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": 5,
   "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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            120\n",
       "driver_count    120\n",
       "type            120\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.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            0\n",
       "driver_count    0\n",
       "type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            object\n",
       "driver_count     int64\n",
       "type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "# Driver_count column has to be numerical data type to perform calculations\n",
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Suburban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "ride_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "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>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": 19,
     "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": 20,
   "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 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": 20,
     "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": 21,
   "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": 23,
   "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": 23,
     "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "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": 25,
     "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": 26,
   "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": 27,
   "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": 27,
     "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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x124b0cd0688>"
      ]
     },
     "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 urban cities.\n",
    "plt.scatter(urban_ride_count, urban_avg_fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x124b10ed208>"
      ]
     },
     "execution_count": 31,
     "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 ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x124b114b308>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for suburban cities.\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x124b11c7088>"
      ]
     },
     "execution_count": 33,
     "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 ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add the scatter charts for each type of city.\n",
    "plt.subplots(figsize=(10,6))\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "\n",
    "# Incorporate the other graph properties\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\", fontsize=20)\n",
    "plt.ylabel(\"Average Fare ($)\", 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",
    "\n",
    "# Incorporate a text label about circle size.\n",
    "plt.text(42, 35, \"Note: Circle size correlates with driver count per city.\", fontsize=\"12\")\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig(\"analysis/Fig1.png\", bbox_inches = \"tight\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "suburban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "rural_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.62, 17.36, 6.94)"
      ]
     },
     "execution_count": 56,
     "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.0, 17.0, 6.0)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the median 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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 70,
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModeResult(mode=array([6], dtype=int64), count=array([5]))\n"
     ]
    }
   ],
   "source": [
    "mode_rural_rd_count = sts.mode(rural_ride_count)\n",
    "print(mode_rural_rd_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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": 110,
     "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": 78,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": 87,
     "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": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for urban cities is 37.\n",
      "The median driver count for urban cities is 37.\n",
      "The mode driver count for urban cities ModeResult(mode=array([39], dtype=int64), count=array([86])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average driver count for the urban cities.\n",
    "mean_urban_drivers = np.mean(urban_drivers)\n",
    "print(f\"The mean driver count for urban cities is {mean_urban_drivers:.0f}.\")\n",
    "\n",
    "median_urban_drivers = np.median(urban_drivers)\n",
    "print(f\"The median driver count for urban cities is {median_urban_drivers:.0f}.\")\n",
    "\n",
    "mode_urban_drivers = sts.mode(urban_drivers)\n",
    "print(f\"The mode driver count for urban cities {mode_urban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 85,
     "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": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for suburban cities is 14.\n",
      "The median driver count for suburban cities is 16.\n",
      "The mode driver count for suburban cities ModeResult(mode=array([20], dtype=int64), count=array([79])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average driver count for the suburban cities.\n",
    "mean_suburban_drivers = np.mean(suburban_drivers)\n",
    "print(f\"The mean driver count for suburban cities is {mean_suburban_drivers:.0f}.\")\n",
    "\n",
    "median_suburban_drivers = np.median(suburban_drivers)\n",
    "print(f\"The median driver count for suburban cities is {median_suburban_drivers:.0f}.\")\n",
    "\n",
    "mode_suburban_drivers = sts.mode(suburban_drivers)\n",
    "print(f\"The mode driver count for suburban cities {mode_suburban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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": 117,
     "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": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for rural cities is 4.\n",
      "The median driver count for rural cities is 4.\n",
      "The mode driver count for rural cities ModeResult(mode=array([1], dtype=int64), count=array([32])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average driver count for the rural cities.\n",
    "mean_rural_drivers = np.mean(rural_drivers)\n",
    "print(f\"The mean driver count for rural cities is {mean_rural_drivers:.0f}.\")\n",
    "\n",
    "median_rural_drivers = np.median(rural_drivers)\n",
    "print(f\"The median driver count for rural cities is {median_rural_drivers:.0f}.\")\n",
    "\n",
    "mode_rural_drivers = sts.mode(rural_drivers)\n",
    "print(f\"The mode driver count for rural cities {mode_rural_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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 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": 96,
   "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(8, 29, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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(2, 14, step=1.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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": 102,
   "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": 103,
   "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": 103,
     "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": 107,
   "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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the suburban fare data.\n",
    "x_labels = [\"Subrban\"]\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($USD)')\n",
    "ax.set_yticks(np.arange(10, 60, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean      34.623440\n",
       "std       14.558046\n",
       "min       10.110000\n",
       "25%       19.900000\n",
       "50%       37.050000\n",
       "75%       47.070000\n",
       "max       58.550000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_fares = rural_cities_df[\"fare\"]\n",
    "\n",
    "# Create a box-and-whisker plot for the rural fare data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(5, 66, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cWV+SJKknJmKSJEk92S0n/ZYkabVL0nmZXQ15tZqYiEmStBva2aQoiQnVMmLTpCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT1xERMkiSpJyZikiRJPTERkyRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT1xERMkiSpJ50mYkm2JPlakrOSbGrXbUhyUbvurCSP7jImSZKkvuzZQ5mHVdWlI+teU1Wv7CEWSZKk3tg0KUmS1JOua8QK+ESSAt5YVW9q1x+b5OnAJuD4qto2emCS9cB6gKmpKQaDQUchC2B2dtbPXCue17lWC6/z5SNV1V1hya9V1feS3BY4HXgucC5wKU2SdiKwtqqO2d55pqena9OmTUser64zGAyYmZnpOwxpSXmdazVIQpff/YIkm6tqety2Tpsmq+p77eMlwIeAQ6rqB1V1TVVdC7wZOKTLmCRJkvrSWSKW5OZJbjH3HHgEcHaStUO7PR44u6uYJEmS+tRlH7Ep4ENJ5sp9T1WdluSfkxxE0zS5BXhOhzFJkiT1prNErKrOBw4cs/6ormKQJElaThy+QpIkqScmYpIkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqSd7dllYki3A5cA1wNVVNZ1kP+B9wDpgC/B7VbWty7gkSZL60EeN2GFVdVBVTbevXwicUVX7A2e0ryVJkla85dA0+TjgpPb5ScAR/YUiSZLUna4TsQI+kWRzkvXtuqmquhigfbxtxzFJkiT1otM+YsCDqup7SW4LnJ7km5Me2CZu6wGmpqYYDAZLFKLGmZ2d9TPXbuOwww7rvMyNGzd2Xqa0s/x7vnykqvopONkAzALPBmaq6uIka4FBVR2wvWOnp6dr06ZNHUSpOYPBgJmZmb7DkJZUEvr6myh1xeu8e0k2D/WNv57OmiaT3DzJLeaeA48AzgY+DBzd7nY0cEpXMUmSJPWpy6bJKeBDSebKfU9VnZbky8DJSZ4FfBc4ssOYJEmSetNZIlZV5wMHjln/I+DwruKQJElaLpbD8BWSJEmrkomYJElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST7qca1KSJI3Yb7/92LZtW6dltvM+d2LNmjVs3bq1s/J2NyZikiT1aNu2bVRVZ+UNBgNmZmY6K6/LpG93ZNOkJElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknnSdiSfZIcmaSU9vXG5JclOSsdnl01zFJkiT1oY9xxI4DzgFuObTuNVX1yh5ikSRJ6k2nNWJJbg/8NvCWLsuVJElajrquEXst8OfALUbWH5vk6cAm4PiqusFcD0nWA+sBpqamGAwGSxvpCnXYYYd1XubGjRs7L1PaWf5tUR+6vO5mZ2c7v879vZpfuppWIcljgEdX1R8lmQFeUFWPSTIFXAoUcCKwtqqO2d65pqena9OmTUsdsoYk6XQKDqkPXufqQ9fXXR9THK3236skm6tqety2LmvEHgQ8tu2Mf1PglkneVVVPm9shyZuBUzuMSZIkqTed9RGrqhdV1e2rah3wZOBTVfW0JGuHdns8cHZXMUmSJPWpj7smR70iyUE0TZNbgOf0Go0kSVJHeknEqmoADNrnR/URgyRJUt8cWV+SJKknJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknnSdiSfZIcmaSU9vX+yU5Pcl57eOarmOSJEnqQx81YscB5wy9fiFwRlXtD5zRvpYkSVrxOk3Ektwe+G3gLUOrHwec1D4/CTiiy5gkSZL60nWN2GuBPweuHVo3VVUXA7SPt+04JkmSpF7s2VVBSR4DXFJVm5PM7MTx64H1AFNTUwwGg0WNTzvmZ67VwOtcfejyupudne38Ovf3an6pqm4KSv4WOAq4GrgpcEvgg8D9gJmqujjJWmBQVQds71zT09O1adOmpQ5ZQ5LQ1bUizdlvv/3Ytm1b32EsmTVr1rB169a+w1DPuv77OhgMmJmZ6aw8vz8gyeaqmh63rbOmyap6UVXdvqrWAU8GPlVVTwM+DBzd7nY0cEpXMUla3rZt20ZVdbZs3Lix0/JWcpIpaTLLYRyxlwMPT3Ie8PD2tSRJ0orXWR+xYVU1AAbt8x8Bh/cRhyRJUp+WQ42YJEnSqmQiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSemIiJkmS1JOJpzhKcnfgKcBDgHXAzYAfAl8BPgZ8oKquXIIYJUmSVqQdJmJJ7gu8Angw8Dng88D7gSuA/YB7AS8F/iHJK4DXmpBJkjSZOuGWsGGfzsqbgXa2527UCbfsrrDd0CQ1Yh+iScSOrKpt8+2U5AHAnwLHAy9bnPAkSVrZ8pLLqKrOyhsMBszMzHRWXhJqQ2fF7XYmScT2r6qrdrRTVX0B+EKSvXY9LEmSpJVvh531J0nCdmV/SZKk1WohnfUDPB14InAnoIDzgX8F3l1d1qtKkiStAAsZvuIDwNuBOwBfA74O3BF4J03nfUmSJC3ARDViSZ4KPAL4rao6fWTbbwEfSPK/quo9SxCjJEnSijRpjdjTgL8bTcIAqurjwN+3+8wryU2TfCnJfyb5epKXtOs3JLkoyVnt8uiFvglJkqTd0aR9xA4EXryd7R8BnrODc1wJPLSqZpPcGPhsko+1215TVa+cMBZJkqQVYdJE7FbAxdvZfjHN4K7zajvzz7Yvb9wudvCXJEmr1qSJ2I2BX2xn+9XtPtuVZA9gM3AX4A1V9cUkjwKOTfJ0YBNw/LiBY5OsB9YDTE1NMRgMJgxdi8XPXH3o8rqbnZ3t/Dr390rgdb6aZZJRJ5JcC7wN+Nk8u/wK8Myq2mOiQpN9aUbsfy7NfJWX0tSOnQisrapjtnf89PR0bdq0aZKitEiSdDryswTdX3e9jDju79Wq53W+8iXZXFXT47ZNWiP2GeDOE+wzkar6cZIB8MjhvmFJ3gycOul5JEmSdmcTJWJVNbOrBSW5DfCLNgm7GfAw4O+SrK2quf5njwfO3tWyVoP99tuPbdvmnfpzSTRj+nZjzZo1bN26tbPyJEnqw8Qj64+TZE/gplU1u8OdYS1wUttP7EbAyVV1apJ/TnIQTdPkFnZ896WAbdu2rfiqbEmSVrpJB3Q9HLhVVZ08tO6FwAZgzySfBJ5cVT+e7xxV9VXgPmPWH7XAmCVJklaESQd0fSFw+7kXSQ4BXgb8M/DnNOOM/dWiRydJkrSCTZqI/Q/g00OvjwQ+X1XPrqpXA38CPHaxg5MkSVrJJk3E9gUuGXr9IOC0oddfBm63SDFJkiStCpMmYhfTDl+R5CY0fb2+MLT9FjRTGEmSJGlCkyZiHwNekeShwN8BPwX+fWj7vYFvL3JskiRJK9qkw1f8NfBB4JM080UeXVVXDW0/Bjh9kWOTJEla0SYd0PVS4NAk+wCzVXXNyC5Hct2E3pIkSZrAggZ0raqfzLPeIdAlSZIWaNIBXb9GM/L9qJ8A5wKvqaqvL2ZgkiRJK92kNWLvn2f9vsB9ga8keWhVfW5RopIkSVoFJu0j9pLtbU/yUuBE4KGLEZQkSdJqMOnwFTvybprR9yVJkjShxUrECsginUuSJGlVWKxE7ImAnfUlSZIWYNK7Jp8/z6Z9gIOB3wIetVhBSZIkrQaT3jX53HnWX0YzfMWhVfWFefaRJEnSGJPeNXnHpQ5EkiRptVnQyPqS1KU64ZawYZ/OypsBGHRWXPP+JK1qEydiSW4O7Dk3zVGSGwHHAw8GvgL87chE4KPH3xT4DHCTttz3V9UJSfYD3gesA7YAv1dV23bq3UhaUfKSy6gaN6nH0hgMBszMzHRWXhJqQ2fFSVqGFnLX5DuBPx16/Xzgr4CtwLOBV+3g+CuBh1bVgcBBwCOT3B94IXBGVe0PnNG+liRJWvEWkojdBzh96PUzgD+uqmfSDF/xxO0dXI3Z9uWN26WAxwEntetPAo5YQEySJEm7rR02TSbZ2D69PfCqJD+jGbz1HsBzkzyLJqGbSvIpgKoaO9VRkj2AzcBdgDdU1ReTTFXVxe1xFye57TzHrgfWA0xNTTEYDCZ/lytUl5/B7Oxs55+5P2OB17lWB6/z1SuT9r9IcgFwVFV9JslvA6+pqru22/YBvltVE/WqTbIv8CGaYTE+W1X7Dm3bVlVrtnf89PR0bdq0aaK4V6okK7/vTIfvT8uT17lWA6/zlS/J5qqaHrdtIXdNDoA3J3kX8HTgvUPbDgTOm/REVfXjJAPgkcAPkqxta8PWApcsICZJkqTd1kL6iD0f+BLwezSd6l82tO0I4F3bOzjJbdqaMJLcDHgY8E3gw8DR7W5HA6csICZJkqTd1sQ1YlX1I+CoebbNNwXSsLXASW0/sRsBJ1fVqUm+AJzc9jX7LnDkpDFJkiTtzjob0LWqvkpz5+Xo+h8Bh3cVhyRJ0nKxw6bJJG9JcocJ9kuSpyYZW2smSZKk65ukRuxC4KtJvkjTn2sTcDHwc2ANzTAWDwaeRDMy/nOWJFJJkqQVZoeJWFVtSPIGmtHznwO8bmSXy4FPAs+sqk8sfoiSJEkr00R9xKrqhzR3Sb4syRrgN4CbAZcC36nVPkCIJEnSTlhwZ/12Qm4n5ZYkSdpFCxlHDIAk/yPJ65N8rB2AlSRHJLnBHZGSJEma34ISsSSPAL4M3A54KE3zJMCdgRMWNzRJkqSVbaE1YicCz6+qxwNXDa0fAIcsVlCSJEmrwUITsXsCHx2zfiuw366HI0mStHosNBHbRtMsOeq+NOONSZIkaUILTcTeA/x9ktsDBeyZ5CHAK4F3LnZwkiRJK9lCE7EXA/8FXADsDXwD+BTwWeClixuaJEnSyjbxOGJJbgTsTzO6/v+maY68EXBmVZ23NOFJkiStXAsZ0LWAs4B7VNW3gfOXJCJJkqRVYuKmyXYao3OB2yxdOJIkSavHQvuI/TlNZ/2DkmQpApIkSVotFjrX5MnATYHNwNVJrhzeWFW3XKzAJEmSVrqFJmLH7mxBSX6dZoiLXwWuBd5UVa9LsgF4NvDDdte/rKpxg8ZKkiStKAtKxKrqpF0o62rg+Kr6SpJbAJuTnN5ue01VvXIXzi1JkrTbWWiN2C8l+VVgr+F1VfXd+favqouBi9vnlyc5h/Gj9EuSJK0KC+qsn2SfJCcluQK4iGZw1+Fl0vOsA+4DfLFddWySryZ5W5I1C4lJkiRpd7XQGrFXAgcCRwAfBI6hqdU6Djh+khMk2Rv4APC8qrosyf8BTqQZp+xE4FXteUePWw+sB5iammIwGCww9JWny89gdna288/cn7HA61yrg9f56pVmeLAJd04uBJ5SVf+e5DLgvlX17SRPAY6pqofv4PgbA6cCH6+qV4/Zvg44tarutb3zTE9P16ZNmyaOeyVKwkJ+drtqMBgwMzPTWXldvz8tT17nWg28zle+JJuranrctoXWiO1LM88kwE+AWwHfBr4AvGUHQQR4K3DOcBKWZG3bfwzg8cDZC4xpVaoTbgkb9umsvBmAQWfFNe9PkqQVbqGJ2HeAOwHfBc4BnpzkS8ATgK07OPZBwFHA15Kc1a77S+ApSQ6iaZrcQjOXpXYgL7ls5f8HtaGz4iRJ6sVEiViSewNfB94B3JumbuTlNM2Mx9J0+j9ue+eoqs8C40bjd8wwSZK0Kk1aI3YmsLaqXgOQ5CPA7wN3A6aB86rqa0sToiRJ0so0aSI2WpN1KHCzqjqfpplSkiRJC7TQSb8lSZK0SCZNxKpdRtdJkiRpJy2kafJdSa5sX98UeHOSnw3vVFWPXczgJEmSVrJJE7HRyb7ftdiBSJIkrTYTJWJV9cylDkSSJGm1sbO+JElST0zEJEmSemIiJkmS1BMTMUmSpJ4sdNJvSepUMm6K2pVhzZo1fYcgqWcmYpKWrapux41O0nmZklY3myYlSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqSedJWJJfj3JxiTnJPl6kuPa9fslOT3Jee2j93NLkqRVocsasauB46vq7sD9gT9Ocg/ghcAZVbU/cEb7WpIkacXrLBGrqour6ivt88uBc4DbAY8DTmp3Owk4oquYJEmS+tTLgK5J1gH3Ab4ITFXVxdAka0luO88x64H1AFNTUwwGg26CXca6/AxmZ2c7/8z9GasPXnfqg3/PV6/0MHL13sCngZdW1QeT/Liq9h3avq2qtttPbHp6ujZt2rTEkS5vXY8APhgMmJmZ6aw8RzhXH7zu1IeVPI0XNFN5bd26te8wepVkc1VNj9vWaY1YkhsDHwDeXVUfbFf/IMnatjZsLXBJlzFJktQnp/Ja3bq8azLAW4FzqurVQ5s+DBzdPj8aOKWrmCRJkvrUZY3Yg4CjgK8lOatd95fAy4GTkzwL+C5wZIcxSZIk9aazRKyqPgvM1xB+eFdxSJIkLReOrC9JktQTEzFJkqSemIhJkiT1xERMkiSpJyZikiRJPTERkyRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT1xERMkiSpJyZikiRJPTERkyRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSedJaIJXlbkkuSnD20bkOSi5Kc1S6P7ioeSZKkvnVZI/YO4JFj1r+mqg5ql492GI8kSVKvOkvEquozwNauypMkSVru9uw7AODYJE8HNgHHV9W2cTslWQ+sB5iammIwGHQX4TLV5WcwOzvb+Wfuz1h98LrTauB1vnykqrorLFkHnFpV92pfTwGXAgWcCKytqmN2dJ7p6enatGnTUoa67CWhy5/dYDBgZmams/K6fn8SeN1pdfA6716SzVU1PW5brzViVfWDuedJ3gyc2mM4u50kfYewZNasWdN3CJIkLbleE7Eka6vq4vbl44Gzt7e/rtP1fzP+ByVJ0uLrLBFL8i/ADHDrJBcCJwAzSQ6iaZrcAjynq3gkSZL61lkiVlVPGbP6rV2VL0mStNw4sr4kSVJPTMQkSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST0xEZMkSeqJiZgkSVJPTMQkSZJ60uuk35K0FJJ0fmxV7XSZklYva8QkrThVtVPLxo0bd/pYSdoZJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSemIiJkmS1JPOErEkb0tySZKzh9btl+T0JOe1j2u6ikeSJKlvXdaIvQN45Mi6FwJnVNX+wBnta0mSpFWhs0Ssqj4DbB1Z/TjgpPb5ScARXcUjSZLUt75H1p+qqosBquriJLftOR5JknYLziCxMvSdiE0syXpgPcDU1BSDwaDfgFYhP3OtdLOzs17n2m1s3Lhxp46bnZ1l77333qlj/f1YfOkyu02yDji1qu7Vvj4XmGlrw9YCg6o6YEfnmZ6erk2bNi1tsLqeJP4npBVvMBgwMzPTdxjSkvI6716SzVU1PW5b38NXfBg4un1+NHBKj7FIkiR1qsvhK/4F+AJwQJILkzwLeDnw8CTnAQ9vX0uSJK0KnfURq6qnzLPp8K5ikCRJWk76bpqUJElatUzEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSerJn3wEAJNkCXA5cA1xdVdP9RiRJkrT0lkUi1jqsqi7tOwhJkqSu2DQpSZLUk+VSI1bAJ5IU8MaqetPoDknWA+sBpqamGAwG3UYoP3OteLOzs17nWvG8zpeXVFXfMZDk16rqe0luC5wOPLeqPjPf/tPT07Vp06buAhRJWA7XirSUBoMBMzMzfYchLSmv8+4l2Txf//dl0TRZVd9rHy8BPgQc0m9EkiRJS6/3RCzJzZPcYu458Ajg7H6jkiRJWnrLoY/YFPChJNDE856qOq3fkCRJkpZe74lYVZ0PHNh3HKtFm/B2eqx9yyRJGq/3pkl1q6p2atm4ceNOHytJksYzEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST1ZFolYkkcmOTfJt5O8sO94JEmSutB7IpZkD+ANwKOAewBPSXKPfqOSJElaer0nYsAhwLer6vyqugp4L/C4nmOSJElacsshEbsd8N9Dry9s10mSJK1oe/YdAJAx6+oGOyXrgfXty9kk5y5pVBp1a+DSvoOQlpjXuVYDr/Pu3WG+DcshEbsQ+PWh17cHvje6U1W9CXhTV0Hp+pJsqqrpvuOQlpLXuVYDr/PlZTk0TX4Z2D/JHZPsBTwZ+HDPMUmSJC253mvEqurqJMcCHwf2AN5WVV/vOSxJkqQl13siBlBVHwU+2ncc2i6bhbUaeJ1rNfA6X0ZSdYN+8ZIkSerAcugjJkmStCqZiK0ySdYlOXtk3YYkLxiz7zuS/G530UmTSfJXSb6e5KtJzkrym9vZd+z1vcDyBkm8y0zLUpJr2t+Ds5P8W5J9F/Hcs4t1Lo1nIqaxkiyL/oPSqCQPAB4D3Leq7g08jOsPCr3Y5e2xVOeWFskVVXVQVd0L2Ar88aQH+re+fyZi+qX2v/6XJfk0cFy7+mFJ/j3Jt5I8pt1vXbvuK+3ywHb9THuO9yf5ZpJ3Jxk3YK+0K9YCl1bVlQBVdWlVfS/JliS3BkgynWQwdMyBST6V5Lwkz273mUly6twOSV6f5Bnt8y1J/jrJZ4Ej212eluTzba3DIe1+h7TrzmwfD2jXPyPJB5Oc1pb5iqX9SKRf+gLt7DTDNblJbp1kS/v8GUn+Ncm/AZ9IsneSM9q/519L4jSDHTIT1qh9q+oh0DRNAuuAhwB3BjYmuQtwCfDwqvp5kv2BfwHmmm3uA9yTZlDezwEPAj7b5RvQivcJ4K+TfAv4JPC+qvr0Do65N3B/4ObAmUk+MkE5P6+qBwMk+QPg5lX1wCSHAm8D7gV8Ezi0HYbnYcDLgCe2xx9E8/twJXBukn+oqiWruZPa2tvDgbdOsPsDgHtX1da2VuzxVXVZ+8/MfyT5cHk3XydMxFaf+X6x5ta/b2T9yVV1LXBekvOBuwH/Bbw+yUHANcBdh/b/UlVdCJDkLJpEzkRMi6aqZpMcDPxP4DDgfUleuIPDTqmqK4ArkmwEDgF+vINjRn8X/qUt/zNJbtn2w7kFcFL7D0kBNx7a/4yq+glAkm/QTHFiIqalcLOhv7ebgdMnOOb0qtraPg/wsvafjGtpatSmgO8vfqgaZdPk6vMjYM3Iuv24bt6xn45sG03cCvhT4AfAgTQ1YXsNbb9y6Pk1mOxrCVTVNVU1qKoTgGNpaqGu5rq/aTcdPWTM6+H9xx0zye/CicDGtm/O74ycw98FdeWKqjqIJtnfi+v6iG3vd2L4+n4qcBvg4PY8Pxizv5aIidgqU1WzwMVJDgdIsh/wSOavtToyyY2S3Bm4E3AusA9wcVtTdhTNjAhSJ5Ic0NZAzTkIuADYAhzcrnviyGGPS3LTJLcCZmimVrsAuEeSmyTZh6ZJZ3ue1Jb/YOAnbW3XPsBF7fZn7Mz7kRZLe03+CfCCJDfm+r8T27sDfh/gkqr6RZLD2M4E1Vp8/oe2Oj0deEOSV7WvX1JV35mnX/25wKdpqqn/oO0X9o/AB5IcCWzkhjUH0lLaG/iHtmnwauDbwHrg7sBbk/wl8MWRY74EfAT4DeDEqvoeQJKTga8C5wFn7qDcbUk+D9wSOKZd9wqapsnnA5/axfcl7bKqOjPJf9LM2/xK4OQkR7H96/PdwL8l2QScRdP3UR1xZH1JkqSe2DQpSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST0xEZO0W2jnf3xB33FI0mIyEZPUuyRTSV6X5DtJrkxyUZKPJXn00G73A/5x6JhKsr1BKicpt3awvGNXzi9JO+KArpJ6lWQdzQTxlwMvAv6T5p/Ew4F/ohmElar64RIUv3bo+WOAN4+su2IJypSkX7JGTFLf/pFm0uHpqjq5qs6tqnOq6vU085kC12+aTLKlXf2vbc3VliTrklyTZHr45EmeneTSJMNzogJQVd+fW2gnAW+f/4Bm2q9nj5xr/7a8+7avK8mxST6S5GdJLkjytJFjbpfkvUm2tctHhqdoSvLrSU5JsrU9xzeTPHnnPkpJuxsTMUm9GZrr9PXtPKjXU1Xb5jn0fu3js2lqsO5XVVuAT3Ld9ENzjgH+uaqumjSuaqYcees85zqrqr4ytO4lwIdp5rx8E/DOuWQwya/QTAP2c+AhwAOAi4FPttugSUR/BTgMuCfwPNqkUNLKZyImqU93oakNO2chBw01U/64rdGae/1m4ClJbgqQ5O7A/WmSqoV6O7B/kvu359qDZp7W0XN9sKreWFXfqqqX0szp97x225Np3t8zq+qrVfVN4Dk082U+pt3nDsBnq+o/q+q/quq0qjptJ+KVtBsyEZPUp7Ezze+CU4CrgCe0r48BvlRVZy/0RG0T5alcVyv2SOBWNBMkD/vCmNf3aJ8fDNwRuDzJbJJZ4CfAGuDO7T6vA16c5AtJ/ibJwQuNVdLuy0RMUp/OAwq4+2KcrKp+AbwTOCbJnsBR7Fxt2Jy3AE9qmxGPoan9mq+5dJwbAWfRNFsOL3cF3tjG/FaaZO3t7frPJ9mwCzFL2o2YiEnqTVVtBT4OHJtk79HtSfbdzuG/APYYs/7NNP2t/gi4BfDeXQjxNOAy4A+A3wHeNmaf+495PdfU+hWa5tdLq+rbI8vWuQOq6sKqelNV/R7w18D6XYhZ0m7ERExS3/6IpolyU5IjkxyQ5G5J/hD46naO2wIcnuRXk6yZW1lV36K54/HvgfdX1WU7G1hVXUOTfP0tcBFwxpjdntDembl/khfRDLvx2nbbu2nuwDwlyUOS3DHJoUleNXfnZDt+2iOT3CnJQTRNoN/Y2Zgl7V5MxCT1qqr+C7gvcDrwdzTJ16eAx9J0bJ/P8TQ1X/8NnDmy7a3AXuxas+Sct7Xnent7N+WoDcATaeL+Q5qO+V8GqKqfAYcC5wP/CnwTOImmj9hcE+eNgH+gSb5Op0ncjl6EuCXtBjL+74ok7b6S/AXwrKq66yKc6zdpBpy9U1V9d2RbAUdW1ft3tRxJq5Mj60taMdp+ZncDjgNeuovnugnw68DfAB8aTcIkaTHYNClpJXk9Te3V52jvStwFTwHOpRmy4vm7eC5JGsumSUmSpJ5YIyZJktQTEzFJkqSemIhJkiT1xERMkiSpJyZikiRJPTERkyRJ6sn/A9OSkzG+eLMXAAAAAElFTkSuQmCC\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_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(ride_fare_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 65, step=5.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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": 131,
     "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": 132,
   "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": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the urban driver count data.\n",
    "x_labels = [\"Subrban\"]\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, 27, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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       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": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the urban 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, 11, step=1.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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",
    "driver_count_data = [urban_drivers, suburban_drivers, rural_drivers]\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": 136,
   "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": 136,
     "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": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 137,
     "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": 138,
   "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": 138,
     "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": 139,
   "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": 139,
     "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": 144,
   "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 percentage of fares by city type 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": 147,
   "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": 147,
     "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": 148,
   "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 percentage of rides by city type 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": 155,
   "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": 155,
     "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",
    "\n",
    "driver_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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 percentage of drivers by city type 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": 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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}