PyBer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ae525e6",
   "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": 9,
   "id": "690c7641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\John\\\\Documents\\\\Berkeley\\\\Classwork\\\\module 5\\\\PyBer_Analysis'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95a59b67",
   "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": 14,
   "id": "abe1f652",
   "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": 14,
     "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": 15,
   "id": "8034170a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "    </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": 15,
     "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": 16,
   "id": "ec00b539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            120\n",
       "driver_count    120\n",
       "type            120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "id": "0588e600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            0\n",
       "driver_count    0\n",
       "type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "id": "111ea758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            object\n",
       "driver_count     int64\n",
       "type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f5db92a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Suburban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "id": "6b17678d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "id": "72e146c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(city_data_df[\"type\"]==\"Rural\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a72e939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "id": "96efdb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "id": "5f9b8a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a16a022e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 30,
     "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": 35,
   "id": "d1b8ded4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 35,
     "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": 36,
   "id": "3596e73f",
   "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\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a6d013e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>Randallchester</td>\n",
       "      <td>2019-04-13 11:13:31</td>\n",
       "      <td>43.22</td>\n",
       "      <td>1076079536213</td>\n",
       "      <td>9</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>North Holly</td>\n",
       "      <td>2019-02-02 14:54:00</td>\n",
       "      <td>12.42</td>\n",
       "      <td>1985256326182</td>\n",
       "      <td>8</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-03-27 18:27:34</td>\n",
       "      <td>54.85</td>\n",
       "      <td>4421836952718</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-02-23 21:12:24</td>\n",
       "      <td>47.90</td>\n",
       "      <td>3269652929887</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-05-06 08:57:56</td>\n",
       "      <td>51.80</td>\n",
       "      <td>4018025271936</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-04-29 17:04:39</td>\n",
       "      <td>13.38</td>\n",
       "      <td>8550365057598</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-01-30 00:05:47</td>\n",
       "      <td>20.76</td>\n",
       "      <td>9018727594352</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>North Jaime</td>\n",
       "      <td>2019-02-10 21:03:50</td>\n",
       "      <td>11.11</td>\n",
       "      <td>2781339863778</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>West Heather</td>\n",
       "      <td>2019-05-07 19:22:15</td>\n",
       "      <td>44.94</td>\n",
       "      <td>4256853490277</td>\n",
       "      <td>4</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>Newtonview</td>\n",
       "      <td>2019-04-25 10:20:13</td>\n",
       "      <td>55.84</td>\n",
       "      <td>9990581345298</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "2250   Randallchester  2019-04-13 11:13:31  43.22  1076079536213   \n",
       "2251      North Holly  2019-02-02 14:54:00  12.42  1985256326182   \n",
       "2252      Michaelberg  2019-03-27 18:27:34  54.85  4421836952718   \n",
       "2253  Lake Latoyabury  2019-02-23 21:12:24  47.90  3269652929887   \n",
       "2254  Lake Latoyabury  2019-05-06 08:57:56  51.80  4018025271936   \n",
       "...               ...                  ...    ...            ...   \n",
       "2370      Michaelberg  2019-04-29 17:04:39  13.38  8550365057598   \n",
       "2371  Lake Latoyabury  2019-01-30 00:05:47  20.76  9018727594352   \n",
       "2372      North Jaime  2019-02-10 21:03:50  11.11  2781339863778   \n",
       "2373     West Heather  2019-05-07 19:22:15  44.94  4256853490277   \n",
       "2374       Newtonview  2019-04-25 10:20:13  55.84  9990581345298   \n",
       "\n",
       "      driver_count   type  \n",
       "2250             9  Rural  \n",
       "2251             8  Rural  \n",
       "2252             6  Rural  \n",
       "2253             2  Rural  \n",
       "2254             2  Rural  \n",
       "...            ...    ...  \n",
       "2370             6  Rural  \n",
       "2371             2  Rural  \n",
       "2372             1  Rural  \n",
       "2373             4  Rural  \n",
       "2374             1  Rural  \n",
       "\n",
       "[125 rows x 6 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_cities_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e35b26a1",
   "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": 39,
     "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": 40,
   "id": "a0dd4411",
   "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": 41,
   "id": "f9d1615b",
   "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": 41,
     "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": 42,
   "id": "16b3c50b",
   "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": 43,
   "id": "73366ad0",
   "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": 43,
     "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": 44,
   "id": "133bdf24",
   "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": 46,
   "id": "b952348b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x185b2a07760>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DGcv38t/le/EaJucO6cL1E3qQluAId2jtlstrcMdby1i6uwSrRWGaMCg7mTdvGUeys2016cPtUPXOGcv3NkjoTruFP1w6NOqvsTV+2HqgyU8qPkMzZ1NxwJN5qzpulFK5wEhgMdAPmKSUWqyUmquUGtvEz0xRSi1TSi0rLi5uc8Di2KrdPi5++Uee/Hwjq/aUsb6ggr99t40znptLYXltuMNrt56dtZklu0pweU2q3Qa1XoO1e8v53acbwh1aQPz+4sE8dekQcjsmYFFgUTCmRwf+dfM4Lht1/GWao5HVgn/RSCOUAlsQxshavJxfKZUEzAWe0lrPVEqtA74D7gfGAv8BeulmXlCW84fGX77ezNR5Oxp8xLMoOGNQJlNvaHZVcMAt3nGQhz5YQ0q8nVeuH0W3JjbaiHXDnphFhcvX4LjDamHT78+JqUFwt8/AqlRQBvqiQVGli4l/moOnkamacTYLs355cqvGEFqynL9F/6eVUnZgBvCu1npm3eG9wEzttwQwgcipOtOO/Wfpnkbn+5oa5mwqwu0L7QrTX7y3kt0lNawvKOf/PouNVujxaOyeAPhMEzOENZJCIc5mbbeJHKBzspO7Tu5NvN1a73i8w8qVY7oHZTD4mH3myj858nVgo9b6uSMe+gg4DfheKdUPcAAHAh6haLXa5ub7an9SibNZm3xOoDnq/qgtShEX4gJi4J/j++/FeTw/ezMHq73YrIqzB3fhtxcMIjPFGbI4JvbpxJzNRRyxfSoKGJXToV0nvlj1q7P60b9LMi/N2caekhqyUp3cfWpvLh3ZNSjna8lslpOAG4C1SqlVdcceBd4A3qjrbvEANzXXxSJCZ0ROGj9sbfx9NTPFSXJcaJcXTL1xDL/9aB0dEh08ceHgkJ5ba80tby7l+80/jdd4Dc1nawr5ev0+Zj9wCj1CNGXu8QsHszyvFJfHwOUzibNZsFstPHXp0JCcX4Te+cOyOH9YVkjOJSVwY9DKvFKu+cciXN76H+vj7Rb+eNkwLglSyyASfbtxP7e91fTvXO+MRL598NSQxVNa7eG9pXms3lPOwKxkrhmfQ+fk0H06ENFJSuC2UyNzOvDKdaP5zYw1VLl9KAVWpXjonAHtKpGDfwZJc7YXV1NU6QpZQu2Q6ODuU/uE5FyifZFkHqMmD+jMokdOZ0tRJV6fZkBWcrusK13QgqmY+8vd0joWUU+SeQyzWFTAFyZEmw4JDsprG04HPFLnFNnnVES/9tdUE+3Kvac136WRmRIX0hktQgSLJHMR0y4f1Y2+nZMafUwpeO2G0SGOSIjgkGQugm73wWr+MW8Hby/aTWm1J6TnVkrx1S9P5tpxOditP62w7J+ZxOf3TWRE9w4hjUeIYJGpiSKoPlmVz0MfrMHQGptFYbVYeP/OExiUHfq+/I2FFWwoLCfV6WBi30447aFbOHWI1pr52w6wvaiKnI4JTO7fWfbGFMckUxNFWHl8Jo9+uA5X3TJ2r6EBk//30Vpm/vykkMVRUFbLlGnL2F5chUUplFJorXn8osFcOaZ7yOIAeOzDdXy0Kh/D1Fgtisn9O/PStSMloYs2k2QugqagrLbRmiMbCitCFoPLa3DZKwsornRjmPVj+d+P15EWb+eswV1CEsuqPWV8uDK/XonYOZuLmLf1AKf0y2jTa1e7fTz5+QY+WlmAzzSZ3L8zT14yhM4yuNtuSJ+5CJrMFCeN9eLldgzdjjOfrSmkotbbIJGDf6OAp7/cFLJY9pTUcPRUf1Nr9pbWtPm175i2jJkr/G8UXkPz7ab9XPr3BXiNxot7eXwms9bvY9rCXazMK0UqcUQ/aZmLoIl3WPnVWX157uutuLwGFgs4rFYeD2F9ls/XFDS70fCe0hoOVrnpmBT8ueZ9OifhO+pNRaHol5ncptfdVlTFirzSelUZDRPKaj3M2VTU4JPHtqJKrnptES6fgc/QWJRiePdU3rxlXFjGEURgSDIXQXXHpN4M65rGR6sKSHRYuXpcDn2amCoYDMdqcCoUoWqTDsxK4Z5T+/Dy99uwWSz4TJPrJ+QwNje9Ta+7t7QGu9XSoBaP16fZW1p/Bay/8NgySqo99a57ZV4Zz83ewqPnDWxTLCJ8JJmLoBvfqyPje3UMy7nPGpzJkl0lTbbOM1Pi6JgYuq307ju9L+cM6cL24mpy0hMCMqtnUHZKo7XSrVbF8O5p9Y6tL6jgYJW7wRuY22fyn6V7JJlHMekzFzHtkpFdSXBYG92PMd5u5aFz+od8JknfzGTOGdIlYNMzOyc7uemEHvU2Qoi3WxmXm86onLR6z610+bA2cb3N1cEXkS/iW+aF5bV8trqQCpeXU/tnMCqng0zjEi2W4LDxwV0ncvO/llBU6casmxJomJoHz+rHhcNjo4rko+cNZGROB95ZtBuX1+Ty0V25ckz3Bn8rw7ql4jUbHxQd17Nt3T0ivCJ60dCXawt54P1VmKbGa2icDiunDejM364eGVP7JYrg01qzbHcpmworSHbaOX1g53a1W/yR3pi/k2dmbT48RdKiwGm3MvPnJ7b7wmyRKqoXDVW6vDzw/qp6gzq1HoM5m4r4ct2+kO3eIWKDUoqxuektHmz0+EzWFZSjgCFdU2OqfPCtE3vSu3MSr87dTmFZLWNy07lnch96hmjHJREcEZvM5289gM1iwb9P9E9qPAYfLN8jyVwETYXLy+V/X0BBmX8mSE7HBD6460QSQ7zdXmvsPFDN1Hk7+GbDfgytGd8znbtO6d1gAPSQU/pltHmhkogsEdvcaGSNR4seE6Ktnp+9hd0Hq6n2GFR7DHYUV/PynG3hDqtJP2wt5rwXfuD9ZXkUV7kpqfYwa/0+rp66iHcW7Q53eCJEIjaZT+rXCV8jAzUJDiuXj+4WhohEe7G9uBqP8VOLwe0z2V5cFcaImlbt9nHn28up9RocudjT1FDrNXjysw3siNDYRWBFbDJPcdr502XD6nYwVyj8iXxin06cP1S6WKJFUYWL33+2gUl//o7T//I9U+dtj/gpcBN6peO0//SnEW+3Mj5CZ3p8tCq/2cd9pubNBbtCE4wIq8jtBAQuHtmVUT068NGqfCpqvUwe0JkTenVs11MTq90+Fu88SHpiHCOa6A+NFAVltZz/4g9UuX11FRPhua+38PGqAmbcfWLELh2fMqkXm/dV8unqAhSKC4ZlcdOJPcMdVqNW7C5ttlyBz9Ss2F0awohEuER0Mgfonp7Afaf1DXcYEaHC5eW8F36gtMaDacItJ+Xy0DkDwh1Wk56ZtZnyWm+9MQ6Xz2RHcRUfrsznmnE54QuuGTarhReuHsmffzYMgDhbZL7pACQ77SjVfNmCBEfkxi8CJ2K7WURDs9bt42CVh2q3Qa3X4LV5OxqtBhgpZq3f1+hgda3XZMbyvaEPqJXibNYWJfLlu0u4/72VXPuPRbw2dzvltd4QROd34fCsZj/hJDis/KyRmu3r8sv53afr+cX0lbw8ZyvFle5ghilCIOJb5uInCQ4bR/Yw2a2q0WXq0SBy34Ja59W523jhm224fAZaw4q8Ul6fv5PP7psYklrio3I6MCgrhbV7y+oN2gJYLYrUeDsXDss+fMztM/j5Oyv4cftBPD4DU0PcegsvfruNR84bwM0R2p0kjk1a5lHk7MGZjM1Nx2G1EGez8OzPhkf0+MHpAzo3WRPl0pHZDR/Av1Jz074KVuaVUu32BTnCpm0rquTFb7byt++2NjkbZH+Fi+dnb6XWaxzu5nB5TQ5Wu0NWJ10pxZu3jGV8r47E2Sw46iYMxNut9O2cxIy7TyT+iG6Wxz5cx4/bD+DyGoc/Nbl9Jm6fyZ++3Mx3m/aHJG4ReNIyjyI2q4U3bxnLgSoPiXFWEhyRffseOmcA87YeoNrtO1zHO85moXt6PJePavjRf9a6Qv73k/WHi0F5TZNrxuXw6HkDQ7oCc9rCXfzh8414TY0CXv5uG09cNJirj+rj/3ZjUaNvVoYJX63fx3OhCZdkp523bxvP9uIq5m4uxjA1Y3umNxggP1Dl5tPVBY1WWAT/VMa/fL2F0wZkhiBqEWiRnQ1EA0opMpKDv5FCIHRPT+DL+yfx8pxtfLNxPw6rhZ+N7sbtk3rVay0CfLtxP/f/Z1WDmtzvLcmjqMLNy9eNCknMJdUenvp8Y72E5zM1T3yynvOGZZFyRD0XHWGdRb0zkuid0XSt+B+3HcBmVTT3gWfzvkqq3b6IXu0qGid3TARVdlo8T106lKcuHdrkc7T2J8ujEzn4B0u/2bifbUVVIdnUYsnOg9itlgatV5vVwrJdJfVaracPyOT/Pt3Q4DWsFjg7RPuKtobP0MferEP5nyeij/SZi7DbW1pLcVXTsylMU/PNxtD05abGOxptcWutSUuov4lFl1Qn95/Rl3i7lUO9LU6bhfTEOB4+N/KmjA7vntboBttH6pDgICVe2njRSO6aCDuvYWJpZiDX0BpvE/28gTa+Zzpp8XZqPT8NEFrrurZGNrJI6+en9mFcbjpvLdxFcaWHU/t14ppxPUhNiLzyun06JzEoK4XVe8sbndIab7dy1ym9I3pQXTRNkrkIu5z0BBxWCzU0vpLRabeGbNs5i0Xx3pQTuOffK9hUWIlGM7RrKi9dO6rJJDcmN50xbdzHM1ReuX40F7/8I2U1nnrdWgkOKyf3y+DmE3PDF5xok4jenEK0H/+Yt4PnZm85vGHCIXaLok9mEl/8YlLIW4wHq9wopUgP4R6hoVDp8vLfZXv595LdVNT66Nkpkdsm9uSMgZmy6UuEiurNKUT7cvuknhRXuXlrwa7D27pZlKJvZhL/unlsWD76d0yKjllDrZXstHPrxJ7cOlEWCMUSSeai1TYWVvDG/J1sKKygc7KTG0/swan9MtqUcJVSPHreQO6Y1IvvNu2n1mMwqkcHhnVLC1zgQsSwYyZzpVR3YBrQBf+2P1O11i8c8fj/AM8AGVrrA8EKVESGN3/cydNfbcLr0xhas54KFu88yMn9Mnj52lFY2/gxPSM5jqvGRmYBLiEiWUumJvqAB7XWA4EJwD1KqUFwONGfCeQFL0QRKbbur+Tprzbh8poYR4y11HgM5m4u5r2l8msgRLgcs2WutS4ECuu+rlRKbQS6AhuA54GHgI+DGaRovaJKF28v3M0XawsxTM2kvhncNrEnuW3YtPfNBbvwGk0vBZ86bwfXje9x3K8vhDh+reozV0rlAiOBxUqpi4B8rfXq5vpKlVJTgCkAOTny8TkU1uWXc/XURXgN8/BKxr2lefx3+V5eumYkZww6vtobm/dV0kQuB6Cw3HVcr3s8PD6Tfy/ezZsLdnGgykPXtHjuPKUXl4zoGnMzMrTWMvdbHFOLk7lSKgmYAfwSf9fLY8BZx/o5rfVUYCr4pyYeV5SixXyGyU3/WkLVUQU4fKbGZxrcN30F839z2nHN1MhOi0fllTa5JDwtPjQLZbyGyQ2vL2bN3jJq6+ZKb95fyWMfrmPulmL+etWIqE9+Wms+WV3A377bxvaiKuxWC2cOyuSBM/uFpKyBiD4tWs6vlLLjT+Tvaq1nAr2BnsBqpdQuoBuwQikVeQUp2pnvNhXh8ja9jZip4b2le47rtW88oQfOJjZriLNZuH5CaLpYZizfWy+RH1LrNZi9YT8/bI3+cfgnPl3PwzPWsq2oCg14DJMv1xVy0UvzWbWnLNzhiQh0zGSu/E2c14GNWuvnALTWa7XWnbXWuVrrXGAvMEprvS+o0YpjWl9QTrW76WTu9pksP849IUf36MAlI7OJP2pnG6fNQs9OidwxqddxvW5rvblgV4NEfkiNx+DtRbtDEkewrN5TxvtL9zZYQGVq//XdP30lRy/201ozZ1MRd7+znFvfXMpHK/ObHN8Qsakl3SwnATcAa5VSq+qOPaq1/iJoUYnjlhhnw25VhzdQbkyy8/iWFyil+MOlQzmxdydembudvIM1pMbbuWFCDjed2LNBWdtgKapovm++oKw2JHEEy7SFu3D7mn5DLq5ysza/vN4c/EdmruWT1QWHN3detOMg05fk8c7t40NaC16ET0tms8wHmu2ArGudiwhw9uAu/OXrLTS1MVuiw8rlo7od9+srpbhweDYXDm98p6BQSIm3U1LT9D6bHRKie/n9zgPVje6deohFKfJLaw8n89V7yvh4VUG9lnyNx2BtfjmfrSng0pHHf79F9JC37BjTo2Mi5wzpgtPe8NY6rIpeGUlM7NMpDJEFzrGKWp3UJzRFuYKlW4cEmhu/1VqTmfrT/qJfr9/X6DhJjcfgo5UFwQhRRCBJ5jHo2SuGc/mobsTZLCTF2UiMsxJnszCpXwb/vmN81E/du35CD2xNXIPNorhkZNcQRxRYzQ00A6Qm2OuV47VbLU2uvHVIF0u7IbVZYpDdauGpS4fy67P7s3D7QXymZnSPDmSnxYclHq9hsmV/JXE2K70zEts8bXB4t1TG9Uxn6a6SemMDcTYLF4/oSlZqeK4zUEb36MB5Q7vwxdp9DQZB4+1Wnr+y/tTLC4Zn8eq87Yf3WT0kwWHlyrEN91oVsUlK4IqgKqvxcPkrCygsd2Fq/0rUV68f3eYaLi6vwR++2Mh/l+3FMDVxdgu3ndST+07v2+bXjgRaa95dlMff525jf7l/F6aJfTvy67MHMKRraoPnvzxnG3/7dite08Q0/TXgzx6cyfMxMOdetKwEriRzEVSPzFzDB8v3Hm5BJzisPHXpkIANynkNk0qXjxSnDVsMdilorXH7TGwWdczr27q/ko9XFeAxDM4e3IVROR0kkccIqWcuwm5bUVW9rhCX12BPSeCmDv59zla+WLufm0/qwTXjYq8ujFIKp71lUz77ZibzP2f3D3JEIlJJMhdBNbFPJ9bmlx/eoizOZmV0jw4Bee3//Xgd0xb6Fwg9MnMdXp/mRtn2TLRTsfe5VESUeyb34cJh2ditini7lV+f3Z+TAjA1cn1BOe8vq1+W4KkvNnKgyt3m1xYiGknLXASVzWrhmSuG8+efDQto/23ewRpsFgv+/VL87FYLBWW1dIrR7d6EaI60zEVIBHogrk/nJHxm/dojPtMkJz0hoOcRIlpIMhdRqW9mMg+dM4A4m4XkOBtOu4XnrxxBWoQv5d9X7uK3H63jvBfm8ZsP1rCnpCbcIYkYIVMTRVTbX+Eiv6yW3I6JpCdGdiIvqfZwxnNzqaj14jM1VuUvjDbrgZOjfqGTCK6WTE2Ulrk4rKTaw/qC8mbroUeazBQno3I6RHwiB3hvSR7Vbt/hlZqG9tdgf2P+zjBHJmKBDIDGKI/P5MOVe3l/2V68hsn5Q7O4ZnwOKc7GdwOau6WYu95ejsUCKU47n943UQYSA2xrUeXhbfwO8RqaLfurwhSRiCXSMo9Bbp/BVa8t5IlPNrB8dylr9pbz/DdbOOf5eRxsYurebz9aR63XoNptUFzp5l8Bbi2WVHsabGXX3pzYuxMJR2/sYbcwqW90V7EUkUGSeQz6z5I9bNpXWa9Ik8trUlzprqt13tCRYyca3Ww97dZasrOECX/8lhP++C3Fle13HvhFI7Lp1yWJhLpNPBIcVnLSE7hmnGx0LtpOulli0PSleQ2q7QF4Tf8mwX+4bGiDx564aDD3/HsFNouFeIeVW07KDVg8m/dXAv6un33lLjKS22f3TZzNygd3ncjsDftZm19O/y7JnDOkC3HNlLsVoqUkmccgVxP7Y4J/Y+DGnD4wk3m/nkx+WS39uyST4Ajcr8YVo7tRXOkiM8XJkK4pAXvdaGSzWjh3aBbnDs0Kdygixkgyj0FnDszkXwt2NroP6ISeTe/S0znFSecUZ5OPHy+n3cqvzpQCUEIEk/SZx6DbJ/UkMc7G0WW94+1WfnPugPAEJYQIKknmMahzipNP753ImYMysVkUFgXje6bz/p0nMDi74cYGQkSLGcv3cttbS5mzuSjcoUQc6WaJUd3TE3jthjFordGaqN/3szGr95Tx+882sK2oiuHd03j8wkH0ykgKd1giSDbtq+Cxj9bi8prM33qAhY+cHhWLxUJFknmMU0o1u9N7tMovq+WafyyixuOftTNvazGX/X0BPz58GolxDX+tK1xe7p++kkU7SkiNt/PsFcOZGEPzu72GyWvztjN98R68hskFw7J44Mx+JDexSCwaeXwm4P9l1poGhdbaO+lmEVFpxjJ/0jpEa39C+3rDvkaf/4vpK5m/7QC1XoN9FS7umLaMvIOxU+Tql/9ZxUvfbSO/rJaiSjfvLM7jytcWYgZywUCYDeuWxn2n9WFwdgpPXjqEzsmBH6yPZtIyF1GpxmtgHJWoTK0Pt9SPtmjHwXqzeywKVuSVktMx+kvm7imp4ZsN++uVCvD4TPIO1jB/2wFO7pcRxugC657Jfbhncp9whxGRpGUuotIFw7Jx2Or/+mr80zIbkxpvb/DcWOlv3V5c1eD/Bfjrvmwtkrov7YUkcxGVhnRN5fcXDyHZacNhs9AxycEr149ucp78Mz8bTrzdSqLDSoLDygm9OzIxANvXHY+8gzXM2VTEjuLAJNo+nZPq+pPrs1kV/TOTA3IOEfmkmyXKaK0pKHeRFGdr0Npsb64Y051LR3altMZLx0RHszN2Tu6XwVe/nMTy3aV0TIpjUp9OIZ/h4/Ia3Dd9JfO2FOOwWfD6TEbndmDqDWMaHbRtqW4dEjh3SBaz1hdSe3jjbAu9MxI5sXfHQIUvIpwk8yiydX8lt09bxr5yF1rDhcOz+NPlw7BZ2+8HLJvV0qJaL1pr9pW72Ffhwmdoqj2+kM/0eOrzjczbUozbZx7u3166q5RHZq7lxWtGtum1/3LlcIb8mMK7i/Pw+EwuHpHNPZP7xOSUVNE42WkoSmitOelP31FY5uLQHXPaLTxwRj/uPKV3WGOLdC6vwU1vLGFtfjlur0Gc3YpS8NYt4xiT23R5g0AyTM3gx79qtG6Ow2ph1eNnBrQejogtstNQDNleXEVZjZcj33pdXpMZK/aGLaZo8cr321m9p4waj4Ghocbjr9t++1vL6k1vDCavYTbarw2gFFS52netd9F2ksyjRGKcrcFUPCCmFoUEy3+W5uFqJJH6TJPFO0pCEoPT7q9d3piUeLvs6iTaTJJ5lMhKjWd8z471pqA57RbulTm3x9R0SWBFjSd0LeLfXTwEp73+n1y83cITFw6Svm3RZpLMo8hrN4zmphN6kJ3qZHB2Cn+7ZhSTB3QOd1jHtGxXCY99uJY/fLGRnQeqQ37+yQM6N6ggCf6uj/E9Qzfb45R+Gbx7+wQm9ulE5+Q4xvdM5/Wbx3L+sOyQxSBilwyAiqCatnAXf/xiEy6vgdWisFstvHP7OEb3CM3AI/jruFzw4g9Uu43Dm3PE2608cGZfppwsg8ci8gVkAFQp1V0pNUcptVEptV4pdX/d8WeUUpuUUmuUUh8qpdICFLcIEJ9hsnZvOav2lFHbxDL3YPL4TP705SZqvQYa8JmaWq/B7z/bGNI4uqbF8/UDp3DbxJ4Mzk7h9AGdef2mMZLIRUxpyVwoH/Cg1nqFUioZWK6Umg3MBh7RWvuUUn8CHgF+E8RYRQtprXl9/k5e/HYrhtYoFIapuXZ8Dg+fOwB7iOall9d68TYyaLsrDF0tGclx/ObcAbI5h4hZx/yr1loXaq1X1H1dCWwEumqtv9ZaHxo9WgR0C16YojX+/NVm/vL1FipcPqrdBlVuH7Veg3cX7+aud5YTqq61jokO0o5apWpRMCa3Q0jOL0R70qommlIqFxgJLD7qoVuBLwMUk2iDokoXr/+4k1pvw24Vl9dk4faDrMgrC0ksFovir1eNOFwTJSnOSnqig8cvHByS8zfG7TNiqiysEIe0eMmZUioJmAH8UmtdccTxx/B3xbzbxM9NAaYA5OTktClYcWyfrymkuUlutV6D/yzNY3SP0LSOT+zTiYWPnMb3m4uJd1g5tX8GcTZrSM59iMtr8M/5O/jX/F2U1niwKMXpAzvzwJn9GNAlJaSxCBEsLUrmSik7/kT+rtZ65hHHbwIuAE7XTXx211pPBaaCfzZLmyMWzSqt8dara300raG40h3CiCAtwcElI7uG9JyHuLwGV722kM37Kg8vHDK15usN+5m35QD/umUsE3o1Pj2xxuNDoYh3hPbNR4jjccxkrpRSwOvARq31c0ccPwf/gOcpWuvY2bIlyvXOSCTRYaW6idkrDqtiYFboW6Nun4FFqZANvh7yzx921Evkh2jt/5Ry9zvLWfrYGfWKla3dW87/+3gt6/L9H0CHd0vlyUuGMihbWvEicrXkL+sk4AbgNKXUqrp/5wEvAcnA7LpjrwYzUNEyZw/ugmpm00+lFNdN6BGyeJbvLuWiv81n4G+/YuBvv+LWN5eytzQ07/1aa974cVejS/kP8Rgm328uPvz9tqIqrpq6kNV7yjFMjWFqVuSVccWrC9h9MPSzcIRoqZbMZpmvtVZa62Fa6xF1/77QWvfRWnc/4thdoQhYNM9pt/LydaOIt1sbrHp02i08dv5AuqbFhySWdfnlXP/PxazJL8fU/nnmczcXc/FLP1JW4wn6+Wu9BuW13maf4/IabCmqPPz9i99uxdXo4LHBy3O2BTxGIQJFlvPHoFP6ZfDhPSdy/tAskuJsJDisTOrbibduGceNJ+SGLI5nZ21uMKvG0P5a4tOX5AX9/I4WdOnYLRaSjtgYYtGOgzQ22cXQ8OO2g4EMT4iAkgLKMWpAlxT+du2osMawfHdpo8ddXn/Xxt2nBrdImM1q4eS+nfh+czFNjbxr4KxBXQ5/nxRno6iJAeJkp/y5iMglLXMRNInNJL+OSaHZTPnBs/rjtDc+G8Vpt3DR8Gy6pP60b+gNE3oQb2/4ZxFvt3LDCaEbaxCitSSZi6C5blxOg5Kv4E+M140PTWIc0jWVf9w4hhSnjcQ4/zhCnM1CnM3C+UOz+MNlQ+s9//oTejC6RzoJR0xHTHBYmdArnavGdA9JzEIcD6maKILG7TO48XX/dm01HgOLAofNwnXje/DbCwaFNJZaj4+rpy5izd5ynHYLf79udJPlg01TM3drMZ+tLkApxUXDs5kYhg2ghTikJVUTpRNQBE2czcr0Oybw4/YDzN6wH6fdysUjshmcnRryWGZvLGJrURUaqPWaPP3lxiaTucWimNy/M5P7R36teCEOkWQugspiUUzqm8GkvhlhjaPG7cM84lNoU4uqhIhW0mcu2oXzhmWRkRxHYpwVp93Cw1IKV8QYaZmL41LrMcgrqSEzJY60hNDMTGmLFKed2Q+cwrr8crqkOunWofHNlYWIVpLMRattLKzgmn8swmuY+AzN05cP49IwFdJqDafdypjc0G1XJ0QoSTeLaLX7pq+krMZLtdvA7TN5eMaaYy6bF0IElyRz0WqF5bX1vrdZFAeqQltWVwhRnyRz0WrjctOxW/1zrhX+7otuHUJTvEsI0ThJ5qLV/nr1SE7um0FKvI0BWSm8N2VCyHcPEkLUJwOgotVS4+28fvPYcIchhDiCtMyFECIGSDIXQogYIMlcCCFigCRzIYSIAZLMhRAiBkgyF0KIGCBTE4WIEaap2XWwGsPU5HZKxN6CDa1F7JBkLkQM+GD5Xp6ZtYmKWh9K+UssTDm5Fz8/tY/skNROSDIXIsq9Pn8nz87aTK23/oYbL8/Zzp7SWv50+bAwRSZCST6HCRHFqtw+npm1qUEiB6j1Gny0Mp/txVVhiEyEmiRzIaLYd5uKsDbTjWKYmo9X5ocwIhEuksyFiGIVtV4MUzf5uM/UlNR4QhiRCBfpMxcRY29pDS98s5XP1xbiNUyGZKfyq7P6hX0z6EjWLzMZi2q6ZZ7gsDI4OzWEEYlwkZa5iAi7D1Zz/ovzmbliLzUeA6+hWbmnjCnTljF9SV64w4tYY3M70DHRQVPpXAEXDc8OZUgiTCSZi4jwxCfrqXR5MY7qMaj1mvzuk/VUuX3hCSzCKaV4/eaxpMTbibP99Odstyri7VZeu2EMiXHyAbw9kLsswq7G42P+tgM01fVrtSi+2bCfS6Jg0+hw6JeZzHcPnsK/l+Tx6eoCfIbmlP4Z3HpST7qnJ4Q7PBEiksxF2FW7jbp+38azuc/UlMkgXrM6JsVx32l9ue+0vuEORYSJdLOIsEtPdNTrIjia1aIYmJUSwoiEiD6SzEXYWS2K2yb2xGlv+OtoUZCZEse4nulhiEyI6CHJPMYdrHKzr9wV7jCO6d7T+nLGwEzi7VYOrYFJjLOSmeJk2q3jUc1MvxNCSJ95TJu2YBdPfrERgKvHdOf/LhkS5oiaZrUoXrp2FBsKKvh0dQHVHh8n9u7IGQMzsUn1PyGO6ZjJXCnVHZgGdAFMYKrW+gWlVDrwHyAX2AVcqbUuDV6oorWe+mIjHp8JwHvL9nDvaX3onOIMc1TNG5SdwqBs6R8XorVa0uTxAQ9qrQcCE4B7lFKDgIeBb7XWfYFv674XEcRx5KCiRupbCxHDjvnXrbUu1FqvqPu6EtgIdAUuBt6qe9pbwCVBilEcp5evHUWK04bTZuHxCwfRIdER7pCEEEGitG66SE+DJyuVC8wDhgB5Wuu0Ix4r1Vp3aORnpgBTAHJyckbv3r27jSELIUT7opRarrUe09xzWvy5WymVBMwAfqm1rmjpz2mtp2qtx2itx2RkSMEkIYQIhhYlc6WUHX8if1drPbPu8H6lVFbd41lAUXBCFEIIcSzHTObKP8H3dWCj1vq5Ix76BLip7uubgI8DH54QQoiWaMk885OAG4C1SqlVdcceBZ4G3ldK3QbkAVcEJUIhhBDHdMxkrrWeD02WSz49sOEIIYQ4HjLxWAghYoAkcyGEiAGtmmfe5pMpVQxE+0TzTsCBcAcRRLF+fRD71yjXF/2OvsYeWutm53aHNJnHAqXUsmNN3o9msX59EPvXKNcX/Y7nGqWbRQghYoAkcyGEiAGSzFtvargDCLJYvz6I/WuU64t+rb5G6TMXQogYIC1zIYSIAZLMhRAiBkgyb4ZS6g2lVJFSat0Rx55QSuUrpVbV/TsvnDG2hVKqu1JqjlJqo1JqvVLq/rrj6Uqp2UqprXX/bVCnPho0c30xcQ+VUk6l1BKl1Oq66/td3fGYuH/Q7DXGxD08RCllVUqtVEp9Vvd9q++h9Jk3Qyl1MlAFTNNaD6k79gRQpbV+NpyxBUJd6eIsrfUKpVQysBz/jlE3AyVa66eVUg8DHbTWvwlfpMenmeu7khi4h3UVTRO11lV1ZarnA/cDlxED9w+avcZziIF7eIhS6lfAGCBFa32BUurPtPIeSsu8GVrreUBJuOMIlljfErCZ64sJ2q+q7lt73T9NjNw/aPYaY4ZSqhtwPvDPIw63+h5KMj8+9yql1tR1w0TtR9gj1W0JOBJYDGRqrQvBnxCBzmEMLSCOuj6IkXtY9/F8Ff7NYWZrrWPu/jVxjRAj9xD4K/AQYB5xrNX3UJJ5670C9AZGAIXAX8IaTQAc75aA0aKR64uZe6i1NrTWI4BuwDil1JAwhxRwTVxjTNxDpdQFQJHWenlbX0uSeStprffX/XKZwD+AceGOqS1ifUvAxq4v1u4hgNa6DPgef19yzNy/Ix15jTF0D08CLlJK7QLeA05TSr3DcdxDSeatdOh/cJ1LgXVNPTfSxfqWgE1dX6zcQ6VUhlIqre7reOAMYBMxcv+g6WuMlXuotX5Ea91Na50LXA18p7W+nuO4hzKbpRlKqenAqfjLUe4HHq/7fgT+QZhdwJ2H+raijVJqIvADsJaf+usexd+v/D6QQ92WgFrrqBsIbub6riEG7qFSahj+wTEr/obZ+1rr/1NKdSQG7h80e41vEwP38EhKqVOB/6mbzdLqeyjJXAghYoB0swghRAyQZC6EEDFAkrkQQsQASeZCCBEDJJkLIUQMkGQuhBAxQJK5EELEgP8PE8+6hyUS+4cAAAAASUVORK5CYII=\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": 47,
   "id": "1d2a767d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x185b2a56cd0>"
      ]
     },
     "execution_count": 47,
     "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 ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6aee97a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x185b2972310>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for suburban cities.\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\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": 49,
   "id": "a82e365d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x185b3af8ac0>"
      ]
     },
     "execution_count": 49,
     "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 ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f524a9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter charts for each city type.\n",
    "plt.subplots(figsize=(10, 6))\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "\n",
    "# Incorporate the other graph properties\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\", fontsize=20)\n",
    "plt.ylabel(\"Average Fare ($)\", fontsize=12)\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\", fontsize=12)\n",
    "plt.grid(True)\n",
    "\n",
    "# Create a legend\n",
    "lgnd = plt.legend(fontsize=\"12\", mode=\"Expanded\",\n",
    "        scatterpoints=1, loc=\"best\", title=\"City Types\")\n",
    "lgnd.legendHandles[0]._sizes = [75]\n",
    "lgnd.legendHandles[1]._sizes = [75]\n",
    "lgnd.legendHandles[2]._sizes = [75]\n",
    "lgnd.get_title().set_fontsize(12)\n",
    "\n",
    "# Incorporate a text label about circle size.\n",
    "plt.text(42, 35, \"Note: \\nCircle size correlates \\nwith driver count per city.\", fontsize=\"12\")\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig1.png\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a8dee21e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "urban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "da29459b",
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.\n",
    "rural_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b4842a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.62, 17.36, 6.94)"
      ]
     },
     "execution_count": 82,
     "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": 85,
   "id": "d3f033f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the urban cities.\n",
    "urban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "413adc68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e52a8da6",
   "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": 88,
   "id": "04fe5855",
   "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": 90,
   "id": "e64aba45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([6], dtype=int64), count=array([5]))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sts.mode(rural_ride_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9aefc7d3",
   "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": 91,
     "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": 92,
   "id": "e66f7c98",
   "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": 94,
   "id": "8b6851af",
   "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": 94,
     "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": 97,
   "id": "b3fe81df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box-and-whisker plot for the urban cities ride count.\n",
    "x_labels = [\"Urban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(urban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(10, 41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2006f06f",
   "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": 99,
   "id": "ee19b255",
   "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": 101,
   "id": "8d909c6e",
   "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": 101,
     "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": 103,
   "id": "3f4aeb9d",
   "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": 103,
     "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": 112,
   "id": "5f2b9395",
   "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_driver_count, suburban_driver_count, rural_driver_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, 80, step=3.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig2.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "1ba45652",
   "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": 113,
     "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": 115,
   "id": "6accb16c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 115,
     "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": 118,
   "id": "bf9f2eab",
   "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": 118,
     "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": 121,
   "id": "f266a45b",
   "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": 122,
   "id": "118981bb",
   "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": 122,
     "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": 123,
   "id": "fd376571",
   "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": 126,
   "id": "bec7ab37",
   "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": 126,
     "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": 127,
   "id": "7295be24",
   "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,
   "id": "4acb39eb",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}