PyBer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Files to load\n",
    "city_data_to_load = \"Resources/city_data.csv\"\n",
    "ride_data_to_load = \"Resources/ride_data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Richardfort</td>\n",
       "      <td>38</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Williamsstad</td>\n",
       "      <td>59</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Angela</td>\n",
       "      <td>67</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West Robert</td>\n",
       "      <td>39</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West Anthony</td>\n",
       "      <td>70</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Martinezhaven</td>\n",
       "      <td>25</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Karenberg</td>\n",
       "      <td>22</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Barajasview</td>\n",
       "      <td>26</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  driver_count   type\n",
       "0    Richardfort            38  Urban\n",
       "1   Williamsstad            59  Urban\n",
       "2    Port Angela            67  Urban\n",
       "3     Rodneyfort            34  Urban\n",
       "4    West Robert            39  Urban\n",
       "5   West Anthony            70  Urban\n",
       "6    West Angela            48  Urban\n",
       "7  Martinezhaven            25  Urban\n",
       "8      Karenberg            22  Urban\n",
       "9    Barajasview            26  Urban"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the city data file and store it in a pandas DataFrame.\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Latoya</td>\n",
       "      <td>2019-03-11 12:26:48</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1994999424437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Paulville</td>\n",
       "      <td>2019-02-27 11:17:56</td>\n",
       "      <td>43.25</td>\n",
       "      <td>793208410091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simpsonburgh</td>\n",
       "      <td>2019-04-26 00:43:24</td>\n",
       "      <td>35.98</td>\n",
       "      <td>111953927754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Karenland</td>\n",
       "      <td>2019-01-08 03:28:48</td>\n",
       "      <td>35.09</td>\n",
       "      <td>7995623208694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>North Jasmine</td>\n",
       "      <td>2019-03-09 06:26:29</td>\n",
       "      <td>42.81</td>\n",
       "      <td>5327642267789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873\n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577\n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003\n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178\n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344\n",
       "5        South Latoya  2019-03-11 12:26:48   9.52  1994999424437\n",
       "6       New Paulville  2019-02-27 11:17:56  43.25   793208410091\n",
       "7        Simpsonburgh  2019-04-26 00:43:24  35.98   111953927754\n",
       "8     South Karenland  2019-01-08 03:28:48  35.09  7995623208694\n",
       "9       North Jasmine  2019-03-09 06:26:29  42.81  5327642267789"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the ride data file and store it in a pandas DataFrame.\n",
    "ride_data_df = pd.read_csv(ride_data_to_load)\n",
    "ride_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            120\n",
       "driver_count    120\n",
       "type            120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "city_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            0\n",
       "driver_count    0\n",
       "type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "city_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            object\n",
       "driver_count     int64\n",
       "type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Suburban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique values of the type of city.\n",
    "city_data_df[\"type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of data points from the Urban cities.\n",
    "sum(city_data_df[\"type\"]==\"Urban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(city_data_df[\"type\"]==\"Suburban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(city_data_df[\"type\"]==\"Rural\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the columns and the rows that are not null.\n",
    "ride_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data into a single dataset\n",
    "pyber_data_df = pd.merge(ride_data_df, city_data_df, how=\"left\", on=[\"city\", \"city\"])\n",
    "\n",
    "# Display the DataFrame\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Urban city DataFrame.\n",
    "urban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Urban\"]\n",
    "urban_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Suburban and Rural city DataFrames.\n",
    "suburban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Suburban\"]\n",
    "rural_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Rural\"]\n",
    "rural_cities_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the suburban and rural ride count.\n",
    "suburban_ride_count = suburban_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "\n",
    "rural_ride_count = rural_cities_df.groupby([\"city\"]).count()[\"ride_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get average fare for each city in the suburban and rural cities.\n",
    "suburban_avg_fare = suburban_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "rural_avg_fare = rural_cities_df.groupby([\"city\"]).mean()[\"fare\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the average number of drivers for each city for the suburban and rural cities.\n",
    "suburban_driver_count = suburban_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]\n",
    "rural_driver_count = rural_cities_df.groupby([\"city\"]).mean()[\"driver_count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1be50f36288>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for urban cities.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=urban_driver_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1be512c2308>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter 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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1be51326c88>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RQCOlVJiIOAK7TBNmuwDtgQpKqSgR0WMJaKlq0qQJpUqVYvnKlaz9aDj379/H09OT1s2bMWH+vGd62EazTQ9CQnAvlt2ssm5ePjy4EJLmfdjb29OgQQMaNGhA+fLlWbBgAQ4ODo+anBL33U/chVNEHiufVJ3Ew0sntY1x48bRsGFD1qxZw6VLlx5rmopvxoqPNzY2Ns3v0xwWayYyjaAaZnrpaPpRGOfjnaaMc5mi9KTWmpny5cvHO8OGsWPL7xw9sI9df2zlvVEjdSLIorw8PQkLvmdW2YcP7uOVxuFIzpw5w7lz/00LfvjwYQoWLEihQoUe9VBatWrVY3XWrl1LZGQkd+/eJSAggOrVq1OwYEFOnjxJVFQUDx48YNu2bSnud9myZYCxGczLywsvLy8ePHjw6H5X/P2GjGbR3kQiYg8cBIoBXyml9opICaCuiEwFIoHhSqn9SdTtD/QHY4+SgIAAS4aaorCwMKvu31yZJU7IPLHqONNXwji9vLwIDQ1Ntmy92i8yfe5PVGnaJtky8U7t/I2R/fqkuL3Ebt26xYgRI3jw4AEODg4UKVKEL774gjNnzjBo0CBy5cpF9erViYuLIzQ0lKioKCpXrkyLFi24evUqI0aMeDRcRYcOHShXrhxFixalfPnyREZGEhoailKKsLCwR2f3cXFxuLm5UbNmTUJDQ/nqq68IDQ1l0KBBDBgwgOnTp1OvXj2UUoSGhhIZGUl0dPSj9xUbG0t4ePhj7zM+vsjIyGf6DGTIHMgi4g2sAd7COPn3H8BQoDqwDCiiUgikWrVqypp9cwMCAh67bLNVmSVOyDyx6jjTV8I4T506RenSpZMtazAYaNqqDbX6vp3iTeTzh/fz14LP2LJxfbo+gJZZxiaKjzOp4ykiB5VS1czZTob0JlJKBQMBQAvgGrDa1Iy0DzAAOTMiDk3TMg87Ozs+mfoBm7+ZxvnDTzQeAMZEsPmbaXwyZbJ+EvkZWbI3US4gRikVLCIuQBPgYyAMaAQEmJqMnIA7lopD07TMq2rVqnw98xNGjh3HHs/slKjdFDcvHx4+uM+ZXZtRYcFP9cCZ9iRL3jPIAyww3TewA5YrpTaIiBPwg4gcB6KBPik1EWlaVmQwGNi9ezcBf+4iMjKKgvn9ad+uHX5+ftYOLUMppVIdZK1q1aps2bThv7GJLhjHJhr/1ht6bCKT9PgKtVgyUEodBSonsTwaeNlS+9U0W3f69GmGvDsCg4sHRV9oSDZvN7af+5c5nbrRsU1LRo8Yjr29vbXDtLhs2bJx9+5ds4axtrOzo169eml6juB5oZTi7t27ZMuW7Zm2o8cm0rQMdPnyZV4bOJja/3uLMi/Wf2xd/W6v8susicR++BETxr1vpQgzTr58+bh27Rq3b9+2dihJioyMfOYv2IwQGRmJt7c3+fLle6bt6GSgaRlo9jffUaZ5pycSAUA2N3c6vjuZH97pQ+9ePSlSpIgVIsw4jo6ONv2MSEBAAJUrP9G4YXPSK07d2KZpGSQ4OJjtu3an2G/e2cWVMg1bsWzFygyMTNN0MtC0DHP16lV8cvs/MXVnYvlKlufM+QsZFJWmGelkoGkZxN7enrgEA5AlJzY6Cofn4AayZlt0MtC0DFKsWDEiQ+5x90bKQxD/e2AXtWqY9dCopqUbnQw0LYM4OTnRtWN7/lq9MNl+4bevXebSwb/o2KFDxganPfd0MtC0DNT/9dexu3edTXM+JSz4/qPlBoOBc4f2suqjkYwfNRwfHx8rRqk9j3TXUk3LQK6ursyfO4cZsz5jwfC++BUthZOLK7cvnyeHuwsfjx9D/fpPdjvVNEvTyUDTMpibmxsT3h/LO0OH8M8//xAVFUW+fPkoXbp0qk/iapql6GSgaVbi4eGhrwI0m6HvGWiapmk6GWiapmk6GWiapmnoewbac2jr1q18/9Mirl6/TsnixXnj1b7UqFHD2mFpmlXpKwPtubJkyVImfvoFhZp2oevkr/Gu2ogho9/njz/+sHZommZV+spAe25ERUXxxXdz6Dz+c3LkzQ9A+bpNcPfJwYwvvqRhw4a6a6f23NJXBtpz4/z587h453yUCOIVKluJoDt3CQ4Otk5gmmYDdDLQnhve3t6E3b9LXGzsY8sjwkJQcXG4uLhYKTJNsz7dTKQ9N/LmzUuZ4kX54+c5+JcoR0xUJE7ZXDi7dyctmjTOFFMcapql6GSgPTf27duHvb0dW5b/hG+x0rh4ehN27zb3Lp2jcMcOnDx5kjJlylg7TE2zCp0MtCxPKcXsr79mydpNVG3Xg3EDxhFngOiYaJydnZG4GA4H/M6rg4by9sB+dOva1doha1qG08lAy/LmzJ3Hqi076PXBV7h5eT9a7obbo99fbNOVUjXq8vkH7+KSLRvt2rWzQqSaZj36BrKWpV27do15i36m86hpjyWCpPj45qHjqI+YOn0mDx8+zJgANc1G6GSgZWnLV66kVL0WuPtkN6t8rnwFyVu2Chs2bLBwZJpmWyyWDEQkm4jsE5EjInJCRCYlWj9cRJSI5LRUDNrzLS4ujhVr1lK5SZs01avYtB0/r1xtoag0zTZZ8sogCmiklKoIVAJaiMgLACKSH2gKXLHg/rXn3IMHD4hVkD23f5rq+RcrxeXLly0UlWZJ4eHhbNmyhUOHDlk7lEzHYjeQlXHG7zDTS0fTT/ws4LOAkcBaS+1f02JjY7F3cExzPXsHR2JjY5OdtF6zTVFRUfTs8woxLl4E37rB6z268ErfPtYOK9MQS37gRcQeOAgUA75SSo0SkXZAY6XUUBG5BFRTSt1Jom5/oD+An59f1aVLl1osztSEhYXh7u5utf2bK7PECRkTq1KKU2fO4FuwWJrGHIqLjeH+jSuULFEi0xxTHafxquBa4C1y+BcgJjqK0KBAihcr+tTbywrHtGHDhgeVUtXM2Y5Fu5YqpeKASiLiDawRkQrAWKCZGXXnAHMAqlWrpho0aGDBSFMWEBCANfdvrswSJ2RcrMtWrcHZ4EnlRq3MrrNzxXyKOkTyRoMGmeaY6jjhxo0bTJr2Mg1ff4crp47gGXqLfq+/9tTbe96OaYb0JlJKBQMBQHugMHDEdFWQD/hHRHJnRBza86d3j24c37re7Caf2JgYTm7fRPeuXSwcmZbe8ubNy/QpEwncuQ5/FcbUSROsHVKmYrErAxHJBcQopYJFxAVoAnyslPJNUOYSyTQTaVp6ePHFF8np+i1/rphPva6vpFjWYDDw+7xZvFC5AkWLPn3zgmY99erVo169etYOI1Oy5JVBHmC7iBwF9gNblFK687aWoezs7Pjmi8+59c+fbFnwFZEPw5IsFxZ8n/WzP8QpOJCPpnyQwVFqmvVZsjfRUaByKmUKWWr/mhYve/bsLPlpPh98OI15Q3tRtEZ9ilSuiZOLKxFhoZzf/ydXjuyjfeuWjHhnKs7OztYOWdMynB6bSHsueHp6Mn3ah9y5c4fVq9dwYM9vhEdE4Onuzku1qtP2o3F4enpaO0xNsxqdDLTnSs6cOenfv5+xz7KmaY/osYk0TdM0fWWgaU8jKiqKW7duERkZibu7O7lz58bOTp9baZmXTgaalgYXL15k6fIV/LJhE07uHjg4ORMRGoKXmwu9u3WhXbt2+t6Dli6UUsTGxuLg4JCmJ+iflk4GmmYGg8HAZ198ydI16yjTsBU9P5qDV07jIzNKKa6dPcEvW9cxe+73fPrhFGrXrm3liLXMKDo6mi1btrBw6XJOnT5NXFwcdnZ2lChenJe7daF58+a4uLhYZN86GWjPlbCwMDZu3Mg/R4/xMDwcDzd3ar9Qg6ZNmybbpVQpxUcff8L2f07Qd8YPuHp4PbZeRMhfshz5S5bj6tkTvD1mPJ9OmUjdunUz4i1pWcS6dev4eNbneBcoRoVmXWk8vCqOztmIjY7m0onD/LB+HR/N/Iy3Bw2ke7du6b5/nQy050J4eDizvviStRs24V+uKgUr1sDVxZWHYaF8u3IDU2fMpGeXTgx84w0cHB7/b7F582Y2/7WfnpO+wNnVLZk9GOUvUZZ2737AiLFj2bhmJTly5LDk29KyiB/nL2Du4mW0Hz0dv4JFHlvn4OREsco1KFa5BndvXOWrT8dz+84d3ho0KF1j0MlAy/JCQkJ4bcBAVM789J7+Ax4+j39BV2nSmns3r/Pb/NkcP/k2X876FCcnp0frv/9pEbW7vZZqIoiXr0QZClarw5pffuH1155+oDTt+bB582bmLF5Gj0lf4Jk95bm+cuTNT4+Jn/HzhCHkzZOHTi+9lG5x6O4PWpamlGLouyNwKlCa1gNHPZEI4mXP7U+nEVO4GWPHpA+mPloeGRnJtaC7FKvyQpr2W7lpexYvX0lcXNwzxa9lbQaDgZmzv6bFwNGpJoJ4rh5etBkyns+//pbY2Nh0i0UnAy1L279/Pxdu3qHx/95MtUeGnb09bQaPYcvOXVy5YpyELywsjKI166e522juwsWIs3fi/PnzTx27lvXt37+fKDsnCpQun6Z6uQsVxSWXPwEBAekWi04GWpa2eNlyyjdtb/aXuaOTM6XrN2fZipWAcR5ll0Q3jM3l5uVDaGjoU9XVng9LVqykbOO2T9V1tHyTtvxs+pymB50MtCwrKiqKHX/upnzdJmmqV6FBS9b/+htgHPU0NibmqfYfGxOtB73TUnT+4mXyFSv9VHX9i5fm4uX0m0Y+xRvIIpINaAPUBfICEcBxYKNS6kS6RaFpFvDgwQOyubnj7OKapnrevnm4d+8eSimcnZ0JOn8yzfuOfBjG/ZvX8ff3T3Nd7fkRGRmJ41OeMDg5uxAREZFusSR7ZSAiE4HdwIvAXuA7YDkQC0wTkS2maSw1zSbZ29tjMKT9Bq7BEIe9vT0AHh4e3LlwmvtBgWnaxrE/t1C/1gv4+Pikef/a88Pd3S3ZOTZSE/kwFHe39JujOaVmov1KqapKqXeVUj8rpbYqpTYopWYqpdoCvQCnFOprmlV5eXkhhjge3AlKU72bF8/h7++PiCAidGrXlr3rl5tdPzY6mqObf6FX9/R/MEjLWqpXrsS/h/Y8Vd2z//xN1UoV0y2WZJOBUmpjShWVUkFKqQPpFommpTMHBwfat27Foa3r01TvyNZ19OjU8dHr117py90T+/lna+oT9cXGxLBu9lReKF+GypVTnNtJ0+jetQsnt28iLo1dRA0GAye2rqdX967pFkuKN5BFpKxpLmNEJIeIzBORpSJSJt0i0DQL6tGtKye3byIiLMSs8veDArl8aC/t2rV7tCx79uzM++YrTm5cwtafviH0/t0k617/9zQrp42mgKsw9YNJGTK4mJa5FSlShJJFCnJk5+Y01Tu9909yebpRvnzauqSmJLUnkL8F4h9xmwrcBI4BPwBpewpH06ygcOHCdG3fhtXT36fzqI9SfIo49P5dVn88hrcHDcDL6/HupAULFmTZop/4+tvvWDjiVfKWq0KeEuVxdM5GRGgIF/bvQCJC6d29K71fflkPZ62ZbdzoUfyv3wC8c+WmSPkqqZa/fu4UO+Z/wdwvZ6XrCUdKN5AnAMWAgabfOwL2QCkgn4iMF5F66RaJplnIO8OGUq9CKX6eMISTf+8gNjaGmNgYoqKjiI2NIToqgkN/bGLJ+Lf4X8c29OzRI8nt5MiRg3Fjx7D99030alyLfNG38Qw6RwnHh0wc9iabN6yjz//+pxOBlibFixdn9qefsOWrqez9dTXRkUn3EIqNjuafrRtZN2Msn06dRIUK6dt/J9krA6XUJBHpAPwM5AbqKaXeAxCRJkqpyekaiaZZiJ2dHe+PeY9SK1cya/Zn/DxtDPnKVsHJzZ3IkAdcO36QMiWKMnnUOzRpkvozCW5ubnTp0iUDIteeF1WrVmXxD3OZ+cWXzBm8gJJ1mlKwXBWcXNyIjgzn6qmjnN75G1XKl2P+t19RuvTTPZuQktSaiSYDO4EYoDsY7yMAd9I9Ek2zEKUUc+d9z5wFiyhRuzH1ylYmPDSEmMgInF1dcX6pOxf/2cN7Ez/g4cOHtG/fPtVtBgUFcenSpUcznZUqVQpX17Q9z6BpCRUpUoTZn83i5s2brFi1imN//0rYw4e4ubpSpWRxPvp5Ifny5bPY/lNMBkqpNcCaRMtOYGwy0sxw+/Ztzp49C0CpUqX0kMZWMOvzL1gXsJs+079PdqC60jXrcef6FT6eNpqIyMgkx4s3GAzs2bOHRUuXse+fw/gWKoaDkzNRD0MJvnGV9q1b0r1rF4oUKZLEHjTNPLlz50734anNkWwyEJFCSqlLKawXwF8pdc0SgWV2V69eZcZnn7N77wHyFCuJUopb589Sr1ZN3h02lLx581o7xOfCli1bWL35D3pN/hIX95Sno8zpX4Au4z5l1oQhlCld+rE22YiICIaPHs2JSzeo2Lwjb7z2Ho7O2R6tf3AniMPbNtDj1X4Mfv0VXu7VS/cm0jKVlK4MpouIHbAWOAjcBrJhvKncEGgMTAB0Mkjk4sWL/O/1/pRu1ol+X77zaDiEyIdhHPj9F3r0eYVFP8wjf/78Vo4065vz4wLqvzww1UQQz8c3D9U69OLHnxYya8Z0AGJiYhg09G0eOHvRe+o32Ds8+d/GK6cv9bu9SqXGbZg7bTRxsXH07dsnXd+LpllSSg+ddQHGASWBr4A/MSaG14EzQCOl1Jbk6otINhHZJyJHROSEiEwyLZ8uIqdF5KiIrBER73R8PzZh9LjxVOrwP2q17/7YuDjZ3Nyp89LLlG3VjTETJlovwOfEyZMnuXHnPkUr1UhTvQp1m/Lnnv3cvn0bgO/mzCUo1p7WA0clmQgS8srpS+f3Pua7hT9z7Nixp45d0zJain3glFInlVJjlVINlFIllVKVlVI9lVKLlFKRqWw7CmPCqAhUAlqIyAvAFqCcUqoCcBZ4Lx3eh804efIkV2/dpXLj1smWqdq0HecuXeXff//NwMieP5u3bqVE7SZp7urp7OpGoSovEBAQgFKKJatW0/DlAWZvxzNHLiq27MyiJcueJmxNswqLTXuplFJA/AhMjqYfpZRK+KjdHqCzpWKwhn379lGoWu0UvzjsHRwoXLU2+/bto1ixYhkY3dOLjY1l7rzv2b1vP7lyZGfY4EEULFjQ2mGl6M7d+3j4Fn9sWVREOMf+3ErghXNERUbg7OJKwdLlKfNCfRwSTHXplsOXe/fu4ezsjHe+IuTIm7YmvYoNmvP9sN7cu3eP7Nmzp8v70TRLEuN3toU2LmKP8X5DMeArpdSoROvXA8uUUouSqNsf6A/g5+dXdenSpRaLMzVhYWG4u5s3OuCdO3cIi4nDwyflKexC7t3G09kxXXsXpSXOtAq8eZOwiCjcvLMTGx1FxIP7FC1S+InJ481lyVjj3QgMJM7eGVdPLwyGOELv3SUiLATHbK44ubgiIhiUgeiHD4mNjsLV0wsPnxyICKH37uDuZI+dnR0P44xTDaZV8K0b5PLxwtPTvPsVzyIjjmd6yCxxQuaJNaU4GzZseFApVc2c7VjsygBAKRUHVDLdF1gjIuWUUscBRGQsxuGwFydTdw4wB6BatWqqQYMGlgw1RQEBAZi7/82bN/PDwuV0HTsjxXJL5w/lvQGvmL1dc6QlzrRQSlHlhVr0/2oZ0aYbsZtnT+XlXDnp2PHpehlbKtaEvv3uO/68dIcarTozf/Jw8patTPV2PXDPnuuJsvcDr7Fn5Xwi7gbR+/1p/L56Ha+2acz94AecdchF8UJV07z/LVt30qlmOYu/T8iY45keMkuckHliTa84U20EFaOXRWS86XUBEUnTHTmlVDAQALQwbaMPxklzeilLXppYQYMGDXhw/RJBVy4mWybwwlki796kTp06GRjZs0k841dsdPSjMf9tVauWLTn15+/89MFIStdvScO+Q5JMBAA+efLRYvBYshcuwcIpo7h24hANGzZERJ56pjNDTDROTnqUdy1zMOeO2NcYJ7iJH7AlFGPvohSJSK74nkIi4gI0AU6LSAtgFNBOKRX+NEHbMicnJ94Z/CZrZ45PckKUu4HXWD9rIiOGvvXUTSwZTUTo+3JPVk8fy9GdW9i26DvCr523+bOmAgUK4O7ohJtvHqq0SX2oXxGhXu83CQ+PoHTRwnh4eODs5Miti2fTvG+lFLcvn9cznWmZhjnfRjWVUlVE5BCAUuq+iJhzupMHWGC6b2AHLFdKbRCRfwFnYIvpoZw9SqkBTxm/Ter00ktER0fz6XtvUKjKCxQoXx2lFFeO7ufKkX2MensIbdq0sXaYaTL4zTfJkzs3f+/bT2kfH2Yu+DFD2sKfhcFgIDoulnIvNCAuJhp7x9Q/trHRUZSq25TQozsB8PHx4dSOedTt3CfVbqUJXTt7AsfYSKpUSX0USk2zBeZ8umNMX+gKjGf8gCG1Skqpo8ATs3sopTJH95ln1KN7d1q1bMnades4cvwgArxUqxJtPxpn81+iSREROnfqROdOnawditn27NmDW648lKv2AoGB1/D2y/vYU8OJRYU/JOT2TWo1b8PPAeu4cOECTk5OlCxckJN/B1C+buqD2MX757c19O7WJcuPYKqUYv/+/azbsInAoCBEhDx+vnRo24YqVarop7AzEXOSwRcYxyfyFZGpGLuCvm/RqLIILy8v/te7t7XDeG5duXIF36Kl8fb2xs7ejhuB17F3dMbF0xtnFxdE7DAYDESFPyQiJBhUHAUL5MfVxRW/QsW4evUqAO8MGUz/oe/gW6AIfgVTH3do/++/EHn9PB06TLTsG7SyLVu2MHP214Qb7CjTsBW5S78IwO3rV3h7whQ8HO0YPmQwDRs2tHKkmjlSTAam4SguAiMxDj8hQAel1KkMiE3LYpRSHDlyhKtXryIiREdb9gZrdHQ09g6OAHh6eOLh7kFoaAh3790n9M5NDAYDdnb2uLq4kNfPFzd3NwTjmay9gyPR0dE4OjpSqVIlPhgzivEfjaDRa+9QvOqLSZ7xR0dGsGf9Mq78tYX5c7/Dw8PDYu/N2hYuXMTXCxbTfOAoCpap+NgVQPHKNanZujMXj/3De1OmMSwoKMmB/zTbktqopQYR+VQp9SJwOoNi0rKgo0ePMmbCJEJjFbkKFad5ldI0bNGK4UMG07FDB4vs09PTk8jQ449eiwienl54eqb+zEB4SDCenp5ERBgnGmnevDnZs2dn6icz2PXzd5Rt3Jb8Jcvh6JyN8NAQzu3/kzO7tvBi9aosXbiAnDlTfs4kM9uyZQtf//QzPSZ9gWeOpHtniQhFKlSl+4TP+WzSUHL7+dl8h4N4169f59ixYxgMBtMJQ9Zu6otnTjPRZhHpBKzOat1AtYxx/vx53hjyNvVeGUapGnUQETwuHaT96OlMnz4WRwcHi9xQf/HFF5ky4zOiIsIfGyMqNcFBN7l/7RIVKlRg7969j5ZXr16dNcuXcvToUZYsX8mhJTuIiIzEw92dWjWqMWPFUvLkyZPu78OWGAwGPv3yK5oPGJVsIkjI2zc3TfsNZ9bsr6lfv34GRPj0DAYDk6d+yMYt28hftjItqpWnRdv2fPvl58/FsOTmJIN3ADcgVkQiMTYVKaVU5rsLqlnFt3O/p0LrrpSuWfex5b4FCtN66Hhmzp5Mq1at0v0MzM/PjxerV+HYrm1Ua9rW7HqHt23gpXZtcHFxeWKdiFCxYkUqVqz4zPHFxMSwe/duwsLCKFOmTKb4wjlw4ABR4kjBMv8N7x0dE01ISAixsbEAODo44OnpiaOp91aRitUI+Gk2R44csUrM5lq0eDF/nfyX1z9fjLOLKx6XDlK23csMeGsov61fm+WvEFJ9d0opD6WUnVLKSSnlaXqtE4Fmlri4OLb88QeVGrZMcr1/sVKQzd1iI3y+0vtlDv6ymJB75k3OF3T1EqcCfqVHt9SfS3hagYGB9B8wgPzFSvLq26MY9ckXvNioGRWqVWfNmjWPvlRt0Zp1GyjdsBUiwsPwh1y+coV/L1wkJCKaKOyJwp4HEdGcu3CBy1evEB4RjohQukFL1m7YaO3wU7Rk5Wrq9ej/2FVkxfrNMWTzYP/+/VaMLGOY1XFaRHyA4hjnMwBAKbXTUkFpWUd0dDQKSXE+ATdvH8LCwpJd/yyqVKlCv17d+HHqCDqN+ghv39zJlr158V9+mTGWCaPepUCBAhaJZ+WqVYweNwHcvOnx8ff4lyoPQHT4Q/as/onBI95j/uKf+f7bb2zyvsPNoCDylavLvfv3uHX7Dm4+OciVK88TXUg9cuQkIiyUy1evktvXlxx58hO4z7b7ndy7d4/suZ98SNDTLw937961QkQZK9VkICKvA0OBfMBh4AXgb6CRRSPTsoRs2bKR3cebG+fPoAwGzh85QMTDUJpXKMbpoN0UKV+FWxfPUahQIYvF8Oqrr+CczZnPxg6g2IuNqNy0Hb75jftTSnH93CkOb1nLlcN7mTJuDM2aNbNIHCtXrWLG13MRF0+6TPqC7Hn/SzhOrm7Ue3kgkaEh3Lt9nT6v9WPJwgU2+UxK6MMwYu7cJXve/I96ayUmYoerhxfO2Vy5FXiV8PCHGRxl2lWqUIHT+3dTpXGrR8uiIyO4euIQ5d8bZr3AMog5jWBDgerAZaVUQ4wPkt22aFRaliEiVCpbmjmj3mDpp5O4FxKKcvUiTim2r/6Zj/q0xcXeDl9fX4vG0atnTzauWk7tgjlZPLofE9vXYnKnekxsX4v1n4ymTbUybNmw1mKJ4ObNm3z82ZdUbNmJ3MXLPJYIEqrYrANhISF4lqrM9JmzLBLLs8iZPTsXT5/A288/2USQkL2jI965/blw6gS5bHwo78ED+rNv+fcc+mMT4aEPiImKZNX092nRsP5zMSuhOckgMn4iGxFxVkqdxjj7mZYBlFJs376d0WPHMWnK1Ew1e5ZSik9nfcb+U+dp9Oow2o78iBov9aZqmy64eWen+eCxNBs4mmx5C9P/zcGEh1t2qKrTp0+zZsNGchUuQZNXh9Bq4Ega93mTbN45WbV2PZcvX7bYvlesWkXxWo1xcHTCI1fyTVWevrkJD31A7Y69+W3rH4SEhFgspqfh7eHG6V1bsXdMPRHEs3d04tSfm8nhk/ZhwDNS+fLlmTv7MyJP/M2Pw14m9PZNejSrx8RxGf+MbXh4OGvXruXLL79k1apVFmtGTcicZHDNNODcLxjHE1oL3LBkUNp/pk2fwaTPvibMrwTXHXPQb+i7bNiwwdphmWXBgp9YH7CbXh98Sd2W7cjp7UX43SBuXz5PXEwMbk72VK/XgG5jPibUxYfho97DYEh1pJOn8vvvvzNiwgfU6zeKl6d8RZ323anWpA31OvWm7/TvKduhL/2HvM0///yT7vs2GAwsW7WGyk3b4eObh9uXziVbNujiObLn9sfd24cClWrY3N/67wP/YG9nx/XTR82uc/noAVxc3dj5997UC1tZ+fLl+e6rL9m/+0+KFytK3z59Mnx03qNHj9KsTTu+X7+VQyHCws27aNq6Lfv27bPofpO9ZyAiLyil9iil4gesnygi2wEv4DeLRqUBcO3aNVZv2MTrny3C2dUNgMIVqvLJp2Np0aKFTY96GhISwlfzfuDlaXMf3TzOnj37o1m/sl06iJ+v36PyLfsPZ8Ho/hw4cIAaNdI2Z3Fq7ty5w7gpH9Hp/ZlJDichIpSqUQenbC4MGT6Sbb9uxNnZOd32HxwcTFRsHLnyFSRH3vxEfTeTK8f/oUC5xwexMxgM/LNhGVVNbdZ5SlTg7PkL6RbHs4qKiuLK1Wu0enM0v337MZ3Gf4ZHjpSb9x4E3WT7vJm0eX0oGz+flEGRZl5RUVEMevtd6r8+nOJVXni0/NKJIwwZMYrN69da7D5SSlcGX8f/IiJ/Ayildiil1imloi0SjfaY06dP41+q/KNEAJCncHFilDyarN1WrV+/ngIVa+CV07x7AfYODlRo1oFFS9N/3uDVq9dQuHq9VMcVKlKhKl75i7J169Z03X90dDQOpj73dnZ2dHhzBFu++pCjW9YRHWFsGrt9+TwbZ47H2d7+UTdcB0cHIqOi0jWWZxEeHo6TiwvlajWkdpsurJw0lKsnDpHUs6hKKS4fPcDqD96mQadelK5ZFwcnZ4td+WUV27Ztw6tAsccSAUChshXJW7Yqv/76q8X2ndKpZcK+YskP9ahZTMGCBbl1/ixxsbGPhk++HxSIISbK5ufVXbF2PZW6DUxTnfJ1mzBn6VxCQkLS9exn9fqN1BvwnlllyzZszaq162ndunW67d/T05Pw0BBioqNwdHKmSIWqvDx2GjtXLWLX4m+xd3DE2dWVGs3aUatdNxxM7fEh9+7g7+OdbnE8K1dXV6IjIlBK8WLbLnjl9GXbT7MxIJRp0BLv3P4opQgOvMaJ7ZtwdHCgbf9hlKpeG4PBQGx0VJZ/cOtZXb9+nRwFkx7YOWfBYly5ds1i+04pGdiZni+wS/D7owShlLpnsag0AIoXL06NSmVZNf19qrXpSlT4Q/5eOZ/+ffukazOGJdwKCiKnf9r66ju7uOLm7cPdu3fTNRkE3Q4ye0L7nP4FOB6Uvlddrq6uvFC9Gid2b6dSwxaA8WG7HqOmEBsdTUxMFM4ubo99URoMBs7t2sLQaR+kayzPwtnZmYIFC3DpxGEKl6tMmRfrU/qFelw+eYQjOzZz84Txfotnjpy89OYICpQu/+j5g/NH9lOqpO53kpp8+fJxd/faJNfdvnSOxg3Stwk1oZSSgRfGyezjE0DCO2sKsP1n57OA6R99xM9LlvDb+p9wyZaNMYP60bx5c2uHZTkWGP/eycmJmKgos8YniomKxNHJ/J4y5urVrQuTPv+Gig2aP/aAloOTEw5JjNx64cgBcni4UqFChSfWWVPvbl34ectaCpczTlUiIhQqW4lCZSulWO/olnX066FHLk1No0aN+GjGTM7s303J6rUfLb94/BA3Tx2i1ScTLbbvZJOBUqqQxfaqmc3JyYm+ffrQt08fa4eSJn6+vty9cRV3bx+z60RHRhB2/x45cuRItkxsbCxXrlwhLCwMV1dXChQokOow2JUqVODcP39TuVGrFMsBnD+0h+qVnn3cocTq1KmD79x57Fj6PfW7v5bipC8Pbt9i67yZfPT+KJubHKZ169Z8+uXXBF25iG+BwmbVCbx4jjvnT9Ks2afs2bPHwhFmbs7Oznz9+SwGDn2bEwG/kqNQCe5fPU/Q2WN8OeMTiw6LrhvwNIvo3K4Nx7anrVvksV3bqFWjWpJNRLdv3+bbb7+jccvW9B08jOEffMxrQ4fTsEUrvpg9m8DAJ+ebjvdy964c27I21ZuXsdHRnNy+ie5du6QpbnPY2dnx1eefcf/EPn6dO5PQ+08Ob2AwGDh3aC9LJg5lyGt9bHJSGDc3Nya8N5I1n4zh3s3rqZa/e+Mqa2e8z+T3x5Atm771aI5y5cqxecM6+nVoTrXs9vRp2YCtmzZQrVo1i+7Xdvsmaplau3bt+PzbuYTcvW3WUMeGuDiObVnLtPfefWLdvn37GDbyPQpUrUOr4R891ivozvUr7N2yjsXdevLx5AlJjplfq1Yt8s77gW2LvqVJ74FJnm3Hxcay8ZuPqVu9KsWKWWZm1uzZs7N4/g/M+uJLFo54lbxlq5C3VAUcHJ0IvXebc39tw9fLnWnjRtv02P+tWrXiYXg4MyYOpWbnVyhXpxFO2R4f4TUqIpxju7axf9V8xrw9hKZNm1op2szJxcWFtm3NH2k3PehkoFmEp6cn/fv+j58/HUfXsdNxcUv+8tZgMPDbvFmU8Pd74hmDw4cPM2TEe7QYMp5CZZ9svsnpX4CmfQdTpk4TRk0cy6wPnahVq9ZjZYxn5bPoP2gwK6a9R7U2XSlcrjIigiEujrMH93Bgw1KK+/owZfKH6XMAkuHu7s64Me/xztAhbNq0idPn/iUqOJpCPl68M/1DypUrZ3NNQ0np0rkzxYoWZd78BcxZOoeiNRvgmcs4l0NI0A3O79tJrRpVmfvFLJu776ElzdxRS+sAxZVSP4pILsBdKXXRsqFpmd3rr73Kvfv3WTJhKPV7v0nh8lWe6FoYdOUify77Hh8VyWezv3iiR83ocRNo+Po7SSaChPyLlaLVkAmMHj+Rbb9uxDHRcAleXl789P081q9fz09Lv+W32Xdxcffg4YP7FC9SmHf69qB58+YZ9rSpm5sbXbqkf3NURqpcuTJfVa5MYGAgW7duJejOHUQE3yolaDpqEH5+fqlvRLMZ5oxaOgGohnE8oh8BR2ARUDulepomIowc/i7lypRm7vy5BMyPoHCNerh4eFErnzdLJr1NxO3r9OzciVdffeWJG8F79+4lyt6ZEtVqJbOHxxUsUwFX33wEBAQk2Szh7OxM586d6dSpE0FBQTx8+BBPT0+bHCo6M8mTJw+9e/e2dhjaMzLnyqAjxpFK/wFQSt0Qkaw707eWrkSE1q1b06pVK44dO8bu3bt5EBqMq703I19/mfr16yc7rMbSlaso16RdmppNyjduy5KVq1JsoxYRfdaqaYmYkwyilVJKRBSAiLilVkGzHRcuXGDp8hVs2ryF4OBgHB0dKViwID07v0Tr1q1xc8uYP6eIUKFChUftxwEBAaneJL1w6Qq1m3RP037yFi/FgRVznzZMTXtumZMMlovId4C3iPQDXgVS/d8mItmAnYCzaT8rlVITRCQ7sAwoBFwCuiql7j9d+FpyIiIiGDNuPH8dPEyZhq3oNOELPLLnwhAXy7Vzp/h56zo+/fJrxo0abpHJ6NNDbGwsdmkcjM/e3oGYmBgLRaRpWVeq/9OUUjNEpCkQgvG+wXil1BYzth0FNFJKhYmII7BLRH4FXgK2KaWmichoYDQw6unfgpZYVFQU/QYOItLTl36fL378CVdHR4qUr0KR8lUIunqJD6ePJSIyki6dO1sv4GTkzJGd4KDAR7OSmSP49k1ypvDQmqZpSTProTOl1Bal1Ail1HAzEwHKKH5GBkfTjwLaAwtMyxcAHdIWspaaGbM+46GLD60GjExyqIN4vvkL0XnMJ8yY/S0nT57MwAjN075VC07uSNto6ce3/0q7lll4uI4EoqOjmff997Tu2Ikz587x/oSJXLPgQGZa1pZqMhCRUBEJSfRzVUTWiEiK4xOJiL2IHAaCgC1Kqb2An1IqEMD0r2XnO3zOhISE8MuGTTTuM9isESKz5/anYqvOLPp5aQZElzatWrUi8NQR7gcl/3RxQg8fBHPh4G46duhg2cBsgFKKd0aMZM2uA9R6dTg+efJzw8GHnn1fTfFpbE1LjiQ1FvljBUQmYZzZ7GeMg9Z1B3IDZ4CBSqkGqe7EOFPaGuAtYJdSyjvBuvtKqScGsBGR/kB/AD8/v6pLl1rvyyosLAx3d3er7d9cYWFhREdHcz8sHG/fPGbXMxjiuHP1IiWKFcuwfvbmHtO7d+9yN/gBPnnyYWeXfGxKGbgfeB0vDzd8c6X+xHN6x5nRIiIiuHLtOjnyF0IQ7KPDiXNyJfTeHbLZQe7cttlbylaPZ1IyS6wpxdmwYcODSimzxrEw5+5cC6VUzQSv54jIHqXUZBEZY85OlFLBIhIAtABuiUgepVSgiOTBeNWQVJ05wByAatWqKWs+nm9OzxdbEBAQwPLVv+BepRFlClVNU91Ny1dTtnRp6tWrZ6HoHmfuMY2fR3nh6vXUf3kgRSpWe+yKRynF5ZNH2LFoDg2qlOe1sWPSdcx8W/3b//DDD+y6fJfGdY33ejwuHSS0UFWux55m30+fsXa5eSdPN27cYMWqVRw5fpI4g4ESRYvQtdNLFC9e3CJx2+rxTEpmiTW94jQnGRhEpCuw0vQ64Z3GZC8rTE8qx5gSgQvQBPgYWAf0AaaZ/k168G7tqYSEhZHLPe2PgTi7eWTIpNtpJSIMf+dtypQqydz5PxCw4EsKVa2Ds5s70REPuXzob7yc7Bnyv1506NAhUwzlkB68vb0Ju3viieUhd2/j45X6xPNxcXF8MuNTVq3fSMm6zSjUoAN2dvacP3uc3gMGU6NiWaZNmYKra+rDfmtZgznJoBfwOcZpMBWwB3jZ9AU/OIV6eYAFImKP8d7EcqXUBtMUmstF5DXgCpC5n8m3Ma4uLo+mUkyLmKgIXFxcUi9oJa1ataJly5YcO3aMAwcOEBIaiodfDip1eJ8qVao8N0kgXpMmTfjk8y+5cvoYBUqVByDiYSh7V//E6IGvpVp/6kfT2H3yPK9/vphsbv81MRStWI1aHXrx29xPeevtd/nuqy9teq5tLf2Y07X0ApDc8Hm7Uqh3FOOTy4mX3wUamxugljZVypdjx5H9j02MkZroyAiunz5O6dLvWzCyZ5f4wbXnmaenJ7Omfcg7o8eQo3BJXmpcl++nfkC3Du1Snfzo1KlT/Lr9T/rO+CHJCX8cHB1pNWAkP08cyh9//EGzZs0s9TY0G2LO2ETZgNeAsiSYC1kp9aoF49KeUqdOL/F9xy406NkPZ1fzni4+tmsbL1SrTO7cuS0cnZaeXnzxRbb9upFdu3YRHh7O2qWL8ff3T7XekuUrKNu4bYozv9nZ2VGlVRd++nmpTgbPCXPutC3E2HuoObADyAeEWjIo7enlypWL+rVfZOfKBY+WKaW4euY4R3Zs5sTu7YTcu/NoXXjIAw6uW8L/evawRrjaM3J1daVZs2Z4e3ublQgA9hw4SMkadVItV7JaLQ4fPUpcXNyzhqllAuY0BhZTSnURkfZKqQUi8jPwu6UD057euDGj6dG7DztXupE9T34CVvxETGwcvoVLEBsdydrvZlK0YlXqtO/B1h9m0a1tiyfmEdCyrujo6BQfRoxn7+CAnb0DsbGxGdblWLMec5JB/EAvwSJSDriJcVwhzUZ5eXnx0w/zaNuhI4EhETQfNJai1Ws/6nIZEfqAfb/8zOcDu/H2oAEMGZxSPwAtq8mXNy9Bly/gk8qzKHeuX8HTwx1nZ+cMikyzJnOSwRwR8QHex9gt1B0YZ9GotGcWGhpKnL0jr388kzh7Z+5cOY+dnT1KKQSo3a4rfrlycvLsgeeuJ87zrttLHfhu1fpUOxkc2rqeri91yJigNKtL8Z6BiNgBIUqp+0qpnUqpIkopX6XUdxkUn/aUli5fQZmGrclXuDgFCxSgVImSFC1SmOLFilKyZAly+/lRq21Xzl66yrlz56wdrpaBmjVrRuStqxwJSH7cp0snjnDh7z/oaoMDGGqWkWIyUEoZSPlZAs1GbdkeQLm6/03wYmdnh6ODIw72DgjGKwE7e3uKvdCAHTt2WCtMzQqcnZ2Z89WXHFo9n83zZ3Pv5vVH68KC7/PnqoX89sUkPv/kI93D7DliTjPRFhEZjnEOgofxC5VS9ywWlfbMwsPDcXH3TLWcs5snD8PT/pCalrkVKVKEFYsXsnDxYlZMfAtHV3dE7Ah/cJ9WzZrw/vzvKVy4sLXD1DKQOckg/nmCQQmWKSDFEUs168qZMyf3bl7Dzcs7xXIhQdfxLaIf4noe5cqVi3eGDWPwm28SGBhIXFwcuXPn1kNQPKfMeQJZnx5kQp3bt2XDtg3kL1ku2TIRD0O5eGA3zcYPz8DINFvj5OREwYIFrR2GZmXmzGfgKiLvi8gc0+viImKb8yRqj3Ro356bxw9w7p89Sa43xMWxed5ntG7WhBx6ZjBNe+6Z8wTyj0A0UMv0+howxWIRaenC29ubrz+bScDcGfyxeM6jCWIMBgPnD+9n2dQR5FQRjB2tZxzVNM28ewZFlVLdRKQHgFIqQnTH9EyhQoUKrFj8E4t+XsKy9wcShxAbE0Oh/PkY0KMbbdu21SNSapoGmJcMok3DVSsAESmKcbJ7LRPImzcvI4e/yzvDhhISEoKDgwMeHh76QTNN0x5jTjKYCPwG5BeRxUBtoK8FY9IswMHBgezZs1s7DKuLjo5m27ZtHD56jIfhEXi4u/FizRrUqVMnXWdI07TMxpzeRJtF5CDwAsY5kIcqpe6kUk3TbEp0dDRz5s5jyarVePkXJn/FGjjn8udWWChbZ88h7sNp9O3Vg5d79dJJQXsumTOfwTpgCbBOKfUwtfKaZmsiIiIYOHgIdyUbL70/ixx58z+2vnaH7gReOMvCBbM5euwEH380VY/SqT13zDkF+hSoC5wUkRUi0tk04Y2m2TylFCNGv0eYe046vjPpiUQQL0+REnQbO4MTN+8xbfqMDI5S06wv1WSglNqhlHoT4xPHc4CuQJClA9O09HDkyBGOnL1Ay37vptr84+DkRIdhE/hl46/cuHEjgyLUNNtgVr9CU2+itkA3oAqwIOUaWlZy+/ZtVq1azfbdf/HgQQjOzs4UL1KIbp07Ua1aNZvumbR46XLKNWmHvZldaJ1d3ShRpykrV6/W8zxozxVznkBeBpwCGgFfYXzu4C1LB6ZZX1hYGCPfG0vLDp3Z8W8gpTu8SuMhE3nh1XeJyFuK4ZM/onXHTuzdu9faoSYpOjqarQEBVKiftjl8KzZqzZr1Gy0UlabZJnNOl34Eeiql4gBEpLaI9FRKDUqlnpaJPXjwgD6v9ydbwdL0+/JnnF3dHlufp3BxqjVrz7+H9/HWyDF8MGYkzZs3t1K0SQsODsYpm6tZo7cmlD23P3fu3EEpZaHIsqaYGOOkiI6OjlaORHsa5nQt/U1EKpmeQO4GXARWWzwyzWoMBgODh72DR8kqNOrVP9lmIBGheOWaeI6ZzvgPR5AnTx4qVLCdEVDt7Oye7gtdKZtu+rIlN2/eZMWqVaxYs5Z79+8DkDNHDrp17ECnTi/h6+tr5Qg1cyXbTCQiJURkvIicAmZjHJNIlFINlVJfZliEWobbs2cP14PDaNizn1lfin4Fi1Cz62t8M/f7DIjOfF5eXsTFRBF2P21TbwRdvUiePHl0QkjF0mXLaNulO39fuUebUZ/w7qLNvLtoM62Gf8TOi0G07tSVVav1eWNmkdI9g9NAY6CtUqqOKQHEZUxYmjUtXracck3ap+nhq/J1GrH/8BGb6oXj6OhIu5YtOPRH2tr/D29dT5cO7SwUVdawdNkyvvxxET2nfkOzV97CN38hRAQRwbdAYZq/OoTuk77k02/msVonhEwhpf/tnYCbwHYRmSsijQGzT5VEJL+IbBeRUyJyQkSGmpZXEpE9InJYRA6ISI1newtaenrw4AF7DvxD+TqNHi2LjY7m6M4tLP5oDHPHDOLHCW+zZeF3j0ZCBXB0zkaJ2o3ZsNG2brz26NaVE39sICrCvNncQu/f5cL+P3mpY0cLR5Z5BQUF8ensb+j83sf4+OZJtlyOvPnp9N7HfDTzc+7d0xMj2rpkk4FSao1SqhtQCggA3gb8ROQbETGne0Ys8K5SqjTGoSwGiUgZ4BNgklKqEjDe9FqzEXfu3MEje04cnY3PFR7f/QczB3Rj37ZfKVSzATW79aNi2x6ERkXz7Yg3WPnZFKIjIwDwyVOAwFu29QhKiRIlaNmgLms/m0xMdMrjK0aEhbB6+lj69+mtx3FKwapVqyn2QkN8/PKmWjZH3vwUqlqbNb/8YvnAtGdizkNnD5VSi5VSbYB8wGFgtBn1ApVS/5h+D8XYPdUf4+in8d07vADbaVfQMBgMj34/9Mev/Dr/G1q/O4UO731CyVqNyFuyHAXKVaHeywPp+8XPxGDHwikjiYmOQkRssgfO2PdGUza3D0snv8OFowefiNEQF8fpfbv4efwQWtepSb/XX7NSpJnD8l/WUqlJW7PLV2rajuVr1lowIi09SEb85xWRQsBOoBzGhPA7xiYnO6CWUupyEnX6A/0B/Pz8qi5dutTicSYnLCwMd3d3q+3fXOkRZ1xcHGf//ZfseQtw5/oVvPPmx8HRKfkKCh7cDsTRwRFEcHe0I1euXBkSa1oFBwdz9949Yg0KJxc3xE5QBgNRD8NwdnIkZ44ceHh4WD3Op5GRcZ48dQq/wiXMLq+UIujSv5QpXSrTHE/IGn/7hg0bHlRKVTNnOxZPBiLiDuwApiqlVovIF8AOpdQqEekK9FdKNUlpG9WqVVMHDhywaJwpCQgIoEGDBlbbv7nSK86+/fpz5aEBz3yFqdPzjVTLPwgKZPm4QXg6O7Bs/jyKFCmSbNkzZ85w8uRJnJycaNWqVYb32FFKceTIEY4dO0ZERATu7u5Uq1aNEiWS/nJ73v72qTEYDFSoVoN3Fm02u4NBTHQUX73enkN7/2bHjh2Z4nhC1vjbi4jZycCiY/WKiCOwClislIrvUtCH/55TWAHoG8g2plfXLpzcs4Nyjc1rCvDyzYO3fwE8nR1TTARrfvmFPgMGs2THfq4F3mT8pMkZ3qwkIlSqVInevXvTv39/evbsmWwi0J5kZ2dHnjx5CLxw1uw6gRfO4u/vr7vq2jiLJQPT1JjfA6eUUjMTrLoB1Df93gg4Z6kYtKdTuXJl7O3scczmYlb5uNhYvHPno0rlismWiYmJYeonM+gybiatBowkh39BAvYe5MiRI+kVtpZBur3UgSNb15td/sjWdfTopHtn2TpLXhnUBnoDjUzdSA+LSCugH/CpiBwBPsR0X0CzHfb29vjmykVUSDBhwSl3CYyNjuJ+4FU8PTwoUrhwsuXCwsLAzv7RENIiQnb/gty9ezddY9cs76WOHbl0cDdBVy+lWvbmxX+5dmQ/7drp5zZsncWSgVJql1JKlFIVlFKVTD+bTMurKqUqKqVqKqUOWioG7el4enri7GhPdg8XVGQ4d65cICz4HnGxMShlwBAXR2R4GPdvXif45jVy++Yi+u5N/P39k92mt7c3uXPlYO+mVRgMBqIjI7h17gSlS5fOwHempYfs2bMzYfQI1kwbTdCVi8mWu3nxX36ZPoYPxo3B0zNt40NpGc+8cX2154q9vT0d2rTm+PbfaNDjdSIjI7h3/z7BgVeJi4tDxA5nZyd8fXzw8MxP2L273DxzjCZNPk52myLC159/xtsjRjFz0Te8NeANZn40hbx5U++rrtme1q1bIyJMnDyM/JVqULFxO/wKFQWlCLz4L0e2ruPG8YNMGT+Wpk2bWjtczQw6GWhJ6t61C137vErFxq3x8c1D3jxJ3z9QSrFz2Q90aNMKV1fXFLdZoEABVi1bQkxMDLt376ZWrVqWCF3LIK1ataJOnTqsX7+eJQs/JzDQ+ES6v78/PTp1pM0nE5/oqqvZLp0MtCQVKlSIYQP68dXUEXQc+SE5/Qs8UcYQF8e2hd+ggi4xdNo4s7ethzjOOjw9PenVqxe9evWydijaM9LJQEtWzx49cHJy4pOJb5G3TGXKNmiJV04/YqIiOX94Lye3b6JiqRJ8M+e7VK8KNE2zbToZaCnq3KkTLVu0YOPGjaz7dTl3793HOZszVSuU571vZ1O8eHFrh6hpWjrQyUBLlZubG127dqVr167WDkXTNAux6BPImqZpWuagk4GmaZqmk4GmaZqmk4GmaZqGTgaapmkaOhlomqZp6GSgaZqmoZOBpmmahk4GmqZpGjoZaJqmaehkoGmapqGTgaZpmoZOBpqmaRo6GWiapmnoZKBpmqahk4GmaZqGTgaapmkaOhlomqZp6GSgaZqmYcFkICL5RWS7iJwSkRMiMjTBurdE5Ixp+SeWikHTNE0zj4MFtx0LvKuU+kdEPICDIrIF8APaAxWUUlEi4mvBGDRN0zQzWCwZKKUCgUDT76EicgrwB/oB05RSUaZ1QZaKQdM0TTOPKKUsvxORQsBOoJzp37VACyASGK6U2p9Enf5AfwA/P7+qS5cutXicyQkLC8Pd3d1q+zdXZokTMk+sOs70lVnihMwTa0pxNmzY8KBSqppZG1JKWfQHcAcOAi+ZXh8HvgAEqAFcxJSUkvupWrWqSovr16+r6Z/OVM3atFP1mjZXg4e9rfbu3ZumbSS0ffv2p66bkTJLnEplnlh1nOkrs8SpVOaJNaU4gQPKzO9qi/YmEhFHYBWwWCm12rT4GrDaFOs+wADkTK99Hj9+nM49e3PkXjSNBk+g4/ufoYpUZtj7k5gzd1567UbTNC1Lsdg9AxER4HvglFJqZoJVvwCNgAARKQE4AXfSY59xcXEMHT6S+q+9Q8nqtR8tr9a0LaWq1+HH99+kZo3qVKxYMT12p2malmVYsjdRbaA3cExEDpuWjQF+AH4QkeNANNDHdDnzzP7880/svXI+lgjiuXv7UKllJxYvXZ6pkoFSisDAQIKDg7Gzs8PX15fs2bNbOyxN07IYS/Ym2oXxvkBSXrbEPs+cOUOe0sl/0RcuX5Ud3/5miV2nu8jISH777Td+WrKM67du45EjF4a4OELu3KJqxQr06taFOnXqYGennxvUNO3ZWfLKIMO5uLgQ9TAw2fXhoSG4ZMuWgRE9nVOnTvHm0Ldxz1+MCh360qZi9Udf+jHRUZz8K4DJX3xLju/m8PUXn5MjRw4rR6xpWmaXpU4rGzZsyIV9O4mJikxy/fEdv9K6WZMMjiptTp8+zasDB1Oj1yA6jZhC8co1Hzv7d3RypmKD5vT64CvcS1fn5VdeIzg42HoBa5qWJWSpZJA/f36a1K3D2s8/ICoi/NFypRQHNq/j3pkjvNSxoxUjTFl0dDQDhwyj7itDKV2zboplRYS6nfvgW6kWo98fn0ERapqWVWWpZiKAiePfZ8pH05j7Vk8KV3kBRxc3rh7dTy4PN+bP/Q4vLy9rh5isrVu34pqnIGVq1jO7Tt0urzBncHcuXbpkucA0TcvyslwycHJyYvKE8Qwa8Aa7du0iKiqKMj3aUrFiRYy9XW3XT0uWUaFZ1zTVcXB0pEyDlixbsZKa1c170FDTNC2xLJcM4vn5+dGpUydrh2G2e/fu8e/FSzSr+mKa65ar14xN00frZKBp2lPLUvcMMrPg4GDcvHyeqquoR/ac+iaypmnPRCcDG+Hg4IDBEPdUdQ1xsTg4ZNmLPE3TMoBOBjYiR44cPAy+T8TD0DTXDbpyidy5c1sgKk3Tnhc6GdgINzc3GtWry9Edm9Nc9+gf6+nWsb0FotI07Xmhk4EN6dmtC8e3rsNgMJhdJ+z+Pa4c3kfbtm0tGJmmaVmdTgY2pHLlypQq4M+W+bMxZ+y+2Oho1n0+id7du+Lp6ZkBEWqallXpZGBDRIRZMz4h5uppfp3z6WNPUScWcvc2S6e8S5Ui+Rn85sAMjFLTtKxId0GxMe7u7vz0/TwmTfmQuW/1pPiLDSlXrxmeOX1RBgO3r13m6Lb1BJ48TN9ePRjwRv8MG7nUYDAQGBiIj48Prq6uGbJPTdMyhk4GNsjV1ZWPP5xCUFAQq1atZuOPn3I/OBh7O3v8/Hzp2a4NbWZ9iJubW4bFFBMTw4DBQzh+9l/sDLF8/dlMKleunGH71zTNsnQysGG+vr4MHDiAgQMHWDsU/vrrL67cD+ONr5ZxYvcffPnNd/ww51trh6VpWjrR9ww0s2TLlo2o8IdEPgwl9N4dXFxsf14ITdPMp68MNLPUqFGDFnVfYO7g7uTPl4/Zsz61dkiapqUjnQw0s4gIY0aNZMyokdYORdM0C9DNRJqmaZpOBpqmaZpOBpqmaRo6GWiapmnoZKBpmqahk4GmaZqGTgaapmkaIOYMlWxtInIbuGzFEHICd6y4f3Nlljgh88Sq40xfmSVOyDyxphRnQaVULnM2kimSgbWJyAGlVDVrx5GazBInZJ5YdZzpK7PECZkn1vSKUzcTaZqmaToZaJqmaToZmGuOtQMwU2aJEzJPrDrO9JVZ4oTME2u6xKnvGWiapmn6ykDTNE3TyUDTNE3jOU8GIvKDiASJyPEEy7KLyBYROWf61yeZui1E5IyI/Csio60Q53QROS0iR0VkjYh4J1P3kogcE5HDInLAknGmEOtEEbluiuGwiLRKpq61j+myBDFeEpHDydTNsGMqIvlFZLuInBKREyIy1LTcpj6nKcRpU5/TFOK0xc9ocrFa5nOqlHpuf4B6QBXgeIJlnwCjTb+PBj5Oop49cB4oAjgBR4AyGRxnM8DB9PvHScVpWncJyGnlYzoRGJ5KPasf00TrPwXGW/uYAnmAKqbfPYCzQBlb+5ymEKdNfU5TiNMWP6NJxmqpz+lzfWWglNoJ3Eu0uD2wwPT7AqBDElVrAP8qpS4opaKBpaZ6GRanUmqzUirW9HIPkM9S+0+LZI6pOax+TOOJiABdgSWW2r+5lFKBSql/TL+HAqcAf2zsc5pcnLb2OU3heJojoz+jKcaa3p/T5zoZJMNPKRUIxj8G4JtEGX/gaoLX1zD/A2UJrwK/JrNOAZtF5KCI9M/AmBIbbGoq+CGZJg1bOqZ1gVtKqXPJrLfKMRWRQkBlYC82/DlNFGdCNvU5TSJOm/2MJnNM0/VzqpPB05Ekllmlj66IjAVigcXJFKmtlKoCtAQGiUi9DAvuP98ARYFKQCDGS9vEbOaYAj1I+Wwrw4+piLgDq4BhSqkQc6slscyixzS5OG3tc5pEnDb7GU3hb5+un1OdDJ50S0TyAJj+DUqizDUgf4LX+YAbGRDbY0SkD9AG6KVMjYSJKaVumP4NAtZgvNTNUEqpW0qpOKWUAZibTAy2ckwdgJeAZcmVyehjKiKOGL8MFiulVpsW29znNJk4be5zmlSctvoZTeGYpvvnVCeDJ60D+ph+7wOsTaLMfqC4iBQWESegu6lehhGRFsAooJ1SKjyZMm4i4hH/O8abeceTKmtJ8V9aJh2TicHqx9SkCXBaKXUtqZUZfUxN7cLfA6eUUjMTrLKpz2lycdra5zSFOG3uM5rC3x4s8Tm11J3wzPCD8RIrEIjBmPVfA3IA24Bzpn+zm8rmBTYlqNsK493988BYK8T5L8b2y8Omn28Tx4mx18MR088JS8eZQqwLgWPAUYz/efLY4jE1LZ8PDEhU1mrHFKiDsSniaIK/dStb+5ymEKdNfU5TiNMWP6NJxmqpz6kejkLTNE3TzUSapmmaTgaapmkaOhlomqZp6GSgaZqmoZOBpmmahk4GGiAiORKMgngz0eiNTonKDhMRVzO2GSAiT0zSbVp+IMHraiISkE7vo6+IzE6PbaWyn1KmY3NIRIomWhc/UuRREdkhIgUTrPsrme3NF5HO6RBXZRGZZ/q9r4jcNsV5UkT6PeO2W4rIATGOoHlaRGaYlg8Qkf8l2GdeM7a1NZnhHjQr0slAQyl1VylVSSlVCfgWmBX/WhkH5EpoGJBqMkiFr4i0fMZtpDsRsTezaAdgrVKqslLqfBLrGyqlKgABwPvxC5VStZ45yJSNAb5M8HqZ6W/aAPhQRPzM2Yjp6daEr8sBs4GXlVKlgXLABQCl1LdKqZ9MRfti7OuemoXAm+bEomUcnQy0JIlIY9OZ7zHTwF3OIjIE43/27SKy3VTuG9MZ4wkRmWTm5qeT4EsywT4fO7MXkQ0i0sD0e5iIfCzGQbe2ikgN01XGBRFpl2Az+UXkNzGOOT8hwbZeFpF9pjPl7+K/+E3bnSwie4EXE8VTSUT2yH9j8fuIcZz7YcDr8ccgBX/z+CiTYaZ/RURmm87YN5JgkDkRqWq6ojgoIr/Lf0NODDGVPyoiS5M4dh5ABaXUkcTrlHE4gvNAwRS2HyAiH4rIDmBook2MBKYqpU6btherlPraVG+iiAw3XdlUAxabjnFrEVmTIL6mIhI/nMI6jOPqaDZEJwMtKdkwPuHYTSlVHnAABiqlvsA4FktDpVRDU9mxSqlqQAWgvohUMGP7fwNRItIw1ZL/cQMClFJVgVBgCtAU49ABkxOUqwH0wjjgWBdTM1RpoBvGgbsqAXGmMvHbPa6UqqmU2pVonz8Bo0xn+ceACUqpTfx39ZRa/C2AX5JY3hEoCZQH+gG14NE4NF8CnU3v8wdgqqnOaKCyKZYBSWyzGskMNyAiRTA+kXo5he0DeCul6iulEg/SVg44mNIbVUqtBA5gHH+oErAJKC0iuUxFXgF+NJW9DziLSI6UtqllLIfUi2jPIXvgolLqrOn1AmAQ8FkSZbuKcXhcB4yTcZTB+Ph8aqZgvDoYZWZM0cBvpt+PAVFKqRgROQYUSlBui1LqLoDpTLQOxtEyqwL7RQTAhf8GdovDOBDYY0TEC+OX4w7TogXACjNj3W5qkgkiiSsgjBPrLFFKxQE3ROQP0/KSGL94t5jitMc4ZAYYj+liEfmFpBNMHuB2omXdRKQOEAW8AeRKYfuQwqBnaaWUUiKyEHhZRH7EeNX1vwRFgjBeZd5Nr31qz0YnAy0pD80pJCKFgeFAdaXUfRGZj/GqIlVKqT9E5APghQSLY3n8ajXhtmLUf2OnGDB+waGUMiRq4048vorCOPTwAqXUe0mEEmn6Uk5PDTEew/kYr1reSaJMUuPACHBCKfViEutaY0wi7YBxIlJW/TdpDEAETx77ZUqpwY82LlI+he1D8n/3ExiT6RNNUKn4EVgPRAIrEsWbzRSzZiN0M5GWlGxAIREpZnrdG4g/Qw7FOAUfgCfGL5AHpjPhtN4UnoqxPTreJaCSiNiJSH6ebhjjpmKcH9gF443e3RgHcussIr7waP7ggilsA6XUA+C+iNQ1LUp4DFKllIrAeG/hfyKSPdHqnUB3EbE3tdnHNzedAXKJyIumOB1FpKyI2AH5lVLbMR4vb8A90TZPAcVIWZLbN+PtTAfGiEgJUz07EUkqwSX8bKCMQyjfwHh1ND9+uRgvS3Jj/HtrNkJfGWhJicTYxrvCdNa9H2M7OcAc4FcRCVRKNRSRQxjPHC9g/OI1m1Jqk4gkbNrYDVzE2Ax0HPjnKWLfhbG3SjHgZ6XUAQAReR/jrE92GEcqHYSxDT0lfYBvxdiV9gLGY2I2pVSgiCwx7euDBKvWAI0wvs+zmJKMUiradCP2C1MzlQPGprmzwCLTMsF4vyI40b5Oi4iXiHgo4xSJScWT3PZPpPI+jorIMGCJ6VgoYGMSRedjPF4RwIumhLgYyKWUOpmgXFVgT6IrBc3K9KilmpZFiMjbQKhSap61Y4knxt5hh5RS3ydY9jmwTim1zXqRaYnpZiJNyzq+wXQvxRaIyEGMvcwWJVp1XCcC26OvDDRN0zR9ZaBpmqbpZKBpmqahk4GmaZqGTgaapmkaOhlomqZpwP8BDNrCkGDzOBAAAAAASUVORK5CYII=\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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1be513ddfc8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for 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 ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add the scatter charts for each type of city.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "\n",
    "#Chart extras\n",
    "plt.title('PyBer Ride-Sharing Data (2019)')\n",
    "plt.ylabel('Average Fare ($)', fontsize=12)\n",
    "plt.xlabel('Total Number of Rides (Per City)', fontsize=12)\n",
    "plt.grid(True)\n",
    "# Create a legend\n",
    "lgnd = plt.legend(fontsize=\"12\", mode=\"Expanded\",\n",
    "         scatterpoints=4, loc=\"best\", title=\"City Types\")\n",
    "lgnd.legendHandles[0]._sizes = [75]\n",
    "lgnd.legendHandles[1]._sizes = [75]\n",
    "lgnd.legendHandles[2]._sizes = [75]\n",
    "lgnd.get_title().set_fontsize(12)\n",
    "# Incorporate a text label about circle size.\n",
    "plt.text(42, 35, \"Note: Circle size correlates with driver count per city.\", fontsize=\"12\")\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig1.png\")\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1625.000000</td>\n",
       "      <td>1.625000e+03</td>\n",
       "      <td>1625.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.525772</td>\n",
       "      <td>4.873485e+12</td>\n",
       "      <td>36.678154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.738649</td>\n",
       "      <td>2.907440e+12</td>\n",
       "      <td>20.075545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.050000</td>\n",
       "      <td>1.458810e+10</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.550000</td>\n",
       "      <td>2.400244e+12</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.640000</td>\n",
       "      <td>4.711188e+12</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.580000</td>\n",
       "      <td>7.451579e+12</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.970000</td>\n",
       "      <td>9.991538e+12</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare       ride_id  driver_count\n",
       "count  1625.000000  1.625000e+03   1625.000000\n",
       "mean     24.525772  4.873485e+12     36.678154\n",
       "std      11.738649  2.907440e+12     20.075545\n",
       "min       4.050000  1.458810e+10      3.000000\n",
       "25%      14.550000  2.400244e+12     22.000000\n",
       "50%      24.640000  4.711188e+12     37.000000\n",
       "75%      34.580000  7.451579e+12     52.000000\n",
       "max      44.970000  9.991538e+12     73.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>625.000000</td>\n",
       "      <td>6.250000e+02</td>\n",
       "      <td>625.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.970128</td>\n",
       "      <td>4.971015e+12</td>\n",
       "      <td>13.712000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.677508</td>\n",
       "      <td>2.912410e+12</td>\n",
       "      <td>8.042372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.050000</td>\n",
       "      <td>3.218337e+08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.970000</td>\n",
       "      <td>2.364253e+12</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.750000</td>\n",
       "      <td>5.053221e+12</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.830000</td>\n",
       "      <td>7.551674e+12</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.960000</td>\n",
       "      <td>9.917744e+12</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fare       ride_id  driver_count\n",
       "count  625.000000  6.250000e+02    625.000000\n",
       "mean    30.970128  4.971015e+12     13.712000\n",
       "std     10.677508  2.912410e+12      8.042372\n",
       "min     12.050000  3.218337e+08      1.000000\n",
       "25%     21.970000  2.364253e+12      5.000000\n",
       "50%     30.750000  5.053221e+12     16.000000\n",
       "75%     39.830000  7.551674e+12     21.000000\n",
       "max     49.960000  9.917744e+12     25.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "suburban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>125.000000</td>\n",
       "      <td>1.250000e+02</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.623440</td>\n",
       "      <td>4.647746e+12</td>\n",
       "      <td>4.296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.558046</td>\n",
       "      <td>2.887834e+12</td>\n",
       "      <td>2.691156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.110000</td>\n",
       "      <td>2.684873e+10</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.900000</td>\n",
       "      <td>2.275364e+12</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.050000</td>\n",
       "      <td>4.023962e+12</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.070000</td>\n",
       "      <td>7.118047e+12</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>58.550000</td>\n",
       "      <td>9.990581e+12</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fare       ride_id  driver_count\n",
       "count  125.000000  1.250000e+02    125.000000\n",
       "mean    34.623440  4.647746e+12      4.296000\n",
       "std     14.558046  2.887834e+12      2.691156\n",
       "min     10.110000  2.684873e+10      1.000000\n",
       "25%     19.900000  2.275364e+12      1.000000\n",
       "50%     37.050000  4.023962e+12      4.000000\n",
       "75%     47.070000  7.118047e+12      7.000000\n",
       "max     58.550000  9.990581e+12      9.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Get summary statistics.\n",
    "rural_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    66.000000\n",
       "mean     24.621212\n",
       "std       5.408726\n",
       "min      12.000000\n",
       "25%      21.000000\n",
       "50%      24.000000\n",
       "75%      28.000000\n",
       "max      39.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36.000000\n",
       "mean     17.361111\n",
       "std       4.323707\n",
       "min       9.000000\n",
       "25%      14.000000\n",
       "50%      17.000000\n",
       "75%      19.250000\n",
       "max      27.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "suburban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18.000000\n",
       "mean      6.944444\n",
       "std       2.508157\n",
       "min       3.000000\n",
       "25%       5.250000\n",
       "50%       6.000000\n",
       "75%       8.750000\n",
       "max      12.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "rural_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.62, 17.36, 6.94)"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.0, 17.0, 6.0)"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for each city type.\n",
    "urban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "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": 42,
   "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": 43,
   "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": 44,
   "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": 44,
     "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": 45,
   "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": 46,
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_fares = suburban_cities_df[\"fare\"]\n",
    "suburban_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for suburban trips is $30.97.\n",
      "The median fare price for suburban trips is $30.75.\n",
      "The mode fare price for suburban trips is ModeResult(mode=array([17.99]), count=array([3])).\n"
     ]
    }
   ],
   "source": [
    "mean_suburban_fares = np.mean(suburban_fares)\n",
    "print(f\"The mean fare price for suburban trips is ${mean_suburban_fares:.2f}.\")\n",
    "\n",
    "median_suburban_fares = np.median(suburban_fares)\n",
    "print(f\"The median fare price for suburban trips is ${median_suburban_fares:.2f}.\")\n",
    "\n",
    "mode_suburban_fares = sts.mode(suburban_fares)\n",
    "print(f\"The mode fare price for suburban trips is {mode_suburban_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for rural trips is $34.62.\n",
      "The median fare price for rural trips is $37.05.\n",
      "The mode fare price for rural trips is ModeResult(mode=array([37.05]), count=array([2])).\n"
     ]
    }
   ],
   "source": [
    "rural_fares = rural_cities_df[\"fare\"]\n",
    "\n",
    "mean_rural_fares = np.mean(rural_fares)\n",
    "print(f\"The mean fare price for rural trips is ${mean_rural_fares:.2f}.\")\n",
    "\n",
    "median_rural_fares = np.median(rural_fares)\n",
    "print(f\"The median fare price for rural trips is ${median_rural_fares:.2f}.\")\n",
    "\n",
    "mode_rural_fares = sts.mode(rural_fares)\n",
    "print(f\"The mode fare price for rural trips is {mode_rural_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1    72\n",
       "2    57\n",
       "3    34\n",
       "4    46\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the urban cities.\n",
    "urban_drivers = urban_cities_df['driver_count']\n",
    "urban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_urban_drivers = np.mean(urban_drivers) \n",
    "median_urban_drivers = np.median(urban_drivers)\n",
    "mode_urban_drivers = sts.mode(urban_drivers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_drivers = suburban_cities_df['driver_count']\n",
    "suburban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "mean_suburban_drivers = np.mean(suburban_drivers)\n",
    "print(f\"{mean_suburban_drivers:.0f}\")\n",
    "median_suburban_drivers = np.median(suburban_drivers)\n",
    "mode_suburban_drivers = sts.mode(suburban_drivers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_drivers = rural_cities_df['driver_count']\n",
    "rural_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_rural_drivers = np.mean(rural_drivers)\n",
    "median_rural_drivers = np.median(rural_drivers)\n",
    "mode_rural_drivers = sts.mode(rural_drivers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nqOd/0lh9QFbQyXFyl+bpnL8EbpA0HXgKOLGzrpuZWSfyFP4ryJ6eNUrSLOAE4Au9bdQinfNF4LA2+2lmZv2k18IfEddJWkxWrAUcFxHLC++ZmZkVomnhl7Rd3eRKYF79soh4qciOmZlZMVod8S8mG9cX8C7g5fR+JNnY/B5Fd87MzPpf06t6ImKPiNgT+BlwTETsEBHbk13Pf3NZHTQzs/6V53LOAyLi9tpERNwBTCyuS2ZmVqQ8V/W8IOkLwHfJhn5OAV4stFdmZlaYPEf8J5PFMf8QuAUYlea1JGl3SQskLU+xzGel+eMk3S1pqaT7JR3Yh/6bmVmb8lzO+RJwVgf7Xgd8LiKWpETPxZLmAxcDF0XEHZI+lqYndbB/MzPrQKvLOb8REZ+V9CPSXbv1ImJqqx2nBM5aCudqScuBXdO+RqTVtgGe7bDvZmbWgVZH/P+Wfl7a10YkdZHdxXsP8FmyXP9LyYaaDm6yjdM5rU+mTp3K6tWrC29HyvNcos4NHz6c2267rdA2rFp6TedsuJH0kYj4z5zrbg0sBGZFxM2SrgAWRsRNkk4CTo+Iw1vtw+mc1okykjO7u7uZNGlSoW04AdQ61XY6p6SNJZ0s6exajr6koyX9F/CtnI1uAtwEXBcRtWv/p/H2fQA3Aj65a2ZWolZDPXOA3YF7gSsk/Q74MDAjIm7pbcfK/v6dAyyPiMvqFj1Ldh9AN3Ao8HhHPTczs460Kvzjgf0iYr2kzYEXgL0j4rmc+/4I8FfAg5KWpnmfJ3sQyzclDSPL+D+9o56bmVlHWhX+NyNiPUBEvCHpsTaKPhGxiCzbp5EPtdFHMzPrR60K/3slPZDeC9grTQuIiNiv8N6ZmVm/a1X4x5TWCzMzK03Twh8RvyuzI2ZmVo48WT1mZvZHxIXfzKxiWt3AdVf6+bVOdtwsnTMtO1PSo2n+xZ3s38zMOtPq5O5oSROBqZK+T49LMyNiSS/7bpbOuRNwLNk9AmsljepD/83MrE2tCv/5wAxgN+CyHsuC7K7bplqkc54GzI6ItWnZys66bmZmnWh1Vc8PgB9I+mJEfLkvjfRI57wEOETSLLI7d8+OiPsabON0Tuuzoj83a9asKeWz6c+/9adc6ZySpgIfTZPdEfHj3A28M53zIeAXZA93OQC4HtgzWnTE6ZzWCadzWtW1nc5Zt+FXyYr0r9PrrDQvT6ON0jmfBm6OzL3AemCHfL+GmZn1VZ6HrR8FjKvl9kiaC/wKOK/VRi3SOW8hOz/QLWlfYFOyADgzMytBnsIPMBJ4Kb3fJuc2zdI5rwGuSUM+bwLTWg3zmJlZ/8pT+L8K/ErSArJLOj9KL0f70Gs65ym5e2hmZv2q18IfEfMkdZOdiBVwbjvxzGZmNrjkGupJ1+T7ac9mZn8EnNVjZlYxLvxmZhXTsvBL2ihdfWNmZn8kWhb+dO3+MknvanfHrdI50/KzJYUk37xlZlaiPCd3RwMPS7oXeK02MyKm9rJdw3TOiPi1pN2BI4CnOu24mZl1Jk/hv6iTHbdI5/w1cDlwDnBrJ/s2M7PO5bmOf6GkdwP7RMTPJW0JbNxOI/XpnCnw7ZmIWJalOjTdxumc1idxwQi4MO+N5p2ZBNBdaBPEBSP8+bd+1Ws6p6TTyArwdhGxl6R9gCsj4rBcDdSlcwI/BRYAR0bEK5KeBMZHRMusHqdzWieczmlV13E6J/AZstydVwEi4nEg11OzGqRz7gXsQXbC+Emyh7wskbRznv2ZmVnf5RnjXxsRb9aGZSQNI3sCV0uN0jkj4kHqvjTyHvGbmVn/yXPEv1DS54EtJB0B3Aj8KMd2tXTOQyUtTa+P9aGvZmbWD/Ic8c8ApgMPAp8Cbgf+pbeNeknnrK3TlaN9MzPrR3mu6lmfHr5yD9kQz6POzzczG7p6LfySjgKuBH5LdgS/h6RPRcQdRXfOzMz6X56hnq8DkyPiNwCS9gJ+Arjwm5kNQXlO7q6sFf3kCWBlQf0xM7OCNT3il3R8evuwpNuBG8jG+E8E7iuhb2ZmVoBWQz3H1L1/HpiY3v8e2LawHpmZWaGaFv6I+GRfdpwSOP8V2BlYD1wVEd+UdAnZl8qbZCeMPxkRq/rSlpmZ5Zfnqp49gDOBrvr1O41lBuYD50XEOklfA84Dzu2w/2Zm1qY8V/XcQha98COyI/dcmsUyR8SddavdDZyQu7dmZtZneQr/GxFxRV8aqY9l7rHoVOD6Jts4ltn6rOjPzZo1a0r5bPrzb/0pTyzzx4F9gDuBtbX5EbEkVwN1scwpobM2fyYwHji+tzuBHctsnXAss1Vds1jmPEf8f0IKW+PtoZ5I07012jOWuTZ/GnA0cJjjH8zMypWn8P85sGdEvNnOjhvFMqf5U8hO5k6MiNfb2aeZmfVdnsK/DBhJ+3fr1mKZH5S0NM37PHAFsBkwP2X83x0RZ7S5bzMz61Cewr8T8Iik+9hwjL/l5ZwtYplvb6uHZmbWr/IU/gsK74WZmZUmTx7/wjI6YlaE2iNDh7Jtt3VCivWvPHfurubtZ+xuCmwCvBYRI4rsmFlflXHBmC+1tKEozxH/8PppSccBBxbVITMzK1aePP4NRMQt5LiG38zMBqc8Qz3H101uRHa3ba9/27ZI59yOLKahC3gSOCkiXm6752Zm1pE8V/XU5/KvIyvWx+bYrlk65yeAuyJitqQZwAyczmlmVpo8Y/wd5fI3S+ck+9KYlFabC3Tjwm9mVppWj148v8V2ERFfzttIj3TOndKXAhGxQtKoJts4ndOGBH82bahpms4p6XMNZm8FTAe2j4itczXQI51T0qqIGFm3/OWIaHmhstM5bbDy5Zw2mLWdzhkRX6/beDhwFvBJ4PvA15tt16PRRumcz0sanY72R9N+BpCZmfVBy8s5JW0n6SvAA2RfEh+MiHMjotdi3SydE7gNmJbeTwNu7ajnZmbWkVZj/JcAxwNXAX8SEWva3HezdM7ZwA2SpgNPASe222kzM+tcqzH+9WRpnOvY8Lp9kZ3cLS2ywWP8Nlh5jN8Gs07G+Nu+q9fMzAY/F3czs4px4TczqxgXfjOzinHhNzOrmMIKv6RrJK2U9FDdvHGS7pa0VNL9kpzrb2ZWsiKP+K8FpvSYdzFwUUSMA85P02ZmVqLCCn9E/DvwUs/ZQO36/22AZ4tq38zMGsuTx9+fPgv8TNKlZF86Bzdb0emcNlT4s2lDTdM7d/tl51kc848jYmyavgJYGBE3SToJOD0iDu9tP75z1wYr37lrg1mzO3fLvqpnGlBL6bwRP7TdzKx0ZRf+Z4GJ6f2hwOMlt29mVnmFjfFLmkf2iMUdJD0NXACcBnxT0jDgDdIYvpmZlaewwh8RJzdZ9KGi2jQzs975zl0zs4px4TczqxgXfjOzinHhNzOrGBd+M7OKceE3M6uYUmOZ0/wzJT0q6WFJTuc0MytZqbHMkiYDxwL7RcT7gUsLbN/MzBooO5b508DsiFib1llZVPtmZtZY2bHM+wKHSJpFFtlwdkTc12hFxzLbUOHPpg01ZccyPwT8AjgLOAC4HtgzeumEY5ltsHIssw1mgyWW+Wng5sjcC6wHdii5D2ZmlVZ24b+FLI4ZSfsCmwIvlNwHM7NKKzuW+RrgmjTk8yYwrbdhHjMz618DEct8SlFtmplZ73znrplZxbjwm5lVjAu/mVnFuPCbmVWMC7+ZWcWUns6Zlp0tKST55i0zs5KVms4JIGl34AjgqQLbNjOzJspO5wS4HDgH8I1bZmYDoNR0TklTgWciYpmk3tZ1OqcNCf5s2lBTWjqnpC2BBcCREfGKpCeB8RHRa1aP0zltsHI6pw1mgyGdcy9gD2BZKvq7AUsk7VxiH8zMKq+0oZ6IeBAYVZtu54jfzMz6T5GXc84Dfgm8R9LTkqYX1ZaZmeU3EOmcteVdRbVtZmbN+c5dM7OKceE3M6sYF34zs4px4TczqxgXfjOziik1nVPSJZIekfSApB9KGllU+2Zm1ljZ6ZzzgbERsR/wGHBege2bmVkDpaZzRsSdEbEuTd5NFttgZmYlKjWds4dTgeubLXQ6pw2EyZMnt71Nb0mzjSxYsKDtbcz6y4AUfkkzgXXAdc3WiYirgKsgS+ecNGlSOZ2zSms3abO7uxt/Nm2oKb3wS5oGHA0cFs6zNTMrXdkPYpkCnAtMjIjXy2zbzMwyZadzfgsYDsyXtFTSlUW1b2ZmjZWdzjmnqPbMzCwf37lrZlYxLvxmZhXjwm9mVjEu/GZmFePCb2ZWMS78ZmYVU3Ys83aS5kt6PP3ctqj2zcyssbJjmWcAd0XEPsBdadrMzEpUaiwzcCwwN72fCxxXVPtmZtZY2SFtO0XECoCIWCFpVLMV62OZgTWSHi2jg2Zt2gF4YaA7YdbEuxvNHMg8/pbqY5nNBitJ90fE+IHuh1k7yr6q53lJowHSz5Ult29mVnllF/7bgGnp/TTg1pLbNzOrvLJjmWcDR0h6HDgiTZsNZR6OtCFHfgiWmVm1+M5dM7OKceE3M6sYF34zQFJXfbxImnehpLMbrHutpBPK651Z/3LhN2uDpEF774tZXi78Zr2Q1C3pHyUtBM5Ksw+X9B+SHpN0dFqvK81bkl4Hp/mT0j5+IOkRSddJ0kD9PmY+ejHLZ2RETIRsqAfoAiYCewELJO1NdkPiERHxhqR9gHlA7a7eDwDvB54F/hP4CLCozF/ArMZH/GaZZtc11+Zf32P+DRGxPiIeB54A3gtsAlwt6UHgRuB9devfGxFPR8R6YCnZF4fZgPARv1nmRaDn8yG2A/5fev9aj2U9vygC+HvgeWB/soOqN+qWr617/xb+f88GkI/4zYCIWAOskHQYZA8NInueRLPhmBMlbSRpL2BP4FFgG2BFOqr/K2Dj4ntu1j4fdZi97a+Bb0v6epq+KCJ+2+Q87KPAQmAn4Iw0rv9PwE2STgQW8M6/EswGBUc2mJlVjId6zMwqxoXfzKxiXPjNzCrGhd/MrGJc+M3MKsaF38ysYlz4zcwq5v8D4g3p5hHWCTcAAAAASUVORK5CYII=\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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    66.000000\n",
       "mean     24.621212\n",
       "std       5.408726\n",
       "min      12.000000\n",
       "25%      21.000000\n",
       "50%      24.000000\n",
       "75%      28.000000\n",
       "max      39.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": [
    "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(10, 30, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    # Create a box-and-whisker plot for the Rural cities ride count.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(2, 13, step=1.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Amandaburgh            18\n",
       "Barajasview            22\n",
       "Carriemouth            27\n",
       "Christopherfurt        27\n",
       "Deanville              19\n",
       "                       ..\n",
       "West Patrickchester    16\n",
       "West Robert            31\n",
       "West Samuelburgh       25\n",
       "Williamsstad           23\n",
       "Williamsview           20\n",
       "Name: ride_id, Length: 66, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urban_ride_count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 62,
   "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": 62,
     "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6K9srJX0aeCdwH/AvwPsqqy5iFDx/GizYtdI+eqEY9KyQ50+rtoOonVZ27r4L2A/YXdIewCnAeRTj/JdIeifFtfkfGLDcFIqdvzuV/fw/2/MlzQSupLgExGrgA7Y3jNk7iijpM09ie+QZR6HRaNDb21tpH5Lwgkq7iJpp9XDMScA04A+AzRRn7gL0n7WrQZZ5Djjc9oHAbOAoSYcC5wCLbe8HLC6nIyKiQ1rZuXuzpMuBLwC7AAts3yLp1cAjtgc9pNPFptbGcnJy+TBwPOUvZGAhxQ/lT43iPURERBtaGuO3fb6k7wCbbN9bNk8C5g23nKQdgGUUl3P+qu07JHXZXlOud005fDTYsvP619/V1UWj0Wil1IgtVP252bhxY0c+m/n8x1jSSGOgkuQRZhppnvLa/VcDHwVutT296bUNtmcMt/6enh739fUNW2fEQJK2nzH+it9HbJ8kLbPdM7C9lTH+pZI+Kuk1A1a4o6TDJS2kuGDbkGw/TjGkcxSwVlJ3uY5uYF1rbyEiIsZCK8F/FMUO3SskPSzpPyTdB6wCTgIusn3ZwIUk7d5/ly5JrwTeDdwDXMtLXxRzgWtG+yYiIqJ1rezcfRb4GvA1SZOB3YBnyq344XQDC8tx/knAItvXSfoxsEjSGcADwPtH8wYiIqI9rZ7A1e9twH62vyVpN2Cq7d8MNqPt5cBBg7Q/ChzRdqURETEmWr6ssqT5FIddnls27QhcXkVRERFRnXaup38ixeUZngaw/TAwtYqiIiKiOu0E//PlIZsGkLRzNSVFRESV2gn+RZL+EZgu6cPAj4BLqikrIiKq0tLOXUmiuLDa64Engf2B823fVGFtERFRgVYv2WBJ37P9FiBhHxExgbUz1HO7pLdWVklERHREO8fxzwHOlHQ/xZE9ovgxcEAllUVERCXaCf6jK6siIiI6puXgt30/QHkZ5SmVVRQREZVq58zd90laBfwGuJnitonXV1RXRERUpJ2duxcAhwK/sv1fKK638++VVBUREZVpJ/hfKC+wNknSJNtLKe6lGxERE0g7O3cfl7QLcAvwbUnrgE3VlBUREVUZcYu/6c5bxwO/Bz4B3AD8GnhvdaVFREQVWtni/x5wsO2nJX3X9p8BC6stKyIiqtLKGL+anr+2qkIiIqIzWgl+D/F8WJL2kbRU0kpJd0v6eNm+QNJDku4qH8e0W3RERGy7VoZ6DpT0JMWW/yvL5/DSJRumDbHcJuBs2z+TNBVYJqn/Am8X2b5wVJVHRMQ2aeVm6ztsy4ptrwHWlM+fkrQS2Gtb1hUREWOn3ZutbxNJsyhuvH4HcBhwlqRTgT6KXwUbBllmHjAPoKuri0aj0YlSYztT9edm48aNHfls5vMfY0nF3RQr7KA49v9m4LO2r5LUBayn2F9wAdBt+/Th1tHT0+O+vr5K64ztjySq/nw3Gg16e3sr7aMT7yO2T5KW2e4Z2N7Ombvb0ulk4LvAt21fBWB7re3Ntl+kuHXjIVXWEBERW6os+MvbNV4KrLT9903t3U2znQisqKqGiIjYWpVj/IcBpwC/kHRX2XYecJKk2RRDPauBMyusISIiBqgs+G3fypYnf/X7QVV9RkTEyCod44+IiJefBH9ERM0k+CMiaibBHxFRMwn+iIiaSfBHRNRMgj8iomYS/BERNZPgj4iomQR/RETNdOR6/BHjpbhW4MQ2Y8aM8S4htjMJ/thudeIa9rlWfkxEGeqJiKiZBH9ERM0k+CMiaibBHxFRMwn+iIiaqfKeu/tIWipppaS7JX28bJ8p6SZJq8q/OVYtIqKDqtzi3wScbfsNwKHA/5D0RuAcYLHt/YDF5XRERHRIZcFve43tn5XPnwJWAnsBxwMLy9kWAidUVUNERGytIydwSZoFHATcAXTZXgPFl4OkPYZYZh4wD6Crq4tGo9GJUiPals9mTDSq+qxDSbsANwOftX2VpMdtT296fYPtYcf5e3p63NfXV2mdEdsiZ+7Gy5mkZbZ7BrZXelSPpMnAd4Fv276qbF4rqbt8vRtYV2UNERGxpSqP6hFwKbDS9t83vXQtMLd8Phe4pqoaIiJia1WO8R8GnAL8QtJdZdt5wOeBRZLOAB4A3l9hDRERMUBlwW/7VmCoa+IeUVW/ERExvJy5GxFRMwn+iIiaSfBHRNRMgj8iomYS/BERNZPgj4iomQR/RETNJPgjImomwR8RUTMJ/oiImknwR0TUTII/IqJmEvwRETWT4I+IqJkEf0REzST4IyJqJsEfEVEzVd5z95uS1kla0dS2QNJDku4qH8dU1X9ERAyuyi3+y4CjBmm/yPbs8vGDCvuPiIhBVBb8tm8BHqtq/RERsW0qu9n6MM6SdCrQB5xte8NgM0maB8wD6OrqotFodK7CiDbksxkTjWxXt3JpFnCd7TeV013AesDABUC37dNHWk9PT4/7+voqqzNiW0miyn9DEaMhaZntnoHtHT2qx/Za25ttvwhcAhzSyf4jIqLDwS+pu2nyRGDFUPNGREQ1Khvjl3QF0AvsJulBYD7QK2k2xVDPauDMqvqPiIjBVRb8tk8apPnSqvqLiIjW5MzdiIiaSfBHRNRMgj8iomYS/BERNZPgj4iomQR/RETNJPgjImomwR8RUTMJ/oiImhmPyzJHvGxJ6sgyuaJnjKds8Uc0sd3WY+nSpW0vk9CP8Zbgj4iomQR/RETNJPgjImomwR8RUTMJ/oiImknwR0TUTGXBL+mbktZJWtHUNlPSTZJWlX9nVNV/REQMrsot/suAowa0nQMstr0fsLicjoiIDqos+G3fAjw2oPl4YGH5fCFwQlX9R0TE4Dp9yYYu22sAbK+RtMdQM0qaB8wrJzdK+mUnCoxo027A+vEuImII+w7W+LK9Vo/ti4GLx7uOiOFI6rPdM951RLSj00f1rJXUDVD+Xdfh/iMiaq/TwX8tMLd8Phe4psP9R0TUXpWHc14B/BjYX9KDks4APg8cKWkVcGQ5HTGRZTgyJhzlErEREfWSM3cjImomwR8RUTMJ/qgFSZ+WdLek5ZLukvS2YeZdIOmTo+yvISmHecbL0sv2OP6IsSLp7cBxwMG2n5O0G7Bjhf3tUNW6I8ZCtvijDrqB9bafA7C93vbDklaXXwJI6pHUaFrmQElLygsKfricp1fSdf0zSPoHSaeVz1dLOl/SrcD7y1lOlnSbpBWSDinnO6Rsu7P8u3/ZfpqkqyTdUPb5xWr/k0SdJfijDm4E9pH0K0lfk/SuFpY5ADgWeDtwvqQ9W1jmWdt/Yvtfyumdbf8x8BHgm2XbPcA7bR8EnA98rmn52cAHgTcDH5S0Twt9RrQtQz2x3bO9UdJbgHcAc4ArJY10ZdhrbD8DPCNpKXAI8PgIy1w5YPqKsv9bJE2TNB2YCiyUtB9gYHLT/IttPwEg6T8orrPy25HeX0S7EvxRC7Y3Aw2gIekXFGeOb+KlX71TBi4yyHTz/IMt83QL67gAWGr7REmzypr6Pdf0fDP59xkVyVBPbPck7V9uYfebDdwPrAbeUrb92YDFjpc0RdKrgV7gp+Uyb5S0k6RdgSNG6PqDZf9/AjxRbs3vCjxUvn7atryfiNHKFkXUwS7AV8qhlk3AvRSX/H4DcKmk84A7BizzE+D7wGuAC2w/DCBpEbAcWAXcOUK/GyTdBkwDTi/bvkgx1PNXwJJRvq+IbZJLNkRE1EyGeiIiaibBHxFRMwn+iIiaSfBHRNRMgj8iomYS/BERNZPgj4iomf8P0L/eQfItR60AAAAASUVORK5CYII=\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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the Suburban fare data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(10, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean      34.623440\n",
       "std       14.558046\n",
       "min       10.110000\n",
       "25%       19.900000\n",
       "50%       37.050000\n",
       "75%       47.070000\n",
       "max       58.550000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the Rural fare data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(5, 61, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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 fare box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "Ride_fare_data = [urban_fares, suburban_fares, rural_fares]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Ride Fare Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Fare ($USD)',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(Ride_fare_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(5, 60, step=3.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 66,
     "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYC0lEQVR4nO3de5gldX3n8feHi7JyR7Qz4mW8EFfEgKYlQY0MkY0aBUQf14AiKo+jG01MNLsSVETRhMXrbqJG9oGVeAFviIiYiDiNqxhxBhUEVFCHgCAXHS4zQRT47h9VLYee6e7TM12np6fer+c5T5/6nbp8T8+Zz6n+1a+qUlVIkvpjq4UuQJI0Wga/JPWMwS9JPWPwS1LPGPyS1DMGvyT1jMGveZPkn5K8ZaHrUCPJXklWbgZ1HJLkjIWuQ/cy+LWeJKuT3JHk9iS3JLkwyauTzPh5qapXV9UJI6zziCQrk6xNcn2SLyV52gi2W0keM8PrL0tyd1vX2iQ/TfJ/k/zuHLbxkSTv2MRSTwDe3a7v/klOSXJ1++/6nSTPnrLNZyT5QZL/SLIiySMGXjuwbbs1yeoN1PuUJBe1675k8N+hqs4G9k7ye5v4fjRPDH5N5+Cq2hF4BHAi8EbglOlmTrJ1F0Wksd7nNMnrgfcDfweMAQ8HPggc2kUdG+GbVbUDsDNwEHAHsCrJ3qPYeJIlwIHAWW3TNsA1wAFtTW8BPpVkaTv/7sCZbftuwErgkwOrXAecCvz3DWxrN+Bs4F3ALsBJwBeS7Dow2+nA8vl4b5oHVeXDx30ewGrgoClt+wH3AHu30x8BPgScSxMKB7Vt72hfvwJ47sDy2wA3A09qp/8QuBC4BfgesGxg3gngncA3aALzMVNq2RlYC7xwhvdwf5ovhuvax/uB+7evvQz4+pT5a3I77fv4APBF4HbgW8Cj29e+1s67rq3hRRvY9nrrb9vPAT4zMP1p4OfAre16H9+2Lwd+A/y63cYX2vZjgB+3NV0OHDbD+38p8JVZ/p0vAV4wsM0LB17bvv3d/+cpyxwErJ7S9lzgsiltPwKOHph+KvDThf5s+2ge7vFrKFV1EXAt8EcDzUfQBPSOwNenLHI6cPjA9DOBm6vq4iR70ITqO2j2Lv8G+GySBw3MfyRNGO0IXD1l3fsD2wGfm6HkN9F8uewL7EPzxfXmGd/kfR0OvA3YFbiK5n1SVU9vX9+nqnaoqk9Os/yGnMl9f39fAvYEHgxcDHy83cbJ7fOT2m0c3M7/43b5ndvaPtbu2W/IE4AfTldIkjHgd4HL2qbH03wB09awrt3e44d4X2kfU9sG/7q5AliaZKch1qeOGfyai+tognrS56vqG1V1T1X9asq8nwAOSfKAdvqItg3gJcC5VXVuu+x5NF0Lfzqw/Eeq6rKququqfjNl3Q+k+RK5a4ZaXwy8vapurKqbaILyyKHfKZxZVRe12/g4zRfIprrP76+qTq2q26vqTuB4YJ8kO0+3cFV9uqqua39nnwSupPlC25BdaP4yWE+SbWne02lV9YO2eQeavzwG3UrzxTubC4GHJDk8ybZJjgIeDTxgYJ7JWnYZYn3qmMGvudgD+OXA9DXTzVhVV9Hs5R3chv8h3Bv8jwBe2B44viXJLcDTgMG912nXDfwC2D3JNjPM8xDu+5fC1W3bsH4+8Pw/aIJxU/3295dk6yQnJvlxkttoutcAdp9u4SQvTfLdgd/Z3jPMv4YNhHZ7vOSjNN1Irx14aS0wdW98J6b58hhUVb+gObbyeuAG4FnAV2j+Qpw0Wcsts61P3TP4NZQkT6YJrsEundku7TrZ3XMocHn7ZQBNqH+0qnYZeGxfVScOue5vAr8CnjfDPNfRfMFMenjbBk3//G/3RpP8zizvY74cBvy/9vkRNL+Xg2i6bpZOltP+vM/7b0fY/B+asH5gVe0CfJ/1u1gmXULTlTO4jtAcoB+j6dsf/EvqMpouscl5t6fZa7+MIVTVBVX15KrajeYvq8cCFw3M8jiaYwO3DbM+dcvg14yS7JTkucAZwMeq6tI5LH4G8CfAf+PevX2Aj9H8JfDMds93uyTLkjx0mJVW1a3AccAHkjwvyQPaLoZnJzmpne104M1JHtSOWDmu3S40fdmPT7Jvku1oulnm4gbgUcPM2L6/Ryb5B2AZTZcTNHvAd9L89fIAmtFJM21je5ovg5va9b6c+/ahT3Ue8KT2/U36EE0AH1xVd0yZ/3M0Qy5f0C5zHHDJZFdQkq3a9m2byWyX5H4D7/OJ7b/BTjRDSK+tqn8dWP8BNMc0tBkw+DWdLyS5nWbv/E3Ae4GXz2UFVXU9zd75UxgYGlhV19Ds7R5LE2TX0AwTHPrzWFXvpelaePPAOl7LvcMX30Fz3OAS4FKag6fvaJf9EfB2mu6IK1n/wPRsjgdOa7tc/us08+yfZC1wG80opZ2AJw98cf4zTffTz2hG6PzblOVPAfZqt3FWVV0OvIfm93kDzcHbb0xXYFXdAHyVdnhr+xfDq2iOVfx84ByDF7fz3wS8gOYg9hrgD4A/G1jl02lG+ZxL89fTHcCXB17/HzSjtq6h6bI7bEpJhwMfnq5ejVaqvBGLtCVKshdwGrBfLeB/9CQHA0dW1XRfkhoxg1+SesauHknqGYNfknrG4JeknpnpBJjNxu67715Lly5d6DKk9axbt47tt99+ocuQNmjVqlU3V9WDprYviuBfunQpK1cu+GXFpfVMTEywbNmyhS5D2qAkU69zBdjVI0m9Y/BLUs8Y/JLUMwa/JPWMwS9JPdNZ8Cd5WHtz5iuSXJbkdW378Ul+1l5X/LtJ/nS2dUmS5k+XwznvAt7Q3mpvR5obTZ/Xvva+qnp3h9uWJE2js+BvL8l7ffv89iRX0NzIQ5K0gEZyAleSpcATgW8BTwVem+SlNNdLf0NVrdnAMstpbrbN2NgYExMToyhVPXfggQeOZDsrVqwYyXakDen8ssxJdgAuAN5ZVWcmGaO5YUMBJwBLquoVM61jfHy8PHNXm6MkeGlzba6SrKqq8antnY7qSbIt8Fng41V1JjR3Bqqqu6vqHpp7iO7XZQ2SpPvqclTP5I2dr2hvkzfZvmRgtsNobhgtSRqRLvv4nwocCVya5Ltt27HA4Un2penqWU1zH1BJ0oh0Oarn60A28NK5XW1TkjQ7z9yVpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqGYNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ4x+CWpZwx+SeqZbRa6AKkru+22G2vWrOl8O0k6Xf+uu+7KL3/5y063oX5xj19brDVr1lBVnT5WrFjR+TZG8eWlfjH4JalnDH5J6hmDX5J6prPgT/KwJCuSXJHksiSva9t3S3Jekivbn7t2VYMkaX1d7vHfBbyhqh4H/CHwmiR7AccA51fVnsD57bQkaUQ6C/6qur6qLm6f3w5cAewBHAqc1s52GvC8rmqQJK1vJOP4kywFngh8Cxirquuh+XJI8uBpllkOLAcYGxtjYmJiFKVqC9P152bt2rUj+Wz6+dd8SlV1u4FkB+AC4J1VdWaSW6pql4HX11TVjP384+PjtXLlyk7r1JYnCV1/vicmJli2bFmn2xjF+9CWKcmqqhqf2t7pqJ4k2wKfBT5eVWe2zTckWdK+vgS4scsaJEn31eWongCnAFdU1XsHXjobOKp9fhTw+a5qkCStr8s+/qcCRwKXJvlu23YscCLwqSRHA/8OvLDDGiRJU3QW/FX1dWC6q1c9o6vtSpJm5pm7ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPXMnII/yfZJtu6qGElS92YM/iRbJTkiyReT3Aj8ALi+vb7+u5LsOZoyJUnzZbY9/hXAo4G/BX6nqh5WVQ8G/gj4N+DEJC/puEZJ0jya7czdg6rqN1Mbq+qXNBdf+2x7ITZJ0iIx4x7/ZOgneXSS+7fPlyX5yyS7DM4jSVochj24+1ng7iSPobni5iOBT3RWlSSpM8MG/z1VdRdwGPD+qvprYEl3ZUmSujJs8P8myeE0188/p22zb1+SFqFhg//lwP40t0/8aZJHAh/rrixJUldmvR5/e8LWsVX122GbVfVTmhuqSJIWmVn3+KvqbuBBSe43gnokSR0b9g5cq4FvJDkbWDfZOOVeupKkRWDY4L+ufWwF7NhdOZKkrg0V/FX1Nmgu0lZV62abX5K0+RpqVE+S/ZNcDlzRTu+T5IOdViZJ6sSwwznfDzwT+AVAVX0PeHpHNUmSOjT09fir6popTXfPcy2SpBEY9uDuNUmeAlQ7rPMvabt9JEmLy7B7/K8GXgPsAVwL7NtOS5IWmWH3+FNVL+60EknSSAy7x39hki8nOXryOvySpMVp2HH8eybZD/gz4E3t0M4zqsoLtWmzVW/dCY7fudNtLAOY6HQTzfuQ5lGqam4LJLsD7wVeXFVbd1LVFOPj47Vy5cpRbEpbkCTM9fM9VxMTEyxbtqzTbYzifWjLlGRVVY1PbR/2BK6dkhyV5EvAhcD1wH7zXKMkaQSGPbj7PeAs4O1V9c3uypEkdW3Y4H9U+bemJG0RZgz+JO+vqr8Czk6yXvBX1SEzLHsq8Fzgxqrau207HnglcFM727FVde7GlS5J2hiz7fF/tP357o1Y90eAfwT+eUr7+6pqY9YnSZoHMwZ/Va1qb734ysFbLw6jqr6WZOmmFCdJmn8LcevF1ya5JMmpSXadp3VKkoY06lsvfgg4Aaj253uAV2xoxiTLgeUAY2NjTExMzHFTEp1/btauXTuSz6aff82noU7gSvLWDbVP3plrhuWWAudMHtwd9rWpPIFLG8MTuNR3053ANadbL85DEUuq6vp28jDg+/OxXknS8Gbt42/P2L04ybr2sTLJS4dY7nTgm8Bjk1yb5GjgpCSXJrkEOBD4601+B5KkOZltHP9Lgb8CXg9cDAR4EvCu9s/PqUM1f6uqDt9A8ykbX6okaT7Mtsf/58BhVbWiqm6tqluq6qvAC9rXJEmLzGzBv1NVrZ7a2LZ5rVhJWoRmC/47NvI1SdJmarZRPY9rD8ROFeBRHdQjSerYrME/kiokSSMz27V6rh5VIZKk0Rj2ZuuSpC2EwS9JPTNj8Cc5v/35P0dTjiSpa7Md3F2S5ADgkCRn0Izm+a2qurizyiRJnZgt+I8DjgEeCky9BHMBf9xFUZKk7sw2quczwGeSvKWqThhRTZKkDg17WeYTkhwCPL1tmqiqc7orS5LUlaFG9ST5e+B1wOXt43VtmyRpkRn21ovPAfatqnsAkpwGfAf4264KkyR1Yy7j+HcZeL7zPNchSRqRYff4/x74TpIVNEM6n457+5K0KA17cPf0JBPAk2mC/41V9fMuC5MkdWPYPX7am6Sf3WEtkqQR8Fo9ktQzBr8k9cyswZ9kqyTfH0UxkqTuzRr87dj97yV5+AjqkSR1bNiDu0uAy5JcBKybbKyqQzqpSpLUmWGD/22dViFJGplhx/FfkOQRwJ5V9ZUkDwC27rY0SVIXhr1I2yuBzwAfbpv2AM7qqCZJUoeGHc75GuCpwG0AVXUl8OCuipIkdWfY4L+zqn49OZFkG5o7cEmSFplhg/+CJMcC/ynJfwE+DXyhu7IkSV0ZNviPAW4CLgVeBZwLvLmroiRJ3Rl2VM897c1XvkXTxfPDqrKrR5IWoaGCP8lzgH8CfkxzWeZHJnlVVX2py+IkSfNv2K6e9wAHVtWyqjoAOBB430wLJDk1yY2D1/lJsluS85Jc2f7cdeNLlyRtjGGD/8aqumpg+ifAjbMs8xHgWVPajgHOr6o9gfPbaUnSCM3Y1ZPk+e3Ty5KcC3yKpo//hcC3Z1q2qr6WZOmU5kOBZe3z04AJ4I1zqliStElm6+M/eOD5DcAB7fObgI3pphlr7+RFVV2fZNqTwJIsB5YDjI2NMTExsRGbU991/blZu3btSD6bfv41n9Ll4Jx2j/+cqtq7nb6lqnYZeH1NVc36BTI+Pl4rV67srE5tmZLQ9eCziYkJli1b1uk2RvE+tGVKsqqqxqe2Dzuq55HAXwBLB5fZiMsy35BkSbu3v4TZjxNIkubZsJdlPgs4heZs3Xs2YXtnA0cBJ7Y/P78J65IkbYRhg/9XVfW/57LiJKfTHMjdPcm1wFtpAv9TSY4G/p3mILEkaYSGDf7/leStwJeBOycbq+ri6RaoqsOneekZw5cnSZpvwwb/E4AjgT/m3q6eaqclSYvIsMF/GPCowUszS5IWp2HP3P0esEuHdUiSRmTYPf4x4AdJvs19+/jnOpxTkrTAhg3+t3ZahSRpZIa9Hv8FXRciSRqNYc/cvZ1777F7P2BbYF1V7dRVYZKkbgy7x7/j4HSS5wH7dVGQJKlbw47quY+qOgvH8EvSojRsV8/zBya3Asa5t+tHkrSIDDuqZ/C6/HcBq2luqiJJWmSG7eN/edeFSJJGY7ZbLx43w8tVVSfMcz2SpI7Ntse/bgNt2wNHAw8EDH5JWmRmDP6qes/k8yQ7Aq8DXg6cAbxnuuUkSZuvWfv4k+wGvB54MXAa8KSqWtN1YZKkbszWx/8u4PnAycATqmrtSKqSJHVmthO43gA8BHgzcF2S29rH7Ulu6748SdJ8m62Pf6PO7JUkbb4MdknqGYNfknrG4JeknjH4JalnDH5J6hmDX5J6xuCXpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqmaFutj7fkqwGbgfuBu6qqvGFqEOS+mhBgr91YFXdvIDbl6ResqtHknpmofb4C/hykgI+XFUnT50hyXJgOcDY2BgTExOjrVBbhCQLXcIm23HHHf38a16lqka/0eQhVXVdkgcD5wF/UVVfm27+8fHxWrly5egKlIaUhIX4PyQNI8mqDR1DXZCunqq6rv15I/A5YL+FqEOS+mjkwZ9k+yQ7Tj4H/gT4/qjrkKS+Wog+/jHgc23f6zbAJ6rqXxagDknqpZEHf1X9BNhn1NuVJDUczilJPWPwS1LPGPyS1DMGvyT1jMEvST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DMGvyT1jMEvST1j8EtSzxj8ktQzBr8k9YzBL0k9Y/BLUs8Y/JLUMwa/JPWMwS9JPWPwS1LPGPyS1DPbLHQB0uYkyUiWqao5LyPNF/f4pQFVNafHihUr5ryMoa+FZvBLUs8Y/JLUMwa/JPXMggR/kmcl+WGSq5IcsxA1SFJfjTz4k2wNfAB4NrAXcHiSvUZdhyT11ULs8e8HXFVVP6mqXwNnAIcuQB2S1EsLMY5/D+CagelrgT+YOlOS5cBygLGxMSYmJkZSnDQXa9eu9bOpRWchgn9DZ7usN7C5qk4GTgYYHx+vZcuWdVyWNHcTExP42dRisxDBfy3wsIHphwLXzbTAqlWrbk5ydadVSRtnd+DmhS5CmsYjNtSYUZ9FmGQb4EfAM4CfAd8Gjqiqy0ZaiDQPkqysqvGFrkOai5Hv8VfVXUleC/wrsDVwqqEvSaMz8j1+aUviHr8WI8/clTbNyQtdgDRX7vFLUs+4xy9JPWPwS1LPGPzqhSRvSnJZkkuSfDfJemeLD8x7fJK/2cTtTSTxoK82S956UVu8JPsDzwWeVFV3JtkduF+H29u6q3VL88E9fvXBEuDmqroToKpurqrrkqxuvwRIMp5kYmCZfZJ8NcmVSV7ZzrMsyTmTMyT5xyQva5+vTnJckq8DL2xneUmSC5N8P8l+7Xz7tW3faX8+tm1/WZIzk/xLu82Tuv2VqM8MfvXBl4GHJflRkg8mOWCIZX4PeA6wP3BckocMscyvquppVXVGO719VT0F+HPg1LbtB8DTq+qJwHHA3w0svy/wIuAJwIuSDF7aRJo3dvVoi1dVa5P8PvBHwIHAJ4e4AdDnq+oO4I4kK2guJ37LLMt8csr06e32v5ZkpyS7ADsCpyXZk+bihNsOzH9+Vd0KkORymuusXIM0zwx+9UJV3Q1MABNJLgWOAu7i3r96t5u6yAamB+ff0DLrhljHCcCKqjosydK2pkl3Djy/G/9/qiN29WiLl+Sx7R72pH2Bq4HVwO+3bS+YstihSbZL8kBgGc3FBK8G9kpy/yQ701xocCYvarf/NODWdm9+Z5qLEwK8bGPej7Sp3KNQH+wA/EPb1XIXcBXNTX4eB5yS5FjgW1OWuQj4IvBw4ISqug4gyaeAS4Arge/Mst01SS4EdgJe0badRNPV83rgq5v4vqSN4iUbJKln7OqRpJ4x+CWpZwx+SeoZg1+Sesbgl6SeMfglqWcMfknqmf8PkOvunpAwH2MAAAAASUVORK5CYII=\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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dWFVr2kG1Z5VkDXAzzfitt800rJQkafHNFfC3zHPesIOq6mcjLitJWiRzBfxDk1w0zfQAD+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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 69,
   "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_data = [urban_driver_count, suburban_driver_count, rural_driver_count]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Driver Count Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Number of Drivers',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(driver_data, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 77, step=5.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig4.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 70,
     "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 71,
     "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": 72,
   "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * sum_fares_by_type / total_fares\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        6.811493\n",
       "Suburban    30.463872\n",
       "Urban       62.724635\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * pyber_data_df.groupby([\"type\"]).sum()[\"fare\"] / pyber_data_df[\"fare\"].sum()\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the percentage of fares by city type pie chart.\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",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import mpl to change the plot configurations using rcParams.\n",
    "import matplotlib as mpl\n",
    "# Build Pie Chart\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(type_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Fares by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig5.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        5.263158\n",
       "Suburban    26.315789\n",
       "Urban       68.421053\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of rides for each city type.\n",
    "ride_percents = 100 * pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"] / pyber_data_df[\"ride_id\"].count()\n",
    "ride_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build percentage of rides by city type pie chart.\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(ride_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Rides by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig6.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        2.623613\n",
       "Suburban    16.481668\n",
       "Urban       80.894719\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of drivers for each city type.\n",
    "driver_percents = 100 *city_data_df.groupby([\"type\"]).sum()[\"driver_count\"] /city_data_df[\"driver_count\"].sum()\n",
    "driver_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build percentage of rides by city type pie chart.\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(driver_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=165)\n",
    "plt.title(\"% of Total Drivers by City Type\")\n",
    "# Change the default font size from 10 to 14.\n",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig7.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}