29NmPGjLzDcM65tiJpppn15rX8ru8qxjnnXDa6tojMb0h0zrlsdWWCaaUelD3ROec6VVcWkbVKD8p+571zrpN1ZYJplRsSWyXROedcFroywbTKDYmtkuiccy4LXZlgWuWGxFZJdM45l4WuTDCt0oNyqyQ655zLQle2IoPW6EHZ77x3znWyrk0wraIVEl278CbdzrUXTzCuLbTSvUvOuXS6sg7GtR9v0u1c+/EE49qCN+l2rv14gnFtwZt0O9d+PMG4tuBNup1rP17J79qCN+l2rv14gnFtw5t0O9deml5EJmk9STdJuk/SvZK+2OwYnHPOZS+PK5g3gC+b2R2SVgZmSrrezO7LIRbnnHMZafoVjJk9aWZ3xNeLgPsBL/dwzrkOk2srMkmjgW2B20qMGy9phqQZCxYsaHZozjnnGpRbgpG0EnA1cLyZvVg83swmmVmvmfX29PQ0P0DnnHMNkZk1f6HSUOCPwFQz+0GK6RcAj2Uc1gjgmYyXUatWjAlaMy6PKZ1WjAlaM65OiGl9M8vtDL3pCUaSgAuB58zs+KYuvAJJM8ysN+84kloxJmjNuDymdFoxJmjNuDymxuVRRLYD8AlgF0l3xr+9cojDOedchpreTNnMbgXU7OU655xrLu+LbJlJeQdQQivGBK0Zl8eUTivGBK0Zl8fUoFwq+Z1zznU+v4JxzjmXCU8wzjnnMtF1CUbSnpIelPQfSSeWGP+l2BHn3ZL+Kmn9FojpGEmzY4u7WyVtlndMiekOkGSSmtJ0MsW2OkLSgkQLxaPzjilOc1Cig9dL845J0g8T2+ghSQuzjillXKNiZ7iz4m8w8xamKWJaPx4L7pZ0s6R1mxDT+ZLmS7qnzHhJOjvGfLek7bKOqS5m1jV/wGDgYWBD4B3AXcBmRdPsDKwQXx8L/LYFYlol8Xpf4Lq8Y4rTrQxMA6YDvS3y/R0B/LTF9ql3ArOA1eL7NfOOqWj644DzW2RbTQKOja83A+a0QExXAofH17sAFzdhW/03sB1wT5nxewF/JrTI3R64LeuY6vnrtiuY9wL/MbNHzOx14HJgv+QEZnaTmb0S304Hsj5bSRNTsiudFYGsW2ZUjSn6NvB94NWM46k1rmZKE9OngZ+Z2fMAZja/BWJKOgS4LOOY0sZlwCrx9arAvBaIaTPgxvj6phLjB5yZTQOeqzDJfsBFFkwHhktaO+u4atVtCWYk8ETi/Vwq9+R8FOEsIUupYpL0OUkPA2cAX8g7pnhJvp6ZXZtxLDXFFR0Qiw2ukrReC8S0CbCJpL9Lmi5pzxaICQjFP8AGLDuA5h3XqcBhkuYCfyJcXeUd013A/vH1R4GVJa2RcVzV1Hosy0W3JZjUJB0G9AJn5h0LgJn9zMw2Ar4GnJxnLJIGAT8AvpxnHGX8ARhtZlsB1xO6JcrbEEIx2VjC1cKvJA3PNaJlDgauMrOleQcSHQJcYGbrEoqBLo77W56+AnxQ0izgg0Af0Crbq6Xl/cU1Wx+QPKNdNw7rR9KHgG8A+5rZa60QU8LlwLhMI6oe08rAFsDNkuYQyoCnNKGiv+q2MrNnE9/ZucC7846JcHY5xcyWmNmjwEOEhJNnTAUH05ziMUgX11HAFQBm9k9geUIHj7nFZGbzzGx/M9uWcFzAzJrSKKKCWo8b+ci7EqiZf4QzyUcIRQKFCr3Ni6bZllDp984Wiumdidf7ADPyjqlo+ptpTiV/mm21duL1R4HpLRDTnsCF8fUIQtHGGnl/f8CmwBziDdct8v39GTgivn4XoQ4ms/hSxjQCGBRffxf4VpO212jKV/J/hP6V/Lc3I6aa1yHvAJq+wuGy+6GYRL4Rh32LcLUCcAPwNHBn/JvSAjH9GLg3xnNTpYN9s2IqmrYpCSbltjo9bqu74rbatAViEqFI8T5gNnBw3jHF96cCE5vxvdWwrTYD/h6/vzuB3VsgpgOBf8dpzgWWa0JMlwFPAksIV8BHAccAxyT2qZ/FmGc36/dX6593FeOccy4T3VYH45xzrkk8wTjnnMuEJxjnnHOZ8ATjnHMuE55gnHPOZcITTBeTtDT2pnuPpCslrZBDDGMlfaDZy00sf6fYw/GdkoYVjTNJlyTeD4k9Nf8xvt+3Uk/TdcTyj/h/tKT/qfGzYyW9ENfjAUln1RnDMZI+WWL46HI9+6ac7/F57F8uX55guttiM9vGzLYAXie0s69K0pABjGEskFuCAQ4FTo/bYXHRuJeBLRKJZzcSd0ub2RQzm9hoAIXtaWaF7TAaqCnBRLeY2TaEm4X3lrRDrTMws1+Y2UV1LLua4wFPMF3GE4wruAXYWNKK8VkUt8dncuwHbz1nZYqkG4G/SlpJ0q8VnlNzt6QD4nS7S/qnpDviVdFKcfgcSafF4bMlbSppNCGpnRDPvHeStI+k2+Kyb5C0Vvx8j6Tr49XGuZIekzQijjssxnunpF9KGly8cpJ2jfOcHddvOYVnxRwEfFvSb8pslz8R7pqGol6H4zb5aXx9QXw+xz8kPSLpwDhcks6MV4mzJX08Dh8r6RZJUwg3YCLppTjricBOcX1OkDRN0jaJ5d4qaetyX2RMlHcSOz+s8J1M1LJnH50Vh50q6Svx9bsl3SXpLuBzieUPjuv0r/jZzyTW6WaFTkYfkPSbuP5fANYBblJ41svguL0K2+SEcuvi2lzed3r6X35/wEvx/xDg94Tn33wPOCwOH064e3lFwnNW5gKrx3HfB36UmNdqhC41pgErxmFfA74ZX88BjouvPwucG1+fCnylaD6FG4CPBv4vvv4pcFJ8vSehW/cRhO5E/gAMjePOAT5ZtJ7LE7pn2SS+vwg4Pr6+ADiw3PYBtgKuivO4k3DF9cc4/gjis2fifK4knLRtRugCHuAAQqebg4G1gMeBteN8XgY2KPF9vLWM+P7wwrYm9Mz8tq6CiuJaDZgJ/Fe57wRYA3gwsa2HF38fwN3Af8fXZxK7LQHGAyfH18sBMwhdrYwFXiD0izUI+CewY+L7HxFfvxu4PhH78Lx/C/6XzZ9fwXS3YZLuJBwgHgfOA3YHTozDbyYcWEfF6a83s8IzKj5E6KoCAAvPOtme2NVH/PzhQPKJoNfE/zMJxUClrAtMlTQbmABsHofvSOjoEzO7Dng+Dt+VcMD6V1zmroSHRyWNAR41s4fi+wsJD3SqyszujrEeQriaqWSymb1pZvcRkkkh7svMbKmZPQ38DXhPHHe7hc4vq7mSUOQ1FPgUIZmVslO82ugDpprZU5T/Tl4gPMfnPEn7A68kZ6TQ2/NwC88lAbg4MXp34JNxfrcRklWh887bzWyumb1JSMijS8T5CLChpJ8oPLrgxRLTuA4wkGXprv0stlBm/xZJAg4wsweLhr+PcMZdiQhJ6JAy4wu9HC+l/L73E+AHZjZF0ljCGXW1ZV5oZidVma4RU4CzCGfolZ4Dkux5WynmW217AmBmr0i6nvCQqYMo30P0LWa2t6QNgOmSrqDCdyLpvYSEfCDwecLTGtMQ4Wp0atH8xtJ/G5T8ns3s+VjEtwehiPQgQuJ0HcavYFyxqcBxMdEgadsy011P/3L51QhPAN1B0sZx2IqSNqmyvEWE7v8LVmVZRfrhieF/JxyIkLQ7oRgI4K/AgZLWjONWV3iIVtKDwOhCXMAnCFcSaZ0PnGZms2v4TMEtwMdjvUMP4crp9iqfKd4mEDpZPBv4V7xaLCteFU0kFIeV/E5iPcyqZvYn4ARg66J5LAQWStoxDjo0MXoqcGy8oiLOb8W06xTrzgaZ2dWEZxu15vPkXcM8wbhi3waGAndLuje+L+U7wGqxovYuYGczW0Col7hM0t2EMvhNqyzvD8BHY4X2ToQrlislzQSeSUx3GrC7QlPZjwFPAYticdTJwF/iMq8n1HG8xcxeBY6M850NvAn8ovqmeOvzc83s7LTTF/kdoS7jLsJTI78ai64quRtYGivYT4gxzCQUJf065XJ/QUhmhfqz4u9kZeCPcditwJdKzONI4GexKCx5RXYuoWHCHfH7+CXVS0MmAddJuonQ+ODmON9LgCyvPl2OvDdl1xYkLQcsNbM3JL0f+Hlx8V4nk7QOoU5s01i/4VzL8zoY1y5GAVcoPD73deDTOcfTNAo3Pn4X+JInF9dO/ArGOedcJrwOxjnnXCY8wTjnnMuEJxjnnHOZ8ATjnHMuE55gnHPOZeL/ATxXvD/7GnJRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    chi_df[\"pct_minority\"],\n",
    "    chi_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Percentage of Minority Residents vs. Cases Per 1,000 in Chicago\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Percentage of Minority Residents\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    chi_df[\"average_household_size\"],\n",
    "    chi_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Average Household Size vs. Cases Per 1,000 in Chicago\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Average Household Size\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    chi_df[\"pct_services\"],\n",
    "    chi_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Percent Services vs. Cases Per 1,000 in Chicago\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Percent Frontline Services\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detroit Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pct_black</th>\n",
       "      <td>0.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <td>0.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_more_than_one_occupant_per_room</th>\n",
       "      <td>-0.566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white</th>\n",
       "      <td>-0.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_minority</th>\n",
       "      <td>0.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic</th>\n",
       "      <td>-0.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_household_size</th>\n",
       "      <td>-0.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <td>-0.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_services</th>\n",
       "      <td>-0.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_median_income</th>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     cases_per_1000\n",
       "pct_black                                     0.702\n",
       "pct_over_age_65                               0.633\n",
       "median_age                                    0.613\n",
       "pct_more_than_one_occupant_per_room          -0.566\n",
       "pct_white                                    -0.535\n",
       "pct_minority                                  0.535\n",
       "pct_hispanic                                 -0.503\n",
       "average_household_size                       -0.452\n",
       "pct_below_poverty_level                      -0.358\n",
       "pct_services                                 -0.265\n",
       "household_median_income                       0.111"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlate_census(det_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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>zip_code</th>\n",
       "      <th>cases_per_1000</th>\n",
       "      <th>pct_black</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48235</td>\n",
       "      <td>20.445557</td>\n",
       "      <td>0.963712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>48207</td>\n",
       "      <td>18.464437</td>\n",
       "      <td>0.803721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>48214</td>\n",
       "      <td>17.594613</td>\n",
       "      <td>0.823769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48208</td>\n",
       "      <td>17.181383</td>\n",
       "      <td>0.721401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48219</td>\n",
       "      <td>16.021916</td>\n",
       "      <td>0.910717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48206</td>\n",
       "      <td>15.369959</td>\n",
       "      <td>0.889870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48221</td>\n",
       "      <td>14.705531</td>\n",
       "      <td>0.900265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48204</td>\n",
       "      <td>14.434839</td>\n",
       "      <td>0.938917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>48205</td>\n",
       "      <td>14.196095</td>\n",
       "      <td>0.936707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>48213</td>\n",
       "      <td>14.082036</td>\n",
       "      <td>0.962711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code  cases_per_1000  pct_black\n",
       "3     48235       20.445557   0.963712\n",
       "19    48207       18.464437   0.803721\n",
       "22    48214       17.594613   0.823769\n",
       "11    48208       17.181383   0.721401\n",
       "0     48219       16.021916   0.910717\n",
       "9     48206       15.369959   0.889870\n",
       "5     48221       14.705531   0.900265\n",
       "7     48204       14.434839   0.938917\n",
       "20    48205       14.196095   0.936707\n",
       "21    48213       14.082036   0.962711"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    det_df.sort_values(\"cases_per_1000\", ascending=False)\n",
    "    [[\"zip_code\", \"cases_per_1000\", \"pct_black\"]]\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    det_df[\"pct_black\"],\n",
    "    det_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Percent Black vs. Cases Per 1,000 in Detroit\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Percent Black\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    det_df[\"pct_over_age_65\"],\n",
    "    det_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Percent Over Age 65 vs. Cases Per 1,000 in Detroit\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Percent Over Age 60\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Philadelphia Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>0.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <td>0.437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black</th>\n",
       "      <td>0.423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_minority</th>\n",
       "      <td>0.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white</th>\n",
       "      <td>-0.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_median_income</th>\n",
       "      <td>-0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_services</th>\n",
       "      <td>0.269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic</th>\n",
       "      <td>-0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_more_than_one_occupant_per_room</th>\n",
       "      <td>-0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_household_size</th>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     cases_per_1000\n",
       "median_age                                    0.471\n",
       "pct_over_age_65                               0.437\n",
       "pct_black                                     0.423\n",
       "pct_minority                                  0.384\n",
       "pct_white                                    -0.384\n",
       "household_median_income                      -0.283\n",
       "pct_services                                  0.269\n",
       "pct_hispanic                                 -0.156\n",
       "pct_more_than_one_occupant_per_room          -0.133\n",
       "average_household_size                        0.089\n",
       "pct_below_poverty_level                       0.004"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlate_census(phi_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "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>zip_code</th>\n",
       "      <th>cases_per_1000</th>\n",
       "      <th>pct_black</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19126</td>\n",
       "      <td>17.083780</td>\n",
       "      <td>0.815167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19115</td>\n",
       "      <td>14.965631</td>\n",
       "      <td>0.088837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19136</td>\n",
       "      <td>14.070206</td>\n",
       "      <td>0.153293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>19150</td>\n",
       "      <td>12.849013</td>\n",
       "      <td>0.935710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19132</td>\n",
       "      <td>11.616350</td>\n",
       "      <td>0.905672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>19142</td>\n",
       "      <td>11.476691</td>\n",
       "      <td>0.840037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19116</td>\n",
       "      <td>11.400092</td>\n",
       "      <td>0.059750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19131</td>\n",
       "      <td>11.029085</td>\n",
       "      <td>0.803900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>19151</td>\n",
       "      <td>10.520287</td>\n",
       "      <td>0.870742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>19139</td>\n",
       "      <td>10.274125</td>\n",
       "      <td>0.870136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code  cases_per_1000  pct_black\n",
       "18    19126       17.083780   0.815167\n",
       "8     19115       14.965631   0.088837\n",
       "28    19136       14.070206   0.153293\n",
       "42    19150       12.849013   0.935710\n",
       "24    19132       11.616350   0.905672\n",
       "34    19142       11.476691   0.840037\n",
       "9     19116       11.400092   0.059750\n",
       "23    19131       11.029085   0.803900\n",
       "43    19151       10.520287   0.870742\n",
       "31    19139       10.274125   0.870136"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    phi_df\n",
    "    .sort_values(\"cases_per_1000\", ascending=False)\n",
    "    [[\"zip_code\", \"cases_per_1000\", \"pct_black\"]]\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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>zip_code</th>\n",
       "      <th>cases_per_1000</th>\n",
       "      <th>pct_white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19137</td>\n",
       "      <td>2.947418</td>\n",
       "      <td>0.883754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19127</td>\n",
       "      <td>2.959553</td>\n",
       "      <td>0.812726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>19154</td>\n",
       "      <td>7.357020</td>\n",
       "      <td>0.779727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19128</td>\n",
       "      <td>6.656399</td>\n",
       "      <td>0.768905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19106</td>\n",
       "      <td>2.747475</td>\n",
       "      <td>0.767273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19114</td>\n",
       "      <td>7.845979</td>\n",
       "      <td>0.755955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19125</td>\n",
       "      <td>5.228175</td>\n",
       "      <td>0.747305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19102</td>\n",
       "      <td>7.291878</td>\n",
       "      <td>0.725744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19103</td>\n",
       "      <td>6.193484</td>\n",
       "      <td>0.721252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19115</td>\n",
       "      <td>14.965631</td>\n",
       "      <td>0.681893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code  cases_per_1000  pct_white\n",
       "29    19137        2.947418   0.883754\n",
       "19    19127        2.959553   0.812726\n",
       "46    19154        7.357020   0.779727\n",
       "20    19128        6.656399   0.768905\n",
       "3     19106        2.747475   0.767273\n",
       "7     19114        7.845979   0.755955\n",
       "17    19125        5.228175   0.747305\n",
       "0     19102        7.291878   0.725744\n",
       "1     19103        6.193484   0.721252\n",
       "8     19115       14.965631   0.681893"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    phi_df\n",
    "    .sort_values(\"pct_white\", ascending=False)\n",
    "    [[\"zip_code\", \"cases_per_1000\", \"pct_white\"]]\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    phi_df[\"pct_black\"],\n",
    "    phi_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Percent Black vs. Cases Per 1,000 in Philadelphia\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Percent Black\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    phi_df[\"pct_over_age_65\"],\n",
    "    phi_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Percent Over Age 65 vs. Cases Per 1,000 in Philadelphia\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Percent Over Age 65\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baltimore Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "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>cases_per_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>0.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic</th>\n",
       "      <td>0.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <td>0.249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_minority</th>\n",
       "      <td>0.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white</th>\n",
       "      <td>-0.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_household_size</th>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black</th>\n",
       "      <td>0.197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_services</th>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_more_than_one_occupant_per_room</th>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_median_income</th>\n",
       "      <td>-0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_below_poverty_level</th>\n",
       "      <td>-0.004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     cases_per_1000\n",
       "median_age                                    0.434\n",
       "pct_hispanic                                  0.328\n",
       "pct_over_age_65                               0.249\n",
       "pct_minority                                  0.247\n",
       "pct_white                                    -0.247\n",
       "average_household_size                        0.238\n",
       "pct_black                                     0.197\n",
       "pct_services                                  0.195\n",
       "pct_more_than_one_occupant_per_room           0.137\n",
       "household_median_income                      -0.113\n",
       "pct_below_poverty_level                      -0.004"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlate_census(balt_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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>zip_code</th>\n",
       "      <th>cases_per_1000</th>\n",
       "      <th>pct_over_age_65</th>\n",
       "      <th>pct_black</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21228</td>\n",
       "      <td>7.167640</td>\n",
       "      <td>0.192072</td>\n",
       "      <td>0.196539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>21215</td>\n",
       "      <td>7.100328</td>\n",
       "      <td>0.194207</td>\n",
       "      <td>0.803090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21224</td>\n",
       "      <td>7.019214</td>\n",
       "      <td>0.101362</td>\n",
       "      <td>0.160569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21208</td>\n",
       "      <td>6.119481</td>\n",
       "      <td>0.256552</td>\n",
       "      <td>0.408578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21207</td>\n",
       "      <td>5.724831</td>\n",
       "      <td>0.191876</td>\n",
       "      <td>0.833351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21229</td>\n",
       "      <td>5.625052</td>\n",
       "      <td>0.135211</td>\n",
       "      <td>0.753505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>21237</td>\n",
       "      <td>4.853728</td>\n",
       "      <td>0.144324</td>\n",
       "      <td>0.296441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21227</td>\n",
       "      <td>4.819348</td>\n",
       "      <td>0.126929</td>\n",
       "      <td>0.149252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21239</td>\n",
       "      <td>4.818039</td>\n",
       "      <td>0.150098</td>\n",
       "      <td>0.783766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21202</td>\n",
       "      <td>4.664446</td>\n",
       "      <td>0.084198</td>\n",
       "      <td>0.579819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code  cases_per_1000  pct_over_age_65  pct_black\n",
       "7     21228        7.167640         0.192072   0.196539\n",
       "24    21215        7.100328         0.194207   0.803090\n",
       "4     21224        7.019214         0.101362   0.160569\n",
       "10    21208        6.119481         0.256552   0.408578\n",
       "1     21207        5.724831         0.191876   0.833351\n",
       "26    21229        5.625052         0.135211   0.753505\n",
       "27    21237        4.853728         0.144324   0.296441\n",
       "3     21227        4.819348         0.126929   0.149252\n",
       "8     21239        4.818039         0.150098   0.783766\n",
       "14    21202        4.664446         0.084198   0.579819"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    balt_df\n",
    "    .sort_values(\"cases_per_1000\", ascending=False)\n",
    "    [[\"zip_code\", \"cases_per_1000\", \"pct_over_age_65\", \"pct_black\"]]\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "plt.scatter(\n",
    "    balt_df[\"pct_over_age_65\"],\n",
    "    balt_df[\"cases_per_1000\"]\n",
    ")\n",
    "plt.title(\"Percent Over Age 65 vs. Cases Per 1,000 in Baltimore\")\n",
    "plt.ylabel(\"Cases Per 1,000 People\")\n",
    "plt.xlabel(\"Percent Over Age 65\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export Data for Mapping and Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_data(df, city_name):\n",
    "    (\n",
    "        gp.GeoDataFrame(\n",
    "            df\n",
    "            [[\n",
    "                \"zip_code\", \"cases\", \"total_population\", \"cases_per_1000\", \"pct_black\",\n",
    "                \"average_household_size\", \"pct_services\", \"household_median_income\",\n",
    "                \"pct_over_age_65\", \"pct_below_poverty_level\"\n",
    "            ]],\n",
    "            geometry = df[\"geometry\"]\n",
    "        )\n",
    "        .to_file(f\"../outputs/geojson/{city_name}_zips.geojson\", driver='GeoJSON')\n",
    "    )\n",
    "    \n",
    "    (\n",
    "        df\n",
    "        .drop(columns = [ \"geometry\" ])\n",
    "        .to_csv(f\"../outputs/csvs/{city_name}_zips.csv\", index = None)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NYC\n",
    "(\n",
    "    gp\n",
    "    .read_file(\"../data/raw/nyc-shapefiles/ZIP_CODE_040114.shp\")\n",
    "    .merge(\n",
    "        nyc_df,\n",
    "        left_on = \"ZIPCODE\",\n",
    "        right_on = \"zip_code\"\n",
    "    )\n",
    "    .rename(\n",
    "        columns = { \"Positive\": \"cases\" }\n",
    "    )\n",
    "    .pipe(export_data, \"nyc\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chicago\n",
    "(\n",
    "    pd.merge(\n",
    "        chi_zips,\n",
    "        chi_df,\n",
    "        right_on = \"zip_code\",\n",
    "        left_on = \"zip\"\n",
    "    )\n",
    "    .rename(\n",
    "        columns = {\"confirmed_cases\": \"cases\"}\n",
    "    )\n",
    "    .pipe(export_data, \"chi\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detroit\n",
    "(\n",
    "    gp\n",
    "    .read_file(\"../data/raw/detroit-shape/detroit-zips.geojson\")\n",
    "    .merge(\n",
    "        det_df,\n",
    "        left_on = \"zipcode\",\n",
    "        right_on = \"zip_code\"\n",
    "    )\n",
    "    .rename(\n",
    "        columns = { \"confirmed_cases\": \"cases\" }\n",
    "    )\n",
    "    .pipe(export_data, \"det\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Philadelphia \n",
    "(\n",
    "    phi_df\n",
    "    .rename(\n",
    "        columns = { \"Positive\": \"cases\" }\n",
    "    )\n",
    "    .pipe(export_data, \"phi\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Baltimore\n",
    "(\n",
    "    pd.merge(\n",
    "        balt_zips,\n",
    "        balt_df,\n",
    "        how = \"left\",\n",
    "        left_on = \"ZIPCODE1\",\n",
    "        right_on = \"zip_code\"\n",
    "    )\n",
    "    .dropna(subset=[ \"total_population\", \"case_count\" ])\n",
    "    .rename(\n",
    "        columns = {\"case_count\": \"cases\"}\n",
    "    )\n",
    "    .pipe(export_data, \"balt\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "----\n",
    "\n",
    "----"
   ]
  }
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