Pyber.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pyber"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies and loading files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "\n",
    "# Dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statistics\n",
    "import os\n",
    "\n",
    "# Files to load\n",
    "city_data_to_load = os.path.join('resources','city_data.csv')\n",
    "ride_data_to_load = os.path.join('resources','ride_data.csv')\n",
    "\n",
    "# Load the CSVs in a dataframe\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "ride_data_df = pd.read_csv(ride_data_to_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Bradshawfurt</td>\n",
       "      <td>7</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>New Ryantown</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Randallchester</td>\n",
       "      <td>9</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Jessicaport</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>South Saramouth</td>\n",
       "      <td>7</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 3 columns</p>\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",
       "..               ...           ...    ...\n",
       "115     Bradshawfurt             7  Rural\n",
       "116     New Ryantown             2  Rural\n",
       "117   Randallchester             9  Rural\n",
       "118      Jessicaport             1  Rural\n",
       "119  South Saramouth             7  Rural\n",
       "\n",
       "[120 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df"
   ]
  },
  {
   "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>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>...</th>\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 4 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",
       "[2375 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            120\n",
       "driver_count    120\n",
       "type            120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the rows and columns that are not null\n",
    "city_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            object\n",
       "driver_count     int64\n",
       "type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Suburban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(city_data_df[\"type\"]==\"Suburban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(city_data_df[\"type\"]==\"Rural\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and rows that are not null from ride data\n",
    "ride_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types from ride data\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining dataframes and creating initial sub-dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "    <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": 13,
     "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",
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Urban city dataframe\n",
    "urban_cities_df = pyber_data_df[pyber_data_df[\"type\"]==\"Urban\"]"
   ]
  },
  {
   "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",
       "    <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>1620</th>\n",
       "      <td>Reynoldsfurt</td>\n",
       "      <td>2019-02-14 11:25:10</td>\n",
       "      <td>39.40</td>\n",
       "      <td>9070692324754</td>\n",
       "      <td>67</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>Christopherfurt</td>\n",
       "      <td>2019-03-13 01:47:52</td>\n",
       "      <td>32.05</td>\n",
       "      <td>2788817929605</td>\n",
       "      <td>41</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>Raymondhaven</td>\n",
       "      <td>2019-03-20 02:20:34</td>\n",
       "      <td>38.53</td>\n",
       "      <td>8658255136559</td>\n",
       "      <td>11</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>Port Johnbury</td>\n",
       "      <td>2019-03-26 07:22:45</td>\n",
       "      <td>7.63</td>\n",
       "      <td>8089282605778</td>\n",
       "      <td>3</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>West Gabriel</td>\n",
       "      <td>2019-05-05 01:24:06</td>\n",
       "      <td>27.57</td>\n",
       "      <td>4428773411209</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1625 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",
       "1620        Reynoldsfurt  2019-02-14 11:25:10  39.40  9070692324754   \n",
       "1621     Christopherfurt  2019-03-13 01:47:52  32.05  2788817929605   \n",
       "1622        Raymondhaven  2019-03-20 02:20:34  38.53  8658255136559   \n",
       "1623       Port Johnbury  2019-03-26 07:22:45   7.63  8089282605778   \n",
       "1624        West Gabriel  2019-05-05 01:24:06  27.57  4428773411209   \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",
       "1620            67  Urban  \n",
       "1621            41  Urban  \n",
       "1622            11  Urban  \n",
       "1623             3  Urban  \n",
       "1624            57  Urban  \n",
       "\n",
       "[1625 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urban_cities_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 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": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_cities_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating initial arrays and variables to use later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the number of rides for urban cities\n",
    "urban_ride_count = urban_cities_df.groupby(['city']).count()['ride_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh            18\n",
       "Barajasview            22\n",
       "Carriemouth            27\n",
       "Christopherfurt        27\n",
       "Deanville              19\n",
       "                       ..\n",
       "West Patrickchester    16\n",
       "West Robert            31\n",
       "West Samuelburgh       25\n",
       "Williamsstad           23\n",
       "Williamsview           20\n",
       "Name: ride_id, Length: 66, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urban_ride_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get number of rides for suburban and rural cities\n",
    "suburban_ride_count = suburban_cities_df.groupby(['city']).count()['ride_id']\n",
    "rural_ride_count = rural_cities_df.groupby(['city']).count()['ride_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get average fare for each urban, suburban, and rural city\n",
    "urban_avg_fare = urban_cities_df.groupby(['city']).mean()['fare']\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": 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",
       "                         ...    \n",
       "West Patrickchester    28.233125\n",
       "West Robert            25.123871\n",
       "West Samuelburgh       21.767600\n",
       "Williamsstad           24.362174\n",
       "Williamsview           26.599000\n",
       "Name: fare, Length: 66, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urban_avg_fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average number of drivers for each city\n",
    "urban_driver_count = urban_cities_df.groupby(['city']).mean()['driver_count']\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": 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",
       "                       ... \n",
       "West Patrickchester    25.0\n",
       "West Robert            39.0\n",
       "West Samuelburgh       73.0\n",
       "Williamsstad           59.0\n",
       "Williamsview           46.0\n",
       "Name: driver_count, Length: 66, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urban_driver_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plots for Avg. Fare by City Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f13f67f70>"
      ]
     },
     "execution_count": 25,
     "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 plot for urban cities\n",
    "plt.scatter(urban_ride_count,\n",
    "            urban_avg_fare,\n",
    "           s=10*urban_driver_count,\n",
    "           c=\"coral\",\n",
    "           edgecolor=\"black\",\n",
    "           linewidths=1,\n",
    "           alpha=0.8,\n",
    "           label=\"Urban\")\n",
    "plt.title(\"Pyber Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Num. of Rides (per City)\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Add legend\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f14807190>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plot for suburban cities\n",
    "plt.scatter(suburban_ride_count,\n",
    "            suburban_avg_fare,\n",
    "           s=10*suburban_driver_count,\n",
    "           c=\"skyblue\",\n",
    "           edgecolor=\"black\",\n",
    "           linewidths=1,\n",
    "           alpha=0.8,\n",
    "           label=\"Suburban\")\n",
    "plt.title(\"Pyber Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Num. of Rides (per City)\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Add legend\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f1479a7c0>"
      ]
     },
     "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 plot for rural cities\n",
    "plt.scatter(rural_ride_count,\n",
    "            rural_avg_fare,\n",
    "           s=10*rural_driver_count,\n",
    "           c=\"gold\",\n",
    "           edgecolor=\"black\",\n",
    "           linewidths=1,\n",
    "           alpha=0.8,\n",
    "           label=\"Rural\")\n",
    "plt.title(\"Pyber Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Num. of Rides (per City)\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Add legend\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combine the scatter plots for each type of city\n",
    "plt.subplots(figsize=(10,6))\n",
    "plt.scatter(urban_ride_count,\n",
    "           urban_avg_fare,\n",
    "           s=10*urban_driver_count,\n",
    "           c=\"coral\",\n",
    "           edgecolor=\"black\",\n",
    "           linewidths=1,\n",
    "           alpha=0.8,\n",
    "           label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count,\n",
    "            suburban_avg_fare,\n",
    "            s=10*suburban_driver_count,\n",
    "            c=\"skyblue\",\n",
    "            edgecolor=\"black\",\n",
    "            linewidths=1,\n",
    "            alpha=0.8,\n",
    "            label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count,\n",
    "            rural_avg_fare,\n",
    "            s=10*rural_driver_count,\n",
    "            c=\"gold\",\n",
    "            edgecolor=\"black\",\n",
    "            linewidths=1,\n",
    "            alpha=0.8,\n",
    "            label=\"Rural\")\n",
    "\n",
    "# Incorporate additional graph properties\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\", fontsize=20)\n",
    "plt.ylabel(\"Average Fare ($)\", fontsize=12)\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "\n",
    "# Add the legend\n",
    "lgnd = plt.legend(fontsize=\"12\",\n",
    "                 mode=\"Expanded\",\n",
    "                 scatterpoints=1,\n",
    "                 loc=\"best\",\n",
    "                 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",
    "# Informational 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 figure.\n",
    "plt.savefig(\"analysis/Fig1.png\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics\n",
    "urban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_cities_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting data and building-up arrays and variables for Ride Count, Fares, and Drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get ride count summary stats\n",
    "urban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    66.00\n",
       "mean     24.62\n",
       "std       5.41\n",
       "min      12.00\n",
       "25%      21.00\n",
       "50%      24.00\n",
       "75%      28.00\n",
       "max      39.00\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(urban_ride_count.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36.00\n",
       "mean     17.36\n",
       "std       4.32\n",
       "min       9.00\n",
       "25%      14.00\n",
       "50%      17.00\n",
       "75%      19.25\n",
       "max      27.00\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(suburban_ride_count.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18.00\n",
       "mean      6.94\n",
       "std       2.51\n",
       "min       3.00\n",
       "25%       5.25\n",
       "50%       6.00\n",
       "75%       8.75\n",
       "max      12.00\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(rural_ride_count.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.62, 17.36, 6.94)"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.0, 17.0, 6.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(urban_ride_count.median(),2), round(suburban_ride_count.median(),2),round(rural_ride_count.median(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    22\n",
       " 1    25\n",
       " dtype: int64,\n",
       " 0    17\n",
       " dtype: int64,\n",
       " 0    6\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for cities, by type\n",
    "urban_ride_count.mode(), suburban_ride_count.mode(), rural_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import stats module from SciPy\n",
    "import scipy.stats as sts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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]), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for 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": 41,
   "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]), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for 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": 42,
   "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]), count=array([5])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for 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": 43,
   "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": 43,
     "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": 44,
   "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 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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the fares for the suburban cities.\n",
    "suburban_fares = suburban_cities_df['fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the fares for the rural cities.\n",
    "rural_fares = rural_cities_df['fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "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 urban 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": 48,
   "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 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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1       72\n",
       "2       57\n",
       "3       34\n",
       "4       46\n",
       "        ..\n",
       "1620    67\n",
       "1621    41\n",
       "1622    11\n",
       "1623     3\n",
       "1624    57\n",
       "Name: driver_count, Length: 1625, dtype: int64"
      ]
     },
     "execution_count": 49,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the driver count data from suburban and rural cities\n",
    "suburban_drivers = suburban_cities_df['driver_count']\n",
    "rural_drivers = rural_cities_df['driver_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for urban cities is 36.68.\n",
      "The median driver count for urban cities is 37.00.\n",
      "The mode driver count for urban cities is ModeResult(mode=array([39]), count=array([86])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the driver counts for urban cities\n",
    "mean_urban_drivers = np.mean(urban_drivers)\n",
    "print(f\"The mean driver count for urban cities is {mean_urban_drivers:.2f}.\")\n",
    "\n",
    "median_urban_drivers = np.median(urban_drivers)\n",
    "print(f\"The median driver count for urban cities is {median_urban_drivers:.2f}.\")\n",
    "\n",
    "mode_urban_drivers = sts.mode(urban_drivers)\n",
    "print(f\"The mode driver count for urban cities is {mode_urban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for suburban cities is 13.71.\n",
      "The median driver count for suburban cities is 16.00.\n",
      "The mode driver count for suburban cities is ModeResult(mode=array([20]), count=array([79])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the driver counts for suburban cities\n",
    "mean_suburban_drivers = np.mean(suburban_drivers)\n",
    "print(f\"The mean driver count for suburban cities is {mean_suburban_drivers:.2f}.\")\n",
    "\n",
    "median_suburban_drivers = np.median(suburban_drivers)\n",
    "print(f\"The median driver count for suburban cities is {median_suburban_drivers:.2f}.\")\n",
    "\n",
    "mode_suburban_drivers = sts.mode(suburban_drivers)\n",
    "print(f\"The mode driver count for suburban cities is {mode_suburban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for rural cities is 4.30.\n",
      "The median driver count for rural cities is 4.00.\n",
      "The mode driver count for rural cities is ModeResult(mode=array([1]), count=array([32])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the driver counts for rural cities\n",
    "mean_rural_drivers = np.mean(rural_drivers)\n",
    "print(f\"The mean driver count for rural cities is {mean_rural_drivers:.2f}.\")\n",
    "\n",
    "median_rural_drivers = np.median(rural_drivers)\n",
    "print(f\"The median driver count for rural cities is {median_rural_drivers:.2f}.\")\n",
    "\n",
    "mode_rural_drivers = sts.mode(rural_drivers)\n",
    "print(f\"The mode driver count for rural cities is {mode_rural_drivers}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box-and-whisker plots for Ride Count Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
    "\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Num. of Rides')\n",
    "ax.set_yticks(np.arange(10,41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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",
    "\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Num. of Rides')\n",
    "ax.set_yticks(np.arange(8,30, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
    "\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Num. of Rides')\n",
    "ax.set_yticks(np.arange(2,14, step=1.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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('Num. 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",
    "\n",
    "# Save the figure\n",
    "plt.savefig('analysis/Fig2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "West Angela has the highest rider count.\n"
     ]
    }
   ],
   "source": [
    "# Get the urban outlier city with 39 rides\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": "markdown",
   "metadata": {},
   "source": [
    "## Box-and-whisker plots for Ride Fare Data"
   ]
  },
  {
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.00\n",
       "mean       24.53\n",
       "std        11.74\n",
       "min         4.05\n",
       "25%        14.55\n",
       "50%        24.64\n",
       "75%        34.58\n",
       "max        44.97\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 59,
     "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",
    "\n",
    "# Add the title, y-axis label, 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",
    "\n",
    "print('Summary Statistics')\n",
    "round(urban_fares.describe(),2)"
   ]
  },
  {
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    625.00\n",
       "mean      30.97\n",
       "std       10.68\n",
       "min       12.05\n",
       "25%       21.97\n",
       "50%       30.75\n",
       "75%       39.83\n",
       "max       49.96\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 60,
     "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",
    "\n",
    "# Add the title, y-axis label, grid\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare ($USD)')\n",
    "ax.set_yticks(np.arange(10,51,step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "\n",
    "print('Summary Statistics')\n",
    "round(suburban_fares.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.00\n",
       "mean      34.62\n",
       "std       14.56\n",
       "min       10.11\n",
       "25%       19.90\n",
       "50%       37.05\n",
       "75%       47.07\n",
       "max       58.55\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the rural fare data.\n",
    "x_labels = ['Rural']\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_fares, labels=x_labels)\n",
    "\n",
    "# Add the title, y-axis label, grid\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare ($USD)')\n",
    "ax.set_yticks(np.arange(9,60,step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "\n",
    "print('Summary Statistics')\n",
    "round(rural_fares.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "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 fare 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, 60, step=5.0))\n",
    "ax.grid()\n",
    "\n",
    "# Save the figure\n",
    "plt.savefig('analysis/Fig3.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box-and-whisker plots for Driver 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    1625.00\n",
       "mean       36.68\n",
       "std        20.08\n",
       "min         3.00\n",
       "25%        22.00\n",
       "50%        37.00\n",
       "75%        52.00\n",
       "max        73.00\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 63,
     "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",
    "\n",
    "# Add title, y-axis label, grid\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Num. of Drivers')\n",
    "ax.set_yticks(np.arange(0,80, step=5.0))\n",
    "ax.grid()\n",
    "\n",
    "# show plot\n",
    "plt.show()\n",
    "\n",
    "print('Summary Statistics')\n",
    "round(urban_drivers.describe(),2)"
   ]
  },
  {
   "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    625.00\n",
       "mean      13.71\n",
       "std        8.04\n",
       "min        1.00\n",
       "25%        5.00\n",
       "50%       16.00\n",
       "75%       21.00\n",
       "max       25.00\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 64,
     "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",
    "\n",
    "# Add title, y-axis label, grid\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Num. of Drivers')\n",
    "ax.set_yticks(np.arange(0,28, step=2.0))\n",
    "ax.grid()\n",
    "\n",
    "# show plot\n",
    "plt.show()\n",
    "\n",
    "print('Summary Statistics')\n",
    "round(suburban_drivers.describe(),2)"
   ]
  },
  {
   "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    125.00\n",
       "mean       4.30\n",
       "std        2.69\n",
       "min        1.00\n",
       "25%        1.00\n",
       "50%        4.00\n",
       "75%        7.00\n",
       "max        9.00\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 65,
     "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",
    "\n",
    "# Add title, y-axis label, grid\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Num. of Drivers')\n",
    "ax.set_yticks(np.arange(0,10, step=1.0))\n",
    "ax.grid()\n",
    "\n",
    "# show plot\n",
    "plt.show()\n",
    "\n",
    "print('Summary Statistics')\n",
    "round(rural_drivers.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RJEla1TpLxKrqEuAg+OnEgd+imW37WcBrq+pVXcUiSZK0I+ira/II4KtVNWefqSRJ0mrX12D9pwLvmvH++CTPBCaBF9TtbzhLkk3AJoCxsTEGg0EXcao1NTXlOdeqZz3XWmA937F0PqFrkjsAVwAHVNVVScZobqZcwMnAhqo6br5jjI+Pl1dNdsurbLQWWM+1FljPu5dkc1WND1vXR9fkY4DPVtVVAFV1VVXdXFW3AG8CDukhJkmSpM71kYg9jRndkkk2zFj3JOCCziOSJEnqQadjxJLcCTgSeO6Mxa9MchBN1+SWWeskSZJWrU4Tsar6IbD3rGXP6DIGSZKkHYUz60uSJPXEREySJKknK/Km39p2STovs+spUiRJWilMxNaYbU2KkphQSZK0xOyalCRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT1xERMkiSpJ50lYkn2T3L+jMf1Sf4wyfokZyW5tH1e11VMkiRJfeosEauqS6rqoKo6CDgY+CHwfuBFwNlVtR9wdvtekiRp1eura/II4KtV9Q3gKODUdvmpwBN7ikmSJKlTfSViTwXe1b4eq6orAdrnfXuKSZIkqVOd32syyR2AJwAvXuR+m4BNAGNjYwwGg6UPTvPynGu1m5qasp5r1bOe71jS9Y2ckxwF/EFV/Wr7/hJgoqquTLIBGFTV/vMdY3x8vCYnJzuIVtO86bfWgsFgwMTERN9hSMvKet69JJuranzYuj66Jp/Grd2SAGcAx7SvjwFO7zwiSZKkHnSaiCW5E3Ak8L4Zi18BHJnk0nbdK7qMSZIkqS+djhGrqh8Ce89a9h2aqyglSZLWFGfWlyRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT1xERMkiSpJyZikiRJPTERkyRJ6omJmCRJUk9MxCRJknpiIiZJktSTThOxJHsleU+Si5NclOThSU5K8q0k57ePx3YZkyRJUl926bi81wEfrqqnJLkDcCfg14DXVtWrOo5FkiSpV50lYkn2AA4DjgWoqhuBG5N0FYIkSdIOpcsWsfsA1wBvTXIgsBk4oV13fJJnApPAC6pq6+ydk2wCNgGMjY0xGAw6CVq38pxrtZuamrKea9Wznu9YUlXdFJSMA58CDq2q85K8DrgeeD1wLVDAycCGqjpuvmONj4/X5OTkcoesGZLQVV2R+jIYDJiYmOg7DGlZWc+7l2RzVY0PW9flYP3Lgcur6rz2/XuAh1TVVVV1c1XdArwJOKTDmCRJknrTWSJWVd8Gvplk/3bREcCFSTbM2OxJwAVdxSRJktSnrq+afB7wjvaKya8BzwL+PslBNF2TW4DndhyTJElSLzpNxKrqfGB2H+kzuoxBkiRpR+HM+pIkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST0xEZMkSeqJiZgkSVJPTMQkSZJ60mkilmSvJO9JcnGSi5I8PMn6JGclubR9XtdlTJIkSX3pukXsdcCHq+r+wIHARcCLgLOraj/g7Pa9JEnSqtdZIpZkD+Aw4M0AVXVjVX0XOAo4td3sVOCJXcUkSZLUp106LOs+wDXAW5McCGwGTgDGqupKgKq6Msm+w3ZOsgnYBDA2NsZgMOgkaN3Kc67VbmpqynquVc96vmNJVXVTUDIOfAo4tKrOS/I64HrgeVW114zttlbVvOPExsfHa3Jyclnj1W0loau6IvVlMBgwMTHRdxjSsrKedy/J5qoaH7auyzFilwOXV9V57fv3AA8BrkqyAaB9vrrDmCRJknrTWSJWVd8Gvplk/3bREcCFwBnAMe2yY4DTu4pJkiSpT12OEQN4HvCOJHcAvgY8iyYZPC3Js4HLgKM7jkmSJKkXnSZiVXU+MKyP9Igu45AkSdoROLO+JElST0zEJEmSemIiJkmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST0zEJEmSemIiJkmS1JNOE7EkW5J8Mcn5SSbbZScl+Va77Pwkj+0yJkmSpL50etPv1uFVde2sZa+tqlf1EIskSVJv7JqUJEnqSdeJWAEfSbI5yaYZy49P8oUkb0myruOYJEmSetF11+ShVXVFkn2Bs5JcDPwTcDJNknYy8GrguNk7tonbJoCxsTEGg0FnQavhOddqNzU1ZT3Xqmc937GkqvopODkJmJo5NizJRuDMqnrQfPuOj4/X5OTk8gao20hCX3VF6spgMGBiYqLvMKRlZT3vXpLNVTU+bF1nXZNJ7pxk9+nXwK8CFyTZMGOzJwEXdBWTJElSn7rsmhwD3p9kutx3VtWHk7w9yUE0XZNbgOd2GJMkSVJvOkvEquprwIFDlj+jqxgkSZJ2JE5fIUmS1BMTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqiYmYJElST7q+16SWyPr169m6dWunZbaT8XZi3bp1XHfddZ2VJ0lSH0zEVqitW7d2eu/Hru9N1mXSJ0lSX+yalCRJ6omJmCRJUk9MxCRJknrS6RixJFuA7wM3AzdV1XiS9cC7gY3AFuA3q6rbUeiSJEk96KNF7PCqOqiqxtv3LwLOrqr9gLPb95IkSavejtA1eRRwavv6VOCJ/YUiSZLUnW3umkyya1X9ZJG7FfCRJAX8c1W9ERirqisBqurKJPvOUd4mYBPA2NgYg8FgW0NfNbo8B1NTU52fc3/G6lof9VzqmvV8x5JR5qJK8nzgW1X13vb9m4FjgK8CT6iqS0YqLPm5qrqiTbbOAp4HnFFVe83YZmtVrZvvOOPj4zU5OTlKkatWklU/j1iXn0+C7uu51AfrefeSbJ4xJOs2Ru2afD5wTXuww4DfBH4bOB949aiBVNUV7fPVwPuBQ4Crkmxoj70BuHrU40mSJK1koyZid6O5ohHg8cC/V9VpwEnAw0Y5QJI7J9l9+jXwq8AFwBk0rWu0z6ePGJMkSdKKNuoYseuBuwKXAUcCf9su/wmw24jHGAPe3966ZhfgnVX14SSfAU5L8uz2+EePeDxJkqQVbdRE7CPAm5J8Drgv8KF2+QHA10c5QFV9DThwyPLvAEeMGIckSdKqMWrX5B8A5wL7AE+pquva5Q8B3rUcgUmSJK12C7aIJdkFeDrwiunB9tOq6sTlCkySJGm1W7BFrKpuohkTtuvyhyNJkrR2jNo1+Sng4OUMRJIkaa0ZdbD+m4BXJbknsBn4wcyVVfXZpQ5MkiRptRs1EXtn+/yaIesK2HlpwpEkSVo7Rk3E7r2sUUiSJK1BIyViVfWN5Q5EkiRprRl1sD5JHpPkzCQXJrlHu+x/JnEyVkmSpG0wUiKW5HeA04BLabopp6ey2Bn4k+UJTZIkaXUbtUXsT4DnVNUfATfNWP4p4KClDkqSJGktGDUR2w/45JDlU8Aeiykwyc5JPpfkzPb9SUm+leT89vHYxRxPkiRppRr1qskrgPsBswftHwZ8dZFlngBcxG0TuNdW1asWeRxJkqQVbdQWsTcCf5/k0Pb9PZIcA7wS+KdRC0tyd+DXgX9ZVJSSJEmr0KjTV7wyyZ7AWcBuwDnADcCrquofF1He39GMN9t91vLjkzwTmAReUFVbF3FMSZKkFWnUrkmq6s+S/BXwQJqWtAuramrU/ZM8Dri6qjYnmZix6p+Ak2lm6D8ZeDVw3JD9NwGbAMbGxhgMBqMWvWp1eQ6mpqY6P+f+jNW1Puq51DXr+Y4lVbXwRskJwDur6pptLij5a+AZNFdd7kYzRux9VfX0GdtsBM6sqgfNd6zx8fGanJzc1lBWhSSM8rNbKoPBgImJic7K6/rzSdB9PZf6YD3vXpLNVTU+bN2oY8ReAHwryYeS/HaSOy02iKp6cVXdvao2Ak8FPlpVT0+yYcZmTwIuWOyxJUmSVqJRE7F7Ab8GXA68HrgqyduT/FqSkWfnn8Mrk3wxyReAw4E/2s7jSZIkrQijDtYvmgH65yT5A+BxwG8D7we+C/zcYgqtqgEwaF8/YzH7SpIkrRaLbs2qqhtpJnf9JM28Yj+71EFJkiStBYu56fceSZ6V5L+Ay4DnAO8C7rtcwUmSJK1mI3VNJnkP8Fjg+8C7gZdU1aeXMzBJkqTVbtR5xG4EngL8Z1XdvIzxSJIkrRmjDtb/7eUORJIkaa2ZMxFL8sfAG6rqx+3rOVXVa5Y8MkmSpFVuvhax5wGnAj8Gnk9zC6JhCjARkyRJWqQ5E7GquveM1xs7iUaSJGkNWXD6iiS7Jjkvyf5dBCRJkrRWLJiIVdVPgHszd9ekJEmStsGoE7qeSjOBqyRJkpbIqPOI3Rn4nSRHApuBH8xcWVXPX+rAJEmSVrtRE7EHAJ9tX99n1rpFdVkm2RmYBL5VVY9Lsp5mtv6NwBbgN6tq62KOKUmStBKNOqHr4UtY5gnARcAe7fsXAWdX1SuSvKh9/6dLWJ4kSdIOaeSbfgMk2SfJ3ttaWJK7A78O/MuMxUfRjEGjfX7ith5fkiRpJVmwRSzJvsDf0CRIe7TLrgfeS3Pz76sXUd7fAX8C7D5j2VhVXQlQVVe25Q2LYxOwCWBsbIzBYLCIYlenLs/B1NRU5+fcn7G61kc9l7pmPd+xpGruIV5J7gx8DlgPvAO4EAhwAPA04Frg4Kr6wZwHufVYjwMeW1W/n2QCeGE7Ruy7VbXXjO22VtW6+Y41Pj5ek5OTCxW5qiVhvp/dUhsMBkxMTHRWXtefT4Lu67nUB+t595JsrqrxYesWahF7HrAr8KCq+vasg74c+ARwPE2L2UIOBZ6Q5LHAbsAeSf4VuCrJhrY1bAOwmBY2SZKkFWuhMWKPB14+OwmDphsR+GvgCaMUVFUvrqq7t7dLeirw0ap6OnAGcEy72THA6SPGLkmStKItlIjdHzh3nvXntttsj1cARya5FDiyfS9JkrTqLdQ1uQdw3Tzrr+PWaShGVlUDYNC+/g5wxGKPIUmStNIt1CK2E3DLPOtrhGNIkiRpiIVaxAJ8LMlN27i/JEmS5rBQIvWyTqKQJElag+ZNxKrKREySJGmZOL5LkiSpJyZikiRJPTERkyRJ6olXPa5QdeIecNKenZU3Ae3Mb92oExc9PZ0kSSvOnIlYkq8BD62q7yR5KfCqqvphd6FpPnnZ9av/pt8ndVacJEm9mK9rcgNwp/b1icBdlj8cSZKktWO+rsnPAW9Jci7NxK4vTDI1bMOq+t/LEZwkSdJqNl8i9izgL4En0tzK6PHAsBn2CzARkyRJWqQ5E7GqugQ4GiDJLcAjq+rqbS0oyW7Ax4Gfact9T1WdmOQk4DnANe2mL6mqD25rOZIkSSvFSFdNVtVSTHNxA/ArVTWVZFfg3CQfate9tqpetQRlSJIkrRgjJ1hJfiHJ25JMJvlMklOT/I9R96/G9BizXdtHd5f9SZIk7WBGahFL8gTgfcB/A9OtWI8APpvkyVX1gRGPszOwGbgv8I9VdV6SxwDHJ3kmMAm8oKq2Dtl3E7AJYGxsjMFgMEqRq1qX52Bqaqrzc+7PWF3ro55LXbOe71gyylxUSb4AvL+qTpy1/H8DR1XVgYsqNNkLeD/wPJqxYdfStI6dDGyoquPm2398fLwmJycXU+Sqk2T1zyPW4eeToPt6LvXBet69JJuranzYulG7Ju8HvH3I8rcD+y82oKr6Ls087Y+uqquq6uaqugV4E3DIYo8nSZK0Eo2aiF0NHDxk+cHAVaMcIMld25YwktwReBRwcZINMzZ7EnDBiDFJkiStaKPea/JNwD8nuS/wCZpuxEcALwT+dsRjbABObceJ7QScVlVnJnl7koPaY24Bnjt6+JIkSSvXqInYXwJTwAtoxnEBXEFz66O/H+UAVfUF4MFDlj9jxBgkSZJWlVHnESvgtcBrk+zeLvv+cgYmSZK02o3aIvZTJmCSJElLYylmzJckSdI2MBGTJEnqiYmYJElST0zEJEmSerJdiViSDUnuuVTBSJIkrSXb2yL2UeDrSxGIJEnSWrPo6StmeTGw51IEIkmStNZsVyJWVf93ieKQJElacxysL0mS1JORErEk65K8LskXknw7ydUzHyMeY7ckn07y+SRfSvKydvn6JGclubR9Xrc9H0iSJGmlGLVr8m3AAcCpwFVAbUNZNwC/UlVTSXYFzk3yIeDJwNlV9YokLwJeBPzpNhxfkiRpRRk1EZsAHllVn93Wgtobh0+1b3dtHwUc1R4fmkRvgImYJElaA0YdI/bVRWw7pyQ7JzkfuBo4q6rOA8aq6kqA9nnf7S1HkiRpJRi1RewE4K+TvBC4oKpu3pbC2v0OSrIX8P4kDxp13ySbgE0AY2NjDAaDbQlhVenyHExNTXV+zv0Zq2t91HOpa9bzHUuaHsMFNkruBrwbePiw9VW186ILTk4EfgA8B5ioqiuTbAAGVbX/fPuOj4/X5OTkYotcVZIwys9uqQwGAyYmJjorr+vPJ0H39Vzqg/W8e0k2V9X4sHWjtoi9i2bi1uezjYP1k9wV+ElVfTfJHYFHAX8DnAEcA7yifT59sceWJElaiUZNxMaBQ6rqgu0oawNwapKdacabnVZVZyb5JHBakmcDlwFHb0cZkiRJK8aoidiFwB7bU1BVfQF48JDl3wGO2J5jS5IkrUSjXgn558BrkjwqyVg7CetPH8sZoCRJ0mo1aovYB9vnj3Db8WFp3y96sL4kSdJaN2oidviyRiFJkrQGjZSIVdXHljsQSZKktWakRCzJQ+Zbvz23PpIkSVqrRu2anKQZC5YZy2aOFXOMmCRJ0iKNmojde9b7XWmmovgz4MVLGpEkSdIaMeoYsW8MWfyVJN8DTgQ+tKRRSZIkrQGjziM2l68DBy1BHJIkSWvOqIP1Z0/aGppbFp0EXLLEMUmSJK0Jo44Ru5bb3+g7wDeB31rSiCRJktaIbZ3Q9RbgGuArVXXT0oYkSZK0NnQ2oWuSewBvA36WJpF7Y1W9LslJwHNoEjuAl1TVB4cfRZIkafWYNxEb9YbeVXXdCJvdBLygqj6bZHdgc5Kz2nWvrapXjVKWJEnSarFQi9iwsWGz1QjHoaquBK5sX38/yUXA3UYJUpIkaTVaKIGa72bfjwZOoGnpWpQkG2kmhD0POBQ4PskzaWbwf0FVbR2yzyZgE8DY2BiDwWCxxa46XZ6Dqampzs+5P2Ntq8MPn+9X1/I455xzOi9T2hZ9/D7X3FK1UIPXrB2a+07+DXAY8M/AyVV1zfx73Wb/uwAfA/6qqt6XZIxbW95OBjZU1XHzHWN8fLwmJycXFfdqk4TF/uy2x2AwYGJiorPyuv58EljvtDZ0/ftckGRzVY0PWzfyhK5J7p3knTStWNcBD6yq5y8yCdsVeC/wjqp6H0BVXVVVN1fVLcCbgENGPZ4kSdJKtmAilmTvJK8DLqa54vHhVfVbVfXVxRSUJMCbgYuq6jUzlm+YsdmTgAsWc1xJkqSVaqGrJl8C/AmwBTiqqj68HWUdCjwD+GKS89tlLwGeluQgmq7JLcBzt6MMSZKkFWOhwfp/CfwIuBz4/SS/P2yjqnrCQgVV1bk0s/HP5pxhkiRpTVooEXsbC09fIUmSpG0wbyJWVcd2FIckSdKaM/JVk5IkSVpaJmKSJEk9MRGTJEnqiYmYJElSTxa8Wbd2XM0cuavTunXr+g5BkqRlZyK2QnV9PzzvwSdJ0tKza1KSJKknJmKSJEk9MRGTJEnqSWeJWJJ7JDknyUVJvpTkhHb5+iRnJbm0fXaUtiQA1q9fT5LOHkCn5a1fv77nMyypb122iN0EvKCqHgA8DPiDJA8EXgScXVX7AWe37yWJrVu3UlWdPc4555xOy9u6dWvfp1hSzzpLxKrqyqr6bPv6+8BFwN2Ao4BT281OBZ7YVUySJEl96mWMWJKNwIOB84CxqroSmmQN2LePmCRJkrrW+TxiSe4CvBf4w6q6ftRJSZNsAjYBjI2NMRgMli1GDec5Vx+6rHdTU1Od13O/V+paH/Vcc0uXk3Qm2RU4E/jPqnpNu+wSYKKqrkyyARhU1f7zHWd8fLwmJyeXP2D9lBO6qg9d17vBYMDExERn5fm9Uh+6rueCJJuranzYui6vmgzwZuCi6SSsdQZwTPv6GOD0rmKSJEnqU5ddk4cCzwC+mOT8dtlLgFcApyV5NnAZcHSHMUmSJPWms0Ssqs4F5hoQdkRXcUiSJO0onFlfkiSpJyZikiRJPTERkyRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT1xERMkiSpJyZikiRJPTERkyRJ6kmXN/2WpEWpE/eAk/bsrLwJgEFnxTWfT9Ka1lkiluQtwOOAq6vqQe2yk4DnANe0m72kqj7YVUySdmx52fVUVWflDQYDJiYmOisvCXVSZ8VJ2gF12TV5CvDoIctfW1UHtQ+TMEmStGZ0lohV1ceB67oqT5IkaUe3I4wROz7JM4FJ4AVVtXXYRkk2AZsAxsbGGAwG3UUoAM+5etFlvZuamuq8nvu9Utf6qOeaW7ocf5FkI3DmjDFiY8C1QAEnAxuq6riFjjM+Pl6Tk5PLGapmSdLpWB0Juq93vYwR83uljnVdzwVJNlfV+LB1vU5fUVVXVdXNVXUL8CbgkD7jkSRJ6lKviViSDTPePgm4oK9YJEmSutbl9BXvopmmZ58klwMnAhNJDqLpmtwCPLereCRJkvrWWSJWVU8bsvjNXZUvSZK0o/EWR5IkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUExMxSZKknpiISZIk9cRETJIkqScmYpIkST0xEZMkSeqJiZgkSVJPOkvEkrwlydVJLpixbH2Ss5Jc2j6v6yoeSZKkvnXZInYK8OhZy14EnF1V+wFnt+8lSZLWhM4Ssar6OHDdrMVHAae2r08FnthVPJIkSX3re4zYWFVdCdA+79tzPJIkSZ3Zpe8ARpVkE7AJYGxsjMFg0G9Aa5DnXH3ost5NTU11Xs/9XqlrfdRzzS1V1V1hyUbgzKp6UPv+EmCiqq5MsgEYVNX+Cx1nfHy8JicnlzdY3UYSuqwrEnRf7waDARMTE52V5/dKfei6nguSbK6q8WHr+u6aPAM4pn19DHB6j7FIkiR1qsvpK94FfBLYP8nlSZ4NvAI4MsmlwJHte0mSpDWhszFiVfW0OVYd0VUMkiStFkk6L9Ou9KW3YgbrS5KkW21rUuTYxB1L32PEJEmS1iwTMUmSpJ6YiEmSJPXEREySJKknJmKSJEk9MRGTJEnqidNXSJLUo/Xr17N169ZOy+xyDrJ169Zx3XXXdVbeSmMiJklSj7Zu3brq76mqudk1KUmS1BMTMUmSpJ6YiEmSJPVkhxgjlmQL8H3gZuCmqhrvNyJJkqTlt0MkYq3Dq+ravoOQJEnqil2TkiRJPdlRErECPpJkc5JNfQcjSZLUhR2la/LQqroiyb7AWUkurqqPz9ygTdA2AYyNjTEYDHoIc23znKsPq3kOot13393vlYBuf79OTU11Xu+s53NLl5PIjSLJScBUVb1qrm3Gx8drcnKyu6BEkk4nHJT6YD1XH7qud31M6LrWv1dJNs91IWLvXZNJ7pxk9+nXwK8CF/QblSRJ0vLbEbomx4D3t90PuwDvrKoP9xuSJEnS8us9EauqrwEH9h2HJEl9qBP3gJP27Ky8CYBBZ8U1n09z6j0RkyRpLcvLrl/9Y8RO6qy4Faf3MWKSJElrlYmYJElST+yaXGO2Z06mbd13rV+2LEnSXGwRW2Oqapse55xzzjbvK0mShjMRkyRJ6omJmCRJUk9MxCRJknpiIiZJktQTEzFJkqSemIhJkiT1xHnEJK06zpenlWZ76uyObt26dX2HsEPbIVrEkjw6ySVJvpLkRX3HI2llc748rSTbWuf6qOfb8rjuuuv6PsU7tN4TsSQ7A/8IPAZ4IPC0JA/sNypJkqTl13siBhwCfKWqvlZVNwL/BhzVc0ySJEnLbkcYI3Y34Jsz3l8O/OLsjZJsAjYBjI2NMRgMOglOjampKc+5Vj3rudYC6/mOZUdIxIaNULzdgIuqeiPwRoDx8fGamJhY5rA002AwwHOu1c56rrXAer5j2RG6Ji8H7jHj/d2BK3qKRZIkqTM7QiL2GWC/JPdOcgfgqcAZPcckSZK07Hrvmqyqm5IcD/wnsDPwlqr6Us9hSZIkLbveEzGAqvog8MG+45AkSerSjtA1KUmStCaZiEmSJPXEREySJKknJmKSJEk9MRGTJEnqSapuN4n9Di/JNcA3+o5jjdkHuLbvIKRlZj3XWmA97969ququw1asyERM3UsyWVXjfcchLSfrudYC6/mOxa5JSZKknpiISZIk9cRETKN6Y98BSB2wnmstsJ7vQBwjJkmS1BNbxCRJknpiIrbGJNmY5IJZy05K8sIh256S5CndRSeNJsmfJflSki8kOT/JL86z7dD6vcjyBkm8ykw7pCQ3t9+DC5J8IMleS3jsqaU6loYzEdNQSXbpOwZpmCQPBx4HPKSqfgF4FPDNZSxv5+U6trREflRVB1XVg4DrgD8YdUd/1/fPREw/1f7X//IkHwNOaBc/Ksl/J/lykse1221sl322ffxSu3yiPcZ7klyc5B1J0tfn0aq1Abi2qm4AqKprq+qKJFuS7AOQZDzJYMY+Byb5aJJLkzyn3WYiyZnTGyR5fZJj29dbkrw0ybnA0e0mT0/yibbV4ZB2u0PaZZ9rn/dvlx+b5H1JPtyW+crlPSXST30SuBvctiU3yT5JtrSvj03y70k+AHwkyV2SnN3+Pv9ikqN6i34NMhPWbHtV1SOh6ZoENgKPBH4eOCfJfYGrgSOr6sdJ9gPeBUx32zwYOAC4Avh/wKHAuV1+AK16HwFemuTLwH8B766qjy2wzy8ADwPuDHwuyX+MUM6Pq+oRAEl+F7hzVf1SksOAtwAPAi4GDquqm5I8Cng58Bvt/gfRfB9uAC5J8g9VtWwtd1LbensE8OYRNn848AtVdV3bKvakqrq+/WfmU0nOKK/m64SJ2Noz1xdrevm7Zy0/rapuAS5N8jXg/sDXgdcnOQi4GbjfjO0/XVWXAyQ5nyaRMxHTkqmqqSQHA78MHA68O8mLFtjt9Kr6EfCjJOcAhwDfXWCf2d+Fd7XlfzzJHu04nN2BU9t/SArYdcb2Z1fV9wCSXAjci2XsQtWadscZv283A2eNsM9ZVXVd+zrAy9t/Mm6haVEbA7699KFqNrsm157vAOtmLVvPrfcd+8GsdbMTtwL+CLgKOJCmJewOM9bfMOP1zZjsaxlU1c1VNaiqE4HjaVqhbuLW32m7zd5lyPuZ2w/bZ5TvwsnAOe3YnMfPOobfBXXlR1V1EE2yfwduHSM233diZv3+HeCuwMHtca4asr2WiYnYGlNVU8CVSY4ASLIeeDRzt1odnWSnJD8P3Ae4BNgTuLJtKXsG4GBmdSbJ/m0L1LSDgG8AW4CD22W/MWu3o5LslmRvYAL4TLvPA5P8TJI9abp05vNbbfmPAL7XtnbtCXyrXX/stnweaam0dfL5wAuT7MptvxPzXQG/J3B1Vf0kyeE0CZ064n9oa9MzgX9M8ur2/cuq6qtzjKu/BPgYTTP177bjwt4AvDfJ0cA53L7lQFpOdwH+oe0avAn4CrAJeADw5iQvAc6btc+ngf8A7gmcXFVXACQ5DfgCcCnwuQXK3ZrkE8AewHHtslfSdE3+MfDR7fxc0narqs8l+TzwVOBVwGlJnsH89fMdwAeSTALn04x9VEecWV+SJKkndk1KkiT1xERMkiSpJyZikiRJPTERkyRJ6omJmCRJUk9MxCStCO39H1/YdxyStJRMxCT1LslYktcl+WqSG5J8K8mHkjx2xmYPBd4wY59KMt8klaOUWws8Ttme40vSQpzQVVKvkmykuUH894EXA5+n+SfxCOD/0EzCSlVdswzFb5jx+nHAm2Yt+9EylClJP2WLmKS+vYHmpsPjVXVaVV1SVRdV1etp7mcK3LZrMsmWdvG/ty1XW5JsTHJzkvGZB0/ynCTXJpl5T1QAqurb0w/am4C3r6+iue3Xc2Yda7+2vIe07yvJ8Un+I8kPk3wjydNn7XO3JP+WZGv7+I+Zt2hKco8kpye5rj3GxUmeum2nUtJKYyImqTcz7nX6+vY+qLdRVVvn2PWh7fNzaFqwHlpVW4D/4tbbD007Dnh7Vd04alzV3HLkzXMc6/yq+uyMZS8DzqC55+UbgbdNJ4NJ7kRzG7AfA48EHg5cCfxXuw6aRPROwOHAAcAf0iaFklY/EzFJfbovTWvYRYvZaUY35XfbFq3p928CnpZkN4AkDwAeRpNULdZbgf2SPKw91s4092mdfaz3VdU/V9WXq+qvaO7p94ftuqfSfL5nVdUXqupi4Lk098t8XLvNvYBzq+rzVfX1qvpwVX14G+KVtAKZiEnq09A7zW+H04EbgSe3748DPl1VFyz2QG0X5Znc2ir2aGBvmhskz/TJIe8f2L4+GLg38P0kU0mmgO8B64Cfb7d5HfDnST6Z5C+THLzYWCWtXCZikvp0KVDAA5biYFX1E+BtwHFJdgGewba1hk37F+C32m7E42hav+bqLh1mJ+B8mm7LmY/7Af/cxvxmmmTtre3yTyQ5aTtilrSCmIhJ6k1VXQf8J3B8krvMXp9kr3l2/wmw85Dlb6IZb/X7wO7Av21HiB8Grgd+F3g88JYh2zxsyPvprtbP0nS/XltVX5n1uG56h6q6vKreWFW/CbwU2LQdMUtaQUzEJPXt92m6KCeTHJ1k/yT3T/J7wBfm2W8LcESSn02ybnphVX2Z5orHvwXeU1XXb2tgVXUzTfL118C3gLOHbPbk9srM/ZK8mGbajb9r172D5grM05M8Msm9kxyW5NXTV06286c9Osl9khxE0wV64bbGLGllMRGT1Kuq+jrwEOAs4G9okq+PAk+gGdg+lxfQtHx9E/jcrHVvBu7A9nVLTntLe6y3tldTznYS8Bs0cf8ezcD8zwBU1Q+Bw4CvAf8OXAycSjNGbLqLcyfgH2iSr7NoErdjliBuSStAhv9ekaSVK8mfAs+uqvstwbF+kWbC2ftU1WWz1hVwdFW9Z3vLkbQ2ObO+pFWjHWd2f+AE4K+281g/A9wD+Evg/bOTMElaCnZNSlpNXk/TevX/aK9K3A5PAy6hmbLij7fzWJI0lF2TkiRJPbFFTJIkqScmYpIkST0xEZMkSeqJiZgkSVJPTMQkSZJ6YiImSZLUk/8PGy2e/F4sP5sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all driver 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('Num. 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",
    "\n",
    "# Save the figure\n",
    "plt.savefig('analysis/Fig4.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build out arrays, lists, vars for % Fares by City pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 68,
     "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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        6.81\n",
       "Suburban    30.46\n",
       "Urban       62.72\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 69,
     "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",
    "round(type_percents,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Percentage of Fares by city pie charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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 configs using rcParams\n",
    "import matplotlib as mpl\n",
    "\n",
    "# Build the 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,\n",
    "        startangle=150)\n",
    "\n",
    "plt.title('% of Total Fares by City Type')\n",
    "\n",
    "# Change default font size\n",
    "mpl.rcParams['font.size'] = 14\n",
    "\n",
    "# Save Figure\n",
    "plt.savefig('analysis/Fig5.png')\n",
    "\n",
    "# Show figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build out series and variables for percentage of rides by city type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        5.26\n",
       "Suburban    26.32\n",
       "Urban       68.42\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 71,
     "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",
    "round(ride_percents,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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,\n",
    "        startangle=150)\n",
    "plt.title(\"% of Total Rides by City Type\")\n",
    "\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig6.png\")\n",
    "\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Driver percentages by City Type calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        2.62\n",
       "Suburban    16.48\n",
       "Urban       80.89\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 73,
     "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",
    "round(driver_percents, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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(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,\n",
    "        startangle=165)\n",
    "\n",
    "plt.title(\"% of Total Drivers by City Type\")\n",
    "\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig7.png\")\n",
    "\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}