PyBer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "547cb46f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "860cc720",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Files to load\n",
    "city_data_to_load = \"Resources/city_data.csv\"\n",
    "ride_data_to_load = \"Resources/ride_data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93198064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Richardfort</td>\n",
       "      <td>38</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Williamsstad</td>\n",
       "      <td>59</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Angela</td>\n",
       "      <td>67</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West Robert</td>\n",
       "      <td>39</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West Anthony</td>\n",
       "      <td>70</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Martinezhaven</td>\n",
       "      <td>25</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Karenberg</td>\n",
       "      <td>22</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Barajasview</td>\n",
       "      <td>26</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  driver_count   type\n",
       "0    Richardfort            38  Urban\n",
       "1   Williamsstad            59  Urban\n",
       "2    Port Angela            67  Urban\n",
       "3     Rodneyfort            34  Urban\n",
       "4    West Robert            39  Urban\n",
       "5   West Anthony            70  Urban\n",
       "6    West Angela            48  Urban\n",
       "7  Martinezhaven            25  Urban\n",
       "8      Karenberg            22  Urban\n",
       "9    Barajasview            26  Urban"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the city data file and store it in a pandas DataFrame.\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1cb81bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Latoya</td>\n",
       "      <td>2019-03-11 12:26:48</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1994999424437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Paulville</td>\n",
       "      <td>2019-02-27 11:17:56</td>\n",
       "      <td>43.25</td>\n",
       "      <td>793208410091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simpsonburgh</td>\n",
       "      <td>2019-04-26 00:43:24</td>\n",
       "      <td>35.98</td>\n",
       "      <td>111953927754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Karenland</td>\n",
       "      <td>2019-01-08 03:28:48</td>\n",
       "      <td>35.09</td>\n",
       "      <td>7995623208694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>North Jasmine</td>\n",
       "      <td>2019-03-09 06:26:29</td>\n",
       "      <td>42.81</td>\n",
       "      <td>5327642267789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873\n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577\n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003\n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178\n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344\n",
       "5        South Latoya  2019-03-11 12:26:48   9.52  1994999424437\n",
       "6       New Paulville  2019-02-27 11:17:56  43.25   793208410091\n",
       "7        Simpsonburgh  2019-04-26 00:43:24  35.98   111953927754\n",
       "8     South Karenland  2019-01-08 03:28:48  35.09  7995623208694\n",
       "9       North Jasmine  2019-03-09 06:26:29  42.81  5327642267789"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the ride data file and store it in a pandas DataFrame.\n",
    "ride_data_df = pd.read_csv(ride_data_to_load)\n",
    "ride_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5935ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            0\n",
       "driver_count    0\n",
       "type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "city_data_df.count()\n",
    "# Get the columns and the rows that are not null.\n",
    "city_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38e29a10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            object\n",
       "driver_count     int64\n",
       "type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "400d6574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Suburban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique values of the type of city.\n",
    "city_data_df[\"type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ea0469f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "id": "50d2eb06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Suburban cities.\n",
    "sum(city_data_df[\"type\"]==\"Suburban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2763aad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Rural cities.\n",
    "sum(city_data_df[\"type\"]==\"Rural\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "44aa1fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "ride_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c9e875b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "ride_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc1262b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b47ab433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data into a single dataset\n",
    "pyber_data_df = pd.merge(ride_data_df, city_data_df, how=\"left\", on=[\"city\", \"city\"])\n",
    "\n",
    "# Display the DataFrame\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5e0d7e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Urban city DataFrame.\n",
    "urban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Urban\"]\n",
    "urban_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1db90b6d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\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",
       "      driver_count   type  \n",
       "2250             9  Rural  \n",
       "2251             8  Rural  \n",
       "2252             6  Rural  \n",
       "2253             2  Rural  \n",
       "2254             2  Rural  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Rural city DataFrames.\n",
    "rural_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Rural\"]\n",
    "rural_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "19bb882b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>Barronchester</td>\n",
       "      <td>2019-01-27 03:08:01</td>\n",
       "      <td>27.79</td>\n",
       "      <td>6653622887913</td>\n",
       "      <td>11</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>East Kentstad</td>\n",
       "      <td>2019-04-07 19:44:19</td>\n",
       "      <td>18.75</td>\n",
       "      <td>6575961095852</td>\n",
       "      <td>20</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>Lake Omar</td>\n",
       "      <td>2019-01-17 21:33:35</td>\n",
       "      <td>21.71</td>\n",
       "      <td>966911700371</td>\n",
       "      <td>22</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>Myersshire</td>\n",
       "      <td>2019-02-27 17:38:39</td>\n",
       "      <td>17.10</td>\n",
       "      <td>5706770909868</td>\n",
       "      <td>19</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>West Hannah</td>\n",
       "      <td>2019-04-19 01:06:59</td>\n",
       "      <td>37.78</td>\n",
       "      <td>2273047151891</td>\n",
       "      <td>12</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               city                 date   fare        ride_id  driver_count  \\\n",
       "1625  Barronchester  2019-01-27 03:08:01  27.79  6653622887913            11   \n",
       "1626  East Kentstad  2019-04-07 19:44:19  18.75  6575961095852            20   \n",
       "1627      Lake Omar  2019-01-17 21:33:35  21.71   966911700371            22   \n",
       "1628     Myersshire  2019-02-27 17:38:39  17.10  5706770909868            19   \n",
       "1629    West Hannah  2019-04-19 01:06:59  37.78  2273047151891            12   \n",
       "\n",
       "          type  \n",
       "1625  Suburban  \n",
       "1626  Suburban  \n",
       "1627  Suburban  \n",
       "1628  Suburban  \n",
       "1629  Suburban  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Suburban city DataFrames.\n",
    "suburban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Suburban\"]\n",
    "suburban_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "55856a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        18\n",
       "Barajasview        22\n",
       "Carriemouth        27\n",
       "Christopherfurt    27\n",
       "Deanville          19\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of rides for urban cities.\n",
    "urban_ride_count = urban_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "urban_ride_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "55bb7beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Barronchester    16\n",
       "Bethanyland      18\n",
       "Brandonfort      19\n",
       "Colemanland      22\n",
       "Davidfurt        17\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the suburban ride count.\n",
    "suburban_ride_count = suburban_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "suburban_ride_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "50b7763d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bradshawfurt      10\n",
       "Garzaport          3\n",
       "Harringtonfort     6\n",
       "Jessicaport        6\n",
       "Lake Jamie         6\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the rural ride count.\n",
    "rural_ride_count = rural_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "rural_ride_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "02b2e7d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        24.641667\n",
       "Barajasview        25.332273\n",
       "Carriemouth        28.314444\n",
       "Christopherfurt    24.501852\n",
       "Deanville          25.842632\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get average fare for each city in the urban cities.\n",
    "urban_avg_fare = urban_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "urban_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6d29f6ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Barronchester    36.422500\n",
       "Bethanyland      32.956111\n",
       "Brandonfort      35.437368\n",
       "Colemanland      30.894545\n",
       "Davidfurt        31.995882\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get average fare for each city in the suburban city.\n",
    "suburban_avg_fare = suburban_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "suburban_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "972c23a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bradshawfurt      40.064000\n",
       "Garzaport         24.123333\n",
       "Harringtonfort    33.470000\n",
       "Jessicaport       36.013333\n",
       "Lake Jamie        34.358333\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get average fare for each city in the rural city.\n",
    "rural_avg_fare = rural_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "rural_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8701e3fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh        12.0\n",
       "Barajasview        26.0\n",
       "Carriemouth        52.0\n",
       "Christopherfurt    41.0\n",
       "Deanville          49.0\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the average number of drivers for each urban city.\n",
    "urban_driver_count = urban_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "urban_driver_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e5f83d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Barronchester    11.0\n",
       "Bethanyland      22.0\n",
       "Brandonfort      10.0\n",
       "Colemanland      23.0\n",
       "Davidfurt        23.0\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the average number of drivers for each suburban city.\n",
    "suburban_driver_count = suburban_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "suburban_driver_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5956a4b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bradshawfurt      7.0\n",
       "Garzaport         7.0\n",
       "Harringtonfort    4.0\n",
       "Jessicaport       1.0\n",
       "Lake Jamie        4.0\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the average number of drivers for each rural city.\n",
    "rural_driver_count = rural_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "rural_driver_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cadb561e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a790983b48>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for urban cities.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "99f85f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a791a76108>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for suburban cities.\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a9e95f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a791b01c88>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r/dWrV9u7775rg5/4u7377ru2evXqrdp/VVq5cmVC68U7ZmC8lfOdmszbXA8GjpPUD8gEGkp6xczOknRumED6hAFuwszywt+LJI0AegKfJzFeV02MGP4c91xbixW10tmhwXy+++47DjnkkKjDcjVcvXr1GDBgQNRhpFTS2u5mdrOZtTGz9sDpwH/C5NAXuBE4zsziPoooqb6krJLPwJHAlGTF6qqXNjt35JMv1rJ6TTFz8qB169ZRh+S2U+Wcn26XtuRYo+jcfYLgmsSo8BbWJwEktZY0MlynJTBG0iRgLPChmX0UQawuAjf96R7y049n8fJMrrvhMXbZZZeoQ4pUUVEREyZM2C5ebFOdZGZmsmTJkhqRJCx8H0RmZuZmlUvJk9Rmlgvkhp/jXhUKu5T6hZ9nAfukIjZX/TRu3Ji77nmE3NxcsrOzow4nUsXFxVx79UUszhtD76Ov5rlnhnDhxb+POqztQps2bZg/fz6//vpr1KFstXXr1lX65V/yRrnN4UNtOFeNzZs3j1kzx/D+kw35dHoaf71/sCeIKlKrVq3NertadZabm0u3bt2qfLueIJyrxho1asSadRmMnbSa/PXFtGjp12Nc6vgN5s5VY02aNOH2u57k78PasnJtPe69/8moQ3I1iCcI56q5Xr168eobn9Cu3S41/oK9Sy1PEM455+LyBOGccy4uTxDOOefi8gThnHMuLk8Qzjnn4vIE4ZxzLi5PEM455+LyBOGccy4uTxDOOefi8gThnHMuLh+szzm3TZk3bx4LFy6ksLAw6lC2e96CcM5tE6ZPn85F55/EoPP68Mxj5zBvzkzOP2cAkydPjjq07ZYnCOdctTdz5kyuuuwUjj9kEh883ZCn765Px3bpnHH4dK676nSmTp0adYjbpaQnCEnpkr6T9EE43VTSKEk/hr+blFOur6QfJP0k6aZkx+mcq76GPHEfg05ey7F9GpORoWCm4MjDGnL1WYUMfvzuaAPcTqWiBXE1MD1m+ibgUzPrBHwaTpciKR0YDBwN7AWcIWmvFMTqnKtmVqxYwXfffk7/wxvFXd63VyNmzhjPokWLUhzZ9i+pCUJSG+AY4NmY2QOAF8PPLwLHxynaE/jJzGaZWSEwLCznnKthVqxYQZOG6WTWif91VauWaN40neXLl6c2sBpAZpa8jUvDgb8CWcD1ZnaspOVm1jhmnWVm1qRMuZOBvmZ2UTh9NrC/mV0RZx+DgEEALVu27D5s2LCkHU8q5Ofn06BBg6jDqBa8LkqrqfVRXFzMTz9OZ9e2aaSnb5yfv24HGmQuprgYfppbxK4d9yQ9doUaZGv+NnJyciaYWY94y5J2m6ukY4FFZjZBUvbmFo8zL24mM7OngacBevToYdnZm7ur6iU3N5dt/RiqitdFaTW5Pj4bPZINv77DZWc1/W1e7swLyd7tOZ5/cyn/t6gvgy7Z5PyxxkjW30Yyn4M4GDhOUj8gE2go6RVgoaRWZvaLpFZAvI7D+cDOMdNtgLwkxuqcq8Yuv/IGLjr/K9avz2PggEbs0DSDoiLjH68s5YMvduDZF26NOsTtUtKuQZjZzWbWxszaA6cD/zGzs4D3gXPD1c4F3otTfBzQSVIHSbXD8u8nK1bnXPXWokULnhv6LmvrnMPJVxXQ55zl/DTPWGZn8MJL/6R169ZRh7hdiuJJ6vuANyVdCMwDTgGQ1Bp41sz6mdkGSVcAHwPpwPNm5jc6O1eDNW/enJtu+Qt/+OOtrFq1ikmTJnHJJZdHHdZ2LSUJwsxygdzw8xKgT5x18oB+MdMjgZGpiM85t+2oVasWTZs2RYp3qdJVJX+S2jnnXFyeIJxzzsXlCcI551xcniCcc87F5QnCOedcXJ4gnHPOxeUJwjnnXFyeIJxzzsXlCcI551xcniCcc87F5QnCOedcXJ4gnHPOxeUJwjnnXFyeIJxzzsXlCcI551xcniCcc87F5QnCOedcXEl7o5ykTOBzoE64n+FmdrukN4Ddw9UaA8vNrGuc8nOAVUARsMHMeiQrVuecc5tK5itHC4DeZpYvqRYwRtK/zOy0khUkPQSsqGAbOWa2OIkxOuecK0fSEoSZGZAfTtYKf6xkuYIXyp4K9E5WDM4557acgu/xchYG3UTHAocCrYG1wBTgQzObWunGpXRgAtARGGxmN8YsOwx4uLyuI0mzgWUESeUpM3u6nPUGAYMAWrZs2X3YsGGVhVWt5efn06BBg6jDqBa8Lkrz+ijN62OjramLnJycCeV+D5eXICTdAfQHcgm+5BcBmcBuQE74+Q9mNrmyACQ1BkYAV5rZlHDeEOAnM3uonDKtzSxPUgtgVFj284r206NHDxs/fnxl4VRrubm5ZGdnRx1GteB1UZrXR2leHxttTV1IKjdBVNTFNM7M7ihn2cPhF3fbRAIws+WScoG+wBRJGcCJQPcKyuSFvxdJGgH0JLjo7ZxzLgXKvc3VzD6sqKCZLTKzck/XJTUPWw5IqgscDswIFx8OzDCz+eWUrS8pq+QzcCRB15ZzzrkUqfA5CEl7S2oefm4m6VlJwyTtlcC2WwGjJU0GxgGjzOyDcNnpwOtl9tVa0shwsiXBXU+TgLEE1zw+SvywnHPOba3K7mJ6kqArCOAe4H/A98DzwAEVFQyvTXQrZ9l5ceblAf3Cz7OAfSqJbbuSl5fHuHHjqFOnTtShOOccUEELQtLtBHcf/T78fAKQDuwBtJH05/BOJLeV5s2bx7kDj2bC6Ov5ed5P5ObmRh2Sc85VeA3iLwQthteAT4EpZnZzOH+2md1Z2V1FLjFjxoyhd8987rymES2awscj34w6JOecq7SL6U6CO4fWE1w3QNLegD/dXIU6dOjAGy+Jz75excp10GGXRC7xOOdcclV4kdrMRphZazNrZ2ZfhfOmmtkJqQlv+1dYWMic2bNYuSaTi27OI3+NsXbdOpYuXRp1aM65Gq6iaxDtKyqoQJsqj6gGKSgo4MrLzuWrf9/GIzcV8/1Hndi1bToFC//BuWcdyy+//BJ1iM65GqyiFsQDkt6WdE54u2sLSW0l9ZZ0F/AlsGeK4twuvfzSC2Slf8Ojtzah6171yKyTRu3a4oZLmnJS74X89e6bow7ROVeDVXSR+hTgNoKhuQcDXwDvARcBPxCM1DoqFUFuj4qLi3ln+HMMOq0eaWnaZPkZ/RsxbcqX5OXlRRCdc85VcpHazKYBf0pRLDXK2rVryV+1lN12aRJ3eZ06aezWIYO5c+fSunXrFEcXrUWLFnHPnTewT7eDWPzrQk4+5bTKCznnqpy/US4itWvXZsMGkb+6qNx1liwron79+imMqnq4795b6Nj8M1o1X8/zT93GzJkzow7JuRrJE0REatWqRa+co3n/3yvjLv9+xlrWbmhG586dUxxZ9Bb/uoADumZSNzON1i3g119/jTok52okTxARuuCiq3h+RCajv1pF7LDrP85exy0Pr+P3l99KWlrN+yc6Y+AV/OnRIubML2Kd7Ub37uUO+uucS6JK3ygXvvltILCLmd0pqS2wo5mNTXp027lOnTrx8OOvc+ftVzPk9QXssQvsuk8x976cwZXXPMzR/Y6JOsRIHN3vGPbauzNTp07luaFv+/hUzkUkkVeO/gMoJng16J3AKuBtYL8kxlVjdOnShbfe+Q+TJk1iwYIFmBkffjyOWrVqRRJPXl4eixcvpnXr1uywww6RxADQrl07Zs+eHVlyWL16NR9+8E+++vIjioo20K17NgOOP5GmTZtGEo9zUUgkQexvZvtK+g7AzJZJqp3kuGoUSXTt2pWuXbuSm5sbSXKYNWsW9997M7P/71tat8hgXt4G9t2vNzfefDfNmzdPeTxR+vHHH7n6ioF06biM/geLjHTx2divOfWlR7n3b8/Ts2fPqEN0LiUSSRDrw3dLGwQvAiJoUbjtRF5eHpdedCKXnprPcTc1IiNDrF1XzEvv/JtLLvqBl1/7sMbcTVVYWMi1V53D1Wcu56hejX6bf9j+cMyUNdxwwwW8+c7n3pJwNUIiV0AfJ3ifdAtJ9wBjgHuTGpVLqVdeepYBOSs5sW9jMjKCh/bqZqZxyZlN6NR6Pu+/9260AabQ6NGjadtiCUf1arjJsn071yO7xzreHfF2BJE5V1pxcTFffPEFd95xIwsW/Mwbw14nPz+/SvdR2Rvl0oDZwA3AX4FfgOPN7K0qjcJFKvc/79O/d4O4ywYcnsHoT99JcUTR+Xb8l2Tvt6Hc5dn7Z/Dt+P+kMCLnNrVmzRouvfhMnn7sAvbY4S2yMlcwacyfOen4XsyYMaPyDSSostFci4GHzGyGmQ02syfMbHoiG5aUKWmspEmSpkr6Szj/DkkLJE0Mf/qVU76vpB8k/STpps0+MpewgoIC6teL/6dQr24a6wsLUhxRdNLS0tlQ/rOLbCgy0tLSUxeQc3E8+vC9tMoay4t/a8ipxzalYVY6917fiBsvWMV1V5/L+vXrq2Q/iXQxfSLppPB2181RQDBe0z5AV6CvpJLXlD5iZl3Dn5FlC4bXPAYDRwN7AWck+B5stwW67nsAn30Tv2n62TcF7LNvzXlx4IEH5zDqv+VfmvtkTDEHHnx0CiNyrrRVq1Yx6uPhXH1ew03Gcet9UEN2br60yt5KmUiCuA54CyiQtFLSKknxH/+NYYGSb51a4Y9VUCRWT+AnM5tlZoXAMGBAgmXdZjrrnMt4+k0x++fSLYVvp6zhn59lcsqpAyOKLPUOOeQQVm9ox6vvLt9k2adfruTbHxpybP/jUh+Yc6H58+fTqjk0bRz/RKb7XoX89GPVDE+j2Cd4q1rYEphA8G7rwWZ2o6Q7gPOAlcB44A9mtqxMuZOBvmZ2UTh9NsHttlfE2ccgYBBAy5Ytuw8bNixpx5MK+fn5NGgQ/3pAMq1YsYKF/5tP/bpG7VrGukKxriCNndq0p169eimPB6Kri/Xr1/PzvDmkq4CsBiBB/mooWJ/Bzm07RPZsRlT1UV3V1PpYv349c2b/QKf2G7s689ftQIPM4EWf/1tURO16rRO+0y4nJ2eCmfWItyyR21yR1AToBGSWzEvkfdRmVgR0ldQYGCGpMzAEuIugNXEX8BBwQdldxttcOft4GngaoEePHpadnV1ZWNVabm4uUR3DqlWr+Pe//83ixYtp06YNvXv3jvQp5ijroqioiDFjxvDfMZ9SXFxEt+4Hc/jhh1O7dnSPAEVZH9VRTa6P887+O+0yZ3DkYcHddrkzLyR7t+dYunwDd961hmFvj6FFixZbvZ9Ehtq4CLgaaANMBA4AviJ4sjohZrZcUi5Bq+DBmG0/A3wQp8h8YOeY6TaAvxghybKysjjhhOjfJltUVMTYsWNZuXIlv/zyC61atUp5DOnp6fTq1YtevXqlfN/OVea6P97NdVedzqrVy+mXEySJsRNX8+DzBZw+8A9VkhwgsWsQVxMMqzHXzHKAbkClw2tKah62HJBUFzgcmCEp9n/7CcCUOMXHAZ0kdQif2j4deD+BWN027ocffmDAsYfy1KPnsWr5zww89VAeuP8uiov92UznSnTp0oW/D3mbL3/Ips85K/hhVhEPvbIT51z8GBdfclmV7SeRBLHOzNYBSKpjZjMI3jJXmVbAaEmTCb7wR5nZB8DfJH0fzs8Brg233VrSSAAz2wBcAXwMTAfeNLOpm3lsbhtT8hTzNQMXM/T+LHbaMY33n8xi2rdDeeft4VGH51y1sueee/Lwo8+SO2Y6nXbbi2FvjeLY/v3Z/BtOy5fINYj5YUvgXWCUpGUk0N1jZpMJWhtl559dzvp5QL+Y6ZHAJrfAuu3Xf//7X9o0X8bhh2x8irlB/XQuH1ibR157jpNPOTXC6JyrnmrXrk1aWlqVJoYS5SYISQeY2ddmVtIpfYek0UAj4KMqj8TVeEuXLqVNy03vRWjTqjZLlyyOICLnaraKWhD/APYFkPSVmR1oZp+lJixXE3Xp0oVnBhuFhcXUrr2x9/Ozb/L5XZcjI4zMFRcX8+WXX/L95Ek0bbYDRx11FE2axH+futt+VHQNIra9klnuWs5VkY4dO9Jtv2O4/r7l/PB/6ygqMt75aBnPDK/DhYOuiTq8GmvNmjVcfOGpPPP4BdRa+QDTv76VkwYcwjfffBN1aC7JKmpBpIXPP6TFfP4taZjZ0mQH52qeO+9+iJde3JsbHhnKMf2N6dOzefwff2D33RO5L8IlwzNPD2anhhO447amvw3t8O2UNdx086V88NE3kT4b4pKrohZEI4KnoMcDDYFvw+mSec5VuYyMDC64cBDvffBfdtt9bx557Dn22suH4YrSRyOHcdGpWaXG/dm3cz067LTGWxHbuXJbEGbWPoVxOFfKwoULKSgooLCw0M9QI7Zu3Toa1N/0LYcN6gXL3PYrkecgnEuZRYsWcfmlZ3HmKQez4OefOLZvT94e/mbUYdVoBx7Uh39+Wnp8zl+XrGfCVKN79+4RReVSIaGxmJxLheLiYq658lyyu87kkesa8d85aTx5RxHX3nszTZs1JycnJ+oQa6RLL7uei88fw/KVyzisZx3m/1LI8+/AeRfc6K9e3c55C8JVG+PHj4f1/8fFpzf+7TbXXdrW4ZpzM3jt5Scijq7matu2LS++OpLiRoP4+xu78tnUHK7/04ucd8HFUYfmkizR0VwPATqZ2QuSmgMNzGx2ckNzNc38+fPZa1c2eSJ0706Z/Pyc/7lFaccdd+Ta624Ebow6FJdClbYgJN1O8FdxczirFvBKMoNyNVOHDh2YON0o+46S76auYZdd94goKudqrkS6mE4AjgNWw29jJmUlMyhXM3Xt2pWGzbrw4DPLyF8dvBh64rQ1PPYynHP+VRFH51zNk0iCKLTglM4AJNVPbkiuppLEo39/geXWn34X5/PjnCLuGNKEa294ggMOOKDyDTjnqlQi1yDelPQU0FjSxQRvf3smuWG5mqphw4bc89fHWL36Xr766iveee8L0tL8XgrnolBpgjCzByUdQfAO6d2BP5vZqKRH5mq0+vXrk5GR4cnBuQgldBdTmBA8KTjnXA2SyDupVxFef4ixgmA8pj+Y2axyymUCnwN1wv0MN7PbJT0A9AcKgf8Dzjez5XHKzwFWAUXABjPrkeAxOeecqwKJtCAeJniD3GsEo7meDuwI/AA8D2SXU64A6G1m+ZJqAWMk/YugJXKzmW2QdD/B7bPl3VydY2b+phjnnItAIh28fc3sKTNbZWYrzexpoJ+ZvQGU+8YQC+SHk7XCHzOzT8J3TgN8DbTZmgNwzjmXHIm0IIolnQqUvDX+5Jhlm74fMoakdILhwTsCg82s7NjAFwBvlFPcgE8kGfBUmJji7WMQMAigZcuW5ObmVhRStZefn7/NH0NV8boozeujNK+PjZJWF2ZW4Q+wC/BPYDHwa/i5I1AXOKSy8uE2GgOjgc4x8/4EjABUTpnW4e8WwCTgsMr20717d9vWjR49OuoQqg2vi9K8Pkrz+thoa+oCGG/lfKcmcpvrLIKLyvGMSTAJLZeUC/QFpkg6FzgW6BMGGK9MXvh7kaQRQE+Ci97OOedSIJG7mDKBC4G9iXk3tZldUEm55sD6MDnUBQ4H7pfUl+CidC8zW1NO2fpAmpmtCj8fCdyZ4DE555yrAolcpH6Z4K6lo4DPCC4qr0qgXCtgtKTJwDhglJl9ADxBMJbTKEkTJT0JIKm1pJFh2ZYEdz1NAsYCH5rZR5txXM4557ZSIhepO5rZKZIGmNmLkl4DPq6skJlNBrrFmd+xnPXzgH7h51nAPgnE5pxzLkkSaUGsD38vl9QZaAS0T1pEzjnnqoVEWhBPS2oC3Aq8DzQAbktqVM455yJXYYKQlAasNLNlBHcQ7ZKSqJxzzkWuwi4mMysGrkhRLM4556qRRK5BjJJ0vaSdJTUt+Ul6ZM455yKVyDWIkucdLo+ZZ3h3k3PObdcSeZK6QyoCcc45V71U2sUkqZ6kWyU9HU53knRs8kNzzjkXpUSuQbxA8HKfg8Lp+cDdSYvIOedctZBIgtjVzP5G+MCcma0leHGQcy5F1qxZQznjWjqXNIkkiMJwsD0DkLQrwdvinHMpMGTwoxyR8ztmzpxG7ujRUYfjapBEEsQdwEfAzpJeBT4FbkhmUM65QF5eHm+/+QQjn2lI21Zw/1//GHVIrgZJ5C6mTyRNAA4g6Fq62vw90c6lhCTMoNgMDILBDZxLjUTuYnqf4H0MuWb2gScH51KnVatWnD7wWo65eBXz/gc33fJA1CG5GiSR05GHgEOBaZLeknRy+BIh51wKXHzJ5eSOmc7uu+/NYb16RR2Oq0ES6WL6DPhMUjrQG7gYeB5omOTYnHOh2rVrRx2Cq4ESGWqD8C6m/sBpwL7Ai8kMyjnnXPQSuQbxBjCdoPUwmOC5iCsTKJcpaaykSZKmSvpLOL+ppFGSfgx/NymnfF9JP0j6SdJNm3dYbksUFxczefJkvvjiC2bMmOH33TtXwyXSgngBONPMigAkHSzpTDO7vJJyBUBvM8uXVIvgHdP/Ak4EPjWz+8Iv/puAG2MLht1Zg4EjCJ7cHifpfTObtllH5xJiZox4ZzhDn3+YerWW0qKZmJdXRGaDdlx62Z/IzsmJOkTnXAQSuQbxkaSuks4g6GKaDbyTQDkD8sPJWuGPAQOA7HD+i0AuZRIE0BP4KXw3NZKGheU8QSTBkH88xuefPM5919Rlr05ZQJA0vpk4n7vuvZiVKx/kuAHHRxukcy7lVF43gqTdgNOBM4AlwBvA9WbWLuGNBy2BCUBHYLCZ3ShpuZk1jllnmZk1KVPuZKCvmV0UTp8N7G9mm7y8SNIgYBBAy5Ytuw8bNizR8Kql/Px8GjRokLL9FRQUMG/uj+yycxrp6ZuOoFJYaMxZYHTstCdpaam9Bz/VdVHdeX2U5vWx0dbURU5OzgQz6xFvWUUtiBnAF0B/M/sJQNK1m7PjsFuqq6TGwAhJnRMsGm+sp7iZzMyeBp4G6NGjh2VnZ29OiNVObm4uqTyG++69g2ZpL9PnyLiXggD487+XU7/+7Qw86+yUxQWpr4vqzuujNK+PjZJVFxWdEp4E/A8YLekZSX3YwkH6zGw5QVdSX2ChpFYA4e9FcYrMB3aOmW4D5G3Jvl3Fpk35hgO6VvxYywH7iOlTx6UoIudcdVFugjCzEWZ2GrAHwZf7tUBLSUMkHVnZhiU1D1sOJbfJHk7QKnkfODdc7VzgvTjFxwGdJHWQVJugq+v9RA/KJS49PZ0NRRXfrbRhg5GWlp6iiJxz1UUiF6lXA68Cr4bvoj6F4M6jTyop2gp4MbwOkQa8aWYfSPoKeFPShcC8cHtIag08a2b9zGyDpCuAj4F04Hkzm7plh+gq0q1HNrlfT6fb3vXKXSd3nDj06MNSGJWrbmbNmsW777zB3DnTqF+/EX2OPIFevXqRkZHQo1RuG7VZVx3NbKmZPWVmvRNYd7KZdTOzLmbW2czuDOcvMbM+ZtYp/L00nJ9nZv1iyo80s93MbFczu2dzD8wl5uRTzuSDz9L5Oa8w7vJJ09Yw+cd69O3bN8WRuerAzHj8sQf4/YVHUXfds5x82FgO6PgRw56/lDNPO5qFCxdGHaJLIk//NVzr1q254pq/csmfb+Sac9aRc2AWtWqJNWuL+VfuSp58I4O7/vokdevWjTpUF4E333iNsZ8/yVuPN6Rh1sZuxuOOgKHDZ3H1Fefw2hv/Svkdbi41PEE4TjjxJHZs1Zqhzz3Cfc98S5NGGSxZXkT3/XJ47B/Xstdee0UdootAUVERL73wGI/cWLdUcihx7kmN+c83s/jyyy859NBDI4jQJZsnCAfAgQceyIEHHsiSJUtYuXIlTZs2pVGjRlGH5SI0Y8YMsuquYLddsuIul8SxvYoY/ekHniC2U54gXCnNmjWjWbNmUYfhqoG1a9fSsEHFXUcNs9JZ93N+heu4bZd3HDrn4mrbti0/zd3A2nXF5a4zaUYx7XfZO4VRuVTyBOGci6tFixZ065HNWx+uiLt84a/r+XhMOsefcHKKI3Op4gnCOVeu666/g9c/bsLzby4lf3URENz6Onbiai69PZ8LBt1CixYtIo7SJYtfg3DOlWunnXbi2Rfe44nH7qP/pR/TukUay1cW0aBRWy696o8c5c/HbNc8QTjnKrTTTjvx17/9neXLl5OXl0f9+vVp27Yt0hYNzea2IZ4gnHMJady4MY0bN446DJdCfg3COedcXJ4gnHPOxeUJwjnnXFyeIJxzzsXlCcI551xcniCcc87F5QnCOedcXEl7DkLSzsBLwI5AMfC0mT0m6Q1g93C1xsByM+sap/wcYBVQBGwwsx7JitU559ymkvmg3AbgD2b2raQsYIKkUWZ2WskKkh4C4o8EFsgxs8VJjNE551w5kpYgzOwX4Jfw8ypJ04GdgGkACp7TPxWo9P3WzjnnUk9mlvydSO2Bz4HOZrYynHcY8HB5XUeSZgPLAAOeMrOny1lvEDAIoGXLlt2HDRtW9QeQQvn5+TRo0CDqMKoFr4vSvD5K8/rYaGvqIicnZ0K5XfhmltQfoAEwATixzPwhBF1Q5ZVrHf5uAUwCDqtsX927d7dt3ejRo6MOodrwuijN66M0r4+NtqYugPFWzndqUu9iklQLeBt41czeiZmfAZwIvFFeWTPLC38vAkYAPZMZq3POudKSliDCawzPAdPN7OEyiw8HZpjZ/HLK1g8vbCOpPnAkMCVZsTrnnNtUMlsQBwNnA70lTQx/+oXLTgdej11ZUmtJI8PJlsAYSZOAscCHZvZREmN1zjlXRjLvYhoDxH2jiJmdF2deHtAv/DwL2CdZsTnnnKucP0ntnHMuLk8Qzjnn4vIE4ZxzLi5PEM455+LyBOGccy4uTxDOOefiSuZors45t12aM2cOjz9yN4t/XcA+3Q7m8iuvJzMzM+qwqpy3IJxzbjMsXbqUSy8+mR67jOaP58xj0aznuf2266MOKym8BeGcc5th3LhxdN51JWcOaALAXbvUodfAkRQWFlK7du2Io6ta3oJwzrnNUKtWLfJX/zbqNGvWFSOlkZa2/X2dbn9H5JxzSXTQQQexfN0u3PXEUkZ8vIwr/7KK0wdeQkbG9tchs/0dkXPOJVFmZibPPD+cl196ge9/WcDJZx/AcQOOjzqspPAE4ZxzmykrK4vLLr8q6jCSzruYnHPOxeUJwjnnXFyeIJxzzsXlCcI551xcyXwn9c6SRkuaLmmqpKvD+XdIWhDnNaRly/eV9IOknyTdlKw4nXPOxZfMu5g2AH8ws28lZQETJI0Klz1iZg+WV1BSOjAYOAKYD4yT9L6ZTUtivM4552IkrQVhZr+Y2bfh51XAdGCnBIv3BH4ys1lmVggMAwYkJ1LnnHPxqORx8aTuRGoPfA50Bq4DzgNWAuMJWhnLyqx/MtDXzC4Kp88G9jezK+JsexAwCKBly5bdhw0blrwDSYH8/HwaNGgQdRjVgtdFaV4fpXl9bLQ1dZGTkzPBzHrEW5b0B+UkNQDeBq4xs5WShgB3ARb+fgi4oGyxOJuKm8nM7GngaYAePXpYdnZ2FUUejdzcXLb1Y6gqXheleX2U5vWxUbLqIql3MUmqRZAcXjWzdwDMbKGZFZlZMfAMQXdSWfOBnWOm2wB5yYzVOedcacm8i0nAc8B0M3s4Zn6rmNVOAKbEKT4O6CSpg6TawOnA+8mK1Tnn3KaS2cV0MHA28L2kieG8W4AzJHUl6DKaA1wCIKk18KyZ9TOzDZKuAD4G0oHnzWxqVQc4d+5c3h7+GjOmjSMzsz69jziJo446irp161b1rpxzbpuTtARhZmOIfy1hZDnr5wH9YqZHlrduVRjxznAGP3YLJx5RxMXH1WbV6iL++eHXvPTCo/zjqWHsuOOOydq1c85tE2rkaK7Tpk3jqSduYeh9mbRptfENUL0Pgpfens9Nf7yUF14aQdBL5pxzNVONHGpj2GvPcVb/DaWSQ4mzTmjE8sVTmTIl3qUR55yrOWpkgpj03Zcc1jP+PcNpaeLQ7kVMnDgxtUE551w1UyMTRHp6BoXri8tdXrhepKenpzAi55yrfmpkgjj40H58MmZt3GWFhcWMHisOPvjgFEflnHPVS41MEKeefjbv/LsO305ZU2p+cbFx/1PL2Wffw2nXrl1E0TnnXPVQI+9i2nnnnbn3b0O54caL6dJxBT27FLFylTHyiwzatD+E+x98KOoQnXMucjUyQQD07NmTf478mo8//piZMyZTp1497rz/SH73u9/57a3OOUcNThAAdevW5fjjjweOjzgS55yrfmrkNQjnnHOV8wThnHMuLk8Qzjnn4vIE4ZxzLq6UvHI0VST9CsyNOo6ttAOwOOogqgmvi9K8Pkrz+thoa+qinZk1j7dgu0oQ2wNJ48t7P2xN43VRmtdHaV4fGyWrLryLyTnnXFyeIJxzzsXlCaL6eTrqAKoRr4vSvD5K8/rYKCl14dcgnHPOxeUtCOecc3F5gnDOOReXJ4hqRFK6pO8kfRB1LFGT1FjScEkzJE2XdGDUMUVF0rWSpkqaIul1SZlRx5RKkp6XtEjSlJh5TSWNkvRj+LtJlDGmUjn18UD4f2WypBGSGlfFvjxBVC9XA9OjDqKaeAz4yMz2APahhtaLpJ2Aq4AeZtYZSAdOjzaqlBsK9C0z7ybgUzPrBHwaTtcUQ9m0PkYBnc2sCzATuLkqduQJopqQ1AY4Bng26liiJqkhcBjwHICZFZrZ8kiDilYGUFdSBlAPyIs4npQys8+BpWVmDwBeDD+/SA0asz9efZjZJ2a2IZz8GmhTFfvyBFF9PArcABRHHEd1sAvwK/BC2OX2rKT6UQcVBTNbADwIzAN+AVaY2SfRRlUttDSzXwDC3y0ijqc6uQD4V1VsyBNENSDpWGCRmU2IOpZqIgPYFxhiZt2A1dSsLoTfhH3rA4AOQGugvqSzoo3KVVeS/gRsAF6tiu15gqgeDgaOkzQHGAb0lvRKtCFFaj4w38y+CaeHEySMmuhwYLaZ/Wpm64F3gIMijqk6WCipFUD4e1HE8URO0rnAscBAq6IH3DxBVANmdrOZtTGz9gQXIP9jZjX2LNHM/gf8LGn3cFYfYFqEIUVpHnCApHoKXpbehxp6wb6M94Fzw8/nAu9FGEvkJPUFbgSOM7M1VbXdGv1OaletXQm8Kqk2MAs4P+J4ImFm30gaDnxL0HXwHTVsiAlJrwPZwA6S5gO3A/cBb0q6kCCJnhJdhKlVTn3cDNQBRgXnEXxtZpdu9b58qA3nnHPxeBeTc865uDxBOOeci8sThHPOubg8QTjnnIvLE4Rzzrm4PEG4hEhqJmli+PM/SQtipmuXWfcaSfUS2GaupE1etB7OHx8z3UNSbhUdx3mSnqiKbVWynz3CuvlO0q5lls2R9H048uZnktrFLPtvOdsbKunkKoirm6Rnw8/nSfo1jHOapIu3cttHSxofjr47Q9KD4fxLJZ0Ts8/WCWzr3zVphNbqyhOES4iZLTGzrmbWFXgSeKRk2swKy6x+DcGgclujhaSjt3IbVU5SeoKrHg+8Z2bdzOz/4izPCUfezAVuLZlpZsl+SvoW4O8x02+E/6bZwL2SWiaykXDgwNjpzsATwFlmtifQmeD5FczsSTN7KVz1PIIhQyrzMnBZIrG45PEE4baYpD7hGfL34Rj1dSRdRfAFMFrS6HC9IeGZ5VRJf0lw8w8Q88UZs89SLQBJH0jKDj/nS7pf0oTwDLRn2BqZJem4mM3sLOkjST9Iuj1mW2dJGhueUT9VkgzC7d4p6Rug1HspJHWV9HXMOPxNJPUjSJIXldRBBb4CdorZXn74W5KeCM/sPyRmMDpJ3cOWxwRJH8cMOXFVuP5kScPi1F0W0MXMJpVdZmaLgP8D2lWw/VxJ90r6jGBo+lg3APeY2YxwexvM7B9huTskXR+2gHoQPAA5UdIxkkbExHeEpHfCyfeBMyqpO5dkniDclsokGJf+NDP7HcFT+b83s8cJhqPOMbOccN0/mVkPoAvQS1KXBLb/FVAgKafSNTeqD+SaWXdgFXA3cARwAnBnzHo9gYFAV+CUsAtrT+A04ODwjLooXKdku1PMbH8zG1Nmny8BN4atge+B281sJBtbWZXF3xd4N878E4Ddgd8BFxOOvySpFkEL4OTwOJ8H7gnL3AR0C2OJ9xRtD2BKnPlI2oVgFN25FWwfoLGZ9TKzh8psojNQ4WCTZjYcGE8wVlBXYCSwp6Tm4SrnAy+E6y4D6khqVtE2XXL5UBtuS6UTDCI3M5x+EbicYNjysk6VNIjg760VsBcwOYF93E3QirgxwZgKgY/Cz98DBWa2XtL3QPuY9UaZ2RKA8Iz1EIJhLLoD4xQMVVCXjQPAFQFvl92ZpEYEX5ifhbNeBN5KMNbRYXfOIuK0lAjeh/G6mRUBeZL+E87fneDLuGRIhXSCYcAhqNNXJb1L/KTTimAY9VinSToEKAAuAZpXsH2ANxI8vkqZmUl6GThL0gsErbNzYlZZRNAaXVJV+3SbxxOE21KrE1lJUgfgemA/M1smaShB66NSZvYfSXcBB8TM3kDplm/sttbHjGJZTPClh5kVl+kzLzu+jAECXjSzeG/iWhd+UVelHII6HErQurkuzjrxxsERMNXM4r2C9RiCxHIccJukvWNeIgOwlk3r/g0zu+K3jUu/q2D7UP6/+1SCBLtJ91UlXgD+CawD3ioTb2YYs4uIdzG5LZUJtJfUMZw+Gyg5k14FZIWfGxJ8qawIz5g398LzPQT92yXmAF0lpUnamaC7aHMdoeCdxnUJLiZ/SfDaypMltYDf3nncroJtYGYrgGWSDg1nxdZBpcxsLcG1inMkNS2z+HPgdAXvKW9FkFAAfgCaK3xHt6RakvaWlAbsbGajCeqrMdCgzDanAx2pWNztJ3A4DwC3SNotLJcmKV7Si/3bwMzyCLokbyVIloTlBexI8O/tIuItCLel1hH0Gb8Vnp2PI+h3h2C00X9J+sXMciR9R3CGOYvgyzhhZjZSUmy3yJfAbIIupCkEo5xurjEEd8l0BF4zs/EAkm4FPgm/bNcTdJnNrWRb5wJPKritd7NHnTWzXxSMznk5cFfMohFAb4LjnEmYeMysMLzY+3jYxZVB0K03E3glnCeC6x/Ly+xrhqRGkrLMbFU58ZS3/amVHMdkSdcAr4d1YcCHcVYdSlBfa4EDwyT5KtDczGKHdO9OMCLphjjbcCnio7k6V4NIuhZYZWbV5t3nCu5K+87MnouZ9xjwvpl9Gl1kzruYnKtZhhBem6kOJE0guLut7BsUp3hyiJ63IJxzzsXlLQjnnHNxeYJwzjkXlycI55xzcXmCcM45F5cnCOecc3H9P+tBbgR1JAlUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for rural cities.\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ecab86a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter charts for each city type.\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "\n",
    "# Incorporate the other graph properties\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\", fontsize=20)\n",
    "plt.ylabel(\"Average Fare ($)\", fontsize=12)\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "\n",
    "# Create a legend\n",
    "lgnd = plt.legend(fontsize=\"12\", mode=\"Expanded\",\n",
    "         scatterpoints=1, loc=\"best\", title=\"City Types\")\n",
    "lgnd.legendHandles[0]._sizes = [75]\n",
    "lgnd.legendHandles[1]._sizes = [75]\n",
    "lgnd.legendHandles[2]._sizes = [75]\n",
    "lgnd.get_title().set_fontsize(12)\n",
    "\n",
    "# incorporate a text label about circle size.\n",
    "plt.text(42, 35, \"Note: Circle size correlates with driver count per city\", fontsize=\"12\")\n",
    "\n",
    "#Save the figure.\n",
    "plt.savefig(\"analysis/Fig1.png\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7db1b5aa",
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "23a9114c",
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "suburban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "599500a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>125.000000</td>\n",
       "      <td>1.250000e+02</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.623440</td>\n",
       "      <td>4.647746e+12</td>\n",
       "      <td>4.296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.558046</td>\n",
       "      <td>2.887834e+12</td>\n",
       "      <td>2.691156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.110000</td>\n",
       "      <td>2.684873e+10</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.900000</td>\n",
       "      <td>2.275364e+12</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.050000</td>\n",
       "      <td>4.023962e+12</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.070000</td>\n",
       "      <td>7.118047e+12</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>58.550000</td>\n",
       "      <td>9.990581e+12</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fare       ride_id  driver_count\n",
       "count  125.000000  1.250000e+02    125.000000\n",
       "mean    34.623440  4.647746e+12      4.296000\n",
       "std     14.558046  2.887834e+12      2.691156\n",
       "min     10.110000  2.684873e+10      1.000000\n",
       "25%     19.900000  2.275364e+12      1.000000\n",
       "50%     37.050000  4.023962e+12      4.000000\n",
       "75%     47.070000  7.118047e+12      7.000000\n",
       "max     58.550000  9.990581e+12      9.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "rural_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "032bf890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.62, 17.36, 6.94)"
      ]
     },
     "execution_count": 47,
     "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": 48,
   "id": "a956f555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.0, 17.0, 6.0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the median of the ride count for each city type.\n",
    "round(urban_ride_count.median(),2), round(suburban_ride_count.median(),2), round(rural_ride_count.median(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "471cd5a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the urban cities.\n",
    "urban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9b997754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the suburban cities.\n",
    "suburban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f27696ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the rural cities.\n",
    "rural_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "af42de73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import NumPy and the stats module from SciPy.\n",
    "import numpy as np\n",
    "import scipy.stats as sts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "466676e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for urban trips is 24.62.\n",
      "The median for the ride counts for urban trips is 24.0.\n",
      "The mode for the ride counts for urban trips is ModeResult(mode=array([22], dtype=int64), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the urban cities.\n",
    "mean_urban_ride_count = np.mean(urban_ride_count)\n",
    "print(f\"The mean for the ride counts for urban trips is {mean_urban_ride_count:.2f}.\")\n",
    "\n",
    "median_urban_ride_count = np.median(urban_ride_count)\n",
    "print(f\"The median for the ride counts for urban trips is {median_urban_ride_count}.\")\n",
    "\n",
    "mode_urban_ride_count = sts.mode(urban_ride_count)\n",
    "print(f\"The mode for the ride counts for urban trips is {mode_urban_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8a21e2d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for suburban trips is 17.36.\n",
      "The median for the ride counts for suburban trips is 17.0.\n",
      "The mode for the ride counts for suburban trips is ModeResult(mode=array([17], dtype=int64), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the suburban cities.\n",
    "mean_suburban_ride_count = np.mean(suburban_ride_count)\n",
    "print(f\"The mean for the ride counts for suburban trips is {mean_suburban_ride_count:.2f}.\")\n",
    "\n",
    "median_suburban_ride_count = np.median(suburban_ride_count)\n",
    "print(f\"The median for the ride counts for suburban trips is {median_suburban_ride_count}.\")\n",
    "\n",
    "mode_suburban_ride_count = sts.mode(suburban_ride_count)\n",
    "print(f\"The mode for the ride counts for suburban trips is {mode_suburban_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "137a689a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for rural trips is 6.94.\n",
      "The median for the ride counts for rural trips is 6.0.\n",
      "The mode for the ride counts for rural trips is ModeResult(mode=array([6], dtype=int64), count=array([5])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the rural cities.\n",
    "mean_rural_ride_count = np.mean(rural_ride_count)\n",
    "print(f\"The mean for the ride counts for rural trips is {mean_rural_ride_count:.2f}.\")\n",
    "\n",
    "median_rural_ride_count = np.median(rural_ride_count)\n",
    "print(f\"The median for the ride counts for rural trips is {median_rural_ride_count}.\")\n",
    "\n",
    "mode_rural_ride_count = sts.mode(rural_ride_count)\n",
    "print(f\"The mode for the ride counts for rural trips is {mode_rural_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bade6c17",
   "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": 56,
     "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": 57,
   "id": "b8563d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1625    27.79\n",
       "1626    18.75\n",
       "1627    21.71\n",
       "1628    17.10\n",
       "1629    37.78\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the suburban cities.\n",
    "suburban_fares = suburban_cities_df[\"fare\"]\n",
    "suburban_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0b4143d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2250    43.22\n",
       "2251    12.42\n",
       "2252    54.85\n",
       "2253    47.90\n",
       "2254    51.80\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the rural cities.\n",
    "rural_fares = rural_cities_df[\"fare\"]\n",
    "rural_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8a30330a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for urban trips is $24.53.\n",
      "The median fare price for urban trips is $24.64.\n",
      "The mode fare price for urban trips is ModeResult(mode=array([22.86]), count=array([5])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the urban cities.\n",
    "mean_urban_fares = np.mean(urban_fares)\n",
    "print(f\"The mean fare price for urban trips is ${mean_urban_fares:.2f}.\")\n",
    "\n",
    "median_urban_fares = np.median(urban_fares)\n",
    "print(f\"The median fare price for urban trips is ${median_urban_fares:.2f}.\")\n",
    "\n",
    "mode_urban_fares = sts.mode(urban_fares)\n",
    "print(f\"The mode fare price for urban trips is {mode_urban_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fee00447",
   "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 the suburban cities.\n",
    "mean_suburban_fares = np.mean(suburban_fares)\n",
    "print(f\"The mean fare price for suburban trips is ${mean_suburban_fares:.2f}.\")\n",
    "\n",
    "median_suburban_fares = np.median(suburban_fares)\n",
    "print(f\"The median fare price for suburban trips is ${median_suburban_fares:.2f}.\")\n",
    "\n",
    "mode_suburban_fares = sts.mode(suburban_fares)\n",
    "print(f\"The mode fare price for suburban trips is {mode_suburban_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "de395410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for rural trips is $34.62.\n",
      "The median fare price for rural trips is $37.05.\n",
      "The mode fare price for rural trips is ModeResult(mode=array([37.05]), count=array([2])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the rural cities.\n",
    "mean_rural_fares = np.mean(rural_fares)\n",
    "print(f\"The mean fare price for rural trips is ${mean_rural_fares:.2f}.\")\n",
    "\n",
    "median_rural_fares = np.median(rural_fares)\n",
    "print(f\"The median fare price for rural trips is ${median_rural_fares:.2f}.\")\n",
    "\n",
    "mode_rural_fares = sts.mode(rural_fares)\n",
    "print(f\"The mode fare price for rural trips is {mode_rural_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c6a6e9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    72\n",
       "2    57\n",
       "3    34\n",
       "4    46\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the urban city.\n",
    "urban_drivers = urban_cities_df['driver_count']\n",
    "urban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a473eba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1625    11\n",
       "1626    20\n",
       "1627    22\n",
       "1628    19\n",
       "1629    12\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the suburban city.\n",
    "suburban_drivers = suburban_cities_df['driver_count']\n",
    "suburban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "78fb826b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2250    9\n",
       "2251    8\n",
       "2252    6\n",
       "2253    2\n",
       "2254    2\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the rural city.\n",
    "rural_drivers = rural_cities_df['driver_count']\n",
    "rural_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "90808823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for rural trips is $4.30.\n",
      "The median driver count for rural trips is $4.00.\n",
      "The mode driver count for rural trips is ModeResult(mode=array([1], dtype=int64), count=array([32])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average driver count for the rural cities.\n",
    "mean_rural_drivers = np.mean(rural_drivers)\n",
    "print(f\"The mean driver count for rural trips is ${mean_rural_drivers:.2f}.\")\n",
    "\n",
    "median_rural_drivers = np.median(rural_drivers)\n",
    "print(f\"The median driver count for rural trips is ${median_rural_drivers:.2f}.\")\n",
    "\n",
    "mode_rural_drivers = sts.mode(rural_drivers)\n",
    "print(f\"The mode driver count for rural trips is {mode_rural_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3d4c817c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for suburban trips is $13.71.\n",
      "The median driver count for suburban trips is $16.00.\n",
      "The mode driver count for suburban trips is ModeResult(mode=array([20], dtype=int64), count=array([79])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average driver count for the suburban cities.\n",
    "mean_suburban_drivers = np.mean(suburban_drivers)\n",
    "print(f\"The mean driver count for suburban trips is ${mean_suburban_drivers:.2f}.\")\n",
    "\n",
    "median_suburban_drivers = np.median(suburban_drivers)\n",
    "print(f\"The median driver count for suburban trips is ${median_suburban_drivers:.2f}.\")\n",
    "\n",
    "mode_suburban_drivers = sts.mode(suburban_drivers)\n",
    "print(f\"The mode driver count for suburban trips is {mode_suburban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b40d3e1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean driver count for urban trips is $36.68.\n",
      "The median driver count for urban trips is $37.00.\n",
      "The mode driver count for urban trips is ModeResult(mode=array([39], dtype=int64), count=array([86])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average driver count for the urban cities.\n",
    "mean_urban_drivers = np.mean(urban_drivers)\n",
    "print(f\"The mean driver count for urban trips is ${mean_urban_drivers:.2f}.\")\n",
    "\n",
    "median_urban_drivers = np.median(urban_drivers)\n",
    "print(f\"The median driver count for urban trips is ${median_urban_drivers:.2f}.\")\n",
    "\n",
    "mode_urban_drivers = sts.mode(urban_drivers)\n",
    "print(f\"The mode driver count for urban trips is {mode_urban_drivers}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "17d1f5d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban cities ride count.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(10, 41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c04ec11e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the suburban cities ride count.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(5, 30, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2215cbdf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the rural cities ride count.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(1, 15, step=1.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1c6efbec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all ride count box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "ride_count_data = [urban_ride_count, suburban_ride_count, rural_ride_count]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Ride Count Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Number of Rides',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(ride_count_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 45, step=3.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig2.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3afec0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "West Angela has the highest rider count.\n"
     ]
    }
   ],
   "source": [
    "# Get the city that matches 39.\n",
    "urban_city_outlier = urban_ride_count[urban_ride_count==39].index[0]\n",
    "print(f\"{urban_city_outlier} has the highest rider count.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7c5f8c0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.000000\n",
       "mean       24.525772\n",
       "std        11.738649\n",
       "min         4.050000\n",
       "25%        14.550000\n",
       "50%        24.640000\n",
       "75%        34.580000\n",
       "max        44.970000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban fare data.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "urban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ad0fdbb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A+ME4m/YDS8o4/1bAJbavkvRPkuZTDfWsAk6ZRNwRETFJnUznvJWNZ+OY6hfAMuA8278dbTvbK4ADRyk/YXKhRkTEVOhkVs/Ro5TNo5rh8xmqoaCIiJghOhnquWeU4nuAmyXdPPUhRUREnTo5uVvn9hER0WOdjPEfNErxXKr78ecB6xERM0xHD1sfsWzgIap5+RdMdUAREVGvTsb4F/QikIiI6I2OxuglbTVi+c8l/WW5c2dERMwgnZ6c/aaklwFI+jDVs3YPAP65rsAiIqIeEyZ+SW8A9gV2Ke9PAD5PlfRfKumPJb2o3jAjImKqdHJyF6oDxI7A9sDTVFfuChi+aldTH1pERNShk5O735V0EfBxYAdgse3vSXoh8KDtTOmMiJhBOurx2z5b0teADbbvLsVbUR6UEhERM0cnF3CpPEbxjvZy2w9SPVTlP+rUFGNEREyhTmb1LJN02sgTuJK2lfRGSUuobtgWEREzQCdDPUcAJwFfl/QHwCPALGBrqqdxnW/7lroCjIiIqTXhM3c3qixtA+wMPFkepzhe3VlU9/J5PtUB5l9tnyNpHnAx1RO9VgHvGO1h6+3yzN2YlMU7TXcEU2fxo9MdQcxAYz1zt9vE/zpgX9tflrQzMNv2L8aoK2B72+vLAeN64HTgT4GHbX9M0pnAXNsfHK/dJP6YjDxsPZpucx62PryDc4APAh8qRdsCF41Vv5wQXl8WtykvA8cAS0r5EuDYTmOIiIjN1+kFXFA9GP1A4CYA2/dLmj3eBuV5u8uBfYDP2r5RUp/t1WUfqyXtOsa2iyjTRfv6+mi1Wl2EGlGp+3uzfv36nnw38/2PqdTxUI+kH9k+WNJNtg+StD3wQ9v7d7DtHOBy4DTgettz2tatsz13vO0z1BOTkaGeaLrNHuoBLpH0eWCOpPcC/wZc2MmG5URwi2qG0AOS+ktQ/cCaLmKIiIjN1OltmUU1E+dfgUuB/YCzbX9mnG12KT19JG0HvAm4A7iSZ+f9LwSumGzwERHRvU5v2WBJ37D9KuDaDvfdDywp4/xbAZfYvkrSD6l+PZwM/BJ4+2QCj4iIyenm5O4Nkl5t+8edVLa9gupk8Mjyh4DDumg3IiKmUDeJfwFwiqR7gCeobsXsTk7uRkTElqObxH9kbVFERETPdJz4bd8DUObdz6otooiIqFU3V+6+TdJdwC+A71LdZ+fbNcUVERE16WYe/7nAIcCdtv+A6gTt92uJKiIiatNN4v99mZGzlaStbC8D5tcTVkRE1KWbk7uPSNqB6lbLX5W0BthQT1gREVGXCXv8bU/eOgb4DfB+4GrgZ8Bb6wstIiLq0EmP/xvAQbafkHSp7T/j2dsqR0TEDNPJGL/a3r+4rkAiIqI3Okn8HuN9RETMQJ0M9Rwg6TGqnv925T08e8uGHWuLLiIiptyEid/21r0IJCIieqObefwREfEckMQfEdEwtSV+SXtKWibpdkm3STq9lC+WdJ+kW8rrqLpiiIiITXVz5W63NgBn2L5J0mxguaThp3edb/u8GtuOiIgx1Jb4ba8GVpf3j0u6Hdi9rvYiIqIzdfb4/4Okvakew3gjcChwqqQTgSGqXwXrRtlmEbAIoK+vj1ar1YtQ4zmm7u/N+vXre/LdzPc/ppLseq/JKjd2+y7wEduXSeoD1lJdDHYu0G/7pPH2MTAw4KGhoVrjjOceSdT9/W61WgwODtbaRi8+Rzw3SVpue2Bkea2zeiRtA1wKfNX2ZQC2H7D9tO1ngAuBg+uMISIiNlbnrB4BXwRut/33beX9bdWOA1bWFUNERGyqzjH+Q4ETgFsl3VLKzgLeLWk+1VDPKuCUGmOIiIgR6pzVcz0b39lz2LfqajMiIiaWK3cjIhomiT8iomGS+CMiGiaJPyKiYZL4IyIaJok/IqJhkvgjIhomiT8iomGS+CMiGqYnt2WOmC7VLaNmtrlz5053CPEck8Qfz1m9uJVxbpkcM1GGeiIiGiaJPyKiYZL4IyIaJok/IqJh6nwC156Slkm6XdJtkk4v5fMkXSvprvI3UxYiInqozh7/BuAM2y8DDgH+m6SXA2cC19neF7iuLEdERI/Ulvhtr7Z9U3n/OHA7sDtwDLCkVFsCHFtXDBERsamezOOXtDdwIHAj0Gd7NVQHB0m7jrHNImARQF9fH61WqxehRnQt382YaVT3xSeSdgC+C3zE9mWSHrE9p239OtvjjvMPDAx4aGio1jgjJiMXcMWWTNJy2wMjy2ud1SNpG+BS4Ku2LyvFD0jqL+v7gTV1xhARERurc1aPgC8Ct9v++7ZVVwILy/uFwBV1xRAREZuqc4z/UOAE4FZJt5Sys4CPAZdIOhn4JfD2GmOIiIgRakv8tq8Hxro14mF1tRsREePLlbsREQ2TxB8R0TBJ/BERDZPEHxHRMEn8ERENk8QfEdEwSfwREQ2TxB8R0TBJ/BERDZPEHxHRMEn8ERENk8QfEdEwSfwREQ2TxB8R0TBJ/BERDVPnE7i+JGmNpJVtZYsl3SfplvI6qq72IyJidHX2+L8CHDFK+fm255fXt2psPyIiRlFb4rf9PeDhuvYfERGTMx1j/KdKWlGGguZOQ/sREY1W58PWR/N/gXMBl7+fBE4araKkRcAigL6+PlqtVo9CjOhOvpsx08h2fTuX9gausv2KbtaNNDAw4KGhoakPMGIzSaLOf0MRm0PSctsDI8t7OtQjqb9t8Thg5Vh1IyKiHrUN9Uj6OjAI7CzpXuAcYFDSfKqhnlXAKXW1HxERo6st8dt+9yjFX6yrvYiI6Eyu3I2IaJgk/oiIhknij4homCT+iIiGSeKPiGiYJP6IiIZJ4o+IaJgk/oiIhknij4homF7fnTNiiyapJ9vkxm4xndLjj2hju6vXsmXLut4mST+mWxJ/RETDJPFHRDRMEn9ERMMk8UdENEwSf0REw9SW+CV9SdIaSSvbyuZJulbSXeXv3Lraj4iI0dXZ4/8KcMSIsjOB62zvC1xXliMioodqS/y2vwc8PKL4GGBJeb8EOLau9iMiYnS9vnK3z/ZqANurJe06VkVJi4BFZXG9pJ/2IsCILu0MrJ3uICLGsNdohVvsLRtsXwBcMN1xRIxH0pDtgemOI6IbvZ7V84CkfoDyd02P24+IaLxeJ/4rgYXl/ULgih63HxHReHVO5/w68ENgP0n3SjoZ+BhwuKS7gMPLcsRMluHImHGUOwVGRDRLrtyNiGiYJP6IiIZJ4o9GkPRhSbdJWiHpFkmvGafuYkkf2Mz2WpIyzTO2SFvsPP6IqSLptcDRwEG2n5K0M7Btje1tXde+I6ZCevzRBP3AWttPAdhea/t+SavKQQBJA5JabdscIGlpuaHge0udQUlXDVeQ9A+S3lPer5J0tqTrgbeXKsdL+oGklZIOLvUOLmU3l7/7lfL3SLpM0tWlzU/U+58kmiyJP5rgGmBPSXdK+j+S3tDBNvsDbwFeC5wtabcOtvmt7dfZ/ueyvL3tPwL+CvhSKbsD+GPbBwJnAx9t234+8E7glcA7Je3ZQZsRXctQTzzn2V4v6VXA64EFwMWSJroz7BW2nwSelLQMOBh4ZIJtLh6x/PXS/vck7ShpDjAbWCJpX8DANm31r7P9KICk/0d1n5VfTfT5IrqVxB+NYPtpoAW0JN1KdeX4Bp791Ttr5CajLLfXH22bJzrYx7nAMtvHSdq7xDTsqbb3T5N/n1GTDPXEc56k/UoPe9h84B5gFfCqUvZnIzY7RtIsSS8EBoEfl21eLun5knYCDpug6XeW9l8HPFp68zsB95X175nM54nYXOlRRBPsAHymDLVsAO6muuX3y4AvSjoLuHHENj8Cvgm8CDjX9v0Aki4BVgB3ATdP0O46ST8AdgROKmWfoBrq+R/A0s38XBGTkls2REQ0TIZ6IiIaJok/IqJhkvgjIhomiT8iomGS+CMiGiaJPyKiYZL4IyIa5v8DnI9dR9bfEPAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    625.000000\n",
       "mean      30.970128\n",
       "std       10.677508\n",
       "min       12.050000\n",
       "25%       21.970000\n",
       "50%       30.750000\n",
       "75%       39.830000\n",
       "max       49.960000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban fare data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(10, 60, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "0c8d7faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nyz8DnwPOtv1quV+KtEVEdFE7t3Pew/rj/AZWAjcDc2y/0sexGwOLKIaI/tn2bZL2APaX9C3gFYpyzbc3OTb1+GPQUo8/YkP91uOXtEuT1RMp7vDZ0vYJ/TZSXA+4GjiZokTzPIrho32By4Bd3UcgqccfA5F6/FF3rerxtzPU82iT1Y8Cd0q6s53Gba+WNJ9iqsbHgavKRP/b8s6gSUC/k7tERMTgtTXZ+kCOl7Rtz50/kjYHDqaYyOUaittCKYd9xlAMHUVERBe0M8b/3iarJ1DU47+1j0N3AC4qx/k3Ai63/fNyjt4LJS2hqO45u69hnoiIGFptTbbea9nAMxSPrVzQ6iDbdwPvabL+NYqTRkREDIN2xvgP7EYgERHRHW2N8UvaqNfyJySdVFbujIiIUaTdi7u/kPROAEmnU8y1uzfFrZkRETGK9Jv4JR0A7A5sW74+DvghRdJ/h6S/kPS2asOMiIih0s7FXShOEFsDWwJvUNx+KYonbylfR0TEKNDOxd1bJF0MfAfYCjjL9q2S3gKssN3XLZ0RETHCtNXjt32GpJ8Ca20/XK7eiLKWTkREjB7tPMAlF+5vXG97BWWZhZ59eh3XtB6/pHOAIyke3noE+LTt1UPxYSIion/t3NVzs6STe1/AlTRG0kxJF1EUbOutpx7/3sBUYJak6cBcYC/bU4AHgdMG9QkiIqIj7Qz1zAKOBy6V9HZgNTAW2JhiNq5zbS/ufVCrevy2b2zYbQHwkYEGHxERnWvn4u4rwPeB75czaU0CXm5neKZZPf5euxxPUZY5IiK6pN3bOXu8H9jd9o8lTQLG2f59q51tvwFM7anHL2kv20vgjw+CrQUuaXZsJmKJoZCJWCI21O9ELH/cUToTmAbsaXsPSTsCV9jer4PjX7Q9R9Js4CTgoIY5fFvKRCwxEJmIJequ1UQsndTj/zBwFPAigO1lwLg+Gmxaj1/SLOBU4Kh2kn5ERAytToZ6XrNtSQaQtGU/+7eqx/8wxS2ecyUBLLB90gBij4iIAegk8V8u6YfAeEknUFyY/ZdWO/dRj3+3jqOMiIgh01biV9E1vwx4B/A8sCdwhu25FcYWEREVaLdkgyVdY3sfigewIiJilOrk4u4CSftWFklERHRFJ2P8BwKflfQoxZ09ovgxMKWSyCIiohKdJP7DKosiIiK6pu3Eb/tRAEnbUdTqiYiIUajtMX5JR0l6CPg9cAuwFLiuorgiIqIinVzc/XtgOvCg7bcDBwH/WUlUERFRmU4S/+u2nwE2krSR7Zsp6uw3Jemtkm6WdJ+keyV9qVw/VdICSYslLZT0vsF9hIiI6EQnF3dXS9qKYlatSyQtp6iu2cpa4Ku275A0DlgkaS7wXeCbtq+TdHi5PGNg4UdERKf67fE3zLx1NPAS8BXgeoppE49sdZztJ23fUb5+AbgP2AkwsHW52zbAsoEGHxERnWunx38N8F7bL0q60vZfAxd10oikyRR1e24DvgzcIGkOxYnngy2OST3+GLTU44/YUL/1+CXdafs9vV+33UAxPHQL8C3bV0n6HnCL7SslfRQ40fbBfb1H6vHHQKQef9TdYOrxu8XrdhrdFLgSuMT2VeXq2UDP6yuAXNyNiOiidoZ69pb0PEWJhs3L17CuZMPWzQ4qK3r+CLjP9v9q2LQMOACYD8wEHhpg7BERMQDtTLa+8QDfez/gOOAeSYvLdd8ATgDOl7QJ8ArlOH5ERHRHp5Ott832ryh+FTSzT1XtRkRE3zp5gCsiIv4EJPFHRNRMEn9ERM0k8UdE1EwSf0REzSTxR0TUTBJ/RETNVJb4W9Xjb9h+iiRLmlRVDBERsaHKHuCiRT1+2/9P0luBQ4DHKmw/IiKaqKzH30c9foBzga/RYdG3iIgYvCp7/H/UWI9f0lHAE7bvKuq4tTwm9fhj0FKPP2JD/dbjH3QDDfX4KWbuuhk41PZzkpYC02yv7Os9Uo8/BiL1+KPuBlOPfzCN9q7H/1+AtwN3lUl/Z+AOSX9WZRwREbFOZUM9zerx274H2K5hn6W00eOPiIihU2WPv6ce/0xJi8u/wytsLyIi2jBc9fh79plcVfsREdFcntyNiKiZJP6IiJpJ4o+IqJkk/oiImknij4iomST+iIiaSeKPiKiZrtfjlzRR0lxJD5X/TqgqhoiI2FCVPf6eevzvBKYDfyvpXcDXgZts7w7cVC5HRESXDEc9/qOBi8rdLgKOqSqGiIjYUFfG+Bvr8QPb234SipMDDUXbIiKiepVPxFLW478S+LLt5/uafKXXcZmIJQat3e/bSDZu3Lh8/2NIVZr4m9TjB3ha0g62n5S0A7C82bG2LwAugGIilqonu4g/Pd2YvCSTpMRoVOVdPRvU4y/9DJhdvp4NXFtVDBERsaEqe/w99fjvkbS4XPcN4GzgckmfAR4D/qbCGCIiopfhqsd/UFXtRkRE3/LkbkREzSTxR0TUTBJ/RETNJPFHRNRMEn9ERM0k8UdE1EwSf0REzSTxR0TUTJUlGy6UtFzSkl7rT5b0QDk5y3eraj8iIpqrssf/E2BW4wpJB1LU459i+93AnArbj4iIJqqciOVW4Nleqz8HnG371XKfppU5IyKiOpXX4+9lD2B/Sd8CXgFOsX17sx1Tjz9Gi3w3Y7RRlbXEy5m3fm57r3J5CTAP+BKwL3AZsKv7CWLatGleuHBhZXFGDFTq8cdIJmmR7Wm913f7rp7Hgatc+C3wJjCpyzFERNRatxP/NcBMAEl7AGOAlV2OISKi1iob45d0KTADmCTpceBM4ELgwnLI5zVgdn/DPBERMbSqnIjl4y02HVtVmxER0b88uRsRUTNJ/BERNZPEHxFRM0n8ERE1k8QfEVEzSfwRETWTxB8RUTNdrccv6RxJ90u6W9LVksZX1X5ERDTX1Xr8wFxgL9tTgAeB0ypsPyIimuhqPX7bN9peWy4uAHauqv2IiGiu2/X4Gx1PUZa5qdTjj9Ei380Ybbpaj79h/enANOCv2inSlnr8MVKlHn+MZK3q8Xe9xy9pNnAEcFAqc0ZEdF9XE7+kWcCpwAG2X+pm2xERUajyds5Lgd8Ae0p6XNJngH8CxgFzJS2W9IOq2o+IiOa6XY//R1W1FxER7cmTuxERNZPEHxFRM8N5H3/EiCOpK8fkhrYYTunxRzSw3dHfzTff3PExSfox3JL4IyJqJok/IqJmkvgjImpmWBK/pK9IulfSEkmXSho7HHFERNRR1xO/pJ2ALwLTyuJtGwMf63YcERF1NVxDPZsAm0vaBNgCWDZMcURE1E7XE7/tJ4A5wGPAk8Bztm/sdhwREXU1HGWZJwBHA28HVgNXSDrW9sW99stELDHirVmzJt/NGHUqnYilaYPS3wCzbH+mXP4kMN325/s4ZgXwaJdCjOjEJGDlcAcR0cIutrftvXI4SjY8BkyXtAXwMnAQ0Of0Ws0CjxgJJC1sNsNRxEg2HGP8twH/BtwB3FPGcEG344iIqKuuD/VE/ClJjz9Gozy5GzE4+bUao056/BERNZMef0REzSTxR0TUTBJ/RANJb0haXBYQ/HdJ44fwvdcM1XtFDEYSf8T6XrY9tSwg+Czwt+0eWNaeihjxkvgjWvsNsBOApPmSppWvJ0laWr7+lKQrJP07cKOkrSTdJOkOSfdIOnrYoo9oIT2UiCYkbUzxVPmP2tj9A8AU28+Wvf4P235e0iRggaSfObfPxQiSHn/E+jaXtBh4BpgIzG3jmLm2ny1fC/i2pLuBX1L8Yti+ikAjBiqJP2J9L9ueCuwCjGHdGP9a1v330nvGuBcbXn8C2BbYp3yfp5vsHzGskvgjmrD9HMVMcadI2hRYCuxTbv5IH4duAyy3/bqkAylOIBEjShJ/RAu27wTuopgadA7wOUm/pijF3MolwDRJCyl6//dXHmhEh1KyISKiZtLjj4iomST+iIiaSeKPiKiZJP6IiJpJ4o+IqJkk/oiImknij4iomf8PnhFkpW8WErsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean      34.623440\n",
       "std       14.558046\n",
       "min       10.110000\n",
       "25%       19.900000\n",
       "50%       37.050000\n",
       "75%       47.070000\n",
       "max       58.550000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban fare data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(8, 60, step=4.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "cd549f70",
   "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_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",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a7cd6654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.000000\n",
       "mean       36.678154\n",
       "std        20.075545\n",
       "min         3.000000\n",
       "25%        22.000000\n",
       "50%        37.000000\n",
       "75%        52.000000\n",
       "max        73.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the urban driver count data.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_drivers,labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Number of Drivers)')\n",
    "ax.set_yticks(np.arange(0, 90, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "urban_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f6acc714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    625.000000\n",
       "mean      13.712000\n",
       "std        8.042372\n",
       "min        1.000000\n",
       "25%        5.000000\n",
       "50%       16.000000\n",
       "75%       21.000000\n",
       "max       25.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the suburban driver count data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_drivers,labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Number of Drivers)')\n",
    "ax.set_yticks(np.arange(0, 30, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6de2a2b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEICAYAAABVv+9nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYVElEQVR4nO3deZhldX3n8feHRRHsBgWsKC6NSoyKgZiSiEYtlMQlgKLjBNwdH1szLhiTSdxxi3F/cGY0kQlGIgZUUAYUIxi5OoqC1QhIg8SFNiAIKLJ0iwrynT/OabhdXcvtqjrV3affr+e5T997tt/3Vp/63FO/c+75paqQJPXPdpu7AElSNwx4SeopA16SesqAl6SeMuAlqacMeEnqKQNemyTJPyZ58+auQ40kD0syuQXUcViSkzZ3HdqQAb+NSrImyS1Jbk5yQ5Jzkrw8yaz7RFW9vKresYR1PifJZJK1Sa5O8sUkf7wE7VaSB88y/0VJftvWtTbJ5Un+OcnvbkIbH0/yzgWW+g7g/e327prkuCQ/bv9fv5PkqVPafFKS7yX5ZZKzkzxgaN5B7bQbk6yZpt7HJDmv3fZFw/8PVXUasG+S31/g+9EiMuC3bYdW1TLgAcC7gb8Fjptp4STbd1FEGhvti0leCxwDvAsYA+4PfAR4ehd1zMM3q+ruwK7AwcAtwKok+y5F40nuDRwEnNpO2gG4AnhCW9ObgU8nWdEuvwfw2Xb6PYFJ4FNDm1wHfAz4H9O0dU/gNOB9wG7Ae4HTk9xjaLETgZWL8d60SKrKxzb4ANYAB0+ZdgBwO7Bv+/rjwD8AZ9D88h/cTntnO/9S4JCh9XcAfgY8sn39aOAc4AbgQmBiaNkB8HfAN2iC8cFTatkVWAs8e5b3cFeaD4Cr2scxwF3beS8Cvj5l+VrfTvs+Pgx8AbgZOBd4UDvva+2y69oa/nyatjfafjv988DJQ68/A/wUuLHd7sPb6SuBW4HftG2c3k5/HfDDtqZLgMNnef8vAL48x//zRcCzhto8Z2jeLu3P/vemrHMwsGbKtEOA1VOm/QfwkqHXjwUu39z7to87Hx7B6w5VdR5wJfC4ocnPoQniZcDXp6xyInDk0OsnAz+rqvOT7EUTnu+kOVr8a+CUJHsOLf98mtBZBvx4yrYPBHYCPjdLyW+k+RDZH9iP5gPqTbO+yQ0dCbwNuAfwA5r3SVU9vp2/X1Xdvao+NcP60/ksG/78vgjsA9wLOB/4ZNvGse3z97ZtHNou/8N2/V3b2k5oj9Sn8wjgspkKSTIG/C6wup30cJoPWtoa1rXtPXyE95X2MXXa8F8rlwIrkiwfYXtaAga8prqKJpDX+79V9Y2qur2qfjVl2X8FDkuyc/v6Oe00gOcBZ1TVGe26Z9F0CTxtaP2PV9Xqqrqtqm6dsu3daT4sbpul1ucCb6+qa6vqOppAfP7I7xQ+W1XntW18kuaDYqE2+PlV1ceq6uaq+jXwVmC/JLvOtHJVfaaqrmp/Zp8Cvk/zwTWd3WiO9DeSZEea93R8VX2vnXx3mr8kht1I8wE7l3OA+yQ5MsmOSV4IPAjYeWiZ9bXsNsL2tAQMeE21F3D90OsrZlqwqn5Ac9R2aBvyh3FnwD8AeHZ7AveGJDcAfwwMH43OuG3g58AeSXaYZZn7sOGR/4/baaP66dDzX9IE4ELd8fNLsn2Sdyf5YZKbaLrFAPaYaeUkL0hywdDPbN9Zlv8F04Rzez7jEzTdP68cmrUWmHp0vZwZPiSGVdXPac59vBa4BngK8GWav/jWW1/LDXNtT0vDgNcdkjyKJqCGu2Lmut3o+m6apwOXtKEPTXh/oqp2G3rsUlXvHnHb3wR+BTxjlmWuovkgWe/+7TRo+s/vOLpM8jtzvI/Fcjjw/9rnz6H5uRxM0+WyYn057b8bvP/2ipb/QxPKu1fVbsDFbNw1st5FNF0ww9sIzYnyMZq+9+G/jFbTdGWtX3YXmqPw1Yygqr5aVY+qqnvS/KX0EOC8oUUeStN3f9Mo21P3DHiRZHmSQ4CTgBOq6rubsPpJwJ8Cf8GdR+8AJ9Ac2T+5PZLdKclEkvuOstGquhF4C/DhJM9IsnPbNfDUJO9tFzsReFOSPdsrRN7StgtNX/PDk+yfZCea7pFNcQ3wwFEWbN/f3kn+FzBB01UEzRHtr2n+GtmZ5mqg2drYhSb0r2u3+2I27OOe6izgke37W+8faIL20Kq6Zcryn6O5lPFZ7TpvAS5a34WTZLt2+o7Ny+yU5C5D7/MP2v+D5TSXZl5ZVV8a2v4TaM45aAthwG/bTk9yM83R9huBDwIv3pQNVNXVNEfbj2HokruquoLm6PUNNIF1Bc3ldyPvc1X1QZougTcNbeOV3HlZ4Dtp+vUvAr5LcxLzne26/wG8naYb4ftsfIJ4Lm8Fjm+7Sv7rDMscmGQtcBPNVUHLgUcNfUD+C0230U9oroj51pT1jwMe1rZxalVdAnyA5ud5Dc1J1G/MVGBVXQN8hfay0fYvgJfRnEv46dA1+s9tl78OeBbNyeRfAH8EHDG0ycfTXFVzBs1fQ7cAZw7N/xuaq6SuoOlqO3xKSUcCH52pXi29VDngh7S1SvIw4HjggNqMv8xJDgWeX1UzfRhqMzDgJamn7KKRpJ4y4CWppwx4Seqp2b5EsuT22GOPWrFixeYuQ9rIunXr2GWXXTZ3GdJGVq1a9bOq2nO6eVtUwK9YsYLJyc1+a2tpI4PBgImJic1dhrSRJFPv43QHu2gkqacMeEnqKQNeknrKgJeknuo04JMcleTiJKuTvKbLtiRJG+os4NtxKV9KM1jBfsAhSfbpqj1J0oa6PIJ/KPCtqvplO2LOV9n47nOSpI50GfAXA49Psns72s/TgPt12J4kaUhnX3SqqkuTvIdmUIK1NAMwbDS+ZpKVNAMvMzY2xmAw6KokCYCDDjpoydo6++yzl6wtaaolu11wknfRjADzkZmWGR8fL7/Jqi1REry1trZESVZV1fh08zq9VUGSe1XVtUnuDzwTOLDL9iRJd+r6XjSnJNkduBV4RVX9ouP2JEmtTgO+qh7X5fYlSTPzm6yS1FMGvCT1lAEvST1lwEtSTxnwktRTBrwk9ZQBL0k9ZcBLUk8Z8JLUUwa8JPWUAS9JPWXAS1JPGfCS1FMGvCT1lAEvST3VacAn+cskq5NcnOTEJDt12Z4k6U6dBXySvYBXA+NVtS+wPXBEV+1JkjbUdRfNDsDdkuwA7Axc1XF7kqRWZ0P2VdVPkrwf+E/gFuDMqjpz6nJJVgIrAcbGxhgMBl2VJC2I+6a2Nqmqbjac3AM4Bfhz4AbgM8DJVXXCTOuMj4/X5ORkJ/VIC5GErn5XpIVIsqqqxqeb12UXzcHA5VV1XVXdCnwWeEyH7UmShnQZ8P8JPDrJzkkCPAm4tMP2JElDOgv4qjoXOBk4H/hu29axXbUnSdpQZydZAarqaODoLtuQJE3Pb7JKUk8Z8JLUUwa8JPWUAS9JPWXAS1JPGfCS1FMGvCT1lAEvST1lwEtSTxnwktRTBrwk9ZQBL0k9ZcBLUk8Z8JLUU50FfJKHJLlg6HFTktd01Z4kaUNdDrp9GbA/QJLtgZ8An+uqPUnShpaqi+ZJwA+r6sdL1J4kbfM6HdFpyBHAidPNSLISWAkwNjbGYDBYopLUF4cddhg333xz5+00Qwt3Z9myZZx22mmdtqFtS6qq2waSuwBXAQ+vqmtmW3Z8fLwmJyc7rUf9k4Su9+PBYMDExESnbSzF+1D/JFlVVePTzVuKLpqnAufPFe6SpMW1FAF/JDN0z0iSutNpwCfZGfgT4LNdtiNJ2linJ1mr6pfA7l22IUmant9klaSe2qSAT7JL+6UlSdIWbtaAT7Jdkuck+UKSa4HvAVcnWZ3kfUn2WZoyJUmbaq4j+LOBBwGvB36nqu5XVfcCHgd8C3h3kud1XKMkaR7mOsl6cFXdOnViVV0PnAKckmTHTiqTJC3IrEfw68M9yYOS3LV9PpHk1Ul2G15GkrRlGfUk6ynAb5M8GDgO2Bv4186qkiQt2KgBf3tV3QYcDhxTVX8J3Lu7siRJCzVqwN+a5EjghcDn22n2vUvSFmzUgH8xcCDwd1V1eZK9gRO6K0uStFBz3qqg/WLTG6rqjsshq+py4N1dFiZJWpg5j+Cr6rfAnu193SVJW4lRbza2BvhGktOAdesnVtUHuyhKkrRwowb8Ve1jO2BZd+VIkhbLSAFfVW+D5mZjVbVuruXXa78M9U/AvkAB/62qvjmPOiVJm2ikq2iSHJjkEuDS9vV+ST4ywqofAv6tqn4P2G/9+pKk7o16meQxwJOBnwNU1YXA42dbIcnydpnj2nV+U1U3zLdQSdKmGXlEp6q6IsnwpN/OscoDgeuAf06yH7AKOGpqF0+SlcBKgLGxMQaDwaglSXfoer9Zu3btkuyb7v9aTKmquRdKTgY+CPxv4NHAq4HxqjpilnXGaW4p/NiqOjfJh4CbqurNM60zPj5ek5OTm/gWtK1Lwij78UIMBgMmJiY6bWMp3of6J8mqqhqfbt6oXTQvB14B7AVcCezfvp7NlcCVVXVu+/pk4JEjtidJWqBRu2hSVc/dlA1X1U+TXJHkIVV1GfAk4JJNrlCSNC+jBvw5SS4HPgWcsgknS18FfLL9FuyPaO5pI0laAqNeB79PkgOAI4A3tpdMnlRVs95wrKouAKbtG5IkdWvUPniq6ryqei1wAHA9cHxnVUmSFmzULzotT/LCJF8EzgGupgl6SdIWatQ++AuBU4G3e6sBSdo6jBrwDywv0JWkrcqsAZ/kmKp6DXBako0CvqoO66owSdLCzHUE/4n23/d3XYgkaXHNGvBVtaodsu+lw0P2SZK2fA7ZJ0k95ZB9ktRTDtknST21SUP2SZK2HnP2wbffYD0/ybr2MZnkBUtRnCRp/ua6Dv4FwGuA1wLnA6G5p/v72sEJ/qXzCiVJ8zLXEfx/Bw6vqrOr6saquqGqvgI8q50nSdpCzdUHv7yq1kydWFVr2kG1Z5VkDXAzzfitt800rJQkafHNFfC3zHPesIOq6mcjLitJWiRzBfxDk1w0zfQAD+ygHknSIpkz4Be4/QLObG9U9tGqOnbqAklWAisBxsbGGAwGC2xS26Ku95u1a9cuyb7p/q/FlC7vApzkPlV1VZJ7AWcBr6qqr820/Pj4eE1OTnZWj/qpvaKr0zYGgwETExOdtrEU70P9k2TVTOc3Rx6ybz6q6qr232uBz+EoUJK0ZDoL+CS7JFm2/jnwp8DFXbUnSdrQrAGf5N/bf98zj22PAV9PciFwHvCFqvq3eWxHkjQPc51kvXeSJwCHJTmJ5uqZO1TV+TOtWFU/AvZbeImSpPmYK+DfArwOuC8w9dbABTyxi6IkSQs314hOJwMnJ3lzVb1jiWqSJC2CUW8X/I4khwGPbycNqurz3ZUlSVqoka6iSfL3wFHAJe3jqHaaJGkLNeqITn8G7F9VtwMkOR74DvD6rgqTJC3MplwHv9vQ810XuQ5J0iIb9Qj+74HvJDmb5lLJx+PRuyRt0UY9yXpikgHwKJqA/9uq+mmXhUmSFmbUI3iq6mrgtA5rkSQtok5vNiZJ2nwMeEnqqTkDPsl2SbwLpCRtZeYM+Pba9wuT3H8J6pEkLZJRT7LeG1id5Dxg3fqJVXVYJ1VJkhZs1IB/23wbSLI9MAn8pKoOme92JEmbZtTr4L+a5AHAPlX15SQ7A9uP2MZRwKXA8nnWKEmah1FvNvZS4GTgo+2kvYBTR1jvvjT3sfmnedYnSZqnUbtoXkEzYPa5AFX1/ST3GmG9Y4C/AZbNtECSlcBKgLGxMQaDwYglSY06ejm8tdvbI00ADDptgjp6ufu/FtWoAf/rqvpN0ozYl2QHmhGdZpTkEODaqlqVZGKm5arqWOBYgPHx8ZqYmHFRaVo56CaqZt0dF2wwGND1vpmEemu3bWjbMuoXnb6a5A3A3ZL8CfAZ4PQ51nkszViua4CTgCcmOWHelUqSNsmoAf864Drgu8DLgDOAN822QlW9vqruW1UrgCOAr1TV8xZQqyRpE4x6Fc3t7SAf59J0zVxWXf9NLElakJECPsmfAf8I/JDmdsF7J3lZVX1xlPWrakDnp6gkScNGPcn6AeCgqvoBQJIHAV8ARgp4SdLSG7UP/tr14d76EXBtB/VIkhbJrEfwSZ7ZPl2d5Azg0zR98M8Gvt1xbZKkBZiri+bQoefXAE9on18H3KOTiiRJi2LWgK+qFy9VIZKkxTXqVTR7A68CVgyv4+2CJWnLNepVNKcCx9F8e/X2zqqRJC2aUQP+V1X1PzutRJK0qEYN+A8lORo4E/j1+olVdX4nVUmSFmzUgH8E8HzgidzZRVPta0nSFmjUgD8ceGBV/abLYiRJi2fUb7JeCOzWYR2SpEU26hH8GPC9JN9mwz54L5OUpC3UqAF/dKdVSJIW3aj3g//qpm44yU7A14C7tu2cXFV+UEjSEhn1m6w3c+cYrHcBdgTWVdXyWVb7NfDEqlqbZEfg60m+WFXfWlDFkqSRjHoEv2z4dZJnAAfMsU4Ba9uXO7YPR4GSpCUyah/8Bqrq1CSvm2u5JNsDq4AHAx+uqnOnWWYlsBJgbGyMwWAwn5K0jet6v1m7du2S7Jvu/1pMGWVo1aH7wkNzaeU48ISqOnCkRpLdgM8Br6qqi2dabnx8vCYnJ0fZpHSHJHQ9RPBgMGBiYqLTNpbifah/kqyqqvHp5o16BD98X/jbgDXA00ctoKpuSDIAngLMGPCSpMUzah/8Jt8XPsmewK1tuN8NOBh4z6ZuR5I0P3MN2feWWWZXVb1jlvn3Bo5v++G3Az5dVZ+fR42SpHmY6wh+3TTTdgFeAuwOzBjwVXUR8AfzL02StBBzDdn3gfXPkywDjgJeDJwEfGCm9SRJm9+cffBJ7gm8FngucDzwyKr6RdeFSZIWZq4++PcBzwSOBR5RVWtnW16StOWY63bBfwXcB3gTcFWSm9rHzUlu6r48SdJ8zdUHP+r94iVJWxgDXJJ6yoCXpJ4y4CWppwx4SeopA16SesqAl6SeMuAlqacMeEnqKQNeknqqs4BPcr8kZye5NMnqJEd11ZYkaWPzGnR7RLcBf1VV57e3Gl6V5KyquqTDNiVJrc6O4Kvq6qo6v31+M3ApsFdX7UmSNrQkffBJVtCM7nTuUrQnSeq2iwaAJHcHTgFeU1Ub3WI4yUpgJcDY2BiDwaDrktRDXe83a9euXZJ90/1fiylV1d3Gkx2BzwNfqqoPzrX8+Ph4TU5OdlaP+ikJXe7H0ATvxMREp20sxftQ/yRZVVXj083r8iqaAMcBl44S7pKkxdVlH/xjgecDT0xyQft4WoftSZKGdNYHX1VfB9LV9iVJs/ObrJLUUwa8JPWUAS9JPWXAS1JPGfCS1FMGvCT1lAEvST1lwEtSTxnwktRTBrwk9ZQBL0k9ZcBLUk8Z8JLUUwa8JPWUAS9JPdXliE4fS3Jtkou7akOSNLMuj+A/Djylw+1LkmbRWcBX1deA67vaviRpdp0N2TeqJCuBlQBjY2MMBoPNW5C2Ss0Y71u3ZcuWuf9rUW32gK+qY4FjAcbHx2tiYmLzFqStTlV13sZgMMB9U1sbr6KRpJ4y4CWpp7q8TPJE4JvAQ5JcmeQlXbUlSdpYZ33wVXVkV9uWJM3NLhpJ6ikDXpJ6yoCXpJ4y4CWppwx4SeopA16SesqAl6SeMuAlqacMeEnqKQNeknrKgJeknjLgJamnDHhJ6ikDXpJ6qtOAT/KUJJcl+UGS13XZliRpQ10O+LE98GHgqcDDgCOTPKyr9iRJG+ryCP4A4AdV9aOq+g1wEvD0DtuTJA3pbEQnYC/giqHXVwJ/NHWhJCuBle3LtUku67Amab72AH62uYuQpvGAmWZ0GfCZZlptNKHqWODYDuuQFizJZFWNb+46pE3RZRfNlcD9hl7fF7iqw/YkSUO6DPhvA/sk2TvJXYAjgNM6bE+SNKSzLpqqui3JK4EvAdsDH6uq1V21J3XMbkRtdVK1Ube4JKkH/CarJPWUAS9JPWXAa5uU5LdJLkhycZLTk+y2iNteu1jbkhbCgNe26paq2r+q9gWuB14x6opJuvz+iLRoDHgJvknzzWuSDJKMt8/3SLKmff6iJJ9JcjpwZpK7J/n3JOcn+W4Sb8OhLY5HItqmtTfFexJw3AiLHwj8flVd3x7FH15VNyXZA/hWktPKy9K0BfEIXtuquyW5APg5cE/grBHWOauqrm+fB3hXkouAL9P8BTDWRaHSfBnw2lbdUlX709yo6S7c2Qd/G3f+Xuw0ZZ11Q8+fC+wJ/GG7nWumWV7arAx4bdOq6kbg1cBfJ9kRWAP8YTv7v8yy6q7AtVV1a5KDmOWOftLmYsBrm1dV3wEupLlf0vuBv0hyDs0tgmfySWA8ySTN0fz3Oi9U2kTeqkCSesojeEnqKQNeknrKgJeknjLgJamnDHhJ6ikDXpJ6yoCXpJ76/1IeOcpnxmcCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean       4.296000\n",
       "std        2.691156\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        4.000000\n",
       "75%        7.000000\n",
       "max        9.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the rural driver count data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_drivers,labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Driver Count Data (2019)')\n",
    "ax.set_ylabel('Number of Drivers)')\n",
    "ax.set_yticks(np.arange(0, 10, step=1.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d22084be",
   "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 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('Number of Drivers',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(driver_count_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 80, step=5.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig4.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "657438d9",
   "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": 105,
     "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": 106,
   "id": "50d4588d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 106,
     "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": 108,
   "id": "4e543a4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        6.811493\n",
       "Suburban    30.463872\n",
       "Urban       62.724635\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * pyber_data_df.groupby([\"type\"]).sum()[\"fare\"] / pyber_data_df[\"fare\"].sum()\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "867e6ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import mpl to change the plot configurations using rcParams.\n",
    "import matplotlib as mpl\n",
    "# Build Pie Chart\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(type_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Fares by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig5.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "3e084fb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         537\n",
       "Suburban     8570\n",
       "Urban       59602\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of the drivers for each city type.\n",
    "sum_driver_by_type = pyber_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "sum_driver_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "015c9deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68709"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of all the drivers.\n",
    "total_drivers = pyber_data_df[\"driver_count\"].sum()\n",
    "total_drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1bfdf8b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        2.623613\n",
       "Suburban    16.481668\n",
       "Urban       80.894719\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of drivers for each city type.\n",
    "driver_percents = 100*city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]/city_data_df[\"driver_count\"].sum()\n",
    "driver_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4728e976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import mpl to change the plot configurations using rcParams.\n",
    "import matplotlib as mpl\n",
    "# Build Pie Chart\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(driver_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Drivers by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig5.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "559e2c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        580968240341287\n",
       "Suburban    3106884522576766\n",
       "Urban       7919412664056093\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of the riders for each city type.\n",
    "sum_riders_by_type = pyber_data_df.groupby([\"type\"]).sum()[\"ride_id\"]\n",
    "sum_riders_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "69281c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11607265426974146"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of all the riders.\n",
    "total_riders = pyber_data_df[\"ride_id\"].sum()\n",
    "total_riders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "90414447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        5.263158\n",
       "Suburban    26.315789\n",
       "Urban       68.421053\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of rides for each city type.\n",
    "ride_percents = 100 * pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"] / pyber_data_df[\"ride_id\"].count()\n",
    "ride_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "65ac6a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import mpl to change the plot configurations using rcParams.\n",
    "import matplotlib as mpl\n",
    "# Build Pie Chart\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(ride_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Rides by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig5.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e176d1b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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