Merged_Data_Consolidated.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09fa788a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526904c7",
   "metadata": {},
   "source": [
    "### Import Physician Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2aed0f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Link as reference:\n",
    "# https://data.cms.gov/provider-data/dataset/mj5m-pzi6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d2e9397",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Steps required for encoding\n",
    "# with open(\"Resources/physician_data.csv\", \"rb\") as csv_file:\n",
    "#     rawdata = csv_file.read()\n",
    "#     chardet.detect(rawdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b3df5bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Steps required for encoding\n",
    "# import chardet\n",
    "# rawdata = open(\"Resources/physician_data.csv\", 'rb').read()\n",
    "# result = chardet.detect(rawdata)\n",
    "# charenc = result['encoding']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91b4e72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5c40af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create functions to normalize zip codes if any are greater than or less than 5 digits\n",
    "\n",
    "def first_five(s):\n",
    "    return s[0:5]\n",
    "\n",
    "def pad_rjust(s):\n",
    "    zip_len = len(s)\n",
    "    if zip_len <= 5:\n",
    "        final_len = 5\n",
    "    else:\n",
    "        final_len = 9\n",
    "    padded_s = s.rjust(final_len, \"0\")\n",
    "    return padded_s\n",
    "\n",
    "def normalize_zip(s):\n",
    "    padded_zip = pad_rjust(s)\n",
    "    stripped_zip = first_five(padded_zip)\n",
    "    return stripped_zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e22b7b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set dtypes prior to reading in csv file\n",
    "physicians_dtypes = {\"zip\": str, \"grd_yr\": str}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aad9fc76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>npi</th>\n",
       "      <th>grd_yr</th>\n",
       "      <th>pri_spec</th>\n",
       "      <th>cty</th>\n",
       "      <th>st</th>\n",
       "      <th>zip</th>\n",
       "      <th>ind_assgn</th>\n",
       "      <th>grp_assgn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1215143243</td>\n",
       "      <td>1985</td>\n",
       "      <td>CHIROPRACTIC</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>WA</td>\n",
       "      <td>985772535</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1215385364</td>\n",
       "      <td>2016</td>\n",
       "      <td>OPTOMETRY</td>\n",
       "      <td>PANAMA CITY</td>\n",
       "      <td>FL</td>\n",
       "      <td>324054508</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1215144167</td>\n",
       "      <td>1989</td>\n",
       "      <td>CHIROPRACTIC</td>\n",
       "      <td>SANTA ROSA</td>\n",
       "      <td>CA</td>\n",
       "      <td>954014642</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1215239728</td>\n",
       "      <td>2010</td>\n",
       "      <td>CHIROPRACTIC</td>\n",
       "      <td>HASLET</td>\n",
       "      <td>TX</td>\n",
       "      <td>760524069</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1215230917</td>\n",
       "      <td>2004</td>\n",
       "      <td>CLINICAL SOCIAL WORKER</td>\n",
       "      <td>BLOOMINGTON</td>\n",
       "      <td>IL</td>\n",
       "      <td>617046037</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</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>2375852</th>\n",
       "      <td>1891957023</td>\n",
       "      <td>2008</td>\n",
       "      <td>PODIATRY</td>\n",
       "      <td>DENVER</td>\n",
       "      <td>CO</td>\n",
       "      <td>802204000</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375853</th>\n",
       "      <td>1316146939</td>\n",
       "      <td>1996</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>ANCHORAGE</td>\n",
       "      <td>AK</td>\n",
       "      <td>995086804</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375854</th>\n",
       "      <td>1710963681</td>\n",
       "      <td>1976</td>\n",
       "      <td>INTERVENTIONAL PAIN MANAGEMENT</td>\n",
       "      <td>ANCHORAGE</td>\n",
       "      <td>AK</td>\n",
       "      <td>995086804</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375855</th>\n",
       "      <td>1487956637</td>\n",
       "      <td>2010</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>CHILLICOTHE</td>\n",
       "      <td>OH</td>\n",
       "      <td>456012535</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375856</th>\n",
       "      <td>1619928488</td>\n",
       "      <td>1992</td>\n",
       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>CHILLICOTHE</td>\n",
       "      <td>OH</td>\n",
       "      <td>456012535</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375857 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                npi grd_yr                        pri_spec          cty  st  \\\n",
       "0        1215143243   1985                    CHIROPRACTIC      RAYMOND  WA   \n",
       "1        1215385364   2016                       OPTOMETRY  PANAMA CITY  FL   \n",
       "2        1215144167   1989                    CHIROPRACTIC   SANTA ROSA  CA   \n",
       "3        1215239728   2010                    CHIROPRACTIC       HASLET  TX   \n",
       "4        1215230917   2004          CLINICAL SOCIAL WORKER  BLOOMINGTON  IL   \n",
       "...             ...    ...                             ...          ...  ..   \n",
       "2375852  1891957023   2008                        PODIATRY       DENVER  CO   \n",
       "2375853  1316146939   1996             PHYSICIAN ASSISTANT    ANCHORAGE  AK   \n",
       "2375854  1710963681   1976  INTERVENTIONAL PAIN MANAGEMENT    ANCHORAGE  AK   \n",
       "2375855  1487956637   2010             PHYSICIAN ASSISTANT  CHILLICOTHE  OH   \n",
       "2375856  1619928488   1992                 FAMILY MEDICINE  CHILLICOTHE  OH   \n",
       "\n",
       "               zip ind_assgn grp_assgn  \n",
       "0        985772535         Y         M  \n",
       "1        324054508         Y         M  \n",
       "2        954014642         M         M  \n",
       "3        760524069         M         M  \n",
       "4        617046037         Y         M  \n",
       "...            ...       ...       ...  \n",
       "2375852  802204000         Y         Y  \n",
       "2375853  995086804         Y         Y  \n",
       "2375854  995086804         Y         Y  \n",
       "2375855  456012535         M         M  \n",
       "2375856  456012535         M         M  \n",
       "\n",
       "[2375857 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in csv and create DataFrame\n",
    "physicians_original_df = pd.read_csv(\"Resources/physician_data.csv\", encoding=\"ISO-8859-1\", dtype=physicians_dtypes)\n",
    "physicians_original_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "88554165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CHIROPRACTIC', 'OPTOMETRY', 'CLINICAL SOCIAL WORKER',\n",
       "       'FAMILY MEDICINE', 'HOSPITALIST', 'PHYSICAL THERAPY',\n",
       "       'ORTHOPEDIC SURGERY', 'GENERAL PRACTICE', 'PODIATRY',\n",
       "       'INTERNAL MEDICINE', 'ALLERGY/IMMUNOLOGY',\n",
       "       'REGISTERED DIETITIAN OR NUTRITION PROFESSIONAL',\n",
       "       'PSYCHOLOGIST, CLINICAL', 'QUALIFIED AUDIOLOGIST', 'OPHTHALMOLOGY',\n",
       "       'NURSE PRACTITIONER', 'ORAL SURGERY', 'OCCUPATIONAL THERAPY',\n",
       "       'INFECTIOUS DISEASE', 'GENERAL SURGERY', 'DERMATOLOGY',\n",
       "       'OBSTETRICS/GYNECOLOGY', 'SLEEP MEDICINE',\n",
       "       'PHYSICAL MEDICINE AND REHABILITATION',\n",
       "       'CERTIFIED REGISTERED NURSE ANESTHETIST (CRNA)', 'OTOLARYNGOLOGY',\n",
       "       'ENDOCRINOLOGY', 'PHYSICIAN ASSISTANT', 'PSYCHIATRY',\n",
       "       'ANESTHESIOLOGY', 'NEPHROLOGY', 'PULMONARY DISEASE',\n",
       "       'DIAGNOSTIC RADIOLOGY', 'PATHOLOGY', 'INTERVENTIONAL RADIOLOGY',\n",
       "       'UROLOGY', 'SPORTS MEDICINE',\n",
       "       'QUALIFIED SPEECH LANGUAGE PATHOLOGIST',\n",
       "       'INTERVENTIONAL CARDIOLOGY', 'PAIN MANAGEMENT', 'NEUROLOGY',\n",
       "       'EMERGENCY MEDICINE', 'INTERVENTIONAL PAIN MANAGEMENT',\n",
       "       'MAXILLOFACIAL SURGERY', 'RHEUMATOLOGY',\n",
       "       'PLASTIC AND RECONSTRUCTIVE SURGERY', 'HAND SURGERY',\n",
       "       'THORACIC SURGERY', 'GASTROENTEROLOGY',\n",
       "       'COLORECTAL SURGERY (PROCTOLOGY)', 'NEUROSURGERY',\n",
       "       'CARDIOVASCULAR DISEASE (CARDIOLOGY)',\n",
       "       'MICROGRAPHIC DERMATOLOGIC SURGERY (MDS)', 'HEMATOLOGY/ONCOLOGY',\n",
       "       'CERTIFIED CLINICAL NURSE SPECIALIST (CNS)', 'MEDICAL ONCOLOGY',\n",
       "       'VASCULAR SURGERY', 'PEDIATRIC MEDICINE',\n",
       "       'OSTEOPATHIC MANIPULATIVE MEDICINE', 'CARDIAC SURGERY',\n",
       "       'ADDICTION MEDICINE', 'GERIATRIC MEDICINE',\n",
       "       'CRITICAL CARE (INTENSIVISTS)', 'RADIATION ONCOLOGY',\n",
       "       'PREVENTATIVE MEDICINE', 'UNDERSEA AND HYPERBARIC MEDICINE',\n",
       "       'DENTIST', 'GYNECOLOGICAL ONCOLOGY', 'NEUROPSYCHIATRY',\n",
       "       'GERIATRIC PSYCHIATRY', 'HOSPICE/PALLIATIVE CARE',\n",
       "       'CARDIAC ELECTROPHYSIOLOGY', 'NUCLEAR MEDICINE',\n",
       "       'CERTIFIED NURSE MIDWIFE (CNM)', 'SURGICAL ONCOLOGY', 'HEMATOLOGY',\n",
       "       'PERIPHERAL VASCULAR DISEASE',\n",
       "       'UNDEFINED PHYSICIAN TYPE (SPECIFY)', 'MEDICAL TOXICOLOGY',\n",
       "       'MEDICAL GENETICS AND GENOMICS', 'MASS IMMUNIZATION ROSTER BILLER',\n",
       "       'UNDEFINED NON-PHYSICIAN TYPE (SPECIFY)',\n",
       "       'ADVANCED HEART FAILURE AND TRANSPLANT CARDIOLOGY',\n",
       "       'ANESTHESIOLOGY ASSISTANT',\n",
       "       'ADULT CONGENITAL HEART DISEASE (ACHD)',\n",
       "       'HEMATOPOIETIC CELL TRANSPLANTATION AND CELLULAR THERAPY'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find all unique names for Primary Specialty\n",
    "physicians_original_df.pri_spec.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4ae2082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>grd_yr</th>\n",
       "      <th>pri_spec</th>\n",
       "      <th>cty</th>\n",
       "      <th>st</th>\n",
       "      <th>zip</th>\n",
       "      <th>ind_assgn</th>\n",
       "      <th>grp_assgn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1215283908</td>\n",
       "      <td>1998</td>\n",
       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>MIAMI</td>\n",
       "      <td>FL</td>\n",
       "      <td>331673407</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1215257605</td>\n",
       "      <td>2007</td>\n",
       "      <td>GENERAL PRACTICE</td>\n",
       "      <td>CAMUY</td>\n",
       "      <td>PR</td>\n",
       "      <td>00627</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1215248273</td>\n",
       "      <td>2010</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>BRAWLEY</td>\n",
       "      <td>CA</td>\n",
       "      <td>922277755</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1215258017</td>\n",
       "      <td>2010</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>OKLAHOMA CITY</td>\n",
       "      <td>OK</td>\n",
       "      <td>731067216</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1215223664</td>\n",
       "      <td>2011</td>\n",
       "      <td>NURSE PRACTITIONER</td>\n",
       "      <td>MCCONNELSVILLE</td>\n",
       "      <td>OH</td>\n",
       "      <td>437562200</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</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>2375838</th>\n",
       "      <td>1235603606</td>\n",
       "      <td>2018</td>\n",
       "      <td>NURSE PRACTITIONER</td>\n",
       "      <td>OKLAHOMA CITY</td>\n",
       "      <td>OK</td>\n",
       "      <td>731208175</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375839</th>\n",
       "      <td>1497033740</td>\n",
       "      <td>2011</td>\n",
       "      <td>NURSE PRACTITIONER</td>\n",
       "      <td>OKLAHOMA CITY</td>\n",
       "      <td>OK</td>\n",
       "      <td>731208175</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375853</th>\n",
       "      <td>1316146939</td>\n",
       "      <td>1996</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>ANCHORAGE</td>\n",
       "      <td>AK</td>\n",
       "      <td>995086804</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375855</th>\n",
       "      <td>1487956637</td>\n",
       "      <td>2010</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>CHILLICOTHE</td>\n",
       "      <td>OH</td>\n",
       "      <td>456012535</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375856</th>\n",
       "      <td>1619928488</td>\n",
       "      <td>1992</td>\n",
       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>CHILLICOTHE</td>\n",
       "      <td>OH</td>\n",
       "      <td>456012535</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1006814 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                npi grd_yr             pri_spec             cty  st  \\\n",
       "7        1215283908   1998      FAMILY MEDICINE           MIAMI  FL   \n",
       "19       1215257605   2007     GENERAL PRACTICE           CAMUY  PR   \n",
       "22       1215248273   2010    INTERNAL MEDICINE         BRAWLEY  CA   \n",
       "31       1215258017   2010    INTERNAL MEDICINE   OKLAHOMA CITY  OK   \n",
       "39       1215223664   2011   NURSE PRACTITIONER  MCCONNELSVILLE  OH   \n",
       "...             ...    ...                  ...             ...  ..   \n",
       "2375838  1235603606   2018   NURSE PRACTITIONER   OKLAHOMA CITY  OK   \n",
       "2375839  1497033740   2011   NURSE PRACTITIONER   OKLAHOMA CITY  OK   \n",
       "2375853  1316146939   1996  PHYSICIAN ASSISTANT       ANCHORAGE  AK   \n",
       "2375855  1487956637   2010  PHYSICIAN ASSISTANT     CHILLICOTHE  OH   \n",
       "2375856  1619928488   1992      FAMILY MEDICINE     CHILLICOTHE  OH   \n",
       "\n",
       "               zip ind_assgn grp_assgn  \n",
       "7        331673407         Y         M  \n",
       "19           00627         Y         M  \n",
       "22       922277755         Y         M  \n",
       "31       731067216         Y         M  \n",
       "39       437562200         Y         M  \n",
       "...            ...       ...       ...  \n",
       "2375838  731208175         Y         Y  \n",
       "2375839  731208175         Y         Y  \n",
       "2375853  995086804         Y         Y  \n",
       "2375855  456012535         M         M  \n",
       "2375856  456012535         M         M  \n",
       "\n",
       "[1006814 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter by general practice / primary care\n",
    "physicians_filtered_df = physicians_original_df.loc[\n",
    "    (physicians_original_df[\"pri_spec\"] == 'FAMILY MEDICINE') |\n",
    "    (physicians_original_df[\"pri_spec\"] == 'NURSE PRACTITIONER') |\n",
    "    (physicians_original_df[\"pri_spec\"] == 'GENERAL PRACTICE') |\n",
    "    (physicians_original_df[\"pri_spec\"] == 'PREVENTATIVE MEDICINE') |\n",
    "    (physicians_original_df[\"pri_spec\"] == 'EMERGENCY MEDICINE') |\n",
    "    (physicians_original_df[\"pri_spec\"] == 'PHYSICIAN ASSISTANT') |\n",
    "    (physicians_original_df[\"pri_spec\"] == 'INTERNAL MEDICINE') |\n",
    "    (physicians_original_df[\"pri_spec\"] == 'PEDIATRIC MEDICINE') |\n",
    "    (physicians_original_df[\"pri_spec\"] == 'OBSTETRICS/GYNECOLOGY'\n",
    ")]\n",
    "physicians_filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bcaa184c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FAMILY MEDICINE', 'GENERAL PRACTICE', 'INTERNAL MEDICINE',\n",
       "       'NURSE PRACTITIONER', 'OBSTETRICS/GYNECOLOGY',\n",
       "       'PHYSICIAN ASSISTANT', 'EMERGENCY MEDICINE', 'PEDIATRIC MEDICINE',\n",
       "       'PREVENTATIVE MEDICINE'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check unique names for Primary Specialty after filtering\n",
    "physicians_filtered_df.pri_spec.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f3335216",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7          331673407\n",
       "19             00627\n",
       "22         922277755\n",
       "31         731067216\n",
       "39         437562200\n",
       "             ...    \n",
       "2375838    731208175\n",
       "2375839    731208175\n",
       "2375853    995086804\n",
       "2375855    456012535\n",
       "2375856    456012535\n",
       "Name: zip, Length: 1006814, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create series of \"zip\"\n",
    "physicians_zip_s = physicians_filtered_df[\"zip\"]\n",
    "physicians_zip_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2fddff66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7          9\n",
       "19         5\n",
       "22         9\n",
       "31         9\n",
       "39         9\n",
       "          ..\n",
       "2375838    9\n",
       "2375839    9\n",
       "2375853    9\n",
       "2375855    9\n",
       "2375856    9\n",
       "Name: zip, Length: 1006814, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check number of digits for each zip\n",
    "physicians_zip_len = physicians_zip_s.map(len)\n",
    "physicians_zip_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a31b8286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    1000804\n",
       "5       6010\n",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of digit lengths\n",
    "physicians_zip_len.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "354642d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7          33167\n",
       "19         00627\n",
       "22         92227\n",
       "31         73106\n",
       "39         43756\n",
       "           ...  \n",
       "2375838    73120\n",
       "2375839    73120\n",
       "2375853    99508\n",
       "2375855    45601\n",
       "2375856    45601\n",
       "Name: zip, Length: 1006814, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply function to standardize number of zip digits\n",
    "physicians_zip_five_s = physicians_zip_s.map(first_five)\n",
    "physicians_zip_five_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f755f865",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Tony\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "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>npi</th>\n",
       "      <th>grd_yr</th>\n",
       "      <th>pri_spec</th>\n",
       "      <th>cty</th>\n",
       "      <th>st</th>\n",
       "      <th>zip</th>\n",
       "      <th>ind_assgn</th>\n",
       "      <th>grp_assgn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1215283908</td>\n",
       "      <td>1998</td>\n",
       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>MIAMI</td>\n",
       "      <td>FL</td>\n",
       "      <td>33167</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1215257605</td>\n",
       "      <td>2007</td>\n",
       "      <td>GENERAL PRACTICE</td>\n",
       "      <td>CAMUY</td>\n",
       "      <td>PR</td>\n",
       "      <td>00627</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1215248273</td>\n",
       "      <td>2010</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>BRAWLEY</td>\n",
       "      <td>CA</td>\n",
       "      <td>92227</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1215258017</td>\n",
       "      <td>2010</td>\n",
       "      <td>INTERNAL MEDICINE</td>\n",
       "      <td>OKLAHOMA CITY</td>\n",
       "      <td>OK</td>\n",
       "      <td>73106</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1215223664</td>\n",
       "      <td>2011</td>\n",
       "      <td>NURSE PRACTITIONER</td>\n",
       "      <td>MCCONNELSVILLE</td>\n",
       "      <td>OH</td>\n",
       "      <td>43756</td>\n",
       "      <td>Y</td>\n",
       "      <td>M</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>2375838</th>\n",
       "      <td>1235603606</td>\n",
       "      <td>2018</td>\n",
       "      <td>NURSE PRACTITIONER</td>\n",
       "      <td>OKLAHOMA CITY</td>\n",
       "      <td>OK</td>\n",
       "      <td>73120</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375839</th>\n",
       "      <td>1497033740</td>\n",
       "      <td>2011</td>\n",
       "      <td>NURSE PRACTITIONER</td>\n",
       "      <td>OKLAHOMA CITY</td>\n",
       "      <td>OK</td>\n",
       "      <td>73120</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375853</th>\n",
       "      <td>1316146939</td>\n",
       "      <td>1996</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>ANCHORAGE</td>\n",
       "      <td>AK</td>\n",
       "      <td>99508</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375855</th>\n",
       "      <td>1487956637</td>\n",
       "      <td>2010</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>CHILLICOTHE</td>\n",
       "      <td>OH</td>\n",
       "      <td>45601</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375856</th>\n",
       "      <td>1619928488</td>\n",
       "      <td>1992</td>\n",
       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>CHILLICOTHE</td>\n",
       "      <td>OH</td>\n",
       "      <td>45601</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1006814 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                npi grd_yr             pri_spec             cty  st    zip  \\\n",
       "7        1215283908   1998      FAMILY MEDICINE           MIAMI  FL  33167   \n",
       "19       1215257605   2007     GENERAL PRACTICE           CAMUY  PR  00627   \n",
       "22       1215248273   2010    INTERNAL MEDICINE         BRAWLEY  CA  92227   \n",
       "31       1215258017   2010    INTERNAL MEDICINE   OKLAHOMA CITY  OK  73106   \n",
       "39       1215223664   2011   NURSE PRACTITIONER  MCCONNELSVILLE  OH  43756   \n",
       "...             ...    ...                  ...             ...  ..    ...   \n",
       "2375838  1235603606   2018   NURSE PRACTITIONER   OKLAHOMA CITY  OK  73120   \n",
       "2375839  1497033740   2011   NURSE PRACTITIONER   OKLAHOMA CITY  OK  73120   \n",
       "2375853  1316146939   1996  PHYSICIAN ASSISTANT       ANCHORAGE  AK  99508   \n",
       "2375855  1487956637   2010  PHYSICIAN ASSISTANT     CHILLICOTHE  OH  45601   \n",
       "2375856  1619928488   1992      FAMILY MEDICINE     CHILLICOTHE  OH  45601   \n",
       "\n",
       "        ind_assgn grp_assgn  \n",
       "7               Y         M  \n",
       "19              Y         M  \n",
       "22              Y         M  \n",
       "31              Y         M  \n",
       "39              Y         M  \n",
       "...           ...       ...  \n",
       "2375838         Y         Y  \n",
       "2375839         Y         Y  \n",
       "2375853         Y         Y  \n",
       "2375855         M         M  \n",
       "2375856         M         M  \n",
       "\n",
       "[1006814 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply function to standardize number of zip digits\n",
    "physicians_filtered_df[\"zip\"] = physicians_zip_five_s\n",
    "physicians_filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f71503c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7          5\n",
       "19         5\n",
       "22         5\n",
       "31         5\n",
       "39         5\n",
       "          ..\n",
       "2375838    5\n",
       "2375839    5\n",
       "2375853    5\n",
       "2375855    5\n",
       "2375856    5\n",
       "Name: zip, Length: 1006814, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check number of digits for each zip\n",
    "physicians_zip_len = physicians_zip_s.map(len)\n",
    "physicians_zip_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc5d01e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    1006814\n",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of digit lengths\n",
    "physicians_zip_len.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aadf7762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column npi has 0 null values\n",
      "Column grd_yr has 560 null values\n",
      "Column pri_spec has 0 null values\n",
      "Column cty has 0 null values\n",
      "Column st has 0 null values\n",
      "Column zip has 0 null values\n",
      "Column ind_assgn has 0 null values\n",
      "Column grp_assgn has 0 null values\n"
     ]
    }
   ],
   "source": [
    "# Find null values\n",
    "for column in physicians_filtered_df.columns:\n",
    "    print(f\"Column {column} has {physicians_filtered_df[column].isnull().sum()} null values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5235a0",
   "metadata": {},
   "source": [
    "### Import Zip Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c81d564",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Link as reference:\n",
    "# https://simplemaps.com/data/us-zips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a4195d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set dtypes before reading in csv\n",
    "zipcode_dtypes = {\"zip\": str, \"county_fips\": str}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3abb70ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>zip</th>\n",
       "      <th>state_id</th>\n",
       "      <th>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>00601</td>\n",
       "      <td>PR</td>\n",
       "      <td>72001</td>\n",
       "      <td>Adjuntas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>00602</td>\n",
       "      <td>PR</td>\n",
       "      <td>72003</td>\n",
       "      <td>Aguada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>00603</td>\n",
       "      <td>PR</td>\n",
       "      <td>72005</td>\n",
       "      <td>Aguadilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>00606</td>\n",
       "      <td>PR</td>\n",
       "      <td>72093</td>\n",
       "      <td>Maricao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>00610</td>\n",
       "      <td>PR</td>\n",
       "      <td>72011</td>\n",
       "      <td>Añasco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32777</th>\n",
       "      <td>33114</td>\n",
       "      <td>99922</td>\n",
       "      <td>AK</td>\n",
       "      <td>02198</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32778</th>\n",
       "      <td>33115</td>\n",
       "      <td>99923</td>\n",
       "      <td>AK</td>\n",
       "      <td>02198</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32779</th>\n",
       "      <td>33116</td>\n",
       "      <td>99925</td>\n",
       "      <td>AK</td>\n",
       "      <td>02198</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32780</th>\n",
       "      <td>33117</td>\n",
       "      <td>99926</td>\n",
       "      <td>AK</td>\n",
       "      <td>02198</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32781</th>\n",
       "      <td>33119</td>\n",
       "      <td>99929</td>\n",
       "      <td>AK</td>\n",
       "      <td>02275</td>\n",
       "      <td>Wrangell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32782 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0    zip state_id county_fips            county_name\n",
       "0               0  00601       PR       72001               Adjuntas\n",
       "1               1  00602       PR       72003                 Aguada\n",
       "2               2  00603       PR       72005              Aguadilla\n",
       "3               3  00606       PR       72093                Maricao\n",
       "4               4  00610       PR       72011                 Añasco\n",
       "...           ...    ...      ...         ...                    ...\n",
       "32777       33114  99922       AK       02198  Prince of Wales-Hyder\n",
       "32778       33115  99923       AK       02198  Prince of Wales-Hyder\n",
       "32779       33116  99925       AK       02198  Prince of Wales-Hyder\n",
       "32780       33117  99926       AK       02198  Prince of Wales-Hyder\n",
       "32781       33119  99929       AK       02275               Wrangell\n",
       "\n",
       "[32782 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in csv and create DataFrame\n",
    "zip_data_df = pd.read_csv(\"Resources/zip_data_df.csv\", dtype=zipcode_dtypes)\n",
    "zip_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5555ae71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename column\n",
    "zip_data_df.rename({'county_fips': 'GeoFips'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f46b49c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      int64\n",
       "zip            object\n",
       "state_id       object\n",
       "GeoFips        object\n",
       "county_name    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes\n",
    "zip_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "82ce6c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip</th>\n",
       "      <th>state_id</th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00601</td>\n",
       "      <td>PR</td>\n",
       "      <td>72001</td>\n",
       "      <td>Adjuntas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00602</td>\n",
       "      <td>PR</td>\n",
       "      <td>72003</td>\n",
       "      <td>Aguada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00603</td>\n",
       "      <td>PR</td>\n",
       "      <td>72005</td>\n",
       "      <td>Aguadilla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00606</td>\n",
       "      <td>PR</td>\n",
       "      <td>72093</td>\n",
       "      <td>Maricao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00610</td>\n",
       "      <td>PR</td>\n",
       "      <td>72011</td>\n",
       "      <td>Añasco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32777</th>\n",
       "      <td>99922</td>\n",
       "      <td>AK</td>\n",
       "      <td>02198</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32778</th>\n",
       "      <td>99923</td>\n",
       "      <td>AK</td>\n",
       "      <td>02198</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32779</th>\n",
       "      <td>99925</td>\n",
       "      <td>AK</td>\n",
       "      <td>02198</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32780</th>\n",
       "      <td>99926</td>\n",
       "      <td>AK</td>\n",
       "      <td>02198</td>\n",
       "      <td>Prince of Wales-Hyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32781</th>\n",
       "      <td>99929</td>\n",
       "      <td>AK</td>\n",
       "      <td>02275</td>\n",
       "      <td>Wrangell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32782 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         zip state_id GeoFips            county_name\n",
       "0      00601       PR   72001               Adjuntas\n",
       "1      00602       PR   72003                 Aguada\n",
       "2      00603       PR   72005              Aguadilla\n",
       "3      00606       PR   72093                Maricao\n",
       "4      00610       PR   72011                 Añasco\n",
       "...      ...      ...     ...                    ...\n",
       "32777  99922       AK   02198  Prince of Wales-Hyder\n",
       "32778  99923       AK   02198  Prince of Wales-Hyder\n",
       "32779  99925       AK   02198  Prince of Wales-Hyder\n",
       "32780  99926       AK   02198  Prince of Wales-Hyder\n",
       "32781  99929       AK   02275               Wrangell\n",
       "\n",
       "[32782 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Unnamed column\n",
    "zip_data_df.drop(columns=[\"Unnamed: 0\"], inplace=True)\n",
    "zip_data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1664c3a",
   "metadata": {},
   "source": [
    "### Merge physician data with zip data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "24550f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>npi</th>\n",
       "      <th>grd_yr</th>\n",
       "      <th>pri_spec</th>\n",
       "      <th>cty</th>\n",
       "      <th>st</th>\n",
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       "      <th>0</th>\n",
       "      <td>1215283908</td>\n",
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       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>MIAMI</td>\n",
       "      <td>FL</td>\n",
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       "      <td>1043627128</td>\n",
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       "      <td>MIAMI</td>\n",
       "      <td>FL</td>\n",
       "      <td>33167</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>FL</td>\n",
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       "      <th>4</th>\n",
       "      <td>1366581118</td>\n",
       "      <td>1979</td>\n",
       "      <td>GENERAL PRACTICE</td>\n",
       "      <td>MIAMI</td>\n",
       "      <td>FL</td>\n",
       "      <td>33167</td>\n",
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       "      <td>Miami-Dade</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>978471</th>\n",
       "      <td>1073730669</td>\n",
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       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>NEGAUNEE</td>\n",
       "      <td>MI</td>\n",
       "      <td>49866</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>MI</td>\n",
       "      <td>26103</td>\n",
       "      <td>Marquette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978472</th>\n",
       "      <td>1255322301</td>\n",
       "      <td>1973</td>\n",
       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>NEGAUNEE</td>\n",
       "      <td>MI</td>\n",
       "      <td>49866</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>MI</td>\n",
       "      <td>26103</td>\n",
       "      <td>Marquette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978473</th>\n",
       "      <td>1598711368</td>\n",
       "      <td>2004</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>NEGAUNEE</td>\n",
       "      <td>MI</td>\n",
       "      <td>49866</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>MI</td>\n",
       "      <td>26103</td>\n",
       "      <td>Marquette</td>\n",
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       "    <tr>\n",
       "      <th>978474</th>\n",
       "      <td>1205327202</td>\n",
       "      <td>2017</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>WEST DEPTFORD</td>\n",
       "      <td>NJ</td>\n",
       "      <td>08051</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NJ</td>\n",
       "      <td>34015</td>\n",
       "      <td>Gloucester</td>\n",
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       "    <tr>\n",
       "      <th>978475</th>\n",
       "      <td>1801296769</td>\n",
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       "      <td>WEST DEPTFORD</td>\n",
       "      <td>NJ</td>\n",
       "      <td>08051</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NJ</td>\n",
       "      <td>34015</td>\n",
       "      <td>Gloucester</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>978476 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi grd_yr             pri_spec            cty  st    zip  \\\n",
       "0       1215283908   1998      FAMILY MEDICINE          MIAMI  FL  33167   \n",
       "1       1053365692   1983    INTERNAL MEDICINE          MIAMI  FL  33167   \n",
       "2       1043627128   1984     GENERAL PRACTICE          MIAMI  FL  33167   \n",
       "3       1043627128   1984     GENERAL PRACTICE          MIAMI  FL  33167   \n",
       "4       1366581118   1979     GENERAL PRACTICE          MIAMI  FL  33167   \n",
       "...            ...    ...                  ...            ...  ..    ...   \n",
       "978471  1073730669   2005      FAMILY MEDICINE       NEGAUNEE  MI  49866   \n",
       "978472  1255322301   1973      FAMILY MEDICINE       NEGAUNEE  MI  49866   \n",
       "978473  1598711368   2004  PHYSICIAN ASSISTANT       NEGAUNEE  MI  49866   \n",
       "978474  1205327202   2017  PHYSICIAN ASSISTANT  WEST DEPTFORD  NJ  08051   \n",
       "978475  1801296769   2014   NURSE PRACTITIONER  WEST DEPTFORD  NJ  08051   \n",
       "\n",
       "       ind_assgn grp_assgn state_id GeoFips county_name  \n",
       "0              Y         M       FL   12086  Miami-Dade  \n",
       "1              Y         Y       FL   12086  Miami-Dade  \n",
       "2              Y         Y       FL   12086  Miami-Dade  \n",
       "3              Y         Y       FL   12086  Miami-Dade  \n",
       "4              Y         Y       FL   12086  Miami-Dade  \n",
       "...          ...       ...      ...     ...         ...  \n",
       "978471         Y         Y       MI   26103   Marquette  \n",
       "978472         Y         Y       MI   26103   Marquette  \n",
       "978473         Y         Y       MI   26103   Marquette  \n",
       "978474         Y         Y       NJ   34015  Gloucester  \n",
       "978475         Y         Y       NJ   34015  Gloucester  \n",
       "\n",
       "[978476 rows x 11 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge physician data with zip data\n",
    "physicians_merge_df = physicians_filtered_df.merge(zip_data_df, left_on=\"zip\", right_on=\"zip\")\n",
    "physicians_merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "799850c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>978471</th>\n",
       "      <td>1073730669</td>\n",
       "      <td>2005</td>\n",
       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>NEGAUNEE</td>\n",
       "      <td>MI</td>\n",
       "      <td>49866</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
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       "      <th>978472</th>\n",
       "      <td>1255322301</td>\n",
       "      <td>1973</td>\n",
       "      <td>FAMILY MEDICINE</td>\n",
       "      <td>NEGAUNEE</td>\n",
       "      <td>MI</td>\n",
       "      <td>49866</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>MI</td>\n",
       "      <td>26103</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978473</th>\n",
       "      <td>1598711368</td>\n",
       "      <td>2004</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>NEGAUNEE</td>\n",
       "      <td>MI</td>\n",
       "      <td>49866</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>MI</td>\n",
       "      <td>26103</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978474</th>\n",
       "      <td>1205327202</td>\n",
       "      <td>2017</td>\n",
       "      <td>PHYSICIAN ASSISTANT</td>\n",
       "      <td>WEST DEPTFORD</td>\n",
       "      <td>NJ</td>\n",
       "      <td>08051</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NJ</td>\n",
       "      <td>34015</td>\n",
       "      <td>Gloucester</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978475</th>\n",
       "      <td>1801296769</td>\n",
       "      <td>2014</td>\n",
       "      <td>NURSE PRACTITIONER</td>\n",
       "      <td>WEST DEPTFORD</td>\n",
       "      <td>NJ</td>\n",
       "      <td>08051</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NJ</td>\n",
       "      <td>34015</td>\n",
       "      <td>Gloucester</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>978476 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi grd_yr             pri_spec            cty  st    zip  \\\n",
       "0       1215283908   1998      FAMILY MEDICINE          MIAMI  FL  33167   \n",
       "1       1053365692   1983    INTERNAL MEDICINE          MIAMI  FL  33167   \n",
       "2       1043627128   1984     GENERAL PRACTICE          MIAMI  FL  33167   \n",
       "3       1043627128   1984     GENERAL PRACTICE          MIAMI  FL  33167   \n",
       "4       1366581118   1979     GENERAL PRACTICE          MIAMI  FL  33167   \n",
       "...            ...    ...                  ...            ...  ..    ...   \n",
       "978471  1073730669   2005      FAMILY MEDICINE       NEGAUNEE  MI  49866   \n",
       "978472  1255322301   1973      FAMILY MEDICINE       NEGAUNEE  MI  49866   \n",
       "978473  1598711368   2004  PHYSICIAN ASSISTANT       NEGAUNEE  MI  49866   \n",
       "978474  1205327202   2017  PHYSICIAN ASSISTANT  WEST DEPTFORD  NJ  08051   \n",
       "978475  1801296769   2014   NURSE PRACTITIONER  WEST DEPTFORD  NJ  08051   \n",
       "\n",
       "       ind_assgn grp_assgn state_id GeoFips county_name  pcp_count  \n",
       "0              Y         M       FL   12086  Miami-Dade      13619  \n",
       "1              Y         Y       FL   12086  Miami-Dade      13619  \n",
       "2              Y         Y       FL   12086  Miami-Dade      13619  \n",
       "3              Y         Y       FL   12086  Miami-Dade      13619  \n",
       "4              Y         Y       FL   12086  Miami-Dade      13619  \n",
       "...          ...       ...      ...     ...         ...        ...  \n",
       "978471         Y         Y       MI   26103   Marquette        313  \n",
       "978472         Y         Y       MI   26103   Marquette        313  \n",
       "978473         Y         Y       MI   26103   Marquette        313  \n",
       "978474         Y         Y       NJ   34015  Gloucester        542  \n",
       "978475         Y         Y       NJ   34015  Gloucester        542  \n",
       "\n",
       "[978476 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add column calculating total number of doctors per GeoFips using transform with count\n",
    "physicians_merge_df[\"pcp_count\"] = physicians_merge_df.groupby([\"GeoFips\"])[\"GeoFips\"].transform(\"count\")\n",
    "physicians_merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4de01725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>st</th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>pcp_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978471</th>\n",
       "      <td>MI</td>\n",
       "      <td>26103</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978472</th>\n",
       "      <td>MI</td>\n",
       "      <td>26103</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978473</th>\n",
       "      <td>MI</td>\n",
       "      <td>26103</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978474</th>\n",
       "      <td>NJ</td>\n",
       "      <td>34015</td>\n",
       "      <td>Gloucester</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978475</th>\n",
       "      <td>NJ</td>\n",
       "      <td>34015</td>\n",
       "      <td>Gloucester</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>978476 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        st GeoFips county_name  pcp_count\n",
       "0       FL   12086  Miami-Dade      13619\n",
       "1       FL   12086  Miami-Dade      13619\n",
       "2       FL   12086  Miami-Dade      13619\n",
       "3       FL   12086  Miami-Dade      13619\n",
       "4       FL   12086  Miami-Dade      13619\n",
       "...     ..     ...         ...        ...\n",
       "978471  MI   26103   Marquette        313\n",
       "978472  MI   26103   Marquette        313\n",
       "978473  MI   26103   Marquette        313\n",
       "978474  NJ   34015  Gloucester        542\n",
       "978475  NJ   34015  Gloucester        542\n",
       "\n",
       "[978476 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop irrelevant columns\n",
    "physicians_merge_df.drop(columns=[\"npi\", \"grd_yr\", \"pri_spec\", \"cty\", \"zip\", \n",
    "                                  \"ind_assgn\", \"grp_assgn\", \"state_id\"], inplace=True)\n",
    "physicians_merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d9c5cc10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>st</th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>pcp_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PR</td>\n",
       "      <td>72027</td>\n",
       "      <td>Camuy</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CA</td>\n",
       "      <td>06025</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>OK</td>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>2914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>OH</td>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978090</th>\n",
       "      <td>CA</td>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978127</th>\n",
       "      <td>TX</td>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978219</th>\n",
       "      <td>NM</td>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978335</th>\n",
       "      <td>MO</td>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978470</th>\n",
       "      <td>NE</td>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3157 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        st GeoFips  county_name  pcp_count\n",
       "0       FL   12086   Miami-Dade      13619\n",
       "12      PR   72027        Camuy         20\n",
       "32      CA   06025     Imperial        236\n",
       "101     OK   40109     Oklahoma       2914\n",
       "108     OH   39115       Morgan         19\n",
       "...     ..     ...          ...        ...\n",
       "978090  CA   06051         Mono         30\n",
       "978127  TX   48169        Garza          2\n",
       "978219  NM   35021      Harding          1\n",
       "978335  MO   29133  Mississippi         14\n",
       "978470  NE   31167      Stanton          1\n",
       "\n",
       "[3157 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop duplicates\n",
    "physicians_merge_df = physicians_merge_df.drop_duplicates()\n",
    "physicians_merge_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5221f6",
   "metadata": {},
   "source": [
    "### Import population data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "09b40a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Link as reference\n",
    "# https://covid19.census.gov/datasets/average-household-size-and-population-density-county/explore?location=34.503894%2C0.315550%2C3.67&showTable=true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "89ca794d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set dtypes before reading in csv\n",
    "population_dtypes = {\"GEOID\": str}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "85876de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FID</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>NAME</th>\n",
       "      <th>State</th>\n",
       "      <th>B25010_001E</th>\n",
       "      <th>B25010_001M</th>\n",
       "      <th>B25010_002E</th>\n",
       "      <th>...</th>\n",
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       "      <th>B01001_001M</th>\n",
       "      <th>B01001_calc_PopDensity</th>\n",
       "      <th>created_user</th>\n",
       "      <th>created_date</th>\n",
       "      <th>last_edited_user</th>\n",
       "      <th>last_edited_date</th>\n",
       "      <th>B01001_calc_PopDensityM</th>\n",
       "      <th>SHAPE_Length</th>\n",
       "      <th>SHAPE_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>161526</td>\n",
       "      <td>01001</td>\n",
       "      <td>1539602123</td>\n",
       "      <td>25706961</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2.59</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.59</td>\n",
       "      <td>...</td>\n",
       "      <td>55200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.853419</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.066037</td>\n",
       "      <td>0.150256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>161527</td>\n",
       "      <td>01003</td>\n",
       "      <td>4117546676</td>\n",
       "      <td>1133055836</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.66</td>\n",
       "      <td>...</td>\n",
       "      <td>208107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.541504</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.483746</td>\n",
       "      <td>0.409904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>161528</td>\n",
       "      <td>01005</td>\n",
       "      <td>2292144655</td>\n",
       "      <td>50538698</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.07</td>\n",
       "      <td>2.44</td>\n",
       "      <td>...</td>\n",
       "      <td>25782</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.247981</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.695262</td>\n",
       "      <td>0.223270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>161529</td>\n",
       "      <td>01007</td>\n",
       "      <td>1612167481</td>\n",
       "      <td>9602089</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.14</td>\n",
       "      <td>3.05</td>\n",
       "      <td>...</td>\n",
       "      <td>22527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.973114</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.887514</td>\n",
       "      <td>0.156473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>161530</td>\n",
       "      <td>01009</td>\n",
       "      <td>1670103911</td>\n",
       "      <td>15015423</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>...</td>\n",
       "      <td>57645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.515816</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.423552</td>\n",
       "      <td>0.164405</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",
       "      <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",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3215</th>\n",
       "      <td>3216</td>\n",
       "      <td>1804553</td>\n",
       "      <td>72145</td>\n",
       "      <td>118777649</td>\n",
       "      <td>57795019</td>\n",
       "      <td>Vega Baja Municipio</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.93</td>\n",
       "      <td>...</td>\n",
       "      <td>53371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>449.335380</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.485650</td>\n",
       "      <td>0.010381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3216</th>\n",
       "      <td>3217</td>\n",
       "      <td>1804554</td>\n",
       "      <td>72147</td>\n",
       "      <td>131541395</td>\n",
       "      <td>552192819</td>\n",
       "      <td>Vieques Municipio</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.38</td>\n",
       "      <td>3.03</td>\n",
       "      <td>...</td>\n",
       "      <td>8771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.678630</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.675819</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3217</th>\n",
       "      <td>3218</td>\n",
       "      <td>1804555</td>\n",
       "      <td>72149</td>\n",
       "      <td>92298569</td>\n",
       "      <td>3622639</td>\n",
       "      <td>Villalba Municipio</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2.96</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.09</td>\n",
       "      <td>...</td>\n",
       "      <td>22993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>249.115455</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.431598</td>\n",
       "      <td>0.008189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3218</th>\n",
       "      <td>3219</td>\n",
       "      <td>1804556</td>\n",
       "      <td>72151</td>\n",
       "      <td>143005179</td>\n",
       "      <td>72592521</td>\n",
       "      <td>Yabucoa Municipio</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.90</td>\n",
       "      <td>...</td>\n",
       "      <td>34149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.795547</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.676628</td>\n",
       "      <td>0.012229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>3220</td>\n",
       "      <td>1804557</td>\n",
       "      <td>72153</td>\n",
       "      <td>175371916</td>\n",
       "      <td>1625259</td>\n",
       "      <td>Yauco Municipio</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.46</td>\n",
       "      <td>...</td>\n",
       "      <td>36439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>207.781273</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>esri_demographics</td>\n",
       "      <td>2020/04/01 20:53:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.717627</td>\n",
       "      <td>0.015087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3220 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FID  COUNTYNS  GEOID       ALAND      AWATER                 NAME  \\\n",
       "0        1    161526  01001  1539602123    25706961       Autauga County   \n",
       "1        2    161527  01003  4117546676  1133055836       Baldwin County   \n",
       "2        3    161528  01005  2292144655    50538698       Barbour County   \n",
       "3        4    161529  01007  1612167481     9602089          Bibb County   \n",
       "4        5    161530  01009  1670103911    15015423        Blount County   \n",
       "...    ...       ...    ...         ...         ...                  ...   \n",
       "3215  3216   1804553  72145   118777649    57795019  Vega Baja Municipio   \n",
       "3216  3217   1804554  72147   131541395   552192819    Vieques Municipio   \n",
       "3217  3218   1804555  72149    92298569     3622639   Villalba Municipio   \n",
       "3218  3219   1804556  72151   143005179    72592521    Yabucoa Municipio   \n",
       "3219  3220   1804557  72153   175371916     1625259      Yauco Municipio   \n",
       "\n",
       "            State  B25010_001E  B25010_001M  B25010_002E  ...  B01001_001E  \\\n",
       "0         Alabama         2.59         0.05         2.59  ...        55200   \n",
       "1         Alabama         2.61         0.04         2.66  ...       208107   \n",
       "2         Alabama         2.49         0.07         2.44  ...        25782   \n",
       "3         Alabama         2.99         0.14         3.05  ...        22527   \n",
       "4         Alabama         2.77         0.05         2.85  ...        57645   \n",
       "...           ...          ...          ...          ...  ...          ...   \n",
       "3215  Puerto Rico         2.93         0.09         2.93  ...        53371   \n",
       "3216  Puerto Rico         3.55         0.38         3.03  ...         8771   \n",
       "3217  Puerto Rico         2.96         0.10         3.09  ...        22993   \n",
       "3218  Puerto Rico         2.91         0.10         2.90  ...        34149   \n",
       "3219  Puerto Rico         3.35         0.12         3.46  ...        36439   \n",
       "\n",
       "      B01001_001M  B01001_calc_PopDensity       created_user  \\\n",
       "0             NaN               35.853419  esri_demographics   \n",
       "1             NaN               50.541504  esri_demographics   \n",
       "2             NaN               11.247981  esri_demographics   \n",
       "3             NaN               13.973114  esri_demographics   \n",
       "4             NaN               34.515816  esri_demographics   \n",
       "...           ...                     ...                ...   \n",
       "3215          NaN              449.335380  esri_demographics   \n",
       "3216          NaN               66.678630  esri_demographics   \n",
       "3217          NaN              249.115455  esri_demographics   \n",
       "3218          NaN              238.795547  esri_demographics   \n",
       "3219          NaN              207.781273  esri_demographics   \n",
       "\n",
       "             created_date   last_edited_user     last_edited_date  \\\n",
       "0     2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "1     2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "2     2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "3     2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "4     2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "...                   ...                ...                  ...   \n",
       "3215  2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "3216  2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "3217  2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "3218  2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "3219  2020/04/01 20:53:36  esri_demographics  2020/04/01 20:53:36   \n",
       "\n",
       "     B01001_calc_PopDensityM SHAPE_Length SHAPE_Area  \n",
       "0                        0.0     2.066037   0.150256  \n",
       "1                        0.0     4.483746   0.409904  \n",
       "2                        0.0     2.695262   0.223270  \n",
       "3                        0.0     1.887514   0.156473  \n",
       "4                        0.0     2.423552   0.164405  \n",
       "...                      ...          ...        ...  \n",
       "3215                     0.0     0.485650   0.010381  \n",
       "3216                     0.0     0.675819   0.012181  \n",
       "3217                     0.0     0.431598   0.008189  \n",
       "3218                     0.0     0.676628   0.012229  \n",
       "3219                     0.0     0.717627   0.015087  \n",
       "\n",
       "[3220 rows x 23 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in csv and create DataFrame\n",
    "population_original_df = pd.read_csv(\"Resources/population_census.csv\", dtype=population_dtypes)\n",
    "population_original_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2e28931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FID                          int64\n",
       "COUNTYNS                     int64\n",
       "GEOID                       object\n",
       "ALAND                        int64\n",
       "AWATER                       int64\n",
       "NAME                        object\n",
       "State                       object\n",
       "B25010_001E                float64\n",
       "B25010_001M                float64\n",
       "B25010_002E                float64\n",
       "B25010_002M                float64\n",
       "B25010_003E                float64\n",
       "B25010_003M                float64\n",
       "B01001_001E                  int64\n",
       "B01001_001M                float64\n",
       "B01001_calc_PopDensity     float64\n",
       "created_user                object\n",
       "created_date                object\n",
       "last_edited_user            object\n",
       "last_edited_date            object\n",
       "B01001_calc_PopDensityM    float64\n",
       "SHAPE_Length               float64\n",
       "SHAPE_Area                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes\n",
    "population_original_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f700cdde",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>GEOID</th>\n",
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       "      <th>B01001_calc_PopDensity</th>\n",
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       "  </thead>\n",
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       "      <td>53371</td>\n",
       "      <td>449.335380</td>\n",
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       "    <tr>\n",
       "      <th>3216</th>\n",
       "      <td>72147</td>\n",
       "      <td>8771</td>\n",
       "      <td>66.678630</td>\n",
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       "    <tr>\n",
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       "      <td>72149</td>\n",
       "      <td>22993</td>\n",
       "      <td>249.115455</td>\n",
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       "    <tr>\n",
       "      <th>3218</th>\n",
       "      <td>72151</td>\n",
       "      <td>34149</td>\n",
       "      <td>238.795547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>72153</td>\n",
       "      <td>36439</td>\n",
       "      <td>207.781273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3220 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      GEOID  B01001_001E  B01001_calc_PopDensity\n",
       "0     01001        55200               35.853419\n",
       "1     01003       208107               50.541504\n",
       "2     01005        25782               11.247981\n",
       "3     01007        22527               13.973114\n",
       "4     01009        57645               34.515816\n",
       "...     ...          ...                     ...\n",
       "3215  72145        53371              449.335380\n",
       "3216  72147         8771               66.678630\n",
       "3217  72149        22993              249.115455\n",
       "3218  72151        34149              238.795547\n",
       "3219  72153        36439              207.781273\n",
       "\n",
       "[3220 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop irrelevant columns\n",
    "population_original_df = population_original_df[[\"GEOID\", \"B01001_001E\", \"B01001_calc_PopDensity\"]]\n",
    "population_original_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "90420eac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Tony\\anaconda3\\envs\\mlenv\\lib\\site-packages\\pandas\\core\\frame.py:5047: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
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       "      <th>GeoFips</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
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       "  <tbody>\n",
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       "      <td>34149</td>\n",
       "      <td>238.795547</td>\n",
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       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>72153</td>\n",
       "      <td>36439</td>\n",
       "      <td>207.781273</td>\n",
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       "<p>3220 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips  population  pop_density\n",
       "0      01001       55200    35.853419\n",
       "1      01003      208107    50.541504\n",
       "2      01005       25782    11.247981\n",
       "3      01007       22527    13.973114\n",
       "4      01009       57645    34.515816\n",
       "...      ...         ...          ...\n",
       "3215   72145       53371   449.335380\n",
       "3216   72147        8771    66.678630\n",
       "3217   72149       22993   249.115455\n",
       "3218   72151       34149   238.795547\n",
       "3219   72153       36439   207.781273\n",
       "\n",
       "[3220 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns\n",
    "population_original_df.rename({'GEOID': 'GeoFips', 'B01001_001E': 'population', 'B01001_calc_PopDensity': 'pop_density'}, axis=1, inplace=True)\n",
    "population_original_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8bcced",
   "metadata": {},
   "source": [
    "### Import income data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b3aef35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Link as reference\n",
    "# https://apps.bea.gov/iTable/iTable.cfm?reqid=70&step=30&isuri=1&major_area=4&area=xx&year=2020&tableid=20&category=720&area_type=4&year_end=-1&classification=non-industry&state=xx&statistic=3&yearbegin=-1&unit_of_measure=levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d51317b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>GeoName</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01001</td>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>46814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01003</td>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>50953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01005</td>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>37850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01007</td>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>34300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01009</td>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>38808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>3154</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3155</th>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3156</th>\n",
       "      <td>Note. All dollar estimates are in thousands of...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3157</th>\n",
       "      <td>(NA) Not available.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3158</th>\n",
       "      <td>Last updated: November 16, 2021-- new statisti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3159 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                GeoFips      GeoName   2020\n",
       "0                                                 01001  Autauga, AL  46814\n",
       "1                                                 01003  Baldwin, AL  50953\n",
       "2                                                 01005  Barbour, AL  37850\n",
       "3                                                 01007     Bibb, AL  34300\n",
       "4                                                 01009   Blount, AL  38808\n",
       "...                                                 ...          ...    ...\n",
       "3154  * Shawano, WI and Menominee, WI are combined a...          NaN    NaN\n",
       "3155  Metropolitan Areas are defined (geographically...          NaN    NaN\n",
       "3156  Note. All dollar estimates are in thousands of...          NaN    NaN\n",
       "3157                                (NA) Not available.          NaN    NaN\n",
       "3158  Last updated: November 16, 2021-- new statisti...          NaN    NaN\n",
       "\n",
       "[3159 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOTE: Skip rows needed to remove header info.  Error occurs otherwise.\n",
    "# Create DataFrame for county and per capita personal income\n",
    "\n",
    "income_original_df = pd.read_csv(\"Resources/bea_income_2020.csv\", skiprows=4)\n",
    "income_original_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "691da570",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3139</th>\n",
       "      <td>56045</td>\n",
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       "    <tr>\n",
       "      <th>3140</th>\n",
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       "      <th>3141</th>\n",
       "      <td>2/ Per capita personal income was computed usi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3142</th>\n",
       "      <td>* Estimates for 1979 forward reflect Alaska Ce...</td>\n",
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       "    <tr>\n",
       "      <th>3143</th>\n",
       "      <td>* Estimates from 1988 forward separate Aleutia...</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3144</th>\n",
       "      <td>* Estimates for 1991 forward separate Denali B...</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3145</th>\n",
       "      <td>* Estimates from 1993 forward separate Skagway...</td>\n",
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       "    <tr>\n",
       "      <th>3146</th>\n",
       "      <td>* Wade Hampton Census Area was renamed Kusilva...</td>\n",
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       "    <tr>\n",
       "      <th>3147</th>\n",
       "      <td>* On January 2, 2019, two new county equivalen...</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3148</th>\n",
       "      <td>* La Paz County, AZ was separated from Yuma Co...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3149</th>\n",
       "      <td>* Broomfield County, CO, was created from part...</td>\n",
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       "    <tr>\n",
       "      <th>3150</th>\n",
       "      <td>* Kalawao County, Hawaii is combined with Maui...</td>\n",
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       "      <th>3151</th>\n",
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       "    <tr>\n",
       "      <th>3152</th>\n",
       "      <td>* Shannon County, SD was renamed to Oglala Lak...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3153</th>\n",
       "      <td>* Virginia combination areas consist of one or...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3154</th>\n",
       "      <td>* Shawano, WI and Menominee, WI are combined a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3155</th>\n",
       "      <td>Metropolitan Areas are defined (geographically...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Note. All dollar estimates are in thousands of...</td>\n",
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       "    <tr>\n",
       "      <th>3158</th>\n",
       "      <td>Last updated: November 16, 2021-- new statisti...</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "                                                GeoFips     GeoName   2020\n",
       "3139                                              56045  Weston, WY  47599\n",
       "3140                                Legend / Footnotes:         NaN    NaN\n",
       "3141  2/ Per capita personal income was computed usi...         NaN    NaN\n",
       "3142  * Estimates for 1979 forward reflect Alaska Ce...         NaN    NaN\n",
       "3143  * Estimates from 1988 forward separate Aleutia...         NaN    NaN\n",
       "3144  * Estimates for 1991 forward separate Denali B...         NaN    NaN\n",
       "3145  * Estimates from 1993 forward separate Skagway...         NaN    NaN\n",
       "3146  * Wade Hampton Census Area was renamed Kusilva...         NaN    NaN\n",
       "3147  * On January 2, 2019, two new county equivalen...         NaN    NaN\n",
       "3148  * La Paz County, AZ was separated from Yuma Co...         NaN    NaN\n",
       "3149  * Broomfield County, CO, was created from part...         NaN    NaN\n",
       "3150  * Kalawao County, Hawaii is combined with Maui...         NaN    NaN\n",
       "3151  * Cibola, NM was separated from Valencia in Ju...         NaN    NaN\n",
       "3152  * Shannon County, SD was renamed to Oglala Lak...         NaN    NaN\n",
       "3153  * Virginia combination areas consist of one or...         NaN    NaN\n",
       "3154  * Shawano, WI and Menominee, WI are combined a...         NaN    NaN\n",
       "3155  Metropolitan Areas are defined (geographically...         NaN    NaN\n",
       "3156  Note. All dollar estimates are in thousands of...         NaN    NaN\n",
       "3157                                (NA) Not available.         NaN    NaN\n",
       "3158  Last updated: November 16, 2021-- new statisti...         NaN    NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check footer info\n",
    "income_original_df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1461ad7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "3154     True\n",
       "3155     True\n",
       "3156     True\n",
       "3157     True\n",
       "3158     True\n",
       "Length: 3159, dtype: bool"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use NAN in last two columns to find where to remove footer rows\n",
    "both_columns_null_idx = income_original_df[[\"GeoName\", \"2020\"]].isna().all(axis=\"columns\")\n",
    "both_columns_null_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2eb2384f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3146</th>\n",
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       "      <th>3151</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3152</th>\n",
       "      <td>* Shannon County, SD was renamed to Oglala Lak...</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3153</th>\n",
       "      <td>* Virginia combination areas consist of one or...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3154</th>\n",
       "      <td>* Shawano, WI and Menominee, WI are combined a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3155</th>\n",
       "      <td>Metropolitan Areas are defined (geographically...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3156</th>\n",
       "      <td>Note. All dollar estimates are in thousands of...</td>\n",
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       "    <tr>\n",
       "      <th>3157</th>\n",
       "      <td>(NA) Not available.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3158</th>\n",
       "      <td>Last updated: November 16, 2021-- new statisti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                                                GeoFips GeoName 2020\n",
       "3140                                Legend / Footnotes:     NaN  NaN\n",
       "3141  2/ Per capita personal income was computed usi...     NaN  NaN\n",
       "3142  * Estimates for 1979 forward reflect Alaska Ce...     NaN  NaN\n",
       "3143  * Estimates from 1988 forward separate Aleutia...     NaN  NaN\n",
       "3144  * Estimates for 1991 forward separate Denali B...     NaN  NaN\n",
       "3145  * Estimates from 1993 forward separate Skagway...     NaN  NaN\n",
       "3146  * Wade Hampton Census Area was renamed Kusilva...     NaN  NaN\n",
       "3147  * On January 2, 2019, two new county equivalen...     NaN  NaN\n",
       "3148  * La Paz County, AZ was separated from Yuma Co...     NaN  NaN\n",
       "3149  * Broomfield County, CO, was created from part...     NaN  NaN\n",
       "3150  * Kalawao County, Hawaii is combined with Maui...     NaN  NaN\n",
       "3151  * Cibola, NM was separated from Valencia in Ju...     NaN  NaN\n",
       "3152  * Shannon County, SD was renamed to Oglala Lak...     NaN  NaN\n",
       "3153  * Virginia combination areas consist of one or...     NaN  NaN\n",
       "3154  * Shawano, WI and Menominee, WI are combined a...     NaN  NaN\n",
       "3155  Metropolitan Areas are defined (geographically...     NaN  NaN\n",
       "3156  Note. All dollar estimates are in thousands of...     NaN  NaN\n",
       "3157                                (NA) Not available.     NaN  NaN\n",
       "3158  Last updated: November 16, 2021-- new statisti...     NaN  NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use NAN in last two columns to find where to remove footer rows\n",
    "income_original_df.loc[both_columns_null_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d0a8198d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>GeoName</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01001</td>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>46814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01003</td>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>50953</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01005</td>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>37850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01007</td>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>34300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01009</td>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>38808</td>\n",
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       "      <td>56037</td>\n",
       "      <td>Sweetwater, WY</td>\n",
       "      <td>54000</td>\n",
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       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>56039</td>\n",
       "      <td>Teton, WY</td>\n",
       "      <td>220645</td>\n",
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       "    <tr>\n",
       "      <th>3137</th>\n",
       "      <td>56041</td>\n",
       "      <td>Uinta, WY</td>\n",
       "      <td>42854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3138</th>\n",
       "      <td>56043</td>\n",
       "      <td>Washakie, WY</td>\n",
       "      <td>54361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston, WY</td>\n",
       "      <td>47599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3140 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips         GeoName    2020\n",
       "0      01001     Autauga, AL   46814\n",
       "1      01003     Baldwin, AL   50953\n",
       "2      01005     Barbour, AL   37850\n",
       "3      01007        Bibb, AL   34300\n",
       "4      01009      Blount, AL   38808\n",
       "...      ...             ...     ...\n",
       "3135   56037  Sweetwater, WY   54000\n",
       "3136   56039       Teton, WY  220645\n",
       "3137   56041       Uinta, WY   42854\n",
       "3138   56043    Washakie, WY   54361\n",
       "3139   56045      Weston, WY   47599\n",
       "\n",
       "[3140 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop footer rows\n",
    "income_original_df.drop(income_original_df.tail(19).index, inplace = True)\n",
    "income_original_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "768ef1a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>GeoName</th>\n",
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       "    <tr>\n",
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       "      <td>56041</td>\n",
       "      <td>Uinta, WY</td>\n",
       "      <td>42854</td>\n",
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       "    <tr>\n",
       "      <th>3138</th>\n",
       "      <td>56043</td>\n",
       "      <td>Washakie, WY</td>\n",
       "      <td>54361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston, WY</td>\n",
       "      <td>47599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3140 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips         GeoName per_capita_income\n",
       "0      01001     Autauga, AL             46814\n",
       "1      01003     Baldwin, AL             50953\n",
       "2      01005     Barbour, AL             37850\n",
       "3      01007        Bibb, AL             34300\n",
       "4      01009      Blount, AL             38808\n",
       "...      ...             ...               ...\n",
       "3135   56037  Sweetwater, WY             54000\n",
       "3136   56039       Teton, WY            220645\n",
       "3137   56041       Uinta, WY             42854\n",
       "3138   56043    Washakie, WY             54361\n",
       "3139   56045      Weston, WY             47599\n",
       "\n",
       "[3140 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columnns\n",
    "income_original_df = income_original_df.rename(columns={\"2020\": \"per_capita_income\"})\n",
    "income_original_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7e2aec15",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>220645</td>\n",
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       "      <th>3137</th>\n",
       "      <td>56041</td>\n",
       "      <td>42854</td>\n",
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       "      <th>3138</th>\n",
       "      <td>56043</td>\n",
       "      <td>54361</td>\n",
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       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>56045</td>\n",
       "      <td>47599</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3140 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips per_capita_income\n",
       "0      01001             46814\n",
       "1      01003             50953\n",
       "2      01005             37850\n",
       "3      01007             34300\n",
       "4      01009             38808\n",
       "...      ...               ...\n",
       "3135   56037             54000\n",
       "3136   56039            220645\n",
       "3137   56041             42854\n",
       "3138   56043             54361\n",
       "3139   56045             47599\n",
       "\n",
       "[3140 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop GeoName column\n",
    "income_original_df.drop(columns=[\"GeoName\"], inplace=True)\n",
    "income_original_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b0f69ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoFips              object\n",
       "per_capita_income    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes\n",
    "income_original_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81a3c2b",
   "metadata": {},
   "source": [
    "### Merge all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4fc0c1a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>st</th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>per_capita_income</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>FL</td>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "      <td>57213</td>\n",
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       "      <th>1</th>\n",
       "      <td>CA</td>\n",
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       "      <td>236</td>\n",
       "      <td>44500</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OK</td>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>2914</td>\n",
       "      <td>56971</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
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       "      <td>Morgan</td>\n",
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       "      <td>38583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CO</td>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>1459</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>CA</td>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>30</td>\n",
       "      <td>54586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>TX</td>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>2</td>\n",
       "      <td>31223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>NM</td>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>1</td>\n",
       "      <td>50723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>MO</td>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>14</td>\n",
       "      <td>37483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>NE</td>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>1</td>\n",
       "      <td>52621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3030 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      st GeoFips  county_name  pcp_count per_capita_income\n",
       "0     FL   12086   Miami-Dade      13619             57213\n",
       "1     CA   06025     Imperial        236             44500\n",
       "2     OK   40109     Oklahoma       2914             56971\n",
       "3     OH   39115       Morgan         19             38583\n",
       "4     CO   08059    Jefferson       1459             68829\n",
       "...   ..     ...          ...        ...               ...\n",
       "3025  CA   06051         Mono         30             54586\n",
       "3026  TX   48169        Garza          2             31223\n",
       "3027  NM   35021      Harding          1             50723\n",
       "3028  MO   29133  Mississippi         14             37483\n",
       "3029  NE   31167      Stanton          1             52621\n",
       "\n",
       "[3030 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge physician data to income data\n",
    "phys_income_merged_df = physicians_merge_df.merge(income_original_df, left_on=\"GeoFips\", right_on=\"GeoFips\")\n",
    "phys_income_merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fbec277d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>pcp_count</th>\n",
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       "      <th>0</th>\n",
       "      <td>FL</td>\n",
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       "      <td>2715516</td>\n",
       "      <td>552.187071</td>\n",
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       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>06025</td>\n",
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       "      <td>180216</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2914</td>\n",
       "      <td>56971</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>19</td>\n",
       "      <td>38583</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CO</td>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>1459</td>\n",
       "      <td>68829</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>CA</td>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>30</td>\n",
       "      <td>54586</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>TX</td>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>2</td>\n",
       "      <td>31223</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>NM</td>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>1</td>\n",
       "      <td>50723</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>MO</td>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>14</td>\n",
       "      <td>37483</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>NE</td>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>1</td>\n",
       "      <td>52621</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3030 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      st GeoFips  county_name  pcp_count per_capita_income  population  \\\n",
       "0     FL   12086   Miami-Dade      13619             57213     2715516   \n",
       "1     CA   06025     Imperial        236             44500      180216   \n",
       "2     OK   40109     Oklahoma       2914             56971      782051   \n",
       "3     OH   39115       Morgan         19             38583       14702   \n",
       "4     CO   08059    Jefferson       1459             68829      570427   \n",
       "...   ..     ...          ...        ...               ...         ...   \n",
       "3025  CA   06051         Mono         30             54586       14174   \n",
       "3026  TX   48169        Garza          2             31223        6288   \n",
       "3027  NM   35021      Harding          1             50723         459   \n",
       "3028  MO   29133  Mississippi         14             37483       13748   \n",
       "3029  NE   31167      Stanton          1             52621        5992   \n",
       "\n",
       "      pop_density  \n",
       "0      552.187071  \n",
       "1       16.659991  \n",
       "2      425.989528  \n",
       "3       13.631412  \n",
       "4      288.167016  \n",
       "...           ...  \n",
       "3025     1.794896  \n",
       "3026     2.717454  \n",
       "3027     0.083379  \n",
       "3028    12.896866  \n",
       "3029     5.410380  \n",
       "\n",
       "[3030 rows x 7 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge physician / income data to population data\n",
    "merged_initial_df = phys_income_merged_df.merge(population_original_df, left_on=\"GeoFips\", right_on=\"GeoFips\")\n",
    "merged_initial_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3a3d1901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "st                    object\n",
       "GeoFips               object\n",
       "county_name           object\n",
       "pcp_count              int64\n",
       "per_capita_income     object\n",
       "population             int64\n",
       "pop_density          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes\n",
    "merged_initial_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cc3e86f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>st</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>per_capita_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>FL</td>\n",
       "      <td>13619</td>\n",
       "      <td>2715516</td>\n",
       "      <td>552.187071</td>\n",
       "      <td>57213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06025</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>CA</td>\n",
       "      <td>236</td>\n",
       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "      <td>56971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>OH</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>CO</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
       "      <td>68829</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>CA</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "      <td>54586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>NM</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MO</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>NE</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3030 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips  county_name  st  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade  FL      13619     2715516   552.187071   \n",
       "1      06025     Imperial  CA        236      180216    16.659991   \n",
       "2      40109     Oklahoma  OK       2914      782051   425.989528   \n",
       "3      39115       Morgan  OH         19       14702    13.631412   \n",
       "4      08059    Jefferson  CO       1459      570427   288.167016   \n",
       "...      ...          ...  ..        ...         ...          ...   \n",
       "3025   06051         Mono  CA         30       14174     1.794896   \n",
       "3026   48169        Garza  TX          2        6288     2.717454   \n",
       "3027   35021      Harding  NM          1         459     0.083379   \n",
       "3028   29133  Mississippi  MO         14       13748    12.896866   \n",
       "3029   31167      Stanton  NE          1        5992     5.410380   \n",
       "\n",
       "     per_capita_income  \n",
       "0                57213  \n",
       "1                44500  \n",
       "2                56971  \n",
       "3                38583  \n",
       "4                68829  \n",
       "...                ...  \n",
       "3025             54586  \n",
       "3026             31223  \n",
       "3027             50723  \n",
       "3028             37483  \n",
       "3029             52621  \n",
       "\n",
       "[3030 rows x 7 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re order columns\n",
    "merged_initial_df = merged_initial_df[[\"GeoFips\", \"county_name\", \"st\", \"pcp_count\", \"population\", \"pop_density\", \"per_capita_income\"]]\n",
    "merged_initial_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "de66dd83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>st</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>per_capita_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>NM</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2813</th>\n",
       "      <td>02282</td>\n",
       "      <td>Yakutat</td>\n",
       "      <td>AK</td>\n",
       "      <td>5</td>\n",
       "      <td>689</td>\n",
       "      <td>0.034896</td>\n",
       "      <td>66119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>31091</td>\n",
       "      <td>Hooker</td>\n",
       "      <td>NE</td>\n",
       "      <td>2</td>\n",
       "      <td>691</td>\n",
       "      <td>0.369955</td>\n",
       "      <td>54516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>31075</td>\n",
       "      <td>Grant</td>\n",
       "      <td>NE</td>\n",
       "      <td>1</td>\n",
       "      <td>718</td>\n",
       "      <td>0.356844</td>\n",
       "      <td>54922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2673</th>\n",
       "      <td>46075</td>\n",
       "      <td>Jones</td>\n",
       "      <td>SD</td>\n",
       "      <td>20</td>\n",
       "      <td>735</td>\n",
       "      <td>0.292661</td>\n",
       "      <td>59264</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>48201</td>\n",
       "      <td>Harris</td>\n",
       "      <td>TX</td>\n",
       "      <td>12445</td>\n",
       "      <td>4602523</td>\n",
       "      <td>1042.130651</td>\n",
       "      <td>60183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook</td>\n",
       "      <td>IL</td>\n",
       "      <td>16124</td>\n",
       "      <td>5223719</td>\n",
       "      <td>2134.420727</td>\n",
       "      <td>69935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>06037</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>FL</td>\n",
       "      <td>18526</td>\n",
       "      <td>10098052</td>\n",
       "      <td>960.742778</td>\n",
       "      <td>68272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>06037</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>18526</td>\n",
       "      <td>10098052</td>\n",
       "      <td>960.742778</td>\n",
       "      <td>68272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>06037</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>HI</td>\n",
       "      <td>18526</td>\n",
       "      <td>10098052</td>\n",
       "      <td>960.742778</td>\n",
       "      <td>68272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3030 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips  county_name  st  pcp_count  population  pop_density  \\\n",
       "3027   35021      Harding  NM          1         459     0.083379   \n",
       "2813   02282      Yakutat  AK          5         689     0.034896   \n",
       "2957   31091       Hooker  NE          2         691     0.369955   \n",
       "3022   31075        Grant  NE          1         718     0.356844   \n",
       "2673   46075        Jones  SD         20         735     0.292661   \n",
       "...      ...          ...  ..        ...         ...          ...   \n",
       "25     48201       Harris  TX      12445     4602523  1042.130651   \n",
       "74     17031         Cook  IL      16124     5223719  2134.420727   \n",
       "10     06037  Los Angeles  FL      18526    10098052   960.742778   \n",
       "9      06037  Los Angeles  CA      18526    10098052   960.742778   \n",
       "11     06037  Los Angeles  HI      18526    10098052   960.742778   \n",
       "\n",
       "     per_capita_income  \n",
       "3027             50723  \n",
       "2813             66119  \n",
       "2957             54516  \n",
       "3022             54922  \n",
       "2673             59264  \n",
       "...                ...  \n",
       "25               60183  \n",
       "74               69935  \n",
       "10               68272  \n",
       "9                68272  \n",
       "11               68272  \n",
       "\n",
       "[3030 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for populations with \"(NA)\"\n",
    "merged_initial_df.sort_values([\"population\"], ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "db2b1170",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>st</th>\n",
       "      <th>pcp_count</th>\n",
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       "      <th>0</th>\n",
       "      <td>12086</td>\n",
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       "      <td>552.187071</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06025</td>\n",
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       "      <td>CA</td>\n",
       "      <td>236</td>\n",
       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "      <td>56971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>OH</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>CO</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
       "      <td>68829</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>CA</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "      <td>54586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>NM</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MO</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>NE</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3030 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips  county_name  st  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade  FL      13619     2715516   552.187071   \n",
       "1      06025     Imperial  CA        236      180216    16.659991   \n",
       "2      40109     Oklahoma  OK       2914      782051   425.989528   \n",
       "3      39115       Morgan  OH         19       14702    13.631412   \n",
       "4      08059    Jefferson  CO       1459      570427   288.167016   \n",
       "...      ...          ...  ..        ...         ...          ...   \n",
       "3025   06051         Mono  CA         30       14174     1.794896   \n",
       "3026   48169        Garza  TX          2        6288     2.717454   \n",
       "3027   35021      Harding  NM          1         459     0.083379   \n",
       "3028   29133  Mississippi  MO         14       13748    12.896866   \n",
       "3029   31167      Stanton  NE          1        5992     5.410380   \n",
       "\n",
       "     per_capita_income  \n",
       "0                57213  \n",
       "1                44500  \n",
       "2                56971  \n",
       "3                38583  \n",
       "4                68829  \n",
       "...                ...  \n",
       "3025             54586  \n",
       "3026             31223  \n",
       "3027             50723  \n",
       "3028             37483  \n",
       "3029             52621  \n",
       "\n",
       "[3030 rows x 7 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop population values equal to \"(NA)\" \n",
    "merged_initial_df.drop(merged_initial_df.index[merged_initial_df['population'] == \"(NA)\"], inplace = True)\n",
    "merged_initial_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "66dbd487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>st</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>per_capita_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>02261</td>\n",
       "      <td>Valdez-Cordova</td>\n",
       "      <td>AK</td>\n",
       "      <td>29</td>\n",
       "      <td>9301</td>\n",
       "      <td>0.104937</td>\n",
       "      <td>(NA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2675</th>\n",
       "      <td>46119</td>\n",
       "      <td>Sully</td>\n",
       "      <td>SD</td>\n",
       "      <td>2</td>\n",
       "      <td>1331</td>\n",
       "      <td>0.510470</td>\n",
       "      <td>100301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>51013</td>\n",
       "      <td>Arlington</td>\n",
       "      <td>VA</td>\n",
       "      <td>361</td>\n",
       "      <td>231803</td>\n",
       "      <td>3442.635855</td>\n",
       "      <td>100823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>34027</td>\n",
       "      <td>Morris</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1571</td>\n",
       "      <td>494383</td>\n",
       "      <td>414.453485</td>\n",
       "      <td>102227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>12021</td>\n",
       "      <td>Collier</td>\n",
       "      <td>FL</td>\n",
       "      <td>1164</td>\n",
       "      <td>363922</td>\n",
       "      <td>70.385228</td>\n",
       "      <td>103865</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>34019</td>\n",
       "      <td>Hunterdon</td>\n",
       "      <td>NJ</td>\n",
       "      <td>305</td>\n",
       "      <td>125051</td>\n",
       "      <td>112.855259</td>\n",
       "      <td>95088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>13121</td>\n",
       "      <td>Fulton</td>\n",
       "      <td>GA</td>\n",
       "      <td>4128</td>\n",
       "      <td>1021902</td>\n",
       "      <td>749.058522</td>\n",
       "      <td>95683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau</td>\n",
       "      <td>NY</td>\n",
       "      <td>9091</td>\n",
       "      <td>1356564</td>\n",
       "      <td>1840.443984</td>\n",
       "      <td>96253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>53033</td>\n",
       "      <td>King</td>\n",
       "      <td>WA</td>\n",
       "      <td>7559</td>\n",
       "      <td>2163257</td>\n",
       "      <td>394.855733</td>\n",
       "      <td>96647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>25021</td>\n",
       "      <td>Norfolk</td>\n",
       "      <td>MA</td>\n",
       "      <td>2960</td>\n",
       "      <td>698249</td>\n",
       "      <td>680.629714</td>\n",
       "      <td>98019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3030 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips     county_name  st  pcp_count  population  pop_density  \\\n",
       "1697   02261  Valdez-Cordova  AK         29        9301     0.104937   \n",
       "2675   46119           Sully  SD          2        1331     0.510470   \n",
       "873    51013       Arlington  VA        361      231803  3442.635855   \n",
       "326    34027          Morris  NJ       1571      494383   414.453485   \n",
       "230    12021         Collier  FL       1164      363922    70.385228   \n",
       "...      ...             ...  ..        ...         ...          ...   \n",
       "750    34019       Hunterdon  NJ        305      125051   112.855259   \n",
       "269    13121          Fulton  GA       4128     1021902   749.058522   \n",
       "31     36059          Nassau  NY       9091     1356564  1840.443984   \n",
       "153    53033            King  WA       7559     2163257   394.855733   \n",
       "156    25021         Norfolk  MA       2960      698249   680.629714   \n",
       "\n",
       "     per_capita_income  \n",
       "1697              (NA)  \n",
       "2675            100301  \n",
       "873             100823  \n",
       "326             102227  \n",
       "230             103865  \n",
       "...                ...  \n",
       "750              95088  \n",
       "269              95683  \n",
       "31               96253  \n",
       "153              96647  \n",
       "156              98019  \n",
       "\n",
       "[3030 rows x 7 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for per_capita_income with \"(NA)\"\n",
    "merged_initial_df.sort_values([\"per_capita_income\"], ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3a18bd6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>st</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>per_capita_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>FL</td>\n",
       "      <td>13619</td>\n",
       "      <td>2715516</td>\n",
       "      <td>552.187071</td>\n",
       "      <td>57213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06025</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>CA</td>\n",
       "      <td>236</td>\n",
       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "      <td>56971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>OH</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>CO</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
       "      <td>68829</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>CA</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "      <td>54586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>NM</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MO</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>NE</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3029 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips  county_name  st  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade  FL      13619     2715516   552.187071   \n",
       "1      06025     Imperial  CA        236      180216    16.659991   \n",
       "2      40109     Oklahoma  OK       2914      782051   425.989528   \n",
       "3      39115       Morgan  OH         19       14702    13.631412   \n",
       "4      08059    Jefferson  CO       1459      570427   288.167016   \n",
       "...      ...          ...  ..        ...         ...          ...   \n",
       "3025   06051         Mono  CA         30       14174     1.794896   \n",
       "3026   48169        Garza  TX          2        6288     2.717454   \n",
       "3027   35021      Harding  NM          1         459     0.083379   \n",
       "3028   29133  Mississippi  MO         14       13748    12.896866   \n",
       "3029   31167      Stanton  NE          1        5992     5.410380   \n",
       "\n",
       "     per_capita_income  \n",
       "0                57213  \n",
       "1                44500  \n",
       "2                56971  \n",
       "3                38583  \n",
       "4                68829  \n",
       "...                ...  \n",
       "3025             54586  \n",
       "3026             31223  \n",
       "3027             50723  \n",
       "3028             37483  \n",
       "3029             52621  \n",
       "\n",
       "[3029 rows x 7 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop per_capita_income values equal to \"(NA)\" \n",
    "merged_initial_df.drop(merged_initial_df.index[merged_initial_df['per_capita_income'] == \"(NA)\"], inplace = True)\n",
    "merged_initial_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "be03ad68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoFips               object\n",
       "county_name           object\n",
       "st                    object\n",
       "pcp_count              int64\n",
       "population             int64\n",
       "pop_density          float64\n",
       "per_capita_income     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes\n",
    "merged_initial_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f146a078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoFips               object\n",
       "county_name           object\n",
       "st                    object\n",
       "pcp_count              int64\n",
       "population             int64\n",
       "pop_density          float64\n",
       "per_capita_income      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change per_capita_income to numeric\n",
    "merged_initial_df[\"per_capita_income\"] = pd.to_numeric(merged_initial_df[\"per_capita_income\"])\n",
    "merged_initial_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "49372308",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>st</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
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       "      <td>552.187071</td>\n",
       "      <td>57213</td>\n",
       "      <td>5.015253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06025</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>CA</td>\n",
       "      <td>236</td>\n",
       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
       "      <td>1.309540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "      <td>56971</td>\n",
       "      <td>3.726100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>OH</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "      <td>1.292341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>CO</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
       "      <td>68829</td>\n",
       "      <td>2.557733</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>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>CA</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "      <td>54586</td>\n",
       "      <td>2.116551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "      <td>0.318066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>NM</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "      <td>2.178649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MO</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "      <td>1.018330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>NE</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "      <td>0.166889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3029 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips  county_name  st  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade  FL      13619     2715516   552.187071   \n",
       "1      06025     Imperial  CA        236      180216    16.659991   \n",
       "2      40109     Oklahoma  OK       2914      782051   425.989528   \n",
       "3      39115       Morgan  OH         19       14702    13.631412   \n",
       "4      08059    Jefferson  CO       1459      570427   288.167016   \n",
       "...      ...          ...  ..        ...         ...          ...   \n",
       "3025   06051         Mono  CA         30       14174     1.794896   \n",
       "3026   48169        Garza  TX          2        6288     2.717454   \n",
       "3027   35021      Harding  NM          1         459     0.083379   \n",
       "3028   29133  Mississippi  MO         14       13748    12.896866   \n",
       "3029   31167      Stanton  NE          1        5992     5.410380   \n",
       "\n",
       "      per_capita_income  pcp_per_capita  \n",
       "0                 57213        5.015253  \n",
       "1                 44500        1.309540  \n",
       "2                 56971        3.726100  \n",
       "3                 38583        1.292341  \n",
       "4                 68829        2.557733  \n",
       "...                 ...             ...  \n",
       "3025              54586        2.116551  \n",
       "3026              31223        0.318066  \n",
       "3027              50723        2.178649  \n",
       "3028              37483        1.018330  \n",
       "3029              52621        0.166889  \n",
       "\n",
       "[3029 rows x 8 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add column calculating number of doctors divided by population\n",
    "merged_initial_df[\"pcp_per_capita\"] = merged_initial_df[\"pcp_count\"]/merged_initial_df[\"population\"]*1000\n",
    "merged_initial_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dc3603ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns\n",
    "merged_initial_df.rename({'county_name': 'county', 'st': 'state'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "344ea9e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoFips               object\n",
       "county                object\n",
       "state                 object\n",
       "pcp_count              int64\n",
       "population             int64\n",
       "pop_density          float64\n",
       "per_capita_income      int64\n",
       "pcp_per_capita       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes\n",
    "merged_initial_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b7e9334e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       552.187071\n",
       "1        16.659991\n",
       "2       425.989528\n",
       "3        13.631412\n",
       "4       288.167016\n",
       "           ...    \n",
       "3025      1.794896\n",
       "3026      2.717454\n",
       "3027      0.083379\n",
       "3028     12.896866\n",
       "3029      5.410380\n",
       "Name: pop_density, Length: 3029, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create series of density data\n",
    "density_s = merged_initial_df.pop_density\n",
    "density_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5e0d7279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3029.000000\n",
       "mean       102.016521\n",
       "std        703.920216\n",
       "min          0.014362\n",
       "25%          7.077235\n",
       "50%         17.732294\n",
       "75%         45.403208\n",
       "max      27819.804801\n",
       "Name: pop_density, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the descriptive statistics for density\n",
    "merged_initial_df.pop_density.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e5d089ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       (45.5, 27900.0]\n",
       "1           (7.1, 17.8]\n",
       "2       (45.5, 27900.0]\n",
       "3           (7.1, 17.8]\n",
       "4       (45.5, 27900.0]\n",
       "             ...       \n",
       "3025         (0.0, 7.1]\n",
       "3026         (0.0, 7.1]\n",
       "3027         (0.0, 7.1]\n",
       "3028        (7.1, 17.8]\n",
       "3029         (0.0, 7.1]\n",
       "Name: pop_density, Length: 3029, dtype: category\n",
       "Categories (4, interval[float64, right]): [(0.0, 7.1] < (7.1, 17.8] < (17.8, 45.5] < (45.5, 27900.0]]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cut the density into the ranges.\n",
    "density_bins = [0, 7.1, 17.8, 45.5, 27900]\n",
    "pd.cut(density_s, density_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "eef44f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop_density\n",
       "(0.0, 7.1]         760\n",
       "(7.1, 17.8]        759\n",
       "(17.8, 45.5]       755\n",
       "(45.5, 27900.0]    755\n",
       "Name: pop_density, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the distribution of bins\n",
    "density_s.groupby(pd.cut(density_s, density_bins)).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1c186004",
   "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>GeoFips</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>per_capita_income</th>\n",
       "      <th>pcp_per_capita</th>\n",
       "      <th>pop_density_lvl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>FL</td>\n",
       "      <td>13619</td>\n",
       "      <td>2715516</td>\n",
       "      <td>552.187071</td>\n",
       "      <td>57213</td>\n",
       "      <td>5.015253</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06025</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>CA</td>\n",
       "      <td>236</td>\n",
       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
       "      <td>1.309540</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "      <td>56971</td>\n",
       "      <td>3.726100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>OH</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "      <td>1.292341</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>CO</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
       "      <td>68829</td>\n",
       "      <td>2.557733</td>\n",
       "      <td>3</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>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>CA</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "      <td>54586</td>\n",
       "      <td>2.116551</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "      <td>0.318066</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>NM</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "      <td>2.178649</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MO</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "      <td>1.018330</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>NE</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "      <td>0.166889</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3029 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips       county state  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade    FL      13619     2715516   552.187071   \n",
       "1      06025     Imperial    CA        236      180216    16.659991   \n",
       "2      40109     Oklahoma    OK       2914      782051   425.989528   \n",
       "3      39115       Morgan    OH         19       14702    13.631412   \n",
       "4      08059    Jefferson    CO       1459      570427   288.167016   \n",
       "...      ...          ...   ...        ...         ...          ...   \n",
       "3025   06051         Mono    CA         30       14174     1.794896   \n",
       "3026   48169        Garza    TX          2        6288     2.717454   \n",
       "3027   35021      Harding    NM          1         459     0.083379   \n",
       "3028   29133  Mississippi    MO         14       13748    12.896866   \n",
       "3029   31167      Stanton    NE          1        5992     5.410380   \n",
       "\n",
       "      per_capita_income  pcp_per_capita  pop_density_lvl  \n",
       "0                 57213        5.015253                3  \n",
       "1                 44500        1.309540                1  \n",
       "2                 56971        3.726100                3  \n",
       "3                 38583        1.292341                1  \n",
       "4                 68829        2.557733                3  \n",
       "...                 ...             ...              ...  \n",
       "3025              54586        2.116551                0  \n",
       "3026              31223        0.318066                0  \n",
       "3027              50723        2.178649                0  \n",
       "3028              37483        1.018330                1  \n",
       "3029              52621        0.166889                0  \n",
       "\n",
       "[3029 rows x 9 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Establish the bins\n",
    "merged_initial_df[\"pop_density_lvl\"] = pd.cut(density_s, density_bins, labels=False)\n",
    "merged_initial_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "28db7573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoFips               object\n",
       "county                object\n",
       "state                 object\n",
       "pcp_count              int64\n",
       "population             int64\n",
       "pop_density          float64\n",
       "per_capita_income      int64\n",
       "pcp_per_capita       float64\n",
       "pop_density_lvl        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_initial_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d48c575b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create function to apply parameters for \"region\" (as defined by bea)\n",
    "\n",
    "def region_name(s):\n",
    "    if (s['state'] == \"NH\") or (s['state'] == \"ME\") or (s['state'] == \"VT\") or (s['state'] == \"MA\") or (s['state'] == \"CT\") or (s['state'] == \"RI\"):\n",
    "        return \"New England\"\n",
    "    elif (s['state'] == \"NY\") or (s['state'] == \"PA\") or (s['state'] == \"NJ\") or (s['state'] == \"DE\") or (s['state'] == \"DC\") or (s['state'] == \"MD\"):\n",
    "        return \"Mideast\"\n",
    "    elif (s['state'] == \"WI\") or (s['state'] == \"MI\") or (s['state'] == \"IL\") or (s['state'] == \"IN\") or (s['state'] == \"OH\"):\n",
    "        return \"Great Lakes\"\n",
    "    elif (s['state'] == \"ND\") or (s['state'] == \"SD\") or (s['state'] == \"NE\") or (s['state'] == \"KS\") or (s['state'] == \"MN\") or (s['state'] == \"IA\") or (s['state'] == \"MO\"):\n",
    "        return \"Plains\"\n",
    "    elif (s['state'] == \"WV\") or (s['state'] == \"VA\") or (s['state'] == \"KY\") or (s['state'] == \"TN\") or (s['state'] == \"NC\") or (s['state'] == \"AR\") or (s['state'] == \"LA\") or (s['state'] == \"MS\") or (s['state'] == \"AL\") or (s['state'] == \"GA\") or (s['state'] == \"SC\") or (s['state'] == \"FL\"):\n",
    "        return \"Southeast\"\n",
    "    elif (s['state'] == \"AZ\") or (s['state'] == \"NM\") or (s['state'] == \"OK\") or (s['state'] == \"TX\"):\n",
    "        return \"Southwest\"\n",
    "    elif (s['state'] == \"WA\") or (s['state'] == \"OR\") or (s['state'] == \"NV\") or (s['state'] == \"CA\") or (s['state'] == \"HI\") or (s['state'] == \"AK\"):\n",
    "        return \"Far West\"\n",
    "    else:\n",
    "        return \"Rocky Mountain\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "18a6a8e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>per_capita_income</th>\n",
       "      <th>pcp_per_capita</th>\n",
       "      <th>pop_density_lvl</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>FL</td>\n",
       "      <td>13619</td>\n",
       "      <td>2715516</td>\n",
       "      <td>552.187071</td>\n",
       "      <td>57213</td>\n",
       "      <td>5.015253</td>\n",
       "      <td>3</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06025</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>CA</td>\n",
       "      <td>236</td>\n",
       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
       "      <td>1.309540</td>\n",
       "      <td>1</td>\n",
       "      <td>Far West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "      <td>56971</td>\n",
       "      <td>3.726100</td>\n",
       "      <td>3</td>\n",
       "      <td>Southwest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>OH</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "      <td>1.292341</td>\n",
       "      <td>1</td>\n",
       "      <td>Great Lakes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>CO</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
       "      <td>68829</td>\n",
       "      <td>2.557733</td>\n",
       "      <td>3</td>\n",
       "      <td>Rocky Mountain</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>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>CA</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "      <td>54586</td>\n",
       "      <td>2.116551</td>\n",
       "      <td>0</td>\n",
       "      <td>Far West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "      <td>0.318066</td>\n",
       "      <td>0</td>\n",
       "      <td>Southwest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>NM</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "      <td>2.178649</td>\n",
       "      <td>0</td>\n",
       "      <td>Southwest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MO</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "      <td>1.018330</td>\n",
       "      <td>1</td>\n",
       "      <td>Plains</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>NE</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "      <td>0.166889</td>\n",
       "      <td>0</td>\n",
       "      <td>Plains</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3029 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips       county state  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade    FL      13619     2715516   552.187071   \n",
       "1      06025     Imperial    CA        236      180216    16.659991   \n",
       "2      40109     Oklahoma    OK       2914      782051   425.989528   \n",
       "3      39115       Morgan    OH         19       14702    13.631412   \n",
       "4      08059    Jefferson    CO       1459      570427   288.167016   \n",
       "...      ...          ...   ...        ...         ...          ...   \n",
       "3025   06051         Mono    CA         30       14174     1.794896   \n",
       "3026   48169        Garza    TX          2        6288     2.717454   \n",
       "3027   35021      Harding    NM          1         459     0.083379   \n",
       "3028   29133  Mississippi    MO         14       13748    12.896866   \n",
       "3029   31167      Stanton    NE          1        5992     5.410380   \n",
       "\n",
       "      per_capita_income  pcp_per_capita  pop_density_lvl          region  \n",
       "0                 57213        5.015253                3       Southeast  \n",
       "1                 44500        1.309540                1        Far West  \n",
       "2                 56971        3.726100                3       Southwest  \n",
       "3                 38583        1.292341                1     Great Lakes  \n",
       "4                 68829        2.557733                3  Rocky Mountain  \n",
       "...                 ...             ...              ...             ...  \n",
       "3025              54586        2.116551                0        Far West  \n",
       "3026              31223        0.318066                0       Southwest  \n",
       "3027              50723        2.178649                0       Southwest  \n",
       "3028              37483        1.018330                1          Plains  \n",
       "3029              52621        0.166889                0          Plains  \n",
       "\n",
       "[3029 rows x 10 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply function to add regions\n",
    "merged_initial_df['region'] = merged_initial_df.apply(region_name, axis=1)\n",
    "merged_initial_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "eea46d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoFips               object\n",
       "county                object\n",
       "state                 object\n",
       "pcp_count              int64\n",
       "population             int64\n",
       "pop_density          float64\n",
       "per_capita_income      int64\n",
       "pcp_per_capita       float64\n",
       "pop_density_lvl        int64\n",
       "region                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes\n",
    "merged_initial_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8d40c80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
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       "      <th>region_Far West</th>\n",
       "      <th>region_Great Lakes</th>\n",
       "      <th>region_Mideast</th>\n",
       "      <th>region_New England</th>\n",
       "      <th>region_Plains</th>\n",
       "      <th>region_Rocky Mountain</th>\n",
       "      <th>region_Southeast</th>\n",
       "      <th>region_Southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>FL</td>\n",
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       "      <th>1</th>\n",
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       "      <td>CA</td>\n",
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       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
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       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>OH</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "      <td>1.292341</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>CO</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
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       "      <td>2.557733</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>CA</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
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       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "      <td>0.318066</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>NM</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MO</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "      <td>1.018330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>NE</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "      <td>0.166889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3029 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips       county state  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade    FL      13619     2715516   552.187071   \n",
       "1      06025     Imperial    CA        236      180216    16.659991   \n",
       "2      40109     Oklahoma    OK       2914      782051   425.989528   \n",
       "3      39115       Morgan    OH         19       14702    13.631412   \n",
       "4      08059    Jefferson    CO       1459      570427   288.167016   \n",
       "...      ...          ...   ...        ...         ...          ...   \n",
       "3025   06051         Mono    CA         30       14174     1.794896   \n",
       "3026   48169        Garza    TX          2        6288     2.717454   \n",
       "3027   35021      Harding    NM          1         459     0.083379   \n",
       "3028   29133  Mississippi    MO         14       13748    12.896866   \n",
       "3029   31167      Stanton    NE          1        5992     5.410380   \n",
       "\n",
       "      per_capita_income  pcp_per_capita  pop_density_lvl  region_Far West  \\\n",
       "0                 57213        5.015253                3                0   \n",
       "1                 44500        1.309540                1                1   \n",
       "2                 56971        3.726100                3                0   \n",
       "3                 38583        1.292341                1                0   \n",
       "4                 68829        2.557733                3                0   \n",
       "...                 ...             ...              ...              ...   \n",
       "3025              54586        2.116551                0                1   \n",
       "3026              31223        0.318066                0                0   \n",
       "3027              50723        2.178649                0                0   \n",
       "3028              37483        1.018330                1                0   \n",
       "3029              52621        0.166889                0                0   \n",
       "\n",
       "      region_Great Lakes  region_Mideast  region_New England  region_Plains  \\\n",
       "0                      0               0                   0              0   \n",
       "1                      0               0                   0              0   \n",
       "2                      0               0                   0              0   \n",
       "3                      1               0                   0              0   \n",
       "4                      0               0                   0              0   \n",
       "...                  ...             ...                 ...            ...   \n",
       "3025                   0               0                   0              0   \n",
       "3026                   0               0                   0              0   \n",
       "3027                   0               0                   0              0   \n",
       "3028                   0               0                   0              1   \n",
       "3029                   0               0                   0              1   \n",
       "\n",
       "      region_Rocky Mountain  region_Southeast  region_Southwest  \n",
       "0                         0                 1                 0  \n",
       "1                         0                 0                 0  \n",
       "2                         0                 0                 1  \n",
       "3                         0                 0                 0  \n",
       "4                         1                 0                 0  \n",
       "...                     ...               ...               ...  \n",
       "3025                      0                 0                 0  \n",
       "3026                      0                 0                 1  \n",
       "3027                      0                 0                 1  \n",
       "3028                      0                 0                 0  \n",
       "3029                      0                 0                 0  \n",
       "\n",
       "[3029 rows x 17 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create individual region columns\n",
    "region_df = pd.get_dummies(merged_initial_df, columns=[\"region\"])\n",
    "region_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dced9f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>county</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>per_capita_income</th>\n",
       "      <th>pcp_per_capita</th>\n",
       "      <th>pop_density_lvl</th>\n",
       "      <th>region_Far West</th>\n",
       "      <th>region_Great Lakes</th>\n",
       "      <th>...</th>\n",
       "      <th>state_SD</th>\n",
       "      <th>state_TN</th>\n",
       "      <th>state_TX</th>\n",
       "      <th>state_UT</th>\n",
       "      <th>state_VA</th>\n",
       "      <th>state_VT</th>\n",
       "      <th>state_WA</th>\n",
       "      <th>state_WI</th>\n",
       "      <th>state_WV</th>\n",
       "      <th>state_WY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "      <td>2715516</td>\n",
       "      <td>552.187071</td>\n",
       "      <td>57213</td>\n",
       "      <td>5.015253</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06025</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>236</td>\n",
       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
       "      <td>1.309540</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "      <td>56971</td>\n",
       "      <td>3.726100</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "      <td>1.292341</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
       "      <td>68829</td>\n",
       "      <td>2.557733</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "      <td>54586</td>\n",
       "      <td>2.116551</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "      <td>0.318066</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "      <td>2.178649</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "      <td>1.018330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "      <td>0.166889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3029 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips       county  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade      13619     2715516   552.187071   \n",
       "1      06025     Imperial        236      180216    16.659991   \n",
       "2      40109     Oklahoma       2914      782051   425.989528   \n",
       "3      39115       Morgan         19       14702    13.631412   \n",
       "4      08059    Jefferson       1459      570427   288.167016   \n",
       "...      ...          ...        ...         ...          ...   \n",
       "3025   06051         Mono         30       14174     1.794896   \n",
       "3026   48169        Garza          2        6288     2.717454   \n",
       "3027   35021      Harding          1         459     0.083379   \n",
       "3028   29133  Mississippi         14       13748    12.896866   \n",
       "3029   31167      Stanton          1        5992     5.410380   \n",
       "\n",
       "      per_capita_income  pcp_per_capita  pop_density_lvl  region_Far West  \\\n",
       "0                 57213        5.015253                3                0   \n",
       "1                 44500        1.309540                1                1   \n",
       "2                 56971        3.726100                3                0   \n",
       "3                 38583        1.292341                1                0   \n",
       "4                 68829        2.557733                3                0   \n",
       "...                 ...             ...              ...              ...   \n",
       "3025              54586        2.116551                0                1   \n",
       "3026              31223        0.318066                0                0   \n",
       "3027              50723        2.178649                0                0   \n",
       "3028              37483        1.018330                1                0   \n",
       "3029              52621        0.166889                0                0   \n",
       "\n",
       "      region_Great Lakes  ...  state_SD  state_TN  state_TX  state_UT  \\\n",
       "0                      0  ...         0         0         0         0   \n",
       "1                      0  ...         0         0         0         0   \n",
       "2                      0  ...         0         0         0         0   \n",
       "3                      1  ...         0         0         0         0   \n",
       "4                      0  ...         0         0         0         0   \n",
       "...                  ...  ...       ...       ...       ...       ...   \n",
       "3025                   0  ...         0         0         0         0   \n",
       "3026                   0  ...         0         0         1         0   \n",
       "3027                   0  ...         0         0         0         0   \n",
       "3028                   0  ...         0         0         0         0   \n",
       "3029                   0  ...         0         0         0         0   \n",
       "\n",
       "      state_VA  state_VT  state_WA  state_WI  state_WV  state_WY  \n",
       "0            0         0         0         0         0         0  \n",
       "1            0         0         0         0         0         0  \n",
       "2            0         0         0         0         0         0  \n",
       "3            0         0         0         0         0         0  \n",
       "4            0         0         0         0         0         0  \n",
       "...        ...       ...       ...       ...       ...       ...  \n",
       "3025         0         0         0         0         0         0  \n",
       "3026         0         0         0         0         0         0  \n",
       "3027         0         0         0         0         0         0  \n",
       "3028         0         0         0         0         0         0  \n",
       "3029         0         0         0         0         0         0  \n",
       "\n",
       "[3029 rows x 67 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create individual state columns\n",
    "merged_df = pd.get_dummies(region_df, columns=[\"state\"])\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0a295130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3029.000000\n",
       "mean        2.550960\n",
       "std         2.063406\n",
       "min         0.025139\n",
       "25%         1.392669\n",
       "50%         2.165234\n",
       "75%         3.238274\n",
       "max        37.618786\n",
       "Name: pcp_per_capita, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review calculated info in new pcp_per_capita column\n",
    "merged_df.pcp_per_capita.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "22bf261b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       5.015253\n",
       "1       1.309540\n",
       "2       3.726100\n",
       "3       1.292341\n",
       "4       2.557733\n",
       "          ...   \n",
       "3025    2.116551\n",
       "3026    0.318066\n",
       "3027    2.178649\n",
       "3028    1.018330\n",
       "3029    0.166889\n",
       "Name: pcp_per_capita, Length: 3029, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create series of pcp_per_capita data\n",
    "pcp_per_capita_s = merged_df.pcp_per_capita\n",
    "pcp_per_capita_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c84badfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48755400898805545"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting the criteria for \"underserved\" as one standard deviation from the mean\n",
    "pcp_per_capita_thd = merged_df.pcp_per_capita.mean() - merged_df.pcp_per_capita.std()\n",
    "pcp_per_capita_thd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dcbd6a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "3025    1\n",
       "3026    0\n",
       "3027    1\n",
       "3028    1\n",
       "3029    0\n",
       "Name: pcp_per_capita, Length: 3029, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cut the pcp_per_capita into the ranges.\n",
    "pcp_per_capita_bins = [0, pcp_per_capita_thd, np.inf]\n",
    "pd.cut(pcp_per_capita_s, pcp_per_capita_bins, retbins=False, labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4e7af009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pcp_per_capita\n",
       "(0.0, 0.488]     148\n",
       "(0.488, inf]    2881\n",
       "Name: pcp_per_capita, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the distribution of bins\n",
    "pcp_per_capita_s.groupby(pd.cut(pcp_per_capita_s, pcp_per_capita_bins)).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bda8fcae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>GeoFips</th>\n",
       "      <th>county</th>\n",
       "      <th>pcp_count</th>\n",
       "      <th>population</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>per_capita_income</th>\n",
       "      <th>pcp_per_capita</th>\n",
       "      <th>pop_density_lvl</th>\n",
       "      <th>region_Far West</th>\n",
       "      <th>region_Great Lakes</th>\n",
       "      <th>...</th>\n",
       "      <th>state_TN</th>\n",
       "      <th>state_TX</th>\n",
       "      <th>state_UT</th>\n",
       "      <th>state_VA</th>\n",
       "      <th>state_VT</th>\n",
       "      <th>state_WA</th>\n",
       "      <th>state_WI</th>\n",
       "      <th>state_WV</th>\n",
       "      <th>state_WY</th>\n",
       "      <th>pcp_per_capita_bins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12086</td>\n",
       "      <td>Miami-Dade</td>\n",
       "      <td>13619</td>\n",
       "      <td>2715516</td>\n",
       "      <td>552.187071</td>\n",
       "      <td>57213</td>\n",
       "      <td>5.015253</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06025</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>236</td>\n",
       "      <td>180216</td>\n",
       "      <td>16.659991</td>\n",
       "      <td>44500</td>\n",
       "      <td>1.309540</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40109</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>2914</td>\n",
       "      <td>782051</td>\n",
       "      <td>425.989528</td>\n",
       "      <td>56971</td>\n",
       "      <td>3.726100</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39115</td>\n",
       "      <td>Morgan</td>\n",
       "      <td>19</td>\n",
       "      <td>14702</td>\n",
       "      <td>13.631412</td>\n",
       "      <td>38583</td>\n",
       "      <td>1.292341</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08059</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>1459</td>\n",
       "      <td>570427</td>\n",
       "      <td>288.167016</td>\n",
       "      <td>68829</td>\n",
       "      <td>2.557733</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>06051</td>\n",
       "      <td>Mono</td>\n",
       "      <td>30</td>\n",
       "      <td>14174</td>\n",
       "      <td>1.794896</td>\n",
       "      <td>54586</td>\n",
       "      <td>2.116551</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>48169</td>\n",
       "      <td>Garza</td>\n",
       "      <td>2</td>\n",
       "      <td>6288</td>\n",
       "      <td>2.717454</td>\n",
       "      <td>31223</td>\n",
       "      <td>0.318066</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>35021</td>\n",
       "      <td>Harding</td>\n",
       "      <td>1</td>\n",
       "      <td>459</td>\n",
       "      <td>0.083379</td>\n",
       "      <td>50723</td>\n",
       "      <td>2.178649</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>29133</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>14</td>\n",
       "      <td>13748</td>\n",
       "      <td>12.896866</td>\n",
       "      <td>37483</td>\n",
       "      <td>1.018330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>31167</td>\n",
       "      <td>Stanton</td>\n",
       "      <td>1</td>\n",
       "      <td>5992</td>\n",
       "      <td>5.410380</td>\n",
       "      <td>52621</td>\n",
       "      <td>0.166889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3029 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GeoFips       county  pcp_count  population  pop_density  \\\n",
       "0      12086   Miami-Dade      13619     2715516   552.187071   \n",
       "1      06025     Imperial        236      180216    16.659991   \n",
       "2      40109     Oklahoma       2914      782051   425.989528   \n",
       "3      39115       Morgan         19       14702    13.631412   \n",
       "4      08059    Jefferson       1459      570427   288.167016   \n",
       "...      ...          ...        ...         ...          ...   \n",
       "3025   06051         Mono         30       14174     1.794896   \n",
       "3026   48169        Garza          2        6288     2.717454   \n",
       "3027   35021      Harding          1         459     0.083379   \n",
       "3028   29133  Mississippi         14       13748    12.896866   \n",
       "3029   31167      Stanton          1        5992     5.410380   \n",
       "\n",
       "      per_capita_income  pcp_per_capita  pop_density_lvl  region_Far West  \\\n",
       "0                 57213        5.015253                3                0   \n",
       "1                 44500        1.309540                1                1   \n",
       "2                 56971        3.726100                3                0   \n",
       "3                 38583        1.292341                1                0   \n",
       "4                 68829        2.557733                3                0   \n",
       "...                 ...             ...              ...              ...   \n",
       "3025              54586        2.116551                0                1   \n",
       "3026              31223        0.318066                0                0   \n",
       "3027              50723        2.178649                0                0   \n",
       "3028              37483        1.018330                1                0   \n",
       "3029              52621        0.166889                0                0   \n",
       "\n",
       "      region_Great Lakes  ...  state_TN  state_TX  state_UT  state_VA  \\\n",
       "0                      0  ...         0         0         0         0   \n",
       "1                      0  ...         0         0         0         0   \n",
       "2                      0  ...         0         0         0         0   \n",
       "3                      1  ...         0         0         0         0   \n",
       "4                      0  ...         0         0         0         0   \n",
       "...                  ...  ...       ...       ...       ...       ...   \n",
       "3025                   0  ...         0         0         0         0   \n",
       "3026                   0  ...         0         1         0         0   \n",
       "3027                   0  ...         0         0         0         0   \n",
       "3028                   0  ...         0         0         0         0   \n",
       "3029                   0  ...         0         0         0         0   \n",
       "\n",
       "      state_VT  state_WA  state_WI  state_WV  state_WY  pcp_per_capita_bins  \n",
       "0            0         0         0         0         0                    1  \n",
       "1            0         0         0         0         0                    1  \n",
       "2            0         0         0         0         0                    1  \n",
       "3            0         0         0         0         0                    1  \n",
       "4            0         0         0         0         0                    1  \n",
       "...        ...       ...       ...       ...       ...                  ...  \n",
       "3025         0         0         0         0         0                    1  \n",
       "3026         0         0         0         0         0                    0  \n",
       "3027         0         0         0         0         0                    1  \n",
       "3028         0         0         0         0         0                    1  \n",
       "3029         0         0         0         0         0                    0  \n",
       "\n",
       "[3029 rows x 68 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Establish the bins\n",
    "merged_df[\"pcp_per_capita_bins\"] = pd.cut(pcp_per_capita_s, pcp_per_capita_bins, labels=False)\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f2528081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GeoFips                 object\n",
       "county                  object\n",
       "pcp_count                int64\n",
       "population               int64\n",
       "pop_density            float64\n",
       "                        ...   \n",
       "state_WA                 uint8\n",
       "state_WI                 uint8\n",
       "state_WV                 uint8\n",
       "state_WY                 uint8\n",
       "pcp_per_capita_bins      int64\n",
       "Length: 68, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dtypes\n",
    "merged_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "49ba504e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving file as reference\n",
    "merged_df.to_csv(\"Resources/merged_df.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447c7850",
   "metadata": {},
   "source": [
    "### Graph data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ebd91322",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       5.015253\n",
       "1       1.309540\n",
       "2       3.726100\n",
       "3       1.292341\n",
       "4       2.557733\n",
       "          ...   \n",
       "3025    2.116551\n",
       "3026    0.318066\n",
       "3027    2.178649\n",
       "3028    1.018330\n",
       "3029    0.166889\n",
       "Name: pcp_per_capita, Length: 3029, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out pcp_per_capita\n",
    "pcp_per_capita = merged_df.pcp_per_capita\n",
    "pcp_per_capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1ac1e445",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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11ryRCk2pXmjLmmAY6uVTk7xxfVtFX1tEpJhSWxzfMLPngD7g22bWDSx2uvIVwAF3P+jus8DtwM7CFdz9hLvvAdJl1r3sZLI5xmcyFTv5L2/TmnyLY6KirysispBSp1W/GfgxoM/d08AE80KgiA3A4YLH/eGyUjlwr5k9Zma7ytiuJo2HYxCV7qpqb0rR0ZziJR1ZJSJVUs6fv28kOJ+jcJsvnWX9Yn1ZXsb7XeXuR82sh+D6H8+5+wOveZMgVHYBbNq0qYyXr66RCk+pXmizDskVkSoq9aiqfwQ+C1wNvC38WWxW3H5gY8HjXuBoqYW5+9Hw9gRwB0HXV7H1bnX3Pnfv6+7uLvXlq250KpzgsMItDoBNa1p4WV1VIlIlpbY4+oAd7l5Oi2EPsN3MtgJHgBsILj+7KDNrARLuPhbevwb4b2W8d83Jz1NV6fM4ALasaebuJ49penURqYpSv8WeAtYBxxZbMc/dM2Z2E3APkARuc/enzezj4fO7w9l39xLMtpszs08BO4Au4I7wyK064Mvu/q1S37sWzV2LozmCFsfqZrI558jQFFu6WhbfQETkHJQaHF3AM2b2KDCTX+juP3+2jcKLP909b9nugvvHCbqw5hsFLi2xtmWh0peNLbQ5PCT3pVMTCg4RiVypwfGZKIt4PZjrqopgjGPzGp0EKCLVU1JwuPv3zGwzsN3d7zezZoLuJynRyFSaZMJoqa/8butpbaAxleBlHVklIlVQ6lFVvw78M/B34aINwL9EVNOKNDqVoa2xjsXOuF8KM2Pz6hYFh4hURamH4HwCuIpg7AF3fwHoiaqolSiKKdULbVrTrLPHRaQqSg2OmXDaEADCkwDLOTT3dW9kKl3xs8YLbV7dzCuDk+Ry+rWISLRKDY7vmdnvA01m9m7g/wLfiK6slWc0ggkOC21e08x0OseJsZnFVxYROQelBsfNwADwJPAfCQ6x/YOoilqJRqcrP8Fhoc1zs+Squ0pEolXqUVU5M/sX4F/cXZebW4LRiLuqthScy3Hl+Wsiex8RkbO2OCzwGTM7CTwH7DezATP7dHXKWzmiuBZHoQ2dTaSSxqGTOrJKRKK1WFfVpwiOpnqbu69x99XAlcBVZvafoy5upZhOZ5nJ5CI9qiqZMDatbubQyfHI3kNEBBYPjg8DN7r7ofwCdz8IfCh8TkowNh3OjBvBBIeFtnat4iW1OEQkYosFR6rYpVvDcY7o/nxeYaKcbqTQ1q5mXjo1oUNyRSRSiwXH7BKfkwJRTnBYaGvXKmYyOY6NLnZVXxGRpVus7+RSMxststyAxgjqWZFGp6O7iFOhLV3BZIeHBibY0NEU6XuJyOvXWVsc7p5097YiP63urq6qEuVbHO0RnscBcH7XKgAO6VwOEYmQLhdXBVFeb7zQ2rYGmlJJDg0oOEQkOgqOKqjW4LiZsaWrhZfU4hCRCCk4qmB0KkN9MkFDXfS7+/yuFg6dVHCISHQUHFUQTKkezbU45tvSFcySm87mIn8vEXl9UnBUwehUtNfiKLS1axXZnNM/NFWV9xOR1x8FRxWMTmciHxjP25o/JFdTj4hIRBQcVTBS5RYHoMkORSQyCo4qGJtKRz5PVV5nc4q2xjq1OEQkMgqOKoj6euOFzIyt3ZrsUESiE2lwmNm1ZrbfzA6Y2c1Fnr/IzB4ysxkz++1ytl0u3J3RqeqNcYAOyRWRaEUWHGaWBG4BrgN2ADea2Y55qw0CvwF8dgnbLgszmRyz2Vykl42db8uaFo6OTDGdzlbtPUXk9SPKFscVwAF3P+jus8DtwM7CFdz9hLvvAdLlbrtcnJ6nqnotjq3dLbjDy6fUXSUilRdlcGwADhc87g+XVXRbM9tlZnvNbO/AQO1dDr1a81QV2hpef1zdVSIShSiDo9hp0qVeYajkbd39Vnfvc/e+7u7ukourlmrNU1Vobnp1BYeIRCDK4OgHNhY87gWOVmHbmjI6VZ3LxhZqbUzRtaqBgwM6JFdEKi/K4NgDbDezrWZWD9wA3FmFbWtKHC0OgG09LRxQcIhIBCL7M9jdM2Z2E3APkARuc/enzezj4fO7zWwdsBdoA3Jm9ilgh7uPFts2qlqjVK3Lxs73hrWtfPXxI7h7VSZXFJHXj0j7T9z9buDuect2F9w/TtANVdK2y9Hpy8ZWr6sKYPvaVsZnMhwdmdZlZEWkonTmeMRGp9I0phI01CWr+r5vWNcKwPOvjlX1fUVk5VNwRGxkKl31biqAC3vC4Diu4BCRylJwRKya81QVam9Osbatgedf1QC5iFSWgiNiwTxV1R3fyLtwbau6qkSk4hQcEYurxQFBcLxwYoxcrtTzLkVEFqfgiNhoTGMcABeuXcV0OsfhIc1ZJSKVo+CI2Oh0pqoTHBa6cG0wQP6cBshFpIIUHBFy9/CysfGMcVy0ro1kwnjqyEgs7y8iK5OCI0KTs1myOY+tq6qpPsn2nlX8qF/BISKVo+CI0NyU6jF1VQFc2tvBvv5h3DVALiKVoeCI0NDkLACdzfEFxyUb2xmeTHN4cCq2GkRkZVFwRGh4MmhxdDTXx1bDpb0dAPyofzi2GkRkZVFwRCgfHJ0xBscb1rVSX5dgn4JDRCpEwRGhfFdVR4xdValkgh3r2zRALiIVo+CI0HANBAfAJb3tPHVkhEw2F2sdIrIyKDgiNDSZprk+WfUp1ee7fHMnk7NZnj2mEwFF5NwpOCI0PJmmI8ZDcfPefv4aAB4+eCrmSkRkJVBwRGh4cjbWI6ry1rY1srWrhUcOKThE5NwpOCI0NDlLZ0v8LQ6AK7eu5tFDg2Q1U66InCMFR4SGJ9M10eIAuPL81YxOZ3ju+GjcpYjIMqfgiNDwVG2McQBcuTUY53jk4GDMlYjIcqfgiEgu5wxPzsZ68l+h8zqa2LS6mYc0QC4i50jBEZGx6Qw5j/8cjkJXbVvDwy+e0vkcInJOIg0OM7vWzPab2QEzu7nI82ZmfxM+v8/MLit47iUze9LMnjCzvVHWGYXTExzWRosD4Me3dzM2k9FZ5CJyTiILDjNLArcA1wE7gBvNbMe81a4Dtoc/u4DPzXv+Xe7+Fnfvi6rOqNTCdCPzveOCNZjBgy8MxF2KiCxjUbY4rgAOuPtBd58Fbgd2zltnJ/AlDzwMdJjZ+ghrqpqT40FwdK1qiLmS0zqa67lkQzvff+Fk3KWIyDIWZXBsAA4XPO4Pl5W6jgP3mtljZrZroTcxs11mttfM9g4M1M5f0ifHZwDoaq2d4AC4ensXPzw8zNh0Ou5SRGSZijI4rMiy+WefnW2dq9z9MoLurE+Y2TuLvYm73+rufe7e193dvfRqK+zkWBAca1pqZ4wDgnGObM556EUdXSUiSxNlcPQDGwse9wJHS13H3fO3J4A7CLq+lo2T4zO0NtbRmIp3gsP5LtvUSXN9ku8fUHeViCxNlMGxB9huZlvNrB64Abhz3jp3Ah8Oj656OzDi7sfMrMXMWgHMrAW4Bngqwlor7uT4LN01NL6RV1+X4MqtqzXOISJLVhfVC7t7xsxuAu4BksBt7v60mX08fH43cDdwPXAAmAQ+Gm6+FrjDzPI1ftndvxVVrVEYGJ+pqYHxQldv7+a7+5+hf2iS3s7muMsRkWUmsuAAcPe7CcKhcNnugvsOfKLIdgeBS6OsLWonx2e4aF1r3GUU9c7tXQB8/4WT3HDFppirEZHlRmeOR+Tk2ExNdlUBbOtZxdq2Bh5Ud5WILIGCIwIzmSyj05ma7aoyM67e1s2/vXhS06yLSNkUHBE4lT/5r8bO4Sj0k2/oZngyzeOvDMVdiogsMwqOCMyd/FejLQ4IgqM+meDep4/HXYqILDMKjgicDo7aOvmvUGtjindsW8O9z7xKcIyCiEhpFBwRODo8DcC69saYKzm7a3as4+VTkzx9VFcFFJHSKTgicGR4ilTS6Gmt7eC4/s3rqE8m+Orj/XGXIiLLiIIjAv1DU6xvbyKZKDYVV+3oaK7n3TvW8vUnjjKb0cWdRKQ0Co4IHBmapLezKe4ySvKBvl4GJ2b55lPH4i5FRJYJBUcE+oem2NCxPILjJ7Z3s71nFbd89wA5ndMhIiVQcFTYTCbLibGZZTMHVCJh3PRT23j+1XG+sW/+5MUiIq+l4KiwY+ERVRuWSVcVwHsvOY83b2jnj+56hqGJ2bjLEZEap+CosP6hKYBlM8YBkEwYf/GBSxieTPNb/+cJMlkNlIvIwhQcFXZkeBJg2Yxx5L1xfRt/uPNivrt/gN/5532kFR4isoBIp1V/PTp4coL6ZIL1NX7yXzEfvHIzg+Oz/OV9z3NibIZbfuky2ptTcZclIjVGLY4Ke+HVcc7vbqEuuTx37Sd/ejuf/feX8vDBU1z/Nw9qEkQReY3l+e1Ww144Mca2nlVxl3FOPnB5L//8n95BIgG/uPshdn/vRR2qKyJzFBwVNDadpn9oigvX1uaV/8rxlo0d3PXJH+c9F6/jz775HB/9hz1zkzeKyOubgqOCnjwygjtc0tsedykV0d6U4n/80lv5k/e/Kei6+usHue+ZV+MuS0RipuCooB8dHgHg0t6OeAupIDPjg1du5us3XUVncz2//qW9fOTvH+XfDpxU95XI65SOqqqghw+e4vzuFjpbavc6HEt10bo27vqNq/n7fzvE3/7ri3zwC4/QtaqeyzZ10relk8s3d3Lxee00ppJxlyoiEVNwVMh0Ossjh05xw9s2xV1KZFLJBLveeQEf/rEtfOup4zzw/AB7Xx7i3rD7qj6Z4OINbfRt7uTKrWt429bVtDfpcF6RlUbBUSH/uv8E0+kcP3VRT9ylRK4xleR9b93A+966AYCBsRkef2WIx18e4rGXh/jiQy/z+QcPYQZvXNfGOy/s5gOXb2Bbz/I/aEBEFBwV8+VHD9PT2sA7LlgTdylV193awHsuXsd7Ll4HBK2vJw4P88jBQR4+eIrPP3iQ3d97kUs3dnDj2zbyc5eeR0uDPnoiy1Wkg+Nmdq2Z7TezA2Z2c5Hnzcz+Jnx+n5ldVuq2teTBFwZ44PkBfvXqrcv2xL9Kakwlefv5a/jNn9nOV3a9nYd/76f5g599I1OzGW7+2pNc8Sf383tf28d3nnuVsel03OWKSJnMPZojY8wsCTwPvBvoB/YAN7r7MwXrXA98ErgeuBL4a3e/spRti+nr6/O9e/dG8c8pamo2y33Pvsof3PEkPW2NfOOmq2mq1+DwQtydx18Z5iuPvsJd+44ynQ7mw1rb1sDWrhY2r25hbXsj69oaWd/eSE9bA/UFQTybzZHOOulsjnQmx0x4m846s9ks6YxjBg2pJI11iTNuG+oSNKaS1NclqE8mqK9L0BDeTyxypcZcLnhds8Wv6JjLOWMzGUan0oxMpRmdSjM6nSabg+aGJC31daxqqKO1MfhZ1VA398eGu5PNOTkHM0iYkSjxfUupK53LkTCjLmEVec1KyuacTC5H0oxkifXl91fCbNHf4blydzI5J+dOXSJRsd/Lucjvs3P9nZrZY+7eV842UfYXXAEccPeDAGZ2O7ATKPzy3wl8yYP0etjMOsxsPbClhG1f49ljo/T98f3ho9OBWJiNhTFZGJpnLl98HYCJmQw5DyYI/PyHL1doLMLMuHxzcATWH7/vTTz+8hCPvzLEoZOTHDo5znf2n+Dk+AwR/S2zoFTS5sIk56f/hwxufa6eVNKoSyRIJY1UMkEqmaAuabgH3XOTs1mm0tmy37++LkEufK9ikgmbe8/6gvfNh2o2Hzg5n7ufzTmZrIdhm2P+SycM6pIJ6hLBF3Vwe/pxKmlzlz728D9O8P9DcAtOsG/y+yf/XM7D5eSfK3wcrhP+ezPZINDm/84TBnWJBMmwniBMIJMNfjfBrZ+5fjJBKmGk6hJzv6e6pJFKJMDCmj0IZsfJ5c6sOeeF6/jc7z8d/sEy35n7zub2Z13CSIaflXyeFf77C9+fM/bXmTWcsS/D26wvvs/qksV/p3XJYFmiAoEXZXBsAA4XPO4naFUsts6GErcFwMx2AbsA2s87n2suXnv6uTPWK7hf8MyZy8943WJvd8Y2bY0p3rqpg6u3damLqkyNqSTv2NbFO7Z1nbE8nc0xMDbD8dFpToxOz/viTlBfZ9Qnk8EXaUHrIfgiD77EZzJZptM5ZjJZZtI5pgtuZzM5ZjM5ZsKf2UyO2Wxubnly/pdBIvhrNueQCb+E862eTHiLQXN9kub6OhpTSdqbUrQ11gW3TSnam1IkzJiYzTAxk2F8OsPYdIbR6TTjMxmm0tkz/idP2OkvmnwIpLO5uRBIZ04/hiBYkuFf3fl6k2Zz++TMkDv9hZ3JOdlcLrwNH4dfSvngAcCC/zfMLLw983HwvIWtpNP3LXzy9PqF6wW11hXUmEzY3L83WxCCmayHX+w+FwiFX9S5gi/T/O8knXUy2eDfNpvNzdWbCGtJWPAXer7mhNlcqzL/b0hY8JlL5T9f4b6d21f5fRfuy0zBfju9jp+x/wrfn/z7crqFma/hdI0F+9ogaaf3WT4k8r/T4HNS/Heary+TdXzen8D3U74og6PYN+/82F5onVK2DRa63wrcCkFX1Z++/83l1Cg1JpVMcF5HE+cts2npRZarz32o/G2iDI5+YGPB415g/rVJF1qnvoRtRUQkBlH2r+wBtpvZVjOrB24A7py3zp3Ah8Ojq94OjLj7sRK3FRGRGETW4nD3jJndBNwDJIHb3P1pM/t4+Pxu4G6CI6oOAJPAR8+2bVS1iohI6SI7HDcO1T4cV0RkuVvK4bg6FEhERMqi4BARkbIoOEREpCwKDhERKcuKGhw3szFgf9x1zNMFnIy7iHlUU2lqsSaozbpUU2lqsaY3uHtZ1zxYaXNb7y/36IComdle1bQ41VS6WqxLNZWmVmsqdxt1VYmISFkUHCIiUpaVFhy3xl1AEaqpNKqpdLVYl2oqzYqoaUUNjouISPRWWotDREQipuAQEZGyrKjgMLPPmNkRM3si/Lk+xlquNbP9ZnbAzG6Oq475zOwlM3sy3D+xzAhpZreZ2Qkze6pg2Wozu8/MXghvO2ugplg/T2a20cy+a2bPmtnTZvab4fLY9tVZaoptX5lZo5k9amY/Cmv6w3B53J+pheqK+3OVNLMfmtld4eOy99OKGuMws88A4+7+2ZjrSALPA+8muFjVHuBGdz/rNdOrwcxeAvrcPbaTkMzsncA4wfXm3xQu+wtg0N3/LAzaTnf/3Zhr+gwxfp7MbD2w3t0fN7NW4DHgfcBHiGlfnaWmXySmfWXBdZ5b3H3czFLA94HfBP4d8X6mFqrrWuL9XP0W0Ae0uft7l/L/3opqcdSQK4AD7n7Q3WeB24GdMddUM9z9AWBw3uKdwBfD+18k+DKKu6ZYufsxd388vD8GPAtsIMZ9dZaaYuOB8fBhKvxx4v9MLVRXbMysF/hZ4AsFi8veTysxOG4ys31h10NVm6YFNgCHCx73E/P/XAUcuNfMHjOzXXEXU2BtePVHwtuemOvJq4XPE2a2BXgr8Ag1sq/m1QQx7quw++UJ4ARwn7vXxH5aoC6Ib1/9d+B3gFzBsrL307ILDjO738yeKvKzE/gccAHwFuAY8JdxlVlkWa30CV7l7pcB1wGfCLtopLia+DyZ2Srgq8Cn3H00jhrmK1JTrPvK3bPu/hagF7jCzN5UzfdfyAJ1xbKvzOy9wAl3f+xcX2vZzVXl7j9Tynpm9nngrojLWUg/sLHgcS9wNKZazuDuR8PbE2Z2B0G32gPxVgXAq2a23t2Phf3oJ+IuyN1fzd+P6/MU9o1/Ffhf7v61cHGs+6pYTbWwr8I6hs3sXwnGEWrmM1VYV+HYRpX31VXAz4eD8Y1Am5n9E0vYT8uuxXE24T867/3AUwutG7E9wHYz22pm9cANwJ0x1TLHzFrCAU3MrAW4hvj20Xx3Ar8S3v8V4Osx1gLE/3kKB1f/J/Csu/9VwVOx7auFaopzX5lZt5l1hPebgJ8BniPmz9RCdcW1r9z999y91923EHwnfcfdP8RS9pO7r5gf4B+BJ4F94c5YH2Mt1xMcWfUi8F/j3jdhTecDPwp/no6rLuArBE30NEHr7NeANcC3gRfC29U1UFOsnyfgaoIuzn3AE+HP9XHuq7PUFNu+Ai4Bfhi+91PAp8PlcX+mFqor9u8p4CeBu5a6n1bU4bgiIhK9FdVVJSIi0VNwiIhIWRQcIiJSFgWHiIiURcEhIiJlUXCIiEhZFBwiIlKW/w9nPM3QSsMoMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize pcp_per_capita\n",
    "pcp_per_capita.plot.density()\n",
    "plt.xlim(-5,40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c85e656",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlenv",
   "language": "python",
   "name": "mlenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}