PyBer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Files to load\n",
    "city_data_to_load = \"Resources/city_data.csv\"\n",
    "ride_data_to_load = \"Resources/ride_data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Richardfort</td>\n",
       "      <td>38</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Williamsstad</td>\n",
       "      <td>59</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Angela</td>\n",
       "      <td>67</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West Robert</td>\n",
       "      <td>39</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West Anthony</td>\n",
       "      <td>70</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Martinezhaven</td>\n",
       "      <td>25</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Karenberg</td>\n",
       "      <td>22</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Barajasview</td>\n",
       "      <td>26</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  driver_count   type\n",
       "0    Richardfort            38  Urban\n",
       "1   Williamsstad            59  Urban\n",
       "2    Port Angela            67  Urban\n",
       "3     Rodneyfort            34  Urban\n",
       "4    West Robert            39  Urban\n",
       "5   West Anthony            70  Urban\n",
       "6    West Angela            48  Urban\n",
       "7  Martinezhaven            25  Urban\n",
       "8      Karenberg            22  Urban\n",
       "9    Barajasview            26  Urban"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the city data file and store it in a pandas DataFrame.\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Latoya</td>\n",
       "      <td>2019-03-11 12:26:48</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1994999424437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Paulville</td>\n",
       "      <td>2019-02-27 11:17:56</td>\n",
       "      <td>43.25</td>\n",
       "      <td>793208410091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simpsonburgh</td>\n",
       "      <td>2019-04-26 00:43:24</td>\n",
       "      <td>35.98</td>\n",
       "      <td>111953927754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Karenland</td>\n",
       "      <td>2019-01-08 03:28:48</td>\n",
       "      <td>35.09</td>\n",
       "      <td>7995623208694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>North Jasmine</td>\n",
       "      <td>2019-03-09 06:26:29</td>\n",
       "      <td>42.81</td>\n",
       "      <td>5327642267789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873\n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577\n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003\n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178\n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344\n",
       "5        South Latoya  2019-03-11 12:26:48   9.52  1994999424437\n",
       "6       New Paulville  2019-02-27 11:17:56  43.25   793208410091\n",
       "7        Simpsonburgh  2019-04-26 00:43:24  35.98   111953927754\n",
       "8     South Karenland  2019-01-08 03:28:48  35.09  7995623208694\n",
       "9       North Jasmine  2019-03-09 06:26:29  42.81  5327642267789"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the ride data file and store it in a pandas DataFrame.\n",
    "ride_data_df = pd.read_csv(ride_data_to_load)\n",
    "ride_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            120\n",
       "driver_count    120\n",
       "type            120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "city_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            0\n",
       "driver_count    0\n",
       "type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "city_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            object\n",
       "driver_count     int64\n",
       "type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Suburban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique values of the type of city.\n",
    "city_data_df[\"type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Urban cities.\n",
    "sum(city_data_df[\"type\"]==\"Urban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Suburban cities.\n",
    "sum(city_data_df[\"type\"]==\"Suburban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Rural cities.\n",
    "sum(city_data_df[\"type\"]==\"Rural\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ride Data \n",
    "# Get the columns and the rows that are not null.\n",
    "ride_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "ride_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "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>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data into a single dataset\n",
    "pyber_data_df = pd.merge(ride_data_df, city_data_df, how=\"left\", on=[\"city\", \"city\"])\n",
    "\n",
    "# Display the DataFrame\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "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",
       "    .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>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Urban city DataFrame.\n",
    "urban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Urban\"]\n",
    "urban_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Suburban and Rural city DataFrames.\n",
    "suburban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Suburban\"]\n",
    "rural_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Rural\"]"
   ]
  },
  {
   "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>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>Barronchester</td>\n",
       "      <td>2019-01-27 03:08:01</td>\n",
       "      <td>27.79</td>\n",
       "      <td>6653622887913</td>\n",
       "      <td>11</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>East Kentstad</td>\n",
       "      <td>2019-04-07 19:44:19</td>\n",
       "      <td>18.75</td>\n",
       "      <td>6575961095852</td>\n",
       "      <td>20</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>Lake Omar</td>\n",
       "      <td>2019-01-17 21:33:35</td>\n",
       "      <td>21.71</td>\n",
       "      <td>966911700371</td>\n",
       "      <td>22</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>Myersshire</td>\n",
       "      <td>2019-02-27 17:38:39</td>\n",
       "      <td>17.10</td>\n",
       "      <td>5706770909868</td>\n",
       "      <td>19</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>West Hannah</td>\n",
       "      <td>2019-04-19 01:06:59</td>\n",
       "      <td>37.78</td>\n",
       "      <td>2273047151891</td>\n",
       "      <td>12</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>West Hannah</td>\n",
       "      <td>2019-03-02 17:06:03</td>\n",
       "      <td>13.92</td>\n",
       "      <td>7388937249208</td>\n",
       "      <td>12</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>Lake Robertside</td>\n",
       "      <td>2019-04-03 03:27:45</td>\n",
       "      <td>12.76</td>\n",
       "      <td>1301757653884</td>\n",
       "      <td>4</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>Colemanland</td>\n",
       "      <td>2019-01-10 06:49:58</td>\n",
       "      <td>27.70</td>\n",
       "      <td>8704067171414</td>\n",
       "      <td>23</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2248</th>\n",
       "      <td>East Marymouth</td>\n",
       "      <td>2019-02-12 23:18:53</td>\n",
       "      <td>47.41</td>\n",
       "      <td>7409660239158</td>\n",
       "      <td>5</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>New Raymond</td>\n",
       "      <td>2019-02-11 17:28:27</td>\n",
       "      <td>17.97</td>\n",
       "      <td>7551674475043</td>\n",
       "      <td>17</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>625 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "1625    Barronchester  2019-01-27 03:08:01  27.79  6653622887913   \n",
       "1626    East Kentstad  2019-04-07 19:44:19  18.75  6575961095852   \n",
       "1627        Lake Omar  2019-01-17 21:33:35  21.71   966911700371   \n",
       "1628       Myersshire  2019-02-27 17:38:39  17.10  5706770909868   \n",
       "1629      West Hannah  2019-04-19 01:06:59  37.78  2273047151891   \n",
       "...               ...                  ...    ...            ...   \n",
       "2245      West Hannah  2019-03-02 17:06:03  13.92  7388937249208   \n",
       "2246  Lake Robertside  2019-04-03 03:27:45  12.76  1301757653884   \n",
       "2247      Colemanland  2019-01-10 06:49:58  27.70  8704067171414   \n",
       "2248   East Marymouth  2019-02-12 23:18:53  47.41  7409660239158   \n",
       "2249      New Raymond  2019-02-11 17:28:27  17.97  7551674475043   \n",
       "\n",
       "      driver_count      type  \n",
       "1625            11  Suburban  \n",
       "1626            20  Suburban  \n",
       "1627            22  Suburban  \n",
       "1628            19  Suburban  \n",
       "1629            12  Suburban  \n",
       "...            ...       ...  \n",
       "2245            12  Suburban  \n",
       "2246             4  Suburban  \n",
       "2247            23  Suburban  \n",
       "2248             5  Suburban  \n",
       "2249            17  Suburban  \n",
       "\n",
       "[625 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_cities_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>Randallchester</td>\n",
       "      <td>2019-04-13 11:13:31</td>\n",
       "      <td>43.22</td>\n",
       "      <td>1076079536213</td>\n",
       "      <td>9</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>North Holly</td>\n",
       "      <td>2019-02-02 14:54:00</td>\n",
       "      <td>12.42</td>\n",
       "      <td>1985256326182</td>\n",
       "      <td>8</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-03-27 18:27:34</td>\n",
       "      <td>54.85</td>\n",
       "      <td>4421836952718</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-02-23 21:12:24</td>\n",
       "      <td>47.90</td>\n",
       "      <td>3269652929887</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-05-06 08:57:56</td>\n",
       "      <td>51.80</td>\n",
       "      <td>4018025271936</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-04-29 17:04:39</td>\n",
       "      <td>13.38</td>\n",
       "      <td>8550365057598</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-01-30 00:05:47</td>\n",
       "      <td>20.76</td>\n",
       "      <td>9018727594352</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>North Jaime</td>\n",
       "      <td>2019-02-10 21:03:50</td>\n",
       "      <td>11.11</td>\n",
       "      <td>2781339863778</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>West Heather</td>\n",
       "      <td>2019-05-07 19:22:15</td>\n",
       "      <td>44.94</td>\n",
       "      <td>4256853490277</td>\n",
       "      <td>4</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>Newtonview</td>\n",
       "      <td>2019-04-25 10:20:13</td>\n",
       "      <td>55.84</td>\n",
       "      <td>9990581345298</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "2250   Randallchester  2019-04-13 11:13:31  43.22  1076079536213   \n",
       "2251      North Holly  2019-02-02 14:54:00  12.42  1985256326182   \n",
       "2252      Michaelberg  2019-03-27 18:27:34  54.85  4421836952718   \n",
       "2253  Lake Latoyabury  2019-02-23 21:12:24  47.90  3269652929887   \n",
       "2254  Lake Latoyabury  2019-05-06 08:57:56  51.80  4018025271936   \n",
       "...               ...                  ...    ...            ...   \n",
       "2370      Michaelberg  2019-04-29 17:04:39  13.38  8550365057598   \n",
       "2371  Lake Latoyabury  2019-01-30 00:05:47  20.76  9018727594352   \n",
       "2372      North Jaime  2019-02-10 21:03:50  11.11  2781339863778   \n",
       "2373     West Heather  2019-05-07 19:22:15  44.94  4256853490277   \n",
       "2374       Newtonview  2019-04-25 10:20:13  55.84  9990581345298   \n",
       "\n",
       "      driver_count   type  \n",
       "2250             9  Rural  \n",
       "2251             8  Rural  \n",
       "2252             6  Rural  \n",
       "2253             2  Rural  \n",
       "2254             2  Rural  \n",
       "...            ...    ...  \n",
       "2370             6  Rural  \n",
       "2371             2  Rural  \n",
       "2372             1  Rural  \n",
       "2373             4  Rural  \n",
       "2374             1  Rural  \n",
       "\n",
       "[125 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_cities_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        18\n",
       "Barajasview        22\n",
       "Carriemouth        27\n",
       "Christopherfurt    27\n",
       "Deanville          19\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of rides for urban cities.\n",
    "urban_ride_count = urban_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "urban_ride_count.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the suburban and rural ride count.\n",
    "suburban_ride_count = suburban_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "\n",
    "rural_ride_count = rural_cities_df.groupby([\"city\"]).count()[\"ride_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        24.641667\n",
       "Barajasview        25.332273\n",
       "Carriemouth        28.314444\n",
       "Christopherfurt    24.501852\n",
       "Deanville          25.842632\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get average fare for each city in the urban cities.\n",
    "urban_avg_fare = urban_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "urban_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get average fare for each city in the suburban and rural cities.\n",
    "suburban_avg_fare = suburban_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "rural_avg_fare = rural_cities_df.groupby([\"city\"]).mean()[\"fare\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        12.0\n",
       "Barajasview        26.0\n",
       "Carriemouth        52.0\n",
       "Christopherfurt    41.0\n",
       "Deanville          49.0\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the average number of drivers for each urban city.\n",
    "urban_driver_count = urban_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "urban_driver_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the average number of drivers for each city for the suburban and rural cities.\n",
    "suburban_driver_count = suburban_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "rural_driver_count = rural_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2571bc61fc8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for urban cities.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=urban_driver_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2571cf4db48>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for urban cities.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2571d7e6848>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for suburban cities.\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2571d860d08>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for rural cities.\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter charts for each city type.\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.scatter(urban_ride_count, \n",
    "      urban_avg_fare, \n",
    "      s=10*urban_driver_count, c=\"coral\", \n",
    "      edgecolor=\"black\", linewidths=1, \n",
    "      alpha=0.8, label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count, \n",
    "      suburban_avg_fare, \n",
    "      s=10*suburban_driver_count, c=\"skyblue\", \n",
    "      edgecolor=\"black\", linewidths=1, \n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count, \n",
    "      rural_avg_fare, \n",
    "      s=10*rural_driver_count, c=\"gold\", \n",
    "      edgecolor=\"black\", linewidths=1, \n",
    "      alpha=0.8, label=\"Rural\")\n",
    "\n",
    "# Incorporate the other graph properties\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\", fontsize=20)\n",
    "plt.ylabel(\"Average Fare ($)\", fontsize=12)\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "\n",
    "# Create a legend\n",
    "lgnd = plt.legend(fontsize=\"12\", mode=\"Expanded\",\n",
    "         scatterpoints=1, loc=\"best\", title=\"City Types\")\n",
    "lgnd.legendHandles[0]._sizes = [75]\n",
    "lgnd.legendHandles[1]._sizes = [75]\n",
    "lgnd.legendHandles[2]._sizes = [75]\n",
    "lgnd.get_title().set_fontsize(12)\n",
    "# Incorporate a text label about circle size.\n",
    "plt.text(30, 35, \"Note:\\nCircle size correlates\\nwith driver count per city.\", fontsize=\"12\")\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig1.png\")\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1625.000000</td>\n",
       "      <td>1.625000e+03</td>\n",
       "      <td>1625.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.525772</td>\n",
       "      <td>4.873485e+12</td>\n",
       "      <td>36.678154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.738649</td>\n",
       "      <td>2.907440e+12</td>\n",
       "      <td>20.075545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.050000</td>\n",
       "      <td>1.458810e+10</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.550000</td>\n",
       "      <td>2.400244e+12</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.640000</td>\n",
       "      <td>4.711188e+12</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.580000</td>\n",
       "      <td>7.451579e+12</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.970000</td>\n",
       "      <td>9.991538e+12</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare       ride_id  driver_count\n",
       "count  1625.000000  1.625000e+03   1625.000000\n",
       "mean     24.525772  4.873485e+12     36.678154\n",
       "std      11.738649  2.907440e+12     20.075545\n",
       "min       4.050000  1.458810e+10      3.000000\n",
       "25%      14.550000  2.400244e+12     22.000000\n",
       "50%      24.640000  4.711188e+12     37.000000\n",
       "75%      34.580000  7.451579e+12     52.000000\n",
       "max      44.970000  9.991538e+12     73.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics for Urban cities\n",
    "urban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>625.000000</td>\n",
       "      <td>6.250000e+02</td>\n",
       "      <td>625.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.970128</td>\n",
       "      <td>4.971015e+12</td>\n",
       "      <td>13.712000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.677508</td>\n",
       "      <td>2.912410e+12</td>\n",
       "      <td>8.042372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.050000</td>\n",
       "      <td>3.218337e+08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.970000</td>\n",
       "      <td>2.364253e+12</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.750000</td>\n",
       "      <td>5.053221e+12</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.830000</td>\n",
       "      <td>7.551674e+12</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.960000</td>\n",
       "      <td>9.917744e+12</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fare       ride_id  driver_count\n",
       "count  625.000000  6.250000e+02    625.000000\n",
       "mean    30.970128  4.971015e+12     13.712000\n",
       "std     10.677508  2.912410e+12      8.042372\n",
       "min     12.050000  3.218337e+08      1.000000\n",
       "25%     21.970000  2.364253e+12      5.000000\n",
       "50%     30.750000  5.053221e+12     16.000000\n",
       "75%     39.830000  7.551674e+12     21.000000\n",
       "max     49.960000  9.917744e+12     25.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics for Suburban cities\n",
    "suburban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>125.000000</td>\n",
       "      <td>1.250000e+02</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.623440</td>\n",
       "      <td>4.647746e+12</td>\n",
       "      <td>4.296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.558046</td>\n",
       "      <td>2.887834e+12</td>\n",
       "      <td>2.691156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.110000</td>\n",
       "      <td>2.684873e+10</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.900000</td>\n",
       "      <td>2.275364e+12</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.050000</td>\n",
       "      <td>4.023962e+12</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.070000</td>\n",
       "      <td>7.118047e+12</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>58.550000</td>\n",
       "      <td>9.990581e+12</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fare       ride_id  driver_count\n",
       "count  125.000000  1.250000e+02    125.000000\n",
       "mean    34.623440  4.647746e+12      4.296000\n",
       "std     14.558046  2.887834e+12      2.691156\n",
       "min     10.110000  2.684873e+10      1.000000\n",
       "25%     19.900000  2.275364e+12      1.000000\n",
       "50%     37.050000  4.023962e+12      4.000000\n",
       "75%     47.070000  7.118047e+12      7.000000\n",
       "max     58.550000  9.990581e+12      9.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics for Rural cities\n",
    "rural_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    66.000000\n",
       "mean     24.621212\n",
       "std       5.408726\n",
       "min      12.000000\n",
       "25%      21.000000\n",
       "50%      24.000000\n",
       "75%      28.000000\n",
       "max      39.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics Urban Ride Count.\n",
    "urban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36.000000\n",
       "mean     17.361111\n",
       "std       4.323707\n",
       "min       9.000000\n",
       "25%      14.000000\n",
       "50%      17.000000\n",
       "75%      19.250000\n",
       "max      27.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics Suburban Ride Count.\n",
    "suburban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18.000000\n",
       "mean      6.944444\n",
       "std       2.508157\n",
       "min       3.000000\n",
       "25%       5.250000\n",
       "50%       6.000000\n",
       "75%       8.750000\n",
       "max      12.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics Rural Ride Count.\n",
    "rural_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.62, 17.36, 6.94)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean of the ride count for each city type.\n",
    "round(urban_ride_count.mean(),2), round(suburban_ride_count.mean(),2), round(rural_ride_count.mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.0, 17.0, 6.0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean of the ride count for each city type.\n",
    "round(urban_ride_count.median(),2), round(suburban_ride_count.median(),2), round(rural_ride_count.median(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the urban cities.\n",
    "urban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the suburban cities.\n",
    "suburban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the rural cities.\n",
    "rural_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import NumPy and the stats module from SciPy.\n",
    "import numpy as np\n",
    "import scipy.stats as sts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for urban trips is 24.62.\n",
      "The median for the ride counts for urban trips is 24.0.\n",
      "The mode for the ride counts for urban trips is ModeResult(mode=array([22], dtype=int64), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the urban cities.\n",
    "mean_urban_ride_count = np.mean(urban_ride_count)\n",
    "print(f\"The mean for the ride counts for urban trips is {mean_urban_ride_count:.2f}.\")\n",
    "\n",
    "median_urban_ride_count = np.median(urban_ride_count)\n",
    "print(f\"The median for the ride counts for urban trips is {median_urban_ride_count}.\")\n",
    "\n",
    "mode_urban_ride_count = sts.mode(urban_ride_count)\n",
    "print(f\"The mode for the ride counts for urban trips is {mode_urban_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for suburban trips is 17.36.\n",
      "The median for the ride counts for suburban trips is 17.0.\n",
      "The mode for the ride counts for suburban trips is ModeResult(mode=array([17], dtype=int64), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the suburban cities.\n",
    "mean_suburban_ride_count = np.mean(suburban_ride_count)\n",
    "print(f\"The mean for the ride counts for suburban trips is {mean_suburban_ride_count:.2f}.\")\n",
    "\n",
    "median_suburban_ride_count = np.median(suburban_ride_count)\n",
    "print(f\"The median for the ride counts for suburban trips is {median_suburban_ride_count}.\")\n",
    "\n",
    "mode_suburban_ride_count = sts.mode(suburban_ride_count)\n",
    "print(f\"The mode for the ride counts for suburban trips is {mode_suburban_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for rural trips is 6.94.\n",
      "The median for the ride counts for rural trips is 6.0.\n",
      "The mode for the ride counts for rural trips is ModeResult(mode=array([6], dtype=int64), count=array([5])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the rural cities.\n",
    "mean_rural_ride_count = np.mean(rural_ride_count)\n",
    "print(f\"The mean for the ride counts for rural trips is {mean_rural_ride_count:.2f}.\")\n",
    "\n",
    "median_rural_ride_count = np.median(rural_ride_count)\n",
    "print(f\"The median for the ride counts for rural trips is {median_rural_ride_count}.\")\n",
    "\n",
    "mode_rural_ride_count = sts.mode(rural_ride_count)\n",
    "print(f\"The mode for the ride counts for rural trips is {mode_rural_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13.83\n",
       "1    30.24\n",
       "2    33.44\n",
       "3    23.44\n",
       "4    34.58\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the urban cities.\n",
    "urban_fares = urban_cities_df[\"fare\"]\n",
    "urban_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for urban trips is $24.53.\n",
      "The median fare price for urban trips is $24.64.\n",
      "The mode fare price for urban trips is ModeResult(mode=array([22.86]), count=array([5])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the urban cities.\n",
    "mean_urban_fares = np.mean(urban_fares)\n",
    "print(f\"The mean fare price for urban trips is ${mean_urban_fares:.2f}.\")\n",
    "\n",
    "median_urban_fares = np.median(urban_fares)\n",
    "print(f\"The median fare price for urban trips is ${median_urban_fares:.2f}.\")\n",
    "\n",
    "mode_urban_fares = sts.mode(urban_fares)\n",
    "print(f\"The mode fare price for urban trips is {mode_urban_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1625    27.79\n",
       "1626    18.75\n",
       "1627    21.71\n",
       "1628    17.10\n",
       "1629    37.78\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the suburban cities.\n",
    "suburban_fares = suburban_cities_df[\"fare\"]\n",
    "suburban_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for suburban trips is $30.97.\n",
      "The median fare price for suburban trips is $30.75.\n",
      "The mode fare price for suburban trips is ModeResult(mode=array([17.99]), count=array([3])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the suburban cities.\n",
    "mean_suburban_fares = np.mean(suburban_fares)\n",
    "print(f\"The mean fare price for suburban trips is ${mean_suburban_fares:.2f}.\")\n",
    "\n",
    "median_suburban_fares = np.median(suburban_fares)\n",
    "print(f\"The median fare price for suburban trips is ${median_suburban_fares:.2f}.\")\n",
    "\n",
    "mode_suburban_fares = sts.mode(suburban_fares)\n",
    "print(f\"The mode fare price for suburban trips is {mode_suburban_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2250    43.22\n",
       "2251    12.42\n",
       "2252    54.85\n",
       "2253    47.90\n",
       "2254    51.80\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the rural cities.\n",
    "rural_fares = rural_cities_df[\"fare\"]\n",
    "rural_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for rural trips is $34.62.\n",
      "The median fare price for rural trips is $37.05.\n",
      "The mode fare price for rural trips is ModeResult(mode=array([37.05]), count=array([2])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the rural cities.\n",
    "mean_rural_fares = np.mean(rural_fares)\n",
    "print(f\"The mean fare price for rural trips is ${mean_rural_fares:.2f}.\")\n",
    "\n",
    "median_rural_fares = np.median(rural_fares)\n",
    "print(f\"The median fare price for rural trips is ${median_rural_fares:.2f}.\")\n",
    "\n",
    "mode_rural_fares = sts.mode(rural_fares)\n",
    "print(f\"The mode fare price for rural trips is {mode_rural_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    72\n",
       "2    57\n",
       "3    34\n",
       "4    46\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the urban cities.\n",
    "urban_drivers = urban_cities_df['driver_count']\n",
    "urban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean number of drivers for urban trips is 36.68.\n",
      "The median number of drivers for urban trips is 37.00.\n",
      "The mode number of drivers for urban trips is ModeResult(mode=array([39], dtype=int64), count=array([86])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the driver count for the urban cities.\n",
    "mean_urban_drivers = np.mean(urban_drivers)\n",
    "print(f\"The mean number of drivers for urban trips is {mean_urban_drivers:.2f}.\")\n",
    "\n",
    "median_urban_drivers = np.median(urban_drivers)\n",
    "print(f\"The median number of drivers for urban trips is {median_urban_drivers:.2f}.\")\n",
    "\n",
    "mode_urban_drivers = sts.mode(urban_drivers)\n",
    "print(f\"The mode number of drivers for urban trips is {mode_urban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1625    11\n",
       "1626    20\n",
       "1627    22\n",
       "1628    19\n",
       "1629    12\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the suburban cities.\n",
    "suburban_drivers = suburban_cities_df['driver_count']\n",
    "suburban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean number of drivers for suburban trips is 13.71.\n",
      "The median number of drivers for suburban trips is 16.00.\n",
      "The mode number of drivers for suburban trips is ModeResult(mode=array([20], dtype=int64), count=array([79])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the driver count for the suburban cities.\n",
    "mean_suburban_drivers = np.mean(suburban_drivers)\n",
    "print(f\"The mean number of drivers for suburban trips is {mean_suburban_drivers:.2f}.\")\n",
    "\n",
    "median_suburban_drivers = np.median(suburban_drivers)\n",
    "print(f\"The median number of drivers for suburban trips is {median_suburban_drivers:.2f}.\")\n",
    "\n",
    "mode_suburban_drivers = sts.mode(suburban_drivers)\n",
    "print(f\"The mode number of drivers for suburban trips is {mode_suburban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2250    9\n",
       "2251    8\n",
       "2252    6\n",
       "2253    2\n",
       "2254    2\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the rural cities.\n",
    "rural_drivers = rural_cities_df['driver_count']\n",
    "rural_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean number of drivers for rural trips is 4.30.\n",
      "The median number of drivers for rural trips is 4.00.\n",
      "The mode number of drivers for rural trips is ModeResult(mode=array([1], dtype=int64), count=array([32])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the driver count for the rural cities.\n",
    "mean_rural_drivers = np.mean(rural_drivers)\n",
    "print(f\"The mean number of drivers for rural trips is {mean_rural_drivers:.2f}.\")\n",
    "\n",
    "median_rural_drivers = np.median(rural_drivers)\n",
    "print(f\"The median number of drivers for rural trips is {median_rural_drivers:.2f}.\")\n",
    "\n",
    "mode_rural_drivers = sts.mode(rural_drivers)\n",
    "print(f\"The mode number of drivers for rural trips is {mode_rural_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban cities ride count.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(10, 41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the suburban cities ride count.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(5, 30, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the rural cities ride count.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(0, 16, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all ride count box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "ride_count_data = [urban_ride_count, suburban_ride_count, rural_ride_count]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Ride Count Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Number of Rides',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(ride_count_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 45, step=3.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig2.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "West Angela has the highest rider count.\n"
     ]
    }
   ],
   "source": [
    "# Get the city that matches 39.\n",
    "urban_city_outlier = urban_ride_count[urban_ride_count==39].index[0]\n",
    "print(f\"{urban_city_outlier} has the highest rider count.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.000000\n",
       "mean       24.525772\n",
       "std        11.738649\n",
       "min         4.050000\n",
       "25%        14.550000\n",
       "50%        24.640000\n",
       "75%        34.580000\n",
       "max        44.970000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban fare data.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "urban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    625.000000\n",
       "mean      30.970128\n",
       "std       10.677508\n",
       "min       12.050000\n",
       "25%       21.970000\n",
       "50%       30.750000\n",
       "75%       39.830000\n",
       "max       49.960000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the suburban fare data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean      34.623440\n",
       "std       14.558046\n",
       "min       10.110000\n",
       "25%       19.900000\n",
       "50%       37.050000\n",
       "75%       47.070000\n",
       "max       58.550000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the rural fare data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all fare box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "fare_data = [urban_fares, suburban_fares, rural_fares]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Ride Fare Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Fare ($USD)',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(fare_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 60, step=5.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.000000\n",
       "mean       36.678154\n",
       "std        20.075545\n",
       "min         3.000000\n",
       "25%        22.000000\n",
       "50%        37.000000\n",
       "75%        52.000000\n",
       "max        73.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the urban driver count data.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_drivers,labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Number of Drivers)')\n",
    "ax.set_yticks(np.arange(0, 90, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "urban_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    625.000000\n",
       "mean      13.712000\n",
       "std        8.042372\n",
       "min        1.000000\n",
       "25%        5.000000\n",
       "50%       16.000000\n",
       "75%       21.000000\n",
       "max       25.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the suburban driver count data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_drivers,labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Number of Drivers)')\n",
    "ax.set_yticks(np.arange(0, 35, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean       4.296000\n",
       "std        2.691156\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        4.000000\n",
       "75%        7.000000\n",
       "max        9.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the rural driver count data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_drivers,labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Number of Drivers)')\n",
    "ax.set_yticks(np.arange(0, 12, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2wq8mDryYZrbtpwBvqqo3dBWLJEnSStBX1+RhwDeqas4+U0mSpNWur8H6jwPeN+P9s5M8GZgEXlDDb9K6ieb+YIyNjTEYDLqIU62pqSnPuVY967nWAuv5ytL5hK5JdgMuobnH12VJxmhuplzAq4D1VfXU+Y4xPj5eXjXZLa+y0VpgPddaYD3vXpLNVTU+bF0fXZMPBU6vqssAquqyqrqmqq4F3gkc2ENMkiRJnesjEXs8M7olk6yfse5RwFmdRyRJktSDTseIJbkR8CDgGTMWvy7JRpquyQtnrZMkSVq1Ok3EqupnwD6zlj2pyxgkSZJWCmfWlyRJ6omJmCRJUk92yJt+a9sl6bzMrqdIkSRpR2EitsZsa1KUxIRKkqQlZtekJElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk96SwRS7J/kjNmPH6c5PlJ9k5ySpLz2+d1XcUkSZLUp84Ssao6r6o2VtVG4F7Az4APAy8BTq2q/YBT2/eSJEmrXl9dk4cB36iqbwOHA8e1y48DHtlTTJIkSZ3qKxF7HPC+9vVYVV0K0D7foqeYJEmSOtX5vSaT7AY8AnjpIvfbBGwCGBsbYzAYLH1wmpfnXKvd1NSU9VyrnvV8ZUnXN3JOcjjwp1X14Pb9ecBEVV2aZD0wqKr95zvG+Ph4TU5OdhCtpnnTb60Fg8GAiYmJvsOQlpX1vHtJNlfV+LB1fXRNPp7ruiUBTgaOaF8fAZzUeUSSJEk96DQRS3Ij4EHAh2Ysfi3woCTnt+te22VMkiRJfel0jFhV/QzYZ9ayH9BcRSlJkrSmOLO+JElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSemIiJkmS1BMTMUmSpJ50mogl2SvJiUnOTXJOkoOSHJ3k4iRntI+HdRmTJElSX3bpuLy3AJ+oqsck2Q24EfA7wJuq6g0dxyJJktSrzhKxJHsAhwBHAlTVL4BfJOkqBEmSpBWlyxax2wPfB96T5ABgM/C8dt2zkzwZmAReUFVbZ++cZBOwCWBsbIzBYNBJ0LqO51yr3dTUlPVcq571fGVJVXVTUDIOfAE4uKq+mOQtwI+BtwFXAAW8ClhfVU+d71jj4+M1OTm53CFrhiR0VVekvgwGAyYmJvoOQ1pW1vPuJdlcVePD1nU5WP8i4KKq+mL7/kTgnlV1WVVdU1XXAu8EDuwwJkmSpN50lohV1feA7ybZv110GHB2kvUzNnsUcFZXMUmSJPWp66smnwMc314x+U3gKcA/JNlI0zV5IfCMjmOSJEnqRaeJWFWdAczuI31SlzFIkiStFM6sL0mS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknnSZiSfZKcmKSc5Ock+SgJHsnOSXJ+e3zui5jkiRJ6kvXLWJvAT5RVXcGDgDOAV4CnFpV+wGntu8lSZJWvc4SsSR7AIcA7wKoql9U1Q+Bw4Hj2s2OAx7ZVUySJEl92qXDsm4PfB94T5IDgM3A84CxqroUoKouTXKLYTsn2QRsAhgbG2MwGHQStK7jOddqNzU1ZT3Xqmc9X1lSVd0UlIwDXwAOrqovJnkL8GPgOVW114zttlbVvOPExsfHa3JycnkD1vUkoau6IvVlMBgwMTHRdxjSsrKedy/J5qoaH7auyzFiFwEXVdUX2/cnAvcELkuyHqB9vrzDmCRJknrTWSJWVd8Dvptk/3bRYcDZwMnAEe2yI4CTuopJkiSpT12OEQN4DnB8kt2AbwJPoUkGT0jyNOA7wGM7jkmSJKkXnSZiVXUGMKyP9LAu45AkSVoJnFlfkiSpJyZikiRJPTERkyRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT1xERMkiSpJyZikiRJPTERkyRJ6kmniViSC5N8NckZSSbbZUcnubhddkaSh3UZkyRJUl86vel369CqumLWsjdV1Rt6iEWSJKk3dk1KkiT1pOtErIBPJdmcZNOM5c9OcmaSdydZ13FMkiRJvei6a/LgqrokyS2AU5KcC/wT8CqaJO1VwN8DT529Y5u4bQIYGxtjMBh0FrQannOtdlNTU9ZzrXrW85UlVdVPwcnRwNTMsWFJNgAfqaq7z7fv+Ph4TU5OLmt8ur4k9FVXpK4MBgMmJib6DkNaVtbz7iXZXFXjw9Z11jWZ5MZJbjr9GngwcFaS9TM2exRwVlcxSZIk9anLrskx4MNJpst9b1V9Ism/JdlI0zV5IfCMDmOSJEnqTWeJWFV9EzhgyPIndRWDJEnSSuL0FZIkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknoyUiCW5f5J7z3h/ZJLPJXlHkpssX3iSJEmr16gtYm8GbgmQZH/gHcCZwEHA65cnNEmSpNVt1ETsDsBX29ePBk6pqmcBTwd+bzkCkyRJWu1GTcQK2Ll9fRjwifb194B9ljooSZKktWDUROxLwMuTPAm4H/DxdvkGmmRMkiRJizRqIvZ8YCPwNuDVVfWNdvljgc8vR2CSJEmr3YI3/U6yE3At8NtVNTVr9QuBa0YtLMmFwE/afa6uqvEkewMfoGlduxD4g6raOuoxJUmSdlSjtIgVcAbtVZPXW1H186r65SLLPLSqNlbVePv+JcCpVbUfcGr7XpIkadVbMBGrqgLOA26+TDEcDhzXvj4OeOQylSNJkrSiLNg12XoR8Pokzwa+0iZn26KATyUp4B1VdQwwVlWXAlTVpUluMWzHJJuATQBjY2MMBoNtDEHbynOu1W5qasp6rlXPer6yZJScKslPgN1pWtCuBq6aub6q9hipsOQ3quqSNtk6BXgOcHJV7TVjm61VtW6+44yPj9fk5OQoRa5ae++9N1u3rt6hdOvWrWPLli19h6E1ZjAYMDEx0XcY0rKynncvyeYZQ7KuZ9QWsWcvRSBVdUn7fHmSDwMHApclWd+2hq0HLl+Ksla7rVu3su0Nk4vX9Rc3SWdlSZLUl5ESsao6buGt5pfkxsBOVfWT9vWDgb8GTgaOAF7bPp+0vWVJkiTtCEZtESPJGPAkmtsdvbyqrkhyMHBJVX1rhEOMAR9uWzp2Ad5bVZ9I8iXghCRPA75DMzeZJEnSqjdSIpbkXjRTS3wLuBvNjb6vAB4E3An4o4WOUVXfBA4YsvwHNLdNkiRJWlNGnVn/DcBbquoeXH+g/ieBg5c8KkmSpDVg1ETsXlw319dMl9J0OUqSJGmRRk3ErgSGTSlxZ7zKUZIkaZuMmoidBByV5Abt+0qyAfg74IPLEJckSdKqN2oi9kJgb+D7wI2AzwEXAD8E/mp5QpMkSVrdRp1H7MfAfZM8ALgnTQJ3elX913IGJ0mStJqNOn3FAVX1lar6NPDpZY5JkiRpTRi1a/LLSb6a5EVJbr2sEUmSJK0RoyZidwY+BPwxcGGS05I8NclIN/uWJEnSrxspEauqr1fVUVV1J5oJXL8KvAb4XpITljNASZKk1WrUFrFfqaovVtVzgcOB84BHL3lUkiRJa8CiErEkt0/yV0nOoZnCYitNd+VijrFzki8n+Uj7/tgk30pyRvvYuJjjSZIk7ahGvWryT4EnAPcGzgLeAxxfVRdvQ5nPA84BZo4v+4uqOnEbjiVJkrTDGrVF7CU0LWAbq+qAqnrdtiRh7RWXvwv8y2L3lSRJWm1GahEDbltVtQTlvRl4EXDTWctfneQVwKnAS6rqqiUoS5IkaUWbMxFLck/gjKq6FrhHkjkPUlWnL1RQkocDl1fV5iQTM1a9FPgesBtwDPBi4K+H7L8J2AQwNjbGYDBYqMhVr8tzMDU11fk592esrvVRz6WuWc9XlszV0JXkWuCWVXV5+7qAYdlYVdXOCxaU/C3wJOBqYHeaMWIfqqonzthmAnhhVT18vmONj4/X5OTkQkWuaklYmkbK0QwGAyYmJjorr+vPJ0H39Vzqg/W8e0k2V9X4sHXzdU3uS3OT7+nX26WqXkrT+jUz4XpikvVVdWmaJrdH0lwMIEmStOrNmYhV1bcBkuwK/Cnwj9PLltjxSW5O09p2BvDMZShDkiRpxVlwsH5V/TLJs4C3L1WhVTUABu3rByzVcSVJknYko05f8UnAhEmSJGkJjTp9xanAa5L8JrAZ+OnMlVX1oaUOTJIkabUbNRF7W/v83CHrCljwqklJkiRd30iJWFUt+ubgkiRJmp8JliRJUk8WbBFr5/d6MvBo4PY0XZHfBP6d5sbfzropSZK0DUZpEfsg8B7gdsBXga/RTPD6r8CJyxeaJEnS6jZvi1iSJwAPBn6nqk6Zte53gA8m+aOqeu8yxihJkrQqLdQi9kTg72YnYQBV9Ung9e02kiRJWqSFErEDgI/Ns/6jwMalC0eSJGntWCgR2we4dJ71lwJ7L104kiRJa8dCidiuwC/nWX91u40kSZIWaZQJXf82yc/mWHejxRaYZGdgEri4qh6eZF/g/TQta6cDT6qqXyz2uJIkSTuahRKxzwJ3GGGbxXgecA6wR/v+74A3VdX7k/wz8DTgnxZ5TEmSpB3OvIlYVU0sZWFJbg38LvBq4M/byWIfAPxRu8lxwNGYiEmSpDVg1Jt+L5U3Ay8Cbtq+3wf4YVVd3b6/CLjVsB2TbAI2AYyNjTEYDJY30h1Al+dgamqq83Puz1hd66OeS12znq8snSViSR4OXF5Vm5NMTC8esunQWyZV1THAMQDj4+M1MTExbLM1pctzMBgMOi0Puv18EvRTz6WuWc9Xli5bxA4GHpHkYcDuNGPE3gzslWSXtlXs1sAlHcYkSZLUm1HuNbkkquqlVXXrqtoAPA74dFU9ATgNeEy72RHASV3FJEmS1Kc5E7Ek705y0/b1IUmWq/XsxTQD9y+gGTP2rmUqR5IkaUWZL7l6IvAy4Cc0rVbrgcuXotCqGgCD9vU3gQOX4riSJEk7kvkSsQuB5yT5FM2g+oOSbB22YVUtdi4xSZKkNW++ROwvgHcCL6W5kvHDc2xXwM5LHJckSdKqN2ciVlUnAScl2QvYAtyNJeqalCRJ0gjTV1TVD5McCpw/Y+JVSZIkbaeRroSsqs8kuUGSJwN3pemOPBt4b1VdtZwBSpIkrVYjzSOW5K7A14E3AvcG7gO8Cfh6krssX3iSJEmr16gTur4FOAO4bVXdr6ruB9wW+ArN7PiSJElapFEnaT0Y+K2q+vH0gqr6cZK/BL6wLJFpXnXUHnD0np2VNwHtzG/dqKP26K4wSZJ6Mmoi9nNgryHL92zXqWN55Y+pGnp/9GXR9U1ik1BHd1acJEm9GLVr8j+BdyY5OMnO7eO+wDuAk5cvPEmSpNVr1ETsecD5wH/TtID9HPgMzQD+5y9PaJIkSavbqNNX/BA4PMkdgbvQ3PLo7Kq6YDmDkyRJWs1GHSMGQJt4bVPylWR34LPADdpyT6yqo5IcC9wf+FG76ZFVdca2lCFJkrQjWVQitp2uAh5QVVNJdgU+l+Tj7bq/qKoTO4xFkiSpd50lYtVc4jfVvt21fXR32Z8kSdIK02WLGEl2BjYDdwT+saq+mORPgFcneQVwKvCSYbdNSrIJ2AQwNjbGYDDoLvAVqstzMDU11fk592esrvVRz6WuWc9Xliw0F1WSXWgSoP+oqkuWpNBkL+DDwHOAHwDfA3YDjgG+UVV/Pd/+4+PjNTk5uRSh7LCSrP55xDr8fBJ0X8+lPljPu5dkc1WND1u34PQVVXU18HqarsQl0V6FOQAeUlWXVuMq4D3AgUtVjiRJ0ko26jxiXwDuuT0FJbl52xJGkhsCDwTOTbK+XRbgkcBZ21OOJEnSjmLUMWLvBP4+ye1oxnj9dObKqjp9hGOsB45rx4ntBJxQVR9J8ukkN6eZm+wM4JkjRy9JkrQDGzURe2/7/MYh6wrYeaEDVNWZwD2GLH/AiDFIkiStKqMmYvsuaxSSJElr0Ki3OPr2cgciSZK01ow6WJ8kD03ykSRnJ7lNu+yPkxy2fOFJkiStXiMlYkmeAJwAnE/TTTk9lcXOwIuWJzRJkqTVbV/bhF0AABX8SURBVNQWsRcBT6+qPwOunrH8C8DGJY9KkiRpDRg1EdsP+N8hy6eAPZYuHEmSpLVj1ETsEuBOQ5YfAnxj6cKRJElaO0ZNxI4B/iHJwe372yQ5Angd8E/LEpkkSdIqN+r0Fa9LsidwCrA7cBpwFfCGqvrHZYxPkiRp1Rp1Qleq6i+TvBq4K01L2tlVNbVskUmSJK1yIydirQJ+3r6+ZoljkSRJWlNGnUfsBkneDGwBvgKcCWxJ8pYku494jN2T/F+SryT5WpJXtsv3TfLFJOcn+UCS3bb1w0iSJO1IRh2s/0/AY4A/ppnK4o7t60cBbx/xGFcBD6iqA2jmHntIkvsAfwe8qar2A7YCTxs9fEmSpB3XqInYY4GnVNXxVfXN9nE8TdL0mFEOUI3pMWW7to8CHgCc2C4/DnjkyNFLkiTtwEZNxH4KXDxk+cXAlaMWlmTnJGcAl9NcgfkN4IdVNT1b/0XArUY9niRJ0o5s1MH6bwWOSnJkVV0JkOSGwMvbdSOpqmuAjUn2Aj4M3GXYZsP2TbIJ2AQwNjbGYDAYtdhVq8tzMDU11fk592esrvVRz6WuWc9XllQNzXtIcvKsRRM095k8s33//2gSuc9U1SMWXXByFPAz4MXALavq6iQHAUdX1e/Mt+/4+HhNTk4utshVJQlz/eyWw2AwYGJiorPyuv58EnRfz6U+WM+7l2RzVY0PWzdfi9gPZr3/4Kz331pkEDcHfllVP2xb0x5IM1D/NJpxZu8HjgBOWsxxJUmSdlRzJmJV9ZQlLms9cFySnWnGpp1QVR9Jcjbw/iR/A3wZeNcSlytJkrQiLXZC121WVWcC9xiy/JvAgV3FIUmStFKMlIglWQccDRwK3IJZV1tW1S2WPDJJkqRVbtQWsX8F7kYzz9dlzHFloyRJkkY3aiI2Ady/qk5fxlgkSZLWlFEndP3GIraVJEnSCEZNrp4H/G2SA9qrHiVJkrSdRu2avAC4IXA6NJNtzlRVJmeSJEmLNGoi9j5gT+C5OFhfkiRpSYyaiI0DB1bVWcsZjCRJ0loy6hixs4E9ljMQSZKktWbUROyvgDcmeWCSsSR7z3wsZ4CSJEmr1ahdkx9rnz/F9ceHpX3vYH1JkqRFGjURO3RZo5AkSVqDRkrEquoz21tQktvQ3CrplsC1wDFV9ZYkRwNPB77fbvqyqvrY8KNIkiStHqPe9Pue860f8dZHVwMvqKrTk9wU2JzklHbdm6rqDaPEIkmStFqM2jU5STMWbOZMrjPHii04RqyqLgUubV//JMk5wK1GLF+SJGnVGTUR23fW+12BewB/Cbx0sYUm2dDu/0XgYODZSZ5Mk/C9oKq2DtlnE7AJYGxsjMFgsNhiV50uz8HU1FTn59yfsbbVoYd2P6z1tNNO67xMaVv08ftcc0vVtk+Sn+TBwFFVdfAi9rkJ8Bng1VX1oSRjwBU0LWyvAtZX1VPnO8b4+HhNTk5uc9yrQRK252e3WIPBgImJic7K6/rzSWC909rQ9e9zQZLNVTU+bN2o84jN5VvAxkUEsivwQeD4qvoQQFVdVlXXVNW1wDuBA7czJkmSpB3CqIP1Z0/aGmA9cDRw3ojHCPAu4JyqeuOM5evb8WMAjwK8jZIkSVoTRh0jNt11OFOA7wJ/OOIxDgaeBHw1yRntspcBj0+ysT3+hcAzRjyeJEnSDm1bJ3S9lmberwuq6upRDlBVn+P6V11Oc84wSZK0JnU2oaskSZKub95EbNQbelfVlqUJR5Ikae1YqEVs2Niw2WqE40iSJGmWhRKo+WZFfAjwPJpbF0mSJGmR5k3Eho0Na+87+XfAIcA7aCZhlSRJ0iKNPKFrkn2TvJfmtkRbgLtW1XOr6vvLFp0kSdIqtuDYriT7AK8Angn8D3BQVa3t+wutEM0cuavTunXr+g5BkqRlt9BVky8DXkQz0erhVfWJLoLSwrq+H5734JMkaekt1CL2N8CVwEXAs5I8a9hGVfWIpQ5MkiRptVsoEftXFp6+QpIkSdtgoasmj+woDkmSpDVn5Ksmt1eS2yQ5Lck5Sb6W5Hnt8r2TnJLk/PbZUdqSJGlN6CwRo5n49QVVdRfgPsCfJrkr8BLg1KraDzi1fS9JkrTqdZaIVdWlVXV6+/onwDnArYDDgePazY4DHtlVTJIkSX3q5R6RSTYA96CZHHasqi6FJllLcos+YpK08uy9995s3bq10zK7nJ9v3bp1bNmypbPyJK08nSdiSW4CfBB4flX9eNRfekk2AZsAxsbGGAwGyxajhvOcq2tbt27ltNNO66y8qakpbnKTm3RW3qGHHur3Sp2bmpqy3q0g6XKSziS7Ah8BPllVb2yXnQdMtK1h64FBVe0/33HGx8drctLJ/bvkhK7qQ9f1bjAYMDEx0Vl5fq/Uh67ruSDJ5qoaH7auy6smA7wLOGc6CWudDBzRvj4COKmrmCRJkvrUZdfkwcCTgK8mOaNd9jLgtcAJSZ4GfAd4bIcxSZIk9aazRKyqPgfMNSDssK7ikCRJWim6nEdMkiRJM5iISZIk9cRETJIkqScmYpIkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST3pLBFL8u4klyc5a8ayo5NcnOSM9vGwruKRJEnqW5ctYscCDxmy/E1VtbF9fKzDeCRJknrVWSJWVZ8FtnRVniRJ0kq3S98BAM9O8mRgEnhBVW0dtlGSTcAmgLGxMQaDQXcRCsBzrs7VUXvA0Xt2Vt4EwKCz4qij9vB7pc5NTU1Z71aQVFV3hSUbgI9U1d3b92PAFUABrwLWV9VTFzrO+Ph4TU5OLmOkmi0JXdYVCbqvd4PBgImJic7K83ulPnRdzwVJNlfV+LB1vV41WVWXVdU1VXUt8E7gwD7jkSRJ6lKviViS9TPePgo4a65tJUmSVpvOxogleR/NEIybJbkIOAqYSLKRpmvyQuAZXcUjSZLUt84Ssap6/JDF7+qqfEmSpJXGmfUlSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknnSWiCV5d5LLk5w1Y9neSU5Jcn77vK6reCRJkvrWZYvYscBDZi17CXBqVe0HnNq+lyRJWhM6S8Sq6rPAllmLDweOa18fBzyyq3gkSZL61vcYsbGquhSgfb5Fz/FIkiR1Zpe+AxhVkk3AJoCxsTEGg0G/Aa1BnnP1oct6NzU11Xk993ulrvVRzzW3VFV3hSUbgI9U1d3b9+cBE1V1aZL1wKCq9l/oOOPj4zU5Obmsser6ktBlXZGg+3o3GAyYmJjorDy/V+pD1/VckGRzVY0PW9d31+TJwBHt6yOAk3qMRZIkqVNdTl/xPuB/gf2TXJTkacBrgQclOR94UPtekiRpTehsjFhVPX6OVYd1FYMkSatFks7LtCt96e0wg/UlSdJ1tjUpcmziytL3GDFJkqQ1y0RMkiSpJyZikiRJPTERkyRJ6omJmCRJUk9MxCRJknri9BWSJPVo7733ZuvWrZ2W2eUcZOvWrWPLli2dlbejMRGTJKlHW7duXfX3VNXcTMQkrWir+Zf4unXr+g5BUs9MxCStWF3P/u2M45K65mB9SZKknqyIFrEkFwI/Aa4Brq6q8X4jkiRJWn4rIhFrHVpVV/QdhCRJUlfsmpQkSerJSknECvhUks1JNvUdjCRJUhdWStfkwVV1SZJbAKckObeqPjtzgzZB2wQwNjbGYDDoIcy1zXOutcB6rj50We+mpqY6r+d+r+aWlXapdpKjgamqesNc24yPj9fk5GR3QcnL+rUmWM/Vh67rXR8Tuq7171WSzXNdiNh712SSGye56fRr4MHAWf1GJUmStPxWQtfkGPDhdvbsXYD3VtUn+g1JkiRp+fWeiFXVN4ED+o5DkqQ+1FF7wNF7dlbeBMCgs+Kaz6c59Z6ISZK0luWVP179Y8SO7qy4HU7vY8QkSZLWKhMxSZKkntg1uca0F0V0uu9av2xZkqS52CK2xlTVNj1OO+20bd5XkiQNZyImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST1x+gpJknq2PVMLrXTr1q3rO4QVzURM0qrjfHnakWxr3ekjebOeL70V0TWZ5CFJzktyQZKX9B2PpB2b8+VpLbCerw69J2JJdgb+EXgocFfg8Unu2m9UkiRJy6/3RAw4ELigqr5ZVb8A3g8c3nNMkiRJy24ljBG7FfDdGe8vAu49e6Mkm4BNAGNjYwwGg06CU2NqaspzrlXPeq61wHq+sqyERGzYaMNf64iuqmOAYwDGx8drYmJimcPSTIPBAM+5VjvrudYC6/nKshK6Ji8CbjPj/a2BS3qKRZIkqTMrIRH7ErBfkn2T7AY8Dji555gkSZKWXe9dk1V1dZJnA58EdgbeXVVf6zksSZKkZdd7IgZQVR8DPtZ3HJIkSV1aCV2TkiRJa5KJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSeZEe8m3qS7wPf7juONeZmwBV9ByEtM+u51gLrefduV1U3H7Zih0zE1L0kk1U13ncc0nKynmstsJ6vLHZNSpIk9cRETJIkqScmYhrVMX0HIHXAeq61wHq+gjhGTJIkqSe2iEmSJPXERGyNSbIhyVmzlh2d5IVDtj02yWO6i04aTZK/TPK1JGcmOSPJvefZdmj9XmR5gyReZaYVKck17ffgrCT/mWSvJTz21FIdS8OZiGmoJLv0HYM0TJKDgIcD96yq3wQeCHx3GcvbebmOLS2RK6tqY1XdHdgC/OmoO/q7vn8mYvqV9r/+1yT5DPC8dvEDk/x3kq8neXi73YZ22ent47fb5RPtMU5Mcm6S45Okr8+jVWs9cEVVXQVQVVdU1SVJLkxyM4Ak40kGM/Y5IMmnk5yf5OntNhNJPjK9QZK3JTmyfX1hklck+Rzw2HaTJyb5fNvqcGC73YHtsi+3z/u3y49M8qEkn2jLfN3ynhLpV/4XuBVcvyU3yc2SXNi+PjLJvyf5T+BTSW6S5NT29/lXkxzeW/RrkJmwZturqu4PTdcksAG4P3AH4LQkdwQuBx5UVT9Psh/wPmC62+YewN2AS4D/AQ4GPtflB9Cq9yngFUm+DvwX8IGq+swC+/wmcB/gxsCXk3x0hHJ+XlX3BUjyTODGVfXbSQ4B3g3cHTgXOKSqrk7yQOA1wKPb/TfSfB+uAs5L8taqWraWO6ltvT0MeNcImx8E/GZVbWlbxR5VVT9u/5n5QpKTy6v5OmEitvbM9cWaXv6BWctPqKprgfOTfBO4M/At4G1JNgLXAHeasf3/VdVFAEnOoEnkTMS0ZKpqKsm9gPsBhwIfSPKSBXY7qaquBK5MchpwIPDDBfaZ/V14X1v+Z5Ps0Y7DuSlwXPsPSQG7ztj+1Kr6EUCSs4HbsYxdqFrTbjjj9+1m4JQR9jmlqra0rwO8pv0n41qaFrUx4HvLEKtmsWty7fkBsG7Wsr257r5jP521bnbiVsCfAZcBB9C0hO02Y/1VM15fg8m+lkFVXVNVg6o6Cng2TSvU1Vz3O2332bsMeT9z+2H7jPJdeBVwWjs25/dmHcPvgrpyZVVtpEn2d+O6MWLzfSdm1u8nADcH7tUe57Ih22uZmIitMVU1BVya5DCAJHsDD2HuVqvHJtkpyR2A2wPnAXsCl7YtZU8CHMysziTZv22BmrYR+DZwIXCvdtmjZ+12eJLdk+wDTABfave5a5IbJNmTpktnPn/Yln9f4Edta9eewMXt+iO36QNJS6Stk88FXphkV67/nZjvCvg9gcur6pdJDqVJ6NQR/0Nbm54M/GOSv2/fv7KqvjHHuPrzgM/QNFM/sx0X9nbgg0keC5zGr7ccSMvpJsBb267Bq4ELgE3AXYB3JXkZ8MVZ+/wf8FHgtsCrquoSgCQnAGcC5wNfXqDcrUk+D+wBPLVd9jqarsk/Bz69vR9M2l5V9eUkXwEeB7wBOCHJk5i/fh4P/GeSSeAMmrGP6ogz60uSJPXErklJkqSemIhJkiT1xERMkiSpJyZikiRJPTERkyRJ6omJmKQdQnv/xxf2HYckLSUTMUm9SzKW5C1JvpHkqiQXJ/l4kofN2Oy3gLfP2KeSzDdJ5Sjl1gKPY7fn+JK0ECd0ldSrJBtobhD/E+ClwFdo/kk8DPhnmklYqarvL0Px62e8fjjwzlnLrlyGMiXpV2wRk9S3t9PcdHi8qk6oqvOq6pyqehvN/UyB63dNJrmwXfzvbcvVhUk2JLkmyfjMgyd5epIrksy8JyoAVfW96QftTcDb15fR3Pbr6bOOtV9b3j3b95Xk2Uk+muRnSb6d5Imz9rlVkvcn2do+PjrzFk1JbpPkpCRb2mOcm+Rx23guJe1gTMQk9WbGvU7f1t4H9Xqqauscu/5W+/x0mhas36qqC4H/4rrbD017KvBvVfWLUeOq5pYj75rjWGdU1ekzlr0SOJnmnpfHAP86nQwmuRHNbcB+DtwfOAi4FPivdh00ieiNgEOBuwHPp00KJa1+JmKS+nRHmtawcxaz04xuyh+2LVrT798JPD7J7gBJ7gLchyapWqz3APsluU97rJ1p7tM6+1gfqqp3VNXXq+rVNPf0e3677nE0n+8pVXVmVZ0LPIPmfpkPb7e5HfC5qvpKVX2rqj5RVZ/Yhngl7YBMxCT1aeid5rfDScAvgN9v3z8V+L+qOmuxB2q7KD/Cda1iDwH2oblB8kz/O+T9XdvX9wL2BX6SZCrJFPAjYB1wh3abtwB/leR/k/xNknstNlZJOy4TMUl9Oh8o4C5LcbCq+iXwr8BTk+wCPIltaw2b9i/AH7bdiE+laf2aq7t0mJ2AM2i6LWc+7gS8o435XTTJ2nva5Z9PcvR2xCxpB2IiJqk3VbUF+CTw7CQ3mb0+yV7z7P5LYOchy99JM97qWcBNgfdvR4ifAH4MPBP4PeDdQ7a5z5D3012tp9N0v15RVRfMemyZ3qGqLqqqY6rqD4BXAJu2I2ZJOxATMUl9exZNF+Vkkscm2T/JnZP8CXDmPPtdCByW5JZJ1k0vrKqv01zx+HrgxKr68bYGVlXX0CRffwtcDJw6ZLPfb6/M3C/JS2mm3Xhzu+54miswT0py/yT7Jjkkyd9PXznZzp/2kCS3T7KRpgv07G2NWdKOxURMUq+q6lvAPYFTgL+jSb4+DTyCZmD7XF5A0/L1XeDLs9a9C9iN7euWnPbu9ljvaa+mnO1o4NE0cf8JzcD8LwFU1c+AQ4BvAv8OnAscRzNGbLqLcyfgrTTJ1yk0idsRSxC3pB1Ahv9ekaQdV5IXA0+rqjstwbHuTTPh7O2r6juz1hXw2Ko6cXvLkbQ2ObO+pFWjHWd2Z+B5wKu381g3AG4D/A3w4dlJmCQtBbsmJa0mb6Npvfof2qsSt8PjgfNopqz48+08liQNZdekJElST2wRkyRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT15P8HHgP/Bfwq59IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all ride count box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "driver_count_data = [urban_driver_count, suburban_driver_count, rural_driver_count]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Driver Count Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Number of Drivers',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(driver_count_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 80, step=5.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig4.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        4327.93\n",
       "Suburban    19356.33\n",
       "Urban       39854.38\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of the fares for each city type.\n",
    "sum_fares_by_type = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "sum_fares_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of all the fares.\n",
    "total_fares = pyber_data_df[\"fare\"].sum()\n",
    "total_fares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        6.811493\n",
       "Suburban    30.463872\n",
       "Urban       62.724635\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * sum_fares_by_type / total_fares\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        6.811493\n",
       "Suburban    30.463872\n",
       "Urban       62.724635\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * pyber_data_df.groupby([\"type\"]).sum()[\"fare\"] / pyber_data_df[\"fare\"].sum()\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import mpl to change the plot configurations using rcParams.\n",
    "import matplotlib as mpl\n",
    "# Build Pie Chart\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(type_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Fares by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig5.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        5.263158\n",
       "Suburban    26.315789\n",
       "Urban       68.421053\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of rides for each city type.\n",
    "ride_percents = 100 * pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"] / pyber_data_df[\"ride_id\"].count()\n",
    "ride_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build Pie Chart\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(ride_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Rides by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig6.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        2.623613\n",
       "Suburban    16.481668\n",
       "Urban       80.894719\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of drivers for each city type.\n",
    "driver_percents = 100*city_data_df.groupby([\"type\"]).sum()[\"driver_count\"] / city_data_df[\"driver_count\"].sum()\n",
    "driver_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build Pie Chart\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(driver_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=165)\n",
    "plt.title(\"% of Total Drivers by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig7.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-04-29 17:04:39</td>\n",
       "      <td>13.38</td>\n",
       "      <td>8550365057598</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-01-30 00:05:47</td>\n",
       "      <td>20.76</td>\n",
       "      <td>9018727594352</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>North Jaime</td>\n",
       "      <td>2019-02-10 21:03:50</td>\n",
       "      <td>11.11</td>\n",
       "      <td>2781339863778</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>West Heather</td>\n",
       "      <td>2019-05-07 19:22:15</td>\n",
       "      <td>44.94</td>\n",
       "      <td>4256853490277</td>\n",
       "      <td>4</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>Newtonview</td>\n",
       "      <td>2019-04-25 10:20:13</td>\n",
       "      <td>55.84</td>\n",
       "      <td>9990581345298</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    city                 date   fare        ride_id  \\\n",
       "0     Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1     South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2     Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3             Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4             South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "...                  ...                  ...    ...            ...   \n",
       "2370         Michaelberg  2019-04-29 17:04:39  13.38  8550365057598   \n",
       "2371     Lake Latoyabury  2019-01-30 00:05:47  20.76  9018727594352   \n",
       "2372         North Jaime  2019-02-10 21:03:50  11.11  2781339863778   \n",
       "2373        West Heather  2019-05-07 19:22:15  44.94  4256853490277   \n",
       "2374          Newtonview  2019-04-25 10:20:13  55.84  9990581345298   \n",
       "\n",
       "      driver_count   type  \n",
       "0                5  Urban  \n",
       "1               72  Urban  \n",
       "2               57  Urban  \n",
       "3               34  Urban  \n",
       "4               46  Urban  \n",
       "...            ...    ...  \n",
       "2370             6  Rural  \n",
       "2371             2  Rural  \n",
       "2372             1  Rural  \n",
       "2373             4  Rural  \n",
       "2374             1  Rural  \n",
       "\n",
       "[2375 rows x 6 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate total rides, total drivers, total fares\n",
    "summary_rides = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "summary_drivers = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "summary_fares = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_rides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         78\n",
       "Suburban     490\n",
       "Urban       2405\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        4327.93\n",
       "Suburban    19356.33\n",
       "Urban       39854.38\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_fares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate average fare per ride and per driver\n",
    "average_fare_per_ride = pyber_data_df.groupby([\"type\"]).mean()[\"fare\"]\n",
    "average_fare_per_driver = summary_fares / summary_drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Total Rides</th>\n",
       "      <th>Total Drivers</th>\n",
       "      <th>Total Fares</th>\n",
       "      <th>Average Fare per Ride</th>\n",
       "      <th>Average Fare per Driver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>78</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</td>\n",
       "      <td>55.486282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>19356.33</td>\n",
       "      <td>30.970128</td>\n",
       "      <td>39.502714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>2405</td>\n",
       "      <td>39854.38</td>\n",
       "      <td>24.525772</td>\n",
       "      <td>16.571468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total Rides  Total Drivers  Total Fares  Average Fare per Ride  \\\n",
       "type                                                                       \n",
       "Rural             125             78      4327.93              34.623440   \n",
       "Suburban          625            490     19356.33              30.970128   \n",
       "Urban            1625           2405     39854.38              24.525772   \n",
       "\n",
       "          Average Fare per Driver  \n",
       "type                               \n",
       "Rural                   55.486282  \n",
       "Suburban                39.502714  \n",
       "Urban                   16.571468  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assemble into summary data frame\n",
    "summary_df = pd.DataFrame({\n",
    "        \"Total Rides\": summary_rides,\n",
    "        \"Total Drivers\": summary_drivers,\n",
    "        \"Total Fares\": summary_fares,\n",
    "        \"Average Fare per Ride\": average_fare_per_ride,\n",
    "        \"Average Fare per Driver\": average_fare_per_driver\n",
    "         })\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Total Rides</th>\n",
       "      <th>Total Drivers</th>\n",
       "      <th>Total Fares</th>\n",
       "      <th>Average Fare per Ride</th>\n",
       "      <th>Average Fare per Driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>78</td>\n",
       "      <td>$4,327.93</td>\n",
       "      <td>$34.62</td>\n",
       "      <td>$55.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>$19,356.33</td>\n",
       "      <td>$30.97</td>\n",
       "      <td>$39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>2405</td>\n",
       "      <td>$39,854.38</td>\n",
       "      <td>$24.53</td>\n",
       "      <td>$16.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total Rides  Total Drivers Total Fares Average Fare per Ride  \\\n",
       "Rural             125             78   $4,327.93                $34.62   \n",
       "Suburban          625            490  $19,356.33                $30.97   \n",
       "Urban            1625           2405  $39,854.38                $24.53   \n",
       "\n",
       "         Average Fare per Driver  \n",
       "Rural                     $55.49  \n",
       "Suburban                  $39.50  \n",
       "Urban                     $16.57  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the index name.\n",
    "summary_df.index.name = None\n",
    "\n",
    "# Formatting\n",
    "summary_df[\"Total Fares\"] = summary_df[\"Total Fares\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "summary_df[\"Average Fare per Ride\"] = summary_df[\"Average Fare per Ride\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "summary_df[\"Average Fare per Driver\"] = summary_df[\"Average Fare per Driver\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2375 entries, 0 to 2374\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   City         2375 non-null   object \n",
      " 1   Date         2375 non-null   object \n",
      " 2   Fare         2375 non-null   float64\n",
      " 3   Ride Id      2375 non-null   int64  \n",
      " 4   No. Drivers  2375 non-null   int64  \n",
      " 5   City Type    2375 non-null   object \n",
      "dtypes: float64(1), int64(2), object(3)\n",
      "memory usage: 129.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# rename columns of merged data for deliverable 2\n",
    "pyber_data_df.columns = [\"City\",\"Date\",\"Fare\",\"Ride Id\",\"No. Drivers\",\"City Type\"]\n",
    "pyber_data_df\n",
    "pyber_data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Ride Id</th>\n",
       "      <th>No. Drivers</th>\n",
       "      <th>City Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-14 10:14:22</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04 18:24:09</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24 04:29:00</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10 23:22:03</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06 04:28:35</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-29 17:04:39</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>13.38</td>\n",
       "      <td>8550365057598</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-30 00:05:47</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>20.76</td>\n",
       "      <td>9018727594352</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10 21:03:50</th>\n",
       "      <td>North Jaime</td>\n",
       "      <td>11.11</td>\n",
       "      <td>2781339863778</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 19:22:15</th>\n",
       "      <td>West Heather</td>\n",
       "      <td>44.94</td>\n",
       "      <td>4256853490277</td>\n",
       "      <td>4</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-25 10:20:13</th>\n",
       "      <td>Newtonview</td>\n",
       "      <td>55.84</td>\n",
       "      <td>9990581345298</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   City   Fare        Ride Id  No. Drivers  \\\n",
       "Date                                                                         \n",
       "2019-01-14 10:14:22  Lake Jonathanshire  13.83  5739410935873            5   \n",
       "2019-03-04 18:24:09  South Michelleport  30.24  2343912425577           72   \n",
       "2019-02-24 04:29:00  Port Samanthamouth  33.44  2005065760003           57   \n",
       "2019-02-10 23:22:03          Rodneyfort  23.44  5149245426178           34   \n",
       "2019-03-06 04:28:35          South Jack  34.58  3908451377344           46   \n",
       "...                                 ...    ...            ...          ...   \n",
       "2019-04-29 17:04:39         Michaelberg  13.38  8550365057598            6   \n",
       "2019-01-30 00:05:47     Lake Latoyabury  20.76  9018727594352            2   \n",
       "2019-02-10 21:03:50         North Jaime  11.11  2781339863778            1   \n",
       "2019-05-07 19:22:15        West Heather  44.94  4256853490277            4   \n",
       "2019-04-25 10:20:13          Newtonview  55.84  9990581345298            1   \n",
       "\n",
       "                    City Type  \n",
       "Date                           \n",
       "2019-01-14 10:14:22     Urban  \n",
       "2019-03-04 18:24:09     Urban  \n",
       "2019-02-24 04:29:00     Urban  \n",
       "2019-02-10 23:22:03     Urban  \n",
       "2019-03-06 04:28:35     Urban  \n",
       "...                       ...  \n",
       "2019-04-29 17:04:39     Rural  \n",
       "2019-01-30 00:05:47     Rural  \n",
       "2019-02-10 21:03:50     Rural  \n",
       "2019-05-07 19:22:15     Rural  \n",
       "2019-04-25 10:20:13     Rural  \n",
       "\n",
       "[2375 rows x 5 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set index to the date column\n",
    "pyber_data_df = pyber_data_df.set_index(\"Date\")\n",
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City Type</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-14 10:14:22</th>\n",
       "      <td>Urban</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04 18:24:09</th>\n",
       "      <td>Urban</td>\n",
       "      <td>30.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24 04:29:00</th>\n",
       "      <td>Urban</td>\n",
       "      <td>33.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10 23:22:03</th>\n",
       "      <td>Urban</td>\n",
       "      <td>23.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06 04:28:35</th>\n",
       "      <td>Urban</td>\n",
       "      <td>34.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-29 17:04:39</th>\n",
       "      <td>Rural</td>\n",
       "      <td>13.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-30 00:05:47</th>\n",
       "      <td>Rural</td>\n",
       "      <td>20.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10 21:03:50</th>\n",
       "      <td>Rural</td>\n",
       "      <td>11.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07 19:22:15</th>\n",
       "      <td>Rural</td>\n",
       "      <td>44.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-25 10:20:13</th>\n",
       "      <td>Rural</td>\n",
       "      <td>55.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    City Type   Fare\n",
       "Date                                \n",
       "2019-01-14 10:14:22     Urban  13.83\n",
       "2019-03-04 18:24:09     Urban  30.24\n",
       "2019-02-24 04:29:00     Urban  33.44\n",
       "2019-02-10 23:22:03     Urban  23.44\n",
       "2019-03-06 04:28:35     Urban  34.58\n",
       "...                       ...    ...\n",
       "2019-04-29 17:04:39     Rural  13.38\n",
       "2019-01-30 00:05:47     Rural  20.76\n",
       "2019-02-10 21:03:50     Rural  11.11\n",
       "2019-05-07 19:22:15     Rural  44.94\n",
       "2019-04-25 10:20:13     Rural  55.84\n",
       "\n",
       "[2375 rows x 2 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new DataFrame for fares using copy method\n",
    "pyber_data_df.reset_index()\n",
    "fare_columns = [\"City Type\",\"Fare\"]\n",
    "fare_df = pyber_data_df.copy()\n",
    "fare_df = fare_df[fare_columns]\n",
    "fare_df.reset_index()\n",
    "fare_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2375 entries, 2019-01-14 10:14:22 to 2019-04-25 10:20:13\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   City Type  2375 non-null   object \n",
      " 1   Fare       2375 non-null   float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 55.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# set the index to the datetime data type\n",
    "fare_df.index = pd.to_datetime(fare_df.index)\n",
    "fare_df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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></th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City Type</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Rural</th>\n",
       "      <th>2019-01-01 09:45:36</th>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02 11:18:32</th>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03 19:51:01</th>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 03:31:26</th>\n",
       "      <td>24.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06 07:38:40</th>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Urban</th>\n",
       "      <th>2019-05-08 04:20:00</th>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 04:39:49</th>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 07:29:01</th>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 11:38:35</th>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 13:10:18</th>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Fare\n",
       "City Type Date                      \n",
       "Rural     2019-01-01 09:45:36  43.69\n",
       "          2019-01-02 11:18:32  52.12\n",
       "          2019-01-03 19:51:01  19.90\n",
       "          2019-01-04 03:31:26  24.88\n",
       "          2019-01-06 07:38:40  47.33\n",
       "...                              ...\n",
       "Urban     2019-05-08 04:20:00  21.99\n",
       "          2019-05-08 04:39:49  18.45\n",
       "          2019-05-08 07:29:01  18.55\n",
       "          2019-05-08 11:38:35  19.77\n",
       "          2019-05-08 13:10:18  18.04\n",
       "\n",
       "[2375 rows x 1 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate sum of fares by city type and date using groupby to create series\n",
    "# convert to DataFrame\n",
    "fare_by_citytype_df = pd.DataFrame(fare_df.groupby([\"City Type\",\"Date\"]).sum())\n",
    "fare_by_citytype_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>City Type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:08:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:46:46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:07:24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:46:50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 05:23:21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 04:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 04:39:49</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 07:29:01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 11:38:35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 13:10:18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "City Type            Rural  Suburban  Urban\n",
       "Date                                       \n",
       "2019-01-01 00:08:16    NaN       NaN  37.91\n",
       "2019-01-01 00:46:46    NaN     47.74    NaN\n",
       "2019-01-01 02:07:24    NaN     24.07    NaN\n",
       "2019-01-01 03:46:50    NaN       NaN   7.57\n",
       "2019-01-01 05:23:21    NaN       NaN  10.75\n",
       "...                    ...       ...    ...\n",
       "2019-05-08 04:20:00    NaN       NaN  21.99\n",
       "2019-05-08 04:39:49    NaN       NaN  18.45\n",
       "2019-05-08 07:29:01    NaN       NaN  18.55\n",
       "2019-05-08 11:38:35    NaN       NaN  19.77\n",
       "2019-05-08 13:10:18    NaN       NaN  18.04\n",
       "\n",
       "[2375 rows x 3 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reset the index and create pivot table DataFrame, columns = city type\n",
    "new_df = fare_by_citytype_df.reset_index(level=\"City Type\")\n",
    "pivot_table = pd.pivot_table(new_df,values=\"Fare\",index=[\"Date\"], columns=[\"City Type\"],aggfunc=np.sum)\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>City Type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:08:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:46:46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:07:24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:46:50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 05:23:21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 14:28:36</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 16:29:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 17:26:52</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 17:38:09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>34.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 19:35:03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.96</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "City Type            Rural  Suburban  Urban\n",
       "Date                                       \n",
       "2019-01-01 00:08:16    NaN       NaN  37.91\n",
       "2019-01-01 00:46:46    NaN     47.74    NaN\n",
       "2019-01-01 02:07:24    NaN     24.07    NaN\n",
       "2019-01-01 03:46:50    NaN       NaN   7.57\n",
       "2019-01-01 05:23:21    NaN       NaN  10.75\n",
       "...                    ...       ...    ...\n",
       "2019-04-28 14:28:36    NaN       NaN  11.46\n",
       "2019-04-28 16:29:16    NaN       NaN  36.42\n",
       "2019-04-28 17:26:52    NaN       NaN  31.43\n",
       "2019-04-28 17:38:09    NaN     34.87    NaN\n",
       "2019-04-28 19:35:03    NaN     16.96    NaN\n",
       "\n",
       "[2196 rows x 3 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new DataFrame on the given dates using loc\n",
    "date_range_df = pivot_table.loc[\"2019-01-01\":\"2019-04-28\"]\n",
    "date_range_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>City Type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>187.92</td>\n",
       "      <td>721.60</td>\n",
       "      <td>1661.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>67.65</td>\n",
       "      <td>1105.13</td>\n",
       "      <td>2050.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>306.00</td>\n",
       "      <td>1218.20</td>\n",
       "      <td>1939.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>179.69</td>\n",
       "      <td>1203.28</td>\n",
       "      <td>2129.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>333.08</td>\n",
       "      <td>1042.79</td>\n",
       "      <td>2086.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10</th>\n",
       "      <td>115.80</td>\n",
       "      <td>974.34</td>\n",
       "      <td>2162.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-17</th>\n",
       "      <td>95.82</td>\n",
       "      <td>1045.50</td>\n",
       "      <td>2235.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24</th>\n",
       "      <td>419.06</td>\n",
       "      <td>1412.74</td>\n",
       "      <td>2466.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>175.14</td>\n",
       "      <td>858.46</td>\n",
       "      <td>2218.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-10</th>\n",
       "      <td>303.94</td>\n",
       "      <td>925.27</td>\n",
       "      <td>2470.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-17</th>\n",
       "      <td>163.39</td>\n",
       "      <td>906.20</td>\n",
       "      <td>2044.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-24</th>\n",
       "      <td>189.76</td>\n",
       "      <td>1122.20</td>\n",
       "      <td>2368.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>199.42</td>\n",
       "      <td>1045.06</td>\n",
       "      <td>1942.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-07</th>\n",
       "      <td>501.24</td>\n",
       "      <td>1010.73</td>\n",
       "      <td>2356.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-14</th>\n",
       "      <td>269.79</td>\n",
       "      <td>784.82</td>\n",
       "      <td>2390.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-21</th>\n",
       "      <td>214.14</td>\n",
       "      <td>1149.27</td>\n",
       "      <td>2303.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28</th>\n",
       "      <td>191.85</td>\n",
       "      <td>1357.75</td>\n",
       "      <td>2238.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "City Type    Rural  Suburban    Urban\n",
       "Date                                 \n",
       "2019-01-06  187.92    721.60  1661.68\n",
       "2019-01-13   67.65   1105.13  2050.43\n",
       "2019-01-20  306.00   1218.20  1939.02\n",
       "2019-01-27  179.69   1203.28  2129.51\n",
       "2019-02-03  333.08   1042.79  2086.94\n",
       "2019-02-10  115.80    974.34  2162.64\n",
       "2019-02-17   95.82   1045.50  2235.07\n",
       "2019-02-24  419.06   1412.74  2466.29\n",
       "2019-03-03  175.14    858.46  2218.20\n",
       "2019-03-10  303.94    925.27  2470.93\n",
       "2019-03-17  163.39    906.20  2044.42\n",
       "2019-03-24  189.76   1122.20  2368.37\n",
       "2019-03-31  199.42   1045.06  1942.77\n",
       "2019-04-07  501.24   1010.73  2356.70\n",
       "2019-04-14  269.79    784.82  2390.72\n",
       "2019-04-21  214.14   1149.27  2303.80\n",
       "2019-04-28  191.85   1357.75  2238.29"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new DataFrame in weekly bins\n",
    "weekly_df = date_range_df.resample(\"W\").sum()\n",
    "weekly_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use object oriented method to plot DataFrame\n",
    "# Create the plot with ax.plt()\n",
    "plt.style.use('fivethirtyeight')\n",
    "fig, ax = plt.subplots(figsize=(20, 7.5))\n",
    "ax.plot(weekly_df, label=\"City Type\")\n",
    "ax.set_title(\"Total Fare by City Type\", fontsize=24)\n",
    "ax.set_xlabel(\"2019\", position=(0,0))\n",
    "ax.set_ylabel(\"Fare ($USD)\", fontsize=22)\n",
    "# set x_limits\n",
    "import datetime\n",
    "xmin = datetime.datetime(2019,1,1)\n",
    "xmax = datetime.datetime(2019,4,30)\n",
    "ax.set_xlim(xmin,xmax)\n",
    "# format the ticks\n",
    "import matplotlib.dates as mdates\n",
    "months = mdates.MonthLocator()  # every month\n",
    "months_fmt = mdates.DateFormatter('%b')\n",
    "ax.xaxis.set_major_locator(months)\n",
    "ax.xaxis.set_major_formatter(months_fmt)\n",
    "ax.tick_params(axis = 'both', which = 'major', labelsize =18)\n",
    "# Add the legend.\n",
    "ax.legend([\"Rural\",\"Suburban\",\"Urban\"],title=\"City Type\",loc=10)\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig9.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}