Charging Station EDA_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Charging Station Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides the code for importing alternative fuel station data from the US Department of Energy website, cleaning and manipulating the data to give insight on what regions have the most charging stations, creating bar charts, and creating an interactive choropleth that shows the total number of charging stations and supported levels of charging in each US county."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import, Clean, and Maniplulate the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import necessary libraries\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>access_code</th>\n",
       "      <th>access_days_time</th>\n",
       "      <th>access_detail_code</th>\n",
       "      <th>cards_accepted</th>\n",
       "      <th>date_last_confirmed</th>\n",
       "      <th>expected_date</th>\n",
       "      <th>fuel_type_code</th>\n",
       "      <th>groups_with_access_code</th>\n",
       "      <th>id</th>\n",
       "      <th>open_date</th>\n",
       "      <th>...</th>\n",
       "      <th>ng_fill_type_code</th>\n",
       "      <th>ng_psi</th>\n",
       "      <th>ng_vehicle_class</th>\n",
       "      <th>access_days_time_fr</th>\n",
       "      <th>intersection_directions_fr</th>\n",
       "      <th>bd_blends_fr</th>\n",
       "      <th>groups_with_access_code_fr</th>\n",
       "      <th>ev_pricing_fr</th>\n",
       "      <th>ev_network_ids</th>\n",
       "      <th>federal_agency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>private</td>\n",
       "      <td>Fleet use only</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Private</td>\n",
       "      <td>1517</td>\n",
       "      <td>1999-10-15</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Privé</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>private</td>\n",
       "      <td>Employee use only</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>TEMPORARILY UNAVAILABLE (Private)</td>\n",
       "      <td>1520</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>TEMPORAIREMENT SUSPENDU (Privé)</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily; pay lot</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Public</td>\n",
       "      <td>1523</td>\n",
       "      <td>1995-08-30</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Public</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>private</td>\n",
       "      <td>For fleet and employee use only</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Private</td>\n",
       "      <td>1525</td>\n",
       "      <td>1999-10-15</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Privé</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>private</td>\n",
       "      <td>Fleet use only</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Private</td>\n",
       "      <td>1531</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Privé</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  access_code                 access_days_time access_detail_code  \\\n",
       "0     private                   Fleet use only               None   \n",
       "1     private                Employee use only               None   \n",
       "2      public          24 hours daily; pay lot               None   \n",
       "3     private  For fleet and employee use only               None   \n",
       "4     private                   Fleet use only               None   \n",
       "\n",
       "  cards_accepted date_last_confirmed expected_date fuel_type_code  \\\n",
       "0           None          2020-09-03          None           ELEC   \n",
       "1           None          2021-01-14          None           ELEC   \n",
       "2           None          2020-11-09          None           ELEC   \n",
       "3           None          2020-11-09          None           ELEC   \n",
       "4           None          2020-09-03          None           ELEC   \n",
       "\n",
       "             groups_with_access_code    id   open_date  ... ng_fill_type_code  \\\n",
       "0                            Private  1517  1999-10-15  ...              None   \n",
       "1  TEMPORARILY UNAVAILABLE (Private)  1520        None  ...              None   \n",
       "2                             Public  1523  1995-08-30  ...              None   \n",
       "3                            Private  1525  1999-10-15  ...              None   \n",
       "4                            Private  1531  2018-05-01  ...              None   \n",
       "\n",
       "  ng_psi ng_vehicle_class access_days_time_fr intersection_directions_fr  \\\n",
       "0   None             None                None                       None   \n",
       "1   None             None                None                       None   \n",
       "2   None             None                None                       None   \n",
       "3   None             None                None                       None   \n",
       "4   None             None                None                       None   \n",
       "\n",
       "  bd_blends_fr       groups_with_access_code_fr  ev_pricing_fr  \\\n",
       "0         None                            Privé           None   \n",
       "1         None  TEMPORAIREMENT SUSPENDU (Privé)           None   \n",
       "2         None                           Public           None   \n",
       "3         None                            Privé           None   \n",
       "4         None                            Privé           None   \n",
       "\n",
       "   ev_network_ids federal_agency  \n",
       "0             NaN            NaN  \n",
       "1             NaN            NaN  \n",
       "2             NaN            NaN  \n",
       "3             NaN            NaN  \n",
       "4             NaN            NaN  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pull information on electric cars into json format\n",
    "parameters = {\n",
    "    \"format\":\"json\",\n",
    "    \"api_key\":\"DEMO_KEY\",\n",
    "    \"status\":\"all\",\n",
    "    \"fuel_type\":\"ELEC\"}\n",
    "\n",
    "response = requests.get('https://developer.nrel.gov/api/alt-fuel-stations/v1.json', params=parameters).json()\n",
    "\n",
    "data = pd.DataFrame.from_dict(response[\"fuel_stations\"])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many charging stations, public and private, are included in this dataset? Roughly 43,504 at the time of writing this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are currently 43504 electric vehicle charging stations in the United States (public and private).\n"
     ]
    }
   ],
   "source": [
    "total = len(data)\n",
    "print(\"There are currently {} electric vehicle charging stations in the United States (public and private).\".format(total)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, for the purpose of this exploratory data analysis, only charging stations with public access that are open 24/7 are being looked at. Charging stations have to be readily accessible to the general public at any time of the day for them to help build the case of electric vehicles being practical alternatives to traditional gas cars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>access_days_time_fr</th>\n",
       "      <th>intersection_directions_fr</th>\n",
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       "      <th>groups_with_access_code_fr</th>\n",
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       "      <th>2</th>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily; pay lot</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Public</td>\n",
       "      <td>1523</td>\n",
       "      <td>1995-08-30</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Public</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Public</td>\n",
       "      <td>1583</td>\n",
       "      <td>1996-10-15</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Public</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Public</td>\n",
       "      <td>6355</td>\n",
       "      <td>1997-07-30</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Public</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Public</td>\n",
       "      <td>6384</td>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Public</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily; pay lot</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>None</td>\n",
       "      <td>ELEC</td>\n",
       "      <td>Public</td>\n",
       "      <td>6505</td>\n",
       "      <td>1997-08-30</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Public</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   access_code         access_days_time access_detail_code cards_accepted  \\\n",
       "2       public  24 hours daily; pay lot               None           None   \n",
       "10      public           24 hours daily               None           None   \n",
       "12      public           24 hours daily               None           None   \n",
       "13      public           24 hours daily               None           None   \n",
       "16      public  24 hours daily; pay lot               None           None   \n",
       "\n",
       "   date_last_confirmed expected_date fuel_type_code groups_with_access_code  \\\n",
       "2           2020-11-09          None           ELEC                  Public   \n",
       "10          2021-01-14          None           ELEC                  Public   \n",
       "12          2021-01-14          None           ELEC                  Public   \n",
       "13          2021-01-14          None           ELEC                  Public   \n",
       "16          2020-04-08          None           ELEC                  Public   \n",
       "\n",
       "      id   open_date  ... ng_fill_type_code ng_psi ng_vehicle_class  \\\n",
       "2   1523  1995-08-30  ...              None   None             None   \n",
       "10  1583  1996-10-15  ...              None   None             None   \n",
       "12  6355  1997-07-30  ...              None   None             None   \n",
       "13  6384  2014-09-02  ...              None   None             None   \n",
       "16  6505  1997-08-30  ...              None   None             None   \n",
       "\n",
       "   access_days_time_fr intersection_directions_fr bd_blends_fr  \\\n",
       "2                 None                       None         None   \n",
       "10                None                       None         None   \n",
       "12                None                       None         None   \n",
       "13                None                       None         None   \n",
       "16                None                       None         None   \n",
       "\n",
       "   groups_with_access_code_fr  ev_pricing_fr  ev_network_ids federal_agency  \n",
       "2                      Public           None             NaN            NaN  \n",
       "10                     Public           None             NaN            NaN  \n",
       "12                     Public           None             NaN            NaN  \n",
       "13                     Public           None             NaN            NaN  \n",
       "16                     Public           None             NaN            NaN  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Public Access Only\n",
    "data = data[data['access_code'] == 'public']\n",
    "\n",
    "#Only want chargers with 24/7 accesss... There are multiple variants listed, but they all start with \"24\"\n",
    "data = data[data['access_days_time'].str.startswith('24', na=False)]\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many charging stations are there that are public and open 24/7? Roughly 33,973 at the time of writing this. (compared to 43,504 total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are currently 33973 public, 24/7 EV charging stations nationwide.\n"
     ]
    }
   ],
   "source": [
    "total_accessible = len(data)\n",
    "print(\"There are currently {} public, 24/7 EV charging stations nationwide.\".format(total_accessible)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Data at the State and County Levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answering the question of whether or not the charging infrastructure exists for electric vehicle use to be practical requires looking at the data from different regional perspectives. Some states have more charging stations than other states, and the same is true for counties, so it follows that EV usage practicality differs by region."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "State data will be analyzed first. (Puerto Rico is included.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>11009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>2144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>1623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TX</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MA</td>\n",
       "      <td>1384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  counts\n",
       "0    CA   11009\n",
       "1    NY    2144\n",
       "2    FL    1623\n",
       "3    TX    1562\n",
       "4    MA    1384"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chargers_per_state = data.copy()\n",
    "chargers_per_state = chargers_per_state.groupby('state').size().reset_index(name='counts').sort_values(by='counts', ascending=False).reset_index(drop=True)\n",
    "chargers_per_state.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's polish up the data by referring to states by their full name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>counts</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>11009</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>2144</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>1623</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TX</td>\n",
       "      <td>1562</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MA</td>\n",
       "      <td>1384</td>\n",
       "      <td>Massachusetts</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  counts          State\n",
       "0    CA   11009     California\n",
       "1    NY    2144       New York\n",
       "2    FL    1623        Florida\n",
       "3    TX    1562          Texas\n",
       "4    MA    1384  Massachusetts"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = {\"AL\":\"Alabama\",\"AK\":\"Alaska\",\"AZ\":\"Arizona\",\"AR\":\"Arkansas\",\"CA\":\"California\",\"CO\":\"Colorado\",\"CT\":\"Connecticut\",\"DE\":\"Delaware\",\\\n",
    "          \"FL\":\"Florida\",\"GA\":\"Georgia\",\"HI\":\"Hawaii\",\"ID\":\"Idaho\",\"IL\":\"Illinois\",\"IN\":\"Indiana\",\"IA\":\"Iowa\",\"KS\":\"Kansas\",\"KY\":\"Kentucky\",\\\n",
    "          \"LA\":\"Louisiana\",\"ME\":\"Maine\",\"MD\":\"Maryland\",\"MA\":\"Massachusetts\",\"MI\":\"Michigan\",\"MN\":\"Minnesota\",\"MS\":\"Mississippi\",\"MO\":\"Missouri\",\\\n",
    "          \"MT\":\"Montana\",\"NE\":\"Nebraska\",\"NV\":\"Nevada\",\"NH\":\"New Hampshire\",\"NJ\":\"New Jersey\",\"NM\":\"New Mexico\",\"NY\":\"New York\",\"NC\":\"North Carolina\",\\\n",
    "          \"ND\":\"North Dakota\",\"OH\":\"Ohio\",\"OK\":\"Oklahoma\",\"OR\":\"Oregon\",\"PA\":\"Pennsylvania\",\"RI\":\"Rhode Island\",\"SC\":\"South Carolina\",\"SD\":\"South Dakota\",\\\n",
    "          \"TN\":\"Tennessee\",\"TX\":\"Texas\",\"UT\":\"Utah\",\"VT\":\"Vermont\",\"VA\":\"Virginia\",\"WA\":\"Washington\",\"WV\":\"West Virginia\",\"WI\":\"Wisconsin\",\"WY\":\"Wyoming\",\\\n",
    "         \"PR\":\"Puerto Rico\", \"DC\":\"District of Columbia\"}\n",
    "\n",
    "chargers_per_state['State'] = chargers_per_state.state.map(states)\n",
    "chargers_per_state.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Altair is the library that will be used to create bar charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-86ad4a1aaed645a3b2274bf7b34c9292\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-86ad4a1aaed645a3b2274bf7b34c9292\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-86ad4a1aaed645a3b2274bf7b34c9292\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"usermeta\": {\"embedOptions\": {\"theme\": \"fivethirtyeight\"}}, \"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"strokeWidth\": 0}}, \"layer\": [{\"mark\": {\"type\": \"bar\", \"color\": \"#80c97c\"}, \"encoding\": {\"x\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelAngle\": -45, \"labelColor\": \"black\"}, \"field\": \"State\", \"sort\": \"-y\", \"title\": \"State\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"domain\": false, \"labelColor\": \"black\"}, \"field\": \"counts\", \"title\": \"Number of Charging Stations\"}}}, {\"mark\": {\"type\": \"text\", \"align\": \"center\", \"angle\": 315, \"baseline\": \"top\", \"dx\": 10, \"dy\": -13}, \"encoding\": {\"text\": {\"type\": \"quantitative\", \"field\": \"counts\"}, \"x\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelAngle\": -45, \"labelColor\": \"black\"}, \"field\": \"State\", \"sort\": \"-y\", \"title\": \"State\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"domain\": false, \"labelColor\": \"black\"}, \"field\": \"counts\", \"title\": \"Number of Charging Stations\"}}}], \"data\": {\"name\": \"data-548e6b38b4643b73219481c9c3054c87\"}, \"height\": 320, \"title\": {\"text\": \"Electric Car Charging Stations per State\", \"subtitle\": \"Public & Open 24/7\", \"subtitleFontSize\": 18}, \"width\": 1000, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-548e6b38b4643b73219481c9c3054c87\": [{\"state\": \"CA\", \"counts\": 11009, \"State\": \"California\"}, {\"state\": \"NY\", \"counts\": 2144, \"State\": \"New York\"}, {\"state\": \"FL\", \"counts\": 1623, \"State\": \"Florida\"}, {\"state\": \"TX\", \"counts\": 1562, \"State\": \"Texas\"}, {\"state\": \"MA\", \"counts\": 1384, \"State\": \"Massachusetts\"}, {\"state\": \"GA\", \"counts\": 1141, \"State\": \"Georgia\"}, {\"state\": \"CO\", \"counts\": 1098, \"State\": \"Colorado\"}, {\"state\": \"WA\", \"counts\": 1048, \"State\": \"Washington\"}, {\"state\": \"MO\", \"counts\": 916, \"State\": \"Missouri\"}, {\"state\": \"NC\", \"counts\": 717, \"State\": \"North Carolina\"}, {\"state\": \"PA\", \"counts\": 696, \"State\": \"Pennsylvania\"}, {\"state\": \"UT\", \"counts\": 694, \"State\": \"Utah\"}, {\"state\": \"MD\", \"counts\": 649, \"State\": \"Maryland\"}, {\"state\": \"OH\", \"counts\": 648, \"State\": \"Ohio\"}, {\"state\": \"IL\", \"counts\": 625, \"State\": \"Illinois\"}, {\"state\": \"VA\", \"counts\": 585, \"State\": \"Virginia\"}, {\"state\": \"MI\", \"counts\": 559, \"State\": \"Michigan\"}, {\"state\": \"AZ\", \"counts\": 491, \"State\": \"Arizona\"}, {\"state\": \"OR\", \"counts\": 483, \"State\": \"Oregon\"}, {\"state\": \"MN\", \"counts\": 464, \"State\": \"Minnesota\"}, {\"state\": \"NJ\", \"counts\": 457, \"State\": \"New Jersey\"}, {\"state\": \"KS\", \"counts\": 419, \"State\": \"Kansas\"}, {\"state\": \"TN\", \"counts\": 370, \"State\": \"Tennessee\"}, {\"state\": \"NV\", \"counts\": 359, \"State\": \"Nevada\"}, {\"state\": \"WI\", \"counts\": 359, \"State\": \"Wisconsin\"}, {\"state\": \"CT\", \"counts\": 327, \"State\": \"Connecticut\"}, {\"state\": \"SC\", \"counts\": 250, \"State\": \"South Carolina\"}, {\"state\": \"OK\", \"counts\": 235, \"State\": \"Oklahoma\"}, {\"state\": \"VT\", \"counts\": 219, \"State\": \"Vermont\"}, {\"state\": \"HI\", \"counts\": 216, \"State\": \"Hawaii\"}, {\"state\": \"IN\", \"counts\": 212, \"State\": \"Indiana\"}, {\"state\": \"ME\", \"counts\": 199, \"State\": \"Maine\"}, {\"state\": \"RI\", \"counts\": 189, \"State\": \"Rhode Island\"}, {\"state\": \"IA\", \"counts\": 188, \"State\": \"Iowa\"}, {\"state\": \"DC\", \"counts\": 143, \"State\": \"District of Columbia\"}, {\"state\": \"AL\", \"counts\": 140, \"State\": \"Alabama\"}, {\"state\": \"NM\", \"counts\": 136, \"State\": \"New Mexico\"}, {\"state\": \"KY\", \"counts\": 133, \"State\": \"Kentucky\"}, {\"state\": \"NE\", \"counts\": 116, \"State\": \"Nebraska\"}, {\"state\": \"LA\", \"counts\": 110, \"State\": \"Louisiana\"}, {\"state\": \"NH\", \"counts\": 93, \"State\": \"New Hampshire\"}, {\"state\": \"AR\", \"counts\": 86, \"State\": \"Arkansas\"}, {\"state\": \"ID\", \"counts\": 74, \"State\": \"Idaho\"}, {\"state\": \"MS\", \"counts\": 73, \"State\": \"Mississippi\"}, {\"state\": \"WV\", \"counts\": 68, \"State\": \"West Virginia\"}, {\"state\": \"WY\", \"counts\": 56, \"State\": \"Wyoming\"}, {\"state\": \"MT\", \"counts\": 51, \"State\": \"Montana\"}, {\"state\": \"DE\", \"counts\": 47, \"State\": \"Delaware\"}, {\"state\": \"SD\", \"counts\": 39, \"State\": \"South Dakota\"}, {\"state\": \"ND\", \"counts\": 37, \"State\": \"North Dakota\"}, {\"state\": \"AK\", \"counts\": 33, \"State\": \"Alaska\"}, {\"state\": \"PR\", \"counts\": 3, \"State\": \"Puerto Rico\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "\n",
    "alt.themes.enable('fivethirtyeight')\n",
    "\n",
    "bars = alt.Chart(chargers_per_state).mark_bar(color='#80c97c').encode(\n",
    "    x=alt.Y(\"State:N\", sort='-y', title='State',\n",
    "           axis=alt.Axis(labelAngle=-45, domain=False, labelColor='black')),\n",
    "    y=alt.Y(\"counts:Q\", title='Number of Charging Stations',\n",
    "           axis=alt.Axis(domain=False, labelColor='black'))\n",
    ")\n",
    "\n",
    "text = bars.mark_text(\n",
    "    angle=315,\n",
    "    align='center',\n",
    "    baseline='top',\n",
    "    dx= 10,\n",
    "    dy=-13  # Nudges text to right so it doesn't appear on top of the bar\n",
    ").encode(\n",
    "    text='counts:Q'\n",
    ")\n",
    "\n",
    "(bars + text).properties(\n",
    "    height=320, width=1000, \n",
    "    title={'text': 'Electric Car Charging Stations per State',\n",
    "          'subtitle': 'Public & Open 24/7',\n",
    "          'subtitleFontSize': 18}\n",
    "    ).configure_view(strokeWidth=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "California far outnumbers the other states in terms of public access, 24/7 charging stations, but there are several of other large coastal states with thousands of charging stations. Most of the states that are on the lower end in terms of number of charging stations are either generally more rural and less-populous, or small in terms of area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "County data will now be analyzed. However, the original dataset only contains ZIP codes, so they need to be mapped to counties. \n",
    "Zip Code and county data can be found at https://www.unitedstateszipcodes.org/zip-code-database/. A dictionary to map ZIP and county data will be made with this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load data\n",
    "zip_county_df = pd.read_csv('assets\\zip_code_database.csv')\n",
    "\n",
    "#Create zip-county dictionary from datadrame\n",
    "zip_county_df = zip_county_df[['zip','county','state']]\n",
    "zip_county_df = zip_county_df.set_index('zip')\n",
    "zip_dict = zip_county_df.to_dict('index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dictionary is used to map zip codes in the original public, 24/7 dataframe to their resepctive counties. The dataframe will be grouped by county to provide counts of charging stations in each county. Supporting charging Levels for each station will also be shown and used for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>access_code</th>\n",
       "      <th>access_days_time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>ev_level1_evse_num</th>\n",
       "      <th>ev_level2_evse_num</th>\n",
       "      <th>ev_dc_fast_num</th>\n",
       "      <th>county</th>\n",
       "      <th>county_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>90015.0</td>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily; pay lot</td>\n",
       "      <td>34.040539</td>\n",
       "      <td>-118.271387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Los Angeles County</td>\n",
       "      <td>Los Angeles County, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>El Monte</td>\n",
       "      <td>CA</td>\n",
       "      <td>91731.0</td>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily</td>\n",
       "      <td>34.068720</td>\n",
       "      <td>-118.064000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Los Angeles County</td>\n",
       "      <td>Los Angeles County, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>La Jolla</td>\n",
       "      <td>CA</td>\n",
       "      <td>92037.0</td>\n",
       "      <td>public</td>\n",
       "      <td>24 hours daily</td>\n",
       "      <td>32.899470</td>\n",
       "      <td>-117.243000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Diego County</td>\n",
       "      <td>San Diego County, CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           city state      zip access_code         access_days_time  \\\n",
       "2   Los Angeles    CA  90015.0      public  24 hours daily; pay lot   \n",
       "10     El Monte    CA  91731.0      public           24 hours daily   \n",
       "12     La Jolla    CA  92037.0      public           24 hours daily   \n",
       "\n",
       "     latitude   longitude  ev_level1_evse_num  ev_level2_evse_num  \\\n",
       "2   34.040539 -118.271387                 NaN                12.0   \n",
       "10  34.068720 -118.064000                 NaN                 3.0   \n",
       "12  32.899470 -117.243000                 NaN                 1.0   \n",
       "\n",
       "    ev_dc_fast_num              county            county_state  \n",
       "2              NaN  Los Angeles County  Los Angeles County, CA  \n",
       "10             NaN  Los Angeles County  Los Angeles County, CA  \n",
       "12             NaN    San Diego County    San Diego County, CA  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chargers_per_zip = data.copy()\n",
    "\n",
    "#Reduce columns to relevant information\n",
    "chargers_per_zip = chargers_per_zip[['city','state','zip','access_code','access_days_time', 'latitude',\n",
    "                                'longitude','ev_level1_evse_num','ev_level2_evse_num', 'ev_dc_fast_num']]\n",
    "\n",
    "chargers_per_zip.zip =chargers_per_zip.zip.apply(pd.to_numeric, errors='coerce') #Need to convert zips from objects to numbers to map\n",
    "\n",
    "#Use zip_dict to map to counties, and create a column with county and state in it\n",
    "chargers_per_zip['County'] = chargers_per_zip['zip'].map(zip_dict) #Puts dictionary into county_dict column in form {'county': 'Orange County}\n",
    "df = chargers_per_zip[\"County\"].apply(pd.Series) #Create df with counties separated\n",
    "chargers_per_zip = pd.concat([chargers_per_zip, df['county']], axis=1).drop('County', axis=1) #Concatenate the county names back in\n",
    "\n",
    "#Add county and state for merging with gdf later. (Need both in same column)\n",
    "chargers_per_zip['county_state'] = chargers_per_zip['county'] + ', ' + chargers_per_zip['state'] \n",
    "\n",
    "chargers_per_zip.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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_state</th>\n",
       "      <th>counts</th>\n",
       "      <th>ev_level1_evse_num</th>\n",
       "      <th>ev_level2_evse_num</th>\n",
       "      <th>ev_dc_fast_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Los Angeles County, CA</td>\n",
       "      <td>2699</td>\n",
       "      <td>6</td>\n",
       "      <td>2532</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Santa Clara County, CA</td>\n",
       "      <td>1330</td>\n",
       "      <td>6</td>\n",
       "      <td>1264</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orange County, CA</td>\n",
       "      <td>1253</td>\n",
       "      <td>9</td>\n",
       "      <td>1146</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>San Mateo County, CA</td>\n",
       "      <td>835</td>\n",
       "      <td>4</td>\n",
       "      <td>778</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>San Diego County, CA</td>\n",
       "      <td>755</td>\n",
       "      <td>0</td>\n",
       "      <td>707</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             county_state  counts  ev_level1_evse_num  ev_level2_evse_num  \\\n",
       "0  Los Angeles County, CA    2699                   6                2532   \n",
       "1  Santa Clara County, CA    1330                   6                1264   \n",
       "2       Orange County, CA    1253                   9                1146   \n",
       "3    San Mateo County, CA     835                   4                 778   \n",
       "4    San Diego County, CA     755                   0                 707   \n",
       "\n",
       "   ev_dc_fast_num  \n",
       "0             207  \n",
       "1              83  \n",
       "2             130  \n",
       "3              64  \n",
       "4              66  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group By county_state and aggregate counts (Cities will be summed arbitrarily for total counts)\n",
    "chargers_per_county = chargers_per_zip.groupby('county_state').agg({'city': 'size', 'ev_level1_evse_num': 'count',\\\n",
    "                                                                'ev_level2_evse_num':'count','ev_dc_fast_num':'count'}).\\\n",
    "                                                                    reset_index().sort_values(by='city', ascending=False).reset_index(drop=True)\n",
    "\n",
    "#Fix Columns\n",
    "chargers_per_county.columns = ['county_state','counts','ev_level1_evse_num','ev_level2_evse_num', 'ev_dc_fast_num']\n",
    "\n",
    "chargers_per_county.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a bar chart to visualize the 30 counties with the most charging stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  })({\"usermeta\": {\"embedOptions\": {\"theme\": \"fivethirtyeight\"}}, \"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"strokeWidth\": 0}}, \"layer\": [{\"mark\": {\"type\": \"bar\", \"color\": \"#80c97c\"}, \"encoding\": {\"x\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelAngle\": -45, \"labelColor\": \"black\"}, \"field\": \"county_state\", \"sort\": \"-y\", \"title\": \"Location\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"domain\": false, \"labelColor\": \"black\"}, \"field\": \"counts\", \"title\": \"Number of Charging Stations\"}}}, {\"mark\": {\"type\": \"text\", \"align\": \"center\", \"angle\": 315, \"baseline\": \"top\", \"dx\": 10, \"dy\": -13}, \"encoding\": {\"text\": {\"type\": \"quantitative\", \"field\": \"counts\"}, \"x\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelAngle\": -45, \"labelColor\": \"black\"}, \"field\": \"county_state\", \"sort\": \"-y\", \"title\": \"Location\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"domain\": false, \"labelColor\": \"black\"}, \"field\": \"counts\", \"title\": \"Number of Charging Stations\"}}}], \"data\": {\"name\": \"data-ad1c45c51e806e286d6b3eb8e90a4dea\"}, \"height\": 320, \"title\": {\"text\": \"30 Counties with the Most Electric Car Charging Stations\", \"subtitle\": \"Public & Open 24/7\", \"subtitleFontSize\": 18}, \"width\": 1000, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-ad1c45c51e806e286d6b3eb8e90a4dea\": [{\"county_state\": \"Los Angeles County, CA\", \"counts\": 2699, \"ev_level1_evse_num\": 6, \"ev_level2_evse_num\": 2532, \"ev_dc_fast_num\": 207}, {\"county_state\": \"Santa Clara County, CA\", \"counts\": 1330, \"ev_level1_evse_num\": 6, \"ev_level2_evse_num\": 1264, \"ev_dc_fast_num\": 83}, {\"county_state\": \"Orange County, CA\", \"counts\": 1253, \"ev_level1_evse_num\": 9, \"ev_level2_evse_num\": 1146, \"ev_dc_fast_num\": 130}, {\"county_state\": \"San Mateo County, CA\", \"counts\": 835, \"ev_level1_evse_num\": 4, \"ev_level2_evse_num\": 778, \"ev_dc_fast_num\": 64}, {\"county_state\": \"San Diego County, CA\", \"counts\": 755, \"ev_level1_evse_num\": 0, \"ev_level2_evse_num\": 707, \"ev_dc_fast_num\": 66}, {\"county_state\": \"Alameda County, CA\", \"counts\": 585, \"ev_level1_evse_num\": 10, \"ev_level2_evse_num\": 549, \"ev_dc_fast_num\": 56}, {\"county_state\": \"King County, WA\", \"counts\": 538, \"ev_level1_evse_num\": 4, \"ev_level2_evse_num\": 492, \"ev_dc_fast_num\": 57}, {\"county_state\": \"Fulton County, GA\", \"counts\": 524, \"ev_level1_evse_num\": 1, \"ev_level2_evse_num\": 490, \"ev_dc_fast_num\": 35}, {\"county_state\": \"Travis County, TX\", \"counts\": 457, \"ev_level1_evse_num\": 14, \"ev_level2_evse_num\": 423, \"ev_dc_fast_num\": 38}, {\"county_state\": \"Jackson County, MO\", \"counts\": 453, \"ev_level1_evse_num\": 1, \"ev_level2_evse_num\": 443, \"ev_dc_fast_num\": 10}, {\"county_state\": \"Riverside County, CA\", \"counts\": 406, \"ev_level1_evse_num\": 7, \"ev_level2_evse_num\": 318, \"ev_dc_fast_num\": 99}, {\"county_state\": \"Middlesex County, MA\", \"counts\": 406, \"ev_level1_evse_num\": 7, \"ev_level2_evse_num\": 396, \"ev_dc_fast_num\": 14}, {\"county_state\": \"Salt Lake County, UT\", \"counts\": 370, \"ev_level1_evse_num\": 0, \"ev_level2_evse_num\": 353, \"ev_dc_fast_num\": 21}, {\"county_state\": \"Maricopa County, AZ\", \"counts\": 360, \"ev_level1_evse_num\": 2, \"ev_level2_evse_num\": 334, \"ev_dc_fast_num\": 26}, {\"county_state\": \"Cook County, IL\", \"counts\": 346, \"ev_level1_evse_num\": 12, \"ev_level2_evse_num\": 305, \"ev_dc_fast_num\": 51}, {\"county_state\": \"Johnson County, KS\", \"counts\": 325, \"ev_level1_evse_num\": 2, \"ev_level2_evse_num\": 314, \"ev_dc_fast_num\": 11}, {\"county_state\": \"Sacramento County, CA\", \"counts\": 323, \"ev_level1_evse_num\": 4, \"ev_level2_evse_num\": 290, \"ev_dc_fast_num\": 41}, {\"county_state\": \"San Francisco County, CA\", \"counts\": 304, \"ev_level1_evse_num\": 46, \"ev_level2_evse_num\": 293, \"ev_dc_fast_num\": 14}, {\"county_state\": \"New York County, NY\", \"counts\": 290, \"ev_level1_evse_num\": 0, \"ev_level2_evse_num\": 280, \"ev_dc_fast_num\": 10}, {\"county_state\": \"Suffolk County, MA\", \"counts\": 281, \"ev_level1_evse_num\": 0, \"ev_level2_evse_num\": 267, \"ev_dc_fast_num\": 15}, {\"county_state\": \"San Bernardino County, CA\", \"counts\": 252, \"ev_level1_evse_num\": 0, \"ev_level2_evse_num\": 194, \"ev_dc_fast_num\": 63}, {\"county_state\": \"Franklin County, OH\", \"counts\": 249, \"ev_level1_evse_num\": 0, \"ev_level2_evse_num\": 208, \"ev_dc_fast_num\": 43}, {\"county_state\": \"Harris County, TX\", \"counts\": 221, \"ev_level1_evse_num\": 9, \"ev_level2_evse_num\": 196, \"ev_dc_fast_num\": 24}, {\"county_state\": \"Boulder County, CO\", \"counts\": 220, \"ev_level1_evse_num\": 1, \"ev_level2_evse_num\": 214, \"ev_dc_fast_num\": 9}, {\"county_state\": \"Contra Costa County, CA\", \"counts\": 218, \"ev_level1_evse_num\": 7, \"ev_level2_evse_num\": 198, \"ev_dc_fast_num\": 24}, {\"county_state\": \"Orange County, FL\", \"counts\": 217, \"ev_level1_evse_num\": 6, \"ev_level2_evse_num\": 207, \"ev_dc_fast_num\": 12}, {\"county_state\": \"Miami-Dade County, FL\", \"counts\": 215, \"ev_level1_evse_num\": 0, \"ev_level2_evse_num\": 199, \"ev_dc_fast_num\": 21}, {\"county_state\": \"Clark County, NV\", \"counts\": 208, \"ev_level1_evse_num\": 1, \"ev_level2_evse_num\": 178, \"ev_dc_fast_num\": 30}, {\"county_state\": \"Denver County, CO\", \"counts\": 196, \"ev_level1_evse_num\": 4, \"ev_level2_evse_num\": 186, \"ev_dc_fast_num\": 10}, {\"county_state\": \"Dallas County, TX\", \"counts\": 193, \"ev_level1_evse_num\": 3, \"ev_level2_evse_num\": 185, \"ev_dc_fast_num\": 14}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.themes.enable('fivethirtyeight')\n",
    "\n",
    "bars = alt.Chart(chargers_per_county[:30]).mark_bar(color='#80c97c').encode(\n",
    "    x=alt.Y(\"county_state:N\", sort='-y', title='Location',\n",
    "           axis=alt.Axis(labelAngle=-45, domain=False, labelColor='black')),\n",
    "    y=alt.Y(\"counts:Q\", title='Number of Charging Stations',\n",
    "           axis=alt.Axis(domain=False, labelColor='black'))\n",
    ")\n",
    "\n",
    "text = bars.mark_text(\n",
    "    angle=315,\n",
    "    align='center',\n",
    "    baseline='top',\n",
    "    dx= 10,\n",
    "    dy=-13  # Nudges text to right so it doesn't appear on top of the bar\n",
    ").encode(\n",
    "    text='counts:Q'\n",
    ")\n",
    "\n",
    "(bars + text).properties(\n",
    "    height=320, width=1000, \n",
    "    title={'text': '30 Counties with the Most Electric Car Charging Stations',\n",
    "          'subtitle': 'Public & Open 24/7',\n",
    "          'subtitleFontSize': 18}\n",
    "    ).configure_view(strokeWidth=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The counties with the most EV charging stations are predominantly located in California, with other urban centers appearing in the top 30."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Choropleth of Electric Vehicle Charging Stations by County"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A choropleth will be created to show the number of charging stations in each US county, and a tooltip will be added to provide more specific information like the number of stations that support the different levels of charging. This visualization is intended to address questions concerning where EV ownership is the most practical based on charging locations and what locations could do better to support EV ownership.\n",
    "\n",
    "The folium library will be used to create the choropleth, and geopandas will used to manipulate geographic data in the same way pandas does. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape file that defines the US county boundaries can be found at the US Census Bureau's website: https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas\n",
    "\n",
    "# Suppress all warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#Read in Geographic Information\n",
    "gdf_county = geopandas.read_file('assets\\County_Boundaries\\cb_2019_us_county_20m.shp')\n",
    "gdf_county.crs = \"EPSG:4326\" #Set CRS - I don't know why this is necessary, but it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>geometry</th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAME</th>\n",
       "      <th>STCOUNTYFP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((-94.63203 40.57176, -94.53388 40.570...</td>\n",
       "      <td>29</td>\n",
       "      <td>227</td>\n",
       "      <td>Worth</td>\n",
       "      <td>29227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((-99.17940 40.35068, -98.72683 40.350...</td>\n",
       "      <td>31</td>\n",
       "      <td>061</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>31061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((-79.76195 42.26986, -79.62748 42.324...</td>\n",
       "      <td>36</td>\n",
       "      <td>013</td>\n",
       "      <td>Chautauqua</td>\n",
       "      <td>36013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((-78.49773 36.51467, -78.45728 36.541...</td>\n",
       "      <td>37</td>\n",
       "      <td>181</td>\n",
       "      <td>Vance</td>\n",
       "      <td>37181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((-88.94916 36.41010, -88.81642 36.410...</td>\n",
       "      <td>47</td>\n",
       "      <td>183</td>\n",
       "      <td>Weakley</td>\n",
       "      <td>47183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry STATEFP COUNTYFP  \\\n",
       "0  POLYGON ((-94.63203 40.57176, -94.53388 40.570...      29      227   \n",
       "1  POLYGON ((-99.17940 40.35068, -98.72683 40.350...      31      061   \n",
       "2  POLYGON ((-79.76195 42.26986, -79.62748 42.324...      36      013   \n",
       "3  POLYGON ((-78.49773 36.51467, -78.45728 36.541...      37      181   \n",
       "4  POLYGON ((-88.94916 36.41010, -88.81642 36.410...      47      183   \n",
       "\n",
       "         NAME  STCOUNTYFP  \n",
       "0       Worth       29227  \n",
       "1    Franklin       31061  \n",
       "2  Chautauqua       36013  \n",
       "3       Vance       37181  \n",
       "4     Weakley       47183  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reduce geometric info\n",
    "gdf_county_reduced = gdf_county[['geometry','STATEFP','COUNTYFP','NAME']]\n",
    "gdf_county_reduced.crs = \"EPSG:4326\" #Set CRS\n",
    "\n",
    "#Combine STATEFP and COUNTYFP to map to full State, County Name\n",
    "gdf_county_reduced['STCOUNTYFP'] = gdf_county_reduced['STATEFP'] + gdf_county_reduced['COUNTYFP']\n",
    "gdf_county_reduced['STCOUNTYFP'] = gdf_county_reduced['STCOUNTYFP'].astype(int)\n",
    "\n",
    "gdf_county_reduced.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polygons are now defined for eacy county boundary, but they somehow need to be translated to the county and state names. \n",
    "The data from https://www.kaggle.com/danofer/zipcodes-county-fips-crosswalk?select=ZIP-COUNTY-FIPS_2017-06.csv can be used to map the 'STCOUNTYFP' codes to county names. Afterwards, the data can be merged with the charging station data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>COUNTYNAME</th>\n",
       "      <th>STATE</th>\n",
       "      <th>STCOUNTYFP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga County</td>\n",
       "      <td>AL</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>AL</td>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Barbour County</td>\n",
       "      <td>AL</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        COUNTYNAME STATE  STCOUNTYFP\n",
       "0   Autauga County    AL        1001\n",
       "13  Baldwin County    AL        1003\n",
       "41  Barbour County    AL        1005"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "code_dict_df= pd.read_csv('assets/ZIP-COUNTY-FIPS_2017-06.csv')\n",
    "\n",
    "#Needs to drop duplicates\n",
    "code_dict_df = code_dict_df.drop_duplicates(subset=['STCOUNTYFP'],keep='first')\n",
    "#Drop ZIP and CLASSFP columns\n",
    "code_dict_df = code_dict_df[['COUNTYNAME','STATE','STCOUNTYFP']]\n",
    "                                     \n",
    "code_dict_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>geometry</th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAME</th>\n",
       "      <th>STCOUNTYFP</th>\n",
       "      <th>COUNTYNAME</th>\n",
       "      <th>STATE</th>\n",
       "      <th>county_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((-94.63203 40.57176, -94.53388 40.570...</td>\n",
       "      <td>29</td>\n",
       "      <td>227</td>\n",
       "      <td>Worth</td>\n",
       "      <td>29227</td>\n",
       "      <td>Worth County</td>\n",
       "      <td>MO</td>\n",
       "      <td>Worth County, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((-99.17940 40.35068, -98.72683 40.350...</td>\n",
       "      <td>31</td>\n",
       "      <td>061</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>31061</td>\n",
       "      <td>Franklin County</td>\n",
       "      <td>NE</td>\n",
       "      <td>Franklin County, NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((-79.76195 42.26986, -79.62748 42.324...</td>\n",
       "      <td>36</td>\n",
       "      <td>013</td>\n",
       "      <td>Chautauqua</td>\n",
       "      <td>36013</td>\n",
       "      <td>Chautauqua County</td>\n",
       "      <td>NY</td>\n",
       "      <td>Chautauqua County, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((-78.49773 36.51467, -78.45728 36.541...</td>\n",
       "      <td>37</td>\n",
       "      <td>181</td>\n",
       "      <td>Vance</td>\n",
       "      <td>37181</td>\n",
       "      <td>Vance County</td>\n",
       "      <td>NC</td>\n",
       "      <td>Vance County, NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((-88.94916 36.41010, -88.81642 36.410...</td>\n",
       "      <td>47</td>\n",
       "      <td>183</td>\n",
       "      <td>Weakley</td>\n",
       "      <td>47183</td>\n",
       "      <td>Weakley County</td>\n",
       "      <td>TN</td>\n",
       "      <td>Weakley County, TN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry STATEFP COUNTYFP  \\\n",
       "0  POLYGON ((-94.63203 40.57176, -94.53388 40.570...      29      227   \n",
       "1  POLYGON ((-99.17940 40.35068, -98.72683 40.350...      31      061   \n",
       "2  POLYGON ((-79.76195 42.26986, -79.62748 42.324...      36      013   \n",
       "3  POLYGON ((-78.49773 36.51467, -78.45728 36.541...      37      181   \n",
       "4  POLYGON ((-88.94916 36.41010, -88.81642 36.410...      47      183   \n",
       "\n",
       "         NAME  STCOUNTYFP         COUNTYNAME STATE           county_state  \n",
       "0       Worth       29227       Worth County    MO       Worth County, MO  \n",
       "1    Franklin       31061    Franklin County    NE    Franklin County, NE  \n",
       "2  Chautauqua       36013  Chautauqua County    NY  Chautauqua County, NY  \n",
       "3       Vance       37181       Vance County    NC       Vance County, NC  \n",
       "4     Weakley       47183     Weakley County    TN     Weakley County, TN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge with geo data\n",
    "gdf_county_mapped = gdf_county_reduced.merge(code_dict_df, how='outer', on='STCOUNTYFP')\n",
    "\n",
    "#Combine state and county names into same column for merging (Notice how the county and state names were also combined in chargers_per_county)\n",
    "gdf_county_mapped['county_state'] = gdf_county_mapped['COUNTYNAME'] + ', ' + gdf_county_mapped['STATE'] \n",
    "\n",
    "gdf_county_mapped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the geographic data is to be merged with the data in chargers_per_county to tie county boundaries to the counts of chargers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>geometry</th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAME</th>\n",
       "      <th>STCOUNTYFP</th>\n",
       "      <th>COUNTYNAME</th>\n",
       "      <th>STATE</th>\n",
       "      <th>county_state</th>\n",
       "      <th>counts</th>\n",
       "      <th>ev_level1_evse_num</th>\n",
       "      <th>ev_level2_evse_num</th>\n",
       "      <th>ev_dc_fast_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((-94.63203 40.57176, -94.53388 40.570...</td>\n",
       "      <td>29</td>\n",
       "      <td>227</td>\n",
       "      <td>Worth</td>\n",
       "      <td>29227.0</td>\n",
       "      <td>Worth County</td>\n",
       "      <td>MO</td>\n",
       "      <td>Worth County, MO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((-99.17940 40.35068, -98.72683 40.350...</td>\n",
       "      <td>31</td>\n",
       "      <td>061</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>31061.0</td>\n",
       "      <td>Franklin County</td>\n",
       "      <td>NE</td>\n",
       "      <td>Franklin County, NE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((-79.76195 42.26986, -79.62748 42.324...</td>\n",
       "      <td>36</td>\n",
       "      <td>013</td>\n",
       "      <td>Chautauqua</td>\n",
       "      <td>36013.0</td>\n",
       "      <td>Chautauqua County</td>\n",
       "      <td>NY</td>\n",
       "      <td>Chautauqua County, NY</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((-78.49773 36.51467, -78.45728 36.541...</td>\n",
       "      <td>37</td>\n",
       "      <td>181</td>\n",
       "      <td>Vance</td>\n",
       "      <td>37181.0</td>\n",
       "      <td>Vance County</td>\n",
       "      <td>NC</td>\n",
       "      <td>Vance County, NC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((-88.94916 36.41010, -88.81642 36.410...</td>\n",
       "      <td>47</td>\n",
       "      <td>183</td>\n",
       "      <td>Weakley</td>\n",
       "      <td>47183.0</td>\n",
       "      <td>Weakley County</td>\n",
       "      <td>TN</td>\n",
       "      <td>Weakley County, TN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry STATEFP COUNTYFP  \\\n",
       "0  POLYGON ((-94.63203 40.57176, -94.53388 40.570...      29      227   \n",
       "1  POLYGON ((-99.17940 40.35068, -98.72683 40.350...      31      061   \n",
       "2  POLYGON ((-79.76195 42.26986, -79.62748 42.324...      36      013   \n",
       "3  POLYGON ((-78.49773 36.51467, -78.45728 36.541...      37      181   \n",
       "4  POLYGON ((-88.94916 36.41010, -88.81642 36.410...      47      183   \n",
       "\n",
       "         NAME  STCOUNTYFP         COUNTYNAME STATE           county_state  \\\n",
       "0       Worth     29227.0       Worth County    MO       Worth County, MO   \n",
       "1    Franklin     31061.0    Franklin County    NE    Franklin County, NE   \n",
       "2  Chautauqua     36013.0  Chautauqua County    NY  Chautauqua County, NY   \n",
       "3       Vance     37181.0       Vance County    NC       Vance County, NC   \n",
       "4     Weakley     47183.0     Weakley County    TN     Weakley County, TN   \n",
       "\n",
       "   counts  ev_level1_evse_num  ev_level2_evse_num  ev_dc_fast_num  \n",
       "0     NaN                 NaN                 NaN             NaN  \n",
       "1     NaN                 NaN                 NaN             NaN  \n",
       "2    10.0                 0.0                 8.0             2.0  \n",
       "3     4.0                 0.0                 2.0             2.0  \n",
       "4     NaN                 NaN                 NaN             NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the geometric file with the chargers_per_zip df\n",
    "merged_gdf = gdf_county_mapped.merge(chargers_per_county, how='outer', on='county_state') #Playing with outer to get all geometric\n",
    "\n",
    "merged_gdf.sort_values(by='counts', ascending=True)\n",
    "merged_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counties that do not have charging station have to have NaN's replaced with zero to be put on the choropleth. Also, counties that don't have geometric data are removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>geometry</th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAME</th>\n",
       "      <th>STCOUNTYFP</th>\n",
       "      <th>COUNTYNAME</th>\n",
       "      <th>STATE</th>\n",
       "      <th>county_state</th>\n",
       "      <th>counts</th>\n",
       "      <th>ev_level1_evse_num</th>\n",
       "      <th>ev_level2_evse_num</th>\n",
       "      <th>ev_dc_fast_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((-94.63203 40.57176, -94.53388 40.570...</td>\n",
       "      <td>29</td>\n",
       "      <td>227</td>\n",
       "      <td>Worth</td>\n",
       "      <td>29227.0</td>\n",
       "      <td>Worth County</td>\n",
       "      <td>MO</td>\n",
       "      <td>Worth County, MO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((-99.17940 40.35068, -98.72683 40.350...</td>\n",
       "      <td>31</td>\n",
       "      <td>061</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>31061.0</td>\n",
       "      <td>Franklin County</td>\n",
       "      <td>NE</td>\n",
       "      <td>Franklin County, NE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POLYGON ((-79.76195 42.26986, -79.62748 42.324...</td>\n",
       "      <td>36</td>\n",
       "      <td>013</td>\n",
       "      <td>Chautauqua</td>\n",
       "      <td>36013.0</td>\n",
       "      <td>Chautauqua County</td>\n",
       "      <td>NY</td>\n",
       "      <td>Chautauqua County, NY</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYGON ((-78.49773 36.51467, -78.45728 36.541...</td>\n",
       "      <td>37</td>\n",
       "      <td>181</td>\n",
       "      <td>Vance</td>\n",
       "      <td>37181.0</td>\n",
       "      <td>Vance County</td>\n",
       "      <td>NC</td>\n",
       "      <td>Vance County, NC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POLYGON ((-88.94916 36.41010, -88.81642 36.410...</td>\n",
       "      <td>47</td>\n",
       "      <td>183</td>\n",
       "      <td>Weakley</td>\n",
       "      <td>47183.0</td>\n",
       "      <td>Weakley County</td>\n",
       "      <td>TN</td>\n",
       "      <td>Weakley County, TN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry STATEFP COUNTYFP  \\\n",
       "0  POLYGON ((-94.63203 40.57176, -94.53388 40.570...      29      227   \n",
       "1  POLYGON ((-99.17940 40.35068, -98.72683 40.350...      31      061   \n",
       "2  POLYGON ((-79.76195 42.26986, -79.62748 42.324...      36      013   \n",
       "3  POLYGON ((-78.49773 36.51467, -78.45728 36.541...      37      181   \n",
       "4  POLYGON ((-88.94916 36.41010, -88.81642 36.410...      47      183   \n",
       "\n",
       "         NAME  STCOUNTYFP         COUNTYNAME STATE           county_state  \\\n",
       "0       Worth     29227.0       Worth County    MO       Worth County, MO   \n",
       "1    Franklin     31061.0    Franklin County    NE    Franklin County, NE   \n",
       "2  Chautauqua     36013.0  Chautauqua County    NY  Chautauqua County, NY   \n",
       "3       Vance     37181.0       Vance County    NC       Vance County, NC   \n",
       "4     Weakley     47183.0     Weakley County    TN     Weakley County, TN   \n",
       "\n",
       "   counts  ev_level1_evse_num  ev_level2_evse_num  ev_dc_fast_num  \n",
       "0     0.0                 0.0                 0.0             0.0  \n",
       "1     0.0                 0.0                 0.0             0.0  \n",
       "2    10.0                 0.0                 8.0             2.0  \n",
       "3     4.0                 0.0                 2.0             2.0  \n",
       "4     0.0                 0.0                 0.0             0.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Eliminate rows with no geometry\n",
    "merged_gdf = merged_gdf[merged_gdf['geometry'].notna()]\n",
    "\n",
    "#Fill null values with 0 (Have to do this after removing irrelevant null geometries)\n",
    "merged_gdf = merged_gdf.fillna(0)\n",
    "\n",
    "merged_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before creating the choropleth, HTML code is used to create a draggable and more readable legend.\n",
    "\n",
    "The original template code for the legend was created by Colin Talbert at https://nbviewer.jupyter.org/gist/talbertc-usgs/18f8901fc98f109f2b71156cf3ac81cd, and it has been modified for the purpose of this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from branca.element import Template, MacroElement\n",
    "\n",
    "template = \"\"\"\n",
    "{% macro html(this, kwargs) %}\n",
    "\n",
    "<!doctype html>\n",
    "<html lang=\"en\">\n",
    "<head>\n",
    "  <meta charset=\"utf-8\">\n",
    "  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n",
    "  <title>jQuery UI Draggable - Default functionality</title>\n",
    "  <link rel=\"stylesheet\" href=\"//code.jquery.com/ui/1.12.1/themes/base/jquery-ui.css\">\n",
    "\n",
    "  <script src=\"https://code.jquery.com/jquery-1.12.4.js\"></script>\n",
    "  <script src=\"https://code.jquery.com/ui/1.12.1/jquery-ui.js\"></script>\n",
    "  \n",
    "  <script>\n",
    "  $( function() {\n",
    "    $( \"#maplegend\" ).draggable({\n",
    "                    start: function (event, ui) {\n",
    "                        $(this).css({\n",
    "                            right: \"auto\",\n",
    "                            top: \"auto\",\n",
    "                            bottom: \"auto\"\n",
    "                        });\n",
    "                    }\n",
    "                });\n",
    "});\n",
    "\n",
    "  </script>\n",
    "</head>\n",
    "<body>\n",
    "\n",
    " \n",
    "<div id='maplegend' class='maplegend' \n",
    "    style='position: absolute; z-index:9999; border:2px solid grey; background-color:rgba(255, 255, 255, 0.8);\n",
    "     border-radius:6px; padding: 10px; font-size:14px; right: 20px; bottom: 20px;'>\n",
    "     \n",
    "<div class='legend-title'>Charging</div>\n",
    "<div class='legend-title'>Stations</div>\n",
    "<div class='legend-scale'>\n",
    "  <ul class='legend-labels'>\n",
    "    <li><span style='background:#ffffcc;opacity:0.8;'></span>0</li>\n",
    "    <li><span style='background:#c2e699;opacity:0.8;'></span>1 - 2</li>\n",
    "    <li><span style='background:#78c679;opacity:0.8;'></span>3 - 11</li>\n",
    "    <li><span style='background:#31a354;opacity:0.8;'></span>12 - 65</li>\n",
    "    <li><span style='background:#006837;opacity:0.8;'></span>66 - 2699</li>\n",
    "\n",
    "  </ul>\n",
    "</div>\n",
    "</div>\n",
    " \n",
    "</body>\n",
    "</html>\n",
    "\n",
    "<style type='text/css'>\n",
    "  .maplegend .legend-title {\n",
    "    text-align: left;\n",
    "    margin-bottom: 5px;\n",
    "    font-weight: bold;\n",
    "    font-size: 90%;\n",
    "    }\n",
    "  .maplegend .legend-scale ul {\n",
    "    margin: 0;\n",
    "    margin-bottom: 5px;\n",
    "    padding: 0;\n",
    "    float: left;\n",
    "    list-style: none;\n",
    "    }\n",
    "  .maplegend .legend-scale ul li {\n",
    "    font-size: 80%;\n",
    "    list-style: none;\n",
    "    margin-left: 0;\n",
    "    line-height: 18px;\n",
    "    margin-bottom: 2px;\n",
    "    }\n",
    "  .maplegend ul.legend-labels li span {\n",
    "    display: block;\n",
    "    float: left;\n",
    "    height: 16px;\n",
    "    width: 30px;\n",
    "    margin-right: 5px;\n",
    "    margin-left: 0;\n",
    "    border: 1px solid #999;\n",
    "    }\n",
    "  .maplegend .legend-source {\n",
    "    font-size: 80%;\n",
    "    color: #777;\n",
    "    clear: both;\n",
    "    }\n",
    "  .maplegend a {\n",
    "    color: #777;\n",
    "    }\n",
    "</style>\n",
    "{% endmacro %}\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use folium to create a choropleth, and add a tooltip for information related to supported charging levels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"850\"\n",
       "            height=\"450\"\n",
       "            src=\"us_chargingstations_county.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x236c2f94bc8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "m = folium.Map([39.8283, -98.5795], zoom_start=4)\n",
    "\n",
    "#Mapbox Bright\n",
    "folium.TileLayer(tiles='CartoDB positron',control=True, opacity=0.0).add_to(m) #Baseline\n",
    "    \n",
    "myscale = [0, 1, 3, 12, 66, merged_gdf.counts.max()]\n",
    "\n",
    "colors = ['#d1eca0', '#9ed688', '#62bb6e', '#329750', '#0e723b']\n",
    "\n",
    "choropleth = folium.Choropleth(\n",
    "    geo_data=merged_gdf,\n",
    "    data=merged_gdf,\n",
    "    columns=[\"county_state\", \"counts\"],\n",
    "    key_on=\"feature.properties.county_state\",\n",
    "    fill_color = 'YlGn',\n",
    "    threshold_scale=myscale,\n",
    "    fill_opacity =  1,\n",
    "    line_color='white',\n",
    "    line_weight = .2,\n",
    "    line_opacity=.6,\n",
    "    name=\"Charging Stations\",\n",
    "    show=True,\n",
    "    legend_name = 'Charging Stations in County',\n",
    "    highlight = True,\n",
    "    smooth_factor = .2\n",
    ").add_to(m)\n",
    "\n",
    "macro = MacroElement()\n",
    "macro._template = Template(template)\n",
    "\n",
    "m.get_root().add_child(macro)\n",
    "\n",
    "folium.LayerControl(collapsed=True).add_to(m)\n",
    "\n",
    "choropleth.geojson.add_child(folium.features.GeoJsonTooltip(\n",
    "        fields=['COUNTYNAME', 'STATE', 'counts', 'ev_level1_evse_num', 'ev_level2_evse_num', 'ev_dc_fast_num'],\n",
    "        aliases=['County:     ', 'State:     ', 'Charging Stations:     ',\\\n",
    "                'Number of Stations w/ Lvl. 1 Charging:     ',\n",
    "                'Number of Stations w/ Lvl. 2 Charging:     ',\\\n",
    "                'Number of Stations w/ DC Fast Charging:     '],\n",
    "        style=(\"background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;\"),\n",
    "        localize=True\n",
    "        )\n",
    ")\n",
    "\n",
    "#Delete Default color scale (It overlaps)\n",
    "for key in choropleth._children:\n",
    "    if key.startswith('color_map'):\n",
    "        del(choropleth._children[key])\n",
    "        \n",
    "#Display the map and save\n",
    "m.save(outfile= \"us_chargingstations_county.html\")\n",
    "\n",
    "from IPython.display import IFrame\n",
    "\n",
    "IFrame(src='us_chargingstations_county.html', width=850, height=450)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The choropleth provides a top-down overview of which regions have the most (or least) charging stations. The regions that are shaded a darker green and have relatively more charging stations are the entiere West Coast, Florida, New England, Michigan, and other urban centers. Practicality of Electric Vehicles ultimately comes down to which county an EV user resides in and what their driving habits are like. Hence, the map can somewhat be used as a tool to gage whether or not a particular has enough charging stations for an individual's needs. More specifically, one can use the map to see if there are enough charging stations that support a certain level of charging."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Study: Texas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An interesting case study for the adoption of electric vehicle charging stations is Texas. With 1,562 charging stations, the state ranks 4th in the country for number of 24/7, public charging stations. However, the chasm between the cities with the most and least chargers is quite large. The top 5 cities hold 952 out of Texas' 1,562 public, 24/7 charging stations. Also of note is that 150 out of Texas' 254 counties don't have a single public, 24/7 charging station. The more densely populated areas have the infrastructure to make the lives of EV owners easier, but the more rural areas have catching up to do in terms of practicality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texas has 1562 public, 24/7 charging stations.\n"
     ]
    }
   ],
   "source": [
    "# Reduce data to Texas data only\n",
    "texas_df = data.copy()\n",
    "texas_df = texas_df[texas_df.state=='TX']\n",
    "num_texas = len(texas_df)\n",
    "print(\"Texas has {} public, 24/7 charging stations.\".format(num_texas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group by city, arbitrarily sum up the ID\n",
    "texas_cities = texas_df.groupby('city').size().reset_index(name='counts').sort_values(by='counts', ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texas has 254 counties.\n",
      "Texas has 150 counties with zero public, 24/7 charging stations.\n"
     ]
    }
   ],
   "source": [
    "#How many Texas *counties* don't have charging stations\n",
    "\n",
    "tx_counties = merged_gdf[merged_gdf.STATE=='TX']\n",
    "num_tx_counties = len(tx_counties)\n",
    "tx_counties_0 = tx_counties[tx_counties.counts==0]\n",
    "empty_tx = len(tx_counties_0)\n",
    "\n",
    "print(\"Texas has {} counties.\".format(num_tx_counties))\n",
    "print(\"Texas has {} counties with zero public, 24/7 charging stations.\".format(empty_tx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-1c8647b1dd9243ad856318d3975bad1b\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-1c8647b1dd9243ad856318d3975bad1b\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-1c8647b1dd9243ad856318d3975bad1b\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"usermeta\": {\"embedOptions\": {\"theme\": \"fivethirtyeight\"}}, \"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"strokeWidth\": 0}}, \"layer\": [{\"mark\": {\"type\": \"bar\", \"color\": \"#80c97c\"}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"axis\": {\"domain\": false, \"labelColor\": \"black\"}, \"field\": \"counts\", \"title\": \"Number of Charging Stations\"}, \"y\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelAngle\": 0, \"labelColor\": \"black\"}, \"field\": \"city\", \"sort\": \"-x\", \"title\": \"City\"}}}, {\"mark\": {\"type\": \"text\", \"align\": \"center\", \"angle\": 0, \"baseline\": \"middle\", \"dx\": 10, \"dy\": 0}, \"encoding\": {\"text\": {\"type\": \"quantitative\", \"field\": \"counts\"}, \"x\": {\"type\": \"quantitative\", \"axis\": {\"domain\": false, \"labelColor\": \"black\"}, \"field\": \"counts\", \"title\": \"Number of Charging Stations\"}, \"y\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"labelAngle\": 0, \"labelColor\": \"black\"}, \"field\": \"city\", \"sort\": \"-x\", \"title\": \"City\"}}}], \"data\": {\"name\": \"data-727c545b0a93514323186e82bed50a78\"}, \"height\": 320, \"title\": {\"text\": \"20 Cities in Texas with the Most Electric Car Charging Stations\", \"subtitle\": \"Out of 1,562 Public & 24/7 Charging Stations\", \"subtitleFontSize\": 18}, \"width\": 1000, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-727c545b0a93514323186e82bed50a78\": [{\"city\": \"Austin\", \"counts\": 462}, {\"city\": \"Houston\", \"counts\": 183}, {\"city\": \"Dallas\", \"counts\": 125}, {\"city\": \"San Antonio\", \"counts\": 109}, {\"city\": \"Fort Worth\", \"counts\": 73}, {\"city\": \"Plano\", \"counts\": 22}, {\"city\": \"Irving\", \"counts\": 18}, {\"city\": \"Frisco\", \"counts\": 16}, {\"city\": \"Garland\", \"counts\": 16}, {\"city\": \"Katy\", \"counts\": 15}, {\"city\": \"Coppell\", \"counts\": 14}, {\"city\": \"Spring\", \"counts\": 13}, {\"city\": \"El Paso\", \"counts\": 13}, {\"city\": \"Round Rock\", \"counts\": 12}, {\"city\": \"Arlington\", \"counts\": 10}, {\"city\": \"Denton\", \"counts\": 10}, {\"city\": \"Corpus Christi\", \"counts\": 9}, {\"city\": \"Galveston\", \"counts\": 9}, {\"city\": \"Bryan\", \"counts\": 9}, {\"city\": \"San Marcos\", \"counts\": 9}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualize counts for top 20 cities\n",
    "\n",
    "import altair as alt\n",
    "\n",
    "alt.themes.enable('fivethirtyeight')\n",
    "\n",
    "bars = alt.Chart(texas_cities[:20]).mark_bar(color='#80c97c').encode(\n",
    "    y=alt.Y(\"city:N\", sort='-x', title='City',\n",
    "           axis=alt.Axis(labelAngle=0, domain=False, labelColor='black')),\n",
    "    x=alt.X(\"counts:Q\", title='Number of Charging Stations',\n",
    "           axis=alt.Axis(domain=False, labelColor='black'))\n",
    ")\n",
    "\n",
    "text = bars.mark_text(\n",
    "    angle=0,\n",
    "    align='center',\n",
    "    baseline='middle',\n",
    "    dx= 10,\n",
    "    dy=0  # Nudges text to right so it doesn't appear on top of the bar\n",
    ").encode(\n",
    "    text='counts:Q'\n",
    ")\n",
    "\n",
    "(bars + text).properties(\n",
    "    height=320, width=1000, \n",
    "    title={'text': '20 Cities in Texas with the Most Electric Car Charging Stations',\n",
    "          'subtitle': 'Out of 1,562 Public & 24/7 Charging Stations',\n",
    "          'subtitleFontSize': 18}\n",
    "    ).configure_view(strokeWidth=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the Different Levels of Charging, and What Percent of Charging Stations Support Each?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three different levels of charging: Level 1, Level 2, and Level 3. ( DC Fast Charging) Level 1 is convenient in that anyone with a wall outlet can charge their EV; however, it is slow. Level 2 is available for both residential and commercial applications and charges an EV with 10-20 miles of range per hour. Level 3 is the fastest by far, giving 180-240 miles of range per hour, but it is only available commercially. A charging station that supports higher levels of charging can be said to be more convenient ot EV drivers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The original dataset has to be looked at again to inspect each individual charging station that's 24/7 and public.\n",
    "c_type = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many public, 24/7 charging stations nationwide support Level 1 charging? Roughly 627 at the time of writing this. This makes sense, considering that level one charging is meant for ordinary wall sockets and only gives you 2-5 miles of range/hr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are currently 627 public, 24/7 EV charging stations that support Level 1 charging.\n"
     ]
    }
   ],
   "source": [
    "num_level1 = c_type['ev_level1_evse_num'].count() #Count function doesn't consider NaNs\n",
    "print(\"There are currently {} public, 24/7 EV charging stations that support Level 1 charging.\".format(num_level1)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many public, 24/7 charging stations nationwide support Level 2 charging? Roughly 30,089 at the time of writing this. While it may not be more convenient than DC fast charging, level 2 is older technology and is supported by more adapters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are currently 30122 public, 24/7 EV charging stations that support Level 2 charging.\n"
     ]
    }
   ],
   "source": [
    "num_level2 = c_type['ev_level2_evse_num'].count() #Count function doesn't consider NaNs\n",
    "print(\"There are currently {} public, 24/7 EV charging stations that support Level 2 charging.\".format(num_level2)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many public, 24/7 charging stations support Level 3 charging? Roughly 4,264 at the time of writing this. It is the most convenient for EV drivers, but it is more recent technology, and it will take time to see more widespread adoption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are currently 4264 public, 24/7 EV charging stations that support Level 3 fast charging.\n"
     ]
    }
   ],
   "source": [
    "num_level3 = c_type['ev_dc_fast_num'].count() #Count function doesn't consider NaNs\n",
    "print(\"There are currently {} public, 24/7 EV charging stations that support Level 3 fast charging.\".format(num_level3)) "
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:ddcfabd5-aab3-4dfa-8282-c442b0d701d1.png)"
   ]
  },
  {
   "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>Level</th>\n",
       "      <th>Primary Applications</th>\n",
       "      <th>Charge Rate (Miles/hr)</th>\n",
       "      <th>Charge Ports</th>\n",
       "      <th>Number of Supporting Stations</th>\n",
       "      <th>Percent of Supporting Stations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Level 1 Charging</td>\n",
       "      <td>Residential</td>\n",
       "      <td>2 - 5</td>\n",
       "      <td>J1772</td>\n",
       "      <td>627</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Level 2 Charging</td>\n",
       "      <td>Residental &amp; Commercial</td>\n",
       "      <td>10 - 20</td>\n",
       "      <td>J1772</td>\n",
       "      <td>30122</td>\n",
       "      <td>88.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Level 3 Fast Charging</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>180 - 240</td>\n",
       "      <td>CCS, CHAdeMO, &amp; Tesla</td>\n",
       "      <td>4264</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Level     Primary Applications Charge Rate (Miles/hr)  \\\n",
       "0       Level 1 Charging              Residential                  2 - 5   \n",
       "1       Level 2 Charging  Residental & Commercial                10 - 20   \n",
       "2  Level 3 Fast Charging               Commercial              180 - 240   \n",
       "\n",
       "            Charge Ports  Number of Supporting Stations  \\\n",
       "0                  J1772                            627   \n",
       "1                  J1772                          30122   \n",
       "2  CCS, CHAdeMO, & Tesla                           4264   \n",
       "\n",
       "   Percent of Supporting Stations  \n",
       "0                             1.8  \n",
       "1                            88.7  \n",
       "2                            12.6  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Construct Dataframe with information on Chargers\n",
    "diff_chargers_df= pd.DataFrame({\n",
    "    'Level' : ['Level 1 Charging','Level 2 Charging','Level 3 Fast Charging'],\n",
    "    'Primary Applications' : ['Residential','Residental & Commercial','Commercial'],\n",
    "    'Charge Rate (Miles/hr)' :  ['2 - 5','10 - 20','180 - 240'],\n",
    "    'Charge Ports' : ['J1772','J1772','CCS, CHAdeMO, & Tesla'],\n",
    "    'Number of Supporting Stations' :  [num_level1, num_level2, num_level3]\n",
    "                                })\n",
    "#Calculate percent of supporting stations and format\n",
    "diff_chargers_df['Percent of Supporting Stations'] = \\\n",
    "        (diff_chargers_df['Number of Supporting Stations'] / len(c_type) * 100).round(1).astype(float)\n",
    "diff_chargers_df.Level = diff_chargers_df.Level.astype(str)\n",
    "diff_chargers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    let outputDiv = document.currentScript.previousElementSibling;\n",
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       "      outputDiv = document.getElementById(\"altair-viz-a0e982e60dbf4a4d8e4343762f1ea436\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
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       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"usermeta\": {\"embedOptions\": {\"theme\": \"fivethirtyeight\"}}, \"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"strokeWidth\": 0}, \"axis\": {\"labelColor\": \"black\", \"labelFontSize\": 16, \"labelPadding\": 15}, \"mark\": {}, \"scale\": {\"bandPaddingInner\": 0.1}}, \"layer\": [{\"mark\": {\"type\": \"text\", \"align\": \"left\", \"baseline\": \"middle\", \"dx\": 3}, \"encoding\": {\"text\": {\"type\": \"quantitative\", \"field\": \"Percent of Supporting Stations\"}, \"x\": {\"type\": \"quantitative\", \"axis\": null, \"field\": \"Percent of Supporting Stations\"}, \"y\": {\"type\": \"nominal\", \"axis\": {\"ticks\": false, \"title\": \"\"}, \"field\": \"Level\", \"sort\": \"-x\"}}}, {\"mark\": {\"type\": \"bar\", \"color\": \"#80c97c\", \"size\": 33}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"axis\": null, \"field\": \"Percent of Supporting Stations\"}, \"y\": {\"type\": \"nominal\", \"axis\": {\"ticks\": false, \"title\": \"\"}, \"field\": \"Level\", \"sort\": \"-x\"}}}], \"data\": {\"name\": \"data-3ae9444259333cef110447455a33f9ca\"}, \"height\": 120, \"title\": {\"text\": [\"Percent of Supporting Charging Stations for each Level of Charging\"], \"subtitle\": \"Of 33,935 Public Access, 24/7 Charging Stations Nationwide \", \"subtitleFontSize\": 16, \"fontSize\": 20}, \"width\": 400, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-3ae9444259333cef110447455a33f9ca\": [{\"Level\": \"Level 1 Charging\", \"Primary Applications\": \"Residential\", \"Charge Rate (Miles/hr)\": \"2 - 5\", \"Charge Ports\": \"J1772\", \"Number of Supporting Stations\": 627, \"Percent of Supporting Stations\": 1.8}, {\"Level\": \"Level 2 Charging\", \"Primary Applications\": \"Residental & Commercial\", \"Charge Rate (Miles/hr)\": \"10 - 20\", \"Charge Ports\": \"J1772\", \"Number of Supporting Stations\": 30122, \"Percent of Supporting Stations\": 88.7}, {\"Level\": \"Level 3 Fast Charging\", \"Primary Applications\": \"Commercial\", \"Charge Rate (Miles/hr)\": \"180 - 240\", \"Charge Ports\": \"CCS, CHAdeMO, & Tesla\", \"Number of Supporting Stations\": 4264, \"Percent of Supporting Stations\": 12.6}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bars = alt.Chart(diff_chargers_df).mark_bar(size=33, color='#80c97c').encode(\n",
    "    x=alt.X(\n",
    "        'Percent of Supporting Stations:Q',\n",
    "        axis=None),\n",
    "    y=alt.Y(\n",
    "        # encode y using the name, use the movie name to label the axis, sort using the names_l\n",
    "        'Level:N', sort='-x',\n",
    "         axis=alt.Axis(ticks=False, title='')\n",
    "    )\n",
    ")\n",
    "\n",
    "text = bars.mark_text(\n",
    "    align='left',\n",
    "    baseline='middle',\n",
    "    dx=3  # Nudges text to right so it doesn't appear on top of the bar\n",
    ").encode(\n",
    "    # we'll use the percentage as the text\n",
    "    text=alt.Text('Percent of Supporting Stations:Q')\n",
    ")\n",
    "\n",
    "# finally, we're going to combine the bars and the text and do some styling\n",
    "percent = (text + bars).configure_mark(\n",
    ").configure_view(\n",
    "    # we don't want a stroke around the bars\n",
    "    strokeWidth=0\n",
    ").configure_scale(\n",
    "    # add some padding\n",
    "    bandPaddingInner=0.1\n",
    ").properties(\n",
    "    width=400,\n",
    "    height=120\n",
    ").properties(\n",
    "    # add a title\n",
    "    title={\n",
    "      \"text\": [\"Percent of Supporting Charging Stations for each Level of Charging\"], \n",
    "      \"subtitle\": 'Of 33,935 Public Access, 24/7 Charging Stations Nationwide ',\n",
    "      \"subtitleFontSize\": 16,\n",
    "      'fontSize': 20\n",
    "    }\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    labelColor='black',\n",
    "    labelPadding=15,\n",
    ")\n",
    "\n",
    "percent"
   ]
  }
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