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\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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\n"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": [
    "# Get the columns and the rows that are not null.\n",
    "ride_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Urban city DataFrame.\n",
    "urban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Urban\"]\n",
    "urban_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Suburban and Rural city DataFrames.\n",
    "suburban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Suburban\"]\n",
    "rural_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Rural\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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\"]\n"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "# Now we collected the data that we need, let's start building cool charts :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8af8fc1b90>"
      ]
     },
     "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, urban_avg_fare, s=urban_driver_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8af90f3810>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for urban cities.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=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 ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8af9249d10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for suburban cities.\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8af9311750>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for 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 ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add the scatter charts for each type of city.\n",
    "\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "\n",
    "# Incorporate the other graph properties\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\", fontsize=20)\n",
    "plt.ylabel(\"Average Fare ($)\", fontsize=12)\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "\n",
    "# Add the legend.\n",
    "plt.legend()\n",
    "# Create a legend\n",
    "lgnd = plt.legend(fontsize=\"12\", mode=\"Expanded\",\n",
    "         scatterpoints=1, loc=\"best\", title=\"City Types\")\n",
    "lgnd.legendHandles[0]._sizes = [75]\n",
    "lgnd.legendHandles[1]._sizes = [75]\n",
    "lgnd.legendHandles[2]._sizes = [75]\n",
    "lgnd.get_title().set_fontsize(12)\n",
    "\n",
    "\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",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "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>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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_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>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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36.000000\n",
       "mean     13.611111\n",
       "std       8.022389\n",
       "min       1.000000\n",
       "25%       6.500000\n",
       "50%      15.000000\n",
       "75%      20.250000\n",
       "max      25.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_driver_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18.000000\n",
       "mean      4.333333\n",
       "std       2.828427\n",
       "min       1.000000\n",
       "25%       1.250000\n",
       "50%       4.000000\n",
       "75%       7.000000\n",
       "max       9.000000\n",
       "Name: driver_count, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_driver_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Calculate the mean of the ride count for each city type.\n",
    "# round(urban_ride_count.mean(),2), round(suburban_ride_count.mean(),2), round(rural_ride_count.mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Calculate the mode of the ride count for each city type.\n",
    "# round(urban_ride_count.mode(),2), round(suburban_ride_count.mode(),2), round(rural_ride_count.mode(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "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]), 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}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([17]), count=array([7]))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_suburbant_ride_count = sts.mode(suburban_ride_count)\n",
    "mode_suburbant_ride_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([6]), count=array([5]))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_rural_ride_count = sts.mode(rural_ride_count)\n",
    "mode_rural_ride_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "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": [
    {
     "data": {
      "text/plain": [
       "2250    43.22\n",
       "2251    12.42\n",
       "2252    54.85\n",
       "2253    47.90\n",
       "2254    51.80\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the fares for the rural cities.\n",
    "rural_fares = rural_cities_df[\"fare\"]\n",
    "rural_fares.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": [
    "# Get the fares for the suburban cities.\n",
    "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 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}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for urban trips is $30.97.\n",
      "The median fare price for urban trips is $30.75.\n",
      "The mode fare price for urban 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 urban trips is ${mean_suburban_fares:.2f}.\")\n",
    "\n",
    "median_suburban_fares = np.median(suburban_fares)\n",
    "print(f\"The median fare price for urban trips is ${median_suburban_fares:.2f}.\")\n",
    "\n",
    "mode_suburban_fares = sts.mode(suburban_fares)\n",
    "print(f\"The mode fare price for urban trips is {mode_suburban_fares}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for urban trips is $34.62.\n",
      "The median fare price for urban trips is $37.05.\n",
      "The mode fare price for urban trips is ModeResult(mode=array([37.05]), count=array([2])).\n"
     ]
    }
   ],
   "source": [
    "mean_rural_fares = np.mean(rural_fares)\n",
    "print(f\"The mean fare price for urban trips is ${mean_rural_fares:.2f}.\")\n",
    "\n",
    "median_rural_fares = np.median(rural_fares)\n",
    "print(f\"The median fare price for urban trips is ${median_rural_fares:.2f}.\")\n",
    "\n",
    "mode_rural_fares = sts.mode(rural_fares)\n",
    "print(f\"The mode fare price for urban trips is {mode_rural_fares}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 50,
     "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.68"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_urban_drivers = round(np.mean(urban_drivers),2)\n",
    "mean_urban_drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_drivers = suburban_cities_df['driver_count']\n",
    "suburban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_suburban_drivers = round(np.mean(suburban_drivers))\n",
    "mean_suburban_drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "rural_drivers = rural_cities_df['driver_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.296"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_rural_drivers = np.mean(rural_drivers)\n",
    "mean_rural_drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban cities ride count.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(10, 41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add all ride 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": 58,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.000000\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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban fare data.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_fares, labels=x_labels)\n",
    "# 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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    625.000000\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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the suburban fare data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean      34.623440\n",
       "std       14.558046\n",
       "min       10.110000\n",
       "25%       19.900000\n",
       "50%       37.050000\n",
       "75%       47.070000\n",
       "max       58.550000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the rural fare data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for all fare data.\n",
    "x_labels = [\"Urban\", \"Suburban\", \"Rural\"]\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ride_fare_data = [urban_fares, suburban_fares, rural_fares]\n",
    "ax.boxplot(ride_fare_data, labels=x_labels)\n",
    "ax.set_title('Ride Fare Data (2019)', fontsize=14)\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_xlabel(\"City Types\",fontsize=16)\n",
    "ax.set_yticks(np.arange(0, 56, step=5.0))\n",
    "ax.grid()\n",
    "plt.savefig(\"analysis/Fig3.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1625.000000\n",
       "mean       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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the urban driver count data.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_drivers,labels=x_labels)\n",
    "# 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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    625.000000\n",
       "mean      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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the suburban driver count data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_drivers,labels=x_labels)\n",
    "# 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",
    "suburban_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean       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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for the rural driver count data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_drivers,labels=x_labels)\n",
    "# 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",
    "rural_drivers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+lwAT8x2cZB2wDmBiYoLBYNBhqJqP51zjbmZmxnausWc7X15SVd0UnEwC3wIeUVWnJHk3cCXwsqradWi/jVW14DivycnJmp6e7iROzS8JXbUNabkYDAZMTU2NOgypU7bz/iVZX1WT823rcozXBcAFVXVK+/5Y4CHApUlWt4GtBi7rMAZJkqRlo7PEq6ouAX6cZM921b7AWcDxwIHtugOB47qKQZIkaTnpcowXwMuAjyTZETgfeAFNsndMkhcBPwKe3XEMkiRJy0KniVdVnQ7M18e5b5f1SpIkLUfOXC9JktQTEy9JkqSemHhJkiT1xMRLkiSpJyZekiRJPTHxkiRJ6omJlyRJUk9MvCRJknpi4iVJktQTEy9JkqSemHhJkiT1pNPEK8mGJN9NcnqS6XbdoUkubNednuQJXcYgSZK0XHT6kOzWPlV1+Zx176yqt/dQtyRJ0rJhV6MkSVJPuk68CvhSkvVJ1g2tf2mSM5J8KMluHccgSZK0LHTd1fjIqrowyZ2BE5OcA/wjcBhNUnYY8A7ghXMPbBO1dQATExMMBoOOQ9VcnnONu5mZGdu5xp7tfHlJVfVTUXIoMDM8tivJGuCEqnrQQsdOTk7W9PR0twHq1yShr7YhjcpgMGBqamrUYUidsp33L8n6qpqcb1tnXY1Jdkqy8+xr4LHAmUlWD+32dODMrmKQJElaTrrsapwAPp1ktp6PVtUXkvxLkrU0XY0bgBd3GIMkSdKy0VniVVXnA3vNs/6AruqUJElazpxOQpIkqScmXpIkST0x8ZIkSeqJiZckSVJPTLwkSZJ6YuIlSZLUExMvSZKknnT9rEZtJatWrWLjxo291tlOftuL3XbbjSuuuKK3+iRJGgUTrxVi48aNvT47se9ne/WZ5EmSNCp2NUqSJPXExEuSJKknJl6SJEk96XSMV5INwFXA9cB1VTWZZBVwNLAG2AA8u6r6HTUuSZI0An1c8dqnqtZW1WT7/mDgpKq6L3BS+16SJGnsjaKr8anAUe3ro4CnjSAGSZKk3nU9nUQBX0pSwPur6ghgoqoubrdfAkzMd2CSdcA6gImJCQaDQcehLn99noOZmZnez7m/Y/VtFO1c6pvtfHlJl3NDJdmjqi5McmfgROBlwPFVtevQPhurareFypmcnKzp6enO4lwJkoz9PF59fj4J+m/n0ijYzvuXZP3QEKtf02lXY1Vd2P68DPg0sDdwaZLVbWCrgcu6jEGSJGm56CzxSrJTkp1nXwOPBc4EjgcObHc7EDiuqxgkSZKWky7HeE0An24fBbM98NGq+kKS7wDHJHkR8CPg2R3GIEmStGx0lnhV1fnAXvOs/ymwb1f1SpIkLVfOXC9JktQTEy9JkqSemHhJkiT1ZNHEK8m9k9y6fT2V5OVJdu08MkmSpDGzlCtenwSuT3If4AjgbsBHO41KkiRpDC0l8bqhqq4Dng78Q1X9JbC627AkSZLGz1ISr18l2Z9mstMT2nU7dBeSJEnSeFpK4vUC4OHAm6vqh0nuCfxLt2FJkiSNnwUnUE2yHfD6qnru7Lqq+iHwtq4DkyRJGjcLXvGqquuBeyTZsad4JEmSxtZSHhl0PvD1JMcDV8+urKrDO4tKkiRpDC0l8TqvXW4F7Ly5FbTdldPAhVX1pCRHAn8A/E+7y/Or6vTNLVeSJGmlWTTxqqo3ASS5XVX9fAvqOAg4G9hlaN1fVtWxW1CWJEnSirWUmesfnuQs4Jz2/V5J3reUwpPcFXgi8IFbFKUkSdIYWMp0Eu8CHgf8FKCq/hN41BLLfxfwV8ANc9a/OckZSd45+zgiSZKkcbeUMV5U1Y+TDK+6frFjkjwJuKyq1ieZGtr0WuASYEeaRxC9BvibeY5fB6wDmJiYYDAYLCXUsdbnOZiZmen9nPs7Vt9G0c6lvtnOl5dU1cI7JMcChwPvAX6XZszWZFU9Z5Hj3gIcAFwH3IZmjNenqupPhvaZAl5dVU9aqKzJycmanp5e7LOMtSQs9rvamgaDAVNTU73V1/fnk6D/di6Ngu28f0nWV9XkfNuW0tX4Z8BLgD2AC4G17fsFVdVrq+quVbUGeA7w5ar6kySr26ACPA04cwkxSJIkrXhL6WrM8Mz1W8FHktwJCHA6TWInSZI09paSeH09yQbgaOCTVfWzza2kqgbAoH396M09XpIkaRws2tVYVb8JvAF4IHBqkhOS/Mkih0mSJGmOpYzxoqq+XVWvBPYGrgCO6jQqSZKkMbSUCVR3SXJgks8D3wAupknAJEmStBmWMsbrP4HPAH9TVd/sNhxJkqTxtZTE617lBEuSJEm32CYTryTvqqpXAMcnuVniVVVP6TIwSZKkcbPQFa9/aX++vY9AJEmSxt0mE6/2GYvbAeu28gSqkiRJ26QF72qsquuBeyTZsad4JEmSxtZSBtefTzN7/fHA1bMrq+rwzqKSJEkaQ0tJvM5rl1sBO3cbjiRJ0vhaNPGqqjf1EYgkSdK4W3CMVztj/alJrm6X6STP25wKkmyX5LQkJ7Tv75nklCQ/SHK048ckSdK2YpOJV5IDgVcArwLuAuwB/BVwUJIDNqOOg4Czh96/DXhnVd0H2Ai8aDNjliRJWpEWuuL158DTq+rkqvqfqvpZVX0ZeCbwkqUUnuSuwBOBD7TvAzwaOLbd5SjgaVsYuyRJ0oqy0BivXapqw9yVVbUhyS5LLP9dNFfJZgfl3xH4WVVd176/gOZK2s0kWQesA5iYmGAwGCyxyvHV5zmYmZnp/Zz7O1bfRtHOpb7ZzpeXhRKva7ZwGwBJngRc1k7EOrWZcVFVRwBHAExOTtbU1GYXMXb6PAeDwaDX+qDfzyfBaNq51Dfb+fKyUOJ1/yRnzLM+wL2WUPYjgKckeQJwG2AX4N3Arkm2b6963RW4cDNjliRJWpEWTLxuScFV9VrgtQDtFa9XV9Vzk3wCeBbwceBA4LhbUo8kSdJKsdCzGn/UUZ2vAT6e5G+B04APdlSPJEnSsrKUmetvsaoaAIP29fnA3n3UK0mStJwsOIGqJEmStp6FJlA9qf35tv7CkSRJGl8LdTWuTvJ7NHcmfpzmbsYbVdWpnUYmSZI0ZhZKvN4I/DXNlA+Hz9lWNDPQS5IkaYkWuqvxWODYJH9dVYf1GJMkSdJYWvSuxqo6LMlTgEe1qwZVdUK3YUmSJI2fRe9qTPIW4CDgrHY5KMnfdR2YJEnSuFnKPF5PBNZW1Q0ASY6imfj0dV0Gpl9Xh+wCh96ht/qmoJ15rR91yFKfuy5J0sq11AlUdwWuaF/396+/bpQ3XUlV9VZf3w9VTUId2lt1kiSNxFISr7cApyU5mWZKiUcBB3calSRJ0hhayuD6jyUZAL/TrnpNVV3SaVSSJEljaEldjVV1MXB8x7FIkiSNtc6e1ZjkNkm+neQ/k/xXkje1649M8sMkp7fL2q5ikCRJWk6WOrh+S1wLPLqqZpLsAHwtyefbbX/ZTtAqSZK0zVjwileS7ZKcsyUFV2OmfbtDu/R3W54kSdIys+AVr6q6Psn3kty9qv57cwtPsh2wHrgP8N6qOiXJnwNvTvJG4CTg4Kq6dp5j1wHrACYmJhgMBptb/djp8xzMzMz0fs79Hatvo2jnUt9s58tLFpsbKslXgQcD3waunl1fVU9ZciXJrsCngZcBPwUuAXYEjgDOq6q/Wej4ycnJmp6eXmp1YynJ+M/j1ePnk6D/di6Ngu28f0nWV9XkfNuWMsbrr29pAFX1s3YesMdX1dvb1dcm+TDw6ltaviRJ0kqw6F2NVfUVYAOwQ/v6O8Cpix2X5E7tlS6S3BbYDzgnyep2XYCnAWduYeySJEkryqJXvJL8Kc1Yq1XAvYE9gH8C9l3k0NXAUe04r1sBx1TVCUm+nORONLPgnw782ZaHL0mStHIspavxJcDewCkAVXVukjsvdlBVnUEzNmzu+kdvbpCSJEnjYCkTqF5bVb+cfZNke5wWQpIkabMtJfH6SpLXAbdNsh/wCeCz3YYlSZI0fpaSeB0M/AT4LvBi4HPAG7oMSpIkaRwtOsarqm5IchTNGK8CvldOuCRJkrTZlnJX4xNp7mI8j+ZOxHsmeXFVfX7hIyVJkjRsKXc1vgPYp6p+AJDk3sC/ASZekiRJm2EpY7yumk26WucDV3UUjyRJ0tja5BWvJM9oX04n+RxwDM0Yrz+imb1ekiRJm2GhrsYnD72+FPiD9vVPgNt2FpEkSdKY2mTiVVUv6DMQSZKkcbeUuxrvCbwMWDO8f1U9pbuwJEmSxs9S7mr8DPBBmtnqb1hqwUluA3wVuHVbz7FVdUibyH0cuCOwHjhg+JFEkiRJ42opidcvqur/bUHZ1wKPrqqZJDsAX0vyeeCVwDur6uNJ/gl4EfCPW1C+JEnSirKU6STeneSQJA9P8pDZZbGDqjHTvt2hXQp4NHBsu/4o4GlbELckSdKKs5QrXr8FHECTMM12Nc4mUAtKsh1Nd+J9gPfSzH7/s6q6rt3lAmCPzYxZkiRpRVpK4vVHwL22ZBxWVV0PrE2yK/Bp4H5LPTbJOmAdwMTEBIPBYHOrHzt9noOZmZnez7m/Y/VtFO1c6pvtfHnJYs+7TvIZYF1VXXaLKkreCFwDvAb4jaq6LsnDgUOr6nELHTs5OVnT09O3pPoVLwl9Ppt8MBgwNTXVW319fz4J+m/n0ijYzvuXZH1VTc63bSlXvHYFzknyHZoB88Di00kkuRPwq6r6WZLbAvsBbwNOBp5Fc2fjgcBxS/kQkiRJK91SEq9DtrDs1cBR7TivWwHHVNUJSc4CPp7kb4HTaKaqkCRJGnuLJl5V9ZUtKbiqzgAePM/684G9t6RMSZKklWwpM9dfRXMXI8CONNNCXF1Vu3QZmCRJ0rhZyhWvnWdfJwnwVOBhXQYlSZI0jpYygeqN2klRPwMseBeiJEmSbm4pXY3PGHp7K2AS+EVnEUmSJI2ppdzV+OSh19cBG2i6GyVJkrQZljLG6wV9BCJJkjTuNpl4tTPNb0pV1WEdxCNJkjS2FrridfU863YCXgTcETDxkiRJ2gybTLyq6h2zr5PsDBwEvIDmUT/v2NRxkiRJmt+CY7ySrAJeCTwXOAp4SFVt7CMwSZKkcbPQGK//CzwDOAL4raqa6S0qSZKkMbTQBKqvAu4CvAG4KMmV7XJVkiv7CU+SJGl8bDLxqqpbVdVtq2rnqtplaNl5Kc9pTHK3JCcnOSvJfyU5qF1/aJILk5zeLk/Ymh9IkiRpuVrKBKpb6jrgVVV1ajs4f32SE9tt76yqt3dYtyRJ0rLTWeJVVRcDF7evr0pyNrBHV/VJkiQtd11e8bpRkjXAg4FTgEcAL03yPGCa5qrYze6UTLIOWAcwMTHBYDDoI9Rlrc9zMDMz0/s593esLbXPPvv0XufJJ5/ce53SlhjF33NtWqqq2wqS2wNfAd5cVZ9KMgFcDhTNJKyrq+qFC5UxOTlZ09PTnca53CWh69/VsMFgwNTUVG/19f35JLDdadvQ999zQZL1VTU537aF7mrcGhXvAHwS+EhVfQqgqi6tquur6gbgn4G9u4xBkiRpuegs8UoS4IPA2VV1+ND61UO7PR04s6sYJEmSlpMux3g9AjgA+G6S09t1rwP2T7KWpqtxA/DiDmOQJElaNrq8q/FrQObZ9Lmu6pQkSVrOOh3jJUmSpJuYeEmSJPXExEuSJKknJl6SJEk9MfGSJEnqiYmXJElST3p5VqO2jmZO2vG02267jToESZI6Z+K1QvT9PDmfYSdJ0tZnV6MkSVJPTLwkSZJ6YuIlSZLUk84SryR3S3JykrOS/FeSg9r1q5KcmOTc9qejqiUBsGrVKpL0tgC91rdq1aoRn2FJo9blFa/rgFdV1QOAhwEvSfIA4GDgpKq6L3BS+16S2LhxI1XV23LyySf3Wt/GjRtHfYoljVhniVdVXVxVp7avrwLOBvYAngoc1e52FPC0rmKQJElaTnoZ45VkDfBg4BRgoqoubjddAkz0EYMkSdKodT6PV5LbA58EXlFVVw5PAlpVlWTeyaKSrAPWAUxMTDAYDLoOVXN4zjUKfba7mZmZ3tu53yv1bRTtXJuWLifJTLIDcALwxao6vF33PWCqqi5OshoYVNWeC5UzOTlZ09PTncWpm3MCVY1C3+1uMBgwNTXVW31+rzQKfbdzQZL1VTU537Yu72oM8EHg7Nmkq3U8cGD7+kDguK5ikCRJWk667Gp8BHAA8N0kp7frXge8FTgmyYuAHwHP7jAGSZKkZaOzxKuqvgZs6qnO+3ZVryRJ0nLlzPWSJEk9MfGSJEnqiYmXJElST0y8JEmSemLiJUmS1BMTL0mSpJ6YeEmSJPXExEuSJKknJl6SJEk9MfGSJEnqiYmXJElST7p8SLYkbZY6ZBc49A691TcFMOituubzSdqmdZZ4JfkQ8CTgsqp6ULvuUOBPgZ+0u72uqj7XVQySVpa86Uqqqrf6BoMBU1NTvdWXhDq0t+okLUNddjUeCTx+nvXvrKq17WLSJUmSthmdJV5V9VXgiq7KlyRJWmlGMcbrpUmeB0wDr6qqjfPtlGQdsA5gYmKCwWDQX4QC8JxrJPpsdzMzM723c79X6tso2rk2LV2Op0iyBjhhaIzXBHA5UMBhwOqqeuFi5UxOTtb09HRncermkvQ61kaC/tvdSMZ4+b1Sz/pu54Ik66tqcr5tvU4nUVWXVtX1VXUD8M/A3n3WL0mSNEq9Jl5JVg+9fTpwZp/1S5IkjVKX00l8jGaanN2TXAAcAkwlWUvT1bgBeHFX9UuSJC03nSVeVbX/PKs/2FV9kiRJy52PDJIkSeqJiZckSVJPTLwkSZJ6YuIlSZLUExMvSZKknph4SZIk9cTES5IkqScmXpIkST0x8ZIkSeqJiZckSVJPTLwkSZJ60lnileRDSS5LcubQulVJTkxybvtzt67qlyRJWm66vOJ1JPD4OesOBk6qqvsCJ7XvJUmStgmdJV5V9VXgijmrnwoc1b4+CnhaV/VLkiQtN32P8Zqoqovb15cAEz3XL0mSNDLbj6riqqoktantSdYB6wAmJiYYDAZ9haaW51yj0Ge7m5mZ6b2d+71S30bRzrVpqdpk7nPLC0/WACdU1YPa998Dpqrq4iSrgUFV7blYOZOTkzU9Pd1ZnLq5JHTZNqT59N3uBoMBU1NTvdXn90qj0Hc7FyRZX1WT823ru6vxeODA9vWBwHE91y9JkjQyXU4n8THgm8CeSS5I8iLgrcB+Sc4FHtO+lyRJ2iZ0NsarqvbfxKZ9u6pTkqRxlaT3Ou0a3/pGNrhekiQt3ZYmQY4tXF58ZJAkSVJPTLwkSZJ6YuIlSZLUExMvSZKknph4SZIk9cTES5IkqSdOJyFJUo9WrVrFxo0be62zzznAdtttN6644ore6ltpTLwkSerRxo0bx/6ZpNo0uxolSZJ6YuIlSZLUExMvSZKknoxkjFeSDcBVwPXAdVU1OYo4JEmS+jTKwfX7VNXlI6xfkiSpV3Y1SpIk9WRUiVcBX0qyPsm6EcUgSZLUq1F1NT6yqi5McmfgxCTnVNVXh3doE7J1ABMTEwwGgxGEuW3znGsUxnkOoJ133tnvlYB+/77OzMz03u5s55uWPidxmzeA5FBgpqrevql9Jicna3p6ur+gRJJeJ/iTRsF2rlHou92NYgLVbf17lWT9pm4c7L2rMclOSXaefQ08Fjiz7zgkSZL6Noquxgng0213wvbAR6vqCyOIQ5IkqVe9J15VdT6wV9/1SpK0HNQhu8Chd+itvimAQW/VNZ9Pm+RDsiVJ6lHedOX4j/E6tLfqVhzn8ZIkSeqJiZckSVJP7Gocc7dkTqQtPXZbv41YkqRN8YrXmKuqLVpOPvnkLT5WkiTNz8RLkiSpJyZekiRJPTHxkiRJ6omJlyRJUk9MvCRJknpi4iVJktQT5/GStOI5X51WmlvSZpe73XbbbdQhLGsjueKV5PFJvpfkB0kOHkUMksaH89VpJdnSNjeKdr4lyxVXXDHqU7ys9Z54JdkOeC/wh8ADgP2TPKDvOCRJkvo2iiteewM/qKrzq+qXwMeBp44gDkmSpF6NYozXHsCPh95fAPzu3J2SrAPWAUxMTDAYDHoJTo2ZmRnPucae7VzbAtv58rJsB9dX1RHAEQCTk5M1NTU12oC2MYPBAM+5xp3tXNsC2/nyMoquxguBuw29v2u7TpIkaayNIvH6DnDfJPdMsiPwHOD4EcQhSZLUq967GqvquiQvBb4IbAd8qKr+q+84JEmS+jaSMV5V9Tngc6OoW5IkaVR8ZJAkSVJPTLwkSZJ6YuIlSZLUExMvSZKknph4SZIk9SRVNeoYFpXkJ8CPRh3HNmZ34PJRByF1zHaubYHtvH/3qKo7zbdhRSRe6l+S6aqaHHUcUpds59oW2M6XF7saJUmSemLiJUmS1BMTL23KEaMOQOqB7VzbAtv5MuIYL0mSpJ54xUuSJKknJl5jLsmaJGfOWXdoklfPs++RSZ7VX3TS0iR5fZL/SnJGktOT/O4C+87bvjezvkES7wLTspTk+vZ7cGaSzybZdSuWPbO1ytL8TLwEQJLtRx2DNJ8kDweeBDykqn4beAzw4w7r266rsqWt5JqqWltVDwKuAF6y1AP9Wz96Jl7bsPZ/9e9KMg0c1K5+TJLpJN9P8qR2vzVJ/iPJqe3ye+36qbaMY5Ock+QjSTKqz6OxtRq4vKquBaiqy6vqoiQbkuwOkGQyyWDomL2SfDPJuUn+tN1nKskJszskeU+S57evNyR5W5JTgT9qdzlg6KrC3u1+e7flnpbkG0n2bNc/P8mnknyhrfPvuz0l0o2+CewBv36lNsnuSTa0r5+f5PgkXwZOSnL7JCe1f8+/m+SpI4t+G2Tmqx1nJ9ZLciSwBtgbuDdwcpL7AJcB+1XVL5LcF/gYMNsN82DggcBFwNeBRwBf6/MDaOx9CXhjku8D/w4cXVVfWeSY3wYeBuwEnJbk35ZQz0+r6iEASf4MuF1VrU3yKOBDwIOAc4Dfr6rrkjwG+Dvgme3xa2m+D9cC30vyD1XV2ZU5qb06uy/wwSXs/hDgt6vqivaq19Or6sr2Py/fSnJ8ebddL0y8xt+mvkiz64+es/6YqroBODfJ+cD9gB8C70myFrge+M2h/b9dVRcAJDmdJnEz8dJWU1UzSR4K/D6wD3B0koMXOey4qroGuCbJyTT/mfjZIsfM/S58rK3/q0l2acfR7Awc1f4HpIAdhvY/qar+ByDJWcA96LBLVNu027Z/b/cAzgZOXMIxJ1bVFe3rAH/X/qfihracCeCSDmLVHHY1jr+fArvNWbeKm57bdfWcbXMTtQL+D3ApsBfNla4dh7ZfO/T6ekzm1YGqur6qBlV1CPBSmqtM13HT37DbzD1knvfD+893zFK+C4cBJ7dja548pwy/C+rLNVW1lia5DzeN8VroOzHcvp8L3Al4aFvOpfPsr46YeI25qpoBLk7yaIAkq4DHs+mrUn+U5FZJ7g3cC/gecAfg4vZK2AGAg4/VmyR7tleYZq0FfgRsAB7arnvmnMOemuQ2Se4ITAHfaY95QJJbt1ev9l2k6j9u638k8D/t1aw7ABe225+/BR9H2mqq6ufAy4FXtd2HG7jpO7HQHep3AC6rql8l2YcmgVNP/B/ZtuF5wHuTHN6+f1NVnbeJcfD/DXwb2AX4s3Zc1/uATyZ5HvAFbn5lQOrS7YF/aJOl64AfAOuA+wMfTHIYMJhzzBnAycDuwGFVdRFAkmOAM2m6z09bpN5fJDmNpjvxhe26v6fpanwDsJRxY1Knquq0JGcA+wNvB45Jso6F2+dHgM8m+S4wTTN2UT1x5npJkqSe2NUoSZLUExMvSZKknph4SZIk9cTES5IkqScmXpIkST0x8ZLUuSQPT3JMkouS/DLJT5OcmOTA2YdSt8+TqyRrho47dHYOuq0Qw6Ft+Ystz98a9UnSfJxOQlKnkrwCOBz4MnAUzUSmuwGPBV4A7F9VxyW5E80zQk+bfSB2kgLeXFVv2Apx3BW469CqJwJvoHko9gVD68+rqp/c0vokaT5OoCqpM+2z4A4H3lNVL5+z+bh2Ut+dANpkp7OEp32m6I0JVpL7tS9Pr6ofdFWvJA2zq1FSl14DXAH81Xwbq+q8qjoDbt7V2F7tAnj9UDfgoUleleTa9grZjdI4P8nHtyTQpZabZE0by18kOTzJZUl+nuSE4W7SoePXJfnPJL9IcnmSD7aP7hre56AkZye5JsnGJNNJnr4ln0PS8mbiJakT7ditfYAvVdUvtqCIh7c/j2xfPxz4APBh4AaabsphjwXuCfzTlsS7BeW+Frhvu/9LaJ6R96UkO8zukOStwHuBfweeAvwlzbNSPz80tu25wDuAjwFPoHmA8bE0D7OXNGbsapTUld2B29KM6dpsVfWt9nmiF1bVt4a3JTkaWJfk/9ZNA1VfDJxTVYMtrO+KzSz3KuCp7cPjSfJ9mofPP4/mGZJraBKtN1XV3wzFPrvfk4HP0CSUZwzvA3xuSz6DpOXPK16SVqL30QzE3xcgyWqaROaIHss9djbpAqiqr9OMIZu9Urcfzd/YjyTZfnYBTqFJ2h7V7vcdYG2Sf0jymCS3u4WfQdIyZuIlqSs/Ba4B7rG1C66qbwPrgT9rV/1v4Dqauyb7KvfSTazbo3195/bnD4BfzVl2Bu7Ybv//gD8Hfhf4InBFkk/NN15M0spnV6OkTlTVdUkGwH5Jbj07RcRW9D7g/Un2oEmQPlFVV/RY7sQm1p3evv5p+/OxwMZ59v0pQNul+f62ztlpNt4BHE2TjEkaI17xktSlt9Jc2fn7+TYmuWeS317g+F/SjBObz8douuw+CtydLR9Uv6XlPivJjX9DkzyCZp6wb7arTqQZrH/3qpqeZ/nh3AKramNVHQ0cAzxoK30eScuIV7wkdaaqvprklcDhSR5Ac4fif9NMoLovzRWl/wWcsYkizgKemOQLNFeNLqqqi9qyr0lyJPB/gO9W1Te2UsxLLXdn4DNJ3g/cCXgLcC5N1yFVdV6StwHvSbIn8BXgF8DdaMZ/faCqTk5yBE2i903gMuA3gQOAL22NzyNpefGKl6ROVdW7gEcCPwPeTjOD/ZHA/WnuGPzsAoe/FLi63ec7wLo52z/R/nz/1op3M8p9C834rSNpuidPBR5XVb+a3aGqXkcT86NormIdRzO32UaaJA3g6zRTUbyP5irZ64F/BQ7cOh9F0nLiI4MkrVhJ3gwcBNylqq7so9x20PsPgT+tqg9srTolbRvsapS04iR5MLAnTXJ0xNZKuroqV5JmmXhJWok+TXMH4ReBQ1ZAuZIE2NUoSZLUGwfXS5Ik9cTES5IkqScmXpIkST0x8ZIkSeqJiZckSVJPTLwkSZJ68v8D6IRWIrtvDhcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the box-and-whisker plot for all driver count data.\n",
    "x_labels = [\"Urban\", \"Suburban\", \"Rural\"]\n",
    "driver_count_data = [urban_drivers, suburban_drivers, rural_drivers]\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.boxplot(driver_count_data, labels=x_labels)\n",
    "ax.set_title(\"Driver Count Data(2019)\", fontsize=14)\n",
    "ax.set_ylabel(\"Number of Drivers\")\n",
    "ax.set_xlabel(\"City Types\",fontsize=16)\n",
    "ax.set_yticks(np.arange(0, 80, step = 5.0))\n",
    "ax.grid()\n",
    "plt.savefig('analysis/fig4.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        4327.93\n",
       "Suburban    19356.33\n",
       "Urban       39854.38\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of the fares for each city type.\n",
    "sum_fares_by_type = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "sum_fares_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the sum of all the fares.\n",
    "total_fares = pyber_data_df[\"fare\"].sum()\n",
    "total_fares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * sum_fares_by_type / pyber_data_df[\"fare\"].sum()\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import mpl to change the plot configurations using rcParams.\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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 the percentage of fares by city type pie chart.\n",
    "\n",
    "plt.subplots(figsize=(10, 6))\n",
    "\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",
    "mpl.rcParams['font.size'] = 14\n",
    "# Save Figure\n",
    "plt.savefig(\"analysis/Fig5.png\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a number of rides per city type\n",
    "ride_count_city_type = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "ride_count_city_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of rides for each city type.\n",
    "ride_percents = 100 * ride_count_city_type / pyber_data_df[\"ride_id\"].count()\n",
    "ride_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build percentage of rides by city type pie chart.\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.pie(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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 75,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}