machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Table Name</th>\n",
       "      <th>Tract</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mad Eliza's Cakes &amp; Confections</td>\n",
       "      <td>Topeka</td>\n",
       "      <td>39.001626</td>\n",
       "      <td>-95.782089</td>\n",
       "      <td>bakery</td>\n",
       "      <td>36.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sam's Club Bakery</td>\n",
       "      <td>1401 Southwest Wanamaker Road, Topeka</td>\n",
       "      <td>39.041499</td>\n",
       "      <td>-95.764625</td>\n",
       "      <td>bakery</td>\n",
       "      <td>36.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Cold Stone Creamery</td>\n",
       "      <td>1227 Southwest Wanamaker Road Ste 100, Topeka</td>\n",
       "      <td>39.044995</td>\n",
       "      <td>-95.763936</td>\n",
       "      <td>bakery</td>\n",
       "      <td>24.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Smallcakes Topeka</td>\n",
       "      <td>1223 Southwest Wanamaker Road #300, Topeka</td>\n",
       "      <td>39.044989</td>\n",
       "      <td>-95.762344</td>\n",
       "      <td>bakery</td>\n",
       "      <td>24.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Panera Bread</td>\n",
       "      <td>1421 Southwest Wanamaker Road, Topeka</td>\n",
       "      <td>39.040923</td>\n",
       "      <td>-95.762263</td>\n",
       "      <td>bakery</td>\n",
       "      <td>36.07</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>1880</td>\n",
       "      <td>1880</td>\n",
       "      <td>Nail Benders Home Improvement</td>\n",
       "      <td>3925 Southeast 77th Street, Berryton</td>\n",
       "      <td>38.927405</td>\n",
       "      <td>-95.606345</td>\n",
       "      <td>store</td>\n",
       "      <td>39.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1881</td>\n",
       "      <td>1881</td>\n",
       "      <td>Canvas Man</td>\n",
       "      <td>2730 Northeast 35th Street, Topeka</td>\n",
       "      <td>39.110223</td>\n",
       "      <td>-95.595987</td>\n",
       "      <td>store</td>\n",
       "      <td>33.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1882</td>\n",
       "      <td>1882</td>\n",
       "      <td>Rees Fruit Farm</td>\n",
       "      <td>2476 Kansas 4, Topeka</td>\n",
       "      <td>39.091186</td>\n",
       "      <td>-95.594169</td>\n",
       "      <td>store</td>\n",
       "      <td>201.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1883</td>\n",
       "      <td>1883</td>\n",
       "      <td>Bernadette's Custom Cosmetics</td>\n",
       "      <td>2521 Southeast Cuvier Drive, Topeka</td>\n",
       "      <td>39.020586</td>\n",
       "      <td>-95.595004</td>\n",
       "      <td>store</td>\n",
       "      <td>39.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1884</td>\n",
       "      <td>1884</td>\n",
       "      <td>Kansas Drug Card</td>\n",
       "      <td>4536 Southeast 37th Street, Topeka</td>\n",
       "      <td>39.000583</td>\n",
       "      <td>-95.595594</td>\n",
       "      <td>store</td>\n",
       "      <td>39.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1885 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                             Name  \\\n",
       "0              0  Mad Eliza's Cakes & Confections   \n",
       "1              1                Sam's Club Bakery   \n",
       "2              2              Cold Stone Creamery   \n",
       "3              3                Smallcakes Topeka   \n",
       "4              4                     Panera Bread   \n",
       "...          ...                              ...   \n",
       "1880        1880    Nail Benders Home Improvement   \n",
       "1881        1881                       Canvas Man   \n",
       "1882        1882                  Rees Fruit Farm   \n",
       "1883        1883    Bernadette's Custom Cosmetics   \n",
       "1884        1884                 Kansas Drug Card   \n",
       "\n",
       "                                            Address   Latitude  Longitude  \\\n",
       "0                                            Topeka  39.001626 -95.782089   \n",
       "1             1401 Southwest Wanamaker Road, Topeka  39.041499 -95.764625   \n",
       "2     1227 Southwest Wanamaker Road Ste 100, Topeka  39.044995 -95.763936   \n",
       "3        1223 Southwest Wanamaker Road #300, Topeka  39.044989 -95.762344   \n",
       "4             1421 Southwest Wanamaker Road, Topeka  39.040923 -95.762263   \n",
       "...                                             ...        ...        ...   \n",
       "1880           3925 Southeast 77th Street, Berryton  38.927405 -95.606345   \n",
       "1881             2730 Northeast 35th Street, Topeka  39.110223 -95.595987   \n",
       "1882                          2476 Kansas 4, Topeka  39.091186 -95.594169   \n",
       "1883            2521 Southeast Cuvier Drive, Topeka  39.020586 -95.595004   \n",
       "1884             4536 Southeast 37th Street, Topeka  39.000583 -95.595594   \n",
       "\n",
       "     Table Name   Tract  \n",
       "0        bakery   36.04  \n",
       "1        bakery   36.07  \n",
       "2        bakery   24.01  \n",
       "3        bakery   24.01  \n",
       "4        bakery   36.07  \n",
       "...         ...     ...  \n",
       "1880      store   39.02  \n",
       "1881      store   33.01  \n",
       "1882      store  201.01  \n",
       "1883      store   39.01  \n",
       "1884      store   39.01  \n",
       "\n",
       "[1885 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'Resources/master.csv'\n",
    "df = pd.read_csv(file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Tract</th>\n",
       "      <th>Table Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>36.04</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>36.07</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>24.01</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>24.01</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>36.07</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1880</td>\n",
       "      <td>39.02</td>\n",
       "      <td>store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1881</td>\n",
       "      <td>33.01</td>\n",
       "      <td>store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1882</td>\n",
       "      <td>201.01</td>\n",
       "      <td>store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1883</td>\n",
       "      <td>39.01</td>\n",
       "      <td>store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1884</td>\n",
       "      <td>39.01</td>\n",
       "      <td>store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1885 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tract Table Name\n",
       "0      36.04     bakery\n",
       "1      36.07     bakery\n",
       "2      24.01     bakery\n",
       "3      24.01     bakery\n",
       "4      36.07     bakery\n",
       "...      ...        ...\n",
       "1880   39.02      store\n",
       "1881   33.01      store\n",
       "1882  201.01      store\n",
       "1883   39.01      store\n",
       "1884   39.01      store\n",
       "\n",
       "[1885 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slim_df = df[['Tract', 'Table Name']]\n",
    "slim_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Tract</th>\n",
       "      <th>Table Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6.00</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7.00</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.00</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9.00</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>11.00</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>13.00</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>15.00</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>16.01</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>16.03</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>16.04</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>18.00</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>19.00</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>21.00</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>22.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>24.01</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>24.02</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>25.00</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>26.01</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>26.02</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>27.01</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>27.02</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>28.00</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>29.00</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>30.01</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>30.02</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>31.00</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>33.01</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>34.01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>35.00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>36.01</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>36.04</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>36.05</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>36.06</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>36.07</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>37.01</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>37.02</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>39.01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>39.02</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>40.00</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>41.00</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>201.01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Tract  Table Name\n",
       "0     4.00          38\n",
       "1     5.00           6\n",
       "2     6.00          27\n",
       "3     7.00         119\n",
       "4     8.00         105\n",
       "5     9.00          16\n",
       "6    10.00          20\n",
       "7    11.00          43\n",
       "8    12.00          10\n",
       "9    13.00          16\n",
       "10   15.00          59\n",
       "11   16.01          21\n",
       "12   16.03          20\n",
       "13   16.04          30\n",
       "14   18.00          11\n",
       "15   19.00          17\n",
       "16   21.00          28\n",
       "17   22.00           9\n",
       "18   24.01          84\n",
       "19   24.02          36\n",
       "20   25.00          21\n",
       "21   26.01          66\n",
       "22   26.02         117\n",
       "23   27.01          70\n",
       "24   27.02           7\n",
       "25   28.00          55\n",
       "26   29.00          72\n",
       "27   30.01          21\n",
       "28   30.02          10\n",
       "29   31.00          63\n",
       "30   33.01          11\n",
       "31   34.01           5\n",
       "32   35.00           8\n",
       "33   36.01           5\n",
       "34   36.04          10\n",
       "35   36.05          30\n",
       "36   36.06          27\n",
       "37   36.07          90\n",
       "38   37.01          69\n",
       "39   37.02          71\n",
       "40   39.01          14\n",
       "41   39.02           7\n",
       "42   40.00         142\n",
       "43   41.00          37\n",
       "44  201.01           1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df = slim_df.groupby('Tract')\n",
    "new_df = pd.DataFrame(grouped_df.count())\n",
    "food_count = new_df.reset_index()\n",
    "food_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>ID Year</th>\n",
       "      <th>Year</th>\n",
       "      <th>ID Race</th>\n",
       "      <th>Race</th>\n",
       "      <th>Household Income by Race</th>\n",
       "      <th>Household Income by Race Moe</th>\n",
       "      <th>Geography</th>\n",
       "      <th>ID Geography</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>46838</td>\n",
       "      <td>6268.0</td>\n",
       "      <td>16.01</td>\n",
       "      <td>14000US20177001601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>48500</td>\n",
       "      <td>7503.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>14000US20177004100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>24322</td>\n",
       "      <td>4392.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>14000US20177000400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>55101</td>\n",
       "      <td>7072.0</td>\n",
       "      <td>27.01</td>\n",
       "      <td>14000US20177002701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>58942</td>\n",
       "      <td>9373.0</td>\n",
       "      <td>27.02</td>\n",
       "      <td>14000US20177002702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>25489</td>\n",
       "      <td>5490.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>14000US20177000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>18559</td>\n",
       "      <td>8968.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>14000US20177004000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>49722</td>\n",
       "      <td>8548.0</td>\n",
       "      <td>26.02</td>\n",
       "      <td>14000US20177002602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>34345</td>\n",
       "      <td>2946.0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>14000US20177002800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>26000</td>\n",
       "      <td>5138.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>14000US20177000600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>83717</td>\n",
       "      <td>9687.0</td>\n",
       "      <td>39.02</td>\n",
       "      <td>14000US20177003902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>50227</td>\n",
       "      <td>8832.0</td>\n",
       "      <td>26.01</td>\n",
       "      <td>14000US20177002601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>40542</td>\n",
       "      <td>6260.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>14000US20177000700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>47500</td>\n",
       "      <td>6281.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>14000US20177002500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>35343</td>\n",
       "      <td>7613.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>14000US20177002900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>33229</td>\n",
       "      <td>10310.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>14000US20177000800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>75455</td>\n",
       "      <td>12813.0</td>\n",
       "      <td>39.01</td>\n",
       "      <td>14000US20177003901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>41329</td>\n",
       "      <td>6615.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>14000US20177002400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>39167</td>\n",
       "      <td>12030.0</td>\n",
       "      <td>30.01</td>\n",
       "      <td>14000US20177003001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>35940</td>\n",
       "      <td>5863.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>14000US20177000900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>63638</td>\n",
       "      <td>5179.0</td>\n",
       "      <td>37.00</td>\n",
       "      <td>14000US20177003700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>50658</td>\n",
       "      <td>8233.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>14000US20177002200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>39922</td>\n",
       "      <td>6527.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>14000US20177001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>71343</td>\n",
       "      <td>6033.0</td>\n",
       "      <td>30.02</td>\n",
       "      <td>14000US20177003002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>45369</td>\n",
       "      <td>6993.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>14000US20177002100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>28962</td>\n",
       "      <td>5493.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>14000US20177001100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>88942</td>\n",
       "      <td>10971.0</td>\n",
       "      <td>36.07</td>\n",
       "      <td>14000US20177003607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>64808</td>\n",
       "      <td>9625.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>14000US20177001900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>36297</td>\n",
       "      <td>7186.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>14000US20177003100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>37244</td>\n",
       "      <td>8913.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>14000US20177001200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>65731</td>\n",
       "      <td>7363.0</td>\n",
       "      <td>36.06</td>\n",
       "      <td>14000US20177003606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>49770</td>\n",
       "      <td>6122.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>14000US20177001800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>38750</td>\n",
       "      <td>13186.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14000US20177001300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>65000</td>\n",
       "      <td>10145.0</td>\n",
       "      <td>33.01</td>\n",
       "      <td>14000US20177003301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>56667</td>\n",
       "      <td>8236.0</td>\n",
       "      <td>16.04</td>\n",
       "      <td>14000US20177001604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>41660</td>\n",
       "      <td>5012.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>14000US20177001500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>95430</td>\n",
       "      <td>9770.0</td>\n",
       "      <td>36.05</td>\n",
       "      <td>14000US20177003605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>30675</td>\n",
       "      <td>6426.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>14000US20177001603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>84318</td>\n",
       "      <td>8926.0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>14000US20177003400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID Year  Year  ID Race   Race  Household Income by Race  \\\n",
       "0      2017  2017        0  Total                     46838   \n",
       "1      2017  2017        0  Total                     48500   \n",
       "2      2017  2017        0  Total                     24322   \n",
       "3      2017  2017        0  Total                     55101   \n",
       "4      2017  2017        0  Total                     58942   \n",
       "5      2017  2017        0  Total                     25489   \n",
       "6      2017  2017        0  Total                     18559   \n",
       "7      2017  2017        0  Total                     49722   \n",
       "8      2017  2017        0  Total                     34345   \n",
       "9      2017  2017        0  Total                     26000   \n",
       "10     2017  2017        0  Total                     83717   \n",
       "11     2017  2017        0  Total                     50227   \n",
       "12     2017  2017        0  Total                     40542   \n",
       "13     2017  2017        0  Total                     47500   \n",
       "14     2017  2017        0  Total                     35343   \n",
       "15     2017  2017        0  Total                     33229   \n",
       "16     2017  2017        0  Total                     75455   \n",
       "17     2017  2017        0  Total                     41329   \n",
       "18     2017  2017        0  Total                     39167   \n",
       "19     2017  2017        0  Total                     35940   \n",
       "20     2017  2017        0  Total                     63638   \n",
       "21     2017  2017        0  Total                     50658   \n",
       "22     2017  2017        0  Total                     39922   \n",
       "23     2017  2017        0  Total                     71343   \n",
       "24     2017  2017        0  Total                     45369   \n",
       "25     2017  2017        0  Total                     28962   \n",
       "26     2017  2017        0  Total                     88942   \n",
       "27     2017  2017        0  Total                     64808   \n",
       "28     2017  2017        0  Total                     36297   \n",
       "29     2017  2017        0  Total                     37244   \n",
       "30     2017  2017        0  Total                     65731   \n",
       "31     2017  2017        0  Total                     49770   \n",
       "32     2017  2017        0  Total                     38750   \n",
       "33     2017  2017        0  Total                     65000   \n",
       "34     2017  2017        0  Total                     56667   \n",
       "35     2017  2017        0  Total                     41660   \n",
       "36     2017  2017        0  Total                     95430   \n",
       "37     2017  2017        0  Total                     30675   \n",
       "38     2017  2017        0  Total                     84318   \n",
       "\n",
       "    Household Income by Race Moe  Geography        ID Geography  \n",
       "0                         6268.0      16.01  14000US20177001601  \n",
       "1                         7503.0      41.00  14000US20177004100  \n",
       "2                         4392.0       4.00  14000US20177000400  \n",
       "3                         7072.0      27.01  14000US20177002701  \n",
       "4                         9373.0      27.02  14000US20177002702  \n",
       "5                         5490.0       5.00  14000US20177000500  \n",
       "6                         8968.0      40.00  14000US20177004000  \n",
       "7                         8548.0      26.02  14000US20177002602  \n",
       "8                         2946.0      28.00  14000US20177002800  \n",
       "9                         5138.0       6.00  14000US20177000600  \n",
       "10                        9687.0      39.02  14000US20177003902  \n",
       "11                        8832.0      26.01  14000US20177002601  \n",
       "12                        6260.0       7.00  14000US20177000700  \n",
       "13                        6281.0      25.00  14000US20177002500  \n",
       "14                        7613.0      29.00  14000US20177002900  \n",
       "15                       10310.0       8.00  14000US20177000800  \n",
       "16                       12813.0      39.01  14000US20177003901  \n",
       "17                        6615.0      24.00  14000US20177002400  \n",
       "18                       12030.0      30.01  14000US20177003001  \n",
       "19                        5863.0       9.00  14000US20177000900  \n",
       "20                        5179.0      37.00  14000US20177003700  \n",
       "21                        8233.0      22.00  14000US20177002200  \n",
       "22                        6527.0      10.00  14000US20177001000  \n",
       "23                        6033.0      30.02  14000US20177003002  \n",
       "24                        6993.0      21.00  14000US20177002100  \n",
       "25                        5493.0      11.00  14000US20177001100  \n",
       "26                       10971.0      36.07  14000US20177003607  \n",
       "27                        9625.0      19.00  14000US20177001900  \n",
       "28                        7186.0      31.00  14000US20177003100  \n",
       "29                        8913.0      12.00  14000US20177001200  \n",
       "30                        7363.0      36.06  14000US20177003606  \n",
       "31                        6122.0      18.00  14000US20177001800  \n",
       "32                       13186.0      13.00  14000US20177001300  \n",
       "33                       10145.0      33.01  14000US20177003301  \n",
       "34                        8236.0      16.04  14000US20177001604  \n",
       "35                        5012.0      15.00  14000US20177001500  \n",
       "36                        9770.0      36.05  14000US20177003605  \n",
       "37                        6426.0      16.03  14000US20177001603  \n",
       "38                        8926.0      34.00  14000US20177003400  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'Resources/income_in_topeka.csv'\n",
    "df = pd.read_csv(file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Geography</th>\n",
       "      <th>Household Income by Race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>16.01</td>\n",
       "      <td>46838</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>41.00</td>\n",
       "      <td>48500</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.00</td>\n",
       "      <td>24322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>27.01</td>\n",
       "      <td>55101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>27.02</td>\n",
       "      <td>58942</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>25489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>40.00</td>\n",
       "      <td>18559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>26.02</td>\n",
       "      <td>49722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>28.00</td>\n",
       "      <td>34345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>6.00</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>39.02</td>\n",
       "      <td>83717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>26.01</td>\n",
       "      <td>50227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>7.00</td>\n",
       "      <td>40542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>25.00</td>\n",
       "      <td>47500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>29.00</td>\n",
       "      <td>35343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>8.00</td>\n",
       "      <td>33229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>39.01</td>\n",
       "      <td>75455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>24.00</td>\n",
       "      <td>41329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>30.01</td>\n",
       "      <td>39167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>9.00</td>\n",
       "      <td>35940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>37.00</td>\n",
       "      <td>63638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22.00</td>\n",
       "      <td>50658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>10.00</td>\n",
       "      <td>39922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>30.02</td>\n",
       "      <td>71343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>21.00</td>\n",
       "      <td>45369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>11.00</td>\n",
       "      <td>28962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>36.07</td>\n",
       "      <td>88942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>19.00</td>\n",
       "      <td>64808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>31.00</td>\n",
       "      <td>36297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>12.00</td>\n",
       "      <td>37244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>36.06</td>\n",
       "      <td>65731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>18.00</td>\n",
       "      <td>49770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>13.00</td>\n",
       "      <td>38750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>33.01</td>\n",
       "      <td>65000</td>\n",
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       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>16.04</td>\n",
       "      <td>56667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>15.00</td>\n",
       "      <td>41660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>36.05</td>\n",
       "      <td>95430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>16.03</td>\n",
       "      <td>30675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>34.00</td>\n",
       "      <td>84318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Geography  Household Income by Race\n",
       "0       16.01                     46838\n",
       "1       41.00                     48500\n",
       "2        4.00                     24322\n",
       "3       27.01                     55101\n",
       "4       27.02                     58942\n",
       "5        5.00                     25489\n",
       "6       40.00                     18559\n",
       "7       26.02                     49722\n",
       "8       28.00                     34345\n",
       "9        6.00                     26000\n",
       "10      39.02                     83717\n",
       "11      26.01                     50227\n",
       "12       7.00                     40542\n",
       "13      25.00                     47500\n",
       "14      29.00                     35343\n",
       "15       8.00                     33229\n",
       "16      39.01                     75455\n",
       "17      24.00                     41329\n",
       "18      30.01                     39167\n",
       "19       9.00                     35940\n",
       "20      37.00                     63638\n",
       "21      22.00                     50658\n",
       "22      10.00                     39922\n",
       "23      30.02                     71343\n",
       "24      21.00                     45369\n",
       "25      11.00                     28962\n",
       "26      36.07                     88942\n",
       "27      19.00                     64808\n",
       "28      31.00                     36297\n",
       "29      12.00                     37244\n",
       "30      36.06                     65731\n",
       "31      18.00                     49770\n",
       "32      13.00                     38750\n",
       "33      33.01                     65000\n",
       "34      16.04                     56667\n",
       "35      15.00                     41660\n",
       "36      36.05                     95430\n",
       "37      16.03                     30675\n",
       "38      34.00                     84318"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slim_df = df[['Geography', 'Household Income by Race']]\n",
    "slim_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Tract</th>\n",
       "      <th>Household Income by Race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>24322</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>25489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6.00</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7.00</td>\n",
       "      <td>40542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.00</td>\n",
       "      <td>33229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9.00</td>\n",
       "      <td>35940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>10.00</td>\n",
       "      <td>39922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>11.00</td>\n",
       "      <td>28962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>12.00</td>\n",
       "      <td>37244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>13.00</td>\n",
       "      <td>38750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>15.00</td>\n",
       "      <td>41660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>16.01</td>\n",
       "      <td>46838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>16.03</td>\n",
       "      <td>30675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>16.04</td>\n",
       "      <td>56667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>18.00</td>\n",
       "      <td>49770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>19.00</td>\n",
       "      <td>64808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>21.00</td>\n",
       "      <td>45369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>22.00</td>\n",
       "      <td>50658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>24.00</td>\n",
       "      <td>41329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>25.00</td>\n",
       "      <td>47500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>26.01</td>\n",
       "      <td>50227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>26.02</td>\n",
       "      <td>49722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>27.01</td>\n",
       "      <td>55101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>27.02</td>\n",
       "      <td>58942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>28.00</td>\n",
       "      <td>34345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>29.00</td>\n",
       "      <td>35343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>30.01</td>\n",
       "      <td>39167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>30.02</td>\n",
       "      <td>71343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>31.00</td>\n",
       "      <td>36297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>33.01</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>34.00</td>\n",
       "      <td>84318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>36.05</td>\n",
       "      <td>95430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>36.06</td>\n",
       "      <td>65731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>36.07</td>\n",
       "      <td>88942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>37.00</td>\n",
       "      <td>63638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>39.01</td>\n",
       "      <td>75455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>39.02</td>\n",
       "      <td>83717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>40.00</td>\n",
       "      <td>18559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>41.00</td>\n",
       "      <td>48500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tract  Household Income by Race\n",
       "0    4.00                     24322\n",
       "1    5.00                     25489\n",
       "2    6.00                     26000\n",
       "3    7.00                     40542\n",
       "4    8.00                     33229\n",
       "5    9.00                     35940\n",
       "6   10.00                     39922\n",
       "7   11.00                     28962\n",
       "8   12.00                     37244\n",
       "9   13.00                     38750\n",
       "10  15.00                     41660\n",
       "11  16.01                     46838\n",
       "12  16.03                     30675\n",
       "13  16.04                     56667\n",
       "14  18.00                     49770\n",
       "15  19.00                     64808\n",
       "16  21.00                     45369\n",
       "17  22.00                     50658\n",
       "18  24.00                     41329\n",
       "19  25.00                     47500\n",
       "20  26.01                     50227\n",
       "21  26.02                     49722\n",
       "22  27.01                     55101\n",
       "23  27.02                     58942\n",
       "24  28.00                     34345\n",
       "25  29.00                     35343\n",
       "26  30.01                     39167\n",
       "27  30.02                     71343\n",
       "28  31.00                     36297\n",
       "29  33.01                     65000\n",
       "30  34.00                     84318\n",
       "31  36.05                     95430\n",
       "32  36.06                     65731\n",
       "33  36.07                     88942\n",
       "34  37.00                     63638\n",
       "35  39.01                     75455\n",
       "36  39.02                     83717\n",
       "37  40.00                     18559\n",
       "38  41.00                     48500"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_income = slim_df.groupby('Geography')\n",
    "new_df = pd.DataFrame(grouped_income.mean())\n",
    "new_df = new_df.reset_index()\n",
    "income_avg = new_df.rename(columns = {'Geography': 'Tract'})\n",
    "income_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Tract</th>\n",
       "      <th>Table Name</th>\n",
       "      <th>Household Income by Race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>38</td>\n",
       "      <td>24322</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6</td>\n",
       "      <td>25489</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6.00</td>\n",
       "      <td>27</td>\n",
       "      <td>26000</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7.00</td>\n",
       "      <td>119</td>\n",
       "      <td>40542</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8.00</td>\n",
       "      <td>105</td>\n",
       "      <td>33229</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>9.00</td>\n",
       "      <td>16</td>\n",
       "      <td>35940</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20</td>\n",
       "      <td>39922</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>11.00</td>\n",
       "      <td>43</td>\n",
       "      <td>28962</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10</td>\n",
       "      <td>37244</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>13.00</td>\n",
       "      <td>16</td>\n",
       "      <td>38750</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>15.00</td>\n",
       "      <td>59</td>\n",
       "      <td>41660</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>16.01</td>\n",
       "      <td>21</td>\n",
       "      <td>46838</td>\n",
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       "      <td>12</td>\n",
       "      <td>16.03</td>\n",
       "      <td>20</td>\n",
       "      <td>30675</td>\n",
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       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>16.04</td>\n",
       "      <td>30</td>\n",
       "      <td>56667</td>\n",
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       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>18.00</td>\n",
       "      <td>11</td>\n",
       "      <td>49770</td>\n",
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       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>19.00</td>\n",
       "      <td>17</td>\n",
       "      <td>64808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>21.00</td>\n",
       "      <td>28</td>\n",
       "      <td>45369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>22.00</td>\n",
       "      <td>9</td>\n",
       "      <td>50658</td>\n",
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       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>25.00</td>\n",
       "      <td>21</td>\n",
       "      <td>47500</td>\n",
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       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>26.01</td>\n",
       "      <td>66</td>\n",
       "      <td>50227</td>\n",
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       "      <td>20</td>\n",
       "      <td>26.02</td>\n",
       "      <td>117</td>\n",
       "      <td>49722</td>\n",
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       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>27.01</td>\n",
       "      <td>70</td>\n",
       "      <td>55101</td>\n",
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       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>27.02</td>\n",
       "      <td>7</td>\n",
       "      <td>58942</td>\n",
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       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>28.00</td>\n",
       "      <td>55</td>\n",
       "      <td>34345</td>\n",
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       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>29.00</td>\n",
       "      <td>72</td>\n",
       "      <td>35343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>30.01</td>\n",
       "      <td>21</td>\n",
       "      <td>39167</td>\n",
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       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>30.02</td>\n",
       "      <td>10</td>\n",
       "      <td>71343</td>\n",
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       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>31.00</td>\n",
       "      <td>63</td>\n",
       "      <td>36297</td>\n",
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       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>33.01</td>\n",
       "      <td>11</td>\n",
       "      <td>65000</td>\n",
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       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>36.05</td>\n",
       "      <td>30</td>\n",
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       "      <td>36.06</td>\n",
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       "      <td>41.00</td>\n",
       "      <td>37</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Tract  Table Name  Household Income by Race\n",
       "0    4.00          38                     24322\n",
       "1    5.00           6                     25489\n",
       "2    6.00          27                     26000\n",
       "3    7.00         119                     40542\n",
       "4    8.00         105                     33229\n",
       "5    9.00          16                     35940\n",
       "6   10.00          20                     39922\n",
       "7   11.00          43                     28962\n",
       "8   12.00          10                     37244\n",
       "9   13.00          16                     38750\n",
       "10  15.00          59                     41660\n",
       "11  16.01          21                     46838\n",
       "12  16.03          20                     30675\n",
       "13  16.04          30                     56667\n",
       "14  18.00          11                     49770\n",
       "15  19.00          17                     64808\n",
       "16  21.00          28                     45369\n",
       "17  22.00           9                     50658\n",
       "18  25.00          21                     47500\n",
       "19  26.01          66                     50227\n",
       "20  26.02         117                     49722\n",
       "21  27.01          70                     55101\n",
       "22  27.02           7                     58942\n",
       "23  28.00          55                     34345\n",
       "24  29.00          72                     35343\n",
       "25  30.01          21                     39167\n",
       "26  30.02          10                     71343\n",
       "27  31.00          63                     36297\n",
       "28  33.01          11                     65000\n",
       "29  36.05          30                     95430\n",
       "30  36.06          27                     65731\n",
       "31  36.07          90                     88942\n",
       "32  39.01          14                     75455\n",
       "33  39.02           7                     83717\n",
       "34  40.00         142                     18559\n",
       "35  41.00          37                     48500"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df = food_count.merge(income_avg, on='Tract')\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10</td>\n",
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       "      <td>11</td>\n",
       "      <td>21</td>\n",
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       "      <td>12</td>\n",
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       "      <td>13</td>\n",
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       "      <td>49722</td>\n",
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       "      <td>21</td>\n",
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       "      <td>22</td>\n",
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      ],
      "text/plain": [
       "    Food  Income\n",
       "0     38   24322\n",
       "1      6   25489\n",
       "2     27   26000\n",
       "3    119   40542\n",
       "4    105   33229\n",
       "5     16   35940\n",
       "6     20   39922\n",
       "7     43   28962\n",
       "8     10   37244\n",
       "9     16   38750\n",
       "10    59   41660\n",
       "11    21   46838\n",
       "12    20   30675\n",
       "13    30   56667\n",
       "14    11   49770\n",
       "15    17   64808\n",
       "16    28   45369\n",
       "17     9   50658\n",
       "18    21   47500\n",
       "19    66   50227\n",
       "20   117   49722\n",
       "21    70   55101\n",
       "22     7   58942\n",
       "23    55   34345\n",
       "24    72   35343\n",
       "25    21   39167\n",
       "26    10   71343\n",
       "27    63   36297\n",
       "28    11   65000\n",
       "29    30   95430\n",
       "30    27   65731\n",
       "31    90   88942\n",
       "32    14   75455\n",
       "33     7   83717\n",
       "34   142   18559\n",
       "35    37   48500"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_df = combined_df[['Table Name', 'Household Income by Race']]\n",
    "income_df = income_df.rename(columns = {'Table Name': 'Food', 'Household Income by Race': 'Income'})\n",
    "income_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tract</th>\n",
       "      <th>Population</th>\n",
       "      <th>Housing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>2467</td>\n",
       "      <td>1266</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>33.02</td>\n",
       "      <td>3835</td>\n",
       "      <td>1438</td>\n",
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       "      <td>6491</td>\n",
       "      <td>2292</td>\n",
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       "      <td>4408</td>\n",
       "      <td>1706</td>\n",
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       "      <td>4</td>\n",
       "      <td>13.00</td>\n",
       "      <td>4261</td>\n",
       "      <td>1830</td>\n",
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       "      <td>5</td>\n",
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       "      <td>2868</td>\n",
       "      <td>1521</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>26.02</td>\n",
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       "      <td>3146</td>\n",
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       "      <td>2259</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>16.01</td>\n",
       "      <td>2826</td>\n",
       "      <td>1302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>24.00</td>\n",
       "      <td>5894</td>\n",
       "      <td>3352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>28.00</td>\n",
       "      <td>4006</td>\n",
       "      <td>2340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>33.01</td>\n",
       "      <td>2859</td>\n",
       "      <td>1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>37.00</td>\n",
       "      <td>8827</td>\n",
       "      <td>3320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>30.01</td>\n",
       "      <td>3315</td>\n",
       "      <td>1515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>31.00</td>\n",
       "      <td>5290</td>\n",
       "      <td>2134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>39.02</td>\n",
       "      <td>4429</td>\n",
       "      <td>1858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2781</td>\n",
       "      <td>1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>15.00</td>\n",
       "      <td>2040</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>21.00</td>\n",
       "      <td>3289</td>\n",
       "      <td>1571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>41.00</td>\n",
       "      <td>3785</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2799</td>\n",
       "      <td>1324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>26.01</td>\n",
       "      <td>3633</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>27.01</td>\n",
       "      <td>4489</td>\n",
       "      <td>2075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>16.04</td>\n",
       "      <td>3630</td>\n",
       "      <td>1738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>36.07</td>\n",
       "      <td>6448</td>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>36.06</td>\n",
       "      <td>4830</td>\n",
       "      <td>2062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>16.03</td>\n",
       "      <td>2852</td>\n",
       "      <td>1716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>34.00</td>\n",
       "      <td>9728</td>\n",
       "      <td>3823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>36.01</td>\n",
       "      <td>4897</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3977</td>\n",
       "      <td>2236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3619</td>\n",
       "      <td>1526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2253</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3103</td>\n",
       "      <td>1481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3067</td>\n",
       "      <td>1472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>27.02</td>\n",
       "      <td>4187</td>\n",
       "      <td>2039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>29.00</td>\n",
       "      <td>3540</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>36.04</td>\n",
       "      <td>3963</td>\n",
       "      <td>1588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1929</td>\n",
       "      <td>1445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>9.00</td>\n",
       "      <td>3258</td>\n",
       "      <td>1494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3968</td>\n",
       "      <td>1591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>30.02</td>\n",
       "      <td>5518</td>\n",
       "      <td>2134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>39.01</td>\n",
       "      <td>6561</td>\n",
       "      <td>2425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3572</td>\n",
       "      <td>1603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tract  Population  Housing\n",
       "0   22.00        2467     1266\n",
       "1   33.02        3835     1438\n",
       "2   35.00        6491     2292\n",
       "3   36.05        4408     1706\n",
       "4   13.00        4261     1830\n",
       "5   40.00        2868     1521\n",
       "6   26.02        6641     3146\n",
       "7    6.00        2259     1103\n",
       "8   16.01        2826     1302\n",
       "9   24.00        5894     3352\n",
       "10  28.00        4006     2340\n",
       "11  33.01        2859     1156\n",
       "12  37.00        8827     3320\n",
       "13  30.01        3315     1515\n",
       "14  31.00        5290     2134\n",
       "15  39.02        4429     1858\n",
       "16  11.00        2781     1330\n",
       "17  15.00        2040     1104\n",
       "18  21.00        3289     1571\n",
       "19  41.00        3785     2004\n",
       "20   7.00        2799     1324\n",
       "21  26.01        3633     1957\n",
       "22  27.01        4489     2075\n",
       "23  16.04        3630     1738\n",
       "24  36.07        6448     2600\n",
       "25  36.06        4830     2062\n",
       "26  16.03        2852     1716\n",
       "27  34.00        9728     3823\n",
       "28  36.01        4897     1976\n",
       "29   4.00        3977     2236\n",
       "30  10.00        3619     1526\n",
       "31  12.00        2253      927\n",
       "32  19.00        3103     1481\n",
       "33  25.00        3067     1472\n",
       "34  27.02        4187     2039\n",
       "35  29.00        3540     1338\n",
       "36  36.04        3963     1588\n",
       "37   5.00        1929     1445\n",
       "38   9.00        3258     1494\n",
       "39  18.00        3968     1591\n",
       "40  30.02        5518     2134\n",
       "41  39.01        6561     2425\n",
       "42   8.00        3572     1603"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'Resources/Shawnee_Pop_Housing.csv'\n",
    "pop_housing_df = pd.read_csv(file)\n",
    "pop_housing_df = pop_housing_df.rename(columns = {'Housing Units': 'Housing'})\n",
    "pop_housing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Tract</th>\n",
       "      <th>Population</th>\n",
       "      <th>Food</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>2467</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>35.00</td>\n",
       "      <td>6491</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>36.05</td>\n",
       "      <td>4408</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13.00</td>\n",
       "      <td>4261</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2868</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>26.02</td>\n",
       "      <td>6641</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2259</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>16.01</td>\n",
       "      <td>2826</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>28.00</td>\n",
       "      <td>4006</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>33.01</td>\n",
       "      <td>2859</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>30.01</td>\n",
       "      <td>3315</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>31.00</td>\n",
       "      <td>5290</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>39.02</td>\n",
       "      <td>4429</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2781</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.00</td>\n",
       "      <td>2040</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>21.00</td>\n",
       "      <td>3289</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>41.00</td>\n",
       "      <td>3785</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2799</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>26.01</td>\n",
       "      <td>3633</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>27.01</td>\n",
       "      <td>4489</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>16.04</td>\n",
       "      <td>3630</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>36.07</td>\n",
       "      <td>6448</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>36.06</td>\n",
       "      <td>4830</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>16.03</td>\n",
       "      <td>2852</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>36.01</td>\n",
       "      <td>4897</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3977</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3619</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2253</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3103</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3067</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>27.02</td>\n",
       "      <td>4187</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>29.00</td>\n",
       "      <td>3540</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>36.04</td>\n",
       "      <td>3963</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1929</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>9.00</td>\n",
       "      <td>3258</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3968</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>30.02</td>\n",
       "      <td>5518</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>39.01</td>\n",
       "      <td>6561</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3572</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tract  Population  Food\n",
       "0   22.00        2467     9\n",
       "1   35.00        6491     8\n",
       "2   36.05        4408    30\n",
       "3   13.00        4261    16\n",
       "4   40.00        2868   142\n",
       "5   26.02        6641   117\n",
       "6    6.00        2259    27\n",
       "7   16.01        2826    21\n",
       "8   28.00        4006    55\n",
       "9   33.01        2859    11\n",
       "10  30.01        3315    21\n",
       "11  31.00        5290    63\n",
       "12  39.02        4429     7\n",
       "13  11.00        2781    43\n",
       "14  15.00        2040    59\n",
       "15  21.00        3289    28\n",
       "16  41.00        3785    37\n",
       "17   7.00        2799   119\n",
       "18  26.01        3633    66\n",
       "19  27.01        4489    70\n",
       "20  16.04        3630    30\n",
       "21  36.07        6448    90\n",
       "22  36.06        4830    27\n",
       "23  16.03        2852    20\n",
       "24  36.01        4897     5\n",
       "25   4.00        3977    38\n",
       "26  10.00        3619    20\n",
       "27  12.00        2253    10\n",
       "28  19.00        3103    17\n",
       "29  25.00        3067    21\n",
       "30  27.02        4187     7\n",
       "31  29.00        3540    72\n",
       "32  36.04        3963    10\n",
       "33   5.00        1929     6\n",
       "34   9.00        3258    16\n",
       "35  18.00        3968    11\n",
       "36  30.02        5518    10\n",
       "37  39.01        6561    14\n",
       "38   8.00        3572   105"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slim_df = pop_housing_df[['Tract', 'Population']]\n",
    "population_count = slim_df.merge(food_count, on='Tract')\n",
    "population_count = population_count.rename(columns = {'Table Name': 'Food'})\n",
    "population_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Tract</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Food</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1266</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>35.00</td>\n",
       "      <td>2292</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>36.05</td>\n",
       "      <td>1706</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1830</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>40.00</td>\n",
       "      <td>1521</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>26.02</td>\n",
       "      <td>3146</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1103</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>16.01</td>\n",
       "      <td>1302</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>28.00</td>\n",
       "      <td>2340</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>33.01</td>\n",
       "      <td>1156</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>30.01</td>\n",
       "      <td>1515</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>31.00</td>\n",
       "      <td>2134</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>39.02</td>\n",
       "      <td>1858</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1330</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1104</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1571</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>41.00</td>\n",
       "      <td>2004</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1324</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>26.01</td>\n",
       "      <td>1957</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>27.01</td>\n",
       "      <td>2075</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>16.04</td>\n",
       "      <td>1738</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>36.07</td>\n",
       "      <td>2600</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>36.06</td>\n",
       "      <td>2062</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>16.03</td>\n",
       "      <td>1716</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>36.01</td>\n",
       "      <td>1976</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2236</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1526</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>12.00</td>\n",
       "      <td>927</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>19.00</td>\n",
       "      <td>1481</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1472</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>27.02</td>\n",
       "      <td>2039</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1338</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>36.04</td>\n",
       "      <td>1588</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1445</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1494</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1591</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>30.02</td>\n",
       "      <td>2134</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>39.01</td>\n",
       "      <td>2425</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1603</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tract  Housing  Food\n",
       "0   22.00     1266     9\n",
       "1   35.00     2292     8\n",
       "2   36.05     1706    30\n",
       "3   13.00     1830    16\n",
       "4   40.00     1521   142\n",
       "5   26.02     3146   117\n",
       "6    6.00     1103    27\n",
       "7   16.01     1302    21\n",
       "8   28.00     2340    55\n",
       "9   33.01     1156    11\n",
       "10  30.01     1515    21\n",
       "11  31.00     2134    63\n",
       "12  39.02     1858     7\n",
       "13  11.00     1330    43\n",
       "14  15.00     1104    59\n",
       "15  21.00     1571    28\n",
       "16  41.00     2004    37\n",
       "17   7.00     1324   119\n",
       "18  26.01     1957    66\n",
       "19  27.01     2075    70\n",
       "20  16.04     1738    30\n",
       "21  36.07     2600    90\n",
       "22  36.06     2062    27\n",
       "23  16.03     1716    20\n",
       "24  36.01     1976     5\n",
       "25   4.00     2236    38\n",
       "26  10.00     1526    20\n",
       "27  12.00      927    10\n",
       "28  19.00     1481    17\n",
       "29  25.00     1472    21\n",
       "30  27.02     2039     7\n",
       "31  29.00     1338    72\n",
       "32  36.04     1588    10\n",
       "33   5.00     1445     6\n",
       "34   9.00     1494    16\n",
       "35  18.00     1591    11\n",
       "36  30.02     2134    10\n",
       "37  39.01     2425    14\n",
       "38   8.00     1603   105"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slim_df = pop_housing_df[['Tract', 'Housing']]\n",
    "housing_count = slim_df.merge(food_count, on='Tract')\n",
    "housing_count = housing_count.rename(columns = {'Table Name': 'Food'})\n",
    "housing_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-squared is 0.05012300250449765\n",
      "Weight coefficients:  [[-0.00043659]]\n",
      "y-axis intercept:  [61.47204695]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = income_df.Income.values.reshape(-1, 1)\n",
    "y = income_df.Food.values.reshape(-1, 1)\n",
    "x_values = income_df['Income']\n",
    "y_values = income_df['Food']\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x_values, y_values)\n",
    "r_squared = r_value**2\n",
    "print(f'The r-squared is {r_squared}')\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "print('Weight coefficients: ', model.coef_)\n",
    "print('y-axis intercept: ', model.intercept_)\n",
    "x_min = np.array([[X.min()]])\n",
    "x_max = np.array([[X.max()]])\n",
    "y_min = model.predict(x_min)\n",
    "y_max = model.predict(x_max)\n",
    "plt.scatter(X, y, c='blue')\n",
    "plt.title(\"Avg Income VS Access to Food\")\n",
    "plt.xlabel(\"Avg Income of Census Tract\")\n",
    "plt.ylabel(\"# of Food Sources in Census Tract\")\n",
    "plt.plot([x_min[0], x_max[0]], [y_min[0], y_max[0]], c='red')\n",
    "plt.savefig('Images/income_vs_food.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-squared is 0.008019590151210475\n",
      "Weight coefficients:  [[0.00255527]]\n",
      "y-axis intercept:  [28.06238188]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = population_count.Population.values.reshape(-1, 1)\n",
    "y = population_count.Food.values.reshape(-1, 1)\n",
    "x_values = population_count['Population']\n",
    "y_values = population_count['Food']\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x_values, y_values)\n",
    "r_squared = r_value**2\n",
    "print(f'The r-squared is {r_squared}')\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "print('Weight coefficients: ', model.coef_)\n",
    "print('y-axis intercept: ', model.intercept_)\n",
    "x_min = np.array([[X.min()]])\n",
    "x_max = np.array([[X.max()]])\n",
    "y_min = model.predict(x_min)\n",
    "y_max = model.predict(x_max)\n",
    "plt.scatter(X, y, c='blue')\n",
    "plt.title(\"Total Population VS Access to Food\")\n",
    "plt.xlabel(\"Total Population of Census Tract\")\n",
    "plt.ylabel(\"# of Food Sources in Census Tract\")\n",
    "plt.plot([x_min[0], x_max[0]], [y_min[0], y_max[0]], c='red')\n",
    "plt.savefig('Images/population_vs_food.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-squared is 0.047429453509386585\n",
      "Weight coefficients:  [[0.00255527]]\n",
      "y-axis intercept:  [28.06238188]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = housing_count.Housing.values.reshape(-1, 1)\n",
    "y = housing_count.Food.values.reshape(-1, 1)\n",
    "x_values = housing_count['Housing']\n",
    "y_values = housing_count['Food']\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x_values, y_values)\n",
    "r_squared = r_value**2\n",
    "print(f'The r-squared is {r_squared}')\n",
    "plt.scatter(X, y)\n",
    "print('Weight coefficients: ', model.coef_)\n",
    "print('y-axis intercept: ', model.intercept_)\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "x_min = np.array([[X.min()]])\n",
    "x_max = np.array([[X.max()]])\n",
    "y_min = model.predict(x_min)\n",
    "y_max = model.predict(x_max)\n",
    "plt.scatter(X, y, c='blue')\n",
    "plt.title(\"Housing VS Access to Food\")\n",
    "plt.xlabel(\"Housing Units per Census Tract\")\n",
    "plt.ylabel(\"# of Food Sources in Census Tract\")\n",
    "plt.plot([x_min[0], x_max[0]], [y_min[0], y_max[0]], c='red')\n",
    "plt.savefig('Images/housing_vs_food.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Income</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>24322</td>\n",
       "      <td>3977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>25489</td>\n",
       "      <td>1929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>26000</td>\n",
       "      <td>2259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>40542</td>\n",
       "      <td>2799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33229</td>\n",
       "      <td>3572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>35940</td>\n",
       "      <td>3258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>39922</td>\n",
       "      <td>3619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>28962</td>\n",
       "      <td>2781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>37244</td>\n",
       "      <td>2253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>38750</td>\n",
       "      <td>4261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>41660</td>\n",
       "      <td>2040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>46838</td>\n",
       "      <td>2826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>30675</td>\n",
       "      <td>2852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>56667</td>\n",
       "      <td>3630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>49770</td>\n",
       "      <td>3968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>64808</td>\n",
       "      <td>3103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>45369</td>\n",
       "      <td>3289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>50658</td>\n",
       "      <td>2467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>47500</td>\n",
       "      <td>3067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>50227</td>\n",
       "      <td>3633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>49722</td>\n",
       "      <td>6641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>55101</td>\n",
       "      <td>4489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>58942</td>\n",
       "      <td>4187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>34345</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>35343</td>\n",
       "      <td>3540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>39167</td>\n",
       "      <td>3315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>71343</td>\n",
       "      <td>5518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>36297</td>\n",
       "      <td>5290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>65000</td>\n",
       "      <td>2859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>95430</td>\n",
       "      <td>4408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>65731</td>\n",
       "      <td>4830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>88942</td>\n",
       "      <td>6448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>75455</td>\n",
       "      <td>6561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>83717</td>\n",
       "      <td>4429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>18559</td>\n",
       "      <td>2868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>48500</td>\n",
       "      <td>3785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Income  Population\n",
       "0    24322        3977\n",
       "1    25489        1929\n",
       "2    26000        2259\n",
       "3    40542        2799\n",
       "4    33229        3572\n",
       "5    35940        3258\n",
       "6    39922        3619\n",
       "7    28962        2781\n",
       "8    37244        2253\n",
       "9    38750        4261\n",
       "10   41660        2040\n",
       "11   46838        2826\n",
       "12   30675        2852\n",
       "13   56667        3630\n",
       "14   49770        3968\n",
       "15   64808        3103\n",
       "16   45369        3289\n",
       "17   50658        2467\n",
       "18   47500        3067\n",
       "19   50227        3633\n",
       "20   49722        6641\n",
       "21   55101        4489\n",
       "22   58942        4187\n",
       "23   34345        4006\n",
       "24   35343        3540\n",
       "25   39167        3315\n",
       "26   71343        5518\n",
       "27   36297        5290\n",
       "28   65000        2859\n",
       "29   95430        4408\n",
       "30   65731        4830\n",
       "31   88942        6448\n",
       "32   75455        6561\n",
       "33   83717        4429\n",
       "34   18559        2868\n",
       "35   48500        3785"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_new_df = income_avg.merge(population_count, on='Tract')\n",
    "brand_new_df = brand_new_df.rename(columns = {'Household Income by Race': 'Income'})\n",
    "brand_new_df = brand_new_df[['Income', 'Population']]\n",
    "brand_new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-squared is 0.31818517922556977\n",
      "Weight coefficients:  [[0.03685584]]\n",
      "y-axis intercept:  [1965.80950001]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = brand_new_df.Income.values.reshape(-1, 1)\n",
    "y = brand_new_df.Population.values.reshape(-1, 1)\n",
    "x_values = brand_new_df['Income']\n",
    "y_values = brand_new_df['Population']\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x_values, y_values)\n",
    "r_squared = r_value**2\n",
    "print(f'The r-squared is {r_squared}')\n",
    "plt.scatter(X, y)\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "print('Weight coefficients: ', model.coef_)\n",
    "print('y-axis intercept: ', model.intercept_)\n",
    "x_min = np.array([[X.min()]])\n",
    "x_max = np.array([[X.max()]])\n",
    "y_min = model.predict(x_min)\n",
    "y_max = model.predict(x_max)\n",
    "plt.scatter(X, y, c='blue')\n",
    "plt.title(\"Avg Income VS Population Count\")\n",
    "plt.xlabel(\"Avg. Income\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.plot([x_min[0], x_max[0]], [y_min[0], y_max[0]], c='red')\n",
    "plt.savefig('Images/income_vs_population.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-squared is 0.839922423294193\n",
      "Weight coefficients:  [[2.42278885]]\n",
      "y-axis intercept:  [-350.86214811]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pop_housing_df.Housing.values.reshape(-1, 1)\n",
    "y = pop_housing_df.Population.values.reshape(-1, 1)\n",
    "x_values = pop_housing_df['Housing']\n",
    "y_values = pop_housing_df['Population']\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x_values, y_values)\n",
    "r_squared = r_value**2\n",
    "print(f'The r-squared is {r_squared}')\n",
    "plt.scatter(X, y)\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "print('Weight coefficients: ', model.coef_)\n",
    "print('y-axis intercept: ', model.intercept_)\n",
    "x_min = np.array([[X.min()]])\n",
    "x_max = np.array([[X.max()]])\n",
    "y_min = model.predict(x_min)\n",
    "y_max = model.predict(x_max)\n",
    "plt.scatter(X, y, c='blue')\n",
    "plt.title(\"Housing VS Population\")\n",
    "plt.xlabel(\"Housing Units per Census Tract\")\n",
    "plt.ylabel(\"Population Count per Census Tract\")\n",
    "plt.plot([x_min[0], x_max[0]], [y_min[0], y_max[0]], c='red')\n",
    "plt.savefig('Images/housing_vs_population.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}