per-capita EHR spending correlation with median income analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f56c4b8",
   "metadata": {},
   "source": [
    " # per-capita EHR spending correlation with median income analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "252130e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Taxable Year</th>\n",
       "      <th>County</th>\n",
       "      <th>Population</th>\n",
       "      <th>All Returns</th>\n",
       "      <th>AGI</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Median Rank</th>\n",
       "      <th>Joint Returns</th>\n",
       "      <th>Joint Returns Median Income</th>\n",
       "      <th>Joint Returns Median Rank</th>\n",
       "      <th>Tax Assessed</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>New Georeferenced Column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>Nonresident</td>\n",
       "      <td>NaN</td>\n",
       "      <td>756843</td>\n",
       "      <td>32798066000</td>\n",
       "      <td>14600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371420</td>\n",
       "      <td>20592</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>Unallocated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2885</td>\n",
       "      <td>580682000</td>\n",
       "      <td>51249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1194</td>\n",
       "      <td>127511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19336000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>Unallocated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2880</td>\n",
       "      <td>287037000</td>\n",
       "      <td>36227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1041</td>\n",
       "      <td>86697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12616000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>Unallocated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3710</td>\n",
       "      <td>392781000</td>\n",
       "      <td>31384</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>70468</td>\n",
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       "      <td>14728000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>712700.0</td>\n",
       "      <td>281369</td>\n",
       "      <td>11450530000</td>\n",
       "      <td>26210</td>\n",
       "      <td>10.0</td>\n",
       "      <td>133610</td>\n",
       "      <td>47458</td>\n",
       "      <td>9.0</td>\n",
       "      <td>414669000</td>\n",
       "      <td>34.277091</td>\n",
       "      <td>-119.217850</td>\n",
       "      <td>POINT (-119.2178496 34.27709072)</td>\n",
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       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>2005</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>99236.0</td>\n",
       "      <td>40399</td>\n",
       "      <td>2321926000</td>\n",
       "      <td>35827</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19384</td>\n",
       "      <td>60869</td>\n",
       "      <td>22.0</td>\n",
       "      <td>88583000</td>\n",
       "      <td>39.295489</td>\n",
       "      <td>-120.900873</td>\n",
       "      <td>POINT (-120.9008733 39.29548859)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>2018</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>886618.0</td>\n",
       "      <td>476921</td>\n",
       "      <td>81884509966</td>\n",
       "      <td>67265</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128858</td>\n",
       "      <td>131216</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6641582155</td>\n",
       "      <td>37.757700</td>\n",
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       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>2003</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>2994454.0</td>\n",
       "      <td>1161249</td>\n",
       "      <td>64911102000</td>\n",
       "      <td>32410</td>\n",
       "      <td>22.0</td>\n",
       "      <td>473831</td>\n",
       "      <td>59604</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>32.789640</td>\n",
       "      <td>-117.193658</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>1996</td>\n",
       "      <td>Kern</td>\n",
       "      <td>624100.0</td>\n",
       "      <td>194632</td>\n",
       "      <td>6411332000</td>\n",
       "      <td>21944</td>\n",
       "      <td>37.0</td>\n",
       "      <td>92676</td>\n",
       "      <td>39076</td>\n",
       "      <td>30.0</td>\n",
       "      <td>187469000</td>\n",
       "      <td>35.376768</td>\n",
       "      <td>-118.807357</td>\n",
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       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>2004</td>\n",
       "      <td>Unallocated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3688</td>\n",
       "      <td>256247000</td>\n",
       "      <td>26957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1280</td>\n",
       "      <td>57976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9263000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "<p>1525 rows × 14 columns</p>\n",
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      "text/plain": [
       "      Taxable Year         County  Population  All Returns          AGI  \\\n",
       "0             2017    Nonresident         NaN       756843  32798066000   \n",
       "1             2012    Unallocated         0.0         2885    580682000   \n",
       "2             2010    Unallocated         0.0         2880    287037000   \n",
       "3             2006    Unallocated         0.0         3710    392781000   \n",
       "4             1995        Ventura    712700.0       281369  11450530000   \n",
       "...            ...            ...         ...          ...          ...   \n",
       "1520          2005         Nevada     99236.0        40399   2321926000   \n",
       "1521          2018  San Francisco    886618.0       476921  81884509966   \n",
       "1522          2003      San Diego   2994454.0      1161249  64911102000   \n",
       "1523          1996           Kern    624100.0       194632   6411332000   \n",
       "1524          2004    Unallocated         0.0         3688    256247000   \n",
       "\n",
       "      Median Income  Median Rank  Joint Returns  Joint Returns Median Income  \\\n",
       "0             14600          NaN         371420                        20592   \n",
       "1             51249          0.0           1194                       127511   \n",
       "2             36227          0.0           1041                        86697   \n",
       "3             31384          0.0           1260                        70468   \n",
       "4             26210         10.0         133610                        47458   \n",
       "...             ...          ...            ...                          ...   \n",
       "1520          35827         19.0          19384                        60869   \n",
       "1521          67265          2.0         128858                       131216   \n",
       "1522          32410         22.0         473831                        59604   \n",
       "1523          21944         37.0          92676                        39076   \n",
       "1524          26957          0.0           1280                        57976   \n",
       "\n",
       "      Joint Returns Median Rank  Tax Assessed   Latitude   Longitude  \\\n",
       "0                           NaN    3713047000   0.000000    0.000000   \n",
       "1                           0.0      19336000   0.000000    0.000000   \n",
       "2                           0.0      12616000   0.000000    0.000000   \n",
       "3                           0.0      14728000   0.000000    0.000000   \n",
       "4                           9.0     414669000  34.277091 -119.217850   \n",
       "...                         ...           ...        ...         ...   \n",
       "1520                       22.0      88583000  39.295489 -120.900873   \n",
       "1521                        4.0    6641582155  37.757700 -122.437600   \n",
       "1522                       18.0    2461189000  32.789640 -117.193658   \n",
       "1523                       30.0     187469000  35.376768 -118.807357   \n",
       "1524                        0.0       9263000   0.000000    0.000000   \n",
       "\n",
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       "4     POINT (-119.2178496 34.27709072)  \n",
       "...                                ...  \n",
       "1520  POINT (-120.9008733 39.29548859)  \n",
       "1521         POINT (-122.4376 37.7577)  \n",
       "1522  POINT (-117.1936577 32.78964014)  \n",
       "1523  POINT (-118.8073572 35.37676773)  \n",
       "1524                       POINT (0 0)  \n",
       "\n",
       "[1525 rows x 14 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load GDP dataset\n",
    "df_income = pd.read_excel('B-6__Comparison_By_County.xlsx')\n",
    "df_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "9702657e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2017, 2012, 2010, 2006, 1995, 2019, 2004, 2002, 2015, 1999, 1997,\n",
       "       2013, 2005, 2016, 1998, 2001, 2007, 2009, 2018, 2003, 1996, 2014,\n",
       "       2000, 2011, 2008])"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income[\"Taxable Year\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "5b482692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2017, 2012, 2019, 2015, 2013, 2016, 2018, 2014])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income = df_income[(df_income[\"Taxable Year\"]>=2012) & (df_income[\"Taxable Year\"]<=2019)]\n",
    "df_income[\"Taxable Year\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "213be868",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3f/0c48rx816935875jnxcqj5sr0000gn/T/ipykernel_57388/3067596823.py:1: 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",
      "  df_income['Taxable Year'] = df_income['Taxable Year'].apply(lambda x: str(x))\n"
     ]
    }
   ],
   "source": [
    "df_income['Taxable Year'] = df_income['Taxable Year'].apply(lambda x: str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "ced2a5d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Taxable Year</th>\n",
       "      <th>County</th>\n",
       "      <th>Population</th>\n",
       "      <th>All Returns</th>\n",
       "      <th>AGI</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Median Rank</th>\n",
       "      <th>Joint Returns</th>\n",
       "      <th>Joint Returns Median Income</th>\n",
       "      <th>Joint Returns Median Rank</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>Nonresident</td>\n",
       "      <td>NaN</td>\n",
       "      <td>756843</td>\n",
       "      <td>32798066000</td>\n",
       "      <td>14600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371420</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>Unallocated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2885</td>\n",
       "      <td>580682000</td>\n",
       "      <td>51249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1194</td>\n",
       "      <td>127511</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>San Benito</td>\n",
       "      <td>62789.0</td>\n",
       "      <td>29027</td>\n",
       "      <td>2240420000</td>\n",
       "      <td>47341</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11614</td>\n",
       "      <td>92584</td>\n",
       "      <td>18.0</td>\n",
       "      <td>94246000</td>\n",
       "      <td>36.500249</td>\n",
       "      <td>-121.091510</td>\n",
       "      <td>POINT (-121.0915105 36.50024907)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017</td>\n",
       "      <td>Shasta</td>\n",
       "      <td>178240.0</td>\n",
       "      <td>74833</td>\n",
       "      <td>4272946000</td>\n",
       "      <td>35365</td>\n",
       "      <td>36.0</td>\n",
       "      <td>32083</td>\n",
       "      <td>65014</td>\n",
       "      <td>37.0</td>\n",
       "      <td>151785000</td>\n",
       "      <td>40.535022</td>\n",
       "      <td>-122.502164</td>\n",
       "      <td>POINT (-122.5021643 40.53502155)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>10185487.0</td>\n",
       "      <td>4255233</td>\n",
       "      <td>310621984000</td>\n",
       "      <td>33369</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1325580</td>\n",
       "      <td>68088</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16810162000</td>\n",
       "      <td>34.059483</td>\n",
       "      <td>-118.242048</td>\n",
       "      <td>POINT (-118.242048 34.0594833)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Taxable Year       County  Population  All Returns           AGI  \\\n",
       "0          2017  Nonresident         NaN       756843   32798066000   \n",
       "1          2012  Unallocated         0.0         2885     580682000   \n",
       "8          2019   San Benito     62789.0        29027    2240420000   \n",
       "12         2017       Shasta    178240.0        74833    4272946000   \n",
       "14         2015  Los Angeles  10185487.0      4255233  310621984000   \n",
       "\n",
       "    Median Income  Median Rank  Joint Returns  Joint Returns Median Income  \\\n",
       "0           14600          NaN         371420                        20592   \n",
       "1           51249          0.0           1194                       127511   \n",
       "8           47341         13.0          11614                        92584   \n",
       "12          35365         36.0          32083                        65014   \n",
       "14          33369         38.0        1325580                        68088   \n",
       "\n",
       "    Joint Returns Median Rank  Tax Assessed   Latitude   Longitude  \\\n",
       "0                         NaN    3713047000   0.000000    0.000000   \n",
       "1                         0.0      19336000   0.000000    0.000000   \n",
       "8                        18.0      94246000  36.500249 -121.091510   \n",
       "12                       37.0     151785000  40.535022 -122.502164   \n",
       "14                       25.0   16810162000  34.059483 -118.242048   \n",
       "\n",
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      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "d6764725",
   "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>Taxable Year</th>\n",
       "      <th>County</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>Nonresident</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>Unallocated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>San Benito</td>\n",
       "      <td>62789.0</td>\n",
       "      <td>47341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017</td>\n",
       "      <td>Shasta</td>\n",
       "      <td>178240.0</td>\n",
       "      <td>35365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>10185487.0</td>\n",
       "      <td>33369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Taxable Year       County  Population  Median Income\n",
       "0          2017  Nonresident         NaN          14600\n",
       "1          2012  Unallocated         0.0          51249\n",
       "8          2019   San Benito     62789.0          47341\n",
       "12         2017       Shasta    178240.0          35365\n",
       "14         2015  Los Angeles  10185487.0          33369"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income = df_income.drop(df_income.columns[[3,4,6,7,8,9,10,11,12,13]], axis=1)\n",
    "df_income.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "6cd760be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Taxable Year</th>\n",
       "      <th>County</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Business_1</th>\n",
       "      <th>Payment_Ye</th>\n",
       "      <th>total_paym</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>Shasta</td>\n",
       "      <td>178240.0</td>\n",
       "      <td>35365</td>\n",
       "      <td>Shasta</td>\n",
       "      <td>2017</td>\n",
       "      <td>1500250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>10185487.0</td>\n",
       "      <td>33369</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2015</td>\n",
       "      <td>21013416.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>Modoc</td>\n",
       "      <td>9488.0</td>\n",
       "      <td>31446</td>\n",
       "      <td>Modoc</td>\n",
       "      <td>2017</td>\n",
       "      <td>21250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>Orange</td>\n",
       "      <td>3190832.0</td>\n",
       "      <td>45719</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2019</td>\n",
       "      <td>6307000.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>Stanislaus</td>\n",
       "      <td>555955.0</td>\n",
       "      <td>36697</td>\n",
       "      <td>Stanislaus</td>\n",
       "      <td>2019</td>\n",
       "      <td>1207000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Taxable Year       County  Population  Median Income   Business_1  \\\n",
       "0         2017       Shasta    178240.0          35365       Shasta   \n",
       "1         2015  Los Angeles  10185487.0          33369  Los Angeles   \n",
       "2         2017        Modoc      9488.0          31446        Modoc   \n",
       "3         2019       Orange   3190832.0          45719       Orange   \n",
       "4         2019   Stanislaus    555955.0          36697   Stanislaus   \n",
       "\n",
       "  Payment_Ye   total_paym  \n",
       "0       2017   1500250.00  \n",
       "1       2015  21013416.69  \n",
       "2       2017     21250.00  \n",
       "3       2019   6307000.08  \n",
       "4       2019   1207000.00  "
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = df_income.merge(df_EHR, how='inner', left_on=['County', 'Taxable Year'], right_on=['Business_1', 'Payment_Ye'])\n",
    "df_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "dc0626f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Taxable Year</th>\n",
       "      <th>County</th>\n",
       "      <th>Population</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Business_1</th>\n",
       "      <th>Payment_Ye</th>\n",
       "      <th>total_paym</th>\n",
       "      <th>Per-capita EHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>Shasta</td>\n",
       "      <td>178240.0</td>\n",
       "      <td>35365</td>\n",
       "      <td>Shasta</td>\n",
       "      <td>2017</td>\n",
       "      <td>1500250.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>10185487.0</td>\n",
       "      <td>33369</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2015</td>\n",
       "      <td>21013416.69</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>Modoc</td>\n",
       "      <td>9488.0</td>\n",
       "      <td>31446</td>\n",
       "      <td>Modoc</td>\n",
       "      <td>2017</td>\n",
       "      <td>21250.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>Orange</td>\n",
       "      <td>3190832.0</td>\n",
       "      <td>45719</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2019</td>\n",
       "      <td>6307000.08</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>Stanislaus</td>\n",
       "      <td>555955.0</td>\n",
       "      <td>36697</td>\n",
       "      <td>Stanislaus</td>\n",
       "      <td>2019</td>\n",
       "      <td>1207000.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>830956.0</td>\n",
       "      <td>49777</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>2013</td>\n",
       "      <td>3004750.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>Del Norte</td>\n",
       "      <td>26956.0</td>\n",
       "      <td>33172</td>\n",
       "      <td>Del Norte</td>\n",
       "      <td>2016</td>\n",
       "      <td>68000.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>1637176.0</td>\n",
       "      <td>52963</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>2016</td>\n",
       "      <td>3476500.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>Madera</td>\n",
       "      <td>158794.0</td>\n",
       "      <td>32927</td>\n",
       "      <td>Madera</td>\n",
       "      <td>2019</td>\n",
       "      <td>4403000.00</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>Tuolumne</td>\n",
       "      <td>54118.0</td>\n",
       "      <td>34191</td>\n",
       "      <td>Tuolumne</td>\n",
       "      <td>2013</td>\n",
       "      <td>148750.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Taxable Year         County  Population  Median Income     Business_1  \\\n",
       "0         2017         Shasta    178240.0          35365         Shasta   \n",
       "1         2015    Los Angeles  10185487.0          33369    Los Angeles   \n",
       "2         2017          Modoc      9488.0          31446          Modoc   \n",
       "3         2019         Orange   3190832.0          45719         Orange   \n",
       "4         2019     Stanislaus    555955.0          36697     Stanislaus   \n",
       "5         2013  San Francisco    830956.0          49777  San Francisco   \n",
       "6         2016      Del Norte     26956.0          33172      Del Norte   \n",
       "7         2016        Alameda   1637176.0          52963        Alameda   \n",
       "8         2019         Madera    158794.0          32927         Madera   \n",
       "9         2013       Tuolumne     54118.0          34191       Tuolumne   \n",
       "\n",
       "  Payment_Ye   total_paym  Per-capita EHR  \n",
       "0       2017   1500250.00             8.0  \n",
       "1       2015  21013416.69             2.0  \n",
       "2       2017     21250.00             2.0  \n",
       "3       2019   6307000.08             1.0  \n",
       "4       2019   1207000.00             2.0  \n",
       "5       2013   3004750.00             3.0  \n",
       "6       2016     68000.00             2.0  \n",
       "7       2016   3476500.00             2.0  \n",
       "8       2019   4403000.00            27.0  \n",
       "9       2013    148750.00             2.0  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the per capita payment from EHR \n",
    "df_2[\"Per-capita EHR\"] = (df_2[\"total_paym\"]//df_2[\"Population\"])\n",
    "df_2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "0714fca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output the datasets\n",
    "df_2.to_csv('df_2.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31fc1fb",
   "metadata": {},
   "source": [
    "# Application of Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "4e3f0d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ================================================\n",
    "# Build the model\n",
    "# ================================================\n",
    "\n",
    "# Training data\n",
    "X = df_2.loc[:,[\"Median Income\"]]  # features matrix\n",
    "y = df_2.loc[:,'Per-capita EHR']  # target (response) matix\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Splitting the data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7,test_size=0.3,random_state=100)\n",
    "\n",
    "# Train the model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "c6b71f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Median Income', ylabel='Per-capita EHR'>"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='Median Income', y='Per-capita EHR', data=df_2, ci=None, scatter_kws={'s':100, 'facecolor':'red'})"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}