Join2APIs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dae12e5b-a5c4-4751-b77e-210f4fdb36a6",
   "metadata": {},
   "source": [
    "# DataPrep "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61eb0f7e-0b44-4941-8dc4-5f558c1e6d4d",
   "metadata": {},
   "source": [
    "### Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67f4d67b-2fe1-412f-a585-c2a58077fa9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd474d1-c22a-4012-aecd-e563de3b581c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Read the JSON data fetched from the web & pandas transform it to Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11f3db8f-8b35-4497-b0d8-0b40dafec846",
   "metadata": {},
   "outputs": [],
   "source": [
    "req = requests.get(\"https://api.covid19api.com/live/country/united-states\").json()\n",
    "covid=pd.DataFrame(req)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07f8f620-e3ea-4a1e-8f4a-32e29d725b02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5893 entries, 0 to 5892\n",
      "Data columns (total 13 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   ID           5893 non-null   object\n",
      " 1   Country      5893 non-null   object\n",
      " 2   CountryCode  5893 non-null   object\n",
      " 3   Province     5893 non-null   object\n",
      " 4   City         5893 non-null   object\n",
      " 5   CityCode     5893 non-null   object\n",
      " 6   Lat          5893 non-null   object\n",
      " 7   Lon          5893 non-null   object\n",
      " 8   Confirmed    5893 non-null   int64 \n",
      " 9   Deaths       5893 non-null   int64 \n",
      " 10  Recovered    5893 non-null   int64 \n",
      " 11  Active       5893 non-null   int64 \n",
      " 12  Date         5893 non-null   object\n",
      "dtypes: int64(4), object(9)\n",
      "memory usage: 598.6+ KB\n"
     ]
    }
   ],
   "source": [
    "covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fac111a-0aaf-4a75-a4ad-0e64f367b361",
   "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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.893000e+03</td>\n",
       "      <td>5893.000000</td>\n",
       "      <td>5893.0</td>\n",
       "      <td>5.893000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.852553e+05</td>\n",
       "      <td>11568.342440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.736869e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.349321e+05</td>\n",
       "      <td>14443.925806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.209351e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.400210e+05</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.376790e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.614250e+05</td>\n",
       "      <td>7265.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.532760e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.355200e+05</td>\n",
       "      <td>13938.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.245170e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.773284e+06</td>\n",
       "      <td>69990.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.703294e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Confirmed        Deaths  Recovered        Active\n",
       "count  5.893000e+03   5893.000000     5893.0  5.893000e+03\n",
       "mean   6.852553e+05  11568.342440        0.0  6.736869e+05\n",
       "std    8.349321e+05  14443.925806        0.0  8.209351e+05\n",
       "min    0.000000e+00      0.000000        0.0  0.000000e+00\n",
       "25%    1.400210e+05   2075.000000        0.0  1.376790e+05\n",
       "50%    4.614250e+05   7265.000000        0.0  4.532760e+05\n",
       "75%    8.355200e+05  13938.000000        0.0  8.245170e+05\n",
       "max    4.773284e+06  69990.000000        0.0  4.703294e+06"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0018b13a-4849-4344-922d-fe10e200e754",
   "metadata": {},
   "source": [
    "# Graphing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d89c2d7-bce0-47a5-b023-801238e38c63",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Import  new graphing library called Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2590818-950a-4516-ba83-2dff2bd6b96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x168d1d910>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme(style=\"ticks\")\n",
    "sns.relplot(data=covid,x=\"Lat\",y=\"Lon\",hue=\"Deaths\",size=\"Confirmed\",sizes=(40,400))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1c022c-13a8-46f6-9511-cd41a2b3a454",
   "metadata": {},
   "source": [
    "### Failed! To check why -- Inspect  data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c22b2bdf-f4ee-44c5-84f2-60334a71f6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'18.35'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['Lat'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f5fd14e-700e-4ffa-be22-af4e44f1949e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'-64.93'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['Lon'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d35c70f-77d4-45ae-86fe-6f0aca61108f",
   "metadata": {},
   "source": [
    "### It is a string "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47f20c4-2a27-4967-8510-df7a3db8fca7",
   "metadata": {},
   "source": [
    "### convert it to a floating number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "016e95c8-9deb-4490-ad8d-8b78f141a2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid['Lat']=covid['Lat'].astype('float16')\n",
    "covid['Lon']=covid['Lon'].astype('float16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0ac9365-8392-467f-b391-de0eab89f070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5893 entries, 0 to 5892\n",
      "Data columns (total 13 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   ID           5893 non-null   object \n",
      " 1   Country      5893 non-null   object \n",
      " 2   CountryCode  5893 non-null   object \n",
      " 3   Province     5893 non-null   object \n",
      " 4   City         5893 non-null   object \n",
      " 5   CityCode     5893 non-null   object \n",
      " 6   Lat          5893 non-null   float16\n",
      " 7   Lon          5893 non-null   float16\n",
      " 8   Confirmed    5893 non-null   int64  \n",
      " 9   Deaths       5893 non-null   int64  \n",
      " 10  Recovered    5893 non-null   int64  \n",
      " 11  Active       5893 non-null   int64  \n",
      " 12  Date         5893 non-null   object \n",
      "dtypes: float16(2), int64(4), object(7)\n",
      "memory usage: 529.6+ KB\n"
     ]
    }
   ],
   "source": [
    "covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "878f6d48-704c-4b2c-a6eb-1c21a9100c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1057f3a00>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme(style=\"ticks\")\n",
    "sns.relplot(data=covid,x=\"Lon\",y=\"Lat\",hue=\"Deaths\",size=\"Confirmed\",sizes=(40,400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c3b6cab1-0d9c-4ddc-b960-bbef2c39e7bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=sns.relplot(data=covid, x=\"Lon\",y=\"Lat\", hue=\"Deaths\",size=\"Confirmed\",sizes=(40,400)).set(ylim=(25,50),xlim=(-150,-50))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e871c9e-8b33-4fdf-9ffe-a5cd7ccc24af",
   "metadata": {},
   "source": [
    "# Dataset COVID"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ee671e-fb50-490e-8213-6d35f0640e9f",
   "metadata": {},
   "source": [
    "Our goal is to correlate beds per 10,000 populations to mortality for COVID. Lets bring in another data set that tells us about the number of beds per sample. This table has one-line per hospital. We will need to aggregate or sum by county in order to get the total number of beds per county. First the API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f1f240d-00e5-4132-8e29-df2dbfbe2d17",
   "metadata": {},
   "source": [
    "### Loading from API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "089b4557-a618-4f21-a2bb-c93960d5c3d6",
   "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>hospital_id</th>\n",
       "      <th>hospital_org_id</th>\n",
       "      <th>ein</th>\n",
       "      <th>name</th>\n",
       "      <th>name2</th>\n",
       "      <th>name3</th>\n",
       "      <th>street_address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>...</th>\n",
       "      <th>fips_state_and_county_code</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "      <th>chrch_affl_f</th>\n",
       "      <th>urban_location_f</th>\n",
       "      <th>children_hospital_f</th>\n",
       "      <th>memb_counc_teach_hosps_f</th>\n",
       "      <th>medicare_provider_number</th>\n",
       "      <th>county</th>\n",
       "      <th>hospital_bed_size</th>\n",
       "      <th>updated_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>630307951</td>\n",
       "      <td>Mizell Memorial Hospital</td>\n",
       "      <td></td>\n",
       "      <td>Mizell Memorial Hospital</td>\n",
       "      <td>702 Main Street</td>\n",
       "      <td>Opp</td>\n",
       "      <td>AL</td>\n",
       "      <td>36462</td>\n",
       "      <td>...</td>\n",
       "      <td>01039</td>\n",
       "      <td>99</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>010007</td>\n",
       "      <td>Covington County</td>\n",
       "      <td>&lt;100 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>630578923</td>\n",
       "      <td>St Vincents East</td>\n",
       "      <td></td>\n",
       "      <td>St Vincents East</td>\n",
       "      <td>50 Medical Park Drive East</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>35235</td>\n",
       "      <td>...</td>\n",
       "      <td>01073</td>\n",
       "      <td>362</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>010011</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>&gt;299 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>630312913</td>\n",
       "      <td>Shelby Baptist Medical Center</td>\n",
       "      <td></td>\n",
       "      <td>Shelby Baptist Medical Center</td>\n",
       "      <td>1000 First Street North</td>\n",
       "      <td>Alabaster</td>\n",
       "      <td>AL</td>\n",
       "      <td>35007</td>\n",
       "      <td>...</td>\n",
       "      <td>01117</td>\n",
       "      <td>252</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>010016</td>\n",
       "      <td>Shelby County</td>\n",
       "      <td>100-299 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>630459034</td>\n",
       "      <td>Callahan Eye Foundation Hosp</td>\n",
       "      <td></td>\n",
       "      <td>Callahan Eye Foundation Hosp</td>\n",
       "      <td>1720 University Boulevard</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>35233</td>\n",
       "      <td>...</td>\n",
       "      <td>01073</td>\n",
       "      <td>106</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>010018</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>100-299 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>581973570</td>\n",
       "      <td>Cherokee Medical Center</td>\n",
       "      <td></td>\n",
       "      <td>Cherokee Medical Center</td>\n",
       "      <td>100 Northwood Drive</td>\n",
       "      <td>Centre</td>\n",
       "      <td>AL</td>\n",
       "      <td>35960</td>\n",
       "      <td>...</td>\n",
       "      <td>01019</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>010022</td>\n",
       "      <td>Cherokee County</td>\n",
       "      <td>&lt;100 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  hospital_id hospital_org_id        ein                           name name2  \\\n",
       "0           1               1  630307951       Mizell Memorial Hospital         \n",
       "1           2               2  630578923               St Vincents East         \n",
       "2           3               3  630312913  Shelby Baptist Medical Center         \n",
       "3           4               4  630459034   Callahan Eye Foundation Hosp         \n",
       "4           5               5  581973570        Cherokee Medical Center         \n",
       "\n",
       "                           name3              street_address        city  \\\n",
       "0       Mizell Memorial Hospital             702 Main Street         Opp   \n",
       "1               St Vincents East  50 Medical Park Drive East  Birmingham   \n",
       "2  Shelby Baptist Medical Center     1000 First Street North   Alabaster   \n",
       "3   Callahan Eye Foundation Hosp   1720 University Boulevard  Birmingham   \n",
       "4        Cherokee Medical Center         100 Northwood Drive      Centre   \n",
       "\n",
       "  state zip_code  ... fips_state_and_county_code hospital_bed_count  \\\n",
       "0    AL    36462  ...                      01039                 99   \n",
       "1    AL    35235  ...                      01073                362   \n",
       "2    AL    35007  ...                      01117                252   \n",
       "3    AL    35233  ...                      01073                106   \n",
       "4    AL    35960  ...                      01019                 60   \n",
       "\n",
       "  chrch_affl_f urban_location_f children_hospital_f memb_counc_teach_hosps_f  \\\n",
       "0            N                N                   N                        N   \n",
       "1            N                Y                   N                        Y   \n",
       "2            N                Y                   N                        N   \n",
       "3            N                Y                   N                        Y   \n",
       "4            N                N                   N                        N   \n",
       "\n",
       "  medicare_provider_number            county hospital_bed_size  \\\n",
       "0                   010007  Covington County         <100 beds   \n",
       "1                   010011  Jefferson County         >299 beds   \n",
       "2                   010016     Shelby County      100-299 beds   \n",
       "3                   010018  Jefferson County      100-299 beds   \n",
       "4                   010022   Cherokee County         <100 beds   \n",
       "\n",
       "        updated_dt  \n",
       "0  October 1, 2021  \n",
       "1  October 1, 2021  \n",
       "2  October 1, 2021  \n",
       "3  October 1, 2021  \n",
       "4  October 1, 2021  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "req=requests.get(\"https://www.communitybenefitinsight.org/api/get_hospitals.php\").json()\n",
    "hospitals=pd.DataFrame(req)\n",
    "hospitals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13467ed1-7c0c-4112-99a4-22e679f87120",
   "metadata": {
    "tags": []
   },
   "source": [
    "### CREATE HospitalInfoPerCounty Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f74edac-9983-4e3e-ace9-5133e64498b6",
   "metadata": {},
   "source": [
    " Sum `hospital_bed_count` in each `county`\n",
    " Because `county` is not unique in the US\n",
    " so **create a new column called** `state_county`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec3bdc21-c79e-4bf2-9218-cac78c6462a7",
   "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>hospital_id</th>\n",
       "      <th>hospital_org_id</th>\n",
       "      <th>ein</th>\n",
       "      <th>name</th>\n",
       "      <th>name2</th>\n",
       "      <th>name3</th>\n",
       "      <th>street_address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>...</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "      <th>chrch_affl_f</th>\n",
       "      <th>urban_location_f</th>\n",
       "      <th>children_hospital_f</th>\n",
       "      <th>memb_counc_teach_hosps_f</th>\n",
       "      <th>medicare_provider_number</th>\n",
       "      <th>county</th>\n",
       "      <th>hospital_bed_size</th>\n",
       "      <th>updated_dt</th>\n",
       "      <th>state_county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>630307951</td>\n",
       "      <td>Mizell Memorial Hospital</td>\n",
       "      <td></td>\n",
       "      <td>Mizell Memorial Hospital</td>\n",
       "      <td>702 Main Street</td>\n",
       "      <td>Opp</td>\n",
       "      <td>AL</td>\n",
       "      <td>36462</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>010007</td>\n",
       "      <td>Covington County</td>\n",
       "      <td>&lt;100 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "      <td>AL_Covington County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>630578923</td>\n",
       "      <td>St Vincents East</td>\n",
       "      <td></td>\n",
       "      <td>St Vincents East</td>\n",
       "      <td>50 Medical Park Drive East</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>35235</td>\n",
       "      <td>...</td>\n",
       "      <td>362</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>010011</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>&gt;299 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "      <td>AL_Jefferson County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>630312913</td>\n",
       "      <td>Shelby Baptist Medical Center</td>\n",
       "      <td></td>\n",
       "      <td>Shelby Baptist Medical Center</td>\n",
       "      <td>1000 First Street North</td>\n",
       "      <td>Alabaster</td>\n",
       "      <td>AL</td>\n",
       "      <td>35007</td>\n",
       "      <td>...</td>\n",
       "      <td>252</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>010016</td>\n",
       "      <td>Shelby County</td>\n",
       "      <td>100-299 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "      <td>AL_Shelby County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>630459034</td>\n",
       "      <td>Callahan Eye Foundation Hosp</td>\n",
       "      <td></td>\n",
       "      <td>Callahan Eye Foundation Hosp</td>\n",
       "      <td>1720 University Boulevard</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>35233</td>\n",
       "      <td>...</td>\n",
       "      <td>106</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>010018</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>100-299 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "      <td>AL_Jefferson County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>581973570</td>\n",
       "      <td>Cherokee Medical Center</td>\n",
       "      <td></td>\n",
       "      <td>Cherokee Medical Center</td>\n",
       "      <td>100 Northwood Drive</td>\n",
       "      <td>Centre</td>\n",
       "      <td>AL</td>\n",
       "      <td>35960</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>010022</td>\n",
       "      <td>Cherokee County</td>\n",
       "      <td>&lt;100 beds</td>\n",
       "      <td>October 1, 2021</td>\n",
       "      <td>AL_Cherokee County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  hospital_id hospital_org_id        ein                           name name2  \\\n",
       "0           1               1  630307951       Mizell Memorial Hospital         \n",
       "1           2               2  630578923               St Vincents East         \n",
       "2           3               3  630312913  Shelby Baptist Medical Center         \n",
       "3           4               4  630459034   Callahan Eye Foundation Hosp         \n",
       "4           5               5  581973570        Cherokee Medical Center         \n",
       "\n",
       "                           name3              street_address        city  \\\n",
       "0       Mizell Memorial Hospital             702 Main Street         Opp   \n",
       "1               St Vincents East  50 Medical Park Drive East  Birmingham   \n",
       "2  Shelby Baptist Medical Center     1000 First Street North   Alabaster   \n",
       "3   Callahan Eye Foundation Hosp   1720 University Boulevard  Birmingham   \n",
       "4        Cherokee Medical Center         100 Northwood Drive      Centre   \n",
       "\n",
       "  state zip_code  ... hospital_bed_count chrch_affl_f urban_location_f  \\\n",
       "0    AL    36462  ...                 99            N                N   \n",
       "1    AL    35235  ...                362            N                Y   \n",
       "2    AL    35007  ...                252            N                Y   \n",
       "3    AL    35233  ...                106            N                Y   \n",
       "4    AL    35960  ...                 60            N                N   \n",
       "\n",
       "  children_hospital_f memb_counc_teach_hosps_f medicare_provider_number  \\\n",
       "0                   N                        N                   010007   \n",
       "1                   N                        Y                   010011   \n",
       "2                   N                        N                   010016   \n",
       "3                   N                        Y                   010018   \n",
       "4                   N                        N                   010022   \n",
       "\n",
       "             county hospital_bed_size       updated_dt         state_county  \n",
       "0  Covington County         <100 beds  October 1, 2021  AL_Covington County  \n",
       "1  Jefferson County         >299 beds  October 1, 2021  AL_Jefferson County  \n",
       "2     Shelby County      100-299 beds  October 1, 2021     AL_Shelby County  \n",
       "3  Jefferson County      100-299 beds  October 1, 2021  AL_Jefferson County  \n",
       "4   Cherokee County         <100 beds  October 1, 2021   AL_Cherokee County  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "req=requests.get(\"https://www.communitybenefitinsight.org/api/get_hospitals.php\").json()\n",
    "hospitals=pd.DataFrame(req)\n",
    "hospitals['state_county']=hospitals['state']+'_'+hospitals['county']\n",
    "hospitals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88bdb62d-6918-430e-8a7e-216f4a2f773c",
   "metadata": {},
   "source": [
    "as_index : bool, default True\n",
    "For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output.\n",
    "agg is an alias for aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b57a0a5-5d65-4e4b-a562-d19b683ed01a",
   "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>state_county</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK_Anchorage Municipality</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK_Fairbanks North Star Borough</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK_Kenai Peninsula Borough</td>\n",
       "      <td>49622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK_Ketchikan Gateway Borough</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK_Kodiak Island Borough</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      state_county hospital_bed_count\n",
       "0        AK_Anchorage Municipality                401\n",
       "1  AK_Fairbanks North Star Borough                162\n",
       "2       AK_Kenai Peninsula Borough              49622\n",
       "3     AK_Ketchikan Gateway Borough                 25\n",
       "4         AK_Kodiak Island Borough                 25"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HospitalInfoPerCounty=hospitals.groupby(['state_county'], as_index=False)['hospital_bed_count'].agg('sum')\n",
    "HospitalInfoPerCounty.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "513015d8-0da5-4a8e-870a-8a1bbd8b790b",
   "metadata": {},
   "source": [
    "# It is NOT right"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "603ea200-863d-4909-baaa-4d3784e8c436",
   "metadata": {},
   "source": [
    "Ok – there clearly aren’t 49622 in AK_Kenai Peninsula Borough. Lets inspect further. And we’ll have to address it, but the county have  County in the end. Lets fix one thing at a time. First, lets see the types."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e5b8ce-d2ef-4c49-a4ad-8eafe2186bde",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Turning a string into a number in `hospitals.hospital_bed_count`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81983db9-1e11-43ee-82e2-6cef3f099ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'99'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hospitals.hospital_bed_count[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79d93f54-28f5-449f-aafb-70d8983cd960",
   "metadata": {},
   "outputs": [],
   "source": [
    "hospitals.hospital_bed_count=hospitals.hospital_bed_count.astype('float16')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac674eac-fe8e-43e3-98f8-42d77df0fef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hospitals.hospital_bed_count[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b74b3198-6bd2-4281-92f8-d768c9e6cce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3380 entries, 0 to 3379\n",
      "Data columns (total 22 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   hospital_id                 3380 non-null   object \n",
      " 1   hospital_org_id             3380 non-null   object \n",
      " 2   ein                         3380 non-null   object \n",
      " 3   name                        3380 non-null   object \n",
      " 4   name2                       3380 non-null   object \n",
      " 5   name3                       3380 non-null   object \n",
      " 6   street_address              3380 non-null   object \n",
      " 7   city                        3380 non-null   object \n",
      " 8   state                       3380 non-null   object \n",
      " 9   zip_code                    3380 non-null   object \n",
      " 10  american_association_id     3380 non-null   object \n",
      " 11  fips_state_and_county_code  3380 non-null   object \n",
      " 12  hospital_bed_count          3380 non-null   float16\n",
      " 13  chrch_affl_f                3380 non-null   object \n",
      " 14  urban_location_f            3380 non-null   object \n",
      " 15  children_hospital_f         3380 non-null   object \n",
      " 16  memb_counc_teach_hosps_f    3380 non-null   object \n",
      " 17  medicare_provider_number    3380 non-null   object \n",
      " 18  county                      3379 non-null   object \n",
      " 19  hospital_bed_size           3380 non-null   object \n",
      " 20  updated_dt                  3380 non-null   object \n",
      " 21  state_county                3379 non-null   object \n",
      "dtypes: float16(1), object(21)\n",
      "memory usage: 561.3+ KB\n"
     ]
    }
   ],
   "source": [
    "hospitals.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077a812a-d74e-46ab-a6bd-0f0c1f3d745f",
   "metadata": {},
   "source": [
    "### CREATE `HospitalInfoPerCounty` and summing for `hospital_bed_count`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4123984-0b4d-44eb-ae73-2c39661a72e2",
   "metadata": {},
   "source": [
    "We need to create a dataframe that is unique by state_county, and we need to sum across entries on hospital_bed_count.  This is because the state and county may not be unique (e.g. multiple entries for different days).  Its always important to know what tables are unique for a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4dde70eb-a4db-47a7-9077-1789a8697eb7",
   "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>state_county</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK_Anchorage Municipality</td>\n",
       "      <td>401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK_Fairbanks North Star Borough</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK_Kenai Peninsula Borough</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK_Ketchikan Gateway Borough</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK_Kodiak Island Borough</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      state_county  hospital_bed_count\n",
       "0        AK_Anchorage Municipality               401.0\n",
       "1  AK_Fairbanks North Star Borough               162.0\n",
       "2       AK_Kenai Peninsula Borough                77.0\n",
       "3     AK_Ketchikan Gateway Borough                25.0\n",
       "4         AK_Kodiak Island Borough                25.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HospitalInfoPerCounty=hospitals.groupby(['state_county'], as_index=False)['hospital_bed_count'].agg('sum')\n",
    "HospitalInfoPerCounty.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5114c1-2d73-496b-bf71-80d6af0db677",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "59cdab7d-9f45-4e66-a77d-b7baf25b4e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "hospital_bed_count = hospitals.hospital_bed_count.astype('float16')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8071491-bd10-455b-be74-8e20a09e1f8b",
   "metadata": {},
   "source": [
    "### Substitution and curating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb0d6b9-708b-41ae-afbd-bc2c57c9132f",
   "metadata": {},
   "source": [
    "Sometimes data is not fully structured, as is case here.. This is really sub-optimal, but there are times when you want an approximate solution to understand the scope of the problem. In this case, lets just focus on those that end in County and remove that text, noting we won’t get all matches, but we should get a majority."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "efdfc216-9d95-45e8-ba35-13bc77d9a230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_county</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK_Anchorage Municipality</td>\n",
       "      <td>401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK_Fairbanks North Star Borough</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK_Kenai Peninsula Borough</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK_Ketchikan Gateway Borough</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK_Kodiak Island Borough</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      state_county  hospital_bed_count\n",
       "0        AK_Anchorage Municipality               401.0\n",
       "1  AK_Fairbanks North Star Borough               162.0\n",
       "2       AK_Kenai Peninsula Borough                77.0\n",
       "3     AK_Ketchikan Gateway Borough                25.0\n",
       "4         AK_Kodiak Island Borough                25.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HospitalInfoPerCounty['state_county']=HospitalInfoPerCounty['state_county'].str.replace(' County','')\n",
    "HospitalInfoPerCounty.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e546046b-c1f5-4579-9449-f09d48304237",
   "metadata": {},
   "source": [
    "## Create `CovidPerCounty` DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "65101bba-b2eb-41e5-9028-c6666b7fbc1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\n",
    "req=requests.get(\"https://webhooks.mongodb-stitch.com/api/client/v2.0/app/covid-19-qppza/service/REST-API/incoming_webhook/us_only?min_date=2021-01-15T00:00:00.000Z&max_date=2021-01-15T00:00:00.000Z\").json()\n",
    "covid=pd.DataFrame(req)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be150128-5750-4e93-8d16-b42872908066",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>uid</th>\n",
       "      <th>country_iso2</th>\n",
       "      <th>country_iso3</th>\n",
       "      <th>country_code</th>\n",
       "      <th>fips</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>combined_name</th>\n",
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       "      <th>loc</th>\n",
       "      <th>date</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed_daily</th>\n",
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       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Baldwin, Alabama, US</td>\n",
       "      <td>223234.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-87.7221, 30...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>16002</td>\n",
       "      <td>179</td>\n",
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       "      <td>840</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Barbour, Alabama, US</td>\n",
       "      <td>24686.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-85.3871, 31...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>1712</td>\n",
       "      <td>36</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61625f2601a1c2eeb7597716</td>\n",
       "      <td>84001007</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Bibb, Alabama, US</td>\n",
       "      <td>22394.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-87.1251, 32...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>2130</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61625f2601a1c2eeb7597988</td>\n",
       "      <td>84001009</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>Blount</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Blount, Alabama, US</td>\n",
       "      <td>57826.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-86.5679, 33...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>5264</td>\n",
       "      <td>83</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                        _id       uid country_iso2 country_iso3  country_code  \\\n",
       "0  61625f2601a1c2eeb7596fc0  84001001           US          USA           840   \n",
       "1  61625f2601a1c2eeb7597232  84001003           US          USA           840   \n",
       "2  61625f2601a1c2eeb75974a4  84001005           US          USA           840   \n",
       "3  61625f2601a1c2eeb7597716  84001007           US          USA           840   \n",
       "4  61625f2601a1c2eeb7597988  84001009           US          USA           840   \n",
       "\n",
       "     fips   county    state country         combined_name  population  \\\n",
       "0  1001.0  Autauga  Alabama      US  Autauga, Alabama, US     55869.0   \n",
       "1  1003.0  Baldwin  Alabama      US  Baldwin, Alabama, US    223234.0   \n",
       "2  1005.0  Barbour  Alabama      US  Barbour, Alabama, US     24686.0   \n",
       "3  1007.0     Bibb  Alabama      US     Bibb, Alabama, US     22394.0   \n",
       "4  1009.0   Blount  Alabama      US   Blount, Alabama, US     57826.0   \n",
       "\n",
       "                                                 loc  \\\n",
       "0  {'type': 'Point', 'coordinates': [-86.6441, 32...   \n",
       "1  {'type': 'Point', 'coordinates': [-87.7221, 30...   \n",
       "2  {'type': 'Point', 'coordinates': [-85.3871, 31...   \n",
       "3  {'type': 'Point', 'coordinates': [-87.1251, 32...   \n",
       "4  {'type': 'Point', 'coordinates': [-86.5679, 33...   \n",
       "\n",
       "                       date  confirmed  deaths  confirmed_daily  deaths_daily  \n",
       "0  2021-01-15T00:00:00.000Z       5103      55               28             0  \n",
       "1  2021-01-15T00:00:00.000Z      16002     179              161             2  \n",
       "2  2021-01-15T00:00:00.000Z       1712      36               16             0  \n",
       "3  2021-01-15T00:00:00.000Z       2130      47               17             0  \n",
       "4  2021-01-15T00:00:00.000Z       5264      83               45             3  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7922f6da-5b2f-4d4a-a33a-c35717e2cd4c",
   "metadata": {},
   "source": [
    "### Convert states to a 2-letter code using a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a7b045bd-7b04-439d-8842-02f44393d0c6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61625f2601a1c2eeb7597716</td>\n",
       "      <td>84001007</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "      <td>Bibb, Alabama, US</td>\n",
       "      <td>22394.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-87.1251, 32...</td>\n",
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       "      <td>2130</td>\n",
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       "      <td>17</td>\n",
       "      <td>0</td>\n",
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       "      <td>US</td>\n",
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       "      <td>840</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>Blount</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "      <td>Blount, Alabama, US</td>\n",
       "      <td>57826.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-86.5679, 33...</td>\n",
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      "text/plain": [
       "                        _id       uid country_iso2 country_iso3  country_code  \\\n",
       "0  61625f2601a1c2eeb7596fc0  84001001           US          USA           840   \n",
       "1  61625f2601a1c2eeb7597232  84001003           US          USA           840   \n",
       "2  61625f2601a1c2eeb75974a4  84001005           US          USA           840   \n",
       "3  61625f2601a1c2eeb7597716  84001007           US          USA           840   \n",
       "4  61625f2601a1c2eeb7597988  84001009           US          USA           840   \n",
       "\n",
       "     fips   county state country         combined_name  population  \\\n",
       "0  1001.0  Autauga    AL      US  Autauga, Alabama, US     55869.0   \n",
       "1  1003.0  Baldwin    AL      US  Baldwin, Alabama, US    223234.0   \n",
       "2  1005.0  Barbour    AL      US  Barbour, Alabama, US     24686.0   \n",
       "3  1007.0     Bibb    AL      US     Bibb, Alabama, US     22394.0   \n",
       "4  1009.0   Blount    AL      US   Blount, Alabama, US     57826.0   \n",
       "\n",
       "                                                 loc  \\\n",
       "0  {'type': 'Point', 'coordinates': [-86.6441, 32...   \n",
       "1  {'type': 'Point', 'coordinates': [-87.7221, 30...   \n",
       "2  {'type': 'Point', 'coordinates': [-85.3871, 31...   \n",
       "3  {'type': 'Point', 'coordinates': [-87.1251, 32...   \n",
       "4  {'type': 'Point', 'coordinates': [-86.5679, 33...   \n",
       "\n",
       "                       date  confirmed  deaths  confirmed_daily  deaths_daily  \n",
       "0  2021-01-15T00:00:00.000Z       5103      55               28             0  \n",
       "1  2021-01-15T00:00:00.000Z      16002     179              161             2  \n",
       "2  2021-01-15T00:00:00.000Z       1712      36               16             0  \n",
       "3  2021-01-15T00:00:00.000Z       2130      47               17             0  \n",
       "4  2021-01-15T00:00:00.000Z       5264      83               45             3  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_state_to_abbrev = {\n",
    "\"Alabama\": \"AL\",\"Alaska\": \"AK\",\"Arizona\": \"AZ\",\"Arkansas\": \"AR\", \"California\": \"CA\",\n",
    "\"Colorado\": \"CO\",\"Connecticut\": \"CT\",\"Delaware\": \"DE\",\"Florida\": \"FL\",\"Georgia\": \"GA\",\n",
    "\"Hawaii\": \"HI\",\"Idaho\": \"ID\",\"Illinois\": \"IL\",\"Indiana\": \"IN\",\"Iowa\": \"IA\",\"Kansas\": \"KS\",\n",
    "\"Kentucky\": \"KY\",\"Louisiana\": \"LA\",\"Maine\": \"ME\",\"Maryland\": \"MD\",\"Massachusetts\": \"MA\",\n",
    "\"Michigan\": \"MI\",\"Minnesota\": \"MN\",\"Mississippi\": \"MS\",\"Missouri\": \"MO\",\"Montana\": \"MT\",\n",
    "\"Nebraska\": \"NE\",\"Nevada\": \"NV\",\"New Hampshire\": \"NH\",\"New Jersey\": \"NJ\",\"New Mexico\": \"NM\",\n",
    "\"New York\": \"NY\",\"North Carolina\": \"NC\",\"North Dakota\": \"ND\",\"Ohio\": \"OH\",\n",
    "\"Oklahoma\": \"OK\",\"Oregon\": \"OR\",\"Pennsylvania\": \"PA\",\"Rhode Island\": \"RI\",\n",
    "\"South Carolina\": \"SC\",\"South Dakota\": \"SD\",\"Tennessee\": \"TN\",\"Texas\": \"TX\",\n",
    "\"Utah\": \"UT\",\"Vermont\": \"VT\",\"Virginia\": \"VA\",\"Washington\": \"WA\",\n",
    "\"West Virginia\": \"WV\",\"Wisconsin\": \"WI\",\"Wyoming\": \"WY\",\n",
    "\"District of Columbia\": \"DC\",\"American Samoa\": \"AS\",\"Guam\": \"GU\", \"Northern Mariana Islands\": \"MP\",\n",
    "\"Puerto Rico\": \"PR\",\"United States Minor Outlying Islands\": \"UM\",\"U.S. Virgin Islands\": \"VI\",\n",
    "}\n",
    "covid['state'].replace(us_state_to_abbrev, inplace=True)\n",
    "covid.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf70146-4d28-4de8-8cec-54ae3075e856",
   "metadata": {},
   "source": [
    "When inplace=True is passed, the data is renamed in place (it returns nothing), so you'd use:\n",
    "\n",
    "df.an_operation(inplace=True)\n",
    "When inplace=False is passed (this is the default value, so isn't necessary), performs the operation and returns a copy of the object, so you'd use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd4d1080-f95b-4e67-be3f-40438a389c1a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "                        _id       uid country_iso2 country_iso3  country_code  \\\n",
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       "\n",
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       "\n",
       "                                                 loc  \\\n",
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       "4  {'type': 'Point', 'coordinates': [-86.5679, 33...   \n",
       "\n",
       "                       date  confirmed  deaths  confirmed_daily  deaths_daily  \n",
       "0  2021-01-15T00:00:00.000Z       5103      55               28             0  \n",
       "1  2021-01-15T00:00:00.000Z      16002     179              161             2  \n",
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     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.head(5)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "bc4fb8f6-5e37-440c-8b85-a3750c52ee4a",
   "metadata": {
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      "text/plain": [
       "                           _id       uid country_iso2 country_iso3  \\\n",
       "3332  61625f3b01a1c2eeb7794388  84056029           US          USA   \n",
       "3333  61625f3b01a1c2eeb77945fa  84056031           US          USA   \n",
       "3334  61625f3b01a1c2eeb779486c  84056033           US          USA   \n",
       "3335  61625f3b01a1c2eeb7794ade  84056035           US          USA   \n",
       "3336  61625f3b01a1c2eeb7794d50  84056037           US          USA   \n",
       "3337  61625f3b01a1c2eeb7794fc2  84056039           US          USA   \n",
       "3338  61625f3b01a1c2eeb7795234  84056041           US          USA   \n",
       "3339  61625f3b01a1c2eeb77954a6  84090056           US          USA   \n",
       "3340  61625f3b01a1c2eeb7795718  84056043           US          USA   \n",
       "3341  61625f3b01a1c2eeb779598a  84056045           US          USA   \n",
       "\n",
       "      country_code     fips      county state country  \\\n",
       "3332           840  56029.0        Park    WY      US   \n",
       "3333           840  56031.0      Platte    WY      US   \n",
       "3334           840  56033.0    Sheridan    WY      US   \n",
       "3335           840  56035.0    Sublette    WY      US   \n",
       "3336           840  56037.0  Sweetwater    WY      US   \n",
       "3337           840  56039.0       Teton    WY      US   \n",
       "3338           840  56041.0       Uinta    WY      US   \n",
       "3339           840  90056.0  Unassigned    WY      US   \n",
       "3340           840  56043.0    Washakie    WY      US   \n",
       "3341           840  56045.0      Weston    WY      US   \n",
       "\n",
       "                combined_name  population  \\\n",
       "3332        Park, Wyoming, US     29194.0   \n",
       "3333      Platte, Wyoming, US      8393.0   \n",
       "3334    Sheridan, Wyoming, US     30485.0   \n",
       "3335    Sublette, Wyoming, US      9831.0   \n",
       "3336  Sweetwater, Wyoming, US     42343.0   \n",
       "3337       Teton, Wyoming, US     23464.0   \n",
       "3338       Uinta, Wyoming, US     20226.0   \n",
       "3339  Unassigned, Wyoming, US         NaN   \n",
       "3340    Washakie, Wyoming, US      7805.0   \n",
       "3341      Weston, Wyoming, US      6927.0   \n",
       "\n",
       "                                                    loc  \\\n",
       "3332  {'type': 'Point', 'coordinates': [-109.5853, 4...   \n",
       "3333  {'type': 'Point', 'coordinates': [-104.9663, 4...   \n",
       "3334  {'type': 'Point', 'coordinates': [-106.8862, 4...   \n",
       "3335  {'type': 'Point', 'coordinates': [-109.9131, 4...   \n",
       "3336  {'type': 'Point', 'coordinates': [-108.8828, 4...   \n",
       "3337  {'type': 'Point', 'coordinates': [-110.5891, 4...   \n",
       "3338  {'type': 'Point', 'coordinates': [-110.5476, 4...   \n",
       "3339                                                NaN   \n",
       "3340  {'type': 'Point', 'coordinates': [-107.6802, 4...   \n",
       "3341  {'type': 'Point', 'coordinates': [-104.5675, 4...   \n",
       "\n",
       "                          date  confirmed  deaths  confirmed_daily  \\\n",
       "3332  2021-01-15T00:00:00.000Z       2396      16               28   \n",
       "3333  2021-01-15T00:00:00.000Z        515       9                6   \n",
       "3334  2021-01-15T00:00:00.000Z       2746      17                6   \n",
       "3335  2021-01-15T00:00:00.000Z        633       7                1   \n",
       "3336  2021-01-15T00:00:00.000Z       3262      26               12   \n",
       "3337  2021-01-15T00:00:00.000Z       2605       4               26   \n",
       "3338  2021-01-15T00:00:00.000Z       1806       9               12   \n",
       "3339  2021-01-15T00:00:00.000Z          0       0                0   \n",
       "3340  2021-01-15T00:00:00.000Z        840      23                3   \n",
       "3341  2021-01-15T00:00:00.000Z        602       4                0   \n",
       "\n",
       "      deaths_daily  \n",
       "3332             0  \n",
       "3333             0  \n",
       "3334             0  \n",
       "3335             0  \n",
       "3336             0  \n",
       "3337             0  \n",
       "3338             0  \n",
       "3339             0  \n",
       "3340             0  \n",
       "3341             0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d553ab05-bd0f-40fb-9b88-3d08416082fa",
   "metadata": {},
   "source": [
    "### Create a key to join our tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "897872ad-2957-4f4d-8474-33761320eadb",
   "metadata": {},
   "source": [
    "The dataframe covid may not be fully unique per state or county. Lets create a new column that we can merge on which concatenates both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "554e677a-0850-40c3-923f-1226431a0f8d",
   "metadata": {
    "tags": []
   },
   "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>_id</th>\n",
       "      <th>uid</th>\n",
       "      <th>country_iso2</th>\n",
       "      <th>country_iso3</th>\n",
       "      <th>country_code</th>\n",
       "      <th>fips</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>combined_name</th>\n",
       "      <th>population</th>\n",
       "      <th>loc</th>\n",
       "      <th>date</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed_daily</th>\n",
       "      <th>deaths_daily</th>\n",
       "      <th>state_county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61625f2601a1c2eeb7596fc0</td>\n",
       "      <td>84001001</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "      <td>Autauga, Alabama, US</td>\n",
       "      <td>55869.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-86.6441, 32...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>5103</td>\n",
       "      <td>55</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>AL_Autauga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61625f2601a1c2eeb7597232</td>\n",
       "      <td>84001003</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "      <td>Baldwin, Alabama, US</td>\n",
       "      <td>223234.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-87.7221, 30...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>16002</td>\n",
       "      <td>179</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>AL_Baldwin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61625f2601a1c2eeb75974a4</td>\n",
       "      <td>84001005</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "      <td>Barbour, Alabama, US</td>\n",
       "      <td>24686.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-85.3871, 31...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>1712</td>\n",
       "      <td>36</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>AL_Barbour</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61625f2601a1c2eeb7597716</td>\n",
       "      <td>84001007</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "      <td>Bibb, Alabama, US</td>\n",
       "      <td>22394.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-87.1251, 32...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>2130</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>AL_Bibb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61625f2601a1c2eeb7597988</td>\n",
       "      <td>84001009</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>Blount</td>\n",
       "      <td>AL</td>\n",
       "      <td>US</td>\n",
       "      <td>Blount, Alabama, US</td>\n",
       "      <td>57826.0</td>\n",
       "      <td>{'type': 'Point', 'coordinates': [-86.5679, 33...</td>\n",
       "      <td>2021-01-15T00:00:00.000Z</td>\n",
       "      <td>5264</td>\n",
       "      <td>83</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>AL_Blount</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id       uid country_iso2 country_iso3  country_code  \\\n",
       "0  61625f2601a1c2eeb7596fc0  84001001           US          USA           840   \n",
       "1  61625f2601a1c2eeb7597232  84001003           US          USA           840   \n",
       "2  61625f2601a1c2eeb75974a4  84001005           US          USA           840   \n",
       "3  61625f2601a1c2eeb7597716  84001007           US          USA           840   \n",
       "4  61625f2601a1c2eeb7597988  84001009           US          USA           840   \n",
       "\n",
       "     fips   county state country         combined_name  population  \\\n",
       "0  1001.0  Autauga    AL      US  Autauga, Alabama, US     55869.0   \n",
       "1  1003.0  Baldwin    AL      US  Baldwin, Alabama, US    223234.0   \n",
       "2  1005.0  Barbour    AL      US  Barbour, Alabama, US     24686.0   \n",
       "3  1007.0     Bibb    AL      US     Bibb, Alabama, US     22394.0   \n",
       "4  1009.0   Blount    AL      US   Blount, Alabama, US     57826.0   \n",
       "\n",
       "                                                 loc  \\\n",
       "0  {'type': 'Point', 'coordinates': [-86.6441, 32...   \n",
       "1  {'type': 'Point', 'coordinates': [-87.7221, 30...   \n",
       "2  {'type': 'Point', 'coordinates': [-85.3871, 31...   \n",
       "3  {'type': 'Point', 'coordinates': [-87.1251, 32...   \n",
       "4  {'type': 'Point', 'coordinates': [-86.5679, 33...   \n",
       "\n",
       "                       date  confirmed  deaths  confirmed_daily  deaths_daily  \\\n",
       "0  2021-01-15T00:00:00.000Z       5103      55               28             0   \n",
       "1  2021-01-15T00:00:00.000Z      16002     179              161             2   \n",
       "2  2021-01-15T00:00:00.000Z       1712      36               16             0   \n",
       "3  2021-01-15T00:00:00.000Z       2130      47               17             0   \n",
       "4  2021-01-15T00:00:00.000Z       5264      83               45             3   \n",
       "\n",
       "  state_county  \n",
       "0   AL_Autauga  \n",
       "1   AL_Baldwin  \n",
       "2   AL_Barbour  \n",
       "3      AL_Bibb  \n",
       "4    AL_Blount  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid['state_county']=covid['state']+'_'+covid['county']\n",
    "covid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dafe1b37-9906-4669-ad4d-6a0fcd155d0b",
   "metadata": {},
   "source": [
    "## Create a dataframe with unique CovidPerCount"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c3f805-26c0-43a7-a0be-636e08ea275a",
   "metadata": {},
   "source": [
    "We will group and sum for deaths, population, and confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e7c5387b-3136-4763-b3a8-31cf47a98f01",
   "metadata": {
    "tags": []
   },
   "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>state_county</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>confirmed_daily</th>\n",
       "      <th>deaths_daily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK_Aleutians East</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>3337.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK_Aleutians West</td>\n",
       "      <td>0</td>\n",
       "      <td>242</td>\n",
       "      <td>5634.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK_Anchorage</td>\n",
       "      <td>129</td>\n",
       "      <td>24530</td>\n",
       "      <td>288000.0</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK_Bethel</td>\n",
       "      <td>12</td>\n",
       "      <td>2911</td>\n",
       "      <td>18386.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK_Bristol Bay</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AK_Bristol Bay plus Lake and Peninsula</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>2428.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AK_Chugach</td>\n",
       "      <td>3</td>\n",
       "      <td>241</td>\n",
       "      <td>6751.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AK_Copper River</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>2699.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AK_Denali</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AK_Dillingham</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>4916.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AK_Fairbanks North Star</td>\n",
       "      <td>23</td>\n",
       "      <td>5576</td>\n",
       "      <td>96849.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AK_Haines</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2530.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AK_Hoonah-Angoon</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AK_Juneau</td>\n",
       "      <td>5</td>\n",
       "      <td>1098</td>\n",
       "      <td>31974.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>AK_Kenai Peninsula</td>\n",
       "      <td>19</td>\n",
       "      <td>3821</td>\n",
       "      <td>58708.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              state_county  deaths  confirmed  population  \\\n",
       "0                        AK_Aleutians East       0         40      3337.0   \n",
       "1                        AK_Aleutians West       0        242      5634.0   \n",
       "2                             AK_Anchorage     129      24530    288000.0   \n",
       "3                                AK_Bethel      12       2911     18386.0   \n",
       "4                           AK_Bristol Bay       0          0       836.0   \n",
       "5   AK_Bristol Bay plus Lake and Peninsula       0        163      2428.0   \n",
       "6                               AK_Chugach       3        241      6751.0   \n",
       "7                          AK_Copper River       0        175      2699.0   \n",
       "8                                AK_Denali       0         63      2097.0   \n",
       "9                            AK_Dillingham       1        150      4916.0   \n",
       "10                 AK_Fairbanks North Star      23       5576     96849.0   \n",
       "11                               AK_Haines       0         23      2530.0   \n",
       "12                        AK_Hoonah-Angoon       0          0      2148.0   \n",
       "13                               AK_Juneau       5       1098     31974.0   \n",
       "14                      AK_Kenai Peninsula      19       3821     58708.0   \n",
       "\n",
       "    confirmed_daily  deaths_daily  \n",
       "0                 1             0  \n",
       "1                 3             0  \n",
       "2                92             0  \n",
       "3                26             0  \n",
       "4                 0             0  \n",
       "5                 1             0  \n",
       "6                 0             0  \n",
       "7                 1             0  \n",
       "8                 0             0  \n",
       "9                 1             0  \n",
       "10               46             1  \n",
       "11                0             0  \n",
       "12                0             0  \n",
       "13                4             0  \n",
       "14               11             0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CovidPerCounty=covid.groupby(['state_county'], as_index=False).agg({'deaths':'sum','confirmed':'sum','population':'sum','confirmed_daily':'mean','deaths_daily':'mean'})\n",
    "CovidPerCounty.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f2ae72-69e0-4873-abb8-c8d7b28e354d",
   "metadata": {},
   "source": [
    "Pandas dataframe.groupby() function is used to split the data into groups based on some criteria. pandas objects can be split on any of their axes. The abstract definition of grouping is to provide a mapping of labels to group names."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76fe578-f7b5-49d2-882f-2ada4294c0cc",
   "metadata": {},
   "source": [
    "## Create mortality within CovidPerCounty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "29e12c0b-070e-4b2b-b816-94e3e7285618",
   "metadata": {
    "tags": []
   },
   "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>state_county</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>confirmed_daily</th>\n",
       "      <th>deaths_daily</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK_Aleutians East</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>3337.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK_Aleutians West</td>\n",
       "      <td>0</td>\n",
       "      <td>242</td>\n",
       "      <td>5634.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK_Anchorage</td>\n",
       "      <td>129</td>\n",
       "      <td>24530</td>\n",
       "      <td>288000.0</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK_Bethel</td>\n",
       "      <td>12</td>\n",
       "      <td>2911</td>\n",
       "      <td>18386.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK_Bristol Bay</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state_county  deaths  confirmed  population  confirmed_daily  \\\n",
       "0  AK_Aleutians East       0         40      3337.0                1   \n",
       "1  AK_Aleutians West       0        242      5634.0                3   \n",
       "2       AK_Anchorage     129      24530    288000.0               92   \n",
       "3          AK_Bethel      12       2911     18386.0               26   \n",
       "4     AK_Bristol Bay       0          0       836.0                0   \n",
       "\n",
       "   deaths_daily  mortality  \n",
       "0             0   0.000000  \n",
       "1             0   0.000000  \n",
       "2             0   0.005259  \n",
       "3             0   0.004122  \n",
       "4             0        NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CovidPerCounty['mortality']=CovidPerCounty['deaths']/CovidPerCounty['confirmed']\n",
    "CovidPerCounty.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8c9469-6cf9-40c6-827c-cca2e4bf9f80",
   "metadata": {},
   "source": [
    "## Join HospitalInfoPerCounty and CovidPerCounty by state_county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d184f66f-a570-464f-8102-e549c2013d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_county</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>confirmed_daily</th>\n",
       "      <th>deaths_daily</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL_Baldwin</td>\n",
       "      <td>232.0</td>\n",
       "      <td>179</td>\n",
       "      <td>16002</td>\n",
       "      <td>223234.0</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL_Blount</td>\n",
       "      <td>65.0</td>\n",
       "      <td>83</td>\n",
       "      <td>5264</td>\n",
       "      <td>57826.0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>0.015767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL_Calhoun</td>\n",
       "      <td>552.0</td>\n",
       "      <td>200</td>\n",
       "      <td>10982</td>\n",
       "      <td>113605.0</td>\n",
       "      <td>119</td>\n",
       "      <td>3</td>\n",
       "      <td>0.018212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AL_Cullman</td>\n",
       "      <td>115.0</td>\n",
       "      <td>104</td>\n",
       "      <td>7960</td>\n",
       "      <td>83768.0</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>0.013065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AL_Elmore</td>\n",
       "      <td>69.0</td>\n",
       "      <td>101</td>\n",
       "      <td>7723</td>\n",
       "      <td>81209.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_county  hospital_bed_count  deaths  confirmed  population  \\\n",
       "0   AL_Baldwin               232.0     179      16002    223234.0   \n",
       "1    AL_Blount                65.0      83       5264     57826.0   \n",
       "2   AL_Calhoun               552.0     200      10982    113605.0   \n",
       "8   AL_Cullman               115.0     104       7960     83768.0   \n",
       "9    AL_Elmore                69.0     101       7723     81209.0   \n",
       "\n",
       "   confirmed_daily  deaths_daily  mortality  \n",
       "0              161             2   0.011186  \n",
       "1               45             3   0.015767  \n",
       "2              119             3   0.018212  \n",
       "8               38            10   0.013065  \n",
       "9               53             0   0.013078  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hospital_Covid_PerCounty=pd.merge(HospitalInfoPerCounty,CovidPerCounty,on='state_county')\n",
    "Hospital_Covid_PerCounty=Hospital_Covid_PerCounty[Hospital_Covid_PerCounty['population']>50000]\n",
    "Hospital_Covid_PerCounty=Hospital_Covid_PerCounty[Hospital_Covid_PerCounty['population']<300000]\n",
    "Hospital_Covid_PerCounty=Hospital_Covid_PerCounty[Hospital_Covid_PerCounty['confirmed']>10]\n",
    "Hospital_Covid_PerCounty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8e96b25e-90f2-49e9-9ec2-056d82eda4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['state_county',\n",
       " 'hospital_bed_count',\n",
       " 'deaths',\n",
       " 'confirmed',\n",
       " 'population',\n",
       " 'confirmed_daily',\n",
       " 'deaths_daily',\n",
       " 'mortality']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in Hospital_Covid_PerCounty]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e8710d1b-9e6b-491c-b484-9a9adf2836cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       232.0\n",
       "1        65.0\n",
       "2       552.0\n",
       "8       115.0\n",
       "9        69.0\n",
       "        ...  \n",
       "1636    720.0\n",
       "1641     70.0\n",
       "1642    173.0\n",
       "1649    343.0\n",
       "1653    217.0\n",
       "Name: hospital_bed_count, Length: 560, dtype: float16"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hospital_Covid_PerCounty['hospital_bed_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ddf3ff8c-4188-4818-adec-a8b9e0d1dda6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       0.001039\n",
      "1       0.001124\n",
      "2       0.004859\n",
      "8       0.001373\n",
      "9       0.000850\n",
      "          ...   \n",
      "1636    0.006817\n",
      "1641    0.001240\n",
      "1642    0.002358\n",
      "1649    0.004107\n",
      "1653    0.002717\n",
      "Length: 560, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "bed_per_people=Hospital_Covid_PerCounty['hospital_bed_count']/Hospital_Covid_PerCounty['population']\n",
    "print(bed_per_people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "78873099-c5bd-499e-b0b3-eaf1f9ffcfac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_county</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>confirmed_daily</th>\n",
       "      <th>deaths_daily</th>\n",
       "      <th>mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL_Baldwin</td>\n",
       "      <td>232.0</td>\n",
       "      <td>179</td>\n",
       "      <td>16002</td>\n",
       "      <td>223234.0</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL_Blount</td>\n",
       "      <td>65.0</td>\n",
       "      <td>83</td>\n",
       "      <td>5264</td>\n",
       "      <td>57826.0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>0.015767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL_Calhoun</td>\n",
       "      <td>552.0</td>\n",
       "      <td>200</td>\n",
       "      <td>10982</td>\n",
       "      <td>113605.0</td>\n",
       "      <td>119</td>\n",
       "      <td>3</td>\n",
       "      <td>0.018212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AL_Cullman</td>\n",
       "      <td>115.0</td>\n",
       "      <td>104</td>\n",
       "      <td>7960</td>\n",
       "      <td>83768.0</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>0.013065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AL_Elmore</td>\n",
       "      <td>69.0</td>\n",
       "      <td>101</td>\n",
       "      <td>7723</td>\n",
       "      <td>81209.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>WV_Monongalia</td>\n",
       "      <td>720.0</td>\n",
       "      <td>52</td>\n",
       "      <td>6311</td>\n",
       "      <td>105612.0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>WV_Putnam</td>\n",
       "      <td>70.0</td>\n",
       "      <td>63</td>\n",
       "      <td>3444</td>\n",
       "      <td>56450.0</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.018293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>WV_Raleigh</td>\n",
       "      <td>173.0</td>\n",
       "      <td>37</td>\n",
       "      <td>3505</td>\n",
       "      <td>73361.0</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>WV_Wood</td>\n",
       "      <td>343.0</td>\n",
       "      <td>76</td>\n",
       "      <td>5993</td>\n",
       "      <td>83518.0</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>0.012681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>WY_Natrona</td>\n",
       "      <td>217.0</td>\n",
       "      <td>103</td>\n",
       "      <td>7039</td>\n",
       "      <td>79858.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       state_county  hospital_bed_count  deaths  confirmed  population  \\\n",
       "0        AL_Baldwin               232.0     179      16002    223234.0   \n",
       "1         AL_Blount                65.0      83       5264     57826.0   \n",
       "2        AL_Calhoun               552.0     200      10982    113605.0   \n",
       "8        AL_Cullman               115.0     104       7960     83768.0   \n",
       "9         AL_Elmore                69.0     101       7723     81209.0   \n",
       "...             ...                 ...     ...        ...         ...   \n",
       "1636  WV_Monongalia               720.0      52       6311    105612.0   \n",
       "1641      WV_Putnam                70.0      63       3444     56450.0   \n",
       "1642     WV_Raleigh               173.0      37       3505     73361.0   \n",
       "1649        WV_Wood               343.0      76       5993     83518.0   \n",
       "1653     WY_Natrona               217.0     103       7039     79858.0   \n",
       "\n",
       "      confirmed_daily  deaths_daily  mortality  \n",
       "0                 161             2   0.011186  \n",
       "1                  45             3   0.015767  \n",
       "2                 119             3   0.018212  \n",
       "8                  38            10   0.013065  \n",
       "9                  53             0   0.013078  \n",
       "...               ...           ...        ...  \n",
       "1636               67             1   0.008240  \n",
       "1641               37             2   0.018293  \n",
       "1642               79             1   0.010556  \n",
       "1649               54             6   0.012681  \n",
       "1653               18             0   0.014633  \n",
       "\n",
       "[560 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=Hospital_Covid_PerCounty\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "063da8cb-d323-4401-a6a9-df6e35692fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_county</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>confirmed_daily</th>\n",
       "      <th>deaths_daily</th>\n",
       "      <th>mortality</th>\n",
       "      <th>bed_per_people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL_Baldwin</td>\n",
       "      <td>232.0</td>\n",
       "      <td>179</td>\n",
       "      <td>16002</td>\n",
       "      <td>223234.0</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011186</td>\n",
       "      <td>0.001039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL_Blount</td>\n",
       "      <td>65.0</td>\n",
       "      <td>83</td>\n",
       "      <td>5264</td>\n",
       "      <td>57826.0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>0.001124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL_Calhoun</td>\n",
       "      <td>552.0</td>\n",
       "      <td>200</td>\n",
       "      <td>10982</td>\n",
       "      <td>113605.0</td>\n",
       "      <td>119</td>\n",
       "      <td>3</td>\n",
       "      <td>0.018212</td>\n",
       "      <td>0.004859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AL_Cullman</td>\n",
       "      <td>115.0</td>\n",
       "      <td>104</td>\n",
       "      <td>7960</td>\n",
       "      <td>83768.0</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>0.013065</td>\n",
       "      <td>0.001373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AL_Elmore</td>\n",
       "      <td>69.0</td>\n",
       "      <td>101</td>\n",
       "      <td>7723</td>\n",
       "      <td>81209.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013078</td>\n",
       "      <td>0.000850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>WV_Monongalia</td>\n",
       "      <td>720.0</td>\n",
       "      <td>52</td>\n",
       "      <td>6311</td>\n",
       "      <td>105612.0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008240</td>\n",
       "      <td>0.006817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>WV_Putnam</td>\n",
       "      <td>70.0</td>\n",
       "      <td>63</td>\n",
       "      <td>3444</td>\n",
       "      <td>56450.0</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.001240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>WV_Raleigh</td>\n",
       "      <td>173.0</td>\n",
       "      <td>37</td>\n",
       "      <td>3505</td>\n",
       "      <td>73361.0</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010556</td>\n",
       "      <td>0.002358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>WV_Wood</td>\n",
       "      <td>343.0</td>\n",
       "      <td>76</td>\n",
       "      <td>5993</td>\n",
       "      <td>83518.0</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>0.012681</td>\n",
       "      <td>0.004107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>WY_Natrona</td>\n",
       "      <td>217.0</td>\n",
       "      <td>103</td>\n",
       "      <td>7039</td>\n",
       "      <td>79858.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014633</td>\n",
       "      <td>0.002717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       state_county  hospital_bed_count  deaths  confirmed  population  \\\n",
       "0        AL_Baldwin               232.0     179      16002    223234.0   \n",
       "1         AL_Blount                65.0      83       5264     57826.0   \n",
       "2        AL_Calhoun               552.0     200      10982    113605.0   \n",
       "8        AL_Cullman               115.0     104       7960     83768.0   \n",
       "9         AL_Elmore                69.0     101       7723     81209.0   \n",
       "...             ...                 ...     ...        ...         ...   \n",
       "1636  WV_Monongalia               720.0      52       6311    105612.0   \n",
       "1641      WV_Putnam                70.0      63       3444     56450.0   \n",
       "1642     WV_Raleigh               173.0      37       3505     73361.0   \n",
       "1649        WV_Wood               343.0      76       5993     83518.0   \n",
       "1653     WY_Natrona               217.0     103       7039     79858.0   \n",
       "\n",
       "      confirmed_daily  deaths_daily  mortality  bed_per_people  \n",
       "0                 161             2   0.011186        0.001039  \n",
       "1                  45             3   0.015767        0.001124  \n",
       "2                 119             3   0.018212        0.004859  \n",
       "8                  38            10   0.013065        0.001373  \n",
       "9                  53             0   0.013078        0.000850  \n",
       "...               ...           ...        ...             ...  \n",
       "1636               67             1   0.008240        0.006817  \n",
       "1641               37             2   0.018293        0.001240  \n",
       "1642               79             1   0.010556        0.002358  \n",
       "1649               54             6   0.012681        0.004107  \n",
       "1653               18             0   0.014633        0.002717  \n",
       "\n",
       "[560 rows x 9 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bed_per_people']= bed_per_people\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "31fea1d5-2c4c-41a4-8c62-174853ec0ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_plot=sns.relplot(data=Hospital_Covid_PerCounty, x=\"bed_per_people\",y=\"mortality\",hue=\"confirmed\",size=\"population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "669ba4b1-1cb8-404e-8147-d97cc44c46e7",
   "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>state_county</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>confirmed_daily</th>\n",
       "      <th>deaths_daily</th>\n",
       "      <th>mortality</th>\n",
       "      <th>bed_per_people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL_Baldwin</td>\n",
       "      <td>232.0</td>\n",
       "      <td>179</td>\n",
       "      <td>16002</td>\n",
       "      <td>223234.0</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011186</td>\n",
       "      <td>0.001039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL_Blount</td>\n",
       "      <td>65.0</td>\n",
       "      <td>83</td>\n",
       "      <td>5264</td>\n",
       "      <td>57826.0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>0.001124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL_Calhoun</td>\n",
       "      <td>552.0</td>\n",
       "      <td>200</td>\n",
       "      <td>10982</td>\n",
       "      <td>113605.0</td>\n",
       "      <td>119</td>\n",
       "      <td>3</td>\n",
       "      <td>0.018212</td>\n",
       "      <td>0.004859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AL_Cullman</td>\n",
       "      <td>115.0</td>\n",
       "      <td>104</td>\n",
       "      <td>7960</td>\n",
       "      <td>83768.0</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>0.013065</td>\n",
       "      <td>0.001373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AL_Elmore</td>\n",
       "      <td>69.0</td>\n",
       "      <td>101</td>\n",
       "      <td>7723</td>\n",
       "      <td>81209.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013078</td>\n",
       "      <td>0.000850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>WV_Monongalia</td>\n",
       "      <td>720.0</td>\n",
       "      <td>52</td>\n",
       "      <td>6311</td>\n",
       "      <td>105612.0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008240</td>\n",
       "      <td>0.006817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>WV_Putnam</td>\n",
       "      <td>70.0</td>\n",
       "      <td>63</td>\n",
       "      <td>3444</td>\n",
       "      <td>56450.0</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.001240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>WV_Raleigh</td>\n",
       "      <td>173.0</td>\n",
       "      <td>37</td>\n",
       "      <td>3505</td>\n",
       "      <td>73361.0</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010556</td>\n",
       "      <td>0.002358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>WV_Wood</td>\n",
       "      <td>343.0</td>\n",
       "      <td>76</td>\n",
       "      <td>5993</td>\n",
       "      <td>83518.0</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>0.012681</td>\n",
       "      <td>0.004107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>WY_Natrona</td>\n",
       "      <td>217.0</td>\n",
       "      <td>103</td>\n",
       "      <td>7039</td>\n",
       "      <td>79858.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014633</td>\n",
       "      <td>0.002717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       state_county  hospital_bed_count  deaths  confirmed  population  \\\n",
       "0        AL_Baldwin               232.0     179      16002    223234.0   \n",
       "1         AL_Blount                65.0      83       5264     57826.0   \n",
       "2        AL_Calhoun               552.0     200      10982    113605.0   \n",
       "8        AL_Cullman               115.0     104       7960     83768.0   \n",
       "9         AL_Elmore                69.0     101       7723     81209.0   \n",
       "...             ...                 ...     ...        ...         ...   \n",
       "1636  WV_Monongalia               720.0      52       6311    105612.0   \n",
       "1641      WV_Putnam                70.0      63       3444     56450.0   \n",
       "1642     WV_Raleigh               173.0      37       3505     73361.0   \n",
       "1649        WV_Wood               343.0      76       5993     83518.0   \n",
       "1653     WY_Natrona               217.0     103       7039     79858.0   \n",
       "\n",
       "      confirmed_daily  deaths_daily  mortality  bed_per_people  \n",
       "0                 161             2   0.011186        0.001039  \n",
       "1                  45             3   0.015767        0.001124  \n",
       "2                 119             3   0.018212        0.004859  \n",
       "8                  38            10   0.013065        0.001373  \n",
       "9                  53             0   0.013078        0.000850  \n",
       "...               ...           ...        ...             ...  \n",
       "1636               67             1   0.008240        0.006817  \n",
       "1641               37             2   0.018293        0.001240  \n",
       "1642               79             1   0.010556        0.002358  \n",
       "1649               54             6   0.012681        0.004107  \n",
       "1653               18             0   0.014633        0.002717  \n",
       "\n",
       "[560 rows x 9 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2a934630-30fe-40f7-8eb7-399f70436e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_county</th>\n",
       "      <th>hospital_bed_count</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>confirmed_daily</th>\n",
       "      <th>deaths_daily</th>\n",
       "      <th>mortality</th>\n",
       "      <th>bed_per_people</th>\n",
       "      <th>people_per_bed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL_Baldwin</td>\n",
       "      <td>232.0</td>\n",
       "      <td>179</td>\n",
       "      <td>16002</td>\n",
       "      <td>223234.0</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>0.011186</td>\n",
       "      <td>0.001039</td>\n",
       "      <td>962.215517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL_Blount</td>\n",
       "      <td>65.0</td>\n",
       "      <td>83</td>\n",
       "      <td>5264</td>\n",
       "      <td>57826.0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>0.001124</td>\n",
       "      <td>889.630769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL_Calhoun</td>\n",
       "      <td>552.0</td>\n",
       "      <td>200</td>\n",
       "      <td>10982</td>\n",
       "      <td>113605.0</td>\n",
       "      <td>119</td>\n",
       "      <td>3</td>\n",
       "      <td>0.018212</td>\n",
       "      <td>0.004859</td>\n",
       "      <td>205.806159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AL_Cullman</td>\n",
       "      <td>115.0</td>\n",
       "      <td>104</td>\n",
       "      <td>7960</td>\n",
       "      <td>83768.0</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>0.013065</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>728.417391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AL_Elmore</td>\n",
       "      <td>69.0</td>\n",
       "      <td>101</td>\n",
       "      <td>7723</td>\n",
       "      <td>81209.0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013078</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>1176.942029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>WV_Monongalia</td>\n",
       "      <td>720.0</td>\n",
       "      <td>52</td>\n",
       "      <td>6311</td>\n",
       "      <td>105612.0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008240</td>\n",
       "      <td>0.006817</td>\n",
       "      <td>146.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>WV_Putnam</td>\n",
       "      <td>70.0</td>\n",
       "      <td>63</td>\n",
       "      <td>3444</td>\n",
       "      <td>56450.0</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>806.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>WV_Raleigh</td>\n",
       "      <td>173.0</td>\n",
       "      <td>37</td>\n",
       "      <td>3505</td>\n",
       "      <td>73361.0</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010556</td>\n",
       "      <td>0.002358</td>\n",
       "      <td>424.052023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>WV_Wood</td>\n",
       "      <td>343.0</td>\n",
       "      <td>76</td>\n",
       "      <td>5993</td>\n",
       "      <td>83518.0</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>0.012681</td>\n",
       "      <td>0.004107</td>\n",
       "      <td>243.492711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>WY_Natrona</td>\n",
       "      <td>217.0</td>\n",
       "      <td>103</td>\n",
       "      <td>7039</td>\n",
       "      <td>79858.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014633</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>368.009217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       state_county  hospital_bed_count  deaths  confirmed  population  \\\n",
       "0        AL_Baldwin               232.0     179      16002    223234.0   \n",
       "1         AL_Blount                65.0      83       5264     57826.0   \n",
       "2        AL_Calhoun               552.0     200      10982    113605.0   \n",
       "8        AL_Cullman               115.0     104       7960     83768.0   \n",
       "9         AL_Elmore                69.0     101       7723     81209.0   \n",
       "...             ...                 ...     ...        ...         ...   \n",
       "1636  WV_Monongalia               720.0      52       6311    105612.0   \n",
       "1641      WV_Putnam                70.0      63       3444     56450.0   \n",
       "1642     WV_Raleigh               173.0      37       3505     73361.0   \n",
       "1649        WV_Wood               343.0      76       5993     83518.0   \n",
       "1653     WY_Natrona               217.0     103       7039     79858.0   \n",
       "\n",
       "      confirmed_daily  deaths_daily  mortality  bed_per_people  people_per_bed  \n",
       "0                 161             2   0.011186        0.001039      962.215517  \n",
       "1                  45             3   0.015767        0.001124      889.630769  \n",
       "2                 119             3   0.018212        0.004859      205.806159  \n",
       "8                  38            10   0.013065        0.001373      728.417391  \n",
       "9                  53             0   0.013078        0.000850     1176.942029  \n",
       "...               ...           ...        ...             ...             ...  \n",
       "1636               67             1   0.008240        0.006817      146.683333  \n",
       "1641               37             2   0.018293        0.001240      806.428571  \n",
       "1642               79             1   0.010556        0.002358      424.052023  \n",
       "1649               54             6   0.012681        0.004107      243.492711  \n",
       "1653               18             0   0.014633        0.002717      368.009217  \n",
       "\n",
       "[560 rows x 10 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['people_per_bed']=df['population']/df['hospital_bed_count']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c3f9ae-a074-494e-b0c3-d6e7f07befb8",
   "metadata": {},
   "source": [
    "# People per hospital bed vs mortality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0243a2b9-97d5-4b80-b896-5130725d8807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_plot1=sns.relplot(data=df, x=\"people_per_bed\",y=\"mortality\",hue=\"confirmed\",size=\"population\",sizes=(40, 400))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}