analysis_v3.6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis Version 3 (a version that actually works)\n",
    "##### *Highlights from model, state-by-state analysis, and aggregate review.*\n",
    "\n",
    "Following the results of the updated optimisation model, this notebook aims to shed light on the outcomes. In general, it seeks to answer the question '*how can biofuels complement electrification?*' at a county level, across the set of chosen states (MN, TX, CA).\n",
    "\n",
    "Between model v0.1 and model v0.2, the following changes were made:\n",
    "- Solutions involve running linear regression in order to better understand county-level factors allowing\n",
    "- Adding at-home charging to EV viability index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from mpl_toolkits import mplot3d\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "pd.set_option('display.float_format', '{:.2f}'.format)\n",
    "pd.options.mode.chained_assignment = None\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_states = ['CA','TX','MN']\n",
    "vdf = pd.read_csv('optimisation_data/visual_df.csv')\n",
    "del vdf['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "520760468398.2461"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf['annual_ghg_emissions'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall_mean</th>\n",
       "      <th>mn_mean</th>\n",
       "      <th>tx_mean</th>\n",
       "      <th>ca_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>annual_ghg_emissions</th>\n",
       "      <td>520760468398.25</td>\n",
       "      <td>1504381095330.31</td>\n",
       "      <td>348879536151.38</td>\n",
       "      <td>366826680221.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_operating_cost</th>\n",
       "      <td>43985842.79</td>\n",
       "      <td>168284900.52</td>\n",
       "      <td>19948291.42</td>\n",
       "      <td>31298402.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_annual_cost</th>\n",
       "      <td>948112519.17</td>\n",
       "      <td>3100346634.16</td>\n",
       "      <td>594335818.82</td>\n",
       "      <td>546155084.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_fuel_use</th>\n",
       "      <td>22546952.26</td>\n",
       "      <td>71845909.70</td>\n",
       "      <td>13680793.30</td>\n",
       "      <td>15566088.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_vehicle_count</th>\n",
       "      <td>154795.45</td>\n",
       "      <td>507595.84</td>\n",
       "      <td>98121.35</td>\n",
       "      <td>85057.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_split_BEV</th>\n",
       "      <td>121483.41</td>\n",
       "      <td>434534.34</td>\n",
       "      <td>70894.22</td>\n",
       "      <td>60479.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_split_FFV</th>\n",
       "      <td>10648.13</td>\n",
       "      <td>33241.04</td>\n",
       "      <td>5538.78</td>\n",
       "      <td>10503.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_split_SIDI</th>\n",
       "      <td>22663.90</td>\n",
       "      <td>39820.47</td>\n",
       "      <td>21688.35</td>\n",
       "      <td>14074.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <td>33.52</td>\n",
       "      <td>65.47</td>\n",
       "      <td>27.78</td>\n",
       "      <td>28.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <td>6.57</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.23</td>\n",
       "      <td>22.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <td>59.91</td>\n",
       "      <td>32.56</td>\n",
       "      <td>69.99</td>\n",
       "      <td>48.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         overall_mean          mn_mean         tx_mean  \\\n",
       "annual_ghg_emissions  520760468398.25 1504381095330.31 348879536151.38   \n",
       "annual_operating_cost     43985842.79     168284900.52     19948291.42   \n",
       "total_annual_cost        948112519.17    3100346634.16    594335818.82   \n",
       "annual_fuel_use           22546952.26      71845909.70     13680793.30   \n",
       "total_vehicle_count         154795.45        507595.84        98121.35   \n",
       "vehicle_split_BEV           121483.41        434534.34        70894.22   \n",
       "vehicle_split_FFV            10648.13         33241.04         5538.78   \n",
       "vehicle_split_SIDI           22663.90         39820.47        21688.35   \n",
       "vehicle_pc_BEV                  33.52            65.47           27.78   \n",
       "vehicle_pc_FFV                   6.57             1.98            2.23   \n",
       "vehicle_pc_SIDI                 59.91            32.56           69.99   \n",
       "\n",
       "                              ca_mean  \n",
       "annual_ghg_emissions  366826680221.75  \n",
       "annual_operating_cost     31298402.56  \n",
       "total_annual_cost        546155084.90  \n",
       "annual_fuel_use           15566088.39  \n",
       "total_vehicle_count          85057.48  \n",
       "vehicle_split_BEV            60479.99  \n",
       "vehicle_split_FFV            10503.15  \n",
       "vehicle_split_SIDI           14074.34  \n",
       "vehicle_pc_BEV                  28.97  \n",
       "vehicle_pc_FFV                  22.31  \n",
       "vehicle_pc_SIDI                 48.71  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_vdf = vdf[vdf['State']=='CA']\n",
    "tx_vdf = vdf[vdf['State']=='TX']\n",
    "mn_vdf = vdf[vdf['State']=='MN']\n",
    "a = pd.Series(vdf.mean(), name='overall_mean')\n",
    "b = pd.Series(ca_vdf.mean(), name='mn_mean')\n",
    "c = pd.Series(tx_vdf.mean(), name='tx_mean')\n",
    "d = pd.Series(mn_vdf.mean(), name='ca_mean')\n",
    "pd.DataFrame([a,b,c,d]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "evi = pd.read_csv('efuels_vi.csv')\n",
    "del evi['Unnamed: 0']\n",
    "evi.columns\n",
    "evi.efuels_area = evi.efuels_area*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>census_area</th>\n",
       "      <th>efuels_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>Lassen</td>\n",
       "      <td>4541.18</td>\n",
       "      <td>3.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>Modoc</td>\n",
       "      <td>3917.77</td>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>46.87</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>2735.09</td>\n",
       "      <td>73.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>953.21</td>\n",
       "      <td>15.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state         county  census_area  efuels_area\n",
       "0    CA         Lassen      4541.18         3.21\n",
       "1    CA          Modoc      3917.77         5.29\n",
       "2    CA  San Francisco        46.87       100.00\n",
       "3    CA  Santa Barbara      2735.09        73.62\n",
       "4    CA         Sierra       953.21        15.63"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evi[evi['NAME']==\"Anoka\"]\n",
    "evi.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evi.NAME = evi.NAME.str.replace('_',' ')\n",
    "# evi = evi[['NAME','efuels_area','CENSUSAREA']]\n",
    "vdf = vdf.merge(evi, left_on='County',right_on='county')\n",
    "vdf.rename(columns={'CENSUSAREA':'census_area'}, inplace=True)\n",
    "vdf.drop('county', axis=1,inplace=True)\n",
    "vdf.drop('state', axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ru = pd.read_csv('preprocessed_data/rural_urban.csv')\n",
    "del ru['Note']\n",
    "ru = ru[ru.State.isin(selected_states)]\n",
    "ru.rename(columns = {'2015 Geography Name':'County','2010 Census \\nPercent Rural' : 'pct_rural',\n",
    "                    '2010 Census Total Population':'census_totpop', '2010 Census Rural Population':'census_rurpop',\n",
    "                    '2010 Census Urban Population':'census_urbpop'},inplace=True)\n",
    "\n",
    "ru.County = ru.County.str.replace(' County, Texas','')\n",
    "ru.County = ru.County.str.replace(' County, California','')\n",
    "ru.County = ru.County.str.replace(' County, Minnesota','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning up the document here to ensure that we have the right names\n",
    "repeated_list = ['Orange','Cass','Lake','Trinity','Houston','Polk','Brown','Clay','Jackson','Washington','Martin']\n",
    "ru.loc[ru['County'].isin(repeated_list), 'County'] = ru['County'] + ' ' + ru['State']\n",
    "vdf = vdf.merge(ru)\n",
    "#checking if the sets are the same\n",
    "misfits = set(ru.County).symmetric_difference(set(vdf.County))\n",
    "len(misfits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vdf['census_totpop'] = vdf['census_totpop'].apply(lambda x: x.replace(',','')).astype(int)\n",
    "vdf['census_urbpop'] = vdf['census_urbpop'].apply(lambda x: x.replace(',','')).astype(int)\n",
    "vdf['census_rurpop'] = vdf['census_rurpop'].apply(lambda x: x.replace(',','')).astype(int)\n",
    "vdf['pop_density'] =vdf['census_totpop']/vdf['census_area']\n",
    "trunc = lambda x: math.trunc(1000 * x) / 1000\n",
    "vdf['census_area'] = vdf['census_area'].apply(trunc)\n",
    "\n",
    "vdf.loc[vdf['pop_density'] >= 750, 'county_class'] = 'urban'\n",
    "vdf.loc[vdf['pop_density'] <= 750, 'county_class'] = 'suburban'\n",
    "vdf.loc[vdf['pct_rural'] >= 50, 'county_class'] = 'rural'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vdf.loc[vdf['county_class'] == 'suburban','efuels_area'] = vdf['efuels_area']+40\n",
    "# vdf.loc[vdf['county_class'] == 'urban','efuels_area'] = vdf['efuels_area']+25\n",
    "\n",
    "# vdf['efuels_area'] = vdf['efuels_area'].apply(lambda x: 100 if x >= 100 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   399.00\n",
       "mean     33.51\n",
       "std      32.37\n",
       "min       0.00\n",
       "25%       0.00\n",
       "50%      36.77\n",
       "75%      55.62\n",
       "max     100.00\n",
       "Name: efuels_area, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf['efuels_area'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>efuels_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Jeff Davis</td>\n",
       "      <td>TX</td>\n",
       "      <td>4.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         County State  efuels_area\n",
       "266  Jeff Davis    TX         4.89"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf[vdf['County']=='Jeff Davis'][['County','State','efuels_area']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.90927318295743"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf['vehicle_pc_SIDI'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41042060639905903"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf['census_rurpop'].corr(vdf['efuels_area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.410420606399059"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf['efuels_area'].corr(vdf['census_rurpop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>County</th>\n",
       "      <th>census_area</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>census_totpop</th>\n",
       "      <th>census_urbpop</th>\n",
       "      <th>census_rurpop</th>\n",
       "      <th>pct_rural</th>\n",
       "      <th>pop_density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>46.87</td>\n",
       "      <td>100.00</td>\n",
       "      <td>805235</td>\n",
       "      <td>805235</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17179.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Orange CA</td>\n",
       "      <td>790.57</td>\n",
       "      <td>100.00</td>\n",
       "      <td>3010232</td>\n",
       "      <td>3005917</td>\n",
       "      <td>4315</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3807.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Ramsey</td>\n",
       "      <td>152.21</td>\n",
       "      <td>100.00</td>\n",
       "      <td>508640</td>\n",
       "      <td>507688</td>\n",
       "      <td>952</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3341.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>871.28</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2368139</td>\n",
       "      <td>2351839</td>\n",
       "      <td>16300</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2718.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>4057.88</td>\n",
       "      <td>87.97</td>\n",
       "      <td>9818605</td>\n",
       "      <td>9759181</td>\n",
       "      <td>59424</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2419.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            County  census_area  efuels_area  census_totpop  census_urbpop  \\\n",
       "37   San Francisco        46.87       100.00         805235         805235   \n",
       "29       Orange CA       790.57       100.00        3010232        3005917   \n",
       "119         Ramsey       152.21       100.00         508640         507688   \n",
       "201         Dallas       871.28       100.00        2368139        2351839   \n",
       "18     Los Angeles      4057.88        87.97        9818605        9759181   \n",
       "\n",
       "     census_rurpop  pct_rural  pop_density  \n",
       "37               0       0.00     17179.08  \n",
       "29            4315       0.10      3807.68  \n",
       "119            952       0.20      3341.66  \n",
       "201          16300       0.70      2718.00  \n",
       "18           59424       0.60      2419.64  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf.sort_values(by='pop_density',ascending=False).head()[['County','census_area','efuels_area','census_totpop','census_urbpop','census_rurpop','pct_rural','pop_density']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.51434334951582"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "X = vdf['efuels_area'].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "y = vdf['pop_density'].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "X.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, y)  # perform linear regression\n",
    "y_pred = linear_regressor.predict(X)  # make predictions\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, y_pred, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_new = vdf[vdf.County != 'San Francisco']['efuels_area'].values.reshape(-1,1)\n",
    "y_new = vdf[vdf.County != 'San Francisco']['pop_density'].values.reshape(-1,1)\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X_new, y_new)  # perform linear regression\n",
    "y_newpred = linear_regressor.predict(X_new)  # make predictions\n",
    "plt.scatter(X_new, y_new)\n",
    "plt.plot(X_new, y_newpred, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Overall results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>efuels_area</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>CA</th>\n",
       "      <td>65.47</td>\n",
       "      <td>1.98</td>\n",
       "      <td>32.56</td>\n",
       "      <td>65.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <td>28.97</td>\n",
       "      <td>22.31</td>\n",
       "      <td>48.71</td>\n",
       "      <td>28.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>27.78</td>\n",
       "      <td>2.23</td>\n",
       "      <td>69.99</td>\n",
       "      <td>27.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  efuels_area\n",
       "State                                                              \n",
       "CA              65.47            1.98            32.56        65.46\n",
       "MN              28.97           22.31            48.71        28.97\n",
       "TX              27.78            2.23            69.99        27.78"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf[['State','vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI','efuels_area']].groupby(by='State').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>County</th>\n",
       "      <th>annual_ghg_emissions</th>\n",
       "      <th>annual_operating_cost</th>\n",
       "      <th>total_annual_cost</th>\n",
       "      <th>annual_fuel_use</th>\n",
       "      <th>total_vehicle_count</th>\n",
       "      <th>vehicle_split_BEV</th>\n",
       "      <th>vehicle_split_FFV</th>\n",
       "      <th>vehicle_split_SIDI</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>State</th>\n",
       "      <th>census_area</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>census_totpop</th>\n",
       "      <th>census_urbpop</th>\n",
       "      <th>census_rurpop</th>\n",
       "      <th>pct_rural</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>county_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Waseca</td>\n",
       "      <td>118811080000.00</td>\n",
       "      <td>12959585.50</td>\n",
       "      <td>187710128.00</td>\n",
       "      <td>8960031.00</td>\n",
       "      <td>26698.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18181.09</td>\n",
       "      <td>8516.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68.10</td>\n",
       "      <td>31.90</td>\n",
       "      <td>MN</td>\n",
       "      <td>423.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19136</td>\n",
       "      <td>9537</td>\n",
       "      <td>9599</td>\n",
       "      <td>50.20</td>\n",
       "      <td>45.20</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Cottonwood</td>\n",
       "      <td>60027191000.00</td>\n",
       "      <td>7994397.30</td>\n",
       "      <td>114724579.00</td>\n",
       "      <td>5434810.40</td>\n",
       "      <td>16306.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10135.02</td>\n",
       "      <td>6170.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.20</td>\n",
       "      <td>37.80</td>\n",
       "      <td>MN</td>\n",
       "      <td>638.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11687</td>\n",
       "      <td>4409</td>\n",
       "      <td>7278</td>\n",
       "      <td>62.30</td>\n",
       "      <td>18.30</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Yellow Medicine</td>\n",
       "      <td>53904179100.00</td>\n",
       "      <td>7100187.86</td>\n",
       "      <td>102226657.20</td>\n",
       "      <td>4792604.15</td>\n",
       "      <td>14563.00</td>\n",
       "      <td>281.56</td>\n",
       "      <td>8778.78</td>\n",
       "      <td>5502.66</td>\n",
       "      <td>1.90</td>\n",
       "      <td>60.30</td>\n",
       "      <td>37.80</td>\n",
       "      <td>MN</td>\n",
       "      <td>759.10</td>\n",
       "      <td>1.93</td>\n",
       "      <td>10438</td>\n",
       "      <td>2002</td>\n",
       "      <td>8436</td>\n",
       "      <td>80.80</td>\n",
       "      <td>13.75</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Chisago</td>\n",
       "      <td>262738283000.00</td>\n",
       "      <td>29027294.60</td>\n",
       "      <td>499582700.00</td>\n",
       "      <td>18584589.00</td>\n",
       "      <td>75183.00</td>\n",
       "      <td>31120.72</td>\n",
       "      <td>44062.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.40</td>\n",
       "      <td>58.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>MN</td>\n",
       "      <td>414.86</td>\n",
       "      <td>41.39</td>\n",
       "      <td>53887</td>\n",
       "      <td>23932</td>\n",
       "      <td>29955</td>\n",
       "      <td>55.60</td>\n",
       "      <td>129.89</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Le Sueur</td>\n",
       "      <td>142842975000.00</td>\n",
       "      <td>17362337.30</td>\n",
       "      <td>264758822.00</td>\n",
       "      <td>11338910.00</td>\n",
       "      <td>38651.00</td>\n",
       "      <td>8074.76</td>\n",
       "      <td>21699.13</td>\n",
       "      <td>8877.11</td>\n",
       "      <td>20.90</td>\n",
       "      <td>56.10</td>\n",
       "      <td>23.00</td>\n",
       "      <td>MN</td>\n",
       "      <td>448.76</td>\n",
       "      <td>20.89</td>\n",
       "      <td>27703</td>\n",
       "      <td>10483</td>\n",
       "      <td>17220</td>\n",
       "      <td>62.20</td>\n",
       "      <td>61.73</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Jackson MN</td>\n",
       "      <td>51676333000.00</td>\n",
       "      <td>7094753.80</td>\n",
       "      <td>100845299.00</td>\n",
       "      <td>4739442.50</td>\n",
       "      <td>14323.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8014.81</td>\n",
       "      <td>6308.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>MN</td>\n",
       "      <td>702.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10266</td>\n",
       "      <td>3169</td>\n",
       "      <td>7097</td>\n",
       "      <td>69.10</td>\n",
       "      <td>14.60</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Renville</td>\n",
       "      <td>83133503000.00</td>\n",
       "      <td>10104880.40</td>\n",
       "      <td>151224580.00</td>\n",
       "      <td>6587460.35</td>\n",
       "      <td>21946.00</td>\n",
       "      <td>3648.81</td>\n",
       "      <td>11824.83</td>\n",
       "      <td>6472.37</td>\n",
       "      <td>16.60</td>\n",
       "      <td>53.90</td>\n",
       "      <td>29.50</td>\n",
       "      <td>MN</td>\n",
       "      <td>982.91</td>\n",
       "      <td>16.63</td>\n",
       "      <td>15730</td>\n",
       "      <td>0</td>\n",
       "      <td>15730</td>\n",
       "      <td>100.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Redwood</td>\n",
       "      <td>87452795000.00</td>\n",
       "      <td>10409496.80</td>\n",
       "      <td>154731846.00</td>\n",
       "      <td>6784123.84</td>\n",
       "      <td>22405.00</td>\n",
       "      <td>3362.48</td>\n",
       "      <td>11907.55</td>\n",
       "      <td>7134.97</td>\n",
       "      <td>15.00</td>\n",
       "      <td>53.10</td>\n",
       "      <td>31.80</td>\n",
       "      <td>MN</td>\n",
       "      <td>878.57</td>\n",
       "      <td>15.01</td>\n",
       "      <td>16059</td>\n",
       "      <td>4958</td>\n",
       "      <td>11101</td>\n",
       "      <td>69.10</td>\n",
       "      <td>18.28</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Stearns</td>\n",
       "      <td>964809632000.00</td>\n",
       "      <td>82870143.10</td>\n",
       "      <td>1387915050.00</td>\n",
       "      <td>50608526.50</td>\n",
       "      <td>210174.99</td>\n",
       "      <td>102013.92</td>\n",
       "      <td>102743.90</td>\n",
       "      <td>5417.17</td>\n",
       "      <td>48.50</td>\n",
       "      <td>48.90</td>\n",
       "      <td>2.60</td>\n",
       "      <td>MN</td>\n",
       "      <td>1343.13</td>\n",
       "      <td>48.54</td>\n",
       "      <td>150642</td>\n",
       "      <td>95005</td>\n",
       "      <td>55637</td>\n",
       "      <td>36.90</td>\n",
       "      <td>112.16</td>\n",
       "      <td>suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Isanti</td>\n",
       "      <td>235166544000.00</td>\n",
       "      <td>22102602.20</td>\n",
       "      <td>353794725.00</td>\n",
       "      <td>13764377.40</td>\n",
       "      <td>52761.00</td>\n",
       "      <td>19714.56</td>\n",
       "      <td>25766.96</td>\n",
       "      <td>7279.48</td>\n",
       "      <td>37.40</td>\n",
       "      <td>48.80</td>\n",
       "      <td>13.80</td>\n",
       "      <td>MN</td>\n",
       "      <td>435.79</td>\n",
       "      <td>37.37</td>\n",
       "      <td>37816</td>\n",
       "      <td>14270</td>\n",
       "      <td>23546</td>\n",
       "      <td>62.30</td>\n",
       "      <td>86.77</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              County  annual_ghg_emissions  annual_operating_cost  \\\n",
       "138           Waseca       118811080000.00            12959585.50   \n",
       "74        Cottonwood        60027191000.00             7994397.30   \n",
       "144  Yellow Medicine        53904179100.00             7100187.86   \n",
       "70           Chisago       262738283000.00            29027294.60   \n",
       "97          Le Sueur       142842975000.00            17362337.30   \n",
       "89        Jackson MN        51676333000.00             7094753.80   \n",
       "122         Renville        83133503000.00            10104880.40   \n",
       "121          Redwood        87452795000.00            10409496.80   \n",
       "130          Stearns       964809632000.00            82870143.10   \n",
       "87            Isanti       235166544000.00            22102602.20   \n",
       "\n",
       "     total_annual_cost  annual_fuel_use  total_vehicle_count  \\\n",
       "138       187710128.00       8960031.00             26698.00   \n",
       "74        114724579.00       5434810.40             16306.00   \n",
       "144       102226657.20       4792604.15             14563.00   \n",
       "70        499582700.00      18584589.00             75183.00   \n",
       "97        264758822.00      11338910.00             38651.00   \n",
       "89        100845299.00       4739442.50             14323.00   \n",
       "122       151224580.00       6587460.35             21946.00   \n",
       "121       154731846.00       6784123.84             22405.00   \n",
       "130      1387915050.00      50608526.50            210174.99   \n",
       "87        353794725.00      13764377.40             52761.00   \n",
       "\n",
       "     vehicle_split_BEV  vehicle_split_FFV  vehicle_split_SIDI  vehicle_pc_BEV  \\\n",
       "138               0.00           18181.09             8516.91            0.00   \n",
       "74                0.00           10135.02             6170.98            0.00   \n",
       "144             281.56            8778.78             5502.66            1.90   \n",
       "70            31120.72           44062.29                0.00           41.40   \n",
       "97             8074.76           21699.13             8877.11           20.90   \n",
       "89                0.00            8014.81             6308.19            0.00   \n",
       "122            3648.81           11824.83             6472.37           16.60   \n",
       "121            3362.48           11907.55             7134.97           15.00   \n",
       "130          102013.92          102743.90             5417.17           48.50   \n",
       "87            19714.56           25766.96             7279.48           37.40   \n",
       "\n",
       "     vehicle_pc_FFV  vehicle_pc_SIDI State  census_area  efuels_area  \\\n",
       "138           68.10            31.90    MN       423.36         0.00   \n",
       "74            62.20            37.80    MN       638.61         0.00   \n",
       "144           60.30            37.80    MN       759.10         1.93   \n",
       "70            58.60             0.00    MN       414.86        41.39   \n",
       "97            56.10            23.00    MN       448.76        20.89   \n",
       "89            56.00            44.00    MN       702.98         0.00   \n",
       "122           53.90            29.50    MN       982.91        16.63   \n",
       "121           53.10            31.80    MN       878.57        15.01   \n",
       "130           48.90             2.60    MN      1343.13        48.54   \n",
       "87            48.80            13.80    MN       435.79        37.37   \n",
       "\n",
       "     census_totpop  census_urbpop  census_rurpop  pct_rural  pop_density  \\\n",
       "138          19136           9537           9599      50.20        45.20   \n",
       "74           11687           4409           7278      62.30        18.30   \n",
       "144          10438           2002           8436      80.80        13.75   \n",
       "70           53887          23932          29955      55.60       129.89   \n",
       "97           27703          10483          17220      62.20        61.73   \n",
       "89           10266           3169           7097      69.10        14.60   \n",
       "122          15730              0          15730     100.00        16.00   \n",
       "121          16059           4958          11101      69.10        18.28   \n",
       "130         150642          95005          55637      36.90       112.16   \n",
       "87           37816          14270          23546      62.30        86.77   \n",
       "\n",
       "    county_class  \n",
       "138        rural  \n",
       "74         rural  \n",
       "144        rural  \n",
       "70         rural  \n",
       "97         rural  \n",
       "89         rural  \n",
       "122        rural  \n",
       "121        rural  \n",
       "130     suburban  \n",
       "87         rural  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vdf[vdf['State']=='CA'].sort_values(by='efuels_area',ascending=True).head()\n",
    "# vdf[(vdf['State']=='TX') & (vdf['vehicle_pc_FFV']>vdf['vehicle_pc_BEV'])].sort_values(by='vehicle_pc_FFV',ascending=False)\n",
    "vdf[(vdf['State']=='MN') & (vdf['vehicle_pc_FFV']>0)].sort_values(by='vehicle_pc_FFV',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vdf[(vdf['State']=='TX')].sort_values(by='annual_ghg_emissions',ascending=False).head(30)\n",
    "# vdf[(vdf['State']=='CA')].sort_values(by='total_vehicle_count',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>County</th>\n",
       "      <th>annual_ghg_emissions</th>\n",
       "      <th>annual_operating_cost</th>\n",
       "      <th>total_annual_cost</th>\n",
       "      <th>annual_fuel_use</th>\n",
       "      <th>total_vehicle_count</th>\n",
       "      <th>vehicle_split_BEV</th>\n",
       "      <th>vehicle_split_FFV</th>\n",
       "      <th>vehicle_split_SIDI</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>State</th>\n",
       "      <th>census_area</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>census_totpop</th>\n",
       "      <th>census_urbpop</th>\n",
       "      <th>census_rurpop</th>\n",
       "      <th>pct_rural</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>county_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Atascosa</td>\n",
       "      <td>291744672500.00</td>\n",
       "      <td>23595841.00</td>\n",
       "      <td>355319474.00</td>\n",
       "      <td>18171949.20</td>\n",
       "      <td>50680.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4936.30</td>\n",
       "      <td>45743.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.70</td>\n",
       "      <td>90.30</td>\n",
       "      <td>TX</td>\n",
       "      <td>1219.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44911</td>\n",
       "      <td>17645</td>\n",
       "      <td>27266</td>\n",
       "      <td>60.70</td>\n",
       "      <td>36.83</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Polk TX</td>\n",
       "      <td>330729548400.00</td>\n",
       "      <td>26663853.40</td>\n",
       "      <td>398877667.00</td>\n",
       "      <td>20509589.40</td>\n",
       "      <td>56866.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4801.87</td>\n",
       "      <td>52064.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.40</td>\n",
       "      <td>91.60</td>\n",
       "      <td>TX</td>\n",
       "      <td>1057.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45413</td>\n",
       "      <td>10155</td>\n",
       "      <td>35258</td>\n",
       "      <td>77.60</td>\n",
       "      <td>42.96</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>Medina</td>\n",
       "      <td>143992358200.00</td>\n",
       "      <td>27755904.87</td>\n",
       "      <td>399910818.20</td>\n",
       "      <td>21203955.66</td>\n",
       "      <td>56857.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>501.54</td>\n",
       "      <td>56355.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>99.10</td>\n",
       "      <td>TX</td>\n",
       "      <td>1325.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46006</td>\n",
       "      <td>17687</td>\n",
       "      <td>28319</td>\n",
       "      <td>61.60</td>\n",
       "      <td>34.71</td>\n",
       "      <td>rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       County  annual_ghg_emissions  annual_operating_cost  total_annual_cost  \\\n",
       "151  Atascosa       291744672500.00            23595841.00       355319474.00   \n",
       "331   Polk TX       330729548400.00            26663853.40       398877667.00   \n",
       "307    Medina       143992358200.00            27755904.87       399910818.20   \n",
       "\n",
       "     annual_fuel_use  total_vehicle_count  vehicle_split_BEV  \\\n",
       "151      18171949.20             50680.00               0.00   \n",
       "331      20509589.40             56866.00               0.00   \n",
       "307      21203955.66             56857.00               0.00   \n",
       "\n",
       "     vehicle_split_FFV  vehicle_split_SIDI  vehicle_pc_BEV  vehicle_pc_FFV  \\\n",
       "151            4936.30            45743.70            0.00            9.70   \n",
       "331            4801.87            52064.13            0.00            8.40   \n",
       "307             501.54            56355.46            0.00            0.90   \n",
       "\n",
       "     vehicle_pc_SIDI State  census_area  efuels_area  census_totpop  \\\n",
       "151            90.30    TX      1219.54         0.00          44911   \n",
       "331            91.60    TX      1057.09         0.00          45413   \n",
       "307            99.10    TX      1325.36         0.00          46006   \n",
       "\n",
       "     census_urbpop  census_rurpop  pct_rural  pop_density county_class  \n",
       "151          17645          27266      60.70        36.83        rural  \n",
       "331          10155          35258      77.60        42.96        rural  \n",
       "307          17687          28319      61.60        34.71        rural  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf[(vdf['State']=='TX') & (vdf['vehicle_pc_BEV']==0) & (vdf['vehicle_pc_FFV']>0)].sort_values(by='vehicle_pc_FFV',ascending=False)\n",
    "# vdf[(vdf['State']=='CA') & (vdf['vehicle_pc_BEV']>0)].sort_values(by='vehicle_pc_FFV',ascending=False).mean()\n",
    "# vdf[(vdf['State']=='TX') & (vdf['County']=='Harris')].sort_values(by='vehicle_pc_FFV',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "interested_counties_ca = ['San Francisco','Los Angeles','San Bernardino'] \n",
    "interested_counties_mn = ['Ramsey','Hennepin','St. Louis']\n",
    "interested_counties_tx = ['Bexar','Dallas','Comal','Lubbock','Travis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#global ranking (across the sample)\n",
    "vdf['ghg_globalrank'] = vdf['annual_ghg_emissions'].rank(ascending=False)\n",
    "vdf['popden_globalrank'] = vdf['pop_density'].rank(ascending=False)\n",
    "vdf['censusarea_globalrank'] = vdf['census_area'].rank(ascending=False)\n",
    "\n",
    "#state ranking (within state)\n",
    "vdf['ghg_staterank'] = vdf.groupby('State')['annual_ghg_emissions'].rank('dense', ascending=False)\n",
    "vdf['popden_staterank'] = vdf.groupby('State')['pop_density'].rank('dense', ascending=False)\n",
    "vdf['censusarea_staterank'] = vdf.groupby('State')['census_area'].rank('dense', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "vdf.to_feather('analysed_vdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>ghg_globalrank</th>\n",
       "      <th>ghg_staterank</th>\n",
       "      <th>popden_globalrank</th>\n",
       "      <th>popden_staterank</th>\n",
       "      <th>censusarea_globalrank</th>\n",
       "      <th>censusarea_staterank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>88.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>CA</td>\n",
       "      <td>49.90</td>\n",
       "      <td>4.30</td>\n",
       "      <td>45.90</td>\n",
       "      <td>11.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>399.00</td>\n",
       "      <td>58.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            County State  vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  \\\n",
       "18     Los Angeles    CA           88.00           12.00             0.00   \n",
       "35  San Bernardino    CA           49.90            4.30            45.90   \n",
       "37   San Francisco    CA          100.00            0.00             0.00   \n",
       "\n",
       "    ghg_globalrank  ghg_staterank  popden_globalrank  popden_staterank  \\\n",
       "18            1.00           1.00               5.00              3.00   \n",
       "35           11.00           6.00              88.00             32.00   \n",
       "37           33.00          16.00               1.00              1.00   \n",
       "\n",
       "    censusarea_globalrank  censusarea_staterank  \n",
       "18                  15.00                 11.00  \n",
       "35                   1.00                  1.00  \n",
       "37                 399.00                 58.00  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vdf[vdf['County']].isin(['San Francisco','Los Angeles','San Bernadino'])\n",
    "vdf[vdf['County'].isin(interested_counties_ca)][['County','State','vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI','ghg_globalrank','ghg_staterank','popden_globalrank','popden_staterank','censusarea_globalrank','censusarea_staterank']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>ghg_globalrank</th>\n",
       "      <th>ghg_staterank</th>\n",
       "      <th>popden_globalrank</th>\n",
       "      <th>popden_staterank</th>\n",
       "      <th>censusarea_globalrank</th>\n",
       "      <th>censusarea_staterank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Hennepin</td>\n",
       "      <td>MN</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>348.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Ramsey</td>\n",
       "      <td>MN</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>397.00</td>\n",
       "      <td>87.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>MN</td>\n",
       "      <td>50.80</td>\n",
       "      <td>3.60</td>\n",
       "      <td>45.60</td>\n",
       "      <td>36.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>184.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        County State  vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  \\\n",
       "84    Hennepin    MN          100.00            0.00             0.00   \n",
       "119     Ramsey    MN          100.00            0.00             0.00   \n",
       "126  St. Louis    MN           50.80            3.60            45.60   \n",
       "\n",
       "     ghg_globalrank  ghg_staterank  popden_globalrank  popden_staterank  \\\n",
       "84             6.00           1.00               8.00              2.00   \n",
       "119           14.00           2.00               3.00              1.00   \n",
       "126           36.00           6.00             184.00             38.00   \n",
       "\n",
       "     censusarea_globalrank  censusarea_staterank  \n",
       "84                  348.00                 60.00  \n",
       "119                 397.00                 87.00  \n",
       "126                   6.00                  1.00  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf[vdf['County'].isin(interested_counties_mn)][['County','State','vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI','ghg_globalrank','ghg_staterank','popden_globalrank','popden_staterank','censusarea_globalrank','censusarea_staterank']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>ghg_globalrank</th>\n",
       "      <th>ghg_staterank</th>\n",
       "      <th>popden_globalrank</th>\n",
       "      <th>popden_staterank</th>\n",
       "      <th>censusarea_globalrank</th>\n",
       "      <th>censusarea_staterank</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Bexar</td>\n",
       "      <td>TX</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>90.70</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Comal</td>\n",
       "      <td>TX</td>\n",
       "      <td>93.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>346.00</td>\n",
       "      <td>234.00</td>\n",
       "      <td>81.60</td>\n",
       "      <td>18.40</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>248.00</td>\n",
       "      <td>177.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>TX</td>\n",
       "      <td>54.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>224.00</td>\n",
       "      <td>155.00</td>\n",
       "      <td>63.70</td>\n",
       "      <td>29.40</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>Travis</td>\n",
       "      <td>TX</td>\n",
       "      <td>31.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>139.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      County State  ghg_globalrank  ghg_staterank  popden_globalrank  \\\n",
       "159    Bexar    TX           10.00           4.00              13.00   \n",
       "190    Comal    TX           93.00          45.00              57.00   \n",
       "201   Dallas    TX            2.00           1.00               4.00   \n",
       "296  Lubbock    TX           54.00          21.00              40.00   \n",
       "371   Travis    TX           31.00          11.00              15.00   \n",
       "\n",
       "     popden_staterank  censusarea_globalrank  censusarea_staterank  \\\n",
       "159              4.00                  91.00                 37.00   \n",
       "190             29.00                 346.00                234.00   \n",
       "201              1.00                 248.00                177.00   \n",
       "296             19.00                 224.00                155.00   \n",
       "371              5.00                 139.00                 76.00   \n",
       "\n",
       "     vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  \n",
       "159           90.70            9.30             0.00  \n",
       "190           81.60           18.40             0.00  \n",
       "201          100.00            0.00             0.00  \n",
       "296           63.70           29.40             6.90  \n",
       "371          100.00            0.00             0.00  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf[vdf['County'].isin(interested_counties_tx)][['County','State','ghg_globalrank','ghg_staterank','popden_globalrank','popden_staterank','censusarea_globalrank','censusarea_staterank','vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>ghg_globalrank</th>\n",
       "      <th>ghg_staterank</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Hennepin</td>\n",
       "      <td>MN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Orange CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>Tarrant</td>\n",
       "      <td>TX</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Ramsey</td>\n",
       "      <td>MN</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>75.80</td>\n",
       "      <td>16.80</td>\n",
       "      <td>7.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Harris</td>\n",
       "      <td>TX</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Dakota</td>\n",
       "      <td>MN</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>CA</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>91.20</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          County State  ghg_globalrank  ghg_staterank  vehicle_pc_BEV  \\\n",
       "18   Los Angeles    CA            1.00           1.00           88.00   \n",
       "201       Dallas    TX            2.00           1.00          100.00   \n",
       "84      Hennepin    MN            6.00           1.00          100.00   \n",
       "29     Orange CA    CA            3.00           2.00          100.00   \n",
       "364      Tarrant    TX            4.00           2.00          100.00   \n",
       "119       Ramsey    MN           14.00           2.00          100.00   \n",
       "32     Riverside    CA            5.00           3.00           75.80   \n",
       "245       Harris    TX            7.00           3.00          100.00   \n",
       "76        Dakota    MN           19.00           3.00          100.00   \n",
       "42   Santa Clara    CA            8.00           4.00           91.20   \n",
       "\n",
       "     vehicle_pc_FFV  vehicle_pc_SIDI  \n",
       "18            12.00             0.00  \n",
       "201            0.00             0.00  \n",
       "84             0.00             0.00  \n",
       "29             0.00             0.00  \n",
       "364            0.00             0.00  \n",
       "119            0.00             0.00  \n",
       "32            16.80             7.40  \n",
       "245            0.00             0.00  \n",
       "76             0.00             0.00  \n",
       "42             8.80             0.00  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf[['County','State','ghg_globalrank','ghg_staterank','vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI']].sort_values(by=['ghg_staterank','ghg_globalrank'],ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>popden_globalrank</th>\n",
       "      <th>popden_staterank</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Ramsey</td>\n",
       "      <td>MN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Orange CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Harris</td>\n",
       "      <td>TX</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Hennepin</td>\n",
       "      <td>MN</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>Tarrant</td>\n",
       "      <td>TX</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Anoka</td>\n",
       "      <td>MN</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>85.70</td>\n",
       "      <td>14.30</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>CA</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            County State  popden_globalrank  popden_staterank  vehicle_pc_BEV  \\\n",
       "37   San Francisco    CA               1.00              1.00          100.00   \n",
       "119         Ramsey    MN               3.00              1.00          100.00   \n",
       "201         Dallas    TX               4.00              1.00          100.00   \n",
       "29       Orange CA    CA               2.00              2.00          100.00   \n",
       "245         Harris    TX               6.00              2.00          100.00   \n",
       "84        Hennepin    MN               8.00              2.00          100.00   \n",
       "18     Los Angeles    CA               5.00              3.00           88.00   \n",
       "364        Tarrant    TX               7.00              3.00          100.00   \n",
       "59           Anoka    MN              18.00              3.00           85.70   \n",
       "0          Alameda    CA               9.00              4.00          100.00   \n",
       "\n",
       "     vehicle_pc_FFV  vehicle_pc_SIDI  \n",
       "37             0.00             0.00  \n",
       "119            0.00             0.00  \n",
       "201            0.00             0.00  \n",
       "29             0.00             0.00  \n",
       "245            0.00             0.00  \n",
       "84             0.00             0.00  \n",
       "18            12.00             0.00  \n",
       "364            0.00             0.00  \n",
       "59            14.30             0.00  \n",
       "0              0.00             0.00  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf[['County','State','popden_globalrank','popden_staterank','vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI']].sort_values(by=['popden_staterank','popden_globalrank'],ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>censusarea_globalrank</th>\n",
       "      <th>censusarea_staterank</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>CA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.90</td>\n",
       "      <td>4.30</td>\n",
       "      <td>45.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>MN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>50.80</td>\n",
       "      <td>3.60</td>\n",
       "      <td>45.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Brewster</td>\n",
       "      <td>TX</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Inyo</td>\n",
       "      <td>CA</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>43.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>56.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>Pecos</td>\n",
       "      <td>TX</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>42.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Koochiching</td>\n",
       "      <td>MN</td>\n",
       "      <td>27.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kern</td>\n",
       "      <td>CA</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>62.80</td>\n",
       "      <td>2.00</td>\n",
       "      <td>35.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Hudspeth</td>\n",
       "      <td>TX</td>\n",
       "      <td>11.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Itasca</td>\n",
       "      <td>MN</td>\n",
       "      <td>32.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.10</td>\n",
       "      <td>95.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>CA</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>75.80</td>\n",
       "      <td>16.80</td>\n",
       "      <td>7.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             County State  censusarea_globalrank  censusarea_staterank  \\\n",
       "35   San Bernardino    CA                   1.00                  1.00   \n",
       "126       St. Louis    MN                   6.00                  1.00   \n",
       "166        Brewster    TX                   7.00                  1.00   \n",
       "13             Inyo    CA                   2.00                  2.00   \n",
       "330           Pecos    TX                  10.00                  2.00   \n",
       "93      Koochiching    MN                  27.00                  2.00   \n",
       "14             Kern    CA                   3.00                  3.00   \n",
       "259        Hudspeth    TX                  11.00                  3.00   \n",
       "88           Itasca    MN                  32.00                  3.00   \n",
       "32        Riverside    CA                   4.00                  4.00   \n",
       "\n",
       "     vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  \n",
       "35            49.90            4.30            45.90  \n",
       "126           50.80            3.60            45.60  \n",
       "166           40.00            0.00            60.00  \n",
       "13            43.40            0.00            56.60  \n",
       "330           42.70            0.00            57.30  \n",
       "93            40.00            0.00            60.00  \n",
       "14            62.80            2.00            35.30  \n",
       "259            0.50            0.00            99.50  \n",
       "88             0.00            4.10            95.90  \n",
       "32            75.80           16.80             7.40  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf[['County','State','censusarea_globalrank','censusarea_staterank','vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI']].sort_values(by=['censusarea_staterank','censusarea_globalrank'],ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#introducing VMT\n",
    "VMT = pd.read_csv('./vmt/vmt_transpose.csv')\n",
    "VMT['Classification'] = VMT['Classification'].str.lower()\n",
    "N = pd.read_csv('https://raw.githubusercontent.com/saif1457/biofuels/master/optimisation_data/N(r).csv')\n",
    "N.county = N.county.str.replace('_',' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 30)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf = vdf.merge(N, left_on='County',right_on='county')\n",
    "vdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 34)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdf = vdf.merge(VMT,left_on=['household_income_ID','State','county_class'],right_on=['household_income_ID','state','Classification'])\n",
    "vdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['County', 'annual_ghg_emissions', 'annual_operating_cost',\n",
       "       'total_annual_cost', 'annual_fuel_use', 'total_vehicle_count',\n",
       "       'vehicle_split_BEV', 'vehicle_split_FFV', 'vehicle_split_SIDI',\n",
       "       'vehicle_pc_BEV', 'vehicle_pc_FFV', 'vehicle_pc_SIDI', 'State',\n",
       "       'census_area', 'efuels_area', 'census_totpop', 'census_urbpop',\n",
       "       'census_rurpop', 'pct_rural', 'pop_density', 'county_class',\n",
       "       'ghg_globalrank', 'popden_globalrank', 'censusarea_globalrank',\n",
       "       'ghg_staterank', 'popden_staterank', 'censusarea_staterank',\n",
       "       'median_household_income_2018', 'household_income_ID', 'Income Class',\n",
       "       'Classification', 'VMT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del vdf['county']\n",
    "del vdf['state']\n",
    "vdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['County', 'State', 'annual_ghg_emissions', 'annual_operating_cost',\n",
       "       'total_annual_cost', 'annual_fuel_use', 'total_vehicle_count',\n",
       "       'vehicle_split_BEV', 'vehicle_split_FFV', 'vehicle_split_SIDI',\n",
       "       'vehicle_pc_BEV', 'vehicle_pc_FFV', 'vehicle_pc_SIDI', 'efuels_area',\n",
       "       'census_area', 'census_totpop', 'census_urbpop', 'census_rurpop',\n",
       "       'pct_rural', 'pop_density', 'county_class', 'ghg_globalrank',\n",
       "       'popden_globalrank', 'censusarea_globalrank', 'ghg_staterank',\n",
       "       'popden_staterank', 'censusarea_staterank',\n",
       "       'median_household_income_2018', 'household_income_ID', 'annual_vmt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning up the table\n",
    "vdf = vdf.rename(columns={'VMT':'annual_vmt'})\n",
    "vdf = vdf[['County','State','annual_ghg_emissions',\n",
    " 'annual_operating_cost',\n",
    " 'total_annual_cost',\n",
    " 'annual_fuel_use',\n",
    " 'total_vehicle_count',\n",
    " 'vehicle_split_BEV',\n",
    " 'vehicle_split_FFV',\n",
    " 'vehicle_split_SIDI',\n",
    " 'vehicle_pc_BEV',\n",
    " 'vehicle_pc_FFV',\n",
    " 'vehicle_pc_SIDI',\n",
    " 'efuels_area',\n",
    " 'census_area',\n",
    " 'census_totpop',\n",
    " 'census_urbpop',\n",
    " 'census_rurpop',\n",
    " 'pct_rural',\n",
    " 'pop_density',\n",
    " 'county_class',\n",
    " 'ghg_globalrank',\n",
    " 'popden_globalrank',\n",
    " 'censusarea_globalrank',\n",
    " 'ghg_staterank',\n",
    " 'popden_staterank',\n",
    " 'censusarea_staterank',\n",
    " 'median_household_income_2018',\n",
    " 'household_income_ID',\n",
    " 'annual_vmt']]\n",
    "vdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "vdf.to_csv('vdf_v4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pd.read_csv('vdf_v4.csv')\n",
    "del model['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 30)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "efuel_station_count = pd.read_csv('efuel_station_count')\n",
    "del efuel_station_count['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 31)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = model.merge(efuel_station_count,how='left')\n",
    "model.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import feather\n",
    "e85_store = pd.read_feather('./e85_store', use_threads=True)\n",
    "e85_store = e85_store[['NAME','e85_area','Station Count']]\n",
    "e85_store = e85_store.rename(columns={'NAME':'County','Station Count':'E85 Station Count'})\n",
    "e85_store.County = e85_store.County.str.replace('_',' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 5)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e85_vi = pd.read_csv('./e85_vi.csv')\n",
    "e85_vi.county = e85_vi.county.str.replace('_',' ')\n",
    "e85_vi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "e85_store = e85_store.merge(e85_vi, left_on='County',right_on='county')\n",
    "e85_store.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "del e85_vi['Unnamed: 0']\n",
    "e85_store = e85_store[['County','e85_area_y','E85 Station Count']]\n",
    "e85_store = e85_store.rename(columns={'e85_area_y':'e85_area'})\n",
    "e85_store.e85_area = e85_store.e85_area*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 3)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e85_store.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.merge(e85_store)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>e85_area</th>\n",
       "      <th>annual_vmt</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>65.47</td>\n",
       "      <td>1.98</td>\n",
       "      <td>32.56</td>\n",
       "      <td>65.46</td>\n",
       "      <td>9.97</td>\n",
       "      <td>10690.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN</th>\n",
       "      <td>28.97</td>\n",
       "      <td>22.31</td>\n",
       "      <td>48.71</td>\n",
       "      <td>28.97</td>\n",
       "      <td>34.34</td>\n",
       "      <td>13610.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TX</th>\n",
       "      <td>27.78</td>\n",
       "      <td>2.23</td>\n",
       "      <td>69.99</td>\n",
       "      <td>27.78</td>\n",
       "      <td>5.36</td>\n",
       "      <td>12040.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  efuels_area  e85_area  \\\n",
       "State                                                                           \n",
       "CA              65.47            1.98            32.56        65.46      9.97   \n",
       "MN              28.97           22.31            48.71        28.97     34.34   \n",
       "TX              27.78            2.23            69.99        27.78      5.36   \n",
       "\n",
       "       annual_vmt  \n",
       "State              \n",
       "CA       10690.08  \n",
       "MN       13610.84  \n",
       "TX       12040.47  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[['State','vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI','efuels_area','e85_area','annual_vmt']].groupby(by='State').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 33)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.46896551724137"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[model['State']=='CA']['vehicle_pc_BEV'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.230708661417323"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[model['State']=='TX']['vehicle_pc_FFV'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.71034482758621"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[model['State']=='MN']['vehicle_pc_SIDI'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>annual_ghg_emissions</th>\n",
       "      <th>annual_operating_cost</th>\n",
       "      <th>total_annual_cost</th>\n",
       "      <th>annual_fuel_use</th>\n",
       "      <th>total_vehicle_count</th>\n",
       "      <th>vehicle_split_BEV</th>\n",
       "      <th>vehicle_split_FFV</th>\n",
       "      <th>vehicle_split_SIDI</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>census_area</th>\n",
       "      <th>census_totpop</th>\n",
       "      <th>census_urbpop</th>\n",
       "      <th>census_rurpop</th>\n",
       "      <th>pct_rural</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>county_class</th>\n",
       "      <th>ghg_globalrank</th>\n",
       "      <th>popden_globalrank</th>\n",
       "      <th>censusarea_globalrank</th>\n",
       "      <th>ghg_staterank</th>\n",
       "      <th>popden_staterank</th>\n",
       "      <th>censusarea_staterank</th>\n",
       "      <th>median_household_income_2018</th>\n",
       "      <th>household_income_ID</th>\n",
       "      <th>annual_vmt</th>\n",
       "      <th>Efuel Station Count</th>\n",
       "      <th>e85_area</th>\n",
       "      <th>E85 Station Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Traverse</td>\n",
       "      <td>MN</td>\n",
       "      <td>23465278000.00</td>\n",
       "      <td>2472513.00</td>\n",
       "      <td>34964149.00</td>\n",
       "      <td>1412864.50</td>\n",
       "      <td>4964.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4964.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>573.90</td>\n",
       "      <td>3558</td>\n",
       "      <td>0</td>\n",
       "      <td>3558</td>\n",
       "      <td>100.00</td>\n",
       "      <td>6.20</td>\n",
       "      <td>rural</td>\n",
       "      <td>339.00</td>\n",
       "      <td>326.00</td>\n",
       "      <td>341.00</td>\n",
       "      <td>85.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>$49,773</td>\n",
       "      <td>5</td>\n",
       "      <td>10517.98</td>\n",
       "      <td>nan</td>\n",
       "      <td>28.72</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      County State  annual_ghg_emissions  annual_operating_cost  \\\n",
       "64  Traverse    MN        23465278000.00             2472513.00   \n",
       "\n",
       "    total_annual_cost  annual_fuel_use  total_vehicle_count  \\\n",
       "64        34964149.00       1412864.50              4964.00   \n",
       "\n",
       "    vehicle_split_BEV  vehicle_split_FFV  vehicle_split_SIDI  vehicle_pc_BEV  \\\n",
       "64               0.00               0.00             4964.00            0.00   \n",
       "\n",
       "    vehicle_pc_FFV  vehicle_pc_SIDI  efuels_area  census_area  census_totpop  \\\n",
       "64            0.00           100.00         0.00       573.90           3558   \n",
       "\n",
       "    census_urbpop  census_rurpop  pct_rural  pop_density county_class  \\\n",
       "64              0           3558     100.00         6.20        rural   \n",
       "\n",
       "    ghg_globalrank  popden_globalrank  censusarea_globalrank  ghg_staterank  \\\n",
       "64          339.00             326.00                 341.00          85.00   \n",
       "\n",
       "    popden_staterank  censusarea_staterank median_household_income_2018  \\\n",
       "64             81.00                 55.00                      $49,773   \n",
       "\n",
       "    household_income_ID  annual_vmt  Efuel Station Count  e85_area  \\\n",
       "64                    5    10517.98                  nan     28.72   \n",
       "\n",
       "    E85 Station Count  \n",
       "64               2.00  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[model['County']=='Traverse']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.514343349515805"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.efuels_area.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>e85_area</th>\n",
       "      <th>E85 Station Count</th>\n",
       "      <th>Efuel Station Count</th>\n",
       "      <th>annual_vmt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county_class</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>rural</th>\n",
       "      <td>5.90</td>\n",
       "      <td>7.21</td>\n",
       "      <td>86.89</td>\n",
       "      <td>5.90</td>\n",
       "      <td>7.89</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2.15</td>\n",
       "      <td>11955.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suburban</th>\n",
       "      <td>58.68</td>\n",
       "      <td>5.98</td>\n",
       "      <td>35.34</td>\n",
       "      <td>58.67</td>\n",
       "      <td>12.11</td>\n",
       "      <td>2.18</td>\n",
       "      <td>24.98</td>\n",
       "      <td>12601.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban</th>\n",
       "      <td>92.41</td>\n",
       "      <td>5.31</td>\n",
       "      <td>2.29</td>\n",
       "      <td>92.40</td>\n",
       "      <td>59.68</td>\n",
       "      <td>15.25</td>\n",
       "      <td>268.40</td>\n",
       "      <td>10886.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  efuels_area  \\\n",
       "county_class                                                                 \n",
       "rural                   5.90            7.21            86.89         5.90   \n",
       "suburban               58.68            5.98            35.34        58.67   \n",
       "urban                  92.41            5.31             2.29        92.40   \n",
       "\n",
       "              e85_area  E85 Station Count  Efuel Station Count  annual_vmt  \n",
       "county_class                                                                \n",
       "rural             7.89               0.64                 2.15    11955.06  \n",
       "suburban         12.11               2.18                24.98    12601.42  \n",
       "urban            59.68              15.25               268.40    10886.00  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[['vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI','efuels_area','e85_area','county_class', 'E85 Station Count','Efuel Station Count', 'annual_vmt']].groupby(by='county_class').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>e85_area</th>\n",
       "      <th>E85 Station Count</th>\n",
       "      <th>Efuel Station Count</th>\n",
       "      <th>annual_vmt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county_class</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>rural</th>\n",
       "      <td>14.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>85.63</td>\n",
       "      <td>14.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.36</td>\n",
       "      <td>9646.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suburban</th>\n",
       "      <td>73.33</td>\n",
       "      <td>2.41</td>\n",
       "      <td>24.26</td>\n",
       "      <td>73.32</td>\n",
       "      <td>5.18</td>\n",
       "      <td>2.46</td>\n",
       "      <td>67.41</td>\n",
       "      <td>10952.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban</th>\n",
       "      <td>97.40</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>97.40</td>\n",
       "      <td>47.05</td>\n",
       "      <td>10.62</td>\n",
       "      <td>515.62</td>\n",
       "      <td>10844.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  efuels_area  \\\n",
       "county_class                                                                 \n",
       "rural                  14.37            0.00            85.63        14.37   \n",
       "suburban               73.33            2.41            24.26        73.32   \n",
       "urban                  97.40            2.60             0.00        97.40   \n",
       "\n",
       "              e85_area  E85 Station Count  Efuel Station Count  annual_vmt  \n",
       "county_class                                                                \n",
       "rural             0.00               0.00                 4.36     9646.37  \n",
       "suburban          5.18               2.46                67.41    10952.73  \n",
       "urban            47.05              10.62               515.62    10844.79  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CALIFORNIA RESULTS\n",
    "model[model['State']=='CA'][['vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI','efuels_area','e85_area','E85 Station Count','Efuel Station Count','county_class','annual_vmt']].groupby(by='county_class').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>e85_area</th>\n",
       "      <th>E85 Station Count</th>\n",
       "      <th>Efuel Station Count</th>\n",
       "      <th>annual_vmt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county_class</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>rural</th>\n",
       "      <td>4.59</td>\n",
       "      <td>1.05</td>\n",
       "      <td>94.35</td>\n",
       "      <td>4.59</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.69</td>\n",
       "      <td>12177.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suburban</th>\n",
       "      <td>51.81</td>\n",
       "      <td>3.23</td>\n",
       "      <td>44.95</td>\n",
       "      <td>51.81</td>\n",
       "      <td>5.93</td>\n",
       "      <td>0.91</td>\n",
       "      <td>5.08</td>\n",
       "      <td>11928.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban</th>\n",
       "      <td>87.02</td>\n",
       "      <td>7.90</td>\n",
       "      <td>5.08</td>\n",
       "      <td>87.01</td>\n",
       "      <td>59.13</td>\n",
       "      <td>12.89</td>\n",
       "      <td>115.11</td>\n",
       "      <td>11320.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  efuels_area  \\\n",
       "county_class                                                                 \n",
       "rural                   4.59            1.05            94.35         4.59   \n",
       "suburban               51.81            3.23            44.95        51.81   \n",
       "urban                  87.02            7.90             5.08        87.01   \n",
       "\n",
       "              e85_area  E85 Station Count  Efuel Station Count  annual_vmt  \n",
       "county_class                                                                \n",
       "rural             1.35               0.12                 1.69    12177.68  \n",
       "suburban          5.93               0.91                 5.08    11928.76  \n",
       "urban            59.13              12.89               115.11    11320.08  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEXAS RESULTS\n",
    "model[model['State']=='TX'][['vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI','efuels_area','e85_area','E85 Station Count','Efuel Station Count','county_class','annual_vmt']].groupby(by='county_class').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>e85_area</th>\n",
       "      <th>E85 Station Count</th>\n",
       "      <th>Efuel Station Count</th>\n",
       "      <th>annual_vmt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county_class</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>rural</th>\n",
       "      <td>7.41</td>\n",
       "      <td>23.57</td>\n",
       "      <td>69.00</td>\n",
       "      <td>7.41</td>\n",
       "      <td>25.34</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.76</td>\n",
       "      <td>11867.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suburban</th>\n",
       "      <td>64.99</td>\n",
       "      <td>21.67</td>\n",
       "      <td>13.34</td>\n",
       "      <td>64.99</td>\n",
       "      <td>45.84</td>\n",
       "      <td>6.71</td>\n",
       "      <td>5.79</td>\n",
       "      <td>17516.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban</th>\n",
       "      <td>95.23</td>\n",
       "      <td>4.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.24</td>\n",
       "      <td>95.02</td>\n",
       "      <td>34.67</td>\n",
       "      <td>69.00</td>\n",
       "      <td>9693.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              vehicle_pc_BEV  vehicle_pc_FFV  vehicle_pc_SIDI  efuels_area  \\\n",
       "county_class                                                                 \n",
       "rural                   7.41           23.57            69.00         7.41   \n",
       "suburban               64.99           21.67            13.34        64.99   \n",
       "urban                  95.23            4.77             0.00        95.24   \n",
       "\n",
       "              e85_area  E85 Station Count  Efuel Station Count  annual_vmt  \n",
       "county_class                                                                \n",
       "rural            25.34               2.04                 1.76    11867.92  \n",
       "suburban         45.84               6.71                 5.79    17516.38  \n",
       "urban            95.02              34.67                69.00     9693.68  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MINNESOTA RESULTS\n",
    "model[model['State']=='MN'][['vehicle_pc_BEV','vehicle_pc_FFV','vehicle_pc_SIDI','efuels_area','e85_area','E85 Station Count','Efuel Station Count','county_class','annual_vmt']].groupby(by='county_class').mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solutions to \"Major Comments\" from 11/18\n",
    "- **Why is there no electricity complement for Waseca, MN?**\n",
    "    - Waseca, MN quick stats:\n",
    "        - 68.1% FFV, 0 BEV, 31.9% SIDI\n",
    "    - There's no BEV infrastructure (efuels area = 0) in Waseca. Hence, there is a zero pc allocation to BEV.\n",
    "- **What's the matter with Red Lake, MN and Traverse, MN?**\n",
    "    - Red Lake, MN quick stats:\n",
    "        - Population Density: 9.45/sqmi\n",
    "        - Vehicles: 5705 vehicles\n",
    "        - Emissions: 1.9x10^10 GHG\n",
    "        - {24.9% FFV, 0 BEV, 75.1% SIDI}\n",
    "    - Traverse, MN quick stats:\n",
    "        - Population Density: 6.19/sqmi\n",
    "        - Vehicles: 4964 vehicles\n",
    "        - Emissions: 2.4x10^10 GHG\n",
    "        - {0% FFV, 0 BEV, 100% SIDI}\n",
    "    - So it turns out that despite having a higher population density and total vehicle count, Red Lake, MN has a lower emission total than Traverse, MN.\n",
    "    - This can be attributed to its almost 25 pc allocation to flex fuel vehicles. Neither county has any BEV infrastructure, and both are classed as 'rural' counties in Minnesota.\n",
    "\n",
    "Introducing VMT (Vehicle Miles Travelled) using the National Household Travel Survey (NHTS, 2017) on the habits and consumption of the American public. From this, we get the state-wide HH income VMT, which can be combined with county average HH to understand the average miles travelled in that particular county. It might be possible to get a more detailed picture than county level, but for the progress of this work, our central interest lies in understanding the overarching area trends for biofuels, and all vehicle splits in terms of mileage comes from optimised percentage applied to total mileage.\n",
    "\n",
    "**FUELTYPE % * TOTAL MILES = MILES USING FUEL TYPE**\n",
    "\n",
    "An obvious problem with this is that its possible for a county that despite having large BEV/FFV allocations, to have a disproportionate amount of VMT with SIDI. However, is this actually a serious concern? Most likely, optimised allocation will reflect usage and consumer behaviour with some outliers (to be expected). Also, the VMT is an explanatory variable, with little in the way of importance to outcomes. For these reasons, it makes sense to include the version outlined above.\n",
    "\n",
    "#### 3D Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Total Vehicle Count')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1=plt.figure(figsize=(14,14))\n",
    "ax1 = fig1.add_subplot(211,projection='3d')\n",
    "zdata = model['total_vehicle_count']\n",
    "xdata = model['annual_ghg_emissions']\n",
    "ydata = model['annual_vmt']\n",
    "ax1.scatter3D(xdata, ydata, zdata, c=xdata, cmap='hsv');\n",
    "ax1.set_ylabel('Annual VMT')\n",
    "ax1.set_xlabel('Annual GHG Emissions')\n",
    "ax1.set_zlabel('Total Vehicle Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Total Vehicle Count')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2=plt.figure(figsize=(14,14))\n",
    "ax2 = fig2.add_subplot(211,projection='3d')\n",
    "xdata = model['total_vehicle_count']\n",
    "ydata = model['annual_ghg_emissions']\n",
    "zdata = model['annual_vmt']\n",
    "ax2.scatter3D(xdata, ydata, zdata, c=xdata, cmap='hsv');\n",
    "ax2.set_zlabel('Annual VMT')\n",
    "ax2.set_ylabel('Annual GHG Emissions')\n",
    "ax2.set_xlabel('Total Vehicle Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Total Vehicle Count')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3=plt.figure(figsize=(14,14))\n",
    "ax3 = fig3.add_subplot(211,projection='3d')\n",
    "xdata = model['total_vehicle_count']\n",
    "zdata = model['annual_ghg_emissions']\n",
    "ydata = model['annual_vmt']\n",
    "ax3.scatter3D(xdata, ydata, zdata, c=xdata, cmap='hsv');\n",
    "ax3.set_ylabel('Annual VMT')\n",
    "ax3.set_zlabel('Annual GHG Emissions')\n",
    "ax3.set_xlabel('Total Vehicle Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Contour 3D Plot](https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-dimensional-plotting.html)**\n",
    "Attempts to make a 3D contour plot were unsuccessful because this requires that the zdata to actually have 2 dimensions, and to be a function of x and y. In other words, currently, it is just a single axis, unrelated to x and y. In short, z needs to be a grid array. This is possible, but requires additional troubleshooting, probably using the [Interpolate Data function from SciPy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html).\n",
    "\n",
    "#### Limitations with 3D Plotting\n",
    "3D plots are most useful when your X, Y and Z values are all continuous variables. For this reason, including state in the diagram is not possible / not useful for analysis. It is also difficult to align the view well, so adjustment might be required. Once a view is chosen from the above selection, delivering the rest of the raw graphs for each of the 3 states will be trivial.\n",
    "\n",
    "#### Future Works - Part 1\n",
    "The first thing that comes to mind is figuring out a budget to pay for the EC2 uptime -- and finally being a part of a paper that enters the 21st century in augmenting the static written paper with a user-friendly portal to actually explore the data. It will probably cost $20 to have the system up all year, depending on traffic.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number of vehicles (meaning automobiles) in the united states: https://www.statista.com/statistics/196072/number-of-registered-automobiles-in-texas/\n",
    "find out annual GHG emissions due to vehicles from each state. Find this out using a combination of knowing the number of vehicles in each type in that state and multiplying by fuel GHG factor. then, find the differential between carbon emissions (2017 optimised versus 2017 not-optimised)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 01/18/2021 Additional Future Works Ideas\n",
    "I also suggest the following:\n",
    "- Expanding the remit of this project beyond CA, TX, MN to the rest of the United States\n",
    "- Creating a recommender engine that discerns where viable growth is to better understand where to put new stations.\n",
    "    - (maybe looking at market trend data for consumer purchasing decisions), fuel pump data\n",
    "- Fuel pump data can also be used to understand the bottom-up figures for carbon emissions.\n",
    "- Expand to trucking and other forms of transport outside of automotive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>annual_ghg_emissions</th>\n",
       "      <th>annual_operating_cost</th>\n",
       "      <th>total_annual_cost</th>\n",
       "      <th>annual_fuel_use</th>\n",
       "      <th>total_vehicle_count</th>\n",
       "      <th>vehicle_split_BEV</th>\n",
       "      <th>vehicle_split_FFV</th>\n",
       "      <th>vehicle_split_SIDI</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>vehicle_pc_SIDI</th>\n",
       "      <th>efuels_area</th>\n",
       "      <th>census_area</th>\n",
       "      <th>census_totpop</th>\n",
       "      <th>census_urbpop</th>\n",
       "      <th>census_rurpop</th>\n",
       "      <th>pct_rural</th>\n",
       "      <th>pop_density</th>\n",
       "      <th>county_class</th>\n",
       "      <th>ghg_globalrank</th>\n",
       "      <th>popden_globalrank</th>\n",
       "      <th>censusarea_globalrank</th>\n",
       "      <th>ghg_staterank</th>\n",
       "      <th>popden_staterank</th>\n",
       "      <th>censusarea_staterank</th>\n",
       "      <th>median_household_income_2018</th>\n",
       "      <th>household_income_ID</th>\n",
       "      <th>annual_vmt</th>\n",
       "      <th>Efuel Station Count</th>\n",
       "      <th>e85_area</th>\n",
       "      <th>E85 Station Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda</td>\n",
       "      <td>CA</td>\n",
       "      <td>1783929600000.00</td>\n",
       "      <td>318966620.00</td>\n",
       "      <td>7192956300.00</td>\n",
       "      <td>136290200.00</td>\n",
       "      <td>1217269.00</td>\n",
       "      <td>1217269.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>739.02</td>\n",
       "      <td>1510271</td>\n",
       "      <td>1504402</td>\n",
       "      <td>5869</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2043.62</td>\n",
       "      <td>urban</td>\n",
       "      <td>28.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>293.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>$101,744</td>\n",
       "      <td>8</td>\n",
       "      <td>10829.72</td>\n",
       "      <td>317.00</td>\n",
       "      <td>52.61</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Contra Costa</td>\n",
       "      <td>CA</td>\n",
       "      <td>4027894200000.00</td>\n",
       "      <td>240519220.00</td>\n",
       "      <td>5423903700.00</td>\n",
       "      <td>102770660.00</td>\n",
       "      <td>917891.00</td>\n",
       "      <td>917891.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>715.94</td>\n",
       "      <td>1049025</td>\n",
       "      <td>1040709</td>\n",
       "      <td>8316</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1465.25</td>\n",
       "      <td>urban</td>\n",
       "      <td>13.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>296.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>$101,463</td>\n",
       "      <td>8</td>\n",
       "      <td>10829.72</td>\n",
       "      <td>162.00</td>\n",
       "      <td>18.49</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>1565396800000.00</td>\n",
       "      <td>119103380.00</td>\n",
       "      <td>2685878000.00</td>\n",
       "      <td>50891291.00</td>\n",
       "      <td>454533.00</td>\n",
       "      <td>454533.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>46.87</td>\n",
       "      <td>805235</td>\n",
       "      <td>805235</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17179.08</td>\n",
       "      <td>urban</td>\n",
       "      <td>33.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>399.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>58.00</td>\n",
       "      <td>$110,601</td>\n",
       "      <td>8</td>\n",
       "      <td>10829.72</td>\n",
       "      <td>223.00</td>\n",
       "      <td>97.86</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>San Mateo</td>\n",
       "      <td>CA</td>\n",
       "      <td>3143350500000.00</td>\n",
       "      <td>187700110.00</td>\n",
       "      <td>4232790000.00</td>\n",
       "      <td>80201763.00</td>\n",
       "      <td>716318.00</td>\n",
       "      <td>716318.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>448.41</td>\n",
       "      <td>718451</td>\n",
       "      <td>704865</td>\n",
       "      <td>13586</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1602.23</td>\n",
       "      <td>urban</td>\n",
       "      <td>17.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>369.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>$122,930</td>\n",
       "      <td>8</td>\n",
       "      <td>10829.72</td>\n",
       "      <td>281.00</td>\n",
       "      <td>37.80</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alpine</td>\n",
       "      <td>CA</td>\n",
       "      <td>3585886300.00</td>\n",
       "      <td>1133274.62</td>\n",
       "      <td>12695918.30</td>\n",
       "      <td>420333.98</td>\n",
       "      <td>1821.00</td>\n",
       "      <td>396.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1424.04</td>\n",
       "      <td>21.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>78.20</td>\n",
       "      <td>21.80</td>\n",
       "      <td>738.33</td>\n",
       "      <td>1175</td>\n",
       "      <td>0</td>\n",
       "      <td>1175</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1.59</td>\n",
       "      <td>rural</td>\n",
       "      <td>393.00</td>\n",
       "      <td>380.00</td>\n",
       "      <td>294.00</td>\n",
       "      <td>58.00</td>\n",
       "      <td>58.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>$55,384</td>\n",
       "      <td>6</td>\n",
       "      <td>9828.84</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amador</td>\n",
       "      <td>CA</td>\n",
       "      <td>71470663000.00</td>\n",
       "      <td>15703854.50</td>\n",
       "      <td>198967876.00</td>\n",
       "      <td>5947661.70</td>\n",
       "      <td>29750.00</td>\n",
       "      <td>12759.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16990.31</td>\n",
       "      <td>42.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57.10</td>\n",
       "      <td>42.89</td>\n",
       "      <td>594.58</td>\n",
       "      <td>38091</td>\n",
       "      <td>15075</td>\n",
       "      <td>23016</td>\n",
       "      <td>60.40</td>\n",
       "      <td>64.06</td>\n",
       "      <td>rural</td>\n",
       "      <td>235.00</td>\n",
       "      <td>117.00</td>\n",
       "      <td>335.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>$58,515</td>\n",
       "      <td>6</td>\n",
       "      <td>9828.84</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Calaveras</td>\n",
       "      <td>CA</td>\n",
       "      <td>98361826000.00</td>\n",
       "      <td>36755748.00</td>\n",
       "      <td>392792573.00</td>\n",
       "      <td>13531418.26</td>\n",
       "      <td>55341.00</td>\n",
       "      <td>6895.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>48445.18</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.50</td>\n",
       "      <td>12.46</td>\n",
       "      <td>1020.01</td>\n",
       "      <td>45578</td>\n",
       "      <td>11208</td>\n",
       "      <td>34370</td>\n",
       "      <td>75.40</td>\n",
       "      <td>44.68</td>\n",
       "      <td>rural</td>\n",
       "      <td>198.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>126.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>$62,984</td>\n",
       "      <td>6</td>\n",
       "      <td>9828.84</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lassen</td>\n",
       "      <td>CA</td>\n",
       "      <td>25027358900.00</td>\n",
       "      <td>10962502.54</td>\n",
       "      <td>112205048.40</td>\n",
       "      <td>4009361.11</td>\n",
       "      <td>15536.00</td>\n",
       "      <td>498.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15037.73</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>96.80</td>\n",
       "      <td>3.21</td>\n",
       "      <td>4541.18</td>\n",
       "      <td>34895</td>\n",
       "      <td>10285</td>\n",
       "      <td>24610</td>\n",
       "      <td>70.50</td>\n",
       "      <td>7.68</td>\n",
       "      <td>rural</td>\n",
       "      <td>335.00</td>\n",
       "      <td>313.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>$57,705</td>\n",
       "      <td>6</td>\n",
       "      <td>9828.84</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mariposa</td>\n",
       "      <td>CA</td>\n",
       "      <td>43932691000.00</td>\n",
       "      <td>11913482.10</td>\n",
       "      <td>136152784.00</td>\n",
       "      <td>4433092.46</td>\n",
       "      <td>19670.00</td>\n",
       "      <td>5019.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14650.17</td>\n",
       "      <td>25.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>74.50</td>\n",
       "      <td>25.52</td>\n",
       "      <td>1448.82</td>\n",
       "      <td>18251</td>\n",
       "      <td>0</td>\n",
       "      <td>18251</td>\n",
       "      <td>100.00</td>\n",
       "      <td>12.60</td>\n",
       "      <td>rural</td>\n",
       "      <td>284.00</td>\n",
       "      <td>276.00</td>\n",
       "      <td>69.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>$51,402</td>\n",
       "      <td>6</td>\n",
       "      <td>9828.84</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Plumas</td>\n",
       "      <td>CA</td>\n",
       "      <td>25952409400.00</td>\n",
       "      <td>10058090.19</td>\n",
       "      <td>106466700.20</td>\n",
       "      <td>3697381.55</td>\n",
       "      <td>14944.00</td>\n",
       "      <td>1565.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13378.25</td>\n",
       "      <td>10.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>89.50</td>\n",
       "      <td>10.48</td>\n",
       "      <td>2553.04</td>\n",
       "      <td>20007</td>\n",
       "      <td>5197</td>\n",
       "      <td>14810</td>\n",
       "      <td>74.00</td>\n",
       "      <td>7.84</td>\n",
       "      <td>rural</td>\n",
       "      <td>331.00</td>\n",
       "      <td>311.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>$55,079</td>\n",
       "      <td>6</td>\n",
       "      <td>9828.84</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sierra</td>\n",
       "      <td>CA</td>\n",
       "      <td>10621278900.00</td>\n",
       "      <td>1645043.82</td>\n",
       "      <td>17856121.80</td>\n",
       "      <td>607089.15</td>\n",
       "      <td>2531.00</td>\n",
       "      <td>395.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2135.33</td>\n",
       "      <td>15.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>84.40</td>\n",
       "      <td>15.63</td>\n",
       "      <td>953.21</td>\n",
       "      <td>3240</td>\n",
       "      <td>9</td>\n",
       "      <td>3231</td>\n",
       "      <td>99.70</td>\n",
       "      <td>3.40</td>\n",
       "      <td>rural</td>\n",
       "      <td>373.00</td>\n",
       "      <td>359.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>$52,308</td>\n",
       "      <td>6</td>\n",
       "      <td>9828.84</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Butte</td>\n",
       "      <td>CA</td>\n",
       "      <td>298096099000.00</td>\n",
       "      <td>51513684.20</td>\n",
       "      <td>809599878.00</td>\n",
       "      <td>21064003.70</td>\n",
       "      <td>127993.00</td>\n",
       "      <td>88698.30</td>\n",
       "      <td>8523.51</td>\n",
       "      <td>30771.19</td>\n",
       "      <td>69.30</td>\n",
       "      <td>6.70</td>\n",
       "      <td>24.00</td>\n",
       "      <td>69.30</td>\n",
       "      <td>1636.46</td>\n",
       "      <td>220000</td>\n",
       "      <td>178416</td>\n",
       "      <td>41584</td>\n",
       "      <td>18.90</td>\n",
       "      <td>134.44</td>\n",
       "      <td>suburban</td>\n",
       "      <td>94.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>$50,945</td>\n",
       "      <td>6</td>\n",
       "      <td>11040.82</td>\n",
       "      <td>20.00</td>\n",
       "      <td>6.66</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           County State  annual_ghg_emissions  annual_operating_cost  \\\n",
       "0         Alameda    CA      1783929600000.00           318966620.00   \n",
       "1    Contra Costa    CA      4027894200000.00           240519220.00   \n",
       "2   San Francisco    CA      1565396800000.00           119103380.00   \n",
       "3       San Mateo    CA      3143350500000.00           187700110.00   \n",
       "4          Alpine    CA         3585886300.00             1133274.62   \n",
       "5          Amador    CA        71470663000.00            15703854.50   \n",
       "6       Calaveras    CA        98361826000.00            36755748.00   \n",
       "7          Lassen    CA        25027358900.00            10962502.54   \n",
       "8        Mariposa    CA        43932691000.00            11913482.10   \n",
       "9          Plumas    CA        25952409400.00            10058090.19   \n",
       "10         Sierra    CA        10621278900.00             1645043.82   \n",
       "11          Butte    CA       298096099000.00            51513684.20   \n",
       "\n",
       "    total_annual_cost  annual_fuel_use  total_vehicle_count  \\\n",
       "0       7192956300.00     136290200.00           1217269.00   \n",
       "1       5423903700.00     102770660.00            917891.00   \n",
       "2       2685878000.00      50891291.00            454533.00   \n",
       "3       4232790000.00      80201763.00            716318.00   \n",
       "4         12695918.30        420333.98              1821.00   \n",
       "5        198967876.00       5947661.70             29750.00   \n",
       "6        392792573.00      13531418.26             55341.00   \n",
       "7        112205048.40       4009361.11             15536.00   \n",
       "8        136152784.00       4433092.46             19670.00   \n",
       "9        106466700.20       3697381.55             14944.00   \n",
       "10        17856121.80        607089.15              2531.00   \n",
       "11       809599878.00      21064003.70            127993.00   \n",
       "\n",
       "    vehicle_split_BEV  vehicle_split_FFV  vehicle_split_SIDI  vehicle_pc_BEV  \\\n",
       "0          1217269.00               0.00                0.00          100.00   \n",
       "1           917891.00               0.00                0.00          100.00   \n",
       "2           454533.00               0.00                0.00          100.00   \n",
       "3           716318.00               0.00                0.00          100.00   \n",
       "4              396.96               0.00             1424.04           21.80   \n",
       "5            12759.69               0.00            16990.31           42.90   \n",
       "6             6895.82               0.00            48445.18           12.50   \n",
       "7              498.27               0.00            15037.73            3.20   \n",
       "8             5019.83               0.00            14650.17           25.50   \n",
       "9             1565.75               0.00            13378.25           10.50   \n",
       "10             395.67               0.00             2135.33           15.60   \n",
       "11           88698.30            8523.51            30771.19           69.30   \n",
       "\n",
       "    vehicle_pc_FFV  vehicle_pc_SIDI  efuels_area  census_area  census_totpop  \\\n",
       "0             0.00             0.00       100.00       739.02        1510271   \n",
       "1             0.00             0.00       100.00       715.94        1049025   \n",
       "2             0.00             0.00       100.00        46.87         805235   \n",
       "3             0.00             0.00       100.00       448.41         718451   \n",
       "4             0.00            78.20        21.80       738.33           1175   \n",
       "5             0.00            57.10        42.89       594.58          38091   \n",
       "6             0.00            87.50        12.46      1020.01          45578   \n",
       "7             0.00            96.80         3.21      4541.18          34895   \n",
       "8             0.00            74.50        25.52      1448.82          18251   \n",
       "9             0.00            89.50        10.48      2553.04          20007   \n",
       "10            0.00            84.40        15.63       953.21           3240   \n",
       "11            6.70            24.00        69.30      1636.46         220000   \n",
       "\n",
       "    census_urbpop  census_rurpop  pct_rural  pop_density county_class  \\\n",
       "0         1504402           5869       0.40      2043.62        urban   \n",
       "1         1040709           8316       0.80      1465.25        urban   \n",
       "2          805235              0       0.00     17179.08        urban   \n",
       "3          704865          13586       1.90      1602.23        urban   \n",
       "4               0           1175     100.00         1.59        rural   \n",
       "5           15075          23016      60.40        64.06        rural   \n",
       "6           11208          34370      75.40        44.68        rural   \n",
       "7           10285          24610      70.50         7.68        rural   \n",
       "8               0          18251     100.00        12.60        rural   \n",
       "9            5197          14810      74.00         7.84        rural   \n",
       "10              9           3231      99.70         3.40        rural   \n",
       "11         178416          41584      18.90       134.44     suburban   \n",
       "\n",
       "    ghg_globalrank  popden_globalrank  censusarea_globalrank  ghg_staterank  \\\n",
       "0            28.00               9.00                 293.00          14.00   \n",
       "1            13.00              12.00                 296.00           8.00   \n",
       "2            33.00               1.00                 399.00          16.00   \n",
       "3            17.00              10.00                 369.00           9.00   \n",
       "4           393.00             380.00                 294.00          58.00   \n",
       "5           235.00             117.00                 335.00          46.00   \n",
       "6           198.00             150.00                 126.00          43.00   \n",
       "7           335.00             313.00                  12.00          55.00   \n",
       "8           284.00             276.00                  69.00          50.00   \n",
       "9           331.00             311.00                  34.00          54.00   \n",
       "10          373.00             359.00                 150.00          57.00   \n",
       "11           94.00              68.00                  53.00          33.00   \n",
       "\n",
       "    popden_staterank  censusarea_staterank median_household_income_2018  \\\n",
       "0               4.00                 49.00                     $101,744   \n",
       "1               7.00                 51.00                     $101,463   \n",
       "2               1.00                 58.00                     $110,601   \n",
       "3               5.00                 56.00                     $122,930   \n",
       "4              58.00                 50.00                      $55,384   \n",
       "5              36.00                 54.00                      $58,515   \n",
       "6              39.00                 40.00                      $62,984   \n",
       "7              51.00                  8.00                      $57,705   \n",
       "8              49.00                 31.00                      $51,402   \n",
       "9              50.00                 22.00                      $55,079   \n",
       "10             55.00                 45.00                      $52,308   \n",
       "11             24.00                 28.00                      $50,945   \n",
       "\n",
       "    household_income_ID  annual_vmt  Efuel Station Count  e85_area  \\\n",
       "0                     8    10829.72               317.00     52.61   \n",
       "1                     8    10829.72               162.00     18.49   \n",
       "2                     8    10829.72               223.00     97.86   \n",
       "3                     8    10829.72               281.00     37.80   \n",
       "4                     6     9828.84                 2.00      0.00   \n",
       "5                     6     9828.84                 9.00      0.00   \n",
       "6                     6     9828.84                 3.00      0.00   \n",
       "7                     6     9828.84                 2.00      0.00   \n",
       "8                     6     9828.84                 7.00      0.00   \n",
       "9                     6     9828.84                 3.00      0.00   \n",
       "10                    6     9828.84                 1.00      0.00   \n",
       "11                    6    11040.82                20.00      6.66   \n",
       "\n",
       "    E85 Station Count  \n",
       "0                6.00  \n",
       "1                1.00  \n",
       "2                1.00  \n",
       "3                2.00  \n",
       "4                0.00  \n",
       "5                0.00  \n",
       "6                0.00  \n",
       "7                0.00  \n",
       "8                0.00  \n",
       "9                0.00  \n",
       "10               0.00  \n",
       "11               1.00  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.to_csv('model_resultsv3.9.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 33)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>vehicle_pc_FFV</th>\n",
       "      <th>e85_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Butte</td>\n",
       "      <td>CA</td>\n",
       "      <td>6.70</td>\n",
       "      <td>6.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kern</td>\n",
       "      <td>CA</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Madera</td>\n",
       "      <td>CA</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>CA</td>\n",
       "      <td>16.80</td>\n",
       "      <td>16.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>CA</td>\n",
       "      <td>4.30</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Stanislaus</td>\n",
       "      <td>CA</td>\n",
       "      <td>14.10</td>\n",
       "      <td>14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Glenn</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Tulare</td>\n",
       "      <td>CA</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Ventura</td>\n",
       "      <td>CA</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Carlton</td>\n",
       "      <td>MN</td>\n",
       "      <td>28.80</td>\n",
       "      <td>28.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Cottonwood</td>\n",
       "      <td>MN</td>\n",
       "      <td>62.20</td>\n",
       "      <td>62.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Faribault</td>\n",
       "      <td>MN</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Grant</td>\n",
       "      <td>MN</td>\n",
       "      <td>30.20</td>\n",
       "      <td>30.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Hubbard</td>\n",
       "      <td>MN</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Itasca</td>\n",
       "      <td>MN</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Jackson MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>56.00</td>\n",
       "      <td>55.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Lac qui Parle</td>\n",
       "      <td>MN</td>\n",
       "      <td>38.30</td>\n",
       "      <td>38.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Murray</td>\n",
       "      <td>MN</td>\n",
       "      <td>25.80</td>\n",
       "      <td>25.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Pine</td>\n",
       "      <td>MN</td>\n",
       "      <td>13.40</td>\n",
       "      <td>13.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Red Lake</td>\n",
       "      <td>MN</td>\n",
       "      <td>24.90</td>\n",
       "      <td>24.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Renville</td>\n",
       "      <td>MN</td>\n",
       "      <td>53.90</td>\n",
       "      <td>53.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Rock</td>\n",
       "      <td>MN</td>\n",
       "      <td>33.20</td>\n",
       "      <td>33.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Waseca</td>\n",
       "      <td>MN</td>\n",
       "      <td>68.10</td>\n",
       "      <td>68.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Watonwan</td>\n",
       "      <td>MN</td>\n",
       "      <td>47.40</td>\n",
       "      <td>47.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Wilkin</td>\n",
       "      <td>MN</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Yellow Medicine</td>\n",
       "      <td>MN</td>\n",
       "      <td>60.30</td>\n",
       "      <td>60.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Blue Earth</td>\n",
       "      <td>MN</td>\n",
       "      <td>38.50</td>\n",
       "      <td>38.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Clay MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>28.70</td>\n",
       "      <td>28.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Kandiyohi</td>\n",
       "      <td>MN</td>\n",
       "      <td>31.50</td>\n",
       "      <td>31.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Nicollet</td>\n",
       "      <td>MN</td>\n",
       "      <td>19.30</td>\n",
       "      <td>19.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Nobles</td>\n",
       "      <td>MN</td>\n",
       "      <td>37.10</td>\n",
       "      <td>37.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Stearns</td>\n",
       "      <td>MN</td>\n",
       "      <td>48.90</td>\n",
       "      <td>48.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Winona</td>\n",
       "      <td>MN</td>\n",
       "      <td>34.10</td>\n",
       "      <td>34.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Dodge</td>\n",
       "      <td>MN</td>\n",
       "      <td>23.60</td>\n",
       "      <td>23.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Midland</td>\n",
       "      <td>TX</td>\n",
       "      <td>9.40</td>\n",
       "      <td>9.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Bandera</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>Bastrop</td>\n",
       "      <td>TX</td>\n",
       "      <td>13.30</td>\n",
       "      <td>13.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Medina</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>Parker</td>\n",
       "      <td>TX</td>\n",
       "      <td>15.80</td>\n",
       "      <td>15.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Caldwell</td>\n",
       "      <td>TX</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>Kerr</td>\n",
       "      <td>TX</td>\n",
       "      <td>11.00</td>\n",
       "      <td>10.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>San Patricio</td>\n",
       "      <td>TX</td>\n",
       "      <td>16.30</td>\n",
       "      <td>16.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              County State  vehicle_pc_FFV  e85_area\n",
       "11             Butte    CA            6.70      6.66\n",
       "15              Kern    CA            2.00      1.95\n",
       "17            Madera    CA            1.40      1.38\n",
       "23         Riverside    CA           16.80     16.79\n",
       "24    San Bernardino    CA            4.30      4.25\n",
       "28        Stanislaus    CA           14.10     14.10\n",
       "33             Glenn    CA            0.60      0.58\n",
       "37            Tulare    CA            2.50      2.46\n",
       "48           Ventura    CA            7.00      6.96\n",
       "70           Carlton    MN           28.80     28.80\n",
       "74        Cottonwood    MN           62.20     62.16\n",
       "77         Faribault    MN            6.20      6.18\n",
       "79             Grant    MN           30.20     30.15\n",
       "80           Hubbard    MN            4.60      4.57\n",
       "82            Itasca    MN            4.10      4.06\n",
       "83        Jackson MN    MN           56.00     55.96\n",
       "86     Lac qui Parle    MN           38.30     38.29\n",
       "96            Murray    MN           25.80     25.76\n",
       "98              Pine    MN           13.40     13.36\n",
       "101         Red Lake    MN           24.90     24.86\n",
       "103         Renville    MN           53.90     53.88\n",
       "104             Rock    MN           33.20     33.16\n",
       "110           Waseca    MN           68.10     68.10\n",
       "111         Watonwan    MN           47.40     47.38\n",
       "112           Wilkin    MN           13.50     13.46\n",
       "113  Yellow Medicine    MN           60.30     60.28\n",
       "115       Blue Earth    MN           38.50     38.49\n",
       "116          Clay MN    MN           28.70     28.65\n",
       "119        Kandiyohi    MN           31.50     31.47\n",
       "123         Nicollet    MN           19.30     19.29\n",
       "124           Nobles    MN           37.10     37.06\n",
       "128          Stearns    MN           48.90     48.88\n",
       "132           Winona    MN           34.10     34.06\n",
       "139            Dodge    MN           23.60     23.58\n",
       "227          Midland    TX            9.40      9.40\n",
       "293          Bandera    TX            0.30      0.28\n",
       "294          Bastrop    TX           13.30     13.28\n",
       "326           Medina    TX            0.90      0.88\n",
       "330           Parker    TX           15.80     15.75\n",
       "347         Caldwell    TX            3.70      3.65\n",
       "363             Kerr    TX           11.00     10.96\n",
       "370     San Patricio    TX           16.30     16.29"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[model['vehicle_pc_FFV']>model['e85_area']][['County','State','vehicle_pc_FFV','e85_area']]\n",
    "# model.e85_area.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>vehicle_pc_BEV</th>\n",
       "      <th>efuels_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alpine</td>\n",
       "      <td>CA</td>\n",
       "      <td>21.80</td>\n",
       "      <td>21.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amador</td>\n",
       "      <td>CA</td>\n",
       "      <td>42.90</td>\n",
       "      <td>42.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Calaveras</td>\n",
       "      <td>CA</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Plumas</td>\n",
       "      <td>CA</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Butte</td>\n",
       "      <td>CA</td>\n",
       "      <td>69.30</td>\n",
       "      <td>69.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Fresno</td>\n",
       "      <td>CA</td>\n",
       "      <td>67.30</td>\n",
       "      <td>67.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Inyo</td>\n",
       "      <td>CA</td>\n",
       "      <td>43.40</td>\n",
       "      <td>43.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kern</td>\n",
       "      <td>CA</td>\n",
       "      <td>62.80</td>\n",
       "      <td>62.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kings</td>\n",
       "      <td>CA</td>\n",
       "      <td>72.20</td>\n",
       "      <td>72.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Madera</td>\n",
       "      <td>CA</td>\n",
       "      <td>70.80</td>\n",
       "      <td>70.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Merced</td>\n",
       "      <td>CA</td>\n",
       "      <td>74.40</td>\n",
       "      <td>74.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Monterey</td>\n",
       "      <td>CA</td>\n",
       "      <td>82.60</td>\n",
       "      <td>82.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riverside</td>\n",
       "      <td>CA</td>\n",
       "      <td>75.80</td>\n",
       "      <td>75.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>San Bernardino</td>\n",
       "      <td>CA</td>\n",
       "      <td>49.90</td>\n",
       "      <td>49.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>San Joaquin</td>\n",
       "      <td>CA</td>\n",
       "      <td>98.10</td>\n",
       "      <td>98.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sutter</td>\n",
       "      <td>CA</td>\n",
       "      <td>57.30</td>\n",
       "      <td>57.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Tuolumne</td>\n",
       "      <td>CA</td>\n",
       "      <td>64.50</td>\n",
       "      <td>64.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Del Norte</td>\n",
       "      <td>CA</td>\n",
       "      <td>59.50</td>\n",
       "      <td>59.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Glenn</td>\n",
       "      <td>CA</td>\n",
       "      <td>48.80</td>\n",
       "      <td>48.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Imperial</td>\n",
       "      <td>CA</td>\n",
       "      <td>44.30</td>\n",
       "      <td>44.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Lake CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>63.10</td>\n",
       "      <td>63.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Tulare</td>\n",
       "      <td>CA</td>\n",
       "      <td>55.70</td>\n",
       "      <td>55.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>San Benito</td>\n",
       "      <td>CA</td>\n",
       "      <td>55.60</td>\n",
       "      <td>55.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>CA</td>\n",
       "      <td>87.70</td>\n",
       "      <td>87.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Ventura</td>\n",
       "      <td>CA</td>\n",
       "      <td>89.40</td>\n",
       "      <td>89.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>88.00</td>\n",
       "      <td>87.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Modoc</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Siskiyou</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.60</td>\n",
       "      <td>5.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Tehama</td>\n",
       "      <td>CA</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>CA</td>\n",
       "      <td>91.20</td>\n",
       "      <td>91.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Beltrami</td>\n",
       "      <td>MN</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Houston MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>8.20</td>\n",
       "      <td>8.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Carlton</td>\n",
       "      <td>MN</td>\n",
       "      <td>22.90</td>\n",
       "      <td>22.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Chippewa</td>\n",
       "      <td>MN</td>\n",
       "      <td>13.90</td>\n",
       "      <td>13.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Cook</td>\n",
       "      <td>MN</td>\n",
       "      <td>14.30</td>\n",
       "      <td>14.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Crow Wing</td>\n",
       "      <td>MN</td>\n",
       "      <td>15.50</td>\n",
       "      <td>15.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Fillmore</td>\n",
       "      <td>MN</td>\n",
       "      <td>19.10</td>\n",
       "      <td>19.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Isanti</td>\n",
       "      <td>MN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>37.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Kanabec</td>\n",
       "      <td>MN</td>\n",
       "      <td>5.90</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Le Sueur</td>\n",
       "      <td>MN</td>\n",
       "      <td>20.90</td>\n",
       "      <td>20.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Pipestone</td>\n",
       "      <td>MN</td>\n",
       "      <td>23.90</td>\n",
       "      <td>23.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Sibley</td>\n",
       "      <td>MN</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Clay MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>46.50</td>\n",
       "      <td>46.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Goodhue</td>\n",
       "      <td>MN</td>\n",
       "      <td>84.00</td>\n",
       "      <td>83.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Kandiyohi</td>\n",
       "      <td>MN</td>\n",
       "      <td>63.70</td>\n",
       "      <td>63.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>McLeod</td>\n",
       "      <td>MN</td>\n",
       "      <td>68.10</td>\n",
       "      <td>68.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Nobles</td>\n",
       "      <td>MN</td>\n",
       "      <td>55.40</td>\n",
       "      <td>55.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Rice</td>\n",
       "      <td>MN</td>\n",
       "      <td>59.20</td>\n",
       "      <td>59.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>MN</td>\n",
       "      <td>50.80</td>\n",
       "      <td>50.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Stevens</td>\n",
       "      <td>MN</td>\n",
       "      <td>63.10</td>\n",
       "      <td>63.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Winona</td>\n",
       "      <td>MN</td>\n",
       "      <td>56.20</td>\n",
       "      <td>56.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Brown MN</td>\n",
       "      <td>MN</td>\n",
       "      <td>51.40</td>\n",
       "      <td>51.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Chisago</td>\n",
       "      <td>MN</td>\n",
       "      <td>41.40</td>\n",
       "      <td>41.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Sherburne</td>\n",
       "      <td>MN</td>\n",
       "      <td>76.00</td>\n",
       "      <td>75.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Wright</td>\n",
       "      <td>MN</td>\n",
       "      <td>87.40</td>\n",
       "      <td>87.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>TX</td>\n",
       "      <td>10.30</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Collingsworth</td>\n",
       "      <td>TX</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>TX</td>\n",
       "      <td>19.20</td>\n",
       "      <td>19.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Culberson</td>\n",
       "      <td>TX</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Hamilton</td>\n",
       "      <td>TX</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>TX</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Hill</td>\n",
       "      <td>TX</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Jasper</td>\n",
       "      <td>TX</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Live Oak</td>\n",
       "      <td>TX</td>\n",
       "      <td>20.60</td>\n",
       "      <td>20.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Madison</td>\n",
       "      <td>TX</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Navarro</td>\n",
       "      <td>TX</td>\n",
       "      <td>10.40</td>\n",
       "      <td>10.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Titus</td>\n",
       "      <td>TX</td>\n",
       "      <td>25.60</td>\n",
       "      <td>25.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Chambers</td>\n",
       "      <td>TX</td>\n",
       "      <td>44.80</td>\n",
       "      <td>44.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Comal</td>\n",
       "      <td>TX</td>\n",
       "      <td>81.60</td>\n",
       "      <td>81.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Fort Bend</td>\n",
       "      <td>TX</td>\n",
       "      <td>77.90</td>\n",
       "      <td>77.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Midland</td>\n",
       "      <td>TX</td>\n",
       "      <td>64.90</td>\n",
       "      <td>64.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Aransas</td>\n",
       "      <td>TX</td>\n",
       "      <td>62.70</td>\n",
       "      <td>62.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Bowie</td>\n",
       "      <td>TX</td>\n",
       "      <td>48.50</td>\n",
       "      <td>48.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Coryell</td>\n",
       "      <td>TX</td>\n",
       "      <td>46.90</td>\n",
       "      <td>46.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Crockett</td>\n",
       "      <td>TX</td>\n",
       "      <td>44.20</td>\n",
       "      <td>44.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>Hidalgo</td>\n",
       "      <td>TX</td>\n",
       "      <td>55.30</td>\n",
       "      <td>55.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Jefferson</td>\n",
       "      <td>TX</td>\n",
       "      <td>64.30</td>\n",
       "      <td>64.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Kimble</td>\n",
       "      <td>TX</td>\n",
       "      <td>50.50</td>\n",
       "      <td>50.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Kleberg</td>\n",
       "      <td>TX</td>\n",
       "      <td>54.10</td>\n",
       "      <td>54.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Nolan</td>\n",
       "      <td>TX</td>\n",
       "      <td>51.90</td>\n",
       "      <td>51.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Presidio</td>\n",
       "      <td>TX</td>\n",
       "      <td>42.90</td>\n",
       "      <td>42.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>Reeves</td>\n",
       "      <td>TX</td>\n",
       "      <td>44.20</td>\n",
       "      <td>44.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Taylor</td>\n",
       "      <td>TX</td>\n",
       "      <td>55.70</td>\n",
       "      <td>55.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Webb</td>\n",
       "      <td>TX</td>\n",
       "      <td>43.20</td>\n",
       "      <td>43.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Wharton</td>\n",
       "      <td>TX</td>\n",
       "      <td>50.60</td>\n",
       "      <td>50.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Wilbarger</td>\n",
       "      <td>TX</td>\n",
       "      <td>51.40</td>\n",
       "      <td>51.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Archer</td>\n",
       "      <td>TX</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "      <td>13.70</td>\n",
       "      <td>13.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Bandera</td>\n",
       "      <td>TX</td>\n",
       "      <td>14.10</td>\n",
       "      <td>14.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>Bastrop</td>\n",
       "      <td>TX</td>\n",
       "      <td>18.50</td>\n",
       "      <td>18.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Burleson</td>\n",
       "      <td>TX</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Clay TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Hopkins</td>\n",
       "      <td>TX</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>Jeff Davis</td>\n",
       "      <td>TX</td>\n",
       "      <td>4.90</td>\n",
       "      <td>4.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Lampasas</td>\n",
       "      <td>TX</td>\n",
       "      <td>26.00</td>\n",
       "      <td>25.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Lavaca</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Lee</td>\n",
       "      <td>TX</td>\n",
       "      <td>17.50</td>\n",
       "      <td>17.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Martin TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Waller</td>\n",
       "      <td>TX</td>\n",
       "      <td>36.80</td>\n",
       "      <td>36.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Bell</td>\n",
       "      <td>TX</td>\n",
       "      <td>76.30</td>\n",
       "      <td>76.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Brazos</td>\n",
       "      <td>TX</td>\n",
       "      <td>90.30</td>\n",
       "      <td>90.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Ector</td>\n",
       "      <td>TX</td>\n",
       "      <td>56.10</td>\n",
       "      <td>56.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>Howard</td>\n",
       "      <td>TX</td>\n",
       "      <td>52.30</td>\n",
       "      <td>52.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>TX</td>\n",
       "      <td>62.50</td>\n",
       "      <td>62.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>Kaufman</td>\n",
       "      <td>TX</td>\n",
       "      <td>48.00</td>\n",
       "      <td>48.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>Kerr</td>\n",
       "      <td>TX</td>\n",
       "      <td>65.00</td>\n",
       "      <td>64.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>Llano</td>\n",
       "      <td>TX</td>\n",
       "      <td>62.70</td>\n",
       "      <td>62.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>Lubbock</td>\n",
       "      <td>TX</td>\n",
       "      <td>63.70</td>\n",
       "      <td>63.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>Moore</td>\n",
       "      <td>TX</td>\n",
       "      <td>52.90</td>\n",
       "      <td>52.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>Nueces</td>\n",
       "      <td>TX</td>\n",
       "      <td>74.40</td>\n",
       "      <td>74.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>Randall</td>\n",
       "      <td>TX</td>\n",
       "      <td>53.00</td>\n",
       "      <td>52.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>Tom Green</td>\n",
       "      <td>TX</td>\n",
       "      <td>47.20</td>\n",
       "      <td>47.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>Victoria</td>\n",
       "      <td>TX</td>\n",
       "      <td>61.40</td>\n",
       "      <td>61.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Galveston</td>\n",
       "      <td>TX</td>\n",
       "      <td>76.40</td>\n",
       "      <td>76.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Collin</td>\n",
       "      <td>TX</td>\n",
       "      <td>75.00</td>\n",
       "      <td>74.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Denton</td>\n",
       "      <td>TX</td>\n",
       "      <td>86.80</td>\n",
       "      <td>86.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>54.30</td>\n",
       "      <td>54.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             County State  vehicle_pc_BEV  efuels_area\n",
       "4            Alpine    CA           21.80        21.80\n",
       "5            Amador    CA           42.90        42.89\n",
       "6         Calaveras    CA           12.50        12.46\n",
       "9            Plumas    CA           10.50        10.48\n",
       "11            Butte    CA           69.30        69.30\n",
       "13           Fresno    CA           67.30        67.29\n",
       "14             Inyo    CA           43.40        43.36\n",
       "15             Kern    CA           62.80        62.77\n",
       "16            Kings    CA           72.20        72.15\n",
       "17           Madera    CA           70.80        70.76\n",
       "19           Merced    CA           74.40        74.37\n",
       "21         Monterey    CA           82.60        82.57\n",
       "23        Riverside    CA           75.80        75.78\n",
       "24   San Bernardino    CA           49.90        49.88\n",
       "25      San Joaquin    CA           98.10        98.07\n",
       "29           Sutter    CA           57.30        57.26\n",
       "30         Tuolumne    CA           64.50        64.47\n",
       "32        Del Norte    CA           59.50        59.49\n",
       "33            Glenn    CA           48.80        48.78\n",
       "35         Imperial    CA           44.30        44.27\n",
       "36          Lake CA    CA           63.10        63.07\n",
       "37           Tulare    CA           55.70        55.69\n",
       "42       San Benito    CA           55.60        55.56\n",
       "43        San Diego    CA           87.70        87.65\n",
       "48          Ventura    CA           89.40        89.39\n",
       "49      Los Angeles    CA           88.00        87.97\n",
       "53            Modoc    CA            5.30         5.29\n",
       "54         Siskiyou    CA            5.60         5.58\n",
       "55           Tehama    CA            8.00         7.98\n",
       "57      Santa Clara    CA           91.20        91.20\n",
       "59         Beltrami    MN            3.40         3.39\n",
       "61       Houston MN    MN            8.20         8.18\n",
       "70          Carlton    MN           22.90        22.89\n",
       "71         Chippewa    MN           13.90        13.86\n",
       "73             Cook    MN           14.30        14.26\n",
       "75        Crow Wing    MN           15.50        15.45\n",
       "78         Fillmore    MN           19.10        19.07\n",
       "81           Isanti    MN           37.40        37.37\n",
       "84          Kanabec    MN            5.90         5.85\n",
       "89         Le Sueur    MN           20.90        20.89\n",
       "99        Pipestone    MN           23.90        23.86\n",
       "106          Sibley    MN            2.90         2.86\n",
       "116         Clay MN    MN           46.50        46.48\n",
       "118         Goodhue    MN           84.00        83.99\n",
       "119       Kandiyohi    MN           63.70        63.66\n",
       "121          McLeod    MN           68.10        68.09\n",
       "124          Nobles    MN           55.40        55.38\n",
       "126            Rice    MN           59.20        59.17\n",
       "127       St. Louis    MN           50.80        50.77\n",
       "130         Stevens    MN           63.10        63.07\n",
       "132          Winona    MN           56.20        56.16\n",
       "133        Brown MN    MN           51.40        51.36\n",
       "138         Chisago    MN           41.40        41.39\n",
       "142       Sherburne    MN           76.00        75.96\n",
       "143          Wright    MN           87.40        87.36\n",
       "145        Anderson    TX           10.30        10.28\n",
       "155   Collingsworth    TX            2.30         2.30\n",
       "156        Colorado    TX           19.20        19.17\n",
       "160       Culberson    TX            3.00         2.98\n",
       "175        Hamilton    TX            8.40         8.37\n",
       "177       Henderson    TX            8.60         8.57\n",
       "178            Hill    TX           11.30        11.26\n",
       "181          Jasper    TX           10.50        10.47\n",
       "189        Live Oak    TX           20.60        20.56\n",
       "191         Madison    TX           23.50        23.47\n",
       "199         Navarro    TX           10.40        10.39\n",
       "216           Titus    TX           25.60        25.56\n",
       "223        Chambers    TX           44.80        44.78\n",
       "224           Comal    TX           81.60        81.56\n",
       "226       Fort Bend    TX           77.90        77.85\n",
       "227         Midland    TX           64.90        64.88\n",
       "232         Aransas    TX           62.70        62.65\n",
       "235           Bowie    TX           48.50        48.48\n",
       "240         Coryell    TX           46.90        46.89\n",
       "241        Crockett    TX           44.20        44.20\n",
       "252         Hidalgo    TX           55.30        55.26\n",
       "254       Jefferson    TX           64.30        64.30\n",
       "258          Kimble    TX           50.50        50.49\n",
       "260         Kleberg    TX           54.10        54.07\n",
       "269           Nolan    TX           51.90        51.88\n",
       "273        Presidio    TX           42.90        42.88\n",
       "274          Reeves    TX           44.20        44.19\n",
       "279          Taylor    TX           55.70        55.68\n",
       "284            Webb    TX           43.20        43.16\n",
       "285         Wharton    TX           50.60        50.56\n",
       "287       Wilbarger    TX           51.40        51.35\n",
       "289          Archer    TX            2.80         2.76\n",
       "292          Austin    TX           13.70        13.65\n",
       "293         Bandera    TX           14.10        14.06\n",
       "294         Bastrop    TX           18.50        18.48\n",
       "297        Burleson    TX            4.00         3.96\n",
       "301         Clay TX    TX           11.30        11.27\n",
       "312         Hopkins    TX           14.00        13.99\n",
       "317      Jeff Davis    TX            4.90         4.89\n",
       "319        Lampasas    TX           26.00        25.97\n",
       "320          Lavaca    TX            0.60         0.57\n",
       "321             Lee    TX           17.50        17.49\n",
       "325       Martin TX    TX            1.00         0.96\n",
       "340          Waller    TX           36.80        36.77\n",
       "343            Bell    TX           76.30        76.26\n",
       "345          Brazos    TX           90.30        90.25\n",
       "351           Ector    TX           56.10        56.06\n",
       "359          Howard    TX           52.30        52.26\n",
       "361         Johnson    TX           62.50        62.45\n",
       "362         Kaufman    TX           48.00        48.00\n",
       "363            Kerr    TX           65.00        64.97\n",
       "364           Llano    TX           62.70        62.68\n",
       "365         Lubbock    TX           63.70        63.67\n",
       "366           Moore    TX           52.90        52.88\n",
       "367          Nueces    TX           74.40        74.39\n",
       "368         Randall    TX           53.00        52.97\n",
       "374       Tom Green    TX           47.20        47.20\n",
       "375        Victoria    TX           61.40        61.36\n",
       "381       Galveston    TX           76.40        76.37\n",
       "389          Collin    TX           75.00        74.98\n",
       "390          Denton    TX           86.80        86.75\n",
       "392         El Paso    TX           54.30        54.29"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[model['vehicle_pc_BEV']>model['efuels_area']][['County','State','vehicle_pc_BEV','efuels_area']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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