notebooks/feature_engineering.ipynb

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    "# Projecting Food Insecurity Rates in the US by County\n",
    "## Feature Engineering\n",
    "The following process imports a cleaned dataset produced from [cleaning_pt2.ipynb.](notebooks/cleaning_pt2) This notebook is used to produce new features that will be used in the modeling process.\n",
    "### Flatiron School Data Science Capstone<br>By Khyatee Desai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from itertools import combinations\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "pd.set_option('display.max_columns', None)\n",
    "import pickle\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Year</th>\n",
       "      <th>coc_number</th>\n",
       "      <th>Houseless_rate</th>\n",
       "      <th>Sheltered_rate</th>\n",
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       "      <th>pop_priv_health</th>\n",
       "      <th>pop_public_health</th>\n",
       "      <th>pop_no_health</th>\n",
       "      <th>pop_total</th>\n",
       "      <th>percent_hh_poverty</th>\n",
       "      <th>hh_avg_size</th>\n",
       "      <th>pop_65+</th>\n",
       "      <th>hh_no_vehicle</th>\n",
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       "      <th>pop_non_citizen</th>\n",
       "      <th>hh_SNAP</th>\n",
       "      <th>State</th>\n",
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       "      <th>TOT_POP</th>\n",
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       "      <th>TOT_ASIAN</th>\n",
       "      <th>TOT_PACIFIC</th>\n",
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       "      <th>FI Rate</th>\n",
       "      <th>Low Threshold Type</th>\n",
       "      <th>High Threshold Type</th>\n",
       "      <th>Cost Per Meal</th>\n",
       "      <th>Num_wholesale</th>\n",
       "      <th>Num_restaraunts</th>\n",
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       "      <th>Total_workforce</th>\n",
       "      <th>Employed</th>\n",
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       "      <td>2.48</td>\n",
       "      <td>87036.0</td>\n",
       "      <td>93630.0</td>\n",
       "      <td>259397.0</td>\n",
       "      <td>17519.0</td>\n",
       "      <td>39967.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>659972.0</td>\n",
       "      <td>312491.0</td>\n",
       "      <td>347481.0</td>\n",
       "      <td>355635.0</td>\n",
       "      <td>284082.0</td>\n",
       "      <td>2029.0</td>\n",
       "      <td>10378.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>24099.0</td>\n",
       "      <td>Jefferson County, AL</td>\n",
       "      <td>0.197</td>\n",
       "      <td>SNAP</td>\n",
       "      <td>Other Nutrition Program</td>\n",
       "      <td>2.93</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2693.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>312131.0</td>\n",
       "      <td>292505.0</td>\n",
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       "      <td>6.3</td>\n",
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       "      <td>18511.0</td>\n",
       "      <td>159655.0</td>\n",
       "      <td>42429.0</td>\n",
       "      <td>19175.0</td>\n",
       "      <td>201168.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.65</td>\n",
       "      <td>23404.0</td>\n",
       "      <td>19762.0</td>\n",
       "      <td>74790.0</td>\n",
       "      <td>7624.0</td>\n",
       "      <td>4706.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Shelby County</td>\n",
       "      <td>206280.0</td>\n",
       "      <td>100304.0</td>\n",
       "      <td>105976.0</td>\n",
       "      <td>174094.0</td>\n",
       "      <td>24247.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>4403.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>11872.0</td>\n",
       "      <td>Shelby County, AL</td>\n",
       "      <td>0.105</td>\n",
       "      <td>SNAP</td>\n",
       "      <td>Other Nutrition Program</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>743.0</td>\n",
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       "      <td>102400.0</td>\n",
       "      <td>4808.0</td>\n",
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       "      <td>7947.0</td>\n",
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       "      <td>Cochise County</td>\n",
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       "      <td>62653.0</td>\n",
       "      <td>112238.0</td>\n",
       "      <td>5737.0</td>\n",
       "      <td>2165.0</td>\n",
       "      <td>2757.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>44374.0</td>\n",
       "      <td>Cochise County, AZ</td>\n",
       "      <td>0.161</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>2.81</td>\n",
       "      <td>800.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>50969.0</td>\n",
       "      <td>46682.0</td>\n",
       "      <td>4287.0</td>\n",
       "      <td>8.4</td>\n",
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       "      <td>273108.0</td>\n",
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       "      <td>1204681.0</td>\n",
       "      <td>646167.0</td>\n",
       "      <td>3947382.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>2.74</td>\n",
       "      <td>507428.0</td>\n",
       "      <td>546028.0</td>\n",
       "      <td>1424244.0</td>\n",
       "      <td>373532.0</td>\n",
       "      <td>171581.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Maricopa County</td>\n",
       "      <td>4093648.0</td>\n",
       "      <td>2024659.0</td>\n",
       "      <td>2068989.0</td>\n",
       "      <td>3449404.0</td>\n",
       "      <td>235660.0</td>\n",
       "      <td>112383.0</td>\n",
       "      <td>172425.0</td>\n",
       "      <td>11190.0</td>\n",
       "      <td>1239835.0</td>\n",
       "      <td>Maricopa County, AZ</td>\n",
       "      <td>0.158</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2389.0</td>\n",
       "      <td>16857.0</td>\n",
       "      <td>6320.0</td>\n",
       "      <td>1961997.0</td>\n",
       "      <td>1848119.0</td>\n",
       "      <td>113878.0</td>\n",
       "      <td>5.8</td>\n",
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       "      <td>2014</td>\n",
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       "      <td>0.002170</td>\n",
       "      <td>0.001705</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>46233.0</td>\n",
       "      <td>133694.0</td>\n",
       "      <td>149147.0</td>\n",
       "      <td>115392.0</td>\n",
       "      <td>81406.0</td>\n",
       "      <td>592298.0</td>\n",
       "      <td>364938.0</td>\n",
       "      <td>141211.0</td>\n",
       "      <td>993144.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>2.50</td>\n",
       "      <td>162075.0</td>\n",
       "      <td>149710.0</td>\n",
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       "      <td>57099.0</td>\n",
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       "      <td>41043.0</td>\n",
       "      <td>42683.0</td>\n",
       "      <td>31905.0</td>\n",
       "      <td>2266.0</td>\n",
       "      <td>363063.0</td>\n",
       "      <td>Pima County, AZ</td>\n",
       "      <td>0.154</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3591.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>1604.0</td>\n",
       "      <td>463126.0</td>\n",
       "      <td>435183.0</td>\n",
       "      <td>27943.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3562</th>\n",
       "      <td>54999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3563</th>\n",
       "      <td>55999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35613 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS         Rent  Year coc_number  Houseless_rate  Sheltered_rate  \\\n",
       "0     01073  1019.995960  2014     AL-500        0.001396        0.000961   \n",
       "1     01117  1229.755051  2014     AL-500        0.001396        0.000961   \n",
       "2     04003  1051.250000  2014     AZ-500        0.001469        0.000826   \n",
       "3     04013  1095.670228  2014     AZ-502        0.001318        0.001084   \n",
       "4     04019   928.546429  2014     AZ-501        0.002170        0.001705   \n",
       "...     ...          ...   ...        ...             ...             ...   \n",
       "3559  49999          NaN   NaN        NaN             NaN             NaN   \n",
       "3560  51999          NaN   NaN        NaN             NaN             NaN   \n",
       "3561  53999          NaN   NaN        NaN             NaN             NaN   \n",
       "3562  54999          NaN   NaN        NaN             NaN             NaN   \n",
       "3563  55999          NaN   NaN        NaN             NaN             NaN   \n",
       "\n",
       "      Unsheltered_rate  hh_med_income  pop_disabled  pop_hs_grad  \\\n",
       "0             0.000435        45239.0       94584.0     117854.0   \n",
       "1             0.000435        69723.0       22792.0      28911.0   \n",
       "2             0.000643        45974.0       20341.0      21109.0   \n",
       "3             0.000235        53689.0      399455.0     593094.0   \n",
       "4             0.000465        46233.0      133694.0     149147.0   \n",
       "...                ...            ...           ...          ...   \n",
       "3559               NaN            NaN           NaN          NaN   \n",
       "3560               NaN            NaN           NaN          NaN   \n",
       "3561               NaN            NaN           NaN          NaN   \n",
       "3562               NaN            NaN           NaN          NaN   \n",
       "3563               NaN            NaN           NaN          NaN   \n",
       "\n",
       "      pop_bachelors  pop_grad_degree  pop_priv_health  pop_public_health  \\\n",
       "0           81626.0          52774.0         431638.0           211570.0   \n",
       "1           35773.0          18511.0         159655.0            42429.0   \n",
       "2           12968.0           7566.0          76099.0            50498.0   \n",
       "3          490927.0         273108.0        2444443.0          1204681.0   \n",
       "4          115392.0          81406.0         592298.0           364938.0   \n",
       "...             ...              ...              ...                ...   \n",
       "3559            NaN              NaN              NaN                NaN   \n",
       "3560            NaN              NaN              NaN                NaN   \n",
       "3561            NaN              NaN              NaN                NaN   \n",
       "3562            NaN              NaN              NaN                NaN   \n",
       "3563            NaN              NaN              NaN                NaN   \n",
       "\n",
       "      pop_no_health  pop_total  percent_hh_poverty  hh_avg_size   pop_65+  \\\n",
       "0           81336.0   658834.0                14.8         2.48   87036.0   \n",
       "1           19175.0   201168.0                 6.2         2.65   23404.0   \n",
       "2           14868.0   130807.0                13.1         2.47   23593.0   \n",
       "3          646167.0  3947382.0                12.7         2.74  507428.0   \n",
       "4          141211.0   993144.0                13.2         2.50  162075.0   \n",
       "...             ...        ...                 ...          ...       ...   \n",
       "3559            NaN        NaN                 NaN          NaN       NaN   \n",
       "3560            NaN        NaN                 NaN          NaN       NaN   \n",
       "3561            NaN        NaN                 NaN          NaN       NaN   \n",
       "3562            NaN        NaN                 NaN          NaN       NaN   \n",
       "3563            NaN        NaN                 NaN          NaN       NaN   \n",
       "\n",
       "      hh_no_vehicle     num_hh  pop_non_citizen   hh_SNAP    State  \\\n",
       "0           93630.0   259397.0          17519.0   39967.0  Alabama   \n",
       "1           19762.0    74790.0           7624.0    4706.0  Alabama   \n",
       "2           16328.0    48846.0           7947.0    7812.0  Arizona   \n",
       "3          546028.0  1424244.0         373532.0  171581.0  Arizona   \n",
       "4          149710.0   386155.0          69636.0   57099.0  Arizona   \n",
       "...             ...        ...              ...       ...      ...   \n",
       "3559            NaN        NaN              NaN       NaN      NaN   \n",
       "3560            NaN        NaN              NaN       NaN      NaN   \n",
       "3561            NaN        NaN              NaN       NaN      NaN   \n",
       "3562            NaN        NaN              NaN       NaN      NaN   \n",
       "3563            NaN        NaN              NaN       NaN      NaN   \n",
       "\n",
       "                County    TOT_POP   TOT_MALE  TOT_FEMALE  TOT_WHITE  \\\n",
       "0     Jefferson County   659972.0   312491.0    347481.0   355635.0   \n",
       "1        Shelby County   206280.0   100304.0    105976.0   174094.0   \n",
       "2       Cochise County   127314.0    64661.0     62653.0   112238.0   \n",
       "3      Maricopa County  4093648.0  2024659.0   2068989.0  3449404.0   \n",
       "4          Pima County  1004229.0   494684.0    509545.0   858334.0   \n",
       "...                ...        ...        ...         ...        ...   \n",
       "3559               NaN        NaN        NaN         NaN        NaN   \n",
       "3560               NaN        NaN        NaN         NaN        NaN   \n",
       "3561               NaN        NaN        NaN         NaN        NaN   \n",
       "3562               NaN        NaN        NaN         NaN        NaN   \n",
       "3563               NaN        NaN        NaN         NaN        NaN   \n",
       "\n",
       "      TOT_BLACK  TOT_NATIVE  TOT_ASIAN  TOT_PACIFIC  TOT_LATINX  \\\n",
       "0      284082.0      2029.0    10378.0        347.0     24099.0   \n",
       "1       24247.0       805.0     4403.0        101.0     11872.0   \n",
       "2        5737.0      2165.0     2757.0        510.0     44374.0   \n",
       "3      235660.0    112383.0   172425.0      11190.0   1239835.0   \n",
       "4       41043.0     42683.0    31905.0       2266.0    363063.0   \n",
       "...         ...         ...        ...          ...         ...   \n",
       "3559        NaN         NaN        NaN          NaN         NaN   \n",
       "3560        NaN         NaN        NaN          NaN         NaN   \n",
       "3561        NaN         NaN        NaN          NaN         NaN   \n",
       "3562        NaN         NaN        NaN          NaN         NaN   \n",
       "3563        NaN         NaN        NaN          NaN         NaN   \n",
       "\n",
       "              State/County  FI Rate              Low Threshold Type  \\\n",
       "0     Jefferson County, AL    0.197                            SNAP   \n",
       "1        Shelby County, AL    0.105                            SNAP   \n",
       "2       Cochise County, AZ    0.161  SNAP, Other Nutrition Programs   \n",
       "3      Maricopa County, AZ    0.158  SNAP, Other Nutrition Programs   \n",
       "4          Pima County, AZ    0.154  SNAP, Other Nutrition Programs   \n",
       "...                    ...      ...                             ...   \n",
       "3559                   NaN      NaN                             NaN   \n",
       "3560                   NaN      NaN                             NaN   \n",
       "3561                   NaN      NaN                             NaN   \n",
       "3562                   NaN      NaN                             NaN   \n",
       "3563                   NaN      NaN                             NaN   \n",
       "\n",
       "                 High Threshold Type  Cost Per Meal  Num_wholesale  \\\n",
       "0            Other Nutrition Program           2.93          483.0   \n",
       "1            Other Nutrition Program           3.37            1.0   \n",
       "2     SNAP, Other Nutrition Programs           2.81          800.0   \n",
       "3     SNAP, Other Nutrition Programs           2.90         2389.0   \n",
       "4     SNAP, Other Nutrition Programs           2.85         3591.0   \n",
       "...                              ...            ...            ...   \n",
       "3559                             NaN            NaN            NaN   \n",
       "3560                             NaN            NaN            NaN   \n",
       "3561                             NaN            NaN            NaN   \n",
       "3562                             NaN            NaN            NaN   \n",
       "3563                             NaN            NaN            NaN   \n",
       "\n",
       "      Num_restaraunts  Num_grocery  Total_workforce   Employed  Unemployed  \\\n",
       "0              2693.0        400.0         312131.0   292505.0     19626.0   \n",
       "1               743.0       2706.0         107208.0   102400.0      4808.0   \n",
       "2                72.0        340.0          50969.0    46682.0      4287.0   \n",
       "3             16857.0       6320.0        1961997.0  1848119.0    113878.0   \n",
       "4               838.0       1604.0         463126.0   435183.0     27943.0   \n",
       "...               ...          ...              ...        ...         ...   \n",
       "3559              NaN          NaN              NaN        NaN         NaN   \n",
       "3560              NaN          NaN              NaN        NaN         NaN   \n",
       "3561              NaN          NaN              NaN        NaN         NaN   \n",
       "3562              NaN          NaN              NaN        NaN         NaN   \n",
       "3563              NaN          NaN              NaN        NaN         NaN   \n",
       "\n",
       "      Unemployment_rate  \n",
       "0                   6.3  \n",
       "1                   4.5  \n",
       "2                   8.4  \n",
       "3                   5.8  \n",
       "4                   6.0  \n",
       "...                 ...  \n",
       "3559                NaN  \n",
       "3560                NaN  \n",
       "3561                NaN  \n",
       "3562                NaN  \n",
       "3563                NaN  \n",
       "\n",
       "[35613 rows x 46 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../pickled/fully_cleaned_data.pickle', \"rb\") as input_file:\n",
    "    df = pickle.load(input_file) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>FIPS</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Year</th>\n",
       "      <th>coc_number</th>\n",
       "      <th>Houseless_rate</th>\n",
       "      <th>Sheltered_rate</th>\n",
       "      <th>Unsheltered_rate</th>\n",
       "      <th>hh_med_income</th>\n",
       "      <th>pop_disabled</th>\n",
       "      <th>pop_hs_grad</th>\n",
       "      <th>pop_bachelors</th>\n",
       "      <th>pop_grad_degree</th>\n",
       "      <th>pop_priv_health</th>\n",
       "      <th>pop_public_health</th>\n",
       "      <th>pop_no_health</th>\n",
       "      <th>pop_total</th>\n",
       "      <th>percent_hh_poverty</th>\n",
       "      <th>hh_avg_size</th>\n",
       "      <th>pop_65+</th>\n",
       "      <th>hh_no_vehicle</th>\n",
       "      <th>num_hh</th>\n",
       "      <th>pop_non_citizen</th>\n",
       "      <th>hh_SNAP</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>TOT_POP</th>\n",
       "      <th>TOT_MALE</th>\n",
       "      <th>TOT_FEMALE</th>\n",
       "      <th>TOT_WHITE</th>\n",
       "      <th>TOT_BLACK</th>\n",
       "      <th>TOT_NATIVE</th>\n",
       "      <th>TOT_ASIAN</th>\n",
       "      <th>TOT_PACIFIC</th>\n",
       "      <th>TOT_LATINX</th>\n",
       "      <th>State/County</th>\n",
       "      <th>FI Rate</th>\n",
       "      <th>Low Threshold Type</th>\n",
       "      <th>High Threshold Type</th>\n",
       "      <th>Cost Per Meal</th>\n",
       "      <th>Num_wholesale</th>\n",
       "      <th>Num_restaraunts</th>\n",
       "      <th>Num_grocery</th>\n",
       "      <th>Total_workforce</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Autauga County, AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25485.100</td>\n",
       "      <td>24078.400</td>\n",
       "      <td>1406.700</td>\n",
       "      <td>5.5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>01003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baldwin County, AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96586.200</td>\n",
       "      <td>90467.700</td>\n",
       "      <td>6118.500</td>\n",
       "      <td>6.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>01005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Barbour County, AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8933.200</td>\n",
       "      <td>8290.600</td>\n",
       "      <td>642.600</td>\n",
       "      <td>7.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>01007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bibb County, AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8685.600</td>\n",
       "      <td>8043.200</td>\n",
       "      <td>642.400</td>\n",
       "      <td>7.3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>01009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blount County, AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24704.900</td>\n",
       "      <td>23569.900</td>\n",
       "      <td>1135.000</td>\n",
       "      <td>4.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32452</th>\n",
       "      <td>72145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vega Baja Municipio, PR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12646.625</td>\n",
       "      <td>11223.375</td>\n",
       "      <td>1423.250</td>\n",
       "      <td>11.2375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32462</th>\n",
       "      <td>72147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vieques Municipio, PR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2476.875</td>\n",
       "      <td>2252.000</td>\n",
       "      <td>224.875</td>\n",
       "      <td>9.0875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32472</th>\n",
       "      <td>72149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Villalba Municipio, PR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6819.875</td>\n",
       "      <td>6013.500</td>\n",
       "      <td>806.375</td>\n",
       "      <td>11.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32482</th>\n",
       "      <td>72151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yabucoa Municipio, PR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8106.625</td>\n",
       "      <td>7232.250</td>\n",
       "      <td>874.375</td>\n",
       "      <td>10.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32492</th>\n",
       "      <td>72153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yauco Municipio, PR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9261.500</td>\n",
       "      <td>8086.875</td>\n",
       "      <td>1174.625</td>\n",
       "      <td>12.5500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3219 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        FIPS  Rent  Year coc_number  Houseless_rate  Sheltered_rate  \\\n",
       "0      01001   NaN  2020        NaN             NaN             NaN   \n",
       "10     01003   NaN  2020        NaN             NaN             NaN   \n",
       "20     01005   NaN  2020        NaN             NaN             NaN   \n",
       "30     01007   NaN  2020        NaN             NaN             NaN   \n",
       "40     01009   NaN  2020        NaN             NaN             NaN   \n",
       "...      ...   ...   ...        ...             ...             ...   \n",
       "32452  72145   NaN  2020        NaN             NaN             NaN   \n",
       "32462  72147   NaN  2020        NaN             NaN             NaN   \n",
       "32472  72149   NaN  2020        NaN             NaN             NaN   \n",
       "32482  72151   NaN  2020        NaN             NaN             NaN   \n",
       "32492  72153   NaN  2020        NaN             NaN             NaN   \n",
       "\n",
       "       Unsheltered_rate  hh_med_income  pop_disabled  pop_hs_grad  \\\n",
       "0                   NaN            NaN           NaN          NaN   \n",
       "10                  NaN            NaN           NaN          NaN   \n",
       "20                  NaN            NaN           NaN          NaN   \n",
       "30                  NaN            NaN           NaN          NaN   \n",
       "40                  NaN            NaN           NaN          NaN   \n",
       "...                 ...            ...           ...          ...   \n",
       "32452               NaN            NaN           NaN          NaN   \n",
       "32462               NaN            NaN           NaN          NaN   \n",
       "32472               NaN            NaN           NaN          NaN   \n",
       "32482               NaN            NaN           NaN          NaN   \n",
       "32492               NaN            NaN           NaN          NaN   \n",
       "\n",
       "       pop_bachelors  pop_grad_degree  pop_priv_health  pop_public_health  \\\n",
       "0                NaN              NaN              NaN                NaN   \n",
       "10               NaN              NaN              NaN                NaN   \n",
       "20               NaN              NaN              NaN                NaN   \n",
       "30               NaN              NaN              NaN                NaN   \n",
       "40               NaN              NaN              NaN                NaN   \n",
       "...              ...              ...              ...                ...   \n",
       "32452            NaN              NaN              NaN                NaN   \n",
       "32462            NaN              NaN              NaN                NaN   \n",
       "32472            NaN              NaN              NaN                NaN   \n",
       "32482            NaN              NaN              NaN                NaN   \n",
       "32492            NaN              NaN              NaN                NaN   \n",
       "\n",
       "       pop_no_health  pop_total  percent_hh_poverty  hh_avg_size  pop_65+  \\\n",
       "0                NaN        NaN                 NaN          NaN      NaN   \n",
       "10               NaN        NaN                 NaN          NaN      NaN   \n",
       "20               NaN        NaN                 NaN          NaN      NaN   \n",
       "30               NaN        NaN                 NaN          NaN      NaN   \n",
       "40               NaN        NaN                 NaN          NaN      NaN   \n",
       "...              ...        ...                 ...          ...      ...   \n",
       "32452            NaN        NaN                 NaN          NaN      NaN   \n",
       "32462            NaN        NaN                 NaN          NaN      NaN   \n",
       "32472            NaN        NaN                 NaN          NaN      NaN   \n",
       "32482            NaN        NaN                 NaN          NaN      NaN   \n",
       "32492            NaN        NaN                 NaN          NaN      NaN   \n",
       "\n",
       "       hh_no_vehicle  num_hh  pop_non_citizen  hh_SNAP State County  TOT_POP  \\\n",
       "0                NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "10               NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "20               NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "30               NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "40               NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "...              ...     ...              ...      ...   ...    ...      ...   \n",
       "32452            NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "32462            NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "32472            NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "32482            NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "32492            NaN     NaN              NaN      NaN   NaN    NaN      NaN   \n",
       "\n",
       "       TOT_MALE  TOT_FEMALE  TOT_WHITE  TOT_BLACK  TOT_NATIVE  TOT_ASIAN  \\\n",
       "0           NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "10          NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "20          NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "30          NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "40          NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "...         ...         ...        ...        ...         ...        ...   \n",
       "32452       NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "32462       NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "32472       NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "32482       NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "32492       NaN         NaN        NaN        NaN         NaN        NaN   \n",
       "\n",
       "       TOT_PACIFIC  TOT_LATINX             State/County  FI Rate  \\\n",
       "0              NaN         NaN       Autauga County, AL      NaN   \n",
       "10             NaN         NaN       Baldwin County, AL      NaN   \n",
       "20             NaN         NaN       Barbour County, AL      NaN   \n",
       "30             NaN         NaN          Bibb County, AL      NaN   \n",
       "40             NaN         NaN        Blount County, AL      NaN   \n",
       "...            ...         ...                      ...      ...   \n",
       "32452          NaN         NaN  Vega Baja Municipio, PR      NaN   \n",
       "32462          NaN         NaN    Vieques Municipio, PR      NaN   \n",
       "32472          NaN         NaN   Villalba Municipio, PR      NaN   \n",
       "32482          NaN         NaN    Yabucoa Municipio, PR      NaN   \n",
       "32492          NaN         NaN      Yauco Municipio, PR      NaN   \n",
       "\n",
       "      Low Threshold Type High Threshold Type  Cost Per Meal  Num_wholesale  \\\n",
       "0                    NaN                 NaN            NaN            NaN   \n",
       "10                   NaN                 NaN            NaN            NaN   \n",
       "20                   NaN                 NaN            NaN            NaN   \n",
       "30                   NaN                 NaN            NaN            NaN   \n",
       "40                   NaN                 NaN            NaN            NaN   \n",
       "...                  ...                 ...            ...            ...   \n",
       "32452                NaN                 NaN            NaN            NaN   \n",
       "32462                NaN                 NaN            NaN            NaN   \n",
       "32472                NaN                 NaN            NaN            NaN   \n",
       "32482                NaN                 NaN            NaN            NaN   \n",
       "32492                NaN                 NaN            NaN            NaN   \n",
       "\n",
       "       Num_restaraunts  Num_grocery  Total_workforce   Employed  Unemployed  \\\n",
       "0                  NaN          NaN        25485.100  24078.400    1406.700   \n",
       "10                 NaN          NaN        96586.200  90467.700    6118.500   \n",
       "20                 NaN          NaN         8933.200   8290.600     642.600   \n",
       "30                 NaN          NaN         8685.600   8043.200     642.400   \n",
       "40                 NaN          NaN        24704.900  23569.900    1135.000   \n",
       "...                ...          ...              ...        ...         ...   \n",
       "32452              NaN          NaN        12646.625  11223.375    1423.250   \n",
       "32462              NaN          NaN         2476.875   2252.000     224.875   \n",
       "32472              NaN          NaN         6819.875   6013.500     806.375   \n",
       "32482              NaN          NaN         8106.625   7232.250     874.375   \n",
       "32492              NaN          NaN         9261.500   8086.875    1174.625   \n",
       "\n",
       "       Unemployment_rate  \n",
       "0                 5.5300  \n",
       "10                6.3600  \n",
       "20                7.1500  \n",
       "30                7.3400  \n",
       "40                4.6100  \n",
       "...                  ...  \n",
       "32452            11.2375  \n",
       "32462             9.0875  \n",
       "32472            11.6500  \n",
       "32482            10.7500  \n",
       "32492            12.5500  \n",
       "\n",
       "[3219 rows x 46 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Year=='2020']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a percentage for each demographic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(-888888888.0,np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Percent_male'] = df['TOT_MALE']/df['TOT_POP']\n",
    "df['Percent_female'] = df['TOT_FEMALE']/df['TOT_POP']\n",
    "df['Percent_white'] = df['TOT_WHITE']/df['TOT_POP']\n",
    "df['Percent_Black'] = df['TOT_BLACK']/df['TOT_POP']\n",
    "df['Percent_native'] = df['TOT_NATIVE']/df['TOT_POP']\n",
    "df['Percent_asian'] = df['TOT_ASIAN']/df['TOT_POP']\n",
    "df['Percent_pacific'] = df['TOT_PACIFIC']/df['TOT_POP']\n",
    "df['Percent_latinX'] = df['TOT_LATINX']/df['TOT_POP']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage PoC\n",
    "Percentage of a county that is not white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Percent_PoC'] = 1-df['Percent_white']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Workforce as a percentage of total population\n",
    "Workforce represented as percentage, so that it can be compared across different counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# percent of population that is working\n",
    "df['Percent_working'] = df['Total_workforce']/df['TOT_POP']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sum of Food Establishments\n",
    "Total number of food retail businesses, which is the sum of the three categories (wholesalers, restaraunts, and grocery stores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total_food_retail'] = df['Num_wholesale'].fillna(0)+ df['Num_restaraunts'].fillna(0)+df['Num_grocery'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population divided by number of food establishments \n",
    "Looking at prevalence of food establishments as a function of population - aka how many Food Retail establishments exist per person within a County"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Food_retail_per_person'] = df['Total_food_retail']/df['TOT_POP']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACS Survey Percentages\n",
    "Take percentages of all features taken from the ACS survey, which use a slightly different (~5%) total population number for the denominator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Percent_disabled'] = df['pop_disabled']/df['TOT_POP']\n",
    "df['Percent_hs_grad'] = df['pop_hs_grad']/df['TOT_POP']\n",
    "df['Percent_bachelors'] = df['pop_bachelors']/df['TOT_POP']\n",
    "df['Percent_grad_degree'] = df['pop_grad_degree']/df['TOT_POP']\n",
    "df['Percent_priv_health'] = df['pop_priv_health']/df['TOT_POP']\n",
    "df['Percent_public_health'] = df['pop_public_health']/df['TOT_POP']\n",
    "df['Percent_no_health'] = df['pop_no_health']/df['TOT_POP']\n",
    "df['Percent_65+'] = df['pop_65+']/df['TOT_POP']\n",
    "df['Percent_non_citizen'] = df['pop_non_citizen']/df['TOT_POP']\n",
    "df['Percent_hh_no_vehicle'] = df['hh_no_vehicle']/df['num_hh']\n",
    "df['Percent_hh_SNAP'] = df['hh_SNAP']/df['num_hh']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop raw count columns after deriving percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['TOT_MALE','TOT_FEMALE','TOT_WHITE','TOT_BLACK','TOT_NATIVE','TOT_ASIAN','TOT_PACIFIC','TOT_LATINX',\n",
    "         'pop_disabled', 'pop_hs_grad','pop_bachelors', 'pop_grad_degree', 'pop_priv_health', 'pop_public_health',\n",
    "        'pop_no_health', 'pop_65+','pop_non_citizen','hh_no_vehicle','hh_SNAP','pop_total'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Percentages for CPS 2020 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../pickled/cps_20_data.pickle', \"rb\") as input_file:\n",
    "    df_cps_20 = pickle.load(input_file) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Education Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cps_20['Percent_hs_grad'] = df_cps_20['pop_hs_grad'] / df_cps_20['Num_respondants_b']\n",
    "df_cps_20['Percent_bachelors'] = df_cps_20['pop_bachelors'] / df_cps_20['Num_respondants_b']\n",
    "df_cps_20['Percent_grad_degree'] = df_cps_20['pop_grad_degree'] / df_cps_20['Num_respondants_b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Citizenship Status Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_cps_20['Percent_non_citizen'] = df_cps_20['pop_non_citizen'] / df_cps_20['Num_respondants_b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disability Status Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cps_20['Percent_disabled'] = df_cps_20['HH_disabled']/ (df_cps_20['HH_disabled']+df_cps_20['HH_not_disabled'] )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Health Insurance columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cps_20['Percent_no_health'] = (df_cps_20['HH_not_insured'])/(df_cps_20['HH_health_insured']+df_cps_20['HH_not_insured']+df_cps_20['HH_some_insured'])\n",
    "df_cps_20['Percent_priv_health'] = (df_cps_20['HH_health_priv']+df_cps_20['HH_some_insured_priv']) /(df_cps_20['HH_not_insured_priv'] + df_cps_20['HH_some_insured_priv']+df_cps_20['HH_health_priv'])\n",
    "df_cps_20['Percent_public_health'] = (df_cps_20['HH_insured_pub']+df_cps_20['HH_some_insured_pub']) /(df_cps_20['HH_no_health_pub'] + df_cps_20['HH_some_insured_pub']+df_cps_20['HH_insured_pub'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cps_20 = df_cps_20.loc[:,['Year','HH_income','HH_size', 'Percent_hs_grad','Percent_bachelors','Percent_grad_degree',\n",
    "    'Percent_non_citizen','Percent_disabled','Percent_no_health','Percent_priv_health','Percent_public_health']].reset_index()\n",
    "\n",
    "df_cps_20.rename(columns={'index':'FIPS', 'HH_income':'hh_med_income','HH_size':'hh_avg_size' },inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate 2020 CPS data with main df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_no_20 = df[~df.Year.isin(['2020'])]\n",
    "df_20 = df[df.Year=='2020']\n",
    "\n",
    "df_20_dropped = df_20.drop(df_cps_20.columns[1:],axis=1)\n",
    "df_20 = df_cps_20.merge(df_20_dropped, on='FIPS', how='outer')\n",
    "df_20['Year'] = '2020'\n",
    "df = pd.concat([df_no_20, df_20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1770     0.457081\n",
       "28318    0.557140\n",
       "912           NaN\n",
       "81       0.375460\n",
       "860      0.547882\n",
       "233      0.501029\n",
       "205      0.506070\n",
       "3009          NaN\n",
       "2040          NaN\n",
       "29370    0.512697\n",
       "dtype: float64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['Total_workforce']/df['TOT_POP']).sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Impute Missing 2020 Data\n",
    "2020 Data is only available for 329 Counties, as the CPS surveys are only performed on a subset of the population. The following cells use survey responses to impute missing values for all of the other counties, by taking an average rate of change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method: \n",
    "- retrieve fips for counties with missing values\n",
    "- determine average change from 2019-2020 for CPS columns\n",
    "- apply average rate of change to missing counties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine which Counties have missing 2020 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_fips = df_20['FIPS'][df_20.hh_med_income.isnull()].tolist()\n",
    "not_missing = df_20['FIPS'][~df_20.hh_med_income.isnull()].tolist()\n",
    "cps_columns = df_cps_20.columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate mean values and percent change for 2019 & 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "scrolled": false
   },
   "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>index</th>\n",
       "      <th>2018 Mean</th>\n",
       "      <th>2020 Mean</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIPS</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Year</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hh_med_income</td>\n",
       "      <td>6.578080e+04</td>\n",
       "      <td>6.872134e+04</td>\n",
       "      <td>0.044702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hh_avg_size</td>\n",
       "      <td>2.638914e+00</td>\n",
       "      <td>1.762491e+00</td>\n",
       "      <td>-0.332115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Percent_hs_grad</td>\n",
       "      <td>1.828338e-01</td>\n",
       "      <td>3.861897e-01</td>\n",
       "      <td>1.112245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Percent_bachelors</td>\n",
       "      <td>1.323612e-01</td>\n",
       "      <td>1.450319e-01</td>\n",
       "      <td>0.095729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Percent_grad_degree</td>\n",
       "      <td>8.363571e-02</td>\n",
       "      <td>8.734611e-02</td>\n",
       "      <td>0.044364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Percent_non_citizen</td>\n",
       "      <td>5.780223e-02</td>\n",
       "      <td>4.038632e-02</td>\n",
       "      <td>-0.301302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Percent_disabled</td>\n",
       "      <td>1.201580e-01</td>\n",
       "      <td>2.024197e-02</td>\n",
       "      <td>-0.831539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Percent_no_health</td>\n",
       "      <td>8.050757e-02</td>\n",
       "      <td>3.898461e-02</td>\n",
       "      <td>-0.515765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Percent_priv_health</td>\n",
       "      <td>6.792832e-01</td>\n",
       "      <td>7.464562e-01</td>\n",
       "      <td>0.098888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Percent_public_health</td>\n",
       "      <td>3.320502e-01</td>\n",
       "      <td>4.756854e-01</td>\n",
       "      <td>0.432571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    index     2018 Mean     2020 Mean  pct_change\n",
       "0                    FIPS           inf           inf         NaN\n",
       "1                    Year           inf           inf         NaN\n",
       "2           hh_med_income  6.578080e+04  6.872134e+04    0.044702\n",
       "3             hh_avg_size  2.638914e+00  1.762491e+00   -0.332115\n",
       "4         Percent_hs_grad  1.828338e-01  3.861897e-01    1.112245\n",
       "5       Percent_bachelors  1.323612e-01  1.450319e-01    0.095729\n",
       "6     Percent_grad_degree  8.363571e-02  8.734611e-02    0.044364\n",
       "7     Percent_non_citizen  5.780223e-02  4.038632e-02   -0.301302\n",
       "8        Percent_disabled  1.201580e-01  2.024197e-02   -0.831539\n",
       "9       Percent_no_health  8.050757e-02  3.898461e-02   -0.515765\n",
       "10    Percent_priv_health  6.792832e-01  7.464562e-01    0.098888\n",
       "11  Percent_public_health  3.320502e-01  4.756854e-01    0.432571"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_19_cps = df[cps_columns][df.Year == '2018']\n",
    "means_19_20 = df_19_cps[df_19_cps['FIPS'].isin(not_missing)].mean().reset_index().rename(columns={0:'2018 Mean'})\n",
    "\n",
    "df_20_cps = df[cps_columns][df.Year == '2020']\n",
    "means_19_20['2020 Mean'] = df_20_cps[df_20_cps['FIPS'].isin(not_missing)].mean().values      \n",
    "means_19_20[['2018 Mean', '2020 Mean']].pct_change(axis=1)\n",
    "means_19_20['pct_change'] = (means_19_20['2020 Mean']-means_19_20['2018 Mean'])/means_19_20['2018 Mean']\n",
    "means_19_20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply percent change to generate missing values for 2020\n",
    "Percent change rate is applied to the 2019 values of counties that are missing values for 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>hh_med_income</th>\n",
       "      <th>hh_avg_size</th>\n",
       "      <th>Percent_hs_grad</th>\n",
       "      <th>Percent_bachelors</th>\n",
       "      <th>Percent_grad_degree</th>\n",
       "      <th>Percent_non_citizen</th>\n",
       "      <th>Percent_disabled</th>\n",
       "      <th>Percent_no_health</th>\n",
       "      <th>Percent_priv_health</th>\n",
       "      <th>Percent_public_health</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>01073</td>\n",
       "      <td>54302.565258</td>\n",
       "      <td>1.649676</td>\n",
       "      <td>0.384496</td>\n",
       "      <td>0.147515</td>\n",
       "      <td>0.089774</td>\n",
       "      <td>0.018711</td>\n",
       "      <td>0.025895</td>\n",
       "      <td>0.045649</td>\n",
       "      <td>0.737636</td>\n",
       "      <td>0.496157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>01117</td>\n",
       "      <td>79147.668222</td>\n",
       "      <td>1.776574</td>\n",
       "      <td>0.285176</td>\n",
       "      <td>0.203412</td>\n",
       "      <td>0.098840</td>\n",
       "      <td>0.023183</td>\n",
       "      <td>0.018425</td>\n",
       "      <td>0.032084</td>\n",
       "      <td>0.864214</td>\n",
       "      <td>0.337877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>04003</td>\n",
       "      <td>50823.707598</td>\n",
       "      <td>1.589566</td>\n",
       "      <td>0.343544</td>\n",
       "      <td>0.108584</td>\n",
       "      <td>0.062698</td>\n",
       "      <td>0.034888</td>\n",
       "      <td>0.026485</td>\n",
       "      <td>0.036652</td>\n",
       "      <td>0.639360</td>\n",
       "      <td>0.661645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>05119</td>\n",
       "      <td>52332.257286</td>\n",
       "      <td>1.642997</td>\n",
       "      <td>0.375347</td>\n",
       "      <td>0.155338</td>\n",
       "      <td>0.093706</td>\n",
       "      <td>0.024338</td>\n",
       "      <td>0.025749</td>\n",
       "      <td>0.041235</td>\n",
       "      <td>0.707271</td>\n",
       "      <td>0.556736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>06013</td>\n",
       "      <td>97901.113824</td>\n",
       "      <td>1.910151</td>\n",
       "      <td>0.250803</td>\n",
       "      <td>0.193429</td>\n",
       "      <td>0.110050</td>\n",
       "      <td>0.079108</td>\n",
       "      <td>0.018558</td>\n",
       "      <td>0.027016</td>\n",
       "      <td>0.802932</td>\n",
       "      <td>0.442811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>52741</th>\n",
       "      <td>05089</td>\n",
       "      <td>37105.725636</td>\n",
       "      <td>1.629639</td>\n",
       "      <td>0.580883</td>\n",
       "      <td>0.088763</td>\n",
       "      <td>0.050009</td>\n",
       "      <td>0.003648</td>\n",
       "      <td>0.037796</td>\n",
       "      <td>0.033038</td>\n",
       "      <td>0.617263</td>\n",
       "      <td>0.796962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52742</th>\n",
       "      <td>36035</td>\n",
       "      <td>52494.186096</td>\n",
       "      <td>1.562851</td>\n",
       "      <td>0.543293</td>\n",
       "      <td>0.074774</td>\n",
       "      <td>0.062175</td>\n",
       "      <td>0.003622</td>\n",
       "      <td>0.026447</td>\n",
       "      <td>0.030156</td>\n",
       "      <td>0.698368</td>\n",
       "      <td>0.652193</td>\n",
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       "    <tr>\n",
       "      <th>52743</th>\n",
       "      <td>20209</td>\n",
       "      <td>46878.912846</td>\n",
       "      <td>1.816647</td>\n",
       "      <td>0.431633</td>\n",
       "      <td>0.079442</td>\n",
       "      <td>0.038295</td>\n",
       "      <td>0.089741</td>\n",
       "      <td>0.024463</td>\n",
       "      <td>0.088502</td>\n",
       "      <td>0.575504</td>\n",
       "      <td>0.532072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52744</th>\n",
       "      <td>22057</td>\n",
       "      <td>55462.184478</td>\n",
       "      <td>1.763216</td>\n",
       "      <td>0.573715</td>\n",
       "      <td>0.084443</td>\n",
       "      <td>0.036421</td>\n",
       "      <td>0.014661</td>\n",
       "      <td>0.027483</td>\n",
       "      <td>0.048675</td>\n",
       "      <td>0.718049</td>\n",
       "      <td>0.513574</td>\n",
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       "    <tr>\n",
       "      <th>52745</th>\n",
       "      <td>22109</td>\n",
       "      <td>51050.407932</td>\n",
       "      <td>1.856720</td>\n",
       "      <td>0.542279</td>\n",
       "      <td>0.083419</td>\n",
       "      <td>0.029885</td>\n",
       "      <td>0.013300</td>\n",
       "      <td>0.027936</td>\n",
       "      <td>0.060584</td>\n",
       "      <td>0.647888</td>\n",
       "      <td>0.557651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3056 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        FIPS  hh_med_income  hh_avg_size  Percent_hs_grad  Percent_bachelors  \\\n",
       "1428   01073   54302.565258     1.649676         0.384496           0.147515   \n",
       "1429   01117   79147.668222     1.776574         0.285176           0.203412   \n",
       "1430   04003   50823.707598     1.589566         0.343544           0.108584   \n",
       "1435   05119   52332.257286     1.642997         0.375347           0.155338   \n",
       "1437   06013   97901.113824     1.910151         0.250803           0.193429   \n",
       "...      ...            ...          ...              ...                ...   \n",
       "52741  05089   37105.725636     1.629639         0.580883           0.088763   \n",
       "52742  36035   52494.186096     1.562851         0.543293           0.074774   \n",
       "52743  20209   46878.912846     1.816647         0.431633           0.079442   \n",
       "52744  22057   55462.184478     1.763216         0.573715           0.084443   \n",
       "52745  22109   51050.407932     1.856720         0.542279           0.083419   \n",
       "\n",
       "       Percent_grad_degree  Percent_non_citizen  Percent_disabled  \\\n",
       "1428              0.089774             0.018711          0.025895   \n",
       "1429              0.098840             0.023183          0.018425   \n",
       "1430              0.062698             0.034888          0.026485   \n",
       "1435              0.093706             0.024338          0.025749   \n",
       "1437              0.110050             0.079108          0.018558   \n",
       "...                    ...                  ...               ...   \n",
       "52741             0.050009             0.003648          0.037796   \n",
       "52742             0.062175             0.003622          0.026447   \n",
       "52743             0.038295             0.089741          0.024463   \n",
       "52744             0.036421             0.014661          0.027483   \n",
       "52745             0.029885             0.013300          0.027936   \n",
       "\n",
       "       Percent_no_health  Percent_priv_health  Percent_public_health  \n",
       "1428            0.045649             0.737636               0.496157  \n",
       "1429            0.032084             0.864214               0.337877  \n",
       "1430            0.036652             0.639360               0.661645  \n",
       "1435            0.041235             0.707271               0.556736  \n",
       "1437            0.027016             0.802932               0.442811  \n",
       "...                  ...                  ...                    ...  \n",
       "52741           0.033038             0.617263               0.796962  \n",
       "52742           0.030156             0.698368               0.652193  \n",
       "52743           0.088502             0.575504               0.532072  \n",
       "52744           0.048675             0.718049               0.513574  \n",
       "52745           0.060584             0.647888               0.557651  \n",
       "\n",
       "[3056 rows x 11 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_19_20 = df_19_cps[df_19_cps.FIPS.isin(missing_fips)].drop('Year',axis=1)\n",
    "df_19_20['hh_med_income'] = df_19_20['hh_med_income']+ df_19_20['hh_med_income']* 0.044702\n",
    "df_19_20['hh_avg_size'] = df_19_20['hh_avg_size']+ df_19_20['hh_avg_size']* -0.332115\n",
    "df_19_20['Percent_hs_grad'] = df_19_20['Percent_hs_grad']+ df_19_20['Percent_hs_grad']* 1.112245\n",
    "df_19_20['Percent_bachelors'] = df_19_20['Percent_bachelors']+ df_19_20['Percent_bachelors']* 0.095729\n",
    "df_19_20['Percent_grad_degree'] = df_19_20['Percent_grad_degree']+ df_19_20['Percent_grad_degree']* 0.044364\n",
    "df_19_20['Percent_non_citizen'] = df_19_20['Percent_non_citizen']+ df_19_20['Percent_non_citizen']* -0.301302\n",
    "df_19_20['Percent_disabled'] = df_19_20['Percent_disabled']+ df_19_20['Percent_disabled']* -0.831539\n",
    "df_19_20['Percent_no_health'] = df_19_20['Percent_no_health']+ df_19_20['Percent_no_health']* -0.515765\n",
    "df_19_20['Percent_priv_health'] = df_19_20['Percent_priv_health']+ df_19_20['Percent_priv_health']* 0.098888\n",
    "df_19_20['Percent_public_health'] = df_19_20['Percent_public_health']+ df_19_20['Percent_public_health']* 0.432571\n",
    "df_19_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_20 = df[~df.Year.isin(['2020'])]\n",
    "df_20 = df[df.Year=='2020']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>0.542857</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.024390</td>\n",
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       "      <td>0.083333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625000</td>\n",
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       "      <td>0.428401</td>\n",
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       "<p>3385 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "       FIPS  hh_med_income  hh_avg_size  Percent_hs_grad  Percent_bachelors  \\\n",
       "1428  01073   54302.565258     1.649676         0.384496           0.147515   \n",
       "1429  01117   79147.668222     1.776574         0.285176           0.203412   \n",
       "1430  04003   50823.707598     1.589566         0.343544           0.108584   \n",
       "1435  05119   52332.257286     1.642997         0.375347           0.155338   \n",
       "1437  06013   97901.113824     1.910151         0.250803           0.193429   \n",
       "...     ...            ...          ...              ...                ...   \n",
       "324   55073   47870.090909     1.848485         0.542857           0.171429   \n",
       "325   55101   97732.240000     2.240000         0.512195           0.146341   \n",
       "326   55105   55234.312500     1.875000         0.533333           0.116667   \n",
       "327   55139   75513.866667     1.700000         0.421875           0.203125   \n",
       "328   56000   53550.643629     1.563715         0.415512           0.092336   \n",
       "\n",
       "      Percent_grad_degree  Percent_non_citizen  Percent_disabled  \\\n",
       "1428             0.089774             0.018711          0.025895   \n",
       "1429             0.098840             0.023183          0.018425   \n",
       "1430             0.062698             0.034888          0.026485   \n",
       "1435             0.093706             0.024338          0.025749   \n",
       "1437             0.110050             0.079108          0.018558   \n",
       "...                   ...                  ...               ...   \n",
       "324              0.000000             0.000000          0.000000   \n",
       "325              0.024390             0.024390          0.025641   \n",
       "326              0.083333             0.033333          0.041667   \n",
       "327              0.062500             0.000000          0.000000   \n",
       "328              0.062327             0.014774          0.023866   \n",
       "\n",
       "      Percent_no_health  Percent_priv_health  Percent_public_health  \n",
       "1428           0.045649             0.737636               0.496157  \n",
       "1429           0.032084             0.864214               0.337877  \n",
       "1430           0.036652             0.639360               0.661645  \n",
       "1435           0.041235             0.707271               0.556736  \n",
       "1437           0.027016             0.802932               0.442811  \n",
       "...                 ...                  ...                    ...  \n",
       "324            0.090909             0.681818               0.545455  \n",
       "325            0.000000             0.846154               0.461538  \n",
       "326            0.000000             0.625000               0.625000  \n",
       "327            0.000000             0.650000               0.550000  \n",
       "328            0.051313             0.787589               0.428401  \n",
       "\n",
       "[3385 rows x 11 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_20_not_missing_cps = df_20[cps_columns][df_20.FIPS.isin(not_missing)].drop('Year',axis=1)\n",
    "df_20_cps_all_fips = pd.concat([df_19_20, df_20_not_missing_cps])\n",
    "df_20_cps_all_fips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_cps_columns = list(set(df_20.columns)-set(cps_columns[1:]))\n",
    "df_20_no_cps = df_20[not_cps_columns]\n",
    "df_20_new = df_20_cps_all_fips.merge(df_20_no_cps,on='FIPS',how='outer')\n",
    "df_20_new['Year']='2020'\n",
    "df = pd.concat([df_no_20,df_20_new])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Impute Missing 2020 Demographic Data\n",
    "Some features, such as demographic information, follow a predictable pattern and are unlikely to be sharply affected by the pandemic, therefore these columns will be imputed using previous yearly data to calculate estimated 2020 values.\n",
    "### Determine yearly percent change for demographics columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Percent_male</th>\n",
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       "      <th>Percent_Black</th>\n",
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       "      <th>Percent_pacific</th>\n",
       "      <th>Percent_latinX</th>\n",
       "      <th>Percent_PoC</th>\n",
       "      <th>Percent_disabled</th>\n",
       "      <th>Percent_hs_grad</th>\n",
       "      <th>Percent_bachelors</th>\n",
       "      <th>Percent_grad_degree</th>\n",
       "      <th>Percent_priv_health</th>\n",
       "      <th>Percent_public_health</th>\n",
       "      <th>Percent_no_health</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.499648</td>\n",
       "      <td>0.500352</td>\n",
       "      <td>0.858055</td>\n",
       "      <td>0.090013</td>\n",
       "      <td>0.021626</td>\n",
       "      <td>0.012630</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.084257</td>\n",
       "      <td>0.141945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.238260</td>\n",
       "      <td>0.084332</td>\n",
       "      <td>0.043167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.499541</td>\n",
       "      <td>0.500459</td>\n",
       "      <td>0.839700</td>\n",
       "      <td>0.110203</td>\n",
       "      <td>0.018216</td>\n",
       "      <td>0.013882</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>0.064932</td>\n",
       "      <td>0.160300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.237480</td>\n",
       "      <td>0.084378</td>\n",
       "      <td>0.043308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.499773</td>\n",
       "      <td>0.500227</td>\n",
       "      <td>0.838104</td>\n",
       "      <td>0.110548</td>\n",
       "      <td>0.018413</td>\n",
       "      <td>0.014356</td>\n",
       "      <td>0.001165</td>\n",
       "      <td>0.066211</td>\n",
       "      <td>0.161896</td>\n",
       "      <td>0.149516</td>\n",
       "      <td>0.236568</td>\n",
       "      <td>0.085763</td>\n",
       "      <td>0.044233</td>\n",
       "      <td>0.627519</td>\n",
       "      <td>0.331782</td>\n",
       "      <td>0.146020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.499853</td>\n",
       "      <td>0.500147</td>\n",
       "      <td>0.835338</td>\n",
       "      <td>0.111947</td>\n",
       "      <td>0.018647</td>\n",
       "      <td>0.014926</td>\n",
       "      <td>0.001204</td>\n",
       "      <td>0.067619</td>\n",
       "      <td>0.164662</td>\n",
       "      <td>0.150693</td>\n",
       "      <td>0.237064</td>\n",
       "      <td>0.087005</td>\n",
       "      <td>0.045306</td>\n",
       "      <td>0.619998</td>\n",
       "      <td>0.339705</td>\n",
       "      <td>0.146622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.500625</td>\n",
       "      <td>0.499375</td>\n",
       "      <td>0.850215</td>\n",
       "      <td>0.092106</td>\n",
       "      <td>0.022659</td>\n",
       "      <td>0.014644</td>\n",
       "      <td>0.001266</td>\n",
       "      <td>0.091117</td>\n",
       "      <td>0.149785</td>\n",
       "      <td>0.150614</td>\n",
       "      <td>0.237156</td>\n",
       "      <td>0.090137</td>\n",
       "      <td>0.047232</td>\n",
       "      <td>0.625153</td>\n",
       "      <td>0.341686</td>\n",
       "      <td>0.140750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.500818</td>\n",
       "      <td>0.499182</td>\n",
       "      <td>0.848638</td>\n",
       "      <td>0.092428</td>\n",
       "      <td>0.022878</td>\n",
       "      <td>0.015110</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.092774</td>\n",
       "      <td>0.151362</td>\n",
       "      <td>0.151874</td>\n",
       "      <td>0.237364</td>\n",
       "      <td>0.091465</td>\n",
       "      <td>0.048343</td>\n",
       "      <td>0.628070</td>\n",
       "      <td>0.350993</td>\n",
       "      <td>0.130266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.500852</td>\n",
       "      <td>0.499148</td>\n",
       "      <td>0.847006</td>\n",
       "      <td>0.092782</td>\n",
       "      <td>0.023124</td>\n",
       "      <td>0.015556</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>0.094446</td>\n",
       "      <td>0.152994</td>\n",
       "      <td>0.153260</td>\n",
       "      <td>0.236615</td>\n",
       "      <td>0.093128</td>\n",
       "      <td>0.049572</td>\n",
       "      <td>0.631643</td>\n",
       "      <td>0.360194</td>\n",
       "      <td>0.118918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.500875</td>\n",
       "      <td>0.499125</td>\n",
       "      <td>0.845457</td>\n",
       "      <td>0.093154</td>\n",
       "      <td>0.023354</td>\n",
       "      <td>0.015933</td>\n",
       "      <td>0.001368</td>\n",
       "      <td>0.096121</td>\n",
       "      <td>0.154543</td>\n",
       "      <td>0.153586</td>\n",
       "      <td>0.235818</td>\n",
       "      <td>0.094946</td>\n",
       "      <td>0.050888</td>\n",
       "      <td>0.635518</td>\n",
       "      <td>0.367423</td>\n",
       "      <td>0.107984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.500904</td>\n",
       "      <td>0.499096</td>\n",
       "      <td>0.843955</td>\n",
       "      <td>0.093561</td>\n",
       "      <td>0.023547</td>\n",
       "      <td>0.016242</td>\n",
       "      <td>0.001411</td>\n",
       "      <td>0.097676</td>\n",
       "      <td>0.156045</td>\n",
       "      <td>0.153959</td>\n",
       "      <td>0.235520</td>\n",
       "      <td>0.096597</td>\n",
       "      <td>0.052234</td>\n",
       "      <td>0.638463</td>\n",
       "      <td>0.376279</td>\n",
       "      <td>0.097646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.500609</td>\n",
       "      <td>0.499391</td>\n",
       "      <td>0.839845</td>\n",
       "      <td>0.094545</td>\n",
       "      <td>0.023384</td>\n",
       "      <td>0.018778</td>\n",
       "      <td>0.001316</td>\n",
       "      <td>0.102870</td>\n",
       "      <td>0.160155</td>\n",
       "      <td>0.972296</td>\n",
       "      <td>0.055520</td>\n",
       "      <td>0.062247</td>\n",
       "      <td>0.100832</td>\n",
       "      <td>0.640464</td>\n",
       "      <td>0.378613</td>\n",
       "      <td>0.092410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.025890</td>\n",
       "      <td>0.496623</td>\n",
       "      <td>0.106129</td>\n",
       "      <td>0.054807</td>\n",
       "      <td>0.701942</td>\n",
       "      <td>0.538651</td>\n",
       "      <td>0.047231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Percent_male  Percent_female  Percent_white  Percent_Black  \\\n",
       "Year                                                               \n",
       "2009           NaN             NaN            NaN            NaN   \n",
       "2010      0.499648        0.500352       0.858055       0.090013   \n",
       "2011      0.499541        0.500459       0.839700       0.110203   \n",
       "2012      0.499773        0.500227       0.838104       0.110548   \n",
       "2013      0.499853        0.500147       0.835338       0.111947   \n",
       "2014      0.500625        0.499375       0.850215       0.092106   \n",
       "2015      0.500818        0.499182       0.848638       0.092428   \n",
       "2016      0.500852        0.499148       0.847006       0.092782   \n",
       "2017      0.500875        0.499125       0.845457       0.093154   \n",
       "2018      0.500904        0.499096       0.843955       0.093561   \n",
       "2019      0.500609        0.499391       0.839845       0.094545   \n",
       "2020           NaN             NaN            NaN            NaN   \n",
       "\n",
       "      Percent_native  Percent_asian  Percent_pacific  Percent_latinX  \\\n",
       "Year                                                                   \n",
       "2009             NaN            NaN              NaN             NaN   \n",
       "2010        0.021626       0.012630         0.000961        0.084257   \n",
       "2011        0.018216       0.013882         0.001130        0.064932   \n",
       "2012        0.018413       0.014356         0.001165        0.066211   \n",
       "2013        0.018647       0.014926         0.001204        0.067619   \n",
       "2014        0.022659       0.014644         0.001266        0.091117   \n",
       "2015        0.022878       0.015110         0.001301        0.092774   \n",
       "2016        0.023124       0.015556         0.001332        0.094446   \n",
       "2017        0.023354       0.015933         0.001368        0.096121   \n",
       "2018        0.023547       0.016242         0.001411        0.097676   \n",
       "2019        0.023384       0.018778         0.001316        0.102870   \n",
       "2020             NaN            NaN              NaN             NaN   \n",
       "\n",
       "      Percent_PoC  Percent_disabled  Percent_hs_grad  Percent_bachelors  \\\n",
       "Year                                                                      \n",
       "2009          NaN               NaN              NaN                NaN   \n",
       "2010     0.141945               NaN         0.238260           0.084332   \n",
       "2011     0.160300               NaN         0.237480           0.084378   \n",
       "2012     0.161896          0.149516         0.236568           0.085763   \n",
       "2013     0.164662          0.150693         0.237064           0.087005   \n",
       "2014     0.149785          0.150614         0.237156           0.090137   \n",
       "2015     0.151362          0.151874         0.237364           0.091465   \n",
       "2016     0.152994          0.153260         0.236615           0.093128   \n",
       "2017     0.154543          0.153586         0.235818           0.094946   \n",
       "2018     0.156045          0.153959         0.235520           0.096597   \n",
       "2019     0.160155          0.972296         0.055520           0.062247   \n",
       "2020          NaN          0.025890         0.496623           0.106129   \n",
       "\n",
       "      Percent_grad_degree  Percent_priv_health  Percent_public_health  \\\n",
       "Year                                                                    \n",
       "2009                  NaN                  NaN                    NaN   \n",
       "2010             0.043167                  NaN                    NaN   \n",
       "2011             0.043308                  NaN                    NaN   \n",
       "2012             0.044233             0.627519               0.331782   \n",
       "2013             0.045306             0.619998               0.339705   \n",
       "2014             0.047232             0.625153               0.341686   \n",
       "2015             0.048343             0.628070               0.350993   \n",
       "2016             0.049572             0.631643               0.360194   \n",
       "2017             0.050888             0.635518               0.367423   \n",
       "2018             0.052234             0.638463               0.376279   \n",
       "2019             0.100832             0.640464               0.378613   \n",
       "2020             0.054807             0.701942               0.538651   \n",
       "\n",
       "      Percent_no_health  \n",
       "Year                     \n",
       "2009                NaN  \n",
       "2010                NaN  \n",
       "2011                NaN  \n",
       "2012           0.146020  \n",
       "2013           0.146622  \n",
       "2014           0.140750  \n",
       "2015           0.130266  \n",
       "2016           0.118918  \n",
       "2017           0.107984  \n",
       "2018           0.097646  \n",
       "2019           0.092410  \n",
       "2020           0.047231  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Year')[['Percent_male','Percent_female','Percent_white','Percent_Black','Percent_native',\n",
    "        'Percent_asian','Percent_pacific','Percent_latinX','Percent_PoC','Percent_disabled', 'Percent_hs_grad',\n",
    "     'Percent_bachelors','Percent_grad_degree','Percent_priv_health','Percent_public_health','Percent_no_health']].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Percent_male       0.000192\n",
       "Percent_female    -0.000192\n",
       "Percent_white     -0.002104\n",
       "Percent_Black      0.008908\n",
       "Percent_native     0.011271\n",
       "Percent_asian      0.041518\n",
       "Percent_pacific    0.033430\n",
       "Percent_latinX     0.028241\n",
       "Percent_PoC        0.013349\n",
       "dtype: float64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_change = df.groupby('Year')[['Percent_male','Percent_female','Percent_white','Percent_Black','Percent_native',\n",
    "        'Percent_asian','Percent_pacific','Percent_latinX','Percent_PoC']].mean().pct_change().mean()\n",
    "percent_change\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiply each 2019 value by the calculated percent change by above, to derive a 2020 estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dems_19_20 = df[['FIPS','Percent_male','Percent_female','Percent_white','Percent_Black','Percent_native',\n",
    "        'Percent_asian','Percent_pacific','Percent_latinX','Percent_PoC']][df.Year=='2019'].drop_duplicates()\n",
    "\n",
    "dems_19_20['Percent_male'] = dems_19_20['Percent_male'] + dems_19_20['Percent_male']* 0.000192\n",
    "dems_19_20['Percent_female'] = dems_19_20['Percent_female'] + dems_19_20['Percent_female']* -0.000192\n",
    "dems_19_20['Percent_white'] = dems_19_20['Percent_white']+ dems_19_20['Percent_white']* -0.002104\n",
    "dems_19_20['Percent_Black'] = dems_19_20['Percent_Black'] +dems_19_20['Percent_Black']* 0.008908\n",
    "dems_19_20['Percent_native'] = dems_19_20['Percent_native']+ dems_19_20['Percent_native']* 0.011271\n",
    "dems_19_20['Percent_asian'] = dems_19_20['Percent_asian'] +dems_19_20['Percent_asian']* 0.041518\n",
    "dems_19_20['Percent_pacific'] = dems_19_20['Percent_pacific'] +dems_19_20['Percent_pacific']* 0.033430\n",
    "dems_19_20['Percent_latinX'] = dems_19_20['Percent_latinX'] +dems_19_20['Percent_latinX']* 0.028241\n",
    "dems_19_20['Percent_PoC'] = dems_19_20['Percent_PoC'] + dems_19_20['Percent_PoC']* 0.013349\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop nulls, and add the newly calculated 2020 values into the main df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_20_dropped_null = df[df.Year=='2020'].drop(['Percent_male','Percent_female','Percent_white','Percent_Black',\n",
    "            'Percent_native','Percent_asian','Percent_pacific','Percent_latinX','Percent_PoC'],axis=1)\n",
    "df_not_20 = df[df.Year!='2020']\n",
    "df_20_new = df_20_dropped_null.merge(dems_19_20,on='FIPS',how='outer')\n",
    "\n",
    "df = pd.concat([df_not_20, df_20_new])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Impute Houselessness Data\n",
    "*NOTE* Decided not to use imputed 2020 houselessness data, because it created significant null values.<br>\n",
    "Data is still widely unavailable for 2020 houselessness rates, however estimates are available at the City level in some areas. Greater Los Angeles is [experiencing a 10% increase](https://www.lahsa.org/documents?id=4585-2020-greater-los-angeles-homeless-count-los-angeles-continuum-of-care-coc-) in houseless rates for 2020, therefore this rate will be applied to each county's 2019 houseless rate, to create an estimate for 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# houseless_19_20 = df[['FIPS','Houseless_rate']][df.Year=='2019'].drop_duplicates()\n",
    "# houseless_19_20['Houseless_rate'] = houseless_19_20['Houseless_rate']*1.13\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_not_20 = df[df.Year!='2020']\n",
    "# df_20_dropped_houseless = df[df.Year=='2020'].drop(['Houseless_rate'],axis=1)\n",
    "# df_20_new = df_20_dropped_houseless.merge(houseless_19_20,on='FIPS',how='outer')\n",
    "# df = pd.concat([df_not_20, df_20_new])\n",
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial Features\n",
    "**Note:** Decided to omit polynomial features, because they decreased model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only using derived percentages, ignore raw counts\n",
    "continuous_features = ['Rent', 'Houseless_rate','Sheltered_rate', 'Unsheltered_rate', 'TOT_POP',\n",
    "       'Cost Per Meal', 'Num_wholesale','Num_restaraunts', 'Num_grocery',  'Unemployment_rate', 'Percent_male', \n",
    "         'Percent_female','Percent_white', 'Percent_Black', 'Percent_native', 'Percent_asian',\n",
    "       'Percent_pacific', 'Percent_latinX','Percent_working', 'Total_food_retail','Food_retail_per_person',\n",
    "        'Percent_disabled','Percent_hs_grad','Percent_bachelors','Percent_grad_degree','Percent_priv_health',\n",
    "            'Percent_public_health','Percent_no_health','Percent_65+','Percent_non_citizen','Percent_hh_no_vehicle',\n",
    "                    'Percent_hh_SNAP','percent_hh_poverty','hh_avg_size','num_hh','hh_med_income']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# add squared and cubed polynomials for each continuous feature\n",
    "for feat in continuous_features:\n",
    "    df[feat+'^2'] = df[feat]**2\n",
    "    df[feat+'^3'] = df[feat]**3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interaction Features\n",
    "Create an interaction feature for each combination of continuous features, and add best ones to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate combinations of features\n",
    "y = df.dropna()[['FI Rate']]\n",
    "X = df.dropna()[continuous_features]\n",
    "interactions = list(combinations(X.columns, 2))\n",
    "interaction_dict = {}\n",
    "\n",
    "# run simple regression model with each possible interaction, and save R-squared for each interaction in a dictionary\n",
    "for interaction in interactions:\n",
    "    X_copy = X.copy()\n",
    "    X_copy['interact'] = X_copy[interaction[0]] * X_copy[interaction[1]] \n",
    "    X_copy = X_copy.replace([np.inf, -np.inf], 0)\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_copy, y)\n",
    "    interaction_dict[model.score(X_copy, y)] = interaction \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add best 50 interactions to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the interactions dictionary, and add best 50 interactions to dataframe\n",
    "top_interactions = sorted(interaction_dict.keys(), reverse = True)[:50]\n",
    "for interaction in top_interactions:\n",
    "    feature1 = interaction_dict[interaction][0]\n",
    "    feature2 = interaction_dict[interaction][1]\n",
    "    df[feature1+'_X_'+feature2] = df[feature1] * df[feature2] #also add to new_features df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Log Transformations\n",
    "Take natural log of each continuous feature, and add these log features to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feat in continuous_features:\n",
    "    df['log_'+feat] = df[feat].map(lambda x: np.log(x))\n",
    "df = df.replace([np.inf, -np.inf], 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy Variables\n",
    "High and Low Threshold programs delineate the assistance programs provided by State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy variables for high and low threshold programs, and add to dataframe\n",
    "hi_thresh_dummies = pd.get_dummies(df['High Threshold Type'].astype(str), dtype=int)\n",
    "hi_thresh_dummies['other'] = hi_thresh_dummies['Other Nutrition Program'] + hi_thresh_dummies['other nutrition pgm']\n",
    "hi_thresh_dummies.drop(['Other Nutrition Program','other nutrition pgm','nan'],axis=1,inplace=True) # drop last col\n",
    "hi_thresh_dummies.rename(columns = {'SNAP, Other Nutrition Programs': 'SNAP_other'}, inplace=True)\n",
    "hi_thresh_dummies.columns = 'Hi_thresh_'+hi_thresh_dummies.columns\n",
    "\n",
    "low_thresh_dummies = pd.get_dummies(df['Low Threshold Type'].astype(str), dtype=int)\n",
    "low_thresh_dummies.drop('nan', axis=1,inplace=True)\n",
    "low_thresh_dummies.rename(columns = {'SNAP, Other Nutrition Programs': 'SNAP_other'}, inplace=True)\n",
    "low_thresh_dummies.columns = 'Lo_thresh_'+low_thresh_dummies.columns\n",
    "\n",
    "df = pd.concat([df, low_thresh_dummies, hi_thresh_dummies],axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pickle the new dataframe\n",
    "Save the dataframe with all new features added (demographic percentages, interactions, logs, and dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>FIPS</th>\n",
       "      <th>Rent</th>\n",
       "      <th>Year</th>\n",
       "      <th>coc_number</th>\n",
       "      <th>Houseless_rate</th>\n",
       "      <th>Sheltered_rate</th>\n",
       "      <th>Unsheltered_rate</th>\n",
       "      <th>hh_med_income</th>\n",
       "      <th>percent_hh_poverty</th>\n",
       "      <th>hh_avg_size</th>\n",
       "      <th>num_hh</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>TOT_POP</th>\n",
       "      <th>State/County</th>\n",
       "      <th>FI Rate</th>\n",
       "      <th>Low Threshold Type</th>\n",
       "      <th>High Threshold Type</th>\n",
       "      <th>Cost Per Meal</th>\n",
       "      <th>Num_wholesale</th>\n",
       "      <th>Num_restaraunts</th>\n",
       "      <th>Num_grocery</th>\n",
       "      <th>Total_workforce</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Unemployed</th>\n",
       "      <th>Unemployment_rate</th>\n",
       "      <th>Percent_male</th>\n",
       "      <th>Percent_female</th>\n",
       "      <th>Percent_white</th>\n",
       "      <th>Percent_Black</th>\n",
       "      <th>Percent_native</th>\n",
       "      <th>Percent_asian</th>\n",
       "      <th>Percent_pacific</th>\n",
       "      <th>Percent_latinX</th>\n",
       "      <th>Percent_PoC</th>\n",
       "      <th>Percent_working</th>\n",
       "      <th>Total_food_retail</th>\n",
       "      <th>Food_retail_per_person</th>\n",
       "      <th>Percent_disabled</th>\n",
       "      <th>Percent_hs_grad</th>\n",
       "      <th>Percent_bachelors</th>\n",
       "      <th>Percent_grad_degree</th>\n",
       "      <th>Percent_priv_health</th>\n",
       "      <th>Percent_public_health</th>\n",
       "      <th>Percent_no_health</th>\n",
       "      <th>Percent_65+</th>\n",
       "      <th>Percent_non_citizen</th>\n",
       "      <th>Percent_hh_no_vehicle</th>\n",
       "      <th>Percent_hh_SNAP</th>\n",
       "      <th>Rent^2</th>\n",
       "      <th>Rent^3</th>\n",
       "      <th>Houseless_rate^2</th>\n",
       "      <th>Houseless_rate^3</th>\n",
       "      <th>Sheltered_rate^2</th>\n",
       "      <th>Sheltered_rate^3</th>\n",
       "      <th>Unsheltered_rate^2</th>\n",
       "      <th>Unsheltered_rate^3</th>\n",
       "      <th>TOT_POP^2</th>\n",
       "      <th>TOT_POP^3</th>\n",
       "      <th>Cost Per Meal^2</th>\n",
       "      <th>Cost Per Meal^3</th>\n",
       "      <th>Num_wholesale^2</th>\n",
       "      <th>Num_wholesale^3</th>\n",
       "      <th>Num_restaraunts^2</th>\n",
       "      <th>Num_restaraunts^3</th>\n",
       "      <th>Num_grocery^2</th>\n",
       "      <th>Num_grocery^3</th>\n",
       "      <th>Unemployment_rate^2</th>\n",
       "      <th>Unemployment_rate^3</th>\n",
       "      <th>Percent_male^2</th>\n",
       "      <th>Percent_male^3</th>\n",
       "      <th>Percent_female^2</th>\n",
       "      <th>Percent_female^3</th>\n",
       "      <th>Percent_white^2</th>\n",
       "      <th>Percent_white^3</th>\n",
       "      <th>Percent_Black^2</th>\n",
       "      <th>Percent_Black^3</th>\n",
       "      <th>Percent_native^2</th>\n",
       "      <th>Percent_native^3</th>\n",
       "      <th>Percent_asian^2</th>\n",
       "      <th>Percent_asian^3</th>\n",
       "      <th>Percent_pacific^2</th>\n",
       "      <th>Percent_pacific^3</th>\n",
       "      <th>Percent_latinX^2</th>\n",
       "      <th>Percent_latinX^3</th>\n",
       "      <th>Percent_working^2</th>\n",
       "      <th>Percent_working^3</th>\n",
       "      <th>Total_food_retail^2</th>\n",
       "      <th>Total_food_retail^3</th>\n",
       "      <th>Food_retail_per_person^2</th>\n",
       "      <th>Food_retail_per_person^3</th>\n",
       "      <th>Percent_disabled^2</th>\n",
       "      <th>Percent_disabled^3</th>\n",
       "      <th>Percent_hs_grad^2</th>\n",
       "      <th>Percent_hs_grad^3</th>\n",
       "      <th>Percent_bachelors^2</th>\n",
       "      <th>Percent_bachelors^3</th>\n",
       "      <th>Percent_grad_degree^2</th>\n",
       "      <th>Percent_grad_degree^3</th>\n",
       "      <th>Percent_priv_health^2</th>\n",
       "      <th>Percent_priv_health^3</th>\n",
       "      <th>Percent_public_health^2</th>\n",
       "      <th>Percent_public_health^3</th>\n",
       "      <th>Percent_no_health^2</th>\n",
       "      <th>Percent_no_health^3</th>\n",
       "      <th>Percent_65+^2</th>\n",
       "      <th>Percent_65+^3</th>\n",
       "      <th>Percent_non_citizen^2</th>\n",
       "      <th>Percent_non_citizen^3</th>\n",
       "      <th>Percent_hh_no_vehicle^2</th>\n",
       "      <th>Percent_hh_no_vehicle^3</th>\n",
       "      <th>Percent_hh_SNAP^2</th>\n",
       "      <th>Percent_hh_SNAP^3</th>\n",
       "      <th>percent_hh_poverty^2</th>\n",
       "      <th>percent_hh_poverty^3</th>\n",
       "      <th>hh_avg_size^2</th>\n",
       "      <th>hh_avg_size^3</th>\n",
       "      <th>num_hh^2</th>\n",
       "      <th>num_hh^3</th>\n",
       "      <th>hh_med_income^2</th>\n",
       "      <th>hh_med_income^3</th>\n",
       "      <th>Unemployment_rate_X_Percent_Black</th>\n",
       "      <th>Unemployment_rate_X_Percent_white</th>\n",
       "      <th>Unemployment_rate_X_Percent_latinX</th>\n",
       "      <th>Houseless_rate_X_Sheltered_rate</th>\n",
       "      <th>Sheltered_rate_X_Percent_grad_degree</th>\n",
       "      <th>Percent_Black_X_Percent_hh_SNAP</th>\n",
       "      <th>Percent_latinX_X_hh_med_income</th>\n",
       "      <th>Cost Per Meal_X_Percent_Black</th>\n",
       "      <th>Unemployment_rate_X_hh_avg_size</th>\n",
       "      <th>Percent_white_X_Percent_hh_SNAP</th>\n",
       "      <th>Rent_X_Unemployment_rate</th>\n",
       "      <th>Percent_Black_X_Percent_grad_degree</th>\n",
       "      <th>Percent_pacific_X_Percent_hs_grad</th>\n",
       "      <th>Percent_latinX_X_Percent_hh_SNAP</th>\n",
       "      <th>Unemployment_rate_X_Percent_non_citizen</th>\n",
       "      <th>Percent_white_X_Percent_grad_degree</th>\n",
       "      <th>Percent_latinX_X_percent_hh_poverty</th>\n",
       "      <th>TOT_POP_X_Percent_latinX</th>\n",
       "      <th>Unemployment_rate_X_Percent_female</th>\n",
       "      <th>Unemployment_rate_X_Percent_male</th>\n",
       "      <th>Percent_pacific_X_Percent_public_health</th>\n",
       "      <th>Percent_Black_X_Percent_hs_grad</th>\n",
       "      <th>Percent_latinX_X_num_hh</th>\n",
       "      <th>Rent_X_Percent_Black</th>\n",
       "      <th>Num_grocery_X_num_hh</th>\n",
       "      <th>Houseless_rate_X_Percent_pacific</th>\n",
       "      <th>Percent_white_X_percent_hh_poverty</th>\n",
       "      <th>Percent_white_X_Percent_priv_health</th>\n",
       "      <th>Percent_white_X_Percent_pacific</th>\n",
       "      <th>Unemployment_rate_X_Percent_hs_grad</th>\n",
       "      <th>Unemployment_rate_X_Percent_disabled</th>\n",
       "      <th>Percent_white_X_Percent_no_health</th>\n",
       "      <th>Percent_female_X_Percent_hs_grad</th>\n",
       "      <th>Sheltered_rate_X_Percent_working</th>\n",
       "      <th>Percent_Black_X_Percent_no_health</th>\n",
       "      <th>Percent_Black_X_percent_hh_poverty</th>\n",
       "      <th>Percent_pacific_X_Percent_disabled</th>\n",
       "      <th>Cost Per Meal_X_Percent_white</th>\n",
       "      <th>Percent_pacific_X_Percent_65+</th>\n",
       "      <th>Sheltered_rate_X_Percent_no_health</th>\n",
       "      <th>TOT_POP_X_Num_grocery</th>\n",
       "      <th>Percent_Black_X_hh_med_income</th>\n",
       "      <th>Percent_Black_X_Percent_priv_health</th>\n",
       "      <th>Percent_hs_grad_X_hh_avg_size</th>\n",
       "      <th>Sheltered_rate_X_Percent_pacific</th>\n",
       "      <th>Unemployment_rate_X_Percent_hh_SNAP</th>\n",
       "      <th>Percent_latinX_X_Percent_priv_health</th>\n",
       "      <th>Total_food_retail_X_num_hh</th>\n",
       "      <th>Houseless_rate_X_Percent_grad_degree</th>\n",
       "      <th>Percent_pacific_X_Percent_hh_SNAP</th>\n",
       "      <th>log_Rent</th>\n",
       "      <th>log_Houseless_rate</th>\n",
       "      <th>log_Sheltered_rate</th>\n",
       "      <th>log_Unsheltered_rate</th>\n",
       "      <th>log_TOT_POP</th>\n",
       "      <th>log_Cost Per Meal</th>\n",
       "      <th>log_Num_wholesale</th>\n",
       "      <th>log_Num_restaraunts</th>\n",
       "      <th>log_Num_grocery</th>\n",
       "      <th>log_Unemployment_rate</th>\n",
       "      <th>log_Percent_male</th>\n",
       "      <th>log_Percent_female</th>\n",
       "      <th>log_Percent_white</th>\n",
       "      <th>log_Percent_Black</th>\n",
       "      <th>log_Percent_native</th>\n",
       "      <th>log_Percent_asian</th>\n",
       "      <th>log_Percent_pacific</th>\n",
       "      <th>log_Percent_latinX</th>\n",
       "      <th>log_Percent_working</th>\n",
       "      <th>log_Total_food_retail</th>\n",
       "      <th>log_Food_retail_per_person</th>\n",
       "      <th>log_Percent_disabled</th>\n",
       "      <th>log_Percent_hs_grad</th>\n",
       "      <th>log_Percent_bachelors</th>\n",
       "      <th>log_Percent_grad_degree</th>\n",
       "      <th>log_Percent_priv_health</th>\n",
       "      <th>log_Percent_public_health</th>\n",
       "      <th>log_Percent_no_health</th>\n",
       "      <th>log_Percent_65+</th>\n",
       "      <th>log_Percent_non_citizen</th>\n",
       "      <th>log_Percent_hh_no_vehicle</th>\n",
       "      <th>log_Percent_hh_SNAP</th>\n",
       "      <th>log_percent_hh_poverty</th>\n",
       "      <th>log_hh_avg_size</th>\n",
       "      <th>log_num_hh</th>\n",
       "      <th>log_hh_med_income</th>\n",
       "      <th>Lo_thresh_SNAP</th>\n",
       "      <th>Lo_thresh_SNAP_other</th>\n",
       "      <th>Hi_thresh_SNAP</th>\n",
       "      <th>Hi_thresh_SNAP_other</th>\n",
       "      <th>Hi_thresh_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01073</td>\n",
       "      <td>1019.995960</td>\n",
       "      <td>2014</td>\n",
       "      <td>AL-500</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>45239.000000</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>259397.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>659972.0</td>\n",
       "      <td>Jefferson County, AL</td>\n",
       "      <td>0.197</td>\n",
       "      <td>SNAP</td>\n",
       "      <td>Other Nutrition Program</td>\n",
       "      <td>2.93</td>\n",
       "      <td>483.0</td>\n",
       "      <td>2693.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>312131.0</td>\n",
       "      <td>292505.0</td>\n",
       "      <td>19626.0</td>\n",
       "      <td>6.30</td>\n",
       "      <td>0.473491</td>\n",
       "      <td>0.526509</td>\n",
       "      <td>0.538864</td>\n",
       "      <td>0.430446</td>\n",
       "      <td>0.003074</td>\n",
       "      <td>0.015725</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.036515</td>\n",
       "      <td>0.461136</td>\n",
       "      <td>0.472946</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.005418</td>\n",
       "      <td>0.143315</td>\n",
       "      <td>0.178574</td>\n",
       "      <td>0.123681</td>\n",
       "      <td>0.079964</td>\n",
       "      <td>0.654025</td>\n",
       "      <td>0.320574</td>\n",
       "      <td>0.123242</td>\n",
       "      <td>0.131878</td>\n",
       "      <td>0.026545</td>\n",
       "      <td>0.360953</td>\n",
       "      <td>0.154077</td>\n",
       "      <td>1.040392e+06</td>\n",
       "      <td>1.061195e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>2.718471e-09</td>\n",
       "      <td>9.232983e-07</td>\n",
       "      <td>8.871827e-10</td>\n",
       "      <td>1.890169e-07</td>\n",
       "      <td>8.217710e-11</td>\n",
       "      <td>4.355630e+11</td>\n",
       "      <td>2.874594e+17</td>\n",
       "      <td>8.5849</td>\n",
       "      <td>25.153757</td>\n",
       "      <td>233289.0</td>\n",
       "      <td>1.126786e+08</td>\n",
       "      <td>7252249.0</td>\n",
       "      <td>1.953031e+10</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>6.400000e+07</td>\n",
       "      <td>39.6900</td>\n",
       "      <td>250.047000</td>\n",
       "      <td>0.224194</td>\n",
       "      <td>0.106154</td>\n",
       "      <td>0.277211</td>\n",
       "      <td>0.145954</td>\n",
       "      <td>0.290374</td>\n",
       "      <td>0.156472</td>\n",
       "      <td>0.185283</td>\n",
       "      <td>7.975439e-02</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>2.905826e-08</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2.764445e-07</td>\n",
       "      <td>1.453489e-10</td>\n",
       "      <td>0.001333</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.223678</td>\n",
       "      <td>0.105787</td>\n",
       "      <td>12787776.0</td>\n",
       "      <td>4.572909e+10</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>1.590802e-07</td>\n",
       "      <td>0.020539</td>\n",
       "      <td>0.002944</td>\n",
       "      <td>0.031889</td>\n",
       "      <td>0.005695</td>\n",
       "      <td>0.015297</td>\n",
       "      <td>0.001892</td>\n",
       "      <td>0.006394</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>0.427748</td>\n",
       "      <td>0.279758</td>\n",
       "      <td>0.102768</td>\n",
       "      <td>0.032945</td>\n",
       "      <td>0.015188</td>\n",
       "      <td>0.001872</td>\n",
       "      <td>0.017392</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.130287</td>\n",
       "      <td>0.047027</td>\n",
       "      <td>0.023740</td>\n",
       "      <td>0.003658</td>\n",
       "      <td>219.04</td>\n",
       "      <td>3241.792</td>\n",
       "      <td>6.150400</td>\n",
       "      <td>15.252992</td>\n",
       "      <td>6.728680e+10</td>\n",
       "      <td>1.745399e+16</td>\n",
       "      <td>2.046567e+09</td>\n",
       "      <td>9.258465e+13</td>\n",
       "      <td>2.711807</td>\n",
       "      <td>3.394842</td>\n",
       "      <td>0.230046</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.066322</td>\n",
       "      <td>1651.910477</td>\n",
       "      <td>1.261205</td>\n",
       "      <td>15.624000</td>\n",
       "      <td>0.083026</td>\n",
       "      <td>6425.974545</td>\n",
       "      <td>0.034420</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.005626</td>\n",
       "      <td>0.167234</td>\n",
       "      <td>0.043090</td>\n",
       "      <td>0.540425</td>\n",
       "      <td>24099.0</td>\n",
       "      <td>3.317005</td>\n",
       "      <td>2.982995</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.076866</td>\n",
       "      <td>9471.929571</td>\n",
       "      <td>439.052706</td>\n",
       "      <td>1.037588e+08</td>\n",
       "      <td>7.338020e-07</td>\n",
       "      <td>7.975184</td>\n",
       "      <td>0.352430</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>1.125018</td>\n",
       "      <td>0.902886</td>\n",
       "      <td>0.066410</td>\n",
       "      <td>0.094021</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.053049</td>\n",
       "      <td>6.370594</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>1.578871</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>2.639888e+08</td>\n",
       "      <td>19472.925515</td>\n",
       "      <td>0.281522</td>\n",
       "      <td>0.442864</td>\n",
       "      <td>5.052135e-07</td>\n",
       "      <td>0.970682</td>\n",
       "      <td>0.023882</td>\n",
       "      <td>9.276037e+08</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>6.927554</td>\n",
       "      <td>-6.574399</td>\n",
       "      <td>-6.947657</td>\n",
       "      <td>-7.740715</td>\n",
       "      <td>13.399953</td>\n",
       "      <td>1.075002</td>\n",
       "      <td>6.180017</td>\n",
       "      <td>7.898411</td>\n",
       "      <td>5.991465</td>\n",
       "      <td>1.840550</td>\n",
       "      <td>-0.747622</td>\n",
       "      <td>-0.641487</td>\n",
       "      <td>-0.618292</td>\n",
       "      <td>-0.842934</td>\n",
       "      <td>-5.784654</td>\n",
       "      <td>-4.152509</td>\n",
       "      <td>-7.550628</td>\n",
       "      <td>-3.310027</td>\n",
       "      <td>-0.748774</td>\n",
       "      <td>8.182000</td>\n",
       "      <td>-5.217953</td>\n",
       "      <td>-1.942709</td>\n",
       "      <td>-1.722751</td>\n",
       "      <td>-2.090050</td>\n",
       "      <td>-2.526179</td>\n",
       "      <td>-0.424610</td>\n",
       "      <td>-1.137641</td>\n",
       "      <td>-2.093609</td>\n",
       "      <td>-2.025876</td>\n",
       "      <td>-3.628911</td>\n",
       "      <td>-1.019009</td>\n",
       "      <td>-1.870306</td>\n",
       "      <td>2.694627</td>\n",
       "      <td>0.908259</td>\n",
       "      <td>12.466115</td>\n",
       "      <td>10.719715</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01117</td>\n",
       "      <td>1229.755051</td>\n",
       "      <td>2014</td>\n",
       "      <td>AL-500</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>69723.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>74790.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Shelby County</td>\n",
       "      <td>206280.0</td>\n",
       "      <td>Shelby County, AL</td>\n",
       "      <td>0.105</td>\n",
       "      <td>SNAP</td>\n",
       "      <td>Other Nutrition Program</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>2706.0</td>\n",
       "      <td>107208.0</td>\n",
       "      <td>102400.0</td>\n",
       "      <td>4808.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.486252</td>\n",
       "      <td>0.513748</td>\n",
       "      <td>0.843969</td>\n",
       "      <td>0.117544</td>\n",
       "      <td>0.003902</td>\n",
       "      <td>0.021345</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>0.057553</td>\n",
       "      <td>0.156031</td>\n",
       "      <td>0.519721</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>0.016725</td>\n",
       "      <td>0.110491</td>\n",
       "      <td>0.140154</td>\n",
       "      <td>0.173420</td>\n",
       "      <td>0.089737</td>\n",
       "      <td>0.773972</td>\n",
       "      <td>0.205686</td>\n",
       "      <td>0.092956</td>\n",
       "      <td>0.113457</td>\n",
       "      <td>0.036959</td>\n",
       "      <td>0.264233</td>\n",
       "      <td>0.062923</td>\n",
       "      <td>1.512297e+06</td>\n",
       "      <td>1.859755e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>2.718471e-09</td>\n",
       "      <td>9.232983e-07</td>\n",
       "      <td>8.871827e-10</td>\n",
       "      <td>1.890169e-07</td>\n",
       "      <td>8.217710e-11</td>\n",
       "      <td>4.255144e+10</td>\n",
       "      <td>8.777511e+15</td>\n",
       "      <td>11.3569</td>\n",
       "      <td>38.272753</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>552049.0</td>\n",
       "      <td>4.101724e+08</td>\n",
       "      <td>7322436.0</td>\n",
       "      <td>1.981451e+10</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>91.125000</td>\n",
       "      <td>0.236441</td>\n",
       "      <td>0.114970</td>\n",
       "      <td>0.263937</td>\n",
       "      <td>0.135597</td>\n",
       "      <td>0.712284</td>\n",
       "      <td>0.601146</td>\n",
       "      <td>0.013817</td>\n",
       "      <td>1.624062e-03</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>5.943144e-08</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>2.397334e-07</td>\n",
       "      <td>1.173796e-10</td>\n",
       "      <td>0.003312</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.270110</td>\n",
       "      <td>0.140382</td>\n",
       "      <td>11902500.0</td>\n",
       "      <td>4.106362e+10</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>4.678277e-06</td>\n",
       "      <td>0.012208</td>\n",
       "      <td>0.001349</td>\n",
       "      <td>0.019643</td>\n",
       "      <td>0.002753</td>\n",
       "      <td>0.030074</td>\n",
       "      <td>0.005215</td>\n",
       "      <td>0.008053</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>0.599033</td>\n",
       "      <td>0.463635</td>\n",
       "      <td>0.042307</td>\n",
       "      <td>0.008702</td>\n",
       "      <td>0.008641</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.012873</td>\n",
       "      <td>0.001460</td>\n",
       "      <td>0.001366</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.069819</td>\n",
       "      <td>0.018449</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>38.44</td>\n",
       "      <td>238.328</td>\n",
       "      <td>7.022500</td>\n",
       "      <td>18.609625</td>\n",
       "      <td>5.593544e+09</td>\n",
       "      <td>4.183412e+14</td>\n",
       "      <td>4.861297e+09</td>\n",
       "      <td>3.389442e+14</td>\n",
       "      <td>0.528949</td>\n",
       "      <td>3.797862</td>\n",
       "      <td>0.258988</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.007396</td>\n",
       "      <td>4012.756719</td>\n",
       "      <td>0.396124</td>\n",
       "      <td>11.925000</td>\n",
       "      <td>0.053105</td>\n",
       "      <td>5533.897727</td>\n",
       "      <td>0.010548</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.003621</td>\n",
       "      <td>0.166318</td>\n",
       "      <td>0.075735</td>\n",
       "      <td>0.356828</td>\n",
       "      <td>11872.0</td>\n",
       "      <td>2.311867</td>\n",
       "      <td>2.188133</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.016474</td>\n",
       "      <td>4304.376963</td>\n",
       "      <td>144.550469</td>\n",
       "      <td>2.023817e+08</td>\n",
       "      <td>6.833436e-07</td>\n",
       "      <td>5.232610</td>\n",
       "      <td>0.653209</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.630694</td>\n",
       "      <td>0.497208</td>\n",
       "      <td>0.078452</td>\n",
       "      <td>0.072004</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.010926</td>\n",
       "      <td>0.728774</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>2.844177</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>5.581937e+08</td>\n",
       "      <td>8195.528316</td>\n",
       "      <td>0.090976</td>\n",
       "      <td>0.371409</td>\n",
       "      <td>4.704736e-07</td>\n",
       "      <td>0.283153</td>\n",
       "      <td>0.044544</td>\n",
       "      <td>2.580255e+08</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>7.114570</td>\n",
       "      <td>-6.574399</td>\n",
       "      <td>-6.947657</td>\n",
       "      <td>-7.740715</td>\n",
       "      <td>12.236990</td>\n",
       "      <td>1.214913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.610696</td>\n",
       "      <td>7.903227</td>\n",
       "      <td>1.504077</td>\n",
       "      <td>-0.721029</td>\n",
       "      <td>-0.666022</td>\n",
       "      <td>-0.169639</td>\n",
       "      <td>-2.140942</td>\n",
       "      <td>-5.546147</td>\n",
       "      <td>-3.846948</td>\n",
       "      <td>-7.621869</td>\n",
       "      <td>-2.855052</td>\n",
       "      <td>-0.654464</td>\n",
       "      <td>8.146130</td>\n",
       "      <td>-4.090860</td>\n",
       "      <td>-2.202825</td>\n",
       "      <td>-1.965012</td>\n",
       "      <td>-1.752041</td>\n",
       "      <td>-2.410869</td>\n",
       "      <td>-0.256219</td>\n",
       "      <td>-1.581402</td>\n",
       "      <td>-2.375627</td>\n",
       "      <td>-2.176328</td>\n",
       "      <td>-3.297933</td>\n",
       "      <td>-1.330923</td>\n",
       "      <td>-2.765846</td>\n",
       "      <td>1.824549</td>\n",
       "      <td>0.974560</td>\n",
       "      <td>11.222439</td>\n",
       "      <td>11.152286</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04003</td>\n",
       "      <td>1051.250000</td>\n",
       "      <td>2014</td>\n",
       "      <td>AZ-500</td>\n",
       "      <td>0.001469</td>\n",
       "      <td>0.000826</td>\n",
       "      <td>0.000643</td>\n",
       "      <td>45974.000000</td>\n",
       "      <td>13.1</td>\n",
       "      <td>2.470000</td>\n",
       "      <td>48846.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Cochise County</td>\n",
       "      <td>127314.0</td>\n",
       "      <td>Cochise County, AZ</td>\n",
       "      <td>0.161</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>2.81</td>\n",
       "      <td>800.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>50969.0</td>\n",
       "      <td>46682.0</td>\n",
       "      <td>4287.0</td>\n",
       "      <td>8.40</td>\n",
       "      <td>0.507886</td>\n",
       "      <td>0.492114</td>\n",
       "      <td>0.881584</td>\n",
       "      <td>0.045062</td>\n",
       "      <td>0.017005</td>\n",
       "      <td>0.021655</td>\n",
       "      <td>0.004006</td>\n",
       "      <td>0.348540</td>\n",
       "      <td>0.118416</td>\n",
       "      <td>0.400341</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>0.009520</td>\n",
       "      <td>0.159770</td>\n",
       "      <td>0.165803</td>\n",
       "      <td>0.101858</td>\n",
       "      <td>0.059428</td>\n",
       "      <td>0.597727</td>\n",
       "      <td>0.396641</td>\n",
       "      <td>0.116782</td>\n",
       "      <td>0.185313</td>\n",
       "      <td>0.062420</td>\n",
       "      <td>0.334275</td>\n",
       "      <td>0.159931</td>\n",
       "      <td>1.105127e+06</td>\n",
       "      <td>1.161764e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>3.169270e-09</td>\n",
       "      <td>6.828076e-07</td>\n",
       "      <td>5.642188e-10</td>\n",
       "      <td>4.128815e-07</td>\n",
       "      <td>2.653006e-10</td>\n",
       "      <td>1.620885e+10</td>\n",
       "      <td>2.063614e+15</td>\n",
       "      <td>7.8961</td>\n",
       "      <td>22.188041</td>\n",
       "      <td>640000.0</td>\n",
       "      <td>5.120000e+08</td>\n",
       "      <td>5184.0</td>\n",
       "      <td>3.732480e+05</td>\n",
       "      <td>115600.0</td>\n",
       "      <td>3.930400e+07</td>\n",
       "      <td>70.5600</td>\n",
       "      <td>592.704000</td>\n",
       "      <td>0.257948</td>\n",
       "      <td>0.131008</td>\n",
       "      <td>0.242176</td>\n",
       "      <td>0.119178</td>\n",
       "      <td>0.777191</td>\n",
       "      <td>0.685159</td>\n",
       "      <td>0.002031</td>\n",
       "      <td>9.150105e-05</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>4.917510e-06</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.604678e-05</td>\n",
       "      <td>6.428091e-08</td>\n",
       "      <td>0.121480</td>\n",
       "      <td>0.042341</td>\n",
       "      <td>0.160273</td>\n",
       "      <td>0.064164</td>\n",
       "      <td>1468944.0</td>\n",
       "      <td>1.780360e+09</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>8.627389e-07</td>\n",
       "      <td>0.025527</td>\n",
       "      <td>0.004078</td>\n",
       "      <td>0.027491</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>0.010375</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.357277</td>\n",
       "      <td>0.213554</td>\n",
       "      <td>0.157324</td>\n",
       "      <td>0.062401</td>\n",
       "      <td>0.013638</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>0.034341</td>\n",
       "      <td>0.006364</td>\n",
       "      <td>0.003896</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.111740</td>\n",
       "      <td>0.037352</td>\n",
       "      <td>0.025578</td>\n",
       "      <td>0.004091</td>\n",
       "      <td>171.61</td>\n",
       "      <td>2248.091</td>\n",
       "      <td>6.100900</td>\n",
       "      <td>15.069223</td>\n",
       "      <td>2.385932e+09</td>\n",
       "      <td>1.165432e+14</td>\n",
       "      <td>2.113609e+09</td>\n",
       "      <td>9.717105e+13</td>\n",
       "      <td>0.378519</td>\n",
       "      <td>7.405307</td>\n",
       "      <td>2.927735</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.007207</td>\n",
       "      <td>16023.770175</td>\n",
       "      <td>0.126624</td>\n",
       "      <td>20.748000</td>\n",
       "      <td>0.140993</td>\n",
       "      <td>8830.500000</td>\n",
       "      <td>0.002678</td>\n",
       "      <td>0.000664</td>\n",
       "      <td>0.055742</td>\n",
       "      <td>0.524332</td>\n",
       "      <td>0.052391</td>\n",
       "      <td>4.565872</td>\n",
       "      <td>44374.0</td>\n",
       "      <td>4.133757</td>\n",
       "      <td>4.266243</td>\n",
       "      <td>0.001589</td>\n",
       "      <td>0.007471</td>\n",
       "      <td>17024.776568</td>\n",
       "      <td>47.371234</td>\n",
       "      <td>1.660764e+07</td>\n",
       "      <td>5.884105e-06</td>\n",
       "      <td>11.548752</td>\n",
       "      <td>0.526947</td>\n",
       "      <td>0.003531</td>\n",
       "      <td>1.392742</td>\n",
       "      <td>1.342071</td>\n",
       "      <td>0.102953</td>\n",
       "      <td>0.081594</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.005262</td>\n",
       "      <td>0.590310</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>2.477251</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>4.328676e+07</td>\n",
       "      <td>2071.671914</td>\n",
       "      <td>0.026935</td>\n",
       "      <td>0.409533</td>\n",
       "      <td>3.310116e-06</td>\n",
       "      <td>1.343422</td>\n",
       "      <td>0.208332</td>\n",
       "      <td>5.920135e+07</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>6.957735</td>\n",
       "      <td>-6.523255</td>\n",
       "      <td>-7.098526</td>\n",
       "      <td>-7.350053</td>\n",
       "      <td>11.754412</td>\n",
       "      <td>1.033184</td>\n",
       "      <td>6.684612</td>\n",
       "      <td>4.276666</td>\n",
       "      <td>5.828946</td>\n",
       "      <td>2.128232</td>\n",
       "      <td>-0.677498</td>\n",
       "      <td>-0.709045</td>\n",
       "      <td>-0.126035</td>\n",
       "      <td>-3.099720</td>\n",
       "      <td>-4.074236</td>\n",
       "      <td>-3.832513</td>\n",
       "      <td>-5.520001</td>\n",
       "      <td>-1.054003</td>\n",
       "      <td>-0.915439</td>\n",
       "      <td>7.100027</td>\n",
       "      <td>-4.654385</td>\n",
       "      <td>-1.834018</td>\n",
       "      <td>-1.796957</td>\n",
       "      <td>-2.284172</td>\n",
       "      <td>-2.822992</td>\n",
       "      <td>-0.514621</td>\n",
       "      <td>-0.924723</td>\n",
       "      <td>-2.147445</td>\n",
       "      <td>-1.685706</td>\n",
       "      <td>-2.773862</td>\n",
       "      <td>-1.095791</td>\n",
       "      <td>-1.833011</td>\n",
       "      <td>2.572612</td>\n",
       "      <td>0.904218</td>\n",
       "      <td>10.796428</td>\n",
       "      <td>10.735831</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04013</td>\n",
       "      <td>1095.670228</td>\n",
       "      <td>2014</td>\n",
       "      <td>AZ-502</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>53689.000000</td>\n",
       "      <td>12.7</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>1424244.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Maricopa County</td>\n",
       "      <td>4093648.0</td>\n",
       "      <td>Maricopa County, AZ</td>\n",
       "      <td>0.158</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2389.0</td>\n",
       "      <td>16857.0</td>\n",
       "      <td>6320.0</td>\n",
       "      <td>1961997.0</td>\n",
       "      <td>1848119.0</td>\n",
       "      <td>113878.0</td>\n",
       "      <td>5.80</td>\n",
       "      <td>0.494586</td>\n",
       "      <td>0.505414</td>\n",
       "      <td>0.842623</td>\n",
       "      <td>0.057567</td>\n",
       "      <td>0.027453</td>\n",
       "      <td>0.042120</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.302868</td>\n",
       "      <td>0.157377</td>\n",
       "      <td>0.479278</td>\n",
       "      <td>25566.0</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>0.097579</td>\n",
       "      <td>0.144882</td>\n",
       "      <td>0.119924</td>\n",
       "      <td>0.066715</td>\n",
       "      <td>0.597131</td>\n",
       "      <td>0.294281</td>\n",
       "      <td>0.157846</td>\n",
       "      <td>0.123955</td>\n",
       "      <td>0.091247</td>\n",
       "      <td>0.383381</td>\n",
       "      <td>0.120472</td>\n",
       "      <td>1.200493e+06</td>\n",
       "      <td>1.315345e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>2.291315e-09</td>\n",
       "      <td>1.174552e-06</td>\n",
       "      <td>1.272941e-09</td>\n",
       "      <td>5.502542e-08</td>\n",
       "      <td>1.290759e-11</td>\n",
       "      <td>1.675795e+13</td>\n",
       "      <td>6.860116e+19</td>\n",
       "      <td>8.4100</td>\n",
       "      <td>24.389000</td>\n",
       "      <td>5707321.0</td>\n",
       "      <td>1.363479e+10</td>\n",
       "      <td>284158449.0</td>\n",
       "      <td>4.790059e+12</td>\n",
       "      <td>39942400.0</td>\n",
       "      <td>2.524360e+11</td>\n",
       "      <td>33.6400</td>\n",
       "      <td>195.112000</td>\n",
       "      <td>0.244615</td>\n",
       "      <td>0.120983</td>\n",
       "      <td>0.255444</td>\n",
       "      <td>0.129105</td>\n",
       "      <td>0.710014</td>\n",
       "      <td>0.598275</td>\n",
       "      <td>0.003314</td>\n",
       "      <td>1.907771e-04</td>\n",
       "      <td>0.000754</td>\n",
       "      <td>2.069047e-05</td>\n",
       "      <td>0.001774</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>7.472040e-06</td>\n",
       "      <td>2.042485e-08</td>\n",
       "      <td>0.091729</td>\n",
       "      <td>0.027782</td>\n",
       "      <td>0.229708</td>\n",
       "      <td>0.110094</td>\n",
       "      <td>653620356.0</td>\n",
       "      <td>1.671046e+13</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>2.435885e-07</td>\n",
       "      <td>0.009522</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>0.020991</td>\n",
       "      <td>0.003041</td>\n",
       "      <td>0.014382</td>\n",
       "      <td>0.001725</td>\n",
       "      <td>0.004451</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.356565</td>\n",
       "      <td>0.212916</td>\n",
       "      <td>0.086601</td>\n",
       "      <td>0.025485</td>\n",
       "      <td>0.024915</td>\n",
       "      <td>0.003933</td>\n",
       "      <td>0.015365</td>\n",
       "      <td>0.001905</td>\n",
       "      <td>0.008326</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.146981</td>\n",
       "      <td>0.056350</td>\n",
       "      <td>0.014513</td>\n",
       "      <td>0.001748</td>\n",
       "      <td>161.29</td>\n",
       "      <td>2048.383</td>\n",
       "      <td>7.507600</td>\n",
       "      <td>20.570824</td>\n",
       "      <td>2.028471e+12</td>\n",
       "      <td>2.889038e+18</td>\n",
       "      <td>2.882509e+09</td>\n",
       "      <td>1.547590e+14</td>\n",
       "      <td>0.333890</td>\n",
       "      <td>4.887216</td>\n",
       "      <td>1.756634</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.006935</td>\n",
       "      <td>16260.680282</td>\n",
       "      <td>0.166945</td>\n",
       "      <td>15.892000</td>\n",
       "      <td>0.101512</td>\n",
       "      <td>6354.887321</td>\n",
       "      <td>0.003841</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.036487</td>\n",
       "      <td>0.529231</td>\n",
       "      <td>0.056216</td>\n",
       "      <td>3.846424</td>\n",
       "      <td>1239835.0</td>\n",
       "      <td>2.931404</td>\n",
       "      <td>2.868596</td>\n",
       "      <td>0.000804</td>\n",
       "      <td>0.008340</td>\n",
       "      <td>431357.937893</td>\n",
       "      <td>63.074706</td>\n",
       "      <td>9.001222e+09</td>\n",
       "      <td>3.603694e-06</td>\n",
       "      <td>10.701318</td>\n",
       "      <td>0.503156</td>\n",
       "      <td>0.002303</td>\n",
       "      <td>0.840313</td>\n",
       "      <td>0.565960</td>\n",
       "      <td>0.133005</td>\n",
       "      <td>0.073225</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.009087</td>\n",
       "      <td>0.731104</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>2.443608</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>2.587186e+10</td>\n",
       "      <td>3090.727327</td>\n",
       "      <td>0.034375</td>\n",
       "      <td>0.396975</td>\n",
       "      <td>2.962482e-06</td>\n",
       "      <td>0.698735</td>\n",
       "      <td>0.180852</td>\n",
       "      <td>3.641222e+10</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>6.999122</td>\n",
       "      <td>-6.631380</td>\n",
       "      <td>-6.827312</td>\n",
       "      <td>-8.357735</td>\n",
       "      <td>15.224947</td>\n",
       "      <td>1.064711</td>\n",
       "      <td>7.778630</td>\n",
       "      <td>9.732521</td>\n",
       "      <td>8.751474</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>-0.704035</td>\n",
       "      <td>-0.682376</td>\n",
       "      <td>-0.171235</td>\n",
       "      <td>-2.854802</td>\n",
       "      <td>-3.595279</td>\n",
       "      <td>-3.167229</td>\n",
       "      <td>-5.902171</td>\n",
       "      <td>-1.194458</td>\n",
       "      <td>-0.735474</td>\n",
       "      <td>10.149019</td>\n",
       "      <td>-5.075928</td>\n",
       "      <td>-2.327091</td>\n",
       "      <td>-1.931839</td>\n",
       "      <td>-2.120896</td>\n",
       "      <td>-2.707324</td>\n",
       "      <td>-0.515619</td>\n",
       "      <td>-1.223222</td>\n",
       "      <td>-1.846134</td>\n",
       "      <td>-2.087837</td>\n",
       "      <td>-2.394188</td>\n",
       "      <td>-0.958726</td>\n",
       "      <td>-2.116341</td>\n",
       "      <td>2.541602</td>\n",
       "      <td>1.007958</td>\n",
       "      <td>14.169152</td>\n",
       "      <td>10.890963</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04019</td>\n",
       "      <td>928.546429</td>\n",
       "      <td>2014</td>\n",
       "      <td>AZ-501</td>\n",
       "      <td>0.002170</td>\n",
       "      <td>0.001705</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>46233.000000</td>\n",
       "      <td>13.2</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>386155.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Pima County</td>\n",
       "      <td>1004229.0</td>\n",
       "      <td>Pima County, AZ</td>\n",
       "      <td>0.154</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3591.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>1604.0</td>\n",
       "      <td>463126.0</td>\n",
       "      <td>435183.0</td>\n",
       "      <td>27943.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.492601</td>\n",
       "      <td>0.507399</td>\n",
       "      <td>0.854719</td>\n",
       "      <td>0.040870</td>\n",
       "      <td>0.042503</td>\n",
       "      <td>0.031771</td>\n",
       "      <td>0.002256</td>\n",
       "      <td>0.361534</td>\n",
       "      <td>0.145281</td>\n",
       "      <td>0.461176</td>\n",
       "      <td>6033.0</td>\n",
       "      <td>0.006008</td>\n",
       "      <td>0.133131</td>\n",
       "      <td>0.148519</td>\n",
       "      <td>0.114906</td>\n",
       "      <td>0.081063</td>\n",
       "      <td>0.589804</td>\n",
       "      <td>0.363401</td>\n",
       "      <td>0.140616</td>\n",
       "      <td>0.161392</td>\n",
       "      <td>0.069343</td>\n",
       "      <td>0.387694</td>\n",
       "      <td>0.147865</td>\n",
       "      <td>8.621985e+05</td>\n",
       "      <td>8.005913e+08</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>1.021582e-08</td>\n",
       "      <td>2.906310e-06</td>\n",
       "      <td>4.954650e-09</td>\n",
       "      <td>2.162560e-07</td>\n",
       "      <td>1.005663e-10</td>\n",
       "      <td>1.008476e+12</td>\n",
       "      <td>1.012741e+18</td>\n",
       "      <td>8.1225</td>\n",
       "      <td>23.149125</td>\n",
       "      <td>12895281.0</td>\n",
       "      <td>4.630695e+10</td>\n",
       "      <td>702244.0</td>\n",
       "      <td>5.884805e+08</td>\n",
       "      <td>2572816.0</td>\n",
       "      <td>4.126797e+09</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>0.242656</td>\n",
       "      <td>0.119532</td>\n",
       "      <td>0.257454</td>\n",
       "      <td>0.130632</td>\n",
       "      <td>0.730545</td>\n",
       "      <td>0.624411</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>6.826829e-05</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>7.678326e-05</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>5.091600e-06</td>\n",
       "      <td>1.148898e-08</td>\n",
       "      <td>0.130707</td>\n",
       "      <td>0.047255</td>\n",
       "      <td>0.212683</td>\n",
       "      <td>0.098084</td>\n",
       "      <td>36397089.0</td>\n",
       "      <td>2.195836e+11</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>2.168212e-07</td>\n",
       "      <td>0.017724</td>\n",
       "      <td>0.002360</td>\n",
       "      <td>0.022058</td>\n",
       "      <td>0.003276</td>\n",
       "      <td>0.013203</td>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.006571</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.347868</td>\n",
       "      <td>0.205174</td>\n",
       "      <td>0.132060</td>\n",
       "      <td>0.047991</td>\n",
       "      <td>0.019773</td>\n",
       "      <td>0.002780</td>\n",
       "      <td>0.026048</td>\n",
       "      <td>0.004204</td>\n",
       "      <td>0.004808</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.150307</td>\n",
       "      <td>0.058273</td>\n",
       "      <td>0.021864</td>\n",
       "      <td>0.003233</td>\n",
       "      <td>174.24</td>\n",
       "      <td>2299.968</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>15.625000</td>\n",
       "      <td>1.491157e+11</td>\n",
       "      <td>5.758177e+16</td>\n",
       "      <td>2.137490e+09</td>\n",
       "      <td>9.882259e+13</td>\n",
       "      <td>0.245221</td>\n",
       "      <td>5.128316</td>\n",
       "      <td>2.169204</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.006043</td>\n",
       "      <td>16714.804770</td>\n",
       "      <td>0.116480</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.126384</td>\n",
       "      <td>5571.278571</td>\n",
       "      <td>0.003313</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.053458</td>\n",
       "      <td>0.416056</td>\n",
       "      <td>0.069286</td>\n",
       "      <td>4.772250</td>\n",
       "      <td>363063.0</td>\n",
       "      <td>3.044395</td>\n",
       "      <td>2.955605</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.006070</td>\n",
       "      <td>139608.189731</td>\n",
       "      <td>37.949841</td>\n",
       "      <td>6.193926e+08</td>\n",
       "      <td>4.896115e-06</td>\n",
       "      <td>11.282296</td>\n",
       "      <td>0.504117</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>0.891113</td>\n",
       "      <td>0.798786</td>\n",
       "      <td>0.120188</td>\n",
       "      <td>0.075358</td>\n",
       "      <td>0.000786</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>0.539486</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>2.435950</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>1.610783e+09</td>\n",
       "      <td>1889.550112</td>\n",
       "      <td>0.024105</td>\n",
       "      <td>0.371297</td>\n",
       "      <td>3.846787e-06</td>\n",
       "      <td>0.887193</td>\n",
       "      <td>0.213234</td>\n",
       "      <td>2.329673e+09</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>6.833620</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1906.580378</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>-1.593934</td>\n",
       "      <td>-2.772589</td>\n",
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       "      <td>-0.597837</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.232072</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>56000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35779 rows × 212 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS         Rent  Year coc_number  Houseless_rate  Sheltered_rate  \\\n",
       "0     01073  1019.995960  2014     AL-500        0.001396        0.000961   \n",
       "1     01117  1229.755051  2014     AL-500        0.001396        0.000961   \n",
       "2     04003  1051.250000  2014     AZ-500        0.001469        0.000826   \n",
       "3     04013  1095.670228  2014     AZ-502        0.001318        0.001084   \n",
       "4     04019   928.546429  2014     AZ-501        0.002170        0.001705   \n",
       "...     ...          ...   ...        ...             ...             ...   \n",
       "3380  55073          NaN  2020        NaN             NaN             NaN   \n",
       "3381  55101          NaN  2020        NaN             NaN             NaN   \n",
       "3382  55105          NaN  2020        NaN             NaN             NaN   \n",
       "3383  55139          NaN  2020        NaN             NaN             NaN   \n",
       "3384  56000          NaN  2020        NaN             NaN             NaN   \n",
       "\n",
       "      Unsheltered_rate  hh_med_income  percent_hh_poverty  hh_avg_size  \\\n",
       "0             0.000435   45239.000000                14.8     2.480000   \n",
       "1             0.000435   69723.000000                 6.2     2.650000   \n",
       "2             0.000643   45974.000000                13.1     2.470000   \n",
       "3             0.000235   53689.000000                12.7     2.740000   \n",
       "4             0.000465   46233.000000                13.2     2.500000   \n",
       "...                ...            ...                 ...          ...   \n",
       "3380               NaN   47870.090909                 NaN     1.848485   \n",
       "3381               NaN   97732.240000                 NaN     2.240000   \n",
       "3382               NaN   55234.312500                 NaN     1.875000   \n",
       "3383               NaN   75513.866667                 NaN     1.700000   \n",
       "3384               NaN   53550.643629                 NaN     1.563715   \n",
       "\n",
       "         num_hh    State            County    TOT_POP          State/County  \\\n",
       "0      259397.0  Alabama  Jefferson County   659972.0  Jefferson County, AL   \n",
       "1       74790.0  Alabama     Shelby County   206280.0     Shelby County, AL   \n",
       "2       48846.0  Arizona    Cochise County   127314.0    Cochise County, AZ   \n",
       "3     1424244.0  Arizona   Maricopa County  4093648.0   Maricopa County, AZ   \n",
       "4      386155.0  Arizona       Pima County  1004229.0       Pima County, AZ   \n",
       "...         ...      ...               ...        ...                   ...   \n",
       "3380        NaN      NaN               NaN        NaN   Marathon County, WI   \n",
       "3381        NaN      NaN               NaN        NaN     Racine County, WI   \n",
       "3382        NaN      NaN               NaN        NaN       Rock County, WI   \n",
       "3383        NaN      NaN               NaN        NaN  Winnebago County, WI   \n",
       "3384        NaN      NaN               NaN        NaN                   NaN   \n",
       "\n",
       "      FI Rate              Low Threshold Type             High Threshold Type  \\\n",
       "0       0.197                            SNAP         Other Nutrition Program   \n",
       "1       0.105                            SNAP         Other Nutrition Program   \n",
       "2       0.161  SNAP, Other Nutrition Programs  SNAP, Other Nutrition Programs   \n",
       "3       0.158  SNAP, Other Nutrition Programs  SNAP, Other Nutrition Programs   \n",
       "4       0.154  SNAP, Other Nutrition Programs  SNAP, Other Nutrition Programs   \n",
       "...       ...                             ...                             ...   \n",
       "3380      NaN                             NaN                             NaN   \n",
       "3381      NaN                             NaN                             NaN   \n",
       "3382      NaN                             NaN                             NaN   \n",
       "3383      NaN                             NaN                             NaN   \n",
       "3384      NaN                             NaN                             NaN   \n",
       "\n",
       "      Cost Per Meal  Num_wholesale  Num_restaraunts  Num_grocery  \\\n",
       "0              2.93          483.0           2693.0        400.0   \n",
       "1              3.37            1.0            743.0       2706.0   \n",
       "2              2.81          800.0             72.0        340.0   \n",
       "3              2.90         2389.0          16857.0       6320.0   \n",
       "4              2.85         3591.0            838.0       1604.0   \n",
       "...             ...            ...              ...          ...   \n",
       "3380            NaN            NaN              NaN          NaN   \n",
       "3381            NaN            NaN              NaN          NaN   \n",
       "3382            NaN            NaN              NaN          NaN   \n",
       "3383            NaN            NaN              NaN          NaN   \n",
       "3384            NaN            NaN              NaN          NaN   \n",
       "\n",
       "      Total_workforce   Employed  Unemployed  Unemployment_rate  Percent_male  \\\n",
       "0            312131.0   292505.0     19626.0               6.30      0.473491   \n",
       "1            107208.0   102400.0      4808.0               4.50      0.486252   \n",
       "2             50969.0    46682.0      4287.0               8.40      0.507886   \n",
       "3           1961997.0  1848119.0    113878.0               5.80      0.494586   \n",
       "4            463126.0   435183.0     27943.0               6.00      0.492601   \n",
       "...               ...        ...         ...                ...           ...   \n",
       "3380          73260.4    69293.6      3966.8               5.44      0.502396   \n",
       "3381         100310.3    92596.6      7713.7               7.66      0.495961   \n",
       "3382          84868.3    78191.9      6676.4               7.86      0.492951   \n",
       "3383          91259.5    85790.2      5469.3               6.01      0.502857   \n",
       "3384              NaN        NaN         NaN                NaN           NaN   \n",
       "\n",
       "      Percent_female  Percent_white  Percent_Black  Percent_native  \\\n",
       "0           0.526509       0.538864       0.430446        0.003074   \n",
       "1           0.513748       0.843969       0.117544        0.003902   \n",
       "2           0.492114       0.881584       0.045062        0.017005   \n",
       "3           0.505414       0.842623       0.057567        0.027453   \n",
       "4           0.507399       0.854719       0.040870        0.042503   \n",
       "...              ...            ...            ...             ...   \n",
       "3380        0.497605       0.905874       0.009168        0.005753   \n",
       "3381        0.504037       0.831618       0.120975        0.007057   \n",
       "3382        0.507047       0.901249       0.053054        0.005782   \n",
       "3383        0.497144       0.915984       0.025248        0.007730   \n",
       "3384             NaN            NaN            NaN             NaN   \n",
       "\n",
       "      Percent_asian  Percent_pacific  Percent_latinX  Percent_PoC  \\\n",
       "0          0.015725         0.000526        0.036515     0.461136   \n",
       "1          0.021345         0.000490        0.057553     0.156031   \n",
       "2          0.021655         0.004006        0.348540     0.118416   \n",
       "3          0.042120         0.002734        0.302868     0.157377   \n",
       "4          0.031771         0.002256        0.361534     0.145281   \n",
       "...             ...              ...             ...          ...   \n",
       "3380       0.063017         0.000548        0.030705     0.093447   \n",
       "3381       0.013794         0.000832        0.140133     0.168853   \n",
       "3382       0.013906         0.000658        0.094015     0.098144   \n",
       "3383       0.031856         0.000607        0.044801     0.083181   \n",
       "3384            NaN              NaN             NaN          NaN   \n",
       "\n",
       "      Percent_working  Total_food_retail  Food_retail_per_person  \\\n",
       "0            0.472946             3576.0                0.005418   \n",
       "1            0.519721             3450.0                0.016725   \n",
       "2            0.400341             1212.0                0.009520   \n",
       "3            0.479278            25566.0                0.006245   \n",
       "4            0.461176             6033.0                0.006008   \n",
       "...               ...                ...                     ...   \n",
       "3380              NaN                0.0                     NaN   \n",
       "3381              NaN                0.0                     NaN   \n",
       "3382              NaN                0.0                     NaN   \n",
       "3383              NaN                0.0                     NaN   \n",
       "3384              NaN                NaN                     NaN   \n",
       "\n",
       "      Percent_disabled  Percent_hs_grad  Percent_bachelors  \\\n",
       "0             0.143315         0.178574           0.123681   \n",
       "1             0.110491         0.140154           0.173420   \n",
       "2             0.159770         0.165803           0.101858   \n",
       "3             0.097579         0.144882           0.119924   \n",
       "4             0.133131         0.148519           0.114906   \n",
       "...                ...              ...                ...   \n",
       "3380          0.000000         0.542857           0.171429   \n",
       "3381          0.025641         0.512195           0.146341   \n",
       "3382          0.041667         0.533333           0.116667   \n",
       "3383          0.000000         0.421875           0.203125   \n",
       "3384          0.023866         0.415512           0.092336   \n",
       "\n",
       "      Percent_grad_degree  Percent_priv_health  Percent_public_health  \\\n",
       "0                0.079964             0.654025               0.320574   \n",
       "1                0.089737             0.773972               0.205686   \n",
       "2                0.059428             0.597727               0.396641   \n",
       "3                0.066715             0.597131               0.294281   \n",
       "4                0.081063             0.589804               0.363401   \n",
       "...                   ...                  ...                    ...   \n",
       "3380             0.000000             0.681818               0.545455   \n",
       "3381             0.024390             0.846154               0.461538   \n",
       "3382             0.083333             0.625000               0.625000   \n",
       "3383             0.062500             0.650000               0.550000   \n",
       "3384             0.062327             0.787589               0.428401   \n",
       "\n",
       "      Percent_no_health  Percent_65+  Percent_non_citizen  \\\n",
       "0              0.123242     0.131878             0.026545   \n",
       "1              0.092956     0.113457             0.036959   \n",
       "2              0.116782     0.185313             0.062420   \n",
       "3              0.157846     0.123955             0.091247   \n",
       "4              0.140616     0.161392             0.069343   \n",
       "...                 ...          ...                  ...   \n",
       "3380           0.090909          NaN             0.000000   \n",
       "3381           0.000000          NaN             0.024390   \n",
       "3382           0.000000          NaN             0.033333   \n",
       "3383           0.000000          NaN             0.000000   \n",
       "3384           0.051313          NaN             0.014774   \n",
       "\n",
       "      Percent_hh_no_vehicle  Percent_hh_SNAP        Rent^2        Rent^3  \\\n",
       "0                  0.360953         0.154077  1.040392e+06  1.061195e+09   \n",
       "1                  0.264233         0.062923  1.512297e+06  1.859755e+09   \n",
       "2                  0.334275         0.159931  1.105127e+06  1.161764e+09   \n",
       "3                  0.383381         0.120472  1.200493e+06  1.315345e+09   \n",
       "4                  0.387694         0.147865  8.621985e+05  8.005913e+08   \n",
       "...                     ...              ...           ...           ...   \n",
       "3380                    NaN              NaN           NaN           NaN   \n",
       "3381                    NaN              NaN           NaN           NaN   \n",
       "3382                    NaN              NaN           NaN           NaN   \n",
       "3383                    NaN              NaN           NaN           NaN   \n",
       "3384                    NaN              NaN           NaN           NaN   \n",
       "\n",
       "      Houseless_rate^2  Houseless_rate^3  Sheltered_rate^2  Sheltered_rate^3  \\\n",
       "0             0.000002      2.718471e-09      9.232983e-07      8.871827e-10   \n",
       "1             0.000002      2.718471e-09      9.232983e-07      8.871827e-10   \n",
       "2             0.000002      3.169270e-09      6.828076e-07      5.642188e-10   \n",
       "3             0.000002      2.291315e-09      1.174552e-06      1.272941e-09   \n",
       "4             0.000005      1.021582e-08      2.906310e-06      4.954650e-09   \n",
       "...                ...               ...               ...               ...   \n",
       "3380               NaN               NaN               NaN               NaN   \n",
       "3381               NaN               NaN               NaN               NaN   \n",
       "3382               NaN               NaN               NaN               NaN   \n",
       "3383               NaN               NaN               NaN               NaN   \n",
       "3384               NaN               NaN               NaN               NaN   \n",
       "\n",
       "      Unsheltered_rate^2  Unsheltered_rate^3     TOT_POP^2     TOT_POP^3  \\\n",
       "0           1.890169e-07        8.217710e-11  4.355630e+11  2.874594e+17   \n",
       "1           1.890169e-07        8.217710e-11  4.255144e+10  8.777511e+15   \n",
       "2           4.128815e-07        2.653006e-10  1.620885e+10  2.063614e+15   \n",
       "3           5.502542e-08        1.290759e-11  1.675795e+13  6.860116e+19   \n",
       "4           2.162560e-07        1.005663e-10  1.008476e+12  1.012741e+18   \n",
       "...                  ...                 ...           ...           ...   \n",
       "3380                 NaN                 NaN           NaN           NaN   \n",
       "3381                 NaN                 NaN           NaN           NaN   \n",
       "3382                 NaN                 NaN           NaN           NaN   \n",
       "3383                 NaN                 NaN           NaN           NaN   \n",
       "3384                 NaN                 NaN           NaN           NaN   \n",
       "\n",
       "      Cost Per Meal^2  Cost Per Meal^3  Num_wholesale^2  Num_wholesale^3  \\\n",
       "0              8.5849        25.153757         233289.0     1.126786e+08   \n",
       "1             11.3569        38.272753              1.0     1.000000e+00   \n",
       "2              7.8961        22.188041         640000.0     5.120000e+08   \n",
       "3              8.4100        24.389000        5707321.0     1.363479e+10   \n",
       "4              8.1225        23.149125       12895281.0     4.630695e+10   \n",
       "...               ...              ...              ...              ...   \n",
       "3380              NaN              NaN              NaN              NaN   \n",
       "3381              NaN              NaN              NaN              NaN   \n",
       "3382              NaN              NaN              NaN              NaN   \n",
       "3383              NaN              NaN              NaN              NaN   \n",
       "3384              NaN              NaN              NaN              NaN   \n",
       "\n",
       "      Num_restaraunts^2  Num_restaraunts^3  Num_grocery^2  Num_grocery^3  \\\n",
       "0             7252249.0       1.953031e+10       160000.0   6.400000e+07   \n",
       "1              552049.0       4.101724e+08      7322436.0   1.981451e+10   \n",
       "2                5184.0       3.732480e+05       115600.0   3.930400e+07   \n",
       "3           284158449.0       4.790059e+12     39942400.0   2.524360e+11   \n",
       "4              702244.0       5.884805e+08      2572816.0   4.126797e+09   \n",
       "...                 ...                ...            ...            ...   \n",
       "3380                NaN                NaN            NaN            NaN   \n",
       "3381                NaN                NaN            NaN            NaN   \n",
       "3382                NaN                NaN            NaN            NaN   \n",
       "3383                NaN                NaN            NaN            NaN   \n",
       "3384                NaN                NaN            NaN            NaN   \n",
       "\n",
       "      Unemployment_rate^2  Unemployment_rate^3  Percent_male^2  \\\n",
       "0                 39.6900           250.047000        0.224194   \n",
       "1                 20.2500            91.125000        0.236441   \n",
       "2                 70.5600           592.704000        0.257948   \n",
       "3                 33.6400           195.112000        0.244615   \n",
       "4                 36.0000           216.000000        0.242656   \n",
       "...                   ...                  ...             ...   \n",
       "3380              29.5936           160.989184        0.252402   \n",
       "3381              58.6756           449.455096        0.245978   \n",
       "3382              61.7796           485.587656        0.243000   \n",
       "3383              36.1201           217.081801        0.252865   \n",
       "3384                  NaN                  NaN             NaN   \n",
       "\n",
       "      Percent_male^3  Percent_female^2  Percent_female^3  Percent_white^2  \\\n",
       "0           0.106154          0.277211          0.145954         0.290374   \n",
       "1           0.114970          0.263937          0.135597         0.712284   \n",
       "2           0.131008          0.242176          0.119178         0.777191   \n",
       "3           0.120983          0.255444          0.129105         0.710014   \n",
       "4           0.119532          0.257454          0.130632         0.730545   \n",
       "...              ...               ...               ...              ...   \n",
       "3380        0.126805          0.247611          0.123212         0.820607   \n",
       "3381        0.121995          0.254053          0.128052         0.691589   \n",
       "3382        0.119787          0.257096          0.130360         0.812249   \n",
       "3383        0.127155          0.247152          0.122870         0.839026   \n",
       "3384             NaN               NaN               NaN              NaN   \n",
       "\n",
       "      Percent_white^3  Percent_Black^2  Percent_Black^3  Percent_native^2  \\\n",
       "0            0.156472         0.185283     7.975439e-02          0.000009   \n",
       "1            0.601146         0.013817     1.624062e-03          0.000015   \n",
       "2            0.685159         0.002031     9.150105e-05          0.000289   \n",
       "3            0.598275         0.003314     1.907771e-04          0.000754   \n",
       "4            0.624411         0.001670     6.826829e-05          0.001807   \n",
       "...               ...              ...              ...               ...   \n",
       "3380         0.743367         0.000084     7.705152e-07          0.000033   \n",
       "3381         0.575138         0.014635     1.770455e-03          0.000050   \n",
       "3382         0.732039         0.002815     1.493292e-04          0.000033   \n",
       "3383         0.768534         0.000637     1.609479e-05          0.000060   \n",
       "3384              NaN              NaN              NaN               NaN   \n",
       "\n",
       "      Percent_native^3  Percent_asian^2  Percent_asian^3  Percent_pacific^2  \\\n",
       "0         2.905826e-08         0.000247         0.000004       2.764445e-07   \n",
       "1         5.943144e-08         0.000456         0.000010       2.397334e-07   \n",
       "2         4.917510e-06         0.000469         0.000010       1.604678e-05   \n",
       "3         2.069047e-05         0.001774         0.000075       7.472040e-06   \n",
       "4         7.678326e-05         0.001009         0.000032       5.091600e-06   \n",
       "...                ...              ...              ...                ...   \n",
       "3380      1.904548e-07         0.003971         0.000250       3.006898e-07   \n",
       "3381      3.515041e-07         0.000190         0.000003       6.918103e-07   \n",
       "3382      1.933098e-07         0.000193         0.000003       4.328817e-07   \n",
       "3383      4.618576e-07         0.001015         0.000032       3.686530e-07   \n",
       "3384               NaN              NaN              NaN                NaN   \n",
       "\n",
       "      Percent_pacific^3  Percent_latinX^2  Percent_latinX^3  \\\n",
       "0          1.453489e-10          0.001333          0.000049   \n",
       "1          1.173796e-10          0.003312          0.000191   \n",
       "2          6.428091e-08          0.121480          0.042341   \n",
       "3          2.042485e-08          0.091729          0.027782   \n",
       "4          1.148898e-08          0.130707          0.047255   \n",
       "...                 ...               ...               ...   \n",
       "3380       1.648838e-10          0.000943          0.000029   \n",
       "3381       5.754142e-10          0.019637          0.002752   \n",
       "3382       2.848091e-10          0.008839          0.000831   \n",
       "3383       2.238343e-10          0.002007          0.000090   \n",
       "3384                NaN               NaN               NaN   \n",
       "\n",
       "      Percent_working^2  Percent_working^3  Total_food_retail^2  \\\n",
       "0              0.223678           0.105787           12787776.0   \n",
       "1              0.270110           0.140382           11902500.0   \n",
       "2              0.160273           0.064164            1468944.0   \n",
       "3              0.229708           0.110094          653620356.0   \n",
       "4              0.212683           0.098084           36397089.0   \n",
       "...                 ...                ...                  ...   \n",
       "3380                NaN                NaN                  0.0   \n",
       "3381                NaN                NaN                  0.0   \n",
       "3382                NaN                NaN                  0.0   \n",
       "3383                NaN                NaN                  0.0   \n",
       "3384                NaN                NaN                  NaN   \n",
       "\n",
       "      Total_food_retail^3  Food_retail_per_person^2  Food_retail_per_person^3  \\\n",
       "0            4.572909e+10                  0.000029              1.590802e-07   \n",
       "1            4.106362e+10                  0.000280              4.678277e-06   \n",
       "2            1.780360e+09                  0.000091              8.627389e-07   \n",
       "3            1.671046e+13                  0.000039              2.435885e-07   \n",
       "4            2.195836e+11                  0.000036              2.168212e-07   \n",
       "...                   ...                       ...                       ...   \n",
       "3380         0.000000e+00                       NaN                       NaN   \n",
       "3381         0.000000e+00                       NaN                       NaN   \n",
       "3382         0.000000e+00                       NaN                       NaN   \n",
       "3383         0.000000e+00                       NaN                       NaN   \n",
       "3384                  NaN                       NaN                       NaN   \n",
       "\n",
       "      Percent_disabled^2  Percent_disabled^3  Percent_hs_grad^2  \\\n",
       "0               0.020539            0.002944           0.031889   \n",
       "1               0.012208            0.001349           0.019643   \n",
       "2               0.025527            0.004078           0.027491   \n",
       "3               0.009522            0.000929           0.020991   \n",
       "4               0.017724            0.002360           0.022058   \n",
       "...                  ...                 ...                ...   \n",
       "3380            0.000000            0.000000           0.294694   \n",
       "3381            0.000657            0.000017           0.262344   \n",
       "3382            0.001736            0.000072           0.284444   \n",
       "3383            0.000000            0.000000           0.177979   \n",
       "3384            0.000570            0.000014           0.172651   \n",
       "\n",
       "      Percent_hs_grad^3  Percent_bachelors^2  Percent_bachelors^3  \\\n",
       "0              0.005695             0.015297             0.001892   \n",
       "1              0.002753             0.030074             0.005215   \n",
       "2              0.004558             0.010375             0.001057   \n",
       "3              0.003041             0.014382             0.001725   \n",
       "4              0.003276             0.013203             0.001517   \n",
       "...                 ...                  ...                  ...   \n",
       "3380           0.159977             0.029388             0.005038   \n",
       "3381           0.134371             0.021416             0.003134   \n",
       "3382           0.151704             0.013611             0.001588   \n",
       "3383           0.075085             0.041260             0.008381   \n",
       "3384           0.071738             0.008526             0.000787   \n",
       "\n",
       "      Percent_grad_degree^2  Percent_grad_degree^3  Percent_priv_health^2  \\\n",
       "0                  0.006394               0.000511               0.427748   \n",
       "1                  0.008053               0.000723               0.599033   \n",
       "2                  0.003532               0.000210               0.357277   \n",
       "3                  0.004451               0.000297               0.356565   \n",
       "4                  0.006571               0.000533               0.347868   \n",
       "...                     ...                    ...                    ...   \n",
       "3380               0.000000               0.000000               0.464876   \n",
       "3381               0.000595               0.000015               0.715976   \n",
       "3382               0.006944               0.000579               0.390625   \n",
       "3383               0.003906               0.000244               0.422500   \n",
       "3384               0.003885               0.000242               0.620297   \n",
       "\n",
       "      Percent_priv_health^3  Percent_public_health^2  Percent_public_health^3  \\\n",
       "0                  0.279758                 0.102768                 0.032945   \n",
       "1                  0.463635                 0.042307                 0.008702   \n",
       "2                  0.213554                 0.157324                 0.062401   \n",
       "3                  0.212916                 0.086601                 0.025485   \n",
       "4                  0.205174                 0.132060                 0.047991   \n",
       "...                     ...                      ...                      ...   \n",
       "3380               0.316961                 0.297521                 0.162284   \n",
       "3381               0.605826                 0.213018                 0.098316   \n",
       "3382               0.244141                 0.390625                 0.244141   \n",
       "3383               0.274625                 0.302500                 0.166375   \n",
       "3384               0.488540                 0.183527                 0.078623   \n",
       "\n",
       "      Percent_no_health^2  Percent_no_health^3  Percent_65+^2  Percent_65+^3  \\\n",
       "0                0.015188             0.001872       0.017392       0.002294   \n",
       "1                0.008641             0.000803       0.012873       0.001460   \n",
       "2                0.013638             0.001593       0.034341       0.006364   \n",
       "3                0.024915             0.003933       0.015365       0.001905   \n",
       "4                0.019773             0.002780       0.026048       0.004204   \n",
       "...                   ...                  ...            ...            ...   \n",
       "3380             0.008264             0.000751            NaN            NaN   \n",
       "3381             0.000000             0.000000            NaN            NaN   \n",
       "3382             0.000000             0.000000            NaN            NaN   \n",
       "3383             0.000000             0.000000            NaN            NaN   \n",
       "3384             0.002633             0.000135            NaN            NaN   \n",
       "\n",
       "      Percent_non_citizen^2  Percent_non_citizen^3  Percent_hh_no_vehicle^2  \\\n",
       "0                  0.000705               0.000019                 0.130287   \n",
       "1                  0.001366               0.000050                 0.069819   \n",
       "2                  0.003896               0.000243                 0.111740   \n",
       "3                  0.008326               0.000760                 0.146981   \n",
       "4                  0.004808               0.000333                 0.150307   \n",
       "...                     ...                    ...                      ...   \n",
       "3380               0.000000               0.000000                      NaN   \n",
       "3381               0.000595               0.000015                      NaN   \n",
       "3382               0.001111               0.000037                      NaN   \n",
       "3383               0.000000               0.000000                      NaN   \n",
       "3384               0.000218               0.000003                      NaN   \n",
       "\n",
       "      Percent_hh_no_vehicle^3  Percent_hh_SNAP^2  Percent_hh_SNAP^3  \\\n",
       "0                    0.047027           0.023740           0.003658   \n",
       "1                    0.018449           0.003959           0.000249   \n",
       "2                    0.037352           0.025578           0.004091   \n",
       "3                    0.056350           0.014513           0.001748   \n",
       "4                    0.058273           0.021864           0.003233   \n",
       "...                       ...                ...                ...   \n",
       "3380                      NaN                NaN                NaN   \n",
       "3381                      NaN                NaN                NaN   \n",
       "3382                      NaN                NaN                NaN   \n",
       "3383                      NaN                NaN                NaN   \n",
       "3384                      NaN                NaN                NaN   \n",
       "\n",
       "      percent_hh_poverty^2  percent_hh_poverty^3  hh_avg_size^2  \\\n",
       "0                   219.04              3241.792       6.150400   \n",
       "1                    38.44               238.328       7.022500   \n",
       "2                   171.61              2248.091       6.100900   \n",
       "3                   161.29              2048.383       7.507600   \n",
       "4                   174.24              2299.968       6.250000   \n",
       "...                    ...                   ...            ...   \n",
       "3380                   NaN                   NaN       3.416896   \n",
       "3381                   NaN                   NaN       5.017600   \n",
       "3382                   NaN                   NaN       3.515625   \n",
       "3383                   NaN                   NaN       2.890000   \n",
       "3384                   NaN                   NaN       2.445204   \n",
       "\n",
       "      hh_avg_size^3      num_hh^2      num_hh^3  hh_med_income^2  \\\n",
       "0         15.252992  6.728680e+10  1.745399e+16     2.046567e+09   \n",
       "1         18.609625  5.593544e+09  4.183412e+14     4.861297e+09   \n",
       "2         15.069223  2.385932e+09  1.165432e+14     2.113609e+09   \n",
       "3         20.570824  2.028471e+12  2.889038e+18     2.882509e+09   \n",
       "4         15.625000  1.491157e+11  5.758177e+16     2.137490e+09   \n",
       "...             ...           ...           ...              ...   \n",
       "3380       6.316081           NaN           NaN     2.291546e+09   \n",
       "3381      11.239424           NaN           NaN     9.551591e+09   \n",
       "3382       6.591797           NaN           NaN     3.050829e+09   \n",
       "3383       4.913000           NaN           NaN     5.702344e+09   \n",
       "3384       3.823602           NaN           NaN     2.867671e+09   \n",
       "\n",
       "      hh_med_income^3  Unemployment_rate_X_Percent_Black  \\\n",
       "0        9.258465e+13                           2.711807   \n",
       "1        3.389442e+14                           0.528949   \n",
       "2        9.717105e+13                           0.378519   \n",
       "3        1.547590e+14                           0.333890   \n",
       "4        9.882259e+13                           0.245221   \n",
       "...               ...                                ...   \n",
       "3380     1.096965e+14                           0.049872   \n",
       "3381     9.334984e+14                           0.926667   \n",
       "3382     1.685105e+14                           0.417001   \n",
       "3383     4.306060e+14                           0.151741   \n",
       "3384     1.535657e+14                                NaN   \n",
       "\n",
       "      Unemployment_rate_X_Percent_white  Unemployment_rate_X_Percent_latinX  \\\n",
       "0                              3.394842                            0.230046   \n",
       "1                              3.797862                            0.258988   \n",
       "2                              7.405307                            2.927735   \n",
       "3                              4.887216                            1.756634   \n",
       "4                              5.128316                            2.169204   \n",
       "...                                 ...                                 ...   \n",
       "3380                           4.927954                            0.167036   \n",
       "3381                           6.370195                            1.073415   \n",
       "3382                           7.083815                            0.738962   \n",
       "3383                           5.505062                            0.269251   \n",
       "3384                                NaN                                 NaN   \n",
       "\n",
       "      Houseless_rate_X_Sheltered_rate  Sheltered_rate_X_Percent_grad_degree  \\\n",
       "0                            0.000001                              0.000077   \n",
       "1                            0.000001                              0.000086   \n",
       "2                            0.000001                              0.000049   \n",
       "3                            0.000001                              0.000072   \n",
       "4                            0.000004                              0.000138   \n",
       "...                               ...                                   ...   \n",
       "3380                              NaN                                   NaN   \n",
       "3381                              NaN                                   NaN   \n",
       "3382                              NaN                                   NaN   \n",
       "3383                              NaN                                   NaN   \n",
       "3384                              NaN                                   NaN   \n",
       "\n",
       "      Percent_Black_X_Percent_hh_SNAP  Percent_latinX_X_hh_med_income  \\\n",
       "0                            0.066322                     1651.910477   \n",
       "1                            0.007396                     4012.756719   \n",
       "2                            0.007207                    16023.770175   \n",
       "3                            0.006935                    16260.680282   \n",
       "4                            0.006043                    16714.804770   \n",
       "...                               ...                             ...   \n",
       "3380                              NaN                     1469.854553   \n",
       "3381                              NaN                    13695.467357   \n",
       "3382                              NaN                     5192.881368   \n",
       "3383                              NaN                     3383.061416   \n",
       "3384                              NaN                             NaN   \n",
       "\n",
       "      Cost Per Meal_X_Percent_Black  Unemployment_rate_X_hh_avg_size  \\\n",
       "0                          1.261205                        15.624000   \n",
       "1                          0.396124                        11.925000   \n",
       "2                          0.126624                        20.748000   \n",
       "3                          0.166945                        15.892000   \n",
       "4                          0.116480                        15.000000   \n",
       "...                             ...                              ...   \n",
       "3380                            NaN                        10.055758   \n",
       "3381                            NaN                        17.158400   \n",
       "3382                            NaN                        14.737500   \n",
       "3383                            NaN                        10.217000   \n",
       "3384                            NaN                              NaN   \n",
       "\n",
       "      Percent_white_X_Percent_hh_SNAP  Rent_X_Unemployment_rate  \\\n",
       "0                            0.083026               6425.974545   \n",
       "1                            0.053105               5533.897727   \n",
       "2                            0.140993               8830.500000   \n",
       "3                            0.101512               6354.887321   \n",
       "4                            0.126384               5571.278571   \n",
       "...                               ...                       ...   \n",
       "3380                              NaN                       NaN   \n",
       "3381                              NaN                       NaN   \n",
       "3382                              NaN                       NaN   \n",
       "3383                              NaN                       NaN   \n",
       "3384                              NaN                       NaN   \n",
       "\n",
       "      Percent_Black_X_Percent_grad_degree  Percent_pacific_X_Percent_hs_grad  \\\n",
       "0                                0.034420                           0.000094   \n",
       "1                                0.010548                           0.000069   \n",
       "2                                0.002678                           0.000664   \n",
       "3                                0.003841                           0.000396   \n",
       "4                                0.003313                           0.000335   \n",
       "...                                   ...                                ...   \n",
       "3380                             0.000000                           0.000298   \n",
       "3381                             0.002951                           0.000426   \n",
       "3382                             0.004421                           0.000351   \n",
       "3383                             0.001578                           0.000256   \n",
       "3384                                  NaN                                NaN   \n",
       "\n",
       "      Percent_latinX_X_Percent_hh_SNAP  \\\n",
       "0                             0.005626   \n",
       "1                             0.003621   \n",
       "2                             0.055742   \n",
       "3                             0.036487   \n",
       "4                             0.053458   \n",
       "...                                ...   \n",
       "3380                               NaN   \n",
       "3381                               NaN   \n",
       "3382                               NaN   \n",
       "3383                               NaN   \n",
       "3384                               NaN   \n",
       "\n",
       "      Unemployment_rate_X_Percent_non_citizen  \\\n",
       "0                                    0.167234   \n",
       "1                                    0.166318   \n",
       "2                                    0.524332   \n",
       "3                                    0.529231   \n",
       "4                                    0.416056   \n",
       "...                                       ...   \n",
       "3380                                 0.000000   \n",
       "3381                                 0.186829   \n",
       "3382                                 0.262000   \n",
       "3383                                 0.000000   \n",
       "3384                                      NaN   \n",
       "\n",
       "      Percent_white_X_Percent_grad_degree  \\\n",
       "0                                0.043090   \n",
       "1                                0.075735   \n",
       "2                                0.052391   \n",
       "3                                0.056216   \n",
       "4                                0.069286   \n",
       "...                                   ...   \n",
       "3380                             0.000000   \n",
       "3381                             0.020283   \n",
       "3382                             0.075104   \n",
       "3383                             0.057249   \n",
       "3384                                  NaN   \n",
       "\n",
       "      Percent_latinX_X_percent_hh_poverty  TOT_POP_X_Percent_latinX  \\\n",
       "0                                0.540425                   24099.0   \n",
       "1                                0.356828                   11872.0   \n",
       "2                                4.565872                   44374.0   \n",
       "3                                3.846424                 1239835.0   \n",
       "4                                4.772250                  363063.0   \n",
       "...                                   ...                       ...   \n",
       "3380                                  NaN                       NaN   \n",
       "3381                                  NaN                       NaN   \n",
       "3382                                  NaN                       NaN   \n",
       "3383                                  NaN                       NaN   \n",
       "3384                                  NaN                       NaN   \n",
       "\n",
       "      Unemployment_rate_X_Percent_female  Unemployment_rate_X_Percent_male  \\\n",
       "0                               3.317005                          2.982995   \n",
       "1                               2.311867                          2.188133   \n",
       "2                               4.133757                          4.266243   \n",
       "3                               2.931404                          2.868596   \n",
       "4                               3.044395                          2.955605   \n",
       "...                                  ...                               ...   \n",
       "3380                            2.706972                          2.733033   \n",
       "3381                            3.860923                          3.799065   \n",
       "3382                            3.985386                          3.874592   \n",
       "3383                            2.987837                          3.022169   \n",
       "3384                                 NaN                               NaN   \n",
       "\n",
       "      Percent_pacific_X_Percent_public_health  \\\n",
       "0                                    0.000169   \n",
       "1                                    0.000101   \n",
       "2                                    0.001589   \n",
       "3                                    0.000804   \n",
       "4                                    0.000820   \n",
       "...                                       ...   \n",
       "3380                                 0.000299   \n",
       "3381                                 0.000384   \n",
       "3382                                 0.000411   \n",
       "3383                                 0.000334   \n",
       "3384                                      NaN   \n",
       "\n",
       "      Percent_Black_X_Percent_hs_grad  Percent_latinX_X_num_hh  \\\n",
       "0                            0.076866              9471.929571   \n",
       "1                            0.016474              4304.376963   \n",
       "2                            0.007471             17024.776568   \n",
       "3                            0.008340            431357.937893   \n",
       "4                            0.006070            139608.189731   \n",
       "...                               ...                      ...   \n",
       "3380                         0.004977                      NaN   \n",
       "3381                         0.061963                      NaN   \n",
       "3382                         0.028295                      NaN   \n",
       "3383                         0.010652                      NaN   \n",
       "3384                              NaN                      NaN   \n",
       "\n",
       "      Rent_X_Percent_Black  Num_grocery_X_num_hh  \\\n",
       "0               439.052706          1.037588e+08   \n",
       "1               144.550469          2.023817e+08   \n",
       "2                47.371234          1.660764e+07   \n",
       "3                63.074706          9.001222e+09   \n",
       "4                37.949841          6.193926e+08   \n",
       "...                    ...                   ...   \n",
       "3380                   NaN                   NaN   \n",
       "3381                   NaN                   NaN   \n",
       "3382                   NaN                   NaN   \n",
       "3383                   NaN                   NaN   \n",
       "3384                   NaN                   NaN   \n",
       "\n",
       "      Houseless_rate_X_Percent_pacific  Percent_white_X_percent_hh_poverty  \\\n",
       "0                         7.338020e-07                            7.975184   \n",
       "1                         6.833436e-07                            5.232610   \n",
       "2                         5.884105e-06                           11.548752   \n",
       "3                         3.603694e-06                           10.701318   \n",
       "4                         4.896115e-06                           11.282296   \n",
       "...                                ...                                 ...   \n",
       "3380                               NaN                                 NaN   \n",
       "3381                               NaN                                 NaN   \n",
       "3382                               NaN                                 NaN   \n",
       "3383                               NaN                                 NaN   \n",
       "3384                               NaN                                 NaN   \n",
       "\n",
       "      Percent_white_X_Percent_priv_health  Percent_white_X_Percent_pacific  \\\n",
       "0                                0.352430                         0.000283   \n",
       "1                                0.653209                         0.000413   \n",
       "2                                0.526947                         0.003531   \n",
       "3                                0.503156                         0.002303   \n",
       "4                                0.504117                         0.001929   \n",
       "...                                   ...                              ...   \n",
       "3380                             0.617641                         0.000497   \n",
       "3381                             0.703677                         0.000692   \n",
       "3382                             0.563280                         0.000593   \n",
       "3383                             0.595389                         0.000556   \n",
       "3384                                  NaN                              NaN   \n",
       "\n",
       "      Unemployment_rate_X_Percent_hs_grad  \\\n",
       "0                                1.125018   \n",
       "1                                0.630694   \n",
       "2                                1.392742   \n",
       "3                                0.840313   \n",
       "4                                0.891113   \n",
       "...                                   ...   \n",
       "3380                             2.953143   \n",
       "3381                             3.923415   \n",
       "3382                             4.192000   \n",
       "3383                             2.535469   \n",
       "3384                                  NaN   \n",
       "\n",
       "      Unemployment_rate_X_Percent_disabled  Percent_white_X_Percent_no_health  \\\n",
       "0                                 0.902886                           0.066410   \n",
       "1                                 0.497208                           0.078452   \n",
       "2                                 1.342071                           0.102953   \n",
       "3                                 0.565960                           0.133005   \n",
       "4                                 0.798786                           0.120188   \n",
       "...                                    ...                                ...   \n",
       "3380                              0.000000                           0.082352   \n",
       "3381                              0.196410                           0.000000   \n",
       "3382                              0.327500                           0.000000   \n",
       "3383                              0.000000                           0.000000   \n",
       "3384                                   NaN                                NaN   \n",
       "\n",
       "      Percent_female_X_Percent_hs_grad  Sheltered_rate_X_Percent_working  \\\n",
       "0                             0.094021                          0.000454   \n",
       "1                             0.072004                          0.000499   \n",
       "2                             0.081594                          0.000331   \n",
       "3                             0.073225                          0.000519   \n",
       "4                             0.075358                          0.000786   \n",
       "...                                ...                               ...   \n",
       "3380                          0.270128                               NaN   \n",
       "3381                          0.258165                               NaN   \n",
       "3382                          0.270425                               NaN   \n",
       "3383                          0.209733                               NaN   \n",
       "3384                               NaN                               NaN   \n",
       "\n",
       "      Percent_Black_X_Percent_no_health  Percent_Black_X_percent_hh_poverty  \\\n",
       "0                              0.053049                            6.370594   \n",
       "1                              0.010926                            0.728774   \n",
       "2                              0.005262                            0.590310   \n",
       "3                              0.009087                            0.731104   \n",
       "4                              0.005747                            0.539486   \n",
       "...                                 ...                                 ...   \n",
       "3380                           0.000833                                 NaN   \n",
       "3381                           0.000000                                 NaN   \n",
       "3382                           0.000000                                 NaN   \n",
       "3383                           0.000000                                 NaN   \n",
       "3384                                NaN                                 NaN   \n",
       "\n",
       "      Percent_pacific_X_Percent_disabled  Cost Per Meal_X_Percent_white  \\\n",
       "0                               0.000075                       1.578871   \n",
       "1                               0.000054                       2.844177   \n",
       "2                               0.000640                       2.477251   \n",
       "3                               0.000267                       2.443608   \n",
       "4                               0.000300                       2.435950   \n",
       "...                                  ...                            ...   \n",
       "3380                            0.000000                            NaN   \n",
       "3381                            0.000021                            NaN   \n",
       "3382                            0.000027                            NaN   \n",
       "3383                            0.000000                            NaN   \n",
       "3384                                 NaN                            NaN   \n",
       "\n",
       "      Percent_pacific_X_Percent_65+  Sheltered_rate_X_Percent_no_health  \\\n",
       "0                          0.000069                            0.000118   \n",
       "1                          0.000056                            0.000089   \n",
       "2                          0.000742                            0.000096   \n",
       "3                          0.000339                            0.000171   \n",
       "4                          0.000364                            0.000240   \n",
       "...                             ...                                 ...   \n",
       "3380                            NaN                                 NaN   \n",
       "3381                            NaN                                 NaN   \n",
       "3382                            NaN                                 NaN   \n",
       "3383                            NaN                                 NaN   \n",
       "3384                            NaN                                 NaN   \n",
       "\n",
       "      TOT_POP_X_Num_grocery  Percent_Black_X_hh_med_income  \\\n",
       "0              2.639888e+08                   19472.925515   \n",
       "1              5.581937e+08                    8195.528316   \n",
       "2              4.328676e+07                    2071.671914   \n",
       "3              2.587186e+10                    3090.727327   \n",
       "4              1.610783e+09                    1889.550112   \n",
       "...                     ...                            ...   \n",
       "3380                    NaN                     438.858638   \n",
       "3381                    NaN                   11823.138909   \n",
       "3382                    NaN                    2930.379780   \n",
       "3383                    NaN                    1906.580378   \n",
       "3384                    NaN                            NaN   \n",
       "\n",
       "      Percent_Black_X_Percent_priv_health  Percent_hs_grad_X_hh_avg_size  \\\n",
       "0                                0.281522                       0.442864   \n",
       "1                                0.090976                       0.371409   \n",
       "2                                0.026935                       0.409533   \n",
       "3                                0.034375                       0.396975   \n",
       "4                                0.024105                       0.371297   \n",
       "...                                   ...                            ...   \n",
       "3380                             0.006251                       1.003463   \n",
       "3381                             0.102363                       1.147317   \n",
       "3382                             0.033159                       1.000000   \n",
       "3383                             0.016411                       0.717187   \n",
       "3384                                  NaN                       0.649743   \n",
       "\n",
       "      Sheltered_rate_X_Percent_pacific  Unemployment_rate_X_Percent_hh_SNAP  \\\n",
       "0                         5.052135e-07                             0.970682   \n",
       "1                         4.704736e-07                             0.283153   \n",
       "2                         3.310116e-06                             1.343422   \n",
       "3                         2.962482e-06                             0.698735   \n",
       "4                         3.846787e-06                             0.887193   \n",
       "...                                ...                                  ...   \n",
       "3380                               NaN                                  NaN   \n",
       "3381                               NaN                                  NaN   \n",
       "3382                               NaN                                  NaN   \n",
       "3383                               NaN                                  NaN   \n",
       "3384                               NaN                                  NaN   \n",
       "\n",
       "      Percent_latinX_X_Percent_priv_health  Total_food_retail_X_num_hh  \\\n",
       "0                                 0.023882                9.276037e+08   \n",
       "1                                 0.044544                2.580255e+08   \n",
       "2                                 0.208332                5.920135e+07   \n",
       "3                                 0.180852                3.641222e+10   \n",
       "4                                 0.213234                2.329673e+09   \n",
       "...                                    ...                         ...   \n",
       "3380                              0.020935                         NaN   \n",
       "3381                              0.118574                         NaN   \n",
       "3382                              0.058760                         NaN   \n",
       "3383                              0.029120                         NaN   \n",
       "3384                                   NaN                         NaN   \n",
       "\n",
       "      Houseless_rate_X_Percent_grad_degree  Percent_pacific_X_Percent_hh_SNAP  \\\n",
       "0                                 0.000112                           0.000081   \n",
       "1                                 0.000125                           0.000031   \n",
       "2                                 0.000087                           0.000641   \n",
       "3                                 0.000088                           0.000329   \n",
       "4                                 0.000176                           0.000334   \n",
       "...                                    ...                                ...   \n",
       "3380                                   NaN                                NaN   \n",
       "3381                                   NaN                                NaN   \n",
       "3382                                   NaN                                NaN   \n",
       "3383                                   NaN                                NaN   \n",
       "3384                                   NaN                                NaN   \n",
       "\n",
       "      log_Rent  log_Houseless_rate  log_Sheltered_rate  log_Unsheltered_rate  \\\n",
       "0     6.927554           -6.574399           -6.947657             -7.740715   \n",
       "1     7.114570           -6.574399           -6.947657             -7.740715   \n",
       "2     6.957735           -6.523255           -7.098526             -7.350053   \n",
       "3     6.999122           -6.631380           -6.827312             -8.357735   \n",
       "4     6.833620           -6.133109           -6.374313             -7.673401   \n",
       "...        ...                 ...                 ...                   ...   \n",
       "3380       NaN                 NaN                 NaN                   NaN   \n",
       "3381       NaN                 NaN                 NaN                   NaN   \n",
       "3382       NaN                 NaN                 NaN                   NaN   \n",
       "3383       NaN                 NaN                 NaN                   NaN   \n",
       "3384       NaN                 NaN                 NaN                   NaN   \n",
       "\n",
       "      log_TOT_POP  log_Cost Per Meal  log_Num_wholesale  log_Num_restaraunts  \\\n",
       "0       13.399953           1.075002           6.180017             7.898411   \n",
       "1       12.236990           1.214913           0.000000             6.610696   \n",
       "2       11.754412           1.033184           6.684612             4.276666   \n",
       "3       15.224947           1.064711           7.778630             9.732521   \n",
       "4       13.819731           1.047319           8.186186             6.731018   \n",
       "...           ...                ...                ...                  ...   \n",
       "3380          NaN                NaN                NaN                  NaN   \n",
       "3381          NaN                NaN                NaN                  NaN   \n",
       "3382          NaN                NaN                NaN                  NaN   \n",
       "3383          NaN                NaN                NaN                  NaN   \n",
       "3384          NaN                NaN                NaN                  NaN   \n",
       "\n",
       "      log_Num_grocery  log_Unemployment_rate  log_Percent_male  \\\n",
       "0            5.991465               1.840550         -0.747622   \n",
       "1            7.903227               1.504077         -0.721029   \n",
       "2            5.828946               2.128232         -0.677498   \n",
       "3            8.751474               1.757858         -0.704035   \n",
       "4            7.380256               1.791759         -0.708056   \n",
       "...               ...                    ...               ...   \n",
       "3380              NaN               1.693779         -0.688367   \n",
       "3381              NaN               2.036012         -0.701257   \n",
       "3382              NaN               2.061787         -0.707346   \n",
       "3383              NaN               1.793425         -0.687450   \n",
       "3384              NaN                    NaN               NaN   \n",
       "\n",
       "      log_Percent_female  log_Percent_white  log_Percent_Black  \\\n",
       "0              -0.641487          -0.618292          -0.842934   \n",
       "1              -0.666022          -0.169639          -2.140942   \n",
       "2              -0.709045          -0.126035          -3.099720   \n",
       "3              -0.682376          -0.171235          -2.854802   \n",
       "4              -0.678457          -0.156982          -3.197355   \n",
       "...                  ...                ...                ...   \n",
       "3380           -0.697948          -0.098855          -4.692069   \n",
       "3381           -0.685106          -0.184382          -2.112173   \n",
       "3382           -0.679152          -0.103974          -2.936452   \n",
       "3383           -0.698875          -0.087757          -3.679005   \n",
       "3384                 NaN                NaN                NaN   \n",
       "\n",
       "      log_Percent_native  log_Percent_asian  log_Percent_pacific  \\\n",
       "0              -5.784654          -4.152509            -7.550628   \n",
       "1              -5.546147          -3.846948            -7.621869   \n",
       "2              -4.074236          -3.832513            -5.520001   \n",
       "3              -3.595279          -3.167229            -5.902171   \n",
       "4              -3.158175          -3.449213            -6.093959   \n",
       "...                  ...                ...                  ...   \n",
       "3380           -5.157950          -2.764355            -7.508593   \n",
       "3381           -4.953681          -4.283509            -7.091977   \n",
       "3382           -5.152991          -4.275457            -7.326401   \n",
       "3383           -4.862670          -3.446524            -7.406705   \n",
       "3384                 NaN                NaN                  NaN   \n",
       "\n",
       "      log_Percent_latinX  log_Percent_working  log_Total_food_retail  \\\n",
       "0              -3.310027            -0.748774               8.182000   \n",
       "1              -2.855052            -0.654464               8.146130   \n",
       "2              -1.054003            -0.915439               7.100027   \n",
       "3              -1.194458            -0.735474              10.149019   \n",
       "4              -1.017399            -0.773976               8.705000   \n",
       "...                  ...                  ...                    ...   \n",
       "3380           -3.483327                  NaN               0.000000   \n",
       "3381           -1.965167                  NaN               0.000000   \n",
       "3382           -2.364296                  NaN               0.000000   \n",
       "3383           -3.105535                  NaN               0.000000   \n",
       "3384                 NaN                  NaN                    NaN   \n",
       "\n",
       "      log_Food_retail_per_person  log_Percent_disabled  log_Percent_hs_grad  \\\n",
       "0                      -5.217953             -1.942709            -1.722751   \n",
       "1                      -4.090860             -2.202825            -1.965012   \n",
       "2                      -4.654385             -1.834018            -1.796957   \n",
       "3                      -5.075928             -2.327091            -1.931839   \n",
       "4                      -5.114731             -2.016422            -1.907043   \n",
       "...                          ...                   ...                  ...   \n",
       "3380                         NaN              0.000000            -0.610909   \n",
       "3381                         NaN             -3.663562            -0.669050   \n",
       "3382                         NaN             -3.178054            -0.628609   \n",
       "3383                         NaN              0.000000            -0.863046   \n",
       "3384                         NaN             -3.735286            -0.878243   \n",
       "\n",
       "      log_Percent_bachelors  log_Percent_grad_degree  log_Percent_priv_health  \\\n",
       "0                 -2.090050                -2.526179                -0.424610   \n",
       "1                 -1.752041                -2.410869                -0.256219   \n",
       "2                 -2.284172                -2.822992                -0.514621   \n",
       "3                 -2.120896                -2.707324                -0.515619   \n",
       "4                 -2.163640                -2.512526                -0.527965   \n",
       "...                     ...                      ...                      ...   \n",
       "3380              -1.763589                 0.000000                -0.382992   \n",
       "3381              -1.921813                -3.713572                -0.167054   \n",
       "3382              -2.148434                -2.484907                -0.470004   \n",
       "3383              -1.593934                -2.772589                -0.430783   \n",
       "3384              -2.382320                -2.775363                -0.238778   \n",
       "\n",
       "      log_Percent_public_health  log_Percent_no_health  log_Percent_65+  \\\n",
       "0                     -1.137641              -2.093609        -2.025876   \n",
       "1                     -1.581402              -2.375627        -2.176328   \n",
       "2                     -0.924723              -2.147445        -1.685706   \n",
       "3                     -1.223222              -1.846134        -2.087837   \n",
       "4                     -1.012248              -1.961720        -1.823916   \n",
       "...                         ...                    ...              ...   \n",
       "3380                  -0.606136              -2.397895              NaN   \n",
       "3381                  -0.773190               0.000000              NaN   \n",
       "3382                  -0.470004               0.000000              NaN   \n",
       "3383                  -0.597837               0.000000              NaN   \n",
       "3384                  -0.847696              -2.969818              NaN   \n",
       "\n",
       "      log_Percent_non_citizen  log_Percent_hh_no_vehicle  log_Percent_hh_SNAP  \\\n",
       "0                   -3.628911                  -1.019009            -1.870306   \n",
       "1                   -3.297933                  -1.330923            -2.765846   \n",
       "2                   -2.773862                  -1.095791            -1.833011   \n",
       "3                   -2.394188                  -0.958726            -2.116341   \n",
       "4                   -2.668694                  -0.947539            -1.911452   \n",
       "...                       ...                        ...                  ...   \n",
       "3380                 0.000000                        NaN                  NaN   \n",
       "3381                -3.713572                        NaN                  NaN   \n",
       "3382                -3.401197                        NaN                  NaN   \n",
       "3383                 0.000000                        NaN                  NaN   \n",
       "3384                -4.214902                        NaN                  NaN   \n",
       "\n",
       "      log_percent_hh_poverty  log_hh_avg_size  log_num_hh  log_hh_med_income  \\\n",
       "0                   2.694627         0.908259   12.466115          10.719715   \n",
       "1                   1.824549         0.974560   11.222439          11.152286   \n",
       "2                   2.572612         0.904218   10.796428          10.735831   \n",
       "3                   2.541602         1.007958   14.169152          10.890963   \n",
       "4                   2.580217         0.916291   12.863994          10.741449   \n",
       "...                      ...              ...         ...                ...   \n",
       "3380                     NaN         0.614366         NaN          10.776246   \n",
       "3381                     NaN         0.806476         NaN          11.489987   \n",
       "3382                     NaN         0.628609         NaN          10.919340   \n",
       "3383                     NaN         0.530628         NaN          11.232072   \n",
       "3384                     NaN         0.447064         NaN          10.888383   \n",
       "\n",
       "      Lo_thresh_SNAP  Lo_thresh_SNAP_other  Hi_thresh_SNAP  \\\n",
       "0                  1                     0               0   \n",
       "1                  1                     0               0   \n",
       "2                  0                     1               0   \n",
       "3                  0                     1               0   \n",
       "4                  0                     1               0   \n",
       "...              ...                   ...             ...   \n",
       "3380               0                     0               0   \n",
       "3381               0                     0               0   \n",
       "3382               0                     0               0   \n",
       "3383               0                     0               0   \n",
       "3384               0                     0               0   \n",
       "\n",
       "      Hi_thresh_SNAP_other  Hi_thresh_other  \n",
       "0                        0                1  \n",
       "1                        0                1  \n",
       "2                        1                0  \n",
       "3                        1                0  \n",
       "4                        1                0  \n",
       "...                    ...              ...  \n",
       "3380                     0                0  \n",
       "3381                     0                0  \n",
       "3382                     0                0  \n",
       "3383                     0                0  \n",
       "3384                     0                0  \n",
       "\n",
       "[35779 rows x 212 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../pickled/feature_engineered_data.pickle', \"wb\") as output_file:\n",
    "    pickle.dump(df, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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