solarpredict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Solar Panel Counts with County Data\n",
    "\n",
    "**Developed by: JC Dy & Wilson Wang**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we will be working with a number of different datasets. The first we examine is a dataset of education by county...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import zipfile\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solar Panel Dataset\n",
    "\n",
    "We will first load the solar panel data set, which was obtained from the Google Project Sunroof. We begin by cleaning the dataset and checking for repeated, missing, or unneeded values and information, and then explore and visualize the data to assist with feature engineering for our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>lat_max</th>\n",
       "      <th>lat_min</th>\n",
       "      <th>lng_max</th>\n",
       "      <th>lng_min</th>\n",
       "      <th>lat_avg</th>\n",
       "      <th>lng_avg</th>\n",
       "      <th>yearly_sunlight_kwh_kw_threshold_avg</th>\n",
       "      <th>count_qualified</th>\n",
       "      <th>...</th>\n",
       "      <th>yearly_sunlight_kwh_n</th>\n",
       "      <th>yearly_sunlight_kwh_s</th>\n",
       "      <th>yearly_sunlight_kwh_e</th>\n",
       "      <th>yearly_sunlight_kwh_w</th>\n",
       "      <th>yearly_sunlight_kwh_f</th>\n",
       "      <th>yearly_sunlight_kwh_median</th>\n",
       "      <th>yearly_sunlight_kwh_total</th>\n",
       "      <th>install_size_kw_buckets_json</th>\n",
       "      <th>carbon_offset_metric_tons</th>\n",
       "      <th>existing_installs_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acadia Parish</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>30.480765</td>\n",
       "      <td>30.054137</td>\n",
       "      <td>-92.141898</td>\n",
       "      <td>-92.629793</td>\n",
       "      <td>30.280735</td>\n",
       "      <td>-92.355786</td>\n",
       "      <td>1087.15</td>\n",
       "      <td>362</td>\n",
       "      <td>...</td>\n",
       "      <td>9.995661e+05</td>\n",
       "      <td>2.387640e+06</td>\n",
       "      <td>1.415127e+06</td>\n",
       "      <td>1.684958e+06</td>\n",
       "      <td>5.161133e+06</td>\n",
       "      <td>18009.16406</td>\n",
       "      <td>1.164842e+07</td>\n",
       "      <td>[[0,62],[5,66],[10,60],[15,59],[20,39],[25,19]...</td>\n",
       "      <td>7.793978e+03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ada County</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>43.805558</td>\n",
       "      <td>43.146687</td>\n",
       "      <td>-115.998789</td>\n",
       "      <td>-116.513522</td>\n",
       "      <td>43.613109</td>\n",
       "      <td>-116.311757</td>\n",
       "      <td>1156.85</td>\n",
       "      <td>133939</td>\n",
       "      <td>...</td>\n",
       "      <td>5.387239e+07</td>\n",
       "      <td>1.055632e+09</td>\n",
       "      <td>4.744878e+08</td>\n",
       "      <td>7.089204e+08</td>\n",
       "      <td>1.092363e+09</td>\n",
       "      <td>16967.80168</td>\n",
       "      <td>3.385276e+09</td>\n",
       "      <td>[[0,12980],[5,32607],[10,38390],[15,24736],[20...</td>\n",
       "      <td>9.001732e+05</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adams County</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>40.000733</td>\n",
       "      <td>39.738577</td>\n",
       "      <td>-103.723147</td>\n",
       "      <td>-105.052979</td>\n",
       "      <td>39.879419</td>\n",
       "      <td>-104.918298</td>\n",
       "      <td>1246.10</td>\n",
       "      <td>117401</td>\n",
       "      <td>...</td>\n",
       "      <td>1.826940e+07</td>\n",
       "      <td>7.158716e+08</td>\n",
       "      <td>3.635472e+08</td>\n",
       "      <td>4.020793e+08</td>\n",
       "      <td>1.415979e+09</td>\n",
       "      <td>11325.30869</td>\n",
       "      <td>2.915746e+09</td>\n",
       "      <td>[[0,24956],[5,49920],[10,23209],[15,8129],[20,...</td>\n",
       "      <td>1.775350e+06</td>\n",
       "      <td>2223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams County</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>40.068780</td>\n",
       "      <td>39.720016</td>\n",
       "      <td>-76.957161</td>\n",
       "      <td>-77.464169</td>\n",
       "      <td>39.846871</td>\n",
       "      <td>-77.150441</td>\n",
       "      <td>997.05</td>\n",
       "      <td>4500</td>\n",
       "      <td>...</td>\n",
       "      <td>4.286321e+06</td>\n",
       "      <td>2.210173e+07</td>\n",
       "      <td>2.234452e+07</td>\n",
       "      <td>1.889155e+07</td>\n",
       "      <td>3.672815e+07</td>\n",
       "      <td>12304.57323</td>\n",
       "      <td>1.043523e+08</td>\n",
       "      <td>[[0,795],[5,1343],[10,877],[15,594],[20,305],[...</td>\n",
       "      <td>6.631176e+04</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aiken County</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>33.871800</td>\n",
       "      <td>33.201889</td>\n",
       "      <td>-81.193502</td>\n",
       "      <td>-82.007226</td>\n",
       "      <td>33.526341</td>\n",
       "      <td>-81.764460</td>\n",
       "      <td>1093.95</td>\n",
       "      <td>49051</td>\n",
       "      <td>...</td>\n",
       "      <td>8.508118e+07</td>\n",
       "      <td>3.292865e+08</td>\n",
       "      <td>1.973077e+08</td>\n",
       "      <td>2.153019e+08</td>\n",
       "      <td>3.459476e+08</td>\n",
       "      <td>15336.44904</td>\n",
       "      <td>1.172925e+09</td>\n",
       "      <td>[[0,5920],[5,12846],[10,12402],[15,8113],[20,4...</td>\n",
       "      <td>6.885615e+05</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     region_name      state_name    lat_max    lat_min     lng_max  \\\n",
       "0  Acadia Parish       Louisiana  30.480765  30.054137  -92.141898   \n",
       "1     Ada County           Idaho  43.805558  43.146687 -115.998789   \n",
       "2   Adams County        Colorado  40.000733  39.738577 -103.723147   \n",
       "3   Adams County    Pennsylvania  40.068780  39.720016  -76.957161   \n",
       "4   Aiken County  South Carolina  33.871800  33.201889  -81.193502   \n",
       "\n",
       "      lng_min    lat_avg     lng_avg  yearly_sunlight_kwh_kw_threshold_avg  \\\n",
       "0  -92.629793  30.280735  -92.355786                               1087.15   \n",
       "1 -116.513522  43.613109 -116.311757                               1156.85   \n",
       "2 -105.052979  39.879419 -104.918298                               1246.10   \n",
       "3  -77.464169  39.846871  -77.150441                                997.05   \n",
       "4  -82.007226  33.526341  -81.764460                               1093.95   \n",
       "\n",
       "   count_qualified           ...             yearly_sunlight_kwh_n  \\\n",
       "0              362           ...                      9.995661e+05   \n",
       "1           133939           ...                      5.387239e+07   \n",
       "2           117401           ...                      1.826940e+07   \n",
       "3             4500           ...                      4.286321e+06   \n",
       "4            49051           ...                      8.508118e+07   \n",
       "\n",
       "   yearly_sunlight_kwh_s  yearly_sunlight_kwh_e  yearly_sunlight_kwh_w  \\\n",
       "0           2.387640e+06           1.415127e+06           1.684958e+06   \n",
       "1           1.055632e+09           4.744878e+08           7.089204e+08   \n",
       "2           7.158716e+08           3.635472e+08           4.020793e+08   \n",
       "3           2.210173e+07           2.234452e+07           1.889155e+07   \n",
       "4           3.292865e+08           1.973077e+08           2.153019e+08   \n",
       "\n",
       "   yearly_sunlight_kwh_f  yearly_sunlight_kwh_median  \\\n",
       "0           5.161133e+06                 18009.16406   \n",
       "1           1.092363e+09                 16967.80168   \n",
       "2           1.415979e+09                 11325.30869   \n",
       "3           3.672815e+07                 12304.57323   \n",
       "4           3.459476e+08                 15336.44904   \n",
       "\n",
       "   yearly_sunlight_kwh_total  \\\n",
       "0               1.164842e+07   \n",
       "1               3.385276e+09   \n",
       "2               2.915746e+09   \n",
       "3               1.043523e+08   \n",
       "4               1.172925e+09   \n",
       "\n",
       "                        install_size_kw_buckets_json  \\\n",
       "0  [[0,62],[5,66],[10,60],[15,59],[20,39],[25,19]...   \n",
       "1  [[0,12980],[5,32607],[10,38390],[15,24736],[20...   \n",
       "2  [[0,24956],[5,49920],[10,23209],[15,8129],[20,...   \n",
       "3  [[0,795],[5,1343],[10,877],[15,594],[20,305],[...   \n",
       "4  [[0,5920],[5,12846],[10,12402],[15,8113],[20,4...   \n",
       "\n",
       "   carbon_offset_metric_tons  existing_installs_count  \n",
       "0               7.793978e+03                        0  \n",
       "1               9.001732e+05                      333  \n",
       "2               1.775350e+06                     2223  \n",
       "3               6.631176e+04                        0  \n",
       "4               6.885615e+05                       83  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sunroof = pd.read_csv(\"data/sunroofByCounty.csv\")\n",
    "sunroof.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region_name', 'state_name', 'lat_max', 'lat_min', 'lng_max', 'lng_min',\n",
       "       'lat_avg', 'lng_avg', 'yearly_sunlight_kwh_kw_threshold_avg',\n",
       "       'count_qualified', 'percent_covered', 'percent_qualified',\n",
       "       'number_of_panels_n', 'number_of_panels_s', 'number_of_panels_e',\n",
       "       'number_of_panels_w', 'number_of_panels_f', 'number_of_panels_median',\n",
       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
       "       'carbon_offset_metric_tons', 'existing_installs_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sunroof.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Solar Panel Dataset\n",
    "\n",
    "Among the columns available to us, we choose to retain only the columns containing data useful to us when constructing our machine learning prediction model.\n",
    "We must keep the state_name and County (region_name) columns, and choose to retain only the 2012-2016 education data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "sunroof = sunroof.loc[:,['region_name', 'state_name', 'count_qualified', 'yearly_sunlight_kwh_kw_threshold_avg',\n",
    "               'percent_covered', 'percent_qualified',\n",
    "               'number_of_panels_n', 'number_of_panels_s', 'number_of_panels_e',\n",
    "               'number_of_panels_w', 'number_of_panels_f', 'number_of_panels_median',\n",
    "               'number_of_panels_total', 'kw_median', 'kw_total',\n",
    "               'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
    "               'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
    "               'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
    "               'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
    "               'carbon_offset_metric_tons', 'existing_installs_count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_state_abbrev = {\n",
    "    'Alabama': 'AL',\n",
    "    'Alaska': 'AK',\n",
    "    'Arizona': 'AZ',\n",
    "    'Arkansas': 'AR',\n",
    "    'California': 'CA',\n",
    "    'Colorado': 'CO',\n",
    "    'Connecticut': 'CT',\n",
    "    'Delaware': 'DE',\n",
    "    'Florida': 'FL',\n",
    "    'Georgia': 'GA',\n",
    "    'Hawaii': 'HI',\n",
    "    'Idaho': 'ID',\n",
    "    'Illinois': 'IL',\n",
    "    'Indiana': 'IN',\n",
    "    'Iowa': 'IA',\n",
    "    'Kansas': 'KS',\n",
    "    'Kentucky': 'KY',\n",
    "    'Louisiana': 'LA',\n",
    "    'Maine': 'ME',\n",
    "    'Maryland': 'MD',\n",
    "    'Massachusetts': 'MA',\n",
    "    'Michigan': 'MI',\n",
    "    'Minnesota': 'MN',\n",
    "    'Mississippi': 'MS',\n",
    "    'Missouri': 'MO',\n",
    "    'Montana': 'MT',\n",
    "    'Nebraska': 'NE',\n",
    "    'Nevada': 'NV',\n",
    "    'New Hampshire': 'NH',\n",
    "    'New Jersey': 'NJ',\n",
    "    'New Mexico': 'NM',\n",
    "    'New York': 'NY',\n",
    "    'North Carolina': 'NC',\n",
    "    'North Dakota': 'ND',\n",
    "    'Ohio': 'OH',\n",
    "    'Oklahoma': 'OK',\n",
    "    'Oregon': 'OR',\n",
    "    'Pennsylvania': 'PA',\n",
    "    'Rhode Island': 'RI',\n",
    "    'South Carolina': 'SC',\n",
    "    'South Dakota': 'SD',\n",
    "    'Tennessee': 'TN',\n",
    "    'Texas': 'TX',\n",
    "    'Utah': 'UT',\n",
    "    'Vermont': 'VT',\n",
    "    'Virginia': 'VA',\n",
    "    'Washington': 'WA',\n",
    "    'West Virginia': 'WV',\n",
    "    'Wisconsin': 'WI',\n",
    "    'Wyoming': 'WY',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "sunroof['State'] = sunroof.apply(lambda row : us_state_abbrev[row['state_name']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>count_qualified</th>\n",
       "      <th>yearly_sunlight_kwh_kw_threshold_avg</th>\n",
       "      <th>percent_covered</th>\n",
       "      <th>percent_qualified</th>\n",
       "      <th>number_of_panels_n</th>\n",
       "      <th>number_of_panels_s</th>\n",
       "      <th>number_of_panels_e</th>\n",
       "      <th>number_of_panels_w</th>\n",
       "      <th>...</th>\n",
       "      <th>yearly_sunlight_kwh_s</th>\n",
       "      <th>yearly_sunlight_kwh_e</th>\n",
       "      <th>yearly_sunlight_kwh_w</th>\n",
       "      <th>yearly_sunlight_kwh_f</th>\n",
       "      <th>yearly_sunlight_kwh_median</th>\n",
       "      <th>yearly_sunlight_kwh_total</th>\n",
       "      <th>install_size_kw_buckets_json</th>\n",
       "      <th>carbon_offset_metric_tons</th>\n",
       "      <th>existing_installs_count</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acadia Parish</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>362</td>\n",
       "      <td>1087.15</td>\n",
       "      <td>1.295852</td>\n",
       "      <td>81.715576</td>\n",
       "      <td>3474</td>\n",
       "      <td>6970</td>\n",
       "      <td>4533</td>\n",
       "      <td>5398</td>\n",
       "      <td>...</td>\n",
       "      <td>2.387640e+06</td>\n",
       "      <td>1.415127e+06</td>\n",
       "      <td>1.684958e+06</td>\n",
       "      <td>5.161133e+06</td>\n",
       "      <td>18009.16406</td>\n",
       "      <td>1.164842e+07</td>\n",
       "      <td>[[0,62],[5,66],[10,60],[15,59],[20,39],[25,19]...</td>\n",
       "      <td>7.793978e+03</td>\n",
       "      <td>0</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ada County</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>133939</td>\n",
       "      <td>1156.85</td>\n",
       "      <td>86.156364</td>\n",
       "      <td>90.224992</td>\n",
       "      <td>183489</td>\n",
       "      <td>2932039</td>\n",
       "      <td>1517496</td>\n",
       "      <td>2190037</td>\n",
       "      <td>...</td>\n",
       "      <td>1.055632e+09</td>\n",
       "      <td>4.744878e+08</td>\n",
       "      <td>7.089204e+08</td>\n",
       "      <td>1.092363e+09</td>\n",
       "      <td>16967.80168</td>\n",
       "      <td>3.385276e+09</td>\n",
       "      <td>[[0,12980],[5,32607],[10,38390],[15,24736],[20...</td>\n",
       "      <td>9.001732e+05</td>\n",
       "      <td>333</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adams County</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>117401</td>\n",
       "      <td>1246.10</td>\n",
       "      <td>92.906285</td>\n",
       "      <td>87.461913</td>\n",
       "      <td>58739</td>\n",
       "      <td>1859313</td>\n",
       "      <td>1050680</td>\n",
       "      <td>1205016</td>\n",
       "      <td>...</td>\n",
       "      <td>7.158716e+08</td>\n",
       "      <td>3.635472e+08</td>\n",
       "      <td>4.020793e+08</td>\n",
       "      <td>1.415979e+09</td>\n",
       "      <td>11325.30869</td>\n",
       "      <td>2.915746e+09</td>\n",
       "      <td>[[0,24956],[5,49920],[10,23209],[15,8129],[20,...</td>\n",
       "      <td>1.775350e+06</td>\n",
       "      <td>2223</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams County</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>4500</td>\n",
       "      <td>997.05</td>\n",
       "      <td>17.958957</td>\n",
       "      <td>87.006961</td>\n",
       "      <td>16651</td>\n",
       "      <td>70798</td>\n",
       "      <td>78169</td>\n",
       "      <td>68634</td>\n",
       "      <td>...</td>\n",
       "      <td>2.210173e+07</td>\n",
       "      <td>2.234452e+07</td>\n",
       "      <td>1.889155e+07</td>\n",
       "      <td>3.672815e+07</td>\n",
       "      <td>12304.57323</td>\n",
       "      <td>1.043523e+08</td>\n",
       "      <td>[[0,795],[5,1343],[10,877],[15,594],[20,305],[...</td>\n",
       "      <td>6.631176e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aiken County</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>49051</td>\n",
       "      <td>1093.95</td>\n",
       "      <td>68.372367</td>\n",
       "      <td>86.296622</td>\n",
       "      <td>297265</td>\n",
       "      <td>984740</td>\n",
       "      <td>645255</td>\n",
       "      <td>693428</td>\n",
       "      <td>...</td>\n",
       "      <td>3.292865e+08</td>\n",
       "      <td>1.973077e+08</td>\n",
       "      <td>2.153019e+08</td>\n",
       "      <td>3.459476e+08</td>\n",
       "      <td>15336.44904</td>\n",
       "      <td>1.172925e+09</td>\n",
       "      <td>[[0,5920],[5,12846],[10,12402],[15,8113],[20,4...</td>\n",
       "      <td>6.885615e+05</td>\n",
       "      <td>83</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     region_name      state_name  count_qualified  \\\n",
       "0  Acadia Parish       Louisiana              362   \n",
       "1     Ada County           Idaho           133939   \n",
       "2   Adams County        Colorado           117401   \n",
       "3   Adams County    Pennsylvania             4500   \n",
       "4   Aiken County  South Carolina            49051   \n",
       "\n",
       "   yearly_sunlight_kwh_kw_threshold_avg  percent_covered  percent_qualified  \\\n",
       "0                               1087.15         1.295852          81.715576   \n",
       "1                               1156.85        86.156364          90.224992   \n",
       "2                               1246.10        92.906285          87.461913   \n",
       "3                                997.05        17.958957          87.006961   \n",
       "4                               1093.95        68.372367          86.296622   \n",
       "\n",
       "   number_of_panels_n  number_of_panels_s  number_of_panels_e  \\\n",
       "0                3474                6970                4533   \n",
       "1              183489             2932039             1517496   \n",
       "2               58739             1859313             1050680   \n",
       "3               16651               70798               78169   \n",
       "4              297265              984740              645255   \n",
       "\n",
       "   number_of_panels_w  ...    yearly_sunlight_kwh_s  yearly_sunlight_kwh_e  \\\n",
       "0                5398  ...             2.387640e+06           1.415127e+06   \n",
       "1             2190037  ...             1.055632e+09           4.744878e+08   \n",
       "2             1205016  ...             7.158716e+08           3.635472e+08   \n",
       "3               68634  ...             2.210173e+07           2.234452e+07   \n",
       "4              693428  ...             3.292865e+08           1.973077e+08   \n",
       "\n",
       "   yearly_sunlight_kwh_w  yearly_sunlight_kwh_f  yearly_sunlight_kwh_median  \\\n",
       "0           1.684958e+06           5.161133e+06                 18009.16406   \n",
       "1           7.089204e+08           1.092363e+09                 16967.80168   \n",
       "2           4.020793e+08           1.415979e+09                 11325.30869   \n",
       "3           1.889155e+07           3.672815e+07                 12304.57323   \n",
       "4           2.153019e+08           3.459476e+08                 15336.44904   \n",
       "\n",
       "   yearly_sunlight_kwh_total  \\\n",
       "0               1.164842e+07   \n",
       "1               3.385276e+09   \n",
       "2               2.915746e+09   \n",
       "3               1.043523e+08   \n",
       "4               1.172925e+09   \n",
       "\n",
       "                        install_size_kw_buckets_json  \\\n",
       "0  [[0,62],[5,66],[10,60],[15,59],[20,39],[25,19]...   \n",
       "1  [[0,12980],[5,32607],[10,38390],[15,24736],[20...   \n",
       "2  [[0,24956],[5,49920],[10,23209],[15,8129],[20,...   \n",
       "3  [[0,795],[5,1343],[10,877],[15,594],[20,305],[...   \n",
       "4  [[0,5920],[5,12846],[10,12402],[15,8113],[20,4...   \n",
       "\n",
       "   carbon_offset_metric_tons  existing_installs_count  State  \n",
       "0               7.793978e+03                        0     LA  \n",
       "1               9.001732e+05                      333     ID  \n",
       "2               1.775350e+06                     2223     CO  \n",
       "3               6.631176e+04                        0     PA  \n",
       "4               6.885615e+05                       83     SC  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sunroof.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Education Dataset\n",
    "\n",
    "We will first load the education data set, which was obtained from the Census Bureau, containing data from 1970, 1980, 1990, 2000, and 2012-2016. We begin by cleaning the dataset and checking for repeated, missing, or unneeded values and information, and then explore and visualize the data to assist with feature engineering for our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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 Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Area name</th>\n",
       "      <th>2003 Rural-urban Continuum Code</th>\n",
       "      <th>2003 Urban Influence Code</th>\n",
       "      <th>2013 Rural-urban Continuum Code</th>\n",
       "      <th>2013 Urban Influence Code</th>\n",
       "      <th>Less than a high school diploma, 1970</th>\n",
       "      <th>High school diploma only, 1970</th>\n",
       "      <th>Some college (1-3 years), 1970</th>\n",
       "      <th>...</th>\n",
       "      <th>Percent of adults with a bachelor's degree or higher, 2000</th>\n",
       "      <th>Less than a high school diploma, 2012-2016</th>\n",
       "      <th>High school diploma only, 2012-2016</th>\n",
       "      <th>Some college or associate's degree, 2012-2016</th>\n",
       "      <th>Bachelor's degree or higher, 2012-2016</th>\n",
       "      <th>Percent of adults with less than a high school diploma, 2012-2016</th>\n",
       "      <th>Percent of adults with a high school diploma only, 2012-2016</th>\n",
       "      <th>Percent of adults completing some college or associate's degree, 2012-2016</th>\n",
       "      <th>Percent of adults with a bachelor's degree or higher, 2012-2016</th>\n",
       "      <th>Unnamed: 47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52,373,312</td>\n",
       "      <td>34,158,051</td>\n",
       "      <td>11,650,730</td>\n",
       "      <td>...</td>\n",
       "      <td>24.4</td>\n",
       "      <td>27,818,380</td>\n",
       "      <td>58,820,411</td>\n",
       "      <td>62,242,569</td>\n",
       "      <td>64,767,787</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.1</td>\n",
       "      <td>30.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,062,306</td>\n",
       "      <td>468,269</td>\n",
       "      <td>136,287</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>496,036</td>\n",
       "      <td>1,009,593</td>\n",
       "      <td>972,703</td>\n",
       "      <td>783,076</td>\n",
       "      <td>15.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6,611</td>\n",
       "      <td>3,757</td>\n",
       "      <td>933</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4,528</td>\n",
       "      <td>12,519</td>\n",
       "      <td>10,451</td>\n",
       "      <td>8,968</td>\n",
       "      <td>12.4</td>\n",
       "      <td>34.3</td>\n",
       "      <td>28.7</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18,726</td>\n",
       "      <td>8,426</td>\n",
       "      <td>2,334</td>\n",
       "      <td>...</td>\n",
       "      <td>23.1</td>\n",
       "      <td>13,956</td>\n",
       "      <td>40,154</td>\n",
       "      <td>44,486</td>\n",
       "      <td>41,350</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.7</td>\n",
       "      <td>31.8</td>\n",
       "      <td>29.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8,120</td>\n",
       "      <td>2,242</td>\n",
       "      <td>581</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9</td>\n",
       "      <td>4,824</td>\n",
       "      <td>6,422</td>\n",
       "      <td>4,775</td>\n",
       "      <td>2,366</td>\n",
       "      <td>26.2</td>\n",
       "      <td>34.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPS Code State       Area name  2003 Rural-urban Continuum Code  \\\n",
       "0        0.0    US   United States                              NaN   \n",
       "1     1000.0    AL         Alabama                              NaN   \n",
       "2     1001.0    AL  Autauga County                              2.0   \n",
       "3     1003.0    AL  Baldwin County                              4.0   \n",
       "4     1005.0    AL  Barbour County                              6.0   \n",
       "\n",
       "   2003 Urban Influence Code  2013 Rural-urban Continuum Code  \\\n",
       "0                        NaN                              NaN   \n",
       "1                        NaN                              NaN   \n",
       "2                        2.0                              2.0   \n",
       "3                        5.0                              3.0   \n",
       "4                        6.0                              6.0   \n",
       "\n",
       "   2013 Urban Influence Code Less than a high school diploma, 1970  \\\n",
       "0                        NaN                            52,373,312   \n",
       "1                        NaN                             1,062,306   \n",
       "2                        2.0                                 6,611   \n",
       "3                        2.0                                18,726   \n",
       "4                        6.0                                 8,120   \n",
       "\n",
       "  High school diploma only, 1970 Some college (1-3 years), 1970     ...       \\\n",
       "0                     34,158,051                     11,650,730     ...        \n",
       "1                        468,269                        136,287     ...        \n",
       "2                          3,757                            933     ...        \n",
       "3                          8,426                          2,334     ...        \n",
       "4                          2,242                            581     ...        \n",
       "\n",
       "  Percent of adults with a bachelor's degree or higher, 2000  \\\n",
       "0                                               24.4           \n",
       "1                                               19.0           \n",
       "2                                               18.0           \n",
       "3                                               23.1           \n",
       "4                                               10.9           \n",
       "\n",
       "   Less than a high school diploma, 2012-2016  \\\n",
       "0                                  27,818,380   \n",
       "1                                     496,036   \n",
       "2                                       4,528   \n",
       "3                                      13,956   \n",
       "4                                       4,824   \n",
       "\n",
       "   High school diploma only, 2012-2016  \\\n",
       "0                           58,820,411   \n",
       "1                            1,009,593   \n",
       "2                               12,519   \n",
       "3                               40,154   \n",
       "4                                6,422   \n",
       "\n",
       "   Some college or associate's degree, 2012-2016  \\\n",
       "0                                     62,242,569   \n",
       "1                                        972,703   \n",
       "2                                         10,451   \n",
       "3                                         44,486   \n",
       "4                                          4,775   \n",
       "\n",
       "   Bachelor's degree or higher, 2012-2016  \\\n",
       "0                              64,767,787   \n",
       "1                                 783,076   \n",
       "2                                   8,968   \n",
       "3                                  41,350   \n",
       "4                                   2,366   \n",
       "\n",
       "  Percent of adults with less than a high school diploma, 2012-2016  \\\n",
       "0                                               13.0                  \n",
       "1                                               15.2                  \n",
       "2                                               12.4                  \n",
       "3                                               10.0                  \n",
       "4                                               26.2                  \n",
       "\n",
       "  Percent of adults with a high school diploma only, 2012-2016  \\\n",
       "0                                               27.5             \n",
       "1                                               31.0             \n",
       "2                                               34.3             \n",
       "3                                               28.7             \n",
       "4                                               34.9             \n",
       "\n",
       "  Percent of adults completing some college or associate's degree, 2012-2016  \\\n",
       "0                                               29.1                           \n",
       "1                                               29.8                           \n",
       "2                                               28.7                           \n",
       "3                                               31.8                           \n",
       "4                                               26.0                           \n",
       "\n",
       "  Percent of adults with a bachelor's degree or higher, 2012-2016  Unnamed: 47  \n",
       "0                                               30.3                       NaN  \n",
       "1                                               24.0                       NaN  \n",
       "2                                               24.6                       NaN  \n",
       "3                                               29.5                       NaN  \n",
       "4                                               12.9                       NaN  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education = pd.read_csv(\"data/educationByCounty.csv\")\n",
    "education.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FIPS Code', 'State', 'Area name', '2003 Rural-urban Continuum Code',\n",
       "       '2003 Urban Influence Code', '2013 Rural-urban Continuum Code',\n",
       "       '2013 Urban Influence Code', 'Less than a high school diploma, 1970',\n",
       "       'High school diploma only, 1970', 'Some college (1-3 years), 1970',\n",
       "       'Four years of college or higher, 1970',\n",
       "       'Percent of adults with less than a high school diploma, 1970',\n",
       "       'Percent of adults with a high school diploma only, 1970',\n",
       "       'Percent of adults completing some college (1-3 years), 1970',\n",
       "       'Percent of adults completing four years of college or higher, 1970',\n",
       "       'Less than a high school diploma, 1980',\n",
       "       'High school diploma only, 1980', 'Some college (1-3 years), 1980',\n",
       "       'Four years of college or higher, 1980',\n",
       "       'Percent of adults with less than a high school diploma, 1980',\n",
       "       'Percent of adults with a high school diploma only, 1980',\n",
       "       'Percent of adults completing some college (1-3 years), 1980',\n",
       "       'Percent of adults completing four years of college or higher, 1980',\n",
       "       'Less than a high school diploma, 1990',\n",
       "       'High school diploma only, 1990',\n",
       "       'Some college or associate's degree, 1990',\n",
       "       'Bachelor's degree or higher, 1990',\n",
       "       'Percent of adults with less than a high school diploma, 1990',\n",
       "       'Percent of adults with a high school diploma only, 1990',\n",
       "       'Percent of adults completing some college or associate's degree, 1990',\n",
       "       'Percent of adults with a bachelor's degree or higher, 1990',\n",
       "       'Less than a high school diploma, 2000',\n",
       "       'High school diploma only, 2000',\n",
       "       'Some college or associate's degree, 2000',\n",
       "       'Bachelor's degree or higher, 2000',\n",
       "       'Percent of adults with less than a high school diploma, 2000',\n",
       "       'Percent of adults with a high school diploma only, 2000',\n",
       "       'Percent of adults completing some college or associate's degree, 2000',\n",
       "       'Percent of adults with a bachelor's degree or higher, 2000',\n",
       "       'Less than a high school diploma, 2012-2016',\n",
       "       'High school diploma only, 2012-2016',\n",
       "       'Some college or associate's degree, 2012-2016',\n",
       "       'Bachelor's degree or higher, 2012-2016',\n",
       "       'Percent of adults with less than a high school diploma, 2012-2016',\n",
       "       'Percent of adults with a high school diploma only, 2012-2016',\n",
       "       'Percent of adults completing some college or associate's degree, 2012-2016',\n",
       "       'Percent of adults with a bachelor's degree or higher, 2012-2016',\n",
       "       'Unnamed: 47'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Education Dataset\n",
    "\n",
    "Notice that we have columns containing extraneous information that will not be useful in building our model. We will focus on data that has been collected more recently. One reason for this is that the other datasets we will be working with have data that is generally 2007 and onwards. In order to ensure accuracy for our predictions, we filter out the outdated data from our dataset.\n",
    "\n",
    "We must keep the State and County (Area name) columns, and choose to retain only the 2012-2016 education data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "education = education.loc[:,['State', 'Area name','Less than a high school diploma, 2012-2016',\n",
    "                   'High school diploma only, 2012-2016',\n",
    "                   \"Some college or associate's degree, 2012-2016\",\n",
    "                   \"Bachelor's degree or higher, 2012-2016\",\n",
    "                   'Percent of adults with less than a high school diploma, 2012-2016',\n",
    "                   'Percent of adults with a high school diploma only, 2012-2016',\n",
    "                   \"Percent of adults completing some college or associate's degree, 2012-2016\",\n",
    "                   \"Percent of adults with a bachelor's degree or higher, 2012-2016\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Area name</th>\n",
       "      <th>Less than a high school diploma, 2012-2016</th>\n",
       "      <th>High school diploma only, 2012-2016</th>\n",
       "      <th>Some college or associate's degree, 2012-2016</th>\n",
       "      <th>Bachelor's degree or higher, 2012-2016</th>\n",
       "      <th>Percent of adults with less than a high school diploma, 2012-2016</th>\n",
       "      <th>Percent of adults with a high school diploma only, 2012-2016</th>\n",
       "      <th>Percent of adults completing some college or associate's degree, 2012-2016</th>\n",
       "      <th>Percent of adults with a bachelor's degree or higher, 2012-2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>27,818,380</td>\n",
       "      <td>58,820,411</td>\n",
       "      <td>62,242,569</td>\n",
       "      <td>64,767,787</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.1</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>496,036</td>\n",
       "      <td>1,009,593</td>\n",
       "      <td>972,703</td>\n",
       "      <td>783,076</td>\n",
       "      <td>15.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>4,528</td>\n",
       "      <td>12,519</td>\n",
       "      <td>10,451</td>\n",
       "      <td>8,968</td>\n",
       "      <td>12.4</td>\n",
       "      <td>34.3</td>\n",
       "      <td>28.7</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>13,956</td>\n",
       "      <td>40,154</td>\n",
       "      <td>44,486</td>\n",
       "      <td>41,350</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.7</td>\n",
       "      <td>31.8</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>4,824</td>\n",
       "      <td>6,422</td>\n",
       "      <td>4,775</td>\n",
       "      <td>2,366</td>\n",
       "      <td>26.2</td>\n",
       "      <td>34.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State       Area name Less than a high school diploma, 2012-2016  \\\n",
       "0    US   United States                                 27,818,380   \n",
       "1    AL         Alabama                                    496,036   \n",
       "2    AL  Autauga County                                      4,528   \n",
       "3    AL  Baldwin County                                     13,956   \n",
       "4    AL  Barbour County                                      4,824   \n",
       "\n",
       "  High school diploma only, 2012-2016  \\\n",
       "0                          58,820,411   \n",
       "1                           1,009,593   \n",
       "2                              12,519   \n",
       "3                              40,154   \n",
       "4                               6,422   \n",
       "\n",
       "  Some college or associate's degree, 2012-2016  \\\n",
       "0                                    62,242,569   \n",
       "1                                       972,703   \n",
       "2                                        10,451   \n",
       "3                                        44,486   \n",
       "4                                         4,775   \n",
       "\n",
       "  Bachelor's degree or higher, 2012-2016  \\\n",
       "0                             64,767,787   \n",
       "1                                783,076   \n",
       "2                                  8,968   \n",
       "3                                 41,350   \n",
       "4                                  2,366   \n",
       "\n",
       "   Percent of adults with less than a high school diploma, 2012-2016  \\\n",
       "0                                               13.0                   \n",
       "1                                               15.2                   \n",
       "2                                               12.4                   \n",
       "3                                               10.0                   \n",
       "4                                               26.2                   \n",
       "\n",
       "   Percent of adults with a high school diploma only, 2012-2016  \\\n",
       "0                                               27.5              \n",
       "1                                               31.0              \n",
       "2                                               34.3              \n",
       "3                                               28.7              \n",
       "4                                               34.9              \n",
       "\n",
       "   Percent of adults completing some college or associate's degree, 2012-2016  \\\n",
       "0                                               29.1                            \n",
       "1                                               29.8                            \n",
       "2                                               28.7                            \n",
       "3                                               31.8                            \n",
       "4                                               26.0                            \n",
       "\n",
       "   Percent of adults with a bachelor's degree or higher, 2012-2016  \n",
       "0                                               30.3                \n",
       "1                                               24.0                \n",
       "2                                               24.6                \n",
       "3                                               29.5                \n",
       "4                                               12.9                "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area name</th>\n",
       "      <th>Less than a high school diploma, 2012-2016</th>\n",
       "      <th>High school diploma only, 2012-2016</th>\n",
       "      <th>Some college or associate's degree, 2012-2016</th>\n",
       "      <th>Bachelor's degree or higher, 2012-2016</th>\n",
       "      <th>Percent of adults with less than a high school diploma, 2012-2016</th>\n",
       "      <th>Percent of adults with a high school diploma only, 2012-2016</th>\n",
       "      <th>Percent of adults completing some college or associate's degree, 2012-2016</th>\n",
       "      <th>Percent of adults with a bachelor's degree or higher, 2012-2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</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>TX</th>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA</th>\n",
       "      <td>136</td>\n",
       "      <td>134</td>\n",
       "      <td>134</td>\n",
       "      <td>134</td>\n",
       "      <td>134</td>\n",
       "      <td>134</td>\n",
       "      <td>134</td>\n",
       "      <td>134</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Area name  Less than a high school diploma, 2012-2016  \\\n",
       "State                                                          \n",
       "TX           255                                         255   \n",
       "GA           160                                         160   \n",
       "VA           136                                         134   \n",
       "KY           121                                         121   \n",
       "MO           116                                         116   \n",
       "\n",
       "       High school diploma only, 2012-2016  \\\n",
       "State                                        \n",
       "TX                                     255   \n",
       "GA                                     160   \n",
       "VA                                     134   \n",
       "KY                                     121   \n",
       "MO                                     116   \n",
       "\n",
       "       Some college or associate's degree, 2012-2016  \\\n",
       "State                                                  \n",
       "TX                                               255   \n",
       "GA                                               160   \n",
       "VA                                               134   \n",
       "KY                                               121   \n",
       "MO                                               116   \n",
       "\n",
       "       Bachelor's degree or higher, 2012-2016  \\\n",
       "State                                           \n",
       "TX                                        255   \n",
       "GA                                        160   \n",
       "VA                                        134   \n",
       "KY                                        121   \n",
       "MO                                        116   \n",
       "\n",
       "       Percent of adults with less than a high school diploma, 2012-2016  \\\n",
       "State                                                                      \n",
       "TX                                                   255                   \n",
       "GA                                                   160                   \n",
       "VA                                                   134                   \n",
       "KY                                                   121                   \n",
       "MO                                                   116                   \n",
       "\n",
       "       Percent of adults with a high school diploma only, 2012-2016  \\\n",
       "State                                                                 \n",
       "TX                                                   255              \n",
       "GA                                                   160              \n",
       "VA                                                   134              \n",
       "KY                                                   121              \n",
       "MO                                                   116              \n",
       "\n",
       "       Percent of adults completing some college or associate's degree, 2012-2016  \\\n",
       "State                                                                               \n",
       "TX                                                   255                            \n",
       "GA                                                   160                            \n",
       "VA                                                   134                            \n",
       "KY                                                   121                            \n",
       "MO                                                   116                            \n",
       "\n",
       "       Percent of adults with a bachelor's degree or higher, 2012-2016  \n",
       "State                                                                   \n",
       "TX                                                   255                \n",
       "GA                                                   160                \n",
       "VA                                                   134                \n",
       "KY                                                   121                \n",
       "MO                                                   116                "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which states have the most county education data?\n",
    "education.groupby('State').agg('count').sort_values(by='Area name', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From above, we know the top states we have most data on\n",
    "top_states = ['TX', 'GA', 'VA', 'KY', 'MO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0de947e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,9))\n",
    "ax = education[education['State'].isin(top_states)]['State'].value_counts().plot(kind='barh')\n",
    "ax.set_ylabel(\"State\")\n",
    "ax.set_xlabel(\"Number of Counties\")\n",
    "ax.set_title(\"Amount of County Education Data By State\");\n",
    "plt.rcParams.update({'font.size': 30})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_states = len(education.groupby(education['State']))\n",
    "state_list = set(education['State'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>Percent of adults with less than a high school diploma, 2012-2016</th>\n",
       "      <th>Percent of adults with a high school diploma only, 2012-2016</th>\n",
       "      <th>Percent of adults completing some college or associate's degree, 2012-2016</th>\n",
       "      <th>Percent of adults with a bachelor's degree or higher, 2012-2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>55.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>9.140000</td>\n",
       "      <td>26.080000</td>\n",
       "      <td>25.773333</td>\n",
       "      <td>38.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RI</th>\n",
       "      <td>10.233333</td>\n",
       "      <td>25.416667</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>38.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>9.133333</td>\n",
       "      <td>28.344444</td>\n",
       "      <td>26.344444</td>\n",
       "      <td>36.188889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>11.018182</td>\n",
       "      <td>29.590909</td>\n",
       "      <td>24.104545</td>\n",
       "      <td>35.286364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Percent of adults with less than a high school diploma, 2012-2016  \\\n",
       "State                                                                      \n",
       "DC                                             10.000000                   \n",
       "MA                                              9.140000                   \n",
       "RI                                             10.233333                   \n",
       "CT                                              9.133333                   \n",
       "NJ                                             11.018182                   \n",
       "\n",
       "       Percent of adults with a high school diploma only, 2012-2016  \\\n",
       "State                                                                 \n",
       "DC                                             18.000000              \n",
       "MA                                             26.080000              \n",
       "RI                                             25.416667              \n",
       "CT                                             28.344444              \n",
       "NJ                                             29.590909              \n",
       "\n",
       "       Percent of adults completing some college or associate's degree, 2012-2016  \\\n",
       "State                                                                               \n",
       "DC                                             16.600000                            \n",
       "MA                                             25.773333                            \n",
       "RI                                             26.333333                            \n",
       "CT                                             26.344444                            \n",
       "NJ                                             24.104545                            \n",
       "\n",
       "       Percent of adults with a bachelor's degree or higher, 2012-2016  \n",
       "State                                                                   \n",
       "DC                                             55.400000                \n",
       "MA                                             38.980000                \n",
       "RI                                             38.016667                \n",
       "CT                                             36.188889                \n",
       "NJ                                             35.286364                "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.groupby('State').agg('mean')\\\n",
    ".sort_values(by=\"Percent of adults with a bachelor's degree or higher, 2012-2016\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Area name</th>\n",
       "      <th>Less than a high school diploma, 2012-2016</th>\n",
       "      <th>High school diploma only, 2012-2016</th>\n",
       "      <th>Some college or associate's degree, 2012-2016</th>\n",
       "      <th>Bachelor's degree or higher, 2012-2016</th>\n",
       "      <th>Percent of adults with less than a high school diploma, 2012-2016</th>\n",
       "      <th>Percent of adults with a high school diploma only, 2012-2016</th>\n",
       "      <th>Percent of adults completing some college or associate's degree, 2012-2016</th>\n",
       "      <th>Percent of adults with a bachelor's degree or higher, 2012-2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>27,818,380</td>\n",
       "      <td>58,820,411</td>\n",
       "      <td>62,242,569</td>\n",
       "      <td>64,767,787</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.1</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>496,036</td>\n",
       "      <td>1,009,593</td>\n",
       "      <td>972,703</td>\n",
       "      <td>783,076</td>\n",
       "      <td>15.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>4,528</td>\n",
       "      <td>12,519</td>\n",
       "      <td>10,451</td>\n",
       "      <td>8,968</td>\n",
       "      <td>12.4</td>\n",
       "      <td>34.3</td>\n",
       "      <td>28.7</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>13,956</td>\n",
       "      <td>40,154</td>\n",
       "      <td>44,486</td>\n",
       "      <td>41,350</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.7</td>\n",
       "      <td>31.8</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>4,824</td>\n",
       "      <td>6,422</td>\n",
       "      <td>4,775</td>\n",
       "      <td>2,366</td>\n",
       "      <td>26.2</td>\n",
       "      <td>34.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State       Area name Less than a high school diploma, 2012-2016  \\\n",
       "0    US   United States                                 27,818,380   \n",
       "1    AL         Alabama                                    496,036   \n",
       "2    AL  Autauga County                                      4,528   \n",
       "3    AL  Baldwin County                                     13,956   \n",
       "4    AL  Barbour County                                      4,824   \n",
       "\n",
       "  High school diploma only, 2012-2016  \\\n",
       "0                          58,820,411   \n",
       "1                           1,009,593   \n",
       "2                              12,519   \n",
       "3                              40,154   \n",
       "4                               6,422   \n",
       "\n",
       "  Some college or associate's degree, 2012-2016  \\\n",
       "0                                    62,242,569   \n",
       "1                                       972,703   \n",
       "2                                        10,451   \n",
       "3                                        44,486   \n",
       "4                                         4,775   \n",
       "\n",
       "  Bachelor's degree or higher, 2012-2016  \\\n",
       "0                             64,767,787   \n",
       "1                                783,076   \n",
       "2                                  8,968   \n",
       "3                                 41,350   \n",
       "4                                  2,366   \n",
       "\n",
       "   Percent of adults with less than a high school diploma, 2012-2016  \\\n",
       "0                                               13.0                   \n",
       "1                                               15.2                   \n",
       "2                                               12.4                   \n",
       "3                                               10.0                   \n",
       "4                                               26.2                   \n",
       "\n",
       "   Percent of adults with a high school diploma only, 2012-2016  \\\n",
       "0                                               27.5              \n",
       "1                                               31.0              \n",
       "2                                               34.3              \n",
       "3                                               28.7              \n",
       "4                                               34.9              \n",
       "\n",
       "   Percent of adults completing some college or associate's degree, 2012-2016  \\\n",
       "0                                               29.1                            \n",
       "1                                               29.8                            \n",
       "2                                               28.7                            \n",
       "3                                               31.8                            \n",
       "4                                               26.0                            \n",
       "\n",
       "   Percent of adults with a bachelor's degree or higher, 2012-2016  \n",
       "0                                               30.3                \n",
       "1                                               24.0                \n",
       "2                                               24.6                \n",
       "3                                               29.5                \n",
       "4                                               12.9                "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poverty Dataset\n",
    "\n",
    "We will first load the poverty data set, which was obtained from the Census Bureau, containing data from 2016. We begin by cleaning the dataset and checking for repeated, missing, or unneeded values and information, and then explore and visualize the data to assist with feature engineering for our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPStxt</th>\n",
       "      <th>State</th>\n",
       "      <th>Area_Name</th>\n",
       "      <th>Rural-urban_Continuum_Code_2003</th>\n",
       "      <th>Urban_Influence_Code_2003</th>\n",
       "      <th>Rural-urban_Continuum_Code_2013</th>\n",
       "      <th>Urban_Influence_Code_2013</th>\n",
       "      <th>POVALL_2016</th>\n",
       "      <th>CI90LBAll_2016</th>\n",
       "      <th>CI90UBALL_2016</th>\n",
       "      <th>...</th>\n",
       "      <th>CI90UB517P_2016</th>\n",
       "      <th>MEDHHINC_2016</th>\n",
       "      <th>CI90LBINC_2016</th>\n",
       "      <th>CI90UBINC_2016</th>\n",
       "      <th>POV05_2016</th>\n",
       "      <th>CI90LB05_2016</th>\n",
       "      <th>CI90UB05_2016</th>\n",
       "      <th>PCTPOV05_2016</th>\n",
       "      <th>CI90LB05P_2016</th>\n",
       "      <th>CI90UB05P_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44,268,996</td>\n",
       "      <td>44,022,086</td>\n",
       "      <td>44,515,906</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>57,617</td>\n",
       "      <td>57,502</td>\n",
       "      <td>57,732</td>\n",
       "      <td>4,156,949</td>\n",
       "      <td>4,108,397</td>\n",
       "      <td>4,205,501</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>814,197</td>\n",
       "      <td>796,927</td>\n",
       "      <td>831,467</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>46,309</td>\n",
       "      <td>45,650</td>\n",
       "      <td>46,968</td>\n",
       "      <td>78,675</td>\n",
       "      <td>74,257</td>\n",
       "      <td>83,093</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7,444</td>\n",
       "      <td>6,255</td>\n",
       "      <td>8,633</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>54,487</td>\n",
       "      <td>50,886</td>\n",
       "      <td>58,088</td>\n",
       "      <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>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24,005</td>\n",
       "      <td>20,132</td>\n",
       "      <td>27,878</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>56,460</td>\n",
       "      <td>53,250</td>\n",
       "      <td>59,670</td>\n",
       "      <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>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6,787</td>\n",
       "      <td>5,551</td>\n",
       "      <td>8,023</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>32,884</td>\n",
       "      <td>29,684</td>\n",
       "      <td>36,084</td>\n",
       "      <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>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPStxt State       Area_Name  Rural-urban_Continuum_Code_2003  \\\n",
       "0        0    US   United States                              NaN   \n",
       "1     1000    AL         Alabama                              NaN   \n",
       "2     1001    AL  Autauga County                              2.0   \n",
       "3     1003    AL  Baldwin County                              4.0   \n",
       "4     1005    AL  Barbour County                              6.0   \n",
       "\n",
       "   Urban_Influence_Code_2003  Rural-urban_Continuum_Code_2013  \\\n",
       "0                        NaN                              NaN   \n",
       "1                        NaN                              NaN   \n",
       "2                        2.0                              2.0   \n",
       "3                        5.0                              3.0   \n",
       "4                        6.0                              6.0   \n",
       "\n",
       "   Urban_Influence_Code_2013 POVALL_2016 CI90LBAll_2016 CI90UBALL_2016  \\\n",
       "0                        NaN  44,268,996     44,022,086     44,515,906   \n",
       "1                        NaN     814,197        796,927        831,467   \n",
       "2                        2.0       7,444          6,255          8,633   \n",
       "3                        2.0      24,005         20,132         27,878   \n",
       "4                        6.0       6,787          5,551          8,023   \n",
       "\n",
       "       ...        CI90UB517P_2016  MEDHHINC_2016  CI90LBINC_2016  \\\n",
       "0      ...                   19.0         57,617          57,502   \n",
       "1      ...                   25.0         46,309          45,650   \n",
       "2      ...                   22.0         54,487          50,886   \n",
       "3      ...                   20.0         56,460          53,250   \n",
       "4      ...                   45.0         32,884          29,684   \n",
       "\n",
       "  CI90UBINC_2016 POV05_2016 CI90LB05_2016  CI90UB05_2016  PCTPOV05_2016  \\\n",
       "0         57,732  4,156,949     4,108,397      4,205,501           21.0   \n",
       "1         46,968     78,675        74,257         83,093           27.0   \n",
       "2         58,088        NaN           NaN            NaN            NaN   \n",
       "3         59,670        NaN           NaN            NaN            NaN   \n",
       "4         36,084        NaN           NaN            NaN            NaN   \n",
       "\n",
       "   CI90LB05P_2016 CI90UB05P_2016  \n",
       "0            21.0           22.0  \n",
       "1            26.0           29.0  \n",
       "2             NaN            NaN  \n",
       "3             NaN            NaN  \n",
       "4             NaN            NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty = pd.read_csv(\"data/povertyByCounty.csv\")\n",
    "poverty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FIPStxt', 'State', 'Area_Name', 'Rural-urban_Continuum_Code_2003',\n",
       "       'Urban_Influence_Code_2003', 'Rural-urban_Continuum_Code_2013',\n",
       "       'Urban_Influence_Code_2013', 'POVALL_2016', 'CI90LBAll_2016',\n",
       "       'CI90UBALL_2016', 'PCTPOVALL_2016', 'CI90LBALLP_2016',\n",
       "       'CI90UBALLP_2016', 'POV017_2016', 'CI90LB017_2016', 'CI90UB017_2016',\n",
       "       'PCTPOV017_2016', 'CI90LB017P_2016', 'CI90UB017P_2016', 'POV517_2016',\n",
       "       'CI90LB517_2016', 'CI90UB517_2016', 'PCTPOV517_2016', 'CI90LB517P_2016',\n",
       "       'CI90UB517P_2016', 'MEDHHINC_2016', 'CI90LBINC_2016', 'CI90UBINC_2016',\n",
       "       'POV05_2016', 'CI90LB05_2016', 'CI90UB05_2016', 'PCTPOV05_2016',\n",
       "       'CI90LB05P_2016', 'CI90UB05P_2016'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Poverty Dataset\n",
    "\n",
    "Notice that we have columns containing extraneous information that will not be useful in building our model. We will focus on data that has been collected more recently. One reason for this is that the other datasets we will be working with have data that is generally 2007 and onwards. In order to ensure accuracy for our predictions, we filter out the outdated data from our dataset.\n",
    "\n",
    "We must keep the State and County (Area_Name) columns, as well as the POVALL_2016, which represents the estimate of people of all ages in poverty in 2016, and PCTPOVALL_2016 which is the estimated percent of people of all ages in poverty 2016. The other estimates are for children, so we exclude those from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty = poverty.loc[:,['State','Area_Name','POVALL_2016', 'PCTPOVALL_2016']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PCTPOVALL_2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MS</th>\n",
       "      <td>24.469880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>22.492308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KY</th>\n",
       "      <td>21.429752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM</th>\n",
       "      <td>21.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GA</th>\n",
       "      <td>20.737500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PCTPOVALL_2016\n",
       "State                \n",
       "MS          24.469880\n",
       "LA          22.492308\n",
       "KY          21.429752\n",
       "NM          21.411765\n",
       "GA          20.737500"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty.groupby('State').agg('mean').sort_values(by='PCTPOVALL_2016', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0de9288c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 12})\n",
    "#ax = sns.countplot(data=rents['Price_2018_10'], x=rents['State'], order=top_states)\n",
    "top_states = ['CA', 'NY', 'TX', 'FL', 'MS', 'LA', 'KY', 'NM', 'GA']\n",
    "ax = poverty.loc[poverty['State'].isin(top_states)].loc[:,['State', 'PCTPOVALL_2016']].groupby([\"State\"]).agg('mean').plot(kind=\"bar\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=0)\n",
    "ax.set_ylabel(\"Percent of people of all ages in poverty\")\n",
    "ax.set_title(\"Poverty Percentages By State in 2016\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Dataset\n",
    "\n",
    "We will first load the population data set, which was obtained from the USDA Economic Research Service, containing data from 2010-2017. We begin by cleaning the dataset and checking for repeated, missing, or unneeded values and information, and then explore and visualize the data to assist with feature engineering for our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>State</th>\n",
       "      <th>Area_Name</th>\n",
       "      <th>Rural-urban_Continuum Code_2003</th>\n",
       "      <th>Rural-urban_Continuum Code_2013</th>\n",
       "      <th>Urban_Influence_Code_2003</th>\n",
       "      <th>Urban_Influence_Code_2013</th>\n",
       "      <th>Economic_typology_2015</th>\n",
       "      <th>CENSUS_2010_POP</th>\n",
       "      <th>ESTIMATES_BASE_2010</th>\n",
       "      <th>...</th>\n",
       "      <th>R_DOMESTIC_MIG_2015</th>\n",
       "      <th>R_DOMESTIC_MIG_2016</th>\n",
       "      <th>R_DOMESTIC_MIG_2017</th>\n",
       "      <th>R_NET_MIG_2011</th>\n",
       "      <th>R_NET_MIG_2012</th>\n",
       "      <th>R_NET_MIG_2013</th>\n",
       "      <th>R_NET_MIG_2014</th>\n",
       "      <th>R_NET_MIG_2015</th>\n",
       "      <th>R_NET_MIG_2016</th>\n",
       "      <th>R_NET_MIG_2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>308,745,538</td>\n",
       "      <td>308,758,105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4,779,736</td>\n",
       "      <td>4,780,135</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54,571</td>\n",
       "      <td>54,571</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>182,265</td>\n",
       "      <td>182,265</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>22.4</td>\n",
       "      <td>16.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>22.6</td>\n",
       "      <td>20.4</td>\n",
       "      <td>17.9</td>\n",
       "      <td>21.3</td>\n",
       "      <td>23.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27,457</td>\n",
       "      <td>27,457</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>-18.9</td>\n",
       "      <td>-19.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FIPS State       Area_Name  Rural-urban_Continuum Code_2003  \\\n",
       "0     0.0    US   United States                              NaN   \n",
       "1  1000.0    AL         Alabama                              NaN   \n",
       "2  1001.0    AL  Autauga County                              2.0   \n",
       "3  1003.0    AL  Baldwin County                              4.0   \n",
       "4  1005.0    AL  Barbour County                              6.0   \n",
       "\n",
       "   Rural-urban_Continuum Code_2013  Urban_Influence_Code_2003  \\\n",
       "0                              NaN                        NaN   \n",
       "1                              NaN                        NaN   \n",
       "2                              2.0                        2.0   \n",
       "3                              3.0                        5.0   \n",
       "4                              6.0                        6.0   \n",
       "\n",
       "   Urban_Influence_Code_2013  Economic_typology_2015 CENSUS_2010_POP  \\\n",
       "0                        NaN                     NaN     308,745,538   \n",
       "1                        NaN                     NaN       4,779,736   \n",
       "2                        2.0                     0.0          54,571   \n",
       "3                        2.0                     5.0         182,265   \n",
       "4                        6.0                     3.0          27,457   \n",
       "\n",
       "  ESTIMATES_BASE_2010      ...       R_DOMESTIC_MIG_2015 R_DOMESTIC_MIG_2016  \\\n",
       "0         308,758,105      ...                       0.0                 0.0   \n",
       "1           4,780,135      ...                      -0.3                -0.4   \n",
       "2              54,571      ...                      -2.0                 4.8   \n",
       "3             182,265      ...                      17.0                20.5   \n",
       "4              27,457      ...                     -16.2               -18.8   \n",
       "\n",
       "  R_DOMESTIC_MIG_2017 R_NET_MIG_2011 R_NET_MIG_2012 R_NET_MIG_2013  \\\n",
       "0                 0.0            2.7            2.9            2.9   \n",
       "1                 0.8            0.5            0.9            1.4   \n",
       "2                 1.0            5.9           -6.1           -4.1   \n",
       "3                22.4           16.3           17.2           22.6   \n",
       "4               -19.0            0.3           -6.8           -8.0   \n",
       "\n",
       "  R_NET_MIG_2014 R_NET_MIG_2015 R_NET_MIG_2016 R_NET_MIG_2017  \n",
       "0            3.2            3.5            3.5            3.4  \n",
       "1            0.7            0.7            0.6            1.7  \n",
       "2            2.1           -1.7            5.1            1.3  \n",
       "3           20.4           17.9           21.3           23.2  \n",
       "4           -5.5          -16.4          -18.9          -19.2  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = pd.read_csv(\"data/populationByCounty.csv\")\n",
    "population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FIPS',\n",
       " 'State',\n",
       " 'Area_Name',\n",
       " 'Rural-urban_Continuum Code_2003',\n",
       " 'Rural-urban_Continuum Code_2013',\n",
       " 'Urban_Influence_Code_2003',\n",
       " 'Urban_Influence_Code_2013',\n",
       " 'Economic_typology_2015',\n",
       " 'CENSUS_2010_POP',\n",
       " 'ESTIMATES_BASE_2010',\n",
       " 'POP_ESTIMATE_2010',\n",
       " 'POP_ESTIMATE_2011',\n",
       " 'POP_ESTIMATE_2012',\n",
       " 'POP_ESTIMATE_2013',\n",
       " 'POP_ESTIMATE_2014',\n",
       " 'POP_ESTIMATE_2015',\n",
       " 'POP_ESTIMATE_2016',\n",
       " 'POP_ESTIMATE_2017',\n",
       " 'N_POP_CHG_2010',\n",
       " 'N_POP_CHG_2011',\n",
       " 'N_POP_CHG_2012',\n",
       " 'N_POP_CHG_2013',\n",
       " 'N_POP_CHG_2014',\n",
       " 'N_POP_CHG_2015',\n",
       " 'N_POP_CHG_2016',\n",
       " 'N_POP_CHG_2017',\n",
       " 'Births_2010',\n",
       " 'Births_2011',\n",
       " 'Births_2012',\n",
       " 'Births_2013',\n",
       " 'Births_2014',\n",
       " 'Births_2015',\n",
       " 'Births_2016',\n",
       " 'Births_2017',\n",
       " 'Deaths_2010',\n",
       " 'Deaths_2011',\n",
       " 'Deaths_2012',\n",
       " 'Deaths_2013',\n",
       " 'Deaths_2014',\n",
       " 'Deaths_2015',\n",
       " 'Deaths_2016',\n",
       " 'Deaths_2017',\n",
       " 'NATURAL_INC_2010',\n",
       " 'NATURAL_INC_2011',\n",
       " 'NATURAL_INC_2012',\n",
       " 'NATURAL_INC_2013',\n",
       " 'NATURAL_INC_2014',\n",
       " 'NATURAL_INC_2015',\n",
       " 'NATURAL_INC_2016',\n",
       " 'NATURAL_INC_2017',\n",
       " 'INTERNATIONALMIG_2010',\n",
       " 'INTERNATIONALMIG-2011',\n",
       " 'INTERNATIONAL_MIG_2012',\n",
       " 'INTERNATIONAL_MIG_2013',\n",
       " 'INTERNATIONAL_MIG_2014',\n",
       " 'INTERNATIONAL_MIG_2015',\n",
       " 'INTERNATIONAL_MIG_2016',\n",
       " 'INTERNATIONAL_MIG_2017',\n",
       " 'DOMESTIC_MIG_2010',\n",
       " 'DOMESTIC_MIG_2011',\n",
       " 'DOMESTIC_MIG_2012',\n",
       " 'DOMESTIC_MIG_2013',\n",
       " 'DOMESTIC_MIG_2014',\n",
       " 'DOMESTIC_MIG_2015',\n",
       " 'DOMESTIC_MIG_2016',\n",
       " 'DOMESTIC_MIG_2017',\n",
       " 'NET_MIG_2010',\n",
       " 'NET_MIG_2011',\n",
       " 'NET_MIG_2012',\n",
       " 'NET_MIG_2013',\n",
       " 'NET_MIG_2014',\n",
       " 'NET_MIG_2015',\n",
       " 'NET_MIG_2016',\n",
       " 'NET_MIG_2017',\n",
       " 'RESIDUAL_2010',\n",
       " 'RESIDUAL_2011',\n",
       " 'RESIDUAL_2012',\n",
       " 'RESIDUAL_2013',\n",
       " 'RESIDUAL_2014',\n",
       " 'RESIDUAL_2015',\n",
       " 'RESIDUAL_2016',\n",
       " 'RESIDUAL_2017',\n",
       " 'GQ_ESTIMATES_BASE_2010',\n",
       " 'GQ_ESTIMATES_2010',\n",
       " 'GQ_ESTIMATES_2011',\n",
       " 'GQ_ESTIMATES_2012',\n",
       " 'GQ_ESTIMATES_2013',\n",
       " 'GQ_ESTIMATES_2014',\n",
       " 'GQ_ESTIMATES_2015',\n",
       " 'GQ_ESTIMATES_2016',\n",
       " 'GQ_ESTIMATES_2017',\n",
       " 'R_birth_2011',\n",
       " 'R_birth_2012',\n",
       " 'R_birth_2013',\n",
       " 'R_birth_2014',\n",
       " 'R_birth_2015',\n",
       " 'R_birth_2016',\n",
       " 'R_birth_2017',\n",
       " 'R_death_2011',\n",
       " 'R_death_2012',\n",
       " 'R_death_2013',\n",
       " 'R_death_2014',\n",
       " 'R_death_2015',\n",
       " 'R_death_2016',\n",
       " 'R_death_2017',\n",
       " 'R_NATURAL_INC_2011',\n",
       " 'R_NATURAL_INC_2012',\n",
       " 'R_NATURAL_INC_2013',\n",
       " 'R_NATURAL_INC_2014',\n",
       " 'R_NATURAL_INC_2015',\n",
       " 'R_NATURAL_INC_2016',\n",
       " 'R_NATURAL_INC_2017',\n",
       " 'R_INTERNATIONAL_MIG_2011',\n",
       " 'R_INTERNATIONAL_MIG_2012',\n",
       " 'R_INTERNATIONAL_MIG_2013',\n",
       " 'R_INTERNATIONAL_MIG_2014',\n",
       " 'R_INTERNATIONAL_MIG_2015',\n",
       " 'R_INTERNATIONAL_MIG_2016',\n",
       " 'R_INTERNATIONAL_MIG_2017',\n",
       " 'R_DOMESTIC_MIG_2011',\n",
       " 'R_DOMESTIC_MIG_2012',\n",
       " 'R_DOMESTIC_MIG_2013',\n",
       " 'R_DOMESTIC_MIG_2014',\n",
       " 'R_DOMESTIC_MIG_2015',\n",
       " 'R_DOMESTIC_MIG_2016',\n",
       " 'R_DOMESTIC_MIG_2017',\n",
       " 'R_NET_MIG_2011',\n",
       " 'R_NET_MIG_2012',\n",
       " 'R_NET_MIG_2013',\n",
       " 'R_NET_MIG_2014',\n",
       " 'R_NET_MIG_2015',\n",
       " 'R_NET_MIG_2016',\n",
       " 'R_NET_MIG_2017']"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(population.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Population Dataset\n",
    "\n",
    "Notice that we have columns containing extraneous information that will not be useful in building our model. We will not retain the information on births, deaths, international or domestic migration.\n",
    "\n",
    "We must keep the State and County (Area_Name) columns for joining purposes, and choose to retain only the population count and change in population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = population.loc[:,['State','Area_Name',\n",
    "                  'POP_ESTIMATE_2010',\n",
    "                 'POP_ESTIMATE_2011',\n",
    "                 'POP_ESTIMATE_2012',\n",
    "                 'POP_ESTIMATE_2013',\n",
    "                 'POP_ESTIMATE_2014',\n",
    "                 'POP_ESTIMATE_2015',\n",
    "                 'POP_ESTIMATE_2016',\n",
    "                 'POP_ESTIMATE_2017',\n",
    "                 'N_POP_CHG_2010',\n",
    "                 'N_POP_CHG_2011',\n",
    "                 'N_POP_CHG_2012',\n",
    "                 'N_POP_CHG_2013',\n",
    "                 'N_POP_CHG_2014',\n",
    "                 'N_POP_CHG_2015',\n",
    "                 'N_POP_CHG_2016',\n",
    "                 'N_POP_CHG_2017',\n",
    "                 'NET_MIG_2010',\n",
    "                 'NET_MIG_2011',\n",
    "                 'NET_MIG_2012',\n",
    "                 'NET_MIG_2013',\n",
    "                 'NET_MIG_2014',\n",
    "                 'NET_MIG_2015',\n",
    "                 'NET_MIG_2016',\n",
    "                 'NET_MIG_2017']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Area_Name</th>\n",
       "      <th>POP_ESTIMATE_2010</th>\n",
       "      <th>POP_ESTIMATE_2011</th>\n",
       "      <th>POP_ESTIMATE_2012</th>\n",
       "      <th>POP_ESTIMATE_2013</th>\n",
       "      <th>POP_ESTIMATE_2014</th>\n",
       "      <th>POP_ESTIMATE_2015</th>\n",
       "      <th>POP_ESTIMATE_2016</th>\n",
       "      <th>POP_ESTIMATE_2017</th>\n",
       "      <th>...</th>\n",
       "      <th>N_POP_CHG_2016</th>\n",
       "      <th>N_POP_CHG_2017</th>\n",
       "      <th>NET_MIG_2010</th>\n",
       "      <th>NET_MIG_2011</th>\n",
       "      <th>NET_MIG_2012</th>\n",
       "      <th>NET_MIG_2013</th>\n",
       "      <th>NET_MIG_2014</th>\n",
       "      <th>NET_MIG_2015</th>\n",
       "      <th>NET_MIG_2016</th>\n",
       "      <th>NET_MIG_2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>309,338,421</td>\n",
       "      <td>311,644,280</td>\n",
       "      <td>313,993,272</td>\n",
       "      <td>316,234,505</td>\n",
       "      <td>318,622,525</td>\n",
       "      <td>321,039,839</td>\n",
       "      <td>323,405,935</td>\n",
       "      <td>325,719,178</td>\n",
       "      <td>...</td>\n",
       "      <td>2,366,096</td>\n",
       "      <td>2,313,243</td>\n",
       "      <td>191,171</td>\n",
       "      <td>844,816</td>\n",
       "      <td>913,547</td>\n",
       "      <td>908,676</td>\n",
       "      <td>1,007,273</td>\n",
       "      <td>1,124,764</td>\n",
       "      <td>1,132,096</td>\n",
       "      <td>1,111,283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4,785,579</td>\n",
       "      <td>4,798,649</td>\n",
       "      <td>4,813,946</td>\n",
       "      <td>4,827,660</td>\n",
       "      <td>4,840,037</td>\n",
       "      <td>4,850,858</td>\n",
       "      <td>4,860,545</td>\n",
       "      <td>4,874,747</td>\n",
       "      <td>...</td>\n",
       "      <td>9,687</td>\n",
       "      <td>14,202</td>\n",
       "      <td>2,313</td>\n",
       "      <td>2,160</td>\n",
       "      <td>4,515</td>\n",
       "      <td>6,577</td>\n",
       "      <td>3,356</td>\n",
       "      <td>3,288</td>\n",
       "      <td>2,714</td>\n",
       "      <td>8,315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>54,750</td>\n",
       "      <td>55,199</td>\n",
       "      <td>54,927</td>\n",
       "      <td>54,695</td>\n",
       "      <td>54,864</td>\n",
       "      <td>54,838</td>\n",
       "      <td>55,278</td>\n",
       "      <td>55,504</td>\n",
       "      <td>...</td>\n",
       "      <td>440</td>\n",
       "      <td>226</td>\n",
       "      <td>172</td>\n",
       "      <td>325</td>\n",
       "      <td>-336</td>\n",
       "      <td>-222</td>\n",
       "      <td>115</td>\n",
       "      <td>-91</td>\n",
       "      <td>281</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>183,110</td>\n",
       "      <td>186,534</td>\n",
       "      <td>190,048</td>\n",
       "      <td>194,736</td>\n",
       "      <td>199,064</td>\n",
       "      <td>202,863</td>\n",
       "      <td>207,509</td>\n",
       "      <td>212,628</td>\n",
       "      <td>...</td>\n",
       "      <td>4,646</td>\n",
       "      <td>5,119</td>\n",
       "      <td>813</td>\n",
       "      <td>3,010</td>\n",
       "      <td>3,238</td>\n",
       "      <td>4,351</td>\n",
       "      <td>4,013</td>\n",
       "      <td>3,598</td>\n",
       "      <td>4,374</td>\n",
       "      <td>4,866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>27,332</td>\n",
       "      <td>27,351</td>\n",
       "      <td>27,175</td>\n",
       "      <td>26,947</td>\n",
       "      <td>26,749</td>\n",
       "      <td>26,264</td>\n",
       "      <td>25,774</td>\n",
       "      <td>25,270</td>\n",
       "      <td>...</td>\n",
       "      <td>-490</td>\n",
       "      <td>-504</td>\n",
       "      <td>-69</td>\n",
       "      <td>7</td>\n",
       "      <td>-186</td>\n",
       "      <td>-217</td>\n",
       "      <td>-149</td>\n",
       "      <td>-435</td>\n",
       "      <td>-493</td>\n",
       "      <td>-489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State       Area_Name POP_ESTIMATE_2010 POP_ESTIMATE_2011 POP_ESTIMATE_2012  \\\n",
       "0    US   United States       309,338,421       311,644,280       313,993,272   \n",
       "1    AL         Alabama         4,785,579         4,798,649         4,813,946   \n",
       "2    AL  Autauga County            54,750            55,199            54,927   \n",
       "3    AL  Baldwin County           183,110           186,534           190,048   \n",
       "4    AL  Barbour County            27,332            27,351            27,175   \n",
       "\n",
       "  POP_ESTIMATE_2013 POP_ESTIMATE_2014 POP_ESTIMATE_2015 POP_ESTIMATE_2016  \\\n",
       "0       316,234,505       318,622,525       321,039,839       323,405,935   \n",
       "1         4,827,660         4,840,037         4,850,858         4,860,545   \n",
       "2            54,695            54,864            54,838            55,278   \n",
       "3           194,736           199,064           202,863           207,509   \n",
       "4            26,947            26,749            26,264            25,774   \n",
       "\n",
       "  POP_ESTIMATE_2017     ...      N_POP_CHG_2016 N_POP_CHG_2017 NET_MIG_2010  \\\n",
       "0       325,719,178     ...           2,366,096      2,313,243      191,171   \n",
       "1         4,874,747     ...               9,687         14,202        2,313   \n",
       "2            55,504     ...                 440            226          172   \n",
       "3           212,628     ...               4,646          5,119          813   \n",
       "4            25,270     ...                -490           -504          -69   \n",
       "\n",
       "  NET_MIG_2011 NET_MIG_2012 NET_MIG_2013 NET_MIG_2014 NET_MIG_2015  \\\n",
       "0      844,816      913,547      908,676    1,007,273    1,124,764   \n",
       "1        2,160        4,515        6,577        3,356        3,288   \n",
       "2          325         -336         -222          115          -91   \n",
       "3        3,010        3,238        4,351        4,013        3,598   \n",
       "4            7         -186         -217         -149         -435   \n",
       "\n",
       "  NET_MIG_2016 NET_MIG_2017  \n",
       "0    1,132,096    1,111,283  \n",
       "1        2,714        8,315  \n",
       "2          281           73  \n",
       "3        4,374        4,866  \n",
       "4         -493         -489  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unemployment Dataset\n",
    "\n",
    "We will first load the unemployment data set, which was obtained from the Bureau of Labor Statistics, containing data from 2007-2017. We begin by cleaning the dataset and checking for repeated, missing, or unneeded values and information, and then explore and visualize the data to assist with feature engineering for our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPStxt</th>\n",
       "      <th>State</th>\n",
       "      <th>Area_name</th>\n",
       "      <th>Rural_urban_continuum_code_2013</th>\n",
       "      <th>Urban_influence_code_2013</th>\n",
       "      <th>Metro_2013</th>\n",
       "      <th>Civilian_labor_force_2007</th>\n",
       "      <th>Employed_2007</th>\n",
       "      <th>Unemployed_2007</th>\n",
       "      <th>Unemployment_rate_2007</th>\n",
       "      <th>...</th>\n",
       "      <th>Civilian_labor_force_2016</th>\n",
       "      <th>Employed_2016</th>\n",
       "      <th>Unemployed_2016</th>\n",
       "      <th>Unemployment_rate_2016</th>\n",
       "      <th>Civilian_labor_force_2017</th>\n",
       "      <th>Employed_2017</th>\n",
       "      <th>Unemployed_2017</th>\n",
       "      <th>Unemployment_rate_2017</th>\n",
       "      <th>Median_Household_Income_2016</th>\n",
       "      <th>Med_HH_Income_Percent_of_State_Total_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152,191,093</td>\n",
       "      <td>145,156,134</td>\n",
       "      <td>7,034,959</td>\n",
       "      <td>4.6</td>\n",
       "      <td>...</td>\n",
       "      <td>158,921,892</td>\n",
       "      <td>151,183,680</td>\n",
       "      <td>7,738,212</td>\n",
       "      <td>4.9</td>\n",
       "      <td>160,588,515</td>\n",
       "      <td>153,594,100</td>\n",
       "      <td>6,994,415</td>\n",
       "      <td>4.4</td>\n",
       "      <td>$57,617</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,175,612</td>\n",
       "      <td>2,089,127</td>\n",
       "      <td>86,485</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2,173,175</td>\n",
       "      <td>2,045,624</td>\n",
       "      <td>127,551</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2168444</td>\n",
       "      <td>2073106</td>\n",
       "      <td>95338</td>\n",
       "      <td>4.4</td>\n",
       "      <td>$46,309</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County, AL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24,383</td>\n",
       "      <td>23,577</td>\n",
       "      <td>806</td>\n",
       "      <td>3.3</td>\n",
       "      <td>...</td>\n",
       "      <td>25,918</td>\n",
       "      <td>24,593</td>\n",
       "      <td>1,325</td>\n",
       "      <td>5.1</td>\n",
       "      <td>25909</td>\n",
       "      <td>24908</td>\n",
       "      <td>1001</td>\n",
       "      <td>3.9</td>\n",
       "      <td>$54,487</td>\n",
       "      <td>117.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County, AL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82,659</td>\n",
       "      <td>80,099</td>\n",
       "      <td>2,560</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>90,500</td>\n",
       "      <td>85,656</td>\n",
       "      <td>4,844</td>\n",
       "      <td>5.4</td>\n",
       "      <td>91567</td>\n",
       "      <td>87915</td>\n",
       "      <td>3652</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$56,460</td>\n",
       "      <td>121.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County, AL</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10,334</td>\n",
       "      <td>9,684</td>\n",
       "      <td>650</td>\n",
       "      <td>6.3</td>\n",
       "      <td>...</td>\n",
       "      <td>8,402</td>\n",
       "      <td>7,700</td>\n",
       "      <td>702</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8236</td>\n",
       "      <td>7750</td>\n",
       "      <td>486</td>\n",
       "      <td>5.9</td>\n",
       "      <td>$32,884</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPStxt State           Area_name  Rural_urban_continuum_code_2013  \\\n",
       "0        0    US       United States                              NaN   \n",
       "1     1000    AL             Alabama                              NaN   \n",
       "2     1001    AL  Autauga County, AL                              2.0   \n",
       "3     1003    AL  Baldwin County, AL                              3.0   \n",
       "4     1005    AL  Barbour County, AL                              6.0   \n",
       "\n",
       "   Urban_influence_code_2013  Metro_2013  Civilian_labor_force_2007   \\\n",
       "0                        NaN         NaN                 152,191,093   \n",
       "1                        NaN         NaN                  2,175,612    \n",
       "2                        2.0         1.0                     24,383    \n",
       "3                        2.0         1.0                     82,659    \n",
       "4                        6.0         0.0                     10,334    \n",
       "\n",
       "   Employed_2007   Unemployed_2007   Unemployment_rate_2007  \\\n",
       "0     145,156,134         7,034,959                     4.6   \n",
       "1      2,089,127            86,485                      4.0   \n",
       "2         23,577               806                      3.3   \n",
       "3         80,099             2,560                      3.1   \n",
       "4          9,684               650                      6.3   \n",
       "\n",
       "                     ...                     Civilian_labor_force_2016   \\\n",
       "0                    ...                                    158,921,892   \n",
       "1                    ...                                     2,173,175    \n",
       "2                    ...                                        25,918    \n",
       "3                    ...                                        90,500    \n",
       "4                    ...                                         8,402    \n",
       "\n",
       "   Employed_2016   Unemployed_2016   Unemployment_rate_2016  \\\n",
       "0     151,183,680         7,738,212                     4.9   \n",
       "1      2,045,624           127,551                      5.9   \n",
       "2         24,593             1,325                      5.1   \n",
       "3         85,656             4,844                      5.4   \n",
       "4          7,700               702                      8.4   \n",
       "\n",
       "  Civilian_labor_force_2017 Employed_2017 Unemployed_2017  \\\n",
       "0               160,588,515   153,594,100       6,994,415   \n",
       "1                   2168444       2073106           95338   \n",
       "2                     25909         24908            1001   \n",
       "3                     91567         87915            3652   \n",
       "4                      8236          7750             486   \n",
       "\n",
       "   Unemployment_rate_2017 Median_Household_Income_2016  \\\n",
       "0                     4.4                      $57,617   \n",
       "1                     4.4                      $46,309   \n",
       "2                     3.9                      $54,487   \n",
       "3                     4.0                      $56,460   \n",
       "4                     5.9                      $32,884   \n",
       "\n",
       "  Med_HH_Income_Percent_of_State_Total_2016  \n",
       "0                                       NaN  \n",
       "1                                     100.0  \n",
       "2                                     117.7  \n",
       "3                                     121.9  \n",
       "4                                      71.0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment = pd.read_csv(\"data/unemploymentByCounty.csv\")\n",
    "unemployment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FIPStxt', 'State', 'Area_name', 'Rural_urban_continuum_code_2013',\n",
       "       'Urban_influence_code_2013', 'Metro_2013',\n",
       "       ' Civilian_labor_force_2007 ', ' Employed_2007 ', ' Unemployed_2007 ',\n",
       "       'Unemployment_rate_2007', ' Civilian_labor_force_2008 ',\n",
       "       ' Employed_2008 ', ' Unemployed_2008 ', 'Unemployment_rate_2008',\n",
       "       'Civilian_labor_force_2009', 'Employed_2009', 'Unemployed_2009',\n",
       "       'Unemployment_rate_2009', ' Civilian_labor_force_2010 ',\n",
       "       ' Employed_2010 ', ' Unemployed_2010 ', 'Unemployment_rate_2010',\n",
       "       ' Civilian_labor_force_2011 ', ' Employed_2011 ', ' Unemployed_2011 ',\n",
       "       'Unemployment_rate_2011', ' Civilian_labor_force_2012 ',\n",
       "       ' Employed_2012 ', ' Unemployed_2012 ', 'Unemployment_rate_2012',\n",
       "       ' Civilian_labor_force_2013 ', ' Employed_2013 ', ' Unemployed_2013 ',\n",
       "       'Unemployment_rate_2013', ' Civilian_labor_force_2014 ',\n",
       "       ' Employed_2014 ', ' Unemployed_2014 ', 'Unemployment_rate_2014',\n",
       "       ' Civilian_labor_force_2015 ', ' Employed_2015 ', ' Unemployed_2015 ',\n",
       "       'Unemployment_rate_2015', ' Civilian_labor_force_2016 ',\n",
       "       ' Employed_2016 ', ' Unemployed_2016 ', 'Unemployment_rate_2016',\n",
       "       'Civilian_labor_force_2017', 'Employed_2017', 'Unemployed_2017',\n",
       "       'Unemployment_rate_2017', 'Median_Household_Income_2016',\n",
       "       'Med_HH_Income_Percent_of_State_Total_2016'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Unemployment Dataset\n",
    "\n",
    "Notice that we have columns containing extraneous information that will not be useful in building our model. We will not retain the information on births, deaths, international or domestic migration.\n",
    "\n",
    "We must keep the State and County (Area_name) columns for joining purposes, and choose to retain the yearly unemployment rate from 2007-2017 and median household income for 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Area_name</th>\n",
       "      <th>Unemployment_rate_2007</th>\n",
       "      <th>Unemployment_rate_2008</th>\n",
       "      <th>Unemployment_rate_2009</th>\n",
       "      <th>Unemployment_rate_2010</th>\n",
       "      <th>Unemployment_rate_2011</th>\n",
       "      <th>Unemployment_rate_2012</th>\n",
       "      <th>Unemployment_rate_2013</th>\n",
       "      <th>Unemployment_rate_2014</th>\n",
       "      <th>Unemployment_rate_2015</th>\n",
       "      <th>Unemployment_rate_2016</th>\n",
       "      <th>Unemployment_rate_2017</th>\n",
       "      <th>Median_Household_Income_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>$57,617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>$46,309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County, AL</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>$54,487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County, AL</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$56,460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County, AL</td>\n",
       "      <td>6.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>12.3</td>\n",
       "      <td>11.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>$32,884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State           Area_name  Unemployment_rate_2007  Unemployment_rate_2008  \\\n",
       "0    US       United States                     4.6                     5.8   \n",
       "1    AL             Alabama                     4.0                     5.7   \n",
       "2    AL  Autauga County, AL                     3.3                     5.1   \n",
       "3    AL  Baldwin County, AL                     3.1                     4.6   \n",
       "4    AL  Barbour County, AL                     6.3                     8.8   \n",
       "\n",
       "   Unemployment_rate_2009  Unemployment_rate_2010  Unemployment_rate_2011  \\\n",
       "0                     9.3                     9.6                     9.0   \n",
       "1                    11.0                    10.5                     9.6   \n",
       "2                     9.7                     8.9                     8.4   \n",
       "3                     9.8                    10.0                     9.0   \n",
       "4                    14.3                    12.3                    11.5   \n",
       "\n",
       "   Unemployment_rate_2012  Unemployment_rate_2013  Unemployment_rate_2014  \\\n",
       "0                     8.1                     7.4                     6.2   \n",
       "1                     8.0                     7.2                     6.8   \n",
       "2                     6.9                     6.2                     5.8   \n",
       "3                     7.5                     6.6                     6.1   \n",
       "4                    11.5                    10.2                    10.5   \n",
       "\n",
       "   Unemployment_rate_2015  Unemployment_rate_2016  Unemployment_rate_2017  \\\n",
       "0                     5.3                     4.9                     4.4   \n",
       "1                     6.0                     5.9                     4.4   \n",
       "2                     5.0                     5.1                     3.9   \n",
       "3                     6.0                     5.4                     4.0   \n",
       "4                     9.0                     8.4                     5.9   \n",
       "\n",
       "  Median_Household_Income_2016  \n",
       "0                      $57,617  \n",
       "1                      $46,309  \n",
       "2                      $54,487  \n",
       "3                      $56,460  \n",
       "4                      $32,884  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.loc[:, ['State', 'Area_name', 'Unemployment_rate_2007', 'Unemployment_rate_2008', \n",
    "                     'Unemployment_rate_2009', 'Unemployment_rate_2010', 'Unemployment_rate_2011', \n",
    "                     'Unemployment_rate_2012', 'Unemployment_rate_2013', 'Unemployment_rate_2014', \n",
    "                     'Unemployment_rate_2015', 'Unemployment_rate_2016', 'Unemployment_rate_2017', \n",
    "                     'Median_Household_Income_2016']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the county names (Area_name) have a comma and state abbreviation. \n",
    "\n",
    "We will have to clean those out in order to be able to merge properly with the sun roof data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment['County'] = unemployment.loc[:,['Area_name']].apply(lambda row : row['Area_name'].split(',')[0], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPStxt</th>\n",
       "      <th>State</th>\n",
       "      <th>Area_name</th>\n",
       "      <th>Rural_urban_continuum_code_2013</th>\n",
       "      <th>Urban_influence_code_2013</th>\n",
       "      <th>Metro_2013</th>\n",
       "      <th>Civilian_labor_force_2007</th>\n",
       "      <th>Employed_2007</th>\n",
       "      <th>Unemployed_2007</th>\n",
       "      <th>Unemployment_rate_2007</th>\n",
       "      <th>...</th>\n",
       "      <th>Employed_2016</th>\n",
       "      <th>Unemployed_2016</th>\n",
       "      <th>Unemployment_rate_2016</th>\n",
       "      <th>Civilian_labor_force_2017</th>\n",
       "      <th>Employed_2017</th>\n",
       "      <th>Unemployed_2017</th>\n",
       "      <th>Unemployment_rate_2017</th>\n",
       "      <th>Median_Household_Income_2016</th>\n",
       "      <th>Med_HH_Income_Percent_of_State_Total_2016</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152,191,093</td>\n",
       "      <td>145,156,134</td>\n",
       "      <td>7,034,959</td>\n",
       "      <td>4.6</td>\n",
       "      <td>...</td>\n",
       "      <td>151,183,680</td>\n",
       "      <td>7,738,212</td>\n",
       "      <td>4.9</td>\n",
       "      <td>160,588,515</td>\n",
       "      <td>153,594,100</td>\n",
       "      <td>6,994,415</td>\n",
       "      <td>4.4</td>\n",
       "      <td>$57,617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,175,612</td>\n",
       "      <td>2,089,127</td>\n",
       "      <td>86,485</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2,045,624</td>\n",
       "      <td>127,551</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2168444</td>\n",
       "      <td>2073106</td>\n",
       "      <td>95338</td>\n",
       "      <td>4.4</td>\n",
       "      <td>$46,309</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga County, AL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24,383</td>\n",
       "      <td>23,577</td>\n",
       "      <td>806</td>\n",
       "      <td>3.3</td>\n",
       "      <td>...</td>\n",
       "      <td>24,593</td>\n",
       "      <td>1,325</td>\n",
       "      <td>5.1</td>\n",
       "      <td>25909</td>\n",
       "      <td>24908</td>\n",
       "      <td>1001</td>\n",
       "      <td>3.9</td>\n",
       "      <td>$54,487</td>\n",
       "      <td>117.7</td>\n",
       "      <td>Autauga County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin County, AL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82,659</td>\n",
       "      <td>80,099</td>\n",
       "      <td>2,560</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>85,656</td>\n",
       "      <td>4,844</td>\n",
       "      <td>5.4</td>\n",
       "      <td>91567</td>\n",
       "      <td>87915</td>\n",
       "      <td>3652</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$56,460</td>\n",
       "      <td>121.9</td>\n",
       "      <td>Baldwin County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour County, AL</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10,334</td>\n",
       "      <td>9,684</td>\n",
       "      <td>650</td>\n",
       "      <td>6.3</td>\n",
       "      <td>...</td>\n",
       "      <td>7,700</td>\n",
       "      <td>702</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8236</td>\n",
       "      <td>7750</td>\n",
       "      <td>486</td>\n",
       "      <td>5.9</td>\n",
       "      <td>$32,884</td>\n",
       "      <td>71.0</td>\n",
       "      <td>Barbour County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIPStxt State           Area_name  Rural_urban_continuum_code_2013  \\\n",
       "0        0    US       United States                              NaN   \n",
       "1     1000    AL             Alabama                              NaN   \n",
       "2     1001    AL  Autauga County, AL                              2.0   \n",
       "3     1003    AL  Baldwin County, AL                              3.0   \n",
       "4     1005    AL  Barbour County, AL                              6.0   \n",
       "\n",
       "   Urban_influence_code_2013  Metro_2013  Civilian_labor_force_2007   \\\n",
       "0                        NaN         NaN                 152,191,093   \n",
       "1                        NaN         NaN                  2,175,612    \n",
       "2                        2.0         1.0                     24,383    \n",
       "3                        2.0         1.0                     82,659    \n",
       "4                        6.0         0.0                     10,334    \n",
       "\n",
       "   Employed_2007   Unemployed_2007   Unemployment_rate_2007       ...        \\\n",
       "0     145,156,134         7,034,959                     4.6       ...         \n",
       "1      2,089,127            86,485                      4.0       ...         \n",
       "2         23,577               806                      3.3       ...         \n",
       "3         80,099             2,560                      3.1       ...         \n",
       "4          9,684               650                      6.3       ...         \n",
       "\n",
       "   Employed_2016   Unemployed_2016  Unemployment_rate_2016  \\\n",
       "0     151,183,680         7,738,212                    4.9   \n",
       "1      2,045,624           127,551                     5.9   \n",
       "2         24,593             1,325                     5.1   \n",
       "3         85,656             4,844                     5.4   \n",
       "4          7,700               702                     8.4   \n",
       "\n",
       "   Civilian_labor_force_2017 Employed_2017 Unemployed_2017  \\\n",
       "0                160,588,515   153,594,100       6,994,415   \n",
       "1                    2168444       2073106           95338   \n",
       "2                      25909         24908            1001   \n",
       "3                      91567         87915            3652   \n",
       "4                       8236          7750             486   \n",
       "\n",
       "  Unemployment_rate_2017  Median_Household_Income_2016  \\\n",
       "0                    4.4                       $57,617   \n",
       "1                    4.4                       $46,309   \n",
       "2                    3.9                       $54,487   \n",
       "3                    4.0                       $56,460   \n",
       "4                    5.9                       $32,884   \n",
       "\n",
       "  Med_HH_Income_Percent_of_State_Total_2016          County  \n",
       "0                                       NaN   United States  \n",
       "1                                     100.0         Alabama  \n",
       "2                                     117.7  Autauga County  \n",
       "3                                     121.9  Baldwin County  \n",
       "4                                      71.0  Barbour County  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also notice that the Median_Household_Income_2016 column has a dollar sign and commas, it is a dtype of object. \n",
    "\n",
    "We will convert into an int data type for ease of data processing later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_dollar_to_int(row):\n",
    "    try:\n",
    "        all_but_dollar_sign = str(row['Median_Household_Income_2016'])[1:]\n",
    "        return int(all_but_dollar_sign.replace(',', ''))\n",
    "    except:\n",
    "        return ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment['Integer_med_household_income_2016'] = unemployment.apply(lambda row : convert_dollar_to_int(row) , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['Median_Household_Income_2016'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3275"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unemployment['Median_Household_Income_2016'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "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>FIPStxt</th>\n",
       "      <th>State</th>\n",
       "      <th>Area_name</th>\n",
       "      <th>Rural_urban_continuum_code_2013</th>\n",
       "      <th>Urban_influence_code_2013</th>\n",
       "      <th>Metro_2013</th>\n",
       "      <th>Civilian_labor_force_2007</th>\n",
       "      <th>Employed_2007</th>\n",
       "      <th>Unemployed_2007</th>\n",
       "      <th>Unemployment_rate_2007</th>\n",
       "      <th>...</th>\n",
       "      <th>Unemployed_2016</th>\n",
       "      <th>Unemployment_rate_2016</th>\n",
       "      <th>Civilian_labor_force_2017</th>\n",
       "      <th>Employed_2017</th>\n",
       "      <th>Unemployed_2017</th>\n",
       "      <th>Unemployment_rate_2017</th>\n",
       "      <th>Median_Household_Income_2016</th>\n",
       "      <th>Med_HH_Income_Percent_of_State_Total_2016</th>\n",
       "      <th>County</th>\n",
       "      <th>int_med_household_income_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3270</th>\n",
       "      <td>72145</td>\n",
       "      <td>PR</td>\n",
       "      <td>Vega Baja Municipio, PR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21,115</td>\n",
       "      <td>18,445</td>\n",
       "      <td>2,670</td>\n",
       "      <td>12.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1,909</td>\n",
       "      <td>13.9</td>\n",
       "      <td>13467</td>\n",
       "      <td>11795</td>\n",
       "      <td>1672</td>\n",
       "      <td>12.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vega Baja Municipio</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3271</th>\n",
       "      <td>72147</td>\n",
       "      <td>PR</td>\n",
       "      <td>Vieques Municipio, PR</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3,317</td>\n",
       "      <td>2,955</td>\n",
       "      <td>362</td>\n",
       "      <td>10.9</td>\n",
       "      <td>...</td>\n",
       "      <td>346</td>\n",
       "      <td>10.6</td>\n",
       "      <td>3228</td>\n",
       "      <td>2787</td>\n",
       "      <td>441</td>\n",
       "      <td>13.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vieques Municipio</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3272</th>\n",
       "      <td>72149</td>\n",
       "      <td>PR</td>\n",
       "      <td>Villalba Municipio, PR</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9,479</td>\n",
       "      <td>8,223</td>\n",
       "      <td>1,256</td>\n",
       "      <td>13.3</td>\n",
       "      <td>...</td>\n",
       "      <td>1,584</td>\n",
       "      <td>20.2</td>\n",
       "      <td>7631</td>\n",
       "      <td>6138</td>\n",
       "      <td>1493</td>\n",
       "      <td>19.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Villalba Municipio</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3273</th>\n",
       "      <td>72151</td>\n",
       "      <td>PR</td>\n",
       "      <td>Yabucoa Municipio, PR</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11,425</td>\n",
       "      <td>9,348</td>\n",
       "      <td>2,077</td>\n",
       "      <td>18.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1,540</td>\n",
       "      <td>16.9</td>\n",
       "      <td>9010</td>\n",
       "      <td>7533</td>\n",
       "      <td>1477</td>\n",
       "      <td>16.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yabucoa Municipio</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>72153</td>\n",
       "      <td>PR</td>\n",
       "      <td>Yauco Municipio, PR</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16,635</td>\n",
       "      <td>14,451</td>\n",
       "      <td>2,184</td>\n",
       "      <td>13.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2,021</td>\n",
       "      <td>18.8</td>\n",
       "      <td>10276</td>\n",
       "      <td>8498</td>\n",
       "      <td>1778</td>\n",
       "      <td>17.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yauco Municipio</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      FIPStxt State                Area_name  Rural_urban_continuum_code_2013  \\\n",
       "3270    72145    PR  Vega Baja Municipio, PR                              1.0   \n",
       "3271    72147    PR    Vieques Municipio, PR                              7.0   \n",
       "3272    72149    PR   Villalba Municipio, PR                              2.0   \n",
       "3273    72151    PR    Yabucoa Municipio, PR                              1.0   \n",
       "3274    72153    PR      Yauco Municipio, PR                              2.0   \n",
       "\n",
       "      Urban_influence_code_2013  Metro_2013  Civilian_labor_force_2007   \\\n",
       "3270                        1.0         1.0                     21,115    \n",
       "3271                       12.0         0.0                      3,317    \n",
       "3272                        2.0         1.0                      9,479    \n",
       "3273                        1.0         1.0                     11,425    \n",
       "3274                        2.0         1.0                     16,635    \n",
       "\n",
       "      Employed_2007   Unemployed_2007   Unemployment_rate_2007  \\\n",
       "3270         18,445             2,670                     12.6   \n",
       "3271          2,955               362                     10.9   \n",
       "3272          8,223             1,256                     13.3   \n",
       "3273          9,348             2,077                     18.2   \n",
       "3274         14,451             2,184                     13.1   \n",
       "\n",
       "                  ...               Unemployed_2016  Unemployment_rate_2016  \\\n",
       "3270              ...                         1,909                    13.9   \n",
       "3271              ...                           346                    10.6   \n",
       "3272              ...                         1,584                    20.2   \n",
       "3273              ...                         1,540                    16.9   \n",
       "3274              ...                         2,021                    18.8   \n",
       "\n",
       "     Civilian_labor_force_2017  Employed_2017 Unemployed_2017  \\\n",
       "3270                     13467          11795            1672   \n",
       "3271                      3228           2787             441   \n",
       "3272                      7631           6138            1493   \n",
       "3273                      9010           7533            1477   \n",
       "3274                     10276           8498            1778   \n",
       "\n",
       "     Unemployment_rate_2017 Median_Household_Income_2016  \\\n",
       "3270                   12.4                          NaN   \n",
       "3271                   13.7                          NaN   \n",
       "3272                   19.6                          NaN   \n",
       "3273                   16.4                          NaN   \n",
       "3274                   17.3                          NaN   \n",
       "\n",
       "      Med_HH_Income_Percent_of_State_Total_2016               County  \\\n",
       "3270                                        NaN  Vega Baja Municipio   \n",
       "3271                                        NaN    Vieques Municipio   \n",
       "3272                                        NaN   Villalba Municipio   \n",
       "3273                                        NaN    Yabucoa Municipio   \n",
       "3274                                        NaN      Yauco Municipio   \n",
       "\n",
       "     int_med_household_income_2016  \n",
       "3270                                \n",
       "3271                                \n",
       "3272                                \n",
       "3273                                \n",
       "3274                                \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining the Datasets\n",
    "\n",
    "We will join the sunroof datasets with all the external datasets that we have now cleaned.\n",
    "We will merge on (state, county) pairs, as we have found that the county names are not unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_and_edu = pd.merge(sunroof, education,  how='left', \\\n",
    "                       left_on=['State','region_name'], right_on = ['State','Area name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_edu_pov = pd.merge(sun_and_edu, poverty, how='left', \\\n",
    "                       left_on=['State','region_name'], right_on=['State', 'Area_Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_eda_pov_pop = pd.merge(sun_edu_pov, population, how='left', \\\n",
    "                       left_on=['State','region_name'], right_on=['State', 'Area_Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_eda_pov_pop_unemp = pd.merge(sun_eda_pov_pop, unemployment, how='left', \\\n",
    "                       left_on=['State','region_name'], right_on=['State', 'County'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(966, 26)"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sunroof.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(966, 116)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sun_eda_pov_pop_unemp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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>region_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>count_qualified</th>\n",
       "      <th>yearly_sunlight_kwh_kw_threshold_avg</th>\n",
       "      <th>percent_covered</th>\n",
       "      <th>percent_qualified</th>\n",
       "      <th>number_of_panels_n</th>\n",
       "      <th>number_of_panels_s</th>\n",
       "      <th>number_of_panels_e</th>\n",
       "      <th>number_of_panels_w</th>\n",
       "      <th>...</th>\n",
       "      <th>Unemployed_2016</th>\n",
       "      <th>Unemployment_rate_2016</th>\n",
       "      <th>Civilian_labor_force_2017</th>\n",
       "      <th>Employed_2017</th>\n",
       "      <th>Unemployed_2017</th>\n",
       "      <th>Unemployment_rate_2017</th>\n",
       "      <th>Median_Household_Income_2016</th>\n",
       "      <th>Med_HH_Income_Percent_of_State_Total_2016</th>\n",
       "      <th>County</th>\n",
       "      <th>int_med_household_income_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acadia Parish</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>362</td>\n",
       "      <td>1087.15</td>\n",
       "      <td>1.295852</td>\n",
       "      <td>81.715576</td>\n",
       "      <td>3474</td>\n",
       "      <td>6970</td>\n",
       "      <td>4533</td>\n",
       "      <td>5398</td>\n",
       "      <td>...</td>\n",
       "      <td>1,809</td>\n",
       "      <td>7.4</td>\n",
       "      <td>24151</td>\n",
       "      <td>22724</td>\n",
       "      <td>1427</td>\n",
       "      <td>5.9</td>\n",
       "      <td>$40,218</td>\n",
       "      <td>88.6</td>\n",
       "      <td>Acadia Parish</td>\n",
       "      <td>40218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ada County</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>133939</td>\n",
       "      <td>1156.85</td>\n",
       "      <td>86.156364</td>\n",
       "      <td>90.224992</td>\n",
       "      <td>183489</td>\n",
       "      <td>2932039</td>\n",
       "      <td>1517496</td>\n",
       "      <td>2190037</td>\n",
       "      <td>...</td>\n",
       "      <td>7,392</td>\n",
       "      <td>3.3</td>\n",
       "      <td>232954</td>\n",
       "      <td>226485</td>\n",
       "      <td>6469</td>\n",
       "      <td>2.8</td>\n",
       "      <td>$61,571</td>\n",
       "      <td>119.2</td>\n",
       "      <td>Ada County</td>\n",
       "      <td>61571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adams County</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>117401</td>\n",
       "      <td>1246.10</td>\n",
       "      <td>92.906285</td>\n",
       "      <td>87.461913</td>\n",
       "      <td>58739</td>\n",
       "      <td>1859313</td>\n",
       "      <td>1050680</td>\n",
       "      <td>1205016</td>\n",
       "      <td>...</td>\n",
       "      <td>9,056</td>\n",
       "      <td>3.6</td>\n",
       "      <td>261302</td>\n",
       "      <td>253318</td>\n",
       "      <td>7984</td>\n",
       "      <td>3.1</td>\n",
       "      <td>$65,442</td>\n",
       "      <td>99.6</td>\n",
       "      <td>Adams County</td>\n",
       "      <td>65442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams County</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>4500</td>\n",
       "      <td>997.05</td>\n",
       "      <td>17.958957</td>\n",
       "      <td>87.006961</td>\n",
       "      <td>16651</td>\n",
       "      <td>70798</td>\n",
       "      <td>78169</td>\n",
       "      <td>68634</td>\n",
       "      <td>...</td>\n",
       "      <td>2,199</td>\n",
       "      <td>4.0</td>\n",
       "      <td>55457</td>\n",
       "      <td>53419</td>\n",
       "      <td>2038</td>\n",
       "      <td>3.7</td>\n",
       "      <td>$59,645</td>\n",
       "      <td>104.8</td>\n",
       "      <td>Adams County</td>\n",
       "      <td>59645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aiken County</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>49051</td>\n",
       "      <td>1093.95</td>\n",
       "      <td>68.372367</td>\n",
       "      <td>86.296622</td>\n",
       "      <td>297265</td>\n",
       "      <td>984740</td>\n",
       "      <td>645255</td>\n",
       "      <td>693428</td>\n",
       "      <td>...</td>\n",
       "      <td>3,691</td>\n",
       "      <td>5.0</td>\n",
       "      <td>74442</td>\n",
       "      <td>71386</td>\n",
       "      <td>3056</td>\n",
       "      <td>4.1</td>\n",
       "      <td>$47,394</td>\n",
       "      <td>95.6</td>\n",
       "      <td>Aiken County</td>\n",
       "      <td>47394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alachua County</td>\n",
       "      <td>Florida</td>\n",
       "      <td>42834</td>\n",
       "      <td>1108.40</td>\n",
       "      <td>77.907543</td>\n",
       "      <td>68.675046</td>\n",
       "      <td>349719</td>\n",
       "      <td>973293</td>\n",
       "      <td>610023</td>\n",
       "      <td>734622</td>\n",
       "      <td>...</td>\n",
       "      <td>5,658</td>\n",
       "      <td>4.3</td>\n",
       "      <td>136013</td>\n",
       "      <td>130980</td>\n",
       "      <td>5033</td>\n",
       "      <td>3.7</td>\n",
       "      <td>$45,294</td>\n",
       "      <td>89.1</td>\n",
       "      <td>Alachua County</td>\n",
       "      <td>45294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alamance County</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>26057</td>\n",
       "      <td>1081.20</td>\n",
       "      <td>41.934685</td>\n",
       "      <td>80.942470</td>\n",
       "      <td>83130</td>\n",
       "      <td>437663</td>\n",
       "      <td>306755</td>\n",
       "      <td>345764</td>\n",
       "      <td>...</td>\n",
       "      <td>3,738</td>\n",
       "      <td>4.7</td>\n",
       "      <td>79767</td>\n",
       "      <td>76334</td>\n",
       "      <td>3433</td>\n",
       "      <td>4.3</td>\n",
       "      <td>$46,060</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Alamance County</td>\n",
       "      <td>46060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alameda County</td>\n",
       "      <td>California</td>\n",
       "      <td>372427</td>\n",
       "      <td>1229.10</td>\n",
       "      <td>96.583429</td>\n",
       "      <td>94.318029</td>\n",
       "      <td>2488057</td>\n",
       "      <td>5840749</td>\n",
       "      <td>4452530</td>\n",
       "      <td>5363907</td>\n",
       "      <td>...</td>\n",
       "      <td>35,809</td>\n",
       "      <td>4.3</td>\n",
       "      <td>848335</td>\n",
       "      <td>817433</td>\n",
       "      <td>30902</td>\n",
       "      <td>3.6</td>\n",
       "      <td>$89,472</td>\n",
       "      <td>132.1</td>\n",
       "      <td>Alameda County</td>\n",
       "      <td>89472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albany County</td>\n",
       "      <td>New York</td>\n",
       "      <td>73594</td>\n",
       "      <td>961.35</td>\n",
       "      <td>90.320851</td>\n",
       "      <td>79.414272</td>\n",
       "      <td>112549</td>\n",
       "      <td>1127695</td>\n",
       "      <td>777304</td>\n",
       "      <td>629049</td>\n",
       "      <td>...</td>\n",
       "      <td>6,465</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159950</td>\n",
       "      <td>153134</td>\n",
       "      <td>6816</td>\n",
       "      <td>4.3</td>\n",
       "      <td>$61,634</td>\n",
       "      <td>98.3</td>\n",
       "      <td>Albany County</td>\n",
       "      <td>61634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Albemarle County</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>10992</td>\n",
       "      <td>1048.90</td>\n",
       "      <td>39.814769</td>\n",
       "      <td>68.002970</td>\n",
       "      <td>29802</td>\n",
       "      <td>185863</td>\n",
       "      <td>148753</td>\n",
       "      <td>130742</td>\n",
       "      <td>...</td>\n",
       "      <td>1,948</td>\n",
       "      <td>3.6</td>\n",
       "      <td>55748</td>\n",
       "      <td>53905</td>\n",
       "      <td>1843</td>\n",
       "      <td>3.3</td>\n",
       "      <td>$73,132</td>\n",
       "      <td>107.3</td>\n",
       "      <td>Albemarle County</td>\n",
       "      <td>73132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Alexander County</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>154</td>\n",
       "      <td>1049.75</td>\n",
       "      <td>3.970588</td>\n",
       "      <td>81.481481</td>\n",
       "      <td>1021</td>\n",
       "      <td>1922</td>\n",
       "      <td>1474</td>\n",
       "      <td>1647</td>\n",
       "      <td>...</td>\n",
       "      <td>203</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2161</td>\n",
       "      <td>1989</td>\n",
       "      <td>172</td>\n",
       "      <td>8.0</td>\n",
       "      <td>$33,344</td>\n",
       "      <td>54.7</td>\n",
       "      <td>Alexander County</td>\n",
       "      <td>33344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alexander County</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>822</td>\n",
       "      <td>1082.05</td>\n",
       "      <td>4.590467</td>\n",
       "      <td>82.862903</td>\n",
       "      <td>3905</td>\n",
       "      <td>10863</td>\n",
       "      <td>11354</td>\n",
       "      <td>13463</td>\n",
       "      <td>...</td>\n",
       "      <td>757</td>\n",
       "      <td>4.2</td>\n",
       "      <td>18159</td>\n",
       "      <td>17492</td>\n",
       "      <td>667</td>\n",
       "      <td>3.7</td>\n",
       "      <td>$46,385</td>\n",
       "      <td>91.7</td>\n",
       "      <td>Alexander County</td>\n",
       "      <td>46385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Allegan County</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>7735</td>\n",
       "      <td>932.45</td>\n",
       "      <td>15.883461</td>\n",
       "      <td>69.515593</td>\n",
       "      <td>64277</td>\n",
       "      <td>161276</td>\n",
       "      <td>123029</td>\n",
       "      <td>158287</td>\n",
       "      <td>...</td>\n",
       "      <td>2,388</td>\n",
       "      <td>3.9</td>\n",
       "      <td>61320</td>\n",
       "      <td>58958</td>\n",
       "      <td>2362</td>\n",
       "      <td>3.9</td>\n",
       "      <td>$58,311</td>\n",
       "      <td>111.2</td>\n",
       "      <td>Allegan County</td>\n",
       "      <td>58311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Allegany County</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>12911</td>\n",
       "      <td>971.55</td>\n",
       "      <td>63.213267</td>\n",
       "      <td>82.016262</td>\n",
       "      <td>42602</td>\n",
       "      <td>143353</td>\n",
       "      <td>129574</td>\n",
       "      <td>115904</td>\n",
       "      <td>...</td>\n",
       "      <td>2,030</td>\n",
       "      <td>6.2</td>\n",
       "      <td>32514</td>\n",
       "      <td>30640</td>\n",
       "      <td>1874</td>\n",
       "      <td>5.8</td>\n",
       "      <td>$44,708</td>\n",
       "      <td>56.7</td>\n",
       "      <td>Allegany County</td>\n",
       "      <td>44708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Allegheny County</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>338425</td>\n",
       "      <td>943.50</td>\n",
       "      <td>83.918545</td>\n",
       "      <td>80.464730</td>\n",
       "      <td>1309325</td>\n",
       "      <td>3937002</td>\n",
       "      <td>2907039</td>\n",
       "      <td>3201278</td>\n",
       "      <td>...</td>\n",
       "      <td>33,548</td>\n",
       "      <td>5.2</td>\n",
       "      <td>645906</td>\n",
       "      <td>615193</td>\n",
       "      <td>30713</td>\n",
       "      <td>4.8</td>\n",
       "      <td>$56,099</td>\n",
       "      <td>98.6</td>\n",
       "      <td>Allegheny County</td>\n",
       "      <td>56099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Allen County</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>112111</td>\n",
       "      <td>957.10</td>\n",
       "      <td>83.983561</td>\n",
       "      <td>83.501661</td>\n",
       "      <td>614639</td>\n",
       "      <td>2130192</td>\n",
       "      <td>1462905</td>\n",
       "      <td>1790269</td>\n",
       "      <td>...</td>\n",
       "      <td>7,700</td>\n",
       "      <td>4.3</td>\n",
       "      <td>180751</td>\n",
       "      <td>174761</td>\n",
       "      <td>5990</td>\n",
       "      <td>3.3</td>\n",
       "      <td>$51,329</td>\n",
       "      <td>98.2</td>\n",
       "      <td>Allen County</td>\n",
       "      <td>51329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Allen County</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>22217</td>\n",
       "      <td>957.95</td>\n",
       "      <td>60.239229</td>\n",
       "      <td>85.660857</td>\n",
       "      <td>161185</td>\n",
       "      <td>375227</td>\n",
       "      <td>226296</td>\n",
       "      <td>310594</td>\n",
       "      <td>...</td>\n",
       "      <td>2,418</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48897</td>\n",
       "      <td>46453</td>\n",
       "      <td>2444</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$47,924</td>\n",
       "      <td>91.5</td>\n",
       "      <td>Allen County</td>\n",
       "      <td>47924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Amherst County</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>2099</td>\n",
       "      <td>1061.65</td>\n",
       "      <td>17.345598</td>\n",
       "      <td>75.721501</td>\n",
       "      <td>8493</td>\n",
       "      <td>35714</td>\n",
       "      <td>18130</td>\n",
       "      <td>19364</td>\n",
       "      <td>...</td>\n",
       "      <td>661</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15085</td>\n",
       "      <td>14455</td>\n",
       "      <td>630</td>\n",
       "      <td>4.2</td>\n",
       "      <td>$46,497</td>\n",
       "      <td>68.3</td>\n",
       "      <td>Amherst County</td>\n",
       "      <td>46497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>48127</td>\n",
       "      <td>742.90</td>\n",
       "      <td>78.044523</td>\n",
       "      <td>75.802489</td>\n",
       "      <td>12320</td>\n",
       "      <td>843399</td>\n",
       "      <td>426902</td>\n",
       "      <td>451753</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <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>19</th>\n",
       "      <td>Anderson County</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>37256</td>\n",
       "      <td>1107.55</td>\n",
       "      <td>45.385773</td>\n",
       "      <td>80.230855</td>\n",
       "      <td>182079</td>\n",
       "      <td>669841</td>\n",
       "      <td>482883</td>\n",
       "      <td>539050</td>\n",
       "      <td>...</td>\n",
       "      <td>4,065</td>\n",
       "      <td>4.6</td>\n",
       "      <td>89559</td>\n",
       "      <td>86045</td>\n",
       "      <td>3514</td>\n",
       "      <td>3.9</td>\n",
       "      <td>$45,821</td>\n",
       "      <td>92.4</td>\n",
       "      <td>Anderson County</td>\n",
       "      <td>45821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         region_name      state_name  count_qualified  \\\n",
       "0      Acadia Parish       Louisiana              362   \n",
       "1         Ada County           Idaho           133939   \n",
       "2       Adams County        Colorado           117401   \n",
       "3       Adams County    Pennsylvania             4500   \n",
       "4       Aiken County  South Carolina            49051   \n",
       "5     Alachua County         Florida            42834   \n",
       "6    Alamance County  North Carolina            26057   \n",
       "7     Alameda County      California           372427   \n",
       "8      Albany County        New York            73594   \n",
       "9   Albemarle County        Virginia            10992   \n",
       "10  Alexander County        Illinois              154   \n",
       "11  Alexander County  North Carolina              822   \n",
       "12    Allegan County        Michigan             7735   \n",
       "13   Allegany County        Maryland            12911   \n",
       "14  Allegheny County    Pennsylvania           338425   \n",
       "15      Allen County         Indiana           112111   \n",
       "16      Allen County            Ohio            22217   \n",
       "17    Amherst County        Virginia             2099   \n",
       "18         Anchorage          Alaska            48127   \n",
       "19   Anderson County  South Carolina            37256   \n",
       "\n",
       "    yearly_sunlight_kwh_kw_threshold_avg  percent_covered  percent_qualified  \\\n",
       "0                                1087.15         1.295852          81.715576   \n",
       "1                                1156.85        86.156364          90.224992   \n",
       "2                                1246.10        92.906285          87.461913   \n",
       "3                                 997.05        17.958957          87.006961   \n",
       "4                                1093.95        68.372367          86.296622   \n",
       "5                                1108.40        77.907543          68.675046   \n",
       "6                                1081.20        41.934685          80.942470   \n",
       "7                                1229.10        96.583429          94.318029   \n",
       "8                                 961.35        90.320851          79.414272   \n",
       "9                                1048.90        39.814769          68.002970   \n",
       "10                               1049.75         3.970588          81.481481   \n",
       "11                               1082.05         4.590467          82.862903   \n",
       "12                                932.45        15.883461          69.515593   \n",
       "13                                971.55        63.213267          82.016262   \n",
       "14                                943.50        83.918545          80.464730   \n",
       "15                                957.10        83.983561          83.501661   \n",
       "16                                957.95        60.239229          85.660857   \n",
       "17                               1061.65        17.345598          75.721501   \n",
       "18                                742.90        78.044523          75.802489   \n",
       "19                               1107.55        45.385773          80.230855   \n",
       "\n",
       "    number_of_panels_n  number_of_panels_s  number_of_panels_e  \\\n",
       "0                 3474                6970                4533   \n",
       "1               183489             2932039             1517496   \n",
       "2                58739             1859313             1050680   \n",
       "3                16651               70798               78169   \n",
       "4               297265              984740              645255   \n",
       "5               349719              973293              610023   \n",
       "6                83130              437663              306755   \n",
       "7              2488057             5840749             4452530   \n",
       "8               112549             1127695              777304   \n",
       "9                29802              185863              148753   \n",
       "10                1021                1922                1474   \n",
       "11                3905               10863               11354   \n",
       "12               64277              161276              123029   \n",
       "13               42602              143353              129574   \n",
       "14             1309325             3937002             2907039   \n",
       "15              614639             2130192             1462905   \n",
       "16              161185              375227              226296   \n",
       "17                8493               35714               18130   \n",
       "18               12320              843399              426902   \n",
       "19              182079              669841              482883   \n",
       "\n",
       "    number_of_panels_w              ...                 Unemployed_2016   \\\n",
       "0                 5398              ...                           1,809    \n",
       "1              2190037              ...                           7,392    \n",
       "2              1205016              ...                           9,056    \n",
       "3                68634              ...                           2,199    \n",
       "4               693428              ...                           3,691    \n",
       "5               734622              ...                           5,658    \n",
       "6               345764              ...                           3,738    \n",
       "7              5363907              ...                          35,809    \n",
       "8               629049              ...                           6,465    \n",
       "9               130742              ...                           1,948    \n",
       "10                1647              ...                             203    \n",
       "11               13463              ...                             757    \n",
       "12              158287              ...                           2,388    \n",
       "13              115904              ...                           2,030    \n",
       "14             3201278              ...                          33,548    \n",
       "15             1790269              ...                           7,700    \n",
       "16              310594              ...                           2,418    \n",
       "17               19364              ...                             661    \n",
       "18              451753              ...                              NaN   \n",
       "19              539050              ...                           4,065    \n",
       "\n",
       "    Unemployment_rate_2016  Civilian_labor_force_2017  Employed_2017  \\\n",
       "0                      7.4                      24151          22724   \n",
       "1                      3.3                     232954         226485   \n",
       "2                      3.6                     261302         253318   \n",
       "3                      4.0                      55457          53419   \n",
       "4                      5.0                      74442          71386   \n",
       "5                      4.3                     136013         130980   \n",
       "6                      4.7                      79767          76334   \n",
       "7                      4.3                     848335         817433   \n",
       "8                      4.1                     159950         153134   \n",
       "9                      3.6                      55748          53905   \n",
       "10                     9.2                       2161           1989   \n",
       "11                     4.2                      18159          17492   \n",
       "12                     3.9                      61320          58958   \n",
       "13                     6.2                      32514          30640   \n",
       "14                     5.2                     645906         615193   \n",
       "15                     4.3                     180751         174761   \n",
       "16                     5.0                      48897          46453   \n",
       "17                     4.4                      15085          14455   \n",
       "18                     NaN                        NaN            NaN   \n",
       "19                     4.6                      89559          86045   \n",
       "\n",
       "    Unemployed_2017  Unemployment_rate_2017  Median_Household_Income_2016  \\\n",
       "0              1427                     5.9                       $40,218   \n",
       "1              6469                     2.8                       $61,571   \n",
       "2              7984                     3.1                       $65,442   \n",
       "3              2038                     3.7                       $59,645   \n",
       "4              3056                     4.1                       $47,394   \n",
       "5              5033                     3.7                       $45,294   \n",
       "6              3433                     4.3                       $46,060   \n",
       "7             30902                     3.6                       $89,472   \n",
       "8              6816                     4.3                       $61,634   \n",
       "9              1843                     3.3                       $73,132   \n",
       "10              172                     8.0                       $33,344   \n",
       "11              667                     3.7                       $46,385   \n",
       "12             2362                     3.9                       $58,311   \n",
       "13             1874                     5.8                       $44,708   \n",
       "14            30713                     4.8                       $56,099   \n",
       "15             5990                     3.3                       $51,329   \n",
       "16             2444                     5.0                       $47,924   \n",
       "17              630                     4.2                       $46,497   \n",
       "18              NaN                     NaN                           NaN   \n",
       "19             3514                     3.9                       $45,821   \n",
       "\n",
       "    Med_HH_Income_Percent_of_State_Total_2016            County  \\\n",
       "0                                        88.6     Acadia Parish   \n",
       "1                                       119.2        Ada County   \n",
       "2                                        99.6      Adams County   \n",
       "3                                       104.8      Adams County   \n",
       "4                                        95.6      Aiken County   \n",
       "5                                        89.1    Alachua County   \n",
       "6                                        91.0   Alamance County   \n",
       "7                                       132.1    Alameda County   \n",
       "8                                        98.3     Albany County   \n",
       "9                                       107.3  Albemarle County   \n",
       "10                                       54.7  Alexander County   \n",
       "11                                       91.7  Alexander County   \n",
       "12                                      111.2    Allegan County   \n",
       "13                                       56.7   Allegany County   \n",
       "14                                       98.6  Allegheny County   \n",
       "15                                       98.2      Allen County   \n",
       "16                                       91.5      Allen County   \n",
       "17                                       68.3    Amherst County   \n",
       "18                                        NaN               NaN   \n",
       "19                                       92.4   Anderson County   \n",
       "\n",
       "    int_med_household_income_2016  \n",
       "0                           40218  \n",
       "1                           61571  \n",
       "2                           65442  \n",
       "3                           59645  \n",
       "4                           47394  \n",
       "5                           45294  \n",
       "6                           46060  \n",
       "7                           89472  \n",
       "8                           61634  \n",
       "9                           73132  \n",
       "10                          33344  \n",
       "11                          46385  \n",
       "12                          58311  \n",
       "13                          44708  \n",
       "14                          56099  \n",
       "15                          51329  \n",
       "16                          47924  \n",
       "17                          46497  \n",
       "18                            NaN  \n",
       "19                          45821  \n",
       "\n",
       "[20 rows x 116 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sun_eda_pov_pop_unemp.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra Datasets\n",
    "\n",
    "Homelessness datasets, rents datasets, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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>Location</th>\n",
       "      <th>Location_Type</th>\n",
       "      <th>State</th>\n",
       "      <th>Bedroom_Size</th>\n",
       "      <th>Price_2014_01</th>\n",
       "      <th>Price_2014_02</th>\n",
       "      <th>Price_2014_03</th>\n",
       "      <th>Price_2014_04</th>\n",
       "      <th>Price_2014_05</th>\n",
       "      <th>Price_2014_06</th>\n",
       "      <th>...</th>\n",
       "      <th>Price_2018_01</th>\n",
       "      <th>Price_2018_02</th>\n",
       "      <th>Price_2018_03</th>\n",
       "      <th>Price_2018_04</th>\n",
       "      <th>Price_2018_05</th>\n",
       "      <th>Price_2018_06</th>\n",
       "      <th>Price_2018_07</th>\n",
       "      <th>Price_2018_08</th>\n",
       "      <th>Price_2018_09</th>\n",
       "      <th>Price_2018_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>City</td>\n",
       "      <td>NY</td>\n",
       "      <td>Studio</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>1859.0</td>\n",
       "      <td>1861.0</td>\n",
       "      <td>1866.0</td>\n",
       "      <td>1883.0</td>\n",
       "      <td>1905.0</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>1896.0</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>City</td>\n",
       "      <td>NY</td>\n",
       "      <td>1br</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2069.0</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>2066.0</td>\n",
       "      <td>2073.0</td>\n",
       "      <td>2091.0</td>\n",
       "      <td>2115.0</td>\n",
       "      <td>2108.0</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>2104.0</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>City</td>\n",
       "      <td>NY</td>\n",
       "      <td>2br</td>\n",
       "      <td>2354.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>2356.0</td>\n",
       "      <td>2362.0</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>2384.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2465.0</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>2462.0</td>\n",
       "      <td>2469.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>2512.0</td>\n",
       "      <td>2509.0</td>\n",
       "      <td>2506.0</td>\n",
       "      <td>2524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>City</td>\n",
       "      <td>NY</td>\n",
       "      <td>3br</td>\n",
       "      <td>3036.0</td>\n",
       "      <td>3033.0</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>3046.0</td>\n",
       "      <td>3060.0</td>\n",
       "      <td>3074.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3178.0</td>\n",
       "      <td>3172.0</td>\n",
       "      <td>3174.0</td>\n",
       "      <td>3183.0</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>3239.0</td>\n",
       "      <td>3235.0</td>\n",
       "      <td>3232.0</td>\n",
       "      <td>3255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>City</td>\n",
       "      <td>NY</td>\n",
       "      <td>4br</td>\n",
       "      <td>3381.0</td>\n",
       "      <td>3378.0</td>\n",
       "      <td>3383.0</td>\n",
       "      <td>3392.0</td>\n",
       "      <td>3407.0</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3540.0</td>\n",
       "      <td>3532.0</td>\n",
       "      <td>3535.0</td>\n",
       "      <td>3546.0</td>\n",
       "      <td>3577.0</td>\n",
       "      <td>3619.0</td>\n",
       "      <td>3607.0</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>3599.0</td>\n",
       "      <td>3625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location Location_Type State Bedroom_Size  Price_2014_01  \\\n",
       "0  New York, NY          City    NY       Studio         1780.0   \n",
       "1  New York, NY          City    NY          1br         1976.0   \n",
       "2  New York, NY          City    NY          2br         2354.0   \n",
       "3  New York, NY          City    NY          3br         3036.0   \n",
       "4  New York, NY          City    NY          4br         3381.0   \n",
       "\n",
       "   Price_2014_02  Price_2014_03  Price_2014_04  Price_2014_05  Price_2014_06  \\\n",
       "0         1778.0         1781.0         1786.0         1794.0         1802.0   \n",
       "1         1975.0         1978.0         1983.0         1992.0         2001.0   \n",
       "2         2352.0         2356.0         2362.0         2373.0         2384.0   \n",
       "3         3033.0         3038.0         3046.0         3060.0         3074.0   \n",
       "4         3378.0         3383.0         3392.0         3407.0         3424.0   \n",
       "\n",
       "       ...        Price_2018_01  Price_2018_02  Price_2018_03  Price_2018_04  \\\n",
       "0      ...               1863.0         1859.0         1861.0         1866.0   \n",
       "1      ...               2069.0         2065.0         2066.0         2073.0   \n",
       "2      ...               2465.0         2460.0         2462.0         2469.0   \n",
       "3      ...               3178.0         3172.0         3174.0         3183.0   \n",
       "4      ...               3540.0         3532.0         3535.0         3546.0   \n",
       "\n",
       "   Price_2018_05  Price_2018_06  Price_2018_07  Price_2018_08  Price_2018_09  \\\n",
       "0         1883.0         1905.0         1899.0         1896.0         1895.0   \n",
       "1         2091.0         2115.0         2108.0         2106.0         2104.0   \n",
       "2         2491.0         2520.0         2512.0         2509.0         2506.0   \n",
       "3         3211.0         3249.0         3239.0         3235.0         3232.0   \n",
       "4         3577.0         3619.0         3607.0         3602.0         3599.0   \n",
       "\n",
       "   Price_2018_10  \n",
       "0           1908  \n",
       "1           2119  \n",
       "2           2524  \n",
       "3           3255  \n",
       "4           3625  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rents = pd.read_csv(\"apartmentrent.csv\")\n",
    "rents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_rents = rents.loc[rents['State'].isin(top_states)].loc[:,['State', 'Price_2018_10']].groupby([\"State\"]).agg('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0de8921dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=\"State\", y=\"Price_2018_10\", data=top_rents.reset_index())\n",
    "ax.set_ylabel(\"Average rent prices in October 2018\")\n",
    "ax.set_title(\"Average Rent Prices By State\");"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}