PyBer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.3.2'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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": 111,
   "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": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>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": 112,
     "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": 113,
   "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": 113,
     "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": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            120\n",
       "driver_count    120\n",
       "type            120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "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": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            0\n",
       "driver_count    0\n",
       "type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "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": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "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": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            object\n",
       "driver_count     int64\n",
       "type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "city_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Suburban', 'Rural'], dtype=object)"
      ]
     },
     "execution_count": 120,
     "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": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.83, 30.24, 33.44, ..., 11.11, 44.94, 55.84])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_data_df[\"fare\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 122,
     "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": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "981"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ride_data_df[\"fare\"]>=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(city_data_df[\"type\"]==\"Suburban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(city_data_df[\"type\"]==\"Rural\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       2375\n",
       "date       2375\n",
       "fare       2375\n",
       "ride_id    2375\n",
       "dtype: int64"
      ]
     },
     "execution_count": 126,
     "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": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       0\n",
       "date       0\n",
       "fare       0\n",
       "ride_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "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": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        object\n",
       "date        object\n",
       "fare       float64\n",
       "ride_id      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data types of each column.\n",
    "ride_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\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": 129,
     "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": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 130,
     "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": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>Barronchester</td>\n",
       "      <td>2019-01-27 03:08:01</td>\n",
       "      <td>27.79</td>\n",
       "      <td>6653622887913</td>\n",
       "      <td>11</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>East Kentstad</td>\n",
       "      <td>2019-04-07 19:44:19</td>\n",
       "      <td>18.75</td>\n",
       "      <td>6575961095852</td>\n",
       "      <td>20</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>Lake Omar</td>\n",
       "      <td>2019-01-17 21:33:35</td>\n",
       "      <td>21.71</td>\n",
       "      <td>966911700371</td>\n",
       "      <td>22</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>Myersshire</td>\n",
       "      <td>2019-02-27 17:38:39</td>\n",
       "      <td>17.10</td>\n",
       "      <td>5706770909868</td>\n",
       "      <td>19</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>West Hannah</td>\n",
       "      <td>2019-04-19 01:06:59</td>\n",
       "      <td>37.78</td>\n",
       "      <td>2273047151891</td>\n",
       "      <td>12</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               city                 date   fare        ride_id  driver_count  \\\n",
       "1625  Barronchester  2019-01-27 03:08:01  27.79  6653622887913            11   \n",
       "1626  East Kentstad  2019-04-07 19:44:19  18.75  6575961095852            20   \n",
       "1627      Lake Omar  2019-01-17 21:33:35  21.71   966911700371            22   \n",
       "1628     Myersshire  2019-02-27 17:38:39  17.10  5706770909868            19   \n",
       "1629    West Hannah  2019-04-19 01:06:59  37.78  2273047151891            12   \n",
       "\n",
       "          type  \n",
       "1625  Suburban  \n",
       "1626  Suburban  \n",
       "1627  Suburban  \n",
       "1628  Suburban  \n",
       "1629  Suburban  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Suburban and Rural city DataFrames.\n",
    "suburban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Suburban\"]\n",
    "suburban_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>Randallchester</td>\n",
       "      <td>2019-04-13 11:13:31</td>\n",
       "      <td>43.22</td>\n",
       "      <td>1076079536213</td>\n",
       "      <td>9</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>North Holly</td>\n",
       "      <td>2019-02-02 14:54:00</td>\n",
       "      <td>12.42</td>\n",
       "      <td>1985256326182</td>\n",
       "      <td>8</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-03-27 18:27:34</td>\n",
       "      <td>54.85</td>\n",
       "      <td>4421836952718</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-02-23 21:12:24</td>\n",
       "      <td>47.90</td>\n",
       "      <td>3269652929887</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-05-06 08:57:56</td>\n",
       "      <td>51.80</td>\n",
       "      <td>4018025271936</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "2250   Randallchester  2019-04-13 11:13:31  43.22  1076079536213   \n",
       "2251      North Holly  2019-02-02 14:54:00  12.42  1985256326182   \n",
       "2252      Michaelberg  2019-03-27 18:27:34  54.85  4421836952718   \n",
       "2253  Lake Latoyabury  2019-02-23 21:12:24  47.90  3269652929887   \n",
       "2254  Lake Latoyabury  2019-05-06 08:57:56  51.80  4018025271936   \n",
       "\n",
       "      driver_count   type  \n",
       "2250             9  Rural  \n",
       "2251             8  Rural  \n",
       "2252             6  Rural  \n",
       "2253             2  Rural  \n",
       "2254             2  Rural  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Rural\"]\n",
    "rural_cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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": 133,
     "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": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Barronchester    16\n",
       "Bethanyland      18\n",
       "Brandonfort      19\n",
       "Colemanland      22\n",
       "Davidfurt        17\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the suburban and rural ride count.\n",
    "suburban_ride_count = suburban_cities_df.groupby([\"city\"]).count()[\"ride_id\"]\n",
    "suburban_ride_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "rural_ride_count = rural_cities_df.groupby([\"city\"]).count()[\"ride_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bradshawfurt      10\n",
       "Garzaport          3\n",
       "Harringtonfort     6\n",
       "Jessicaport        6\n",
       "Lake Jamie         6\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_ride_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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": 138,
     "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": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Barronchester    36.422500\n",
       "Bethanyland      32.956111\n",
       "Brandonfort      35.437368\n",
       "Colemanland      30.894545\n",
       "Davidfurt        31.995882\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "suburban_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bradshawfurt      40.064000\n",
       "Garzaport         24.123333\n",
       "Harringtonfort    33.470000\n",
       "Jessicaport       36.013333\n",
       "Lake Jamie        34.358333\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_avg_fare = rural_cities_df.groupby([\"city\"]).mean()[\"fare\"]\n",
    "rural_avg_fare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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": 141,
     "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": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2398a4af848>"
      ]
     },
     "execution_count": 144,
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2398a51ab48>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for urban cities.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=urban_driver_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2398a53ba88>"
      ]
     },
     "execution_count": 146,
     "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 ($)\")\n",
    "plt.xlabel(\"Total Number of Rides (Per City)\")\n",
    "plt.grid(True)\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2398a5a79c8>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the scatter plots for suburban cities.\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "plt.title(\"PyBer Ride-Sharing Data (2019)\")\n",
    "plt.ylabel(\"Average Fare ($)\")\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": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2398a621d08>"
      ]
     },
     "execution_count": 148,
     "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": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add the scatter charts for each type of city.\n",
    "plt.scatter(urban_ride_count,\n",
    "      urban_avg_fare,\n",
    "      s=10*urban_driver_count, c=\"coral\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Urban\")\n",
    "\n",
    "plt.scatter(suburban_ride_count,\n",
    "      suburban_avg_fare,\n",
    "      s=10*suburban_driver_count, c=\"skyblue\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Suburban\")\n",
    "\n",
    "plt.scatter(rural_ride_count,\n",
    "      rural_avg_fare,\n",
    "      s=10*rural_driver_count, c=\"gold\",\n",
    "      edgecolor=\"black\", linewidths=1,\n",
    "      alpha=0.8, label=\"Rural\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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",
    "# 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",
    "# Incorporate a text label about circle size.\n",
    "plt.text(42, 35, \"Note: Circle size correlates with driver count per city.\", fontsize=\"12\")\n",
    "# Save the figure.\n",
    "plt.savefig(\"Analysis/Fig1.png\")\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics. THIS IS PANDAS METHOD\n",
    "urban_cities_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    66.000000\n",
       "mean     24.621212\n",
       "std       5.408726\n",
       "min      12.000000\n",
       "25%      21.000000\n",
       "50%      24.000000\n",
       "75%      28.000000\n",
       "max      39.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics.  THIS IS A PANDAS METHOD\n",
    "urban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36.000000\n",
       "mean     17.361111\n",
       "std       4.323707\n",
       "min       9.000000\n",
       "25%      14.000000\n",
       "50%      17.000000\n",
       "75%      19.250000\n",
       "max      27.000000\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics. THIS IS A PANDAS METHOD\n",
    "suburban_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics. THIS IS A PANDAS METHOD\n",
    "rural_ride_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.62, 17.36, 6.94)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean of the ride count for each city type. THIS IS PANDAS METHOD\n",
    "round(urban_ride_count.mean(),2), round(suburban_ride_count.mean(),2), round(rural_ride_count.mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24.0, 17.0, 6.0)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean of the ride count for each city type. THIS IS PANDAS METHOD\n",
    "round(urban_ride_count.median(),2), round(suburban_ride_count.median(),2), round(rural_ride_count.median(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mode of the ride count for the urban cities. THIS IS PANDAS METHOD\n",
    "urban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suburban_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rural_ride_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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": 161,
   "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": [
    "# THIS IS NUMPY METHOD\n",
    "# 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",
    "#THIS IS NUMPY METHOD\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",
    "#THIS IS SCIPY METHOD\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": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mode for the ride counts for suburban trips is ModeResult(mode=array([17], dtype=int64), count=array([7])).\n"
     ]
    }
   ],
   "source": [
    "# THIS IS SCIPY METHOD, BECAUSE NUMPY DOESN'T HAVE A MODE THINGY AND PANDAS MODE IS WEAK SAUCE.  \n",
    "# PANDAS ONLY RETURNS THE MODE, BUT SCIPY'S MODE RETURNS MODE, DATA TYPE, AND FREQUENCY.\n",
    "mode_suburban_ride_count = sts.mode(suburban_ride_count)\n",
    "print(f\"The mode for the ride counts for suburban trips is {mode_suburban_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the ride counts for rural trips is 6.94.\n",
      "The median for the ride counts for rural trips is 6.0.\n",
      "The mode for the ride counts for rural trips is ModeResult(mode=array([6], dtype=int64), count=array([5])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the ride count for the rural cities.\n",
    "mean_rural_ride_count = np.mean(rural_ride_count)\n",
    "print(f\"The mean for the ride counts for rural trips is {mean_rural_ride_count:.2f}.\")\n",
    "\n",
    "median_rural_ride_count = np.median(rural_ride_count)\n",
    "print(f\"The median for the ride counts for rural trips is {median_rural_ride_count}.\")\n",
    "\n",
    "mode_rural_ride_count = sts.mode(rural_ride_count)\n",
    "print(f\"The mode for the ride counts for rural trips is {mode_rural_ride_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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": 164,
     "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": 165,
   "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": 166,
   "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": 166,
     "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": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for suburban trips is $30.97.\n",
      "The median fare price for suburban trips is $30.75.\n",
      "The mode fare price for suburban trips is ModeResult(mode=array([17.99]), count=array([3])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the suburban cities.\n",
    "mean_suburban_fares = np.mean(suburban_fares)\n",
    "print(f\"The mean fare price for suburban trips is ${mean_suburban_fares:.2f}.\")\n",
    "\n",
    "median_suburban_fares = np.median(suburban_fares)\n",
    "print(f\"The median fare price for suburban trips is ${median_suburban_fares:.2f}.\")\n",
    "\n",
    "mode_suburban_fares = sts.mode(suburban_fares)\n",
    "print(f\"The mode fare price for suburban trips is {mode_suburban_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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": 168,
     "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": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean fare price for rural trips is $34.62.\n",
      "The median fare price for rural trips is $37.05.\n",
      "The mode fare price for rural trips is ModeResult(mode=array([37.05]), count=array([2])).\n"
     ]
    }
   ],
   "source": [
    "# Calculate the measures of central tendency for the average fare for the rural cities.\n",
    "mean_rural_fares = np.mean(rural_fares)\n",
    "print(f\"The mean fare price for rural trips is ${mean_rural_fares:.2f}.\")\n",
    "\n",
    "median_rural_fares = np.median(rural_fares)\n",
    "print(f\"The median fare price for rural trips is ${median_rural_fares:.2f}.\")\n",
    "\n",
    "mode_rural_fares = sts.mode(rural_fares)\n",
    "print(f\"The mode fare price for rural trips is {mode_rural_fares}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[24.52577230769236, 30.97012800000002, 34.623440000000016]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fares_mean_array = [mean_urban_fares,mean_suburban_fares,mean_rural_fares]\n",
    "fares_mean_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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": 172,
     "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": 173,
   "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": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the urban cities.\n",
    "suburban_drivers = suburban_cities_df['driver_count']\n",
    "suburban_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2250    9\n",
       "2251    8\n",
       "2252    6\n",
       "2253    2\n",
       "2254    2\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the driver count data from the urban cities.\n",
    "rural_drivers = rural_cities_df['driver_count']\n",
    "rural_drivers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the driver counts for urban trips is 36.68.\n",
      "The mean for the driver counts for suburban trips is 13.71.\n",
      "The mean for the driver counts for rural trips is 4.30.\n"
     ]
    }
   ],
   "source": [
    "# SKILL DRILL 5.4.3\n",
    "# THIS IS THE NUMPY METHOD\n",
    "mean_urban_drivers_count = np.mean(urban_drivers)\n",
    "print(f\"The mean for the driver counts for urban trips is {mean_urban_drivers_count:.2f}.\")\n",
    "mean_suburban_drivers_count = np.mean(suburban_drivers)\n",
    "print(f\"The mean for the driver counts for suburban trips is {mean_suburban_drivers_count:.2f}.\")\n",
    "mean_rural_drivers_count = np.mean(rural_drivers)\n",
    "print(f\"The mean for the driver counts for rural trips is {mean_rural_drivers_count:.2f}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[36.67815384615385, 13.712, 4.296]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_drivers_array = [mean_urban_drivers_count,mean_suburban_drivers_count,mean_rural_drivers_count]\n",
    "mean_drivers_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The median for the driver counts for urban trips is 37.00.\n",
      "The median for the driver counts for suburban trips is 16.00.\n",
      "The median for the driver counts for rural trips is 4.00.\n"
     ]
    }
   ],
   "source": [
    "# SKILL DRILL 5.4.3\n",
    "#THIS IS THE NUMPY METHOD\n",
    "median_urban_drivers_count = np.median(urban_drivers)\n",
    "print(f\"The median for the driver counts for urban trips is {median_urban_drivers_count:.2f}.\")\n",
    "median_suburban_drivers_count = np.median(suburban_drivers)\n",
    "print(f\"The median for the driver counts for suburban trips is {median_suburban_drivers_count:.2f}.\")\n",
    "median_rural_drivers_count = np.median(rural_drivers)\n",
    "print(f\"The median for the driver counts for rural trips is {median_rural_drivers_count:.2f}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[37.0, 16.0, 4.0]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_drivers_array = [median_urban_drivers_count,median_suburban_drivers_count,median_rural_drivers_count]\n",
    "median_drivers_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mode for the driver counts for urban trips is ModeResult(mode=array([39], dtype=int64), count=array([86])).\n",
      "The mode for the driver counts for suburban trips is ModeResult(mode=array([20], dtype=int64), count=array([79])).\n",
      "The mode for the driver counts for rural trips is ModeResult(mode=array([1], dtype=int64), count=array([32])).\n"
     ]
    }
   ],
   "source": [
    "# SKILL DRILL 5.4.3\n",
    "# THIS IS THE SCIPY METHOD, NUMPY COULD BE USED TO MAKE AN ARRAY LIKE I DID FOR MEAN AND MEDIAN\n",
    "# OKAY, SO MAYBE THE NUMPY METHOD ISN'T TOTAL WEAKSAUCE.  IT HAS A PURPOSE.\n",
    "mode_urban_drivers_count = sts.mode(urban_drivers)\n",
    "print(f\"The mode for the driver counts for urban trips is {mode_urban_drivers_count}.\")\n",
    "mode_suburban_drivers_count = sts.mode(suburban_drivers)\n",
    "print(f\"The mode for the driver counts for suburban trips is {mode_suburban_drivers_count}.\")\n",
    "mode_rural_drivers_count = sts.mode(rural_drivers)\n",
    "print(f\"The mode for the driver counts for rural trips is {mode_rural_drivers_count}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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": [
    "# NEW SECTION 5.4.4 URBRAN RIDE COUNT \n",
    "# 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": 181,
   "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": [
    "#SKILL DRILL 5.4.4 SUBURBAN RIDE COUNT\n",
    "# Create a box-and-whisker plot for the urban cities ride count.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(10, 41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zi2IXd5tFNuwMHAPsHREvS/LD1s3MKlSk4b8EuBHYSdK5wPHAmYPt1CKy4aPAeRHxcl7X8Jm7ZmZWjkEb/oi4UtIC0lOzBBwbEUuHUsiAyIYLgHfmD5GXgJMj4lcN9nFkg3U9RzbYSNQ0skHSuFY7RsTKQgVsGNlwH/BzYCawL/B9YHK0yI5wZIN1K0c2WDdrFtnQ6ox/AalfX8BrgFV5egzpIem7Fyi0UWTDo8ANuaG/W9JaYAfgycI/jZmZta3pqJ6I2D0iJgM/Bd4dETtExPbAUazL2mmqRWTDTaSoBiTtRRoh9FTbP4GZmQ1JkeGc+0bEj2szEXEr67J2WqlFNhySn7a1KD945TJgcu7yuRqY0aqbx8zMhleRUT1PSToTuILU9XMC8PRgOw0S2XBC4RqamdmwKnLG/wFSHPONpG6anVh3F6+ZmY0wRYZzriSNwDEzsz8DTRt+Sd+IiM9Iupl81269iDi61JqZmVkpWp3x/0d+v7CdA0uaCPw7sAsp4mFWRFxct/5k0s1cO0aER/WYmVWkacMfEQvy++0D10k6oMCxG2b1RMR/5Q+F6aT7AczMrEJNL+5K2lTSBySdLGlKXnaUpF8A3xzswBGxIiIW5unVQC2rB+DrwCk06EIyM7NyterquRSYCNwNXCLpt6Rc/tMi4qahFFKf1SPpaOD3EbE43ePVdB9n9VjXc1aPjUStsnruI0Unr5W0Fenu2j0i4rEhFVCX1QP8BJgHvCsinpX0MNA3WB+/s3qsWzmrx7pZs6yeVuP4X4mItQAR8RLwUBuN/sCsnteRMn4W50Z/N2ChpF2GclwzM2tfq66ev5R0b54W8Lo8LyAiYu9WB26U1RMRS0g3gNW2eZgCZ/xmZjZ8WjX8b9jIY9eyepZIWpSXfaE+98fMzKrXajjnbzfmwINk9dS2mbQxZZiZ2dAVyeoxM7M/I274zcx6TKsbuObm939p58CSJkqaJ2mppPslzczLL5D0gKR7Jd0oaUxbNTczs7a0OuMfL+kg4GhJb5H01vpXgWPXIhveAOwHfErSG4E5wJQ8Kugh4PSN/SHMzKy4VqN6vgScRhprf9GAdUF+fGIzEbECWJGnV0taCkyIiNvqNrsTOH6olTYzs/a1GtVzHXCdpC9GxJc3ppD6yIYBq04Evt9kH0c2WNdzZIONRE0jG9bbKOXrHJhn+yPilsIF1EU25Lt3a8vPAPqA4wZ75q4jG6xbObLBulmzyIZBn8Al6avA24Ar86KZkg6IiEH75htENtSWzwCOAg71g9bNzKpV5GHrRwJTa7k9kmYD9zDIRdlGkQ15+eHAqcBBEfFCuxU3M7P2FGn4AcYAK/P0dgX3aRjZAFwCbAnMybHMd0bExwse08zMNlKRhv+rwD2S5pEiGA6kwBDMFpENzuoxM+ugQRv+iLhKUj+wL6khP3Wo8cxmZtY9CnX15DH5Pyy5LmZmVgFn9ZiZ9ZjSGv4WWT3jJM2RtCy/jy2rDmZmtqGWDb+kTfKzd9vRLKvnNGBuROwJzM3zZmZWkZYNfx67v1jSa4Z64IhYEREL8/RqYCkwATgGmJ03mw0cO9Rjm5lZ+4pc3B0P3C/pbuD52sKIOLpoIQOyenbOF4uJiBWSdmqyj7N6rOs5q8dGoiIN/zkbU0DO6rke+ExEPJdv2hpURMwCZkHK6nEeinUjZ/XYSFRkHP/tkl4L7BkRP5O0DbBpkYM3yep5XNL4fLY/Hnii3cqbmdnQDTqqR9JHgeuA/5sXTQBuKrBfw6we0v0AM/L0DOAHQ6ivmZltpCLDOT9Fyt15DiAilgEN++UHqGX1HCJpUX4dAZwHTJe0DJie583MrCJF+vhfjohXan3zkjYjPYGrpRZZPQCHFq6hmZkNqyJn/LdL+gKwtaTpwLXAzeVWy8zMylKk4T8NeBJYAvwDKV3zzDIrZWZm5SkyqmdtfvjKXaQungeLPDVL0mWkp2w9ERFT8rKpwLeArUh39n4yIu5uv/pmZjZURUb1HAn8N+kBKt8Efi3pbwoc+3Lg8AHLzgfOiYipwJfyvJmZVajIxd2vAQdHxK8BJL0O+BFwa6udIuI/8x276y0GRufp7YDlQ6qtmZlttCIN/xO1Rj/7De3fdPUZ4KeSLiR929i/2YaObLCRwJENNhI1bfglHZcn75f0Y+Aa0hn7e4FftVneJ4DPRsT1kt5HusHrsEYbOrLBRgJHNthI1OqM/911048DB+XpJ4F2M/RnADPz9LXAd9o8jpmZtalpwx8RHy6hvOWkD5B+4BBgWQllmJlZC4P28UvaHTgJmFS//WCxzJKuAqYBO0h6FDgL+Chwcb779yVyH76ZmVWnyMXdm0h98TcDa4seOCI+0GTVPkWPYWZmw69Iw/9SRFxSek3MzKwSRRr+iyWdBdwGvFxbWHusopmZjSxFGv6/Iscrs66rJ/K8mZmNMEUa/vcAkyPilaEcuFFWT15+EvCPpKyeH0XEKUM5rpmZbZwi6ZyLgTFtHPtyBmT1SDoYOAbYOyLeBFzYxnHNzGwjFDnj3xl4QNKvWL+Pv+VwziZZPZ8AzouIl/M2ft6umVnFijT8Zw1jeXsB75R0Lmkc/8kR0TD+wVk9NhI4q8dGoiJ5/LcPc3ljgf2AfYFrJE1ulO/vrB4bCZzVYyNRkTt3V7PuGbtbAJsDz0fE6OZ7NfUocENu6O+WtBbYgZT/Y2ZmFShyxj+qfl7SscDb2izvJtIw0H5Je5E+SJ5q81hmZtaGIqN61hMRN1FgDH/O6vkl8HpJj0r6CHAZMFnSfcDVwIwij3E0M7PhU6Sr57i62U2APtZ1/TTVIqvnhGJVMzOzMhQZ1VOfy/8q8DBpLL6ZmY1ARfr4y8jlNzOzDmn16MUvtdgvIuLLrQ7cLLIhrzsZuADYMSJ8cdfMrEKtLu4+3+AF8BHg1ALHvpwBkQ0AkiYC04FHhlJRMzMbHq0evfi12rSkUaRn5X6YNBrna832q9u/UWQDwNeBU4AfDLWyZma28Vr28UsaB3wO+CAwG3hrRKxqtzBJRwO/j4jFkgbb1pEN1vUc2WAjUas+/guA40ixCX8VEWs2piBJ2wBnAO8qsr0jG2wkcGSDjUSt+vg/D+wKnAksl/Rcfq2W9FwbZb0O2B1YLOlhYDdgoaRd2jiWmZm1qVUf/5Dv6m0lIpYAO9Xmc+Pf51E9ZmbVGtbGvV6TyAYzM+uwInfutqVFZENt/aSyyjYzs+ZKO+M3M7Pu5IbfzKzHlNnHf5mkJ3IEc23ZBZIekHSvpBsljSmrfDMza6zMM/7L2TCyYQ4wJSL2Bh4CTi+xfDMza6C0hj8i/hNYOWDZbRHxap69kzSW38zMKtTJPv4TgVs7WL6ZWU8qbThnK5LOID3U5coW2zirxyp38MEHV1LOvHnzKinHrJHKG35JM0g5/Ye2et6us3qsE4b6CGhJQ97HrNMqbfglHU7K8j8oIl6osmwzM0uqjmz4JjAKmCNpkaRvlVW+mZk1VnVkw6VllWdmZsX4zl0zsx7jht/MrMe44Tcz6zFVZ/WMkzRH0rL8Pras8s3MrLGqs3pOA+ZGxJ7A3DxvZmYVqjSrBzgGmJ2nZwPHllW+mZk1VvWduztHxAqAiFghaadmGzqywUYK/23aSKMybzeXNAm4JSKm5PlnImJM3fpVETFoP39fX1/Mnz+/tHqatcuRDdbNJC2IiL6By6se1fO4pPG5QuOBJyou38ys51Xd8P8QmJGnZwA/qLh8M7OeV3VWz3nAdEnLgOl53szMKlR1Vg/AoWWVaWZmg/Odu2ZmPaYjT+Ayq8K4ceNYtWpV6eVIKvX4Y8eOZeXKgbfEmLWvI2f8kj4r6X5J90m6StJWnaiH/XlbtWoVEVHqa968eaWXUcWHl/WWyht+SROATwN9eXz/psD7q66HmVmv6lQf/2bA1pI2A7YBlneoHmZmPafyPv6I+L2kC4FHgBeB2yLitoHbObLBhkPZfzdr1qyp5G/Tf/82nEqNbGhYYIpivh74W+AZ4Frguoi4otk+jmywdlQRp9Df38+0adNKLcOxENaubolsADgM+H8R8WRE/AG4Adi/A/UwM+tJnWj4HwH2k7SN0ji4Q4GlHaiHmVlPqrzhj4i7gOuAhcCSXIdZVdfDzKxXdeQGrog4CzirE2WbmfU6RzaYmfUYN/xmZj3GDb+ZWY/pVFbPGEnXSXpA0lJJb+9EPczMelGn0jkvBn4SEcdL2oIU22BmZhWovOGXNBo4EPgQQES8ArxSdT3MzHpVJ874JwNPAt+V9GZgATAzIp6v38hZPbax4qzRcPZ2pZYxDaC/1CKIs0b779+GVSeyevqAO4EDIuIuSRcDz0XEF5vt46wea4ezeqzXdVNWz6PAo/kOXkh38b61A/UwM+tJnYhseAz4naTX50WHAv9VdT3MzHpVp0b1nARcmUf0/Ab4cIfqYX/myn4ebhXGjh3b6SrYn5lOZfUsAjbodzIbTlX0i7v/3UYi37lrZtZj3PCbmfWYjjX8kjaVdI+kWzpVBzOzXtTJM/6Z+MlbZmaV61RI227AkcB3OlG+mVkv69Rwzm8ApwCjmm3gyAYbKfy3aSNNJyIbjgKOiIhPSpoGnBwRR7Xax5EN1q08nNO6WTdFNhwAHC3pYeBq4BBJV3SgHmZmPakTkQ2nR8RuETEJeD/w84g4oep6mJn1Ko/jNzPrMZ26uAtARPRTepq5mZnV62jDb9Zt2gl1a2cfXxC2TnJXj1mdiBjSa968eUPex42+dZobfjOzHlN5wy9poqR5kpZKul/SzKrrYGbWyzrRx/8q8PmIWChpFLBA0pyI8FO4zMwq0Ilx/CsiYmGeXk0KaptQdT3MzHpVR0f1SJoEvAW4q8G6P2X1AGskPVhh1cyK2gF4qtOVMGvitY0WVp7V86eCpW2B24FzI+KGjlTCbCNJmt8oC8Wsm3Uqlnlz4HrgSjf6ZmbV6sSoHgGXAksj4qKqyzcz63WdSuf8e1Iq56L8OqID9TAbDrM6XQGzoepYH7+ZmXWG79w1M+sxbvjNzHqMG36zOpL+mK873SfpZkljhvHYa4brWGYbww2/2fpejIipETEFWAl8quiOkhxzbiOCG36z5n5JjhOR1C+pL0/vkJ8ZjaQPSbpW0s3AbZK2lTRX0kJJSyQd07HamzXhMxSzBiRtChxKuudkMG8H9o6Ilfms/z0R8ZykHYA7Jf0wPHzOuojP+M3Wt7WkRcDTwDhgToF95kTEyjwt4J8l3Qv8jPSNYecyKmrWLjf8Zut7MSKmksKttmBdH/+rrPv/stWAfZ6vm/4gsCOwTz7O4w22N+soN/xmDUTEs8CngZNzttTDwD559fEtdt0OeCIi/iDpYJqkI5p1kht+syYi4h5gMfB+4ELgE5J+QYpibuZKoE/SfNLZ/wOlV9RsiBzZYGbWY3zGb2bWY9zwm5n1GDf8ZmY9xg2/mVmPccNvZtZj3PCbmfUYN/xmZj3m/wOmxkKv/G630wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SKILL DRILL 5.4.4 Rural RIDE COUT\n",
    "# Create a box-and-whisker plot for the urban cities ride count.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_ride_count, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Count Data (2019)')\n",
    "ax.set_ylabel('Number of Rides')\n",
    "ax.set_yticks(np.arange(2, 41, step=2.0))\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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": [
    "# RIDE COUNT FOR ALL 3\n",
    "# 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": 188,
   "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": 189,
   "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": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# URBRAN RIDE FARE\n",
    "# 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": 191,
   "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": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SKILL DRILL 5.4.4 SUBURBAN RIDE FARES\n",
    "# Create a box-and-whisker plot for the urban fare data.\n",
    "x_labels = [\"Suburban\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(suburban_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "suburban_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    125.000000\n",
       "mean      34.623440\n",
       "std       14.558046\n",
       "min       10.110000\n",
       "25%       19.900000\n",
       "50%       37.050000\n",
       "75%       47.070000\n",
       "max       58.550000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SKILL DRILL 5.4.4 RURAL RIDE FARES\n",
    "# Create a box-and-whisker plot for the urban fare data.\n",
    "x_labels = [\"Rural\"]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(rural_fares, labels=x_labels)\n",
    "# Add the title, y-axis label and grid.\n",
    "ax.set_title('Ride Fare Data (2019)')\n",
    "ax.set_ylabel('Fare($USD)')\n",
    "ax.set_yticks(np.arange(0, 51, step=5.0))\n",
    "ax.grid()\n",
    "plt.show()\n",
    "print(\"Summary Statistics\")\n",
    "rural_fares.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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": [
    "# SKILL DRILL 5.4.4 MAKE A RIDE FARES B-A-W WITH ALL THREE\n",
    "# Add all ride count box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "ride_fare_array = [urban_fares, suburban_fares, rural_fares]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Ride Fare Data (2019)',fontsize=20)\n",
    "ax.set_ylabel('Fares ($USD)',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(ride_fare_array, 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/Fig3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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": [
    "# SKILL DRILL 5.4.4 MAKE A Driver Coutn B-A-W WITH ALL THREE\n",
    "# Add all ride count box-and-whisker plots to the same graph.\n",
    "x_labels = [\"Urban\", \"Suburban\",\"Rural\"]\n",
    "drivers_count_array = [urban_drivers, suburban_drivers, rural_drivers]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_title('Driver Count (2019)',fontsize=20)\n",
    "ax.set_ylabel('Number of Drivers',fontsize=14)\n",
    "ax.set_xlabel(\"City Types\",fontsize=14)\n",
    "ax.boxplot(drivers_count_array, labels=x_labels)\n",
    "ax.set_yticks(np.arange(0, 75, step=3.0))\n",
    "ax.grid()\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/Fig4.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5.5.1 Groupbys\n",
    "# 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": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63538.64"
      ]
     },
     "execution_count": 209,
     "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": 210,
   "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": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of fare for each city type.\n",
    "type_percents = 100 * sum_fares_by_type / total_fares\n",
    "type_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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": 211,
     "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": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5.5.2 Pie Charts\n",
    "# MatLab approach\n",
    "# Build the percentage of fares by city type pie chart.\n",
    "plt.pie(type_percents, labels=[\"Rural\", \"Suburban\", \"Urban\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QoYB3gQfjkpIKAm1MU8cIRyNh1T339IkKD/+gfXT0iEDbEuTsAR6IS0r6KNCGNGWMcASY2TfeGNIxOvo/3eLi7g4PDQ0PtD3NiHno7os90IY0RYxwBJD5t9xyevfWrae1i4rqEWhbmimVwH+AZ+OSksoDbUxTwghHAPj8yisjesfHT01o0+ba0JCQ5hy81Vj4A/hHXFLS3EAb0lQwwtHAvHnRRcePOe64mZ1iYo4NtC2Gg3ABSej4D/OjOAJGOBqIRJtNLjnhhBvO6d37v7EtWkQF2h5DjXwNTIhLSioJtCGNGSMcDUCizRZx09ChL4/q2fOW8NDQpji9vbmxDrggLilpU6ANaawY4ahnbhw6tO11gwfPGNily+mBtsXgEwXAFXFJSbMDbUhjxAhHPfLvsWNPGtev34zurVt3D7QthjrhAh6NS0qaFGhDGhtGOOqBRJtNLhow4Kqxffq8EdeyZUOu42moHz4HbohLSioNtCGNBSMcfibRZgu/etCgZ87p0+feiNDQxriup6FurALGxiUl5QbakMaAiSHwI4k2W+z5/fpNSbTZ7jeiEXQMBOYUJSfHB9oQEckSkQcCaYMRDj+RaLPFnd2r15uXDRx4nQnqCloGAD8VJSe3OdqCRGS+iLzmJX2CiDT6oWDzBfcDiTZbzOiePV++bsiQv4eFhJjh1uDmJODHouTkuIauWEQiGrrOmjDCcZQk2mxRpyckvHDDySdfFW66J82Fk4HUouTk6PqsREQ+EJEUEXlIRLLRm1hVEy0i00SkRERyPbsuInKfiKwWkf0ikiMi74hIa7fjE6xzzxKRtVa+eSJSq4hmIxxHQaLN1mpE9+7/uXnYsOsiwsLMzNbmxQhgVlFycn0vtDQKOBEYC5zlln4fsB4YjA6Vf1ZELnY77gLuQXevrgSGAa96lB0JPAzcgL6e1sCU2hhlhKOOJNpsLYZ16/b0rcOH3xQZFtZompCGBuUMYKa1pWZ9UQ7coJRaq5Ra45a+RCn1jFJqg1JqKvARWkwAUEq9rJT6WSmVpZRKAx4E/u6xIn4YcIdS6nel1GpgMnBmbVbNN8JRBxJttsjBXbok/2PEiNtahIdHBtoeQ0A5C5helJxcXw+PtUqpCi/pv3l537/6jYiMEZGfRCRbRIqB6UAE4L6yXIVSyn09kh1AOLrlcViMcPhIos0W0b116/tvHzHizlbh4fX5pDE0HRLRe7v4wj7Am4O1NVDk9n6/r8aISA/0VhHrgUuBIejuCGjxqMZzX97qoC7T4vAniTZbaKjIzbePGHFndGSkWUTY4M6VRcnJd/uQ3w5426ZzsHXsSJzi5f166++T0QJxr1LqN6XUBqCLD7YdESMcvjH2hqFDb0po06ZzoA0xNEomFyUnn1HLvG8CPYFXRWSgiNhE5F7gCrSv4UicIiIPi0hvEbkZuBZ4yTq2Ef3bvkdEjhWRK9COUr9hhKOWJNpsfU9LSLjrzOOOGxhoWwyNljDgi6Lk5CM+3ZVSm9HO1d7Aj8DvwOXApUqp72tR14vo0ZZ04Gng/5RSX1llrwbuRjtLM4CbAL9Gmpq5KrUg0WZr0zkm5vlnxo69olV4uOmiGI7Er8CouKQkTx9C0GBaHEcg0WYLDQsJufm+kSPPM6JhqCWnAk8E2oj6xAjHkRl387BhVx3TurXxaxh84eGi5OTRgTaivjDCcRgSbbb+o3r2vPOMY489MdC2GJocIcC0ouTkoNzz1whHDSTabG27xsY+cP2QIacdOmJmMNSKrsA7gTaiPjDC4YVEmy0MuPWW4cNHtQgPbxloewxNmguLkpPPD7QR/sYIh3fOOePYY8+0tW/fM9CGGIKCF+oxJD0gGOHwINFm6xARGnrJVYMGDQm0LYagoTc6riJoMMLhRqLNJsAVE4YMGRDXokXbQNtjCCoeK0pO7hhoI/yFEY6DOQEY3CE6OsQExhn8TCzwbKCN8BcmctQi0WaLAKr3z9h3ao8eXa8YODCxfXR010DaZQgqXMCwuKSk5YE25GgxLY4/cQG5QDsg5tetW3PumjnznRnr1n1TWllZHGDbDMFBCPDfQBvhD0yLww3LxzEQuAZoC+wEKmMiI8NvHjZs5OCuXUeEhYSYdUUNR8uVcUlJnwXaiKPBCIcXEm22SGAMUL2G407A1Ts+vvUNJ598zrFt2/YLnHWGIGA70Lcp7wzXPIUjU8KB3vRVGYfLlmiztQUuAkYCJUAewFm9eiX87fjjx7Zt1SpovOSGBueuuKQkz8WDmwzNTjjGj5GwmW9wB/ACMBV4nL5q7+HOSbTZegJXAb2A3cD+0JAQuWbQoMGjjztuTIuwMDNr1uArdqBfXFJSk/wBNivhGD9GBrSJ44F3nuJv4WFUbwa9Fz0F+k36qhrXT0i02UKAoWgBiUZ3XxztWrVqcfOwYaNO7NRpWIjZwc1v5BYX88ScOfy0cSMlFRUktGnDC+PGcXpCAg6nk6d//pmfNm4kq6CAmMhIRiYkkHT22RzTunWNZd72zTd8tmrVIemtwsPZ8eijAMzbtIkHZs1id0kJiX378tr48USEabdWSUUFZ0ydyieXX06/Dh38cZnnxiUl/eiPghqaZiMc48dIGPDkQzeReNpgTvKSZR1wL33VT4crJ9FmawmcA5wPONEjMeqETp3irxsy5NxucXG9/Gx6s6OwrIxRU6dySvfu3DJsGO2iothaUECnmBhs7dtTVF7OdV98wbWDB3NCp07sKy/nsR9/ZG9pKYtuu42wUO+b6RWVl1PucByUdu5773Fqjx68ceGFuFwu+kyezD2nn85ZvXpx3RdfcNPQodwyfDgAE1NTiY6M5LExY/x1qd/FJSWN91dhDUlzekIO79GF3qcM5IQajg8AfiRTZpIpNf74U+32slS7fQbwCLAaSADarsnNzXtg1qxPpq1Y8em+8vJ8v1vfjHhl0SI6xcQw9eKLGdKtGwlt2jCqZ09s7dsDENeiBd9eey0XH388vePjGdKtGy+NG4c9Lw97Xl6N5ca1aEHHmJgDry0FBWQVFHDd4MEA5JeWkldayk1Dh9KvQwcSbTY2WOUtz85m3qZN/OuM2i4pWiv+WpScXKud0xobzUI4xo+RlsDl11xAQmgoR9rb9XxgHZnyHJkSU1OmVLt9N/AGOhpwH3As0DIlM3PjnTNmvDFv06YfK51Ob/thGI7ArMxMhnTtyvVffkmv557j9Dff5K0lSzhc67i4Qt/q1i1qv2PFh8uX0699e4Z37w5AfFQUnaKj+XnTJsocDn7bupUBHTtS5XRyT0oKL4wbR2SYX0fjQ4Db/VlgQ9EshAM4PS6atif1pbYL8kQA/wI2kik3kul9Z6tUu12l2u12tI/kHbTv45hKpzNk6pIlvz30/fevZOzatUI1l/6gn8gqKODdpUtJaNOGr6+5hn+ccgrJc+bw9u+/e81fWVXFYz/+yNg+fegaV7u9oIvKy5mRkcG1Q/6cyygivH/ppTy/YAHDX3+dEzt35upBg3jl118Z3KULHaKiSHzvPQa/8gr/njfPL9cK3NgA20j6naAPZho/RsKB8686n04R4fg6tbkjWhBuI1Pupq9a5C1Tqt1eBfySaLOlA39F7/NZsbO4eNeTc+d+N7Rbt6VXDRo0tlNMTI+juZbmgkspBnXpQtLZZwMwsHNnNufn887SpQf8DdVUOZ3cMn06ReXlfHbFFbWu44vVq3G6XFx+4sHPkhE9ejDvllsOvN+cn88Hy5ez4NZbufCjj7hh6FAuGjCAMW+9xeCuXTm3T5+juFIA2qAd7m8fbUENSXNocQwMDSHutMEMPooyhgALyZTPyJRjasqUareXpNrtnwOPAhvQ/o+4pdnZufd8990HX65e/WVJZWVRTecbNB0tJ6g7fdq3J7vo4FtX5XRy49dfs27XLmZcey1tW9X+wf3h8uWM79+fNkc4556UFJ78y18IEWHlzp387fjjiYmMZKzNxoItW2p/UYfnTn8V1FA0GeEQkQQRUSJycm3PGT9GBDh//Fm0i4k68n6YteByIJNMeYJMqXFlsFS7fQd6c5zJQCXQA4j8eu3ajLtmzHjt161b51W5XI6azm/unHLMMfyRf7B/+Y/8fI5x64Y4nE6u/+or1u3axXcTJtAxpkZ31CEsy85m7a5dXDv48M+SaenptAoP58IBA3BZvU2H0wlApdOJ0+WqdZ1H4MSi5OSR/iqsIfBJOETkA+vHq0SkSkS2icibItKmvgw8SnoBPRJH1tq3URtaAUloAbm8pkyW/2MN8DgwDT33pVupw6FeWbRoweOzZ7+2MS9vTU3nN2duHzGCpdnZTF6wgM35+Xy7bh1vLVnCTcOGAbqlcd0XX7AsO5t3/vY3BNhVXMyu4mLK3IZbb50+nVunTz+k/A+XL+e4tm05PSGhRhv2lJTwn/nzmfzXvwLQumVL+rVvz2u//caqnTuZmZHBCMup6if+7s/C6pu6+DjmoCeBhaF3x34PvVFu7TuYbohIhFKqsi7n1oKxw08kplM8NXYvjoLuwGdkyh3A3fRVK7xlSrXbK4G5iTbbMuAC4EygbEtBwe7Hf/xx+siEhN8vGzgwMT4qyq97ezZlBnftyieXX86Tc+fyfFoa3eLieOTMM7lp6FAAcvbt43u73l519FtvHXTu6xdcwFWDBgEc0rUBPfoyfe1aHhw1isMtQj3xhx+489RT6ebWynnzoou4/dtveWvJEi4fOJDx/fvXeH4dSPRnYfWNTwFgIvIBEK+UGueW9gIwQSnVrobjTwCXKKWOdy8D+AX4JxChlOogIlejl1frC5QBacA9Sqkc67wEYAswVCm17Ei2jh8jHYFJkx/k5D4JHF/ri6wbLuAD4BH6ql2Hy5hos/VAi2w/9NyXYgGuHDTopLN79TqrZXh4dD3bami89IlLStoYaCNqw1H5OESkJ3oEwdf++ij0vpdjgbOstAh0F2AgMA4tLkcz9Xh0+7aE9eqOXx8LNRAC3ABsIFP+RabUOHqTardvBZ5Dr8sQAvRQEPFJevrKe7/77tVl2dkLnS6XswFsNjQ+mkyroy7CMVZESkSkDNiE7q78x8cyyoEblFJrlVJrAJRS7ymlvldKbVZK/Q7cBowUkW6+Gjh+jEQBZ10whnYhIQ3qAI5Fi8I6MqXGUOJUu92VarevAB4GvgLaA10Ly8urJi9YMPfJuXNfzyooyGwYkw2NiKAWjgXAScAw4FXge+AVH8tYq5Q6KKpSRAaLyAwR2SoixUB1d6QuHqgBQPiQAQRq3YxewAwyZTaZUmOLJ9VuL0+122cBE4El6GuNt+/ZUzAxNfXzd5cu/aigrGx3A9lsCDyji5KTm8Q+PnURjlKl1B9KqTVKqbvQowyPW8dcgKfHKdxLGfvd34hIFDAbKEU7XoeiuzGAz0FbAGd07YCrcwcCHXB1DrCKTHmFzJpHnlLt9vxUu/0d4GlgDzp8PeqnjRu33DljxpSfNm78vqKqqqyBbDYEjhZo53mjxx/N+GTgIRHpgv7Se27OfFItyuiL9mk8opRaoJTKBOo0b3n8GIkF+p9/Jp1D5BARCwRhaCfwRjLldjKlxrkyqXb7H2jxeBOIBLo7Xa7Qd5cuXXr/rFmvrN6583eXUn4LHjA0SppEd+WohUMpNR89Jf0x4GdgkIjcICK9RORB4LRaFLMNqADuFJGeIvJX4Kk6mjQAkBNs2Op4fn3RDngdWEmm1DgvO9Vud6ba7YvR3ZcUtBB3ztu/v+LZefNS/zN//pTsoqJNDWOyIQA0D+GweBG4EchEt0CeAZajQ67fONLJSqk9wHXAhUAGenTlvjracnpcNOVd2ge8m1ITxwNzyZRvyJQat5hMtdtLU+326ejp+2vQ97LNqp079zwwa9a0T9LTP9tXXn7YlcsMTZLjipKTewfaiCMRVAv5WKMpr1yWSOxV53NJoO2pBRXAy8DT9FUlNWWyVl/vC1yN3gF9F1AWGRYWesPJJw8f0aPHGRGhoZENYbChQbg5LimpUe9y32TmqtSS3oAM7k+jV2yLSOAhdPzHBDK9hzJa4evr0S2x94EY4JiKqireXLz414dTU19dv3t3upm+HzTUtNhUoyHYhGMwUHlMZ5raqkqd0YKwhEwZUVOmVLu9KtVuT0OLzU9AN6Bjzr59+5PnzJn58sKFb+8qKdneMCYb6hF/zq2qF4JGOMaPkVDg5G4dKY9uRay/yt25G66bCO1PhRYDof84SPO+nswBZi+EEZdDzBCIHwEX3AEb3GZgp2fAoIshegicfxvsLTxwaKjTxaI+CZJ3yklS49yfVLu9ONVu/x/aIb0JPXwbu2T79p13z5z53tdr1369v7Jy39FctyGgmBZHA9IeiBx6Qt2Gcb1RuA9OuwqUgllTYP0sePVR6NCu5nO2ZGuhGDkE0qfDnPegrBzO+8efeW56HMYMhxVfQVExPOs2T+vVj5HhJ9Ju8f94m0x5nEypcS28VLs9B73NwwvohZMTgMgvV69ee/fMma8t3rYtrcrlqnHldkOjpV1RcrJnWEOjIphWAOsKSO8E/DbL9Ll3oXN7+MgtoP7YIwTAL18Hjir4931Qvdj2w7fAmAmQVwDxbWD9ZvjkeehzLFzxV0iZr/Nt2wEvfwTLvgIgCngSuJFM+Rd91Zfe6ku12xWwOtFmexQYDVwChJRUVu54eeHC+T3btl1x49Ch5xzXrt2Aut4HQ0A4Hr0FR6MkmFocxwLOYzr5Tzi+nQvDT4TL7oUOp8FJF8Frn+gWSE2cfDyEh8E7X4HTCcX74cNvYegJWjQABtrgp1+hqgrmLoYTrYiT25Lhqbv+zGfRA/iCTEkjU06qqd5Uu70y1W7/EXgQWIgOX2+/ee/efY/Onv3Vm4sXv59fWtpov4iGQzgu0AYcjmASjn5AcYe2/hOOzdvhjc+g5zEw+224+xqY+CK8/mnN5yR0hZ/ehaTXIHIgxA2FNRsg5c0/87zzFHw1G447ByLCdYvks1m6pXLWCBj3D33szqfAbV2aM4DlZMpbZEr7Q2vWpNrthal2+wfoBZRz0IIanbZ587Z/zpjx9qzMzJllDsf+ms43NBoatXAERRyHtdnSlF49KHnxIe72V7kRJ8LJA+BXt8n9j7wE38zR/g5v5O6BM66BC8/S3ZDi/fB/1g6hP38A3vZ621sIQy6Bue/Dg5OhX094/DY45ya49Fy446pDTilCd2Nepa+qcUkDa/e5Qej4j9bADsDRpmXLyJuHDTtjYOfOw0NDQo60XYQhMEyPS0r6W6CNqIlgaXF0AhjS/5B5MkdF53jo76H7/XrCtsM0+F//FKJawXP/gkH94YyhMO05SFsKv6Z7P+eB5+H2K3TL5uclcPl5EBGhRePnJV5PiUM7RNeSKX+tyRZr+v5y9PT9r9GrtncpKCurfC4t7aenf/75ja0FBRsOcwsMgaNRtziCRTi6ACHHdfdfNwXgtMFgzzo4bUMW9DhMLaXlEOpxV6vfe1vb9ufFsHI93Hvdn3kc1jhIpQOch5/S1gdIIVO+J1P61pTJmr6fgp7/Uj0VoN363bv3PpSa+tkHy5ZNKywr23PYmgwNTaOORQoW4egFODrF+28oFvSPefEqeGYK/LEVvvwBXpkGd7hFWDz8Ipx1/Z/v/zoKVmRA8uuwMQtWrIPrH4VjOsMQj3GN8gq44yl4+ymo3iDs9MG6jvWb4INv9ftakAisJlNeIlNa15Qp1W7PS7Xbp6Jn4O5Ffzlb/bBhw6Z/zpgxZc7GjakVVVXltarRUN/EFiUnN9rfZ6M1zEf6AsUxUdRuG69aMvQE+PZV+OIHOH48PPqyHvW4/co/8+zcA5u2/fl+zCnw6fMwYy4M+huce7MeZfnhLd2FcSf5dTjvjIMF5ZVHIXMzDL8MBvSCO66ktoQD96Cn7/+jpt3nAFLt9o1oH8lU9Hoq3R0uV8g7S5f+/uD337+yJjd3qSsYnF9Nn9rvZ9nANHnnqLV3ylQg98uX+VdkROO92Q3MKuAe+qr5h8uUaLNFoRdN+it67dhcQA3q0qXDNYMHj+0SG9uom8xBTru4pKRGOQM6GFocUUBYbDShRjQOYiAwj0z5mkxJqClTqt2+P9Vu/xo9fT8D7f9onb5jx+77UlI++t+qVZ8XV1QUNIjFBk8a7fc5GIQjFnB17NwmZvO+fgX7KlubHeIP5mJgPZnyDJkSVVOmVLs9F7127H/Q21MkAC2+Xbcu858zZrz+y5Ytcx1OZ33tf2PwTqMVjmDoqvQD7u8w7PrQXpdNnQCgnJVVqnJfaYhjb3mEc7ejpWtnVbTkqLiQ7JA2odvD2kXkRMRHZrdoG5EbFRbqagzLCzYUO9AjK9PoW/MHn2izhQOnA5ehfSc7AOcxrVtH3zR06Nl94uMHHm4zI4PfGBCXlJQRaCO80eSF49TR/cdWqlYvJpx8bsuTr34iwZdzlXIpVbm/TBwFZWFVeRUtXLnOVmqHMzYkW9qEZoe2Dd8e3i4iJzI+ckerqPCSuiya3FhZgt59znuUiEWizRaL3uPmL+hWyG6AEd27d7nipJMSO0RH+7x1hcEnhsQlJXndITDQNPlJboWqZwW40lt0GtAZ3byuNSIhIpExrYiMaeWkO/vRy68fEtBQBa7yCgeVRaWhVfm6FaN2OmPIUXEh20PahGeHtwvfHhEfsaNlm8jdLUNDVGN/HA8HfiNTpgET6at2eMuUarfvAz5NtNkWoHefOwHI+23bth2Lt21797KBA08Y26fPuS3Cw2vsAhmOikbbVWnywgG0gZDSVnHt63X175CwyHDCOsQpOsRV0I8KoBDwXDVHlTldylFSGuLYWxZWlVfZwpVbFaV2qNiQbNqEbg9tG54dER+Z3SI+MqdVi7CKQN5/QW9FcRGZ8m/gBfoqr/6hVLs9O9Fmm4xeYOYa9O5zuf9btWpNp5iYdqd07z6q4cxuVhjhqEfiAEdEq+hGsZGNhISGSGRcFJFxUVUcSwlQgl4k9CAc4CotqxBHYVmoI788wrXb0cq1wxkjORIXuj2kbVh2WLuI7Ij4iJxWbVrk1+e1RaMXl77Jmr7/tbdM1vT9VYk2WyZ6+v6VQPaa3NwsIxz1hhGOeiQOcISEhjW5awkJbxlJeMtIF50pR++LecigvQK1v8qpnb0F5eFVuytbqlxnFDtcsZId0iasuhWTExkfsSMqIsxR10lrxwJfkSnz0PEfq71lSrXbK4DZiTbbscCJv23dmn3DySc7zWS5eqHR3tMm92PzQgxQ5XJWBe1GzRISFiot2sbQom2Mg+NwAPvwssqLA1z795eLo7A0tCqvsoVzl6MVO10xZKvWYdmhbUK3h8dH5ES2i8xpGRdZWNPT7ExgBZnyNvA4fVVeDflWAcNKHY6q3SUl2zvHxib441oNB1HTvQ84wSAcAiiX0xm0wuELIRFRLYiIauGiK6XoPTUP+fa5QJU4qlRl0YEh60jH9pAodtAxem9Bl5jc0rahm0fFSvbCVutbvR4uZW/SV3kuQZgFKIAtBQVZRjjqhdxAG1ATwSAcDiDE5awya2v6gISGh0nL+FhaxsdW0odKoBjIhc6rnOgVTAFQL4dQFfug9oO4k4vWpYjVO3duObVHj9ENZnzz4RDXWGMhGCJHq9DCYVoc9YKEuAg/2zO1eq1ToPVv27blmEWR/U5xXFJSaaCNqIlgEA4HIEY46pURk9Id3nwiq4EWFVVVzt1mPxd/02hbGxA8wmFaHPVLJOBto6is6j+27N27xctxQ91ptP4NCA7hqALEWeUwwlG/jPaSthsdphK5aufOrAa1JvgxLY56phIIcVWZFkc9c6ZnQnVQGNB68bZtOQ6ns8aFkw0+Y1oc9YwDEKcRjvpm+KR0h7cI1jVAZKXT6dpVUrLNy3FD3TAtjnrGAYQ4yvebdTjqlwjgNC/pWdV/bNm7N8vLcUPdMC2OesYBSOHObWaVqvpntJe0PehA1hbGz+FXNgbagMMRDMJRDITu3pJZ0NTXFmkCHNbPsWTbth1mlbCjRynlBJYF2o7DEQzCkQfgKC+tqizbXxRoY4KcoZPSHd7W3lgLRDhcLlducbHxcxw96+KSkkoCbcThCAbhyMeaM1FeXNQoV4QOIqqXFPTkQAzHZhPPcdSIyGFXZmsMBINw7MW6jtKi/PwA29IcGO0lLR+9rlGLlTt2ZDWkMUHK4kAbcCSCQTj2o5eyCCvZu9u0OOqfmvwcK4E2v2dn76x0Os0I19FhhKO+yUhLUeilKVrt251jWhz1z5BJ6Y4YL+nrgHCny6Vyi4u3NrRRwYJSqghYH2g7jkSTFw6L7UDLvdlbTIuj/gkDRnpJz6r+Y1N+fpaX44ZaICJL45KSGv3wYDAJR4s9WfYC5XI1+pseBIz2krbXerVMN36Oo6HRd1MgeIQjD3BVVZY7K8pKCgNtTDPgcH6O1suys3PNrvd1xghHA7IXa0i2KDfb9K/rn0GT0h1xXtIzgHCXUmqn8XP4jFLKhRGOBiUPfS2ya9O6TYE2phkQinc/xxb0GrBsys838Rw+omBRXFJSk3DwB4VwZKSllKG/tLGbl87fbELPG4RDuivoWI49QKv0nJysBrUmCAgR+TLQNtSWoBAOi6VA3L49O0rLivY26pmFQUJNfo50oPXynJxdFVVVZQ1vVtNE6aed182wGiPBJBwbsfwc+dmbNwfYlubAwEnpjjZe0tcDYQrYsW9fVsOa1HRxKvV7XFKS1z18GyPBJBzb0MsIhu2wrzR+jvonBDjDS3pW9R9/mHiOWhMWEvK/QNvgC0EjHBlpKQ509GLrzUvnbzP7rDQI3rorhei1SKNWGD9HrbCm0X8WaDt8IWiEw2I5EOUoL60qzss107vrH28OUrD8HCt37Nhd7nA02r1BGgtVLtecuKSkRr1UoCfBJhybsPwceVs3Gj9H/XPCpHRHOy/pmUCI8XPUjvDQ0LcDbYOvBJtw7MJaxm7bmiXGz1H/CDDKS/qBeI6NJp7jsDhdrkLgu0Db4StBJRzWTNkVQJvta5bklu0r2BNom5oB3vwc+9CL7UYtz87OanCLmhBKqU/jkpKa3HKLQSUcFqvRK1WRsz59ZWBNaRbU5OdYAbRZnZubV+ZwNOpl8AKFSylnWGjoc4G2oy4Eo3CsR2/SFL7u529WKZfLFWiDgpwBk9Id7b2k27G+XznGz+GVkoqKr+KSkprknJ6gE46MtJQKYAHQoWhX9v7C3G1/BNqmZsBoL2lZ1X9szMvL8nK8WeNyuZwtw8MnBtqOuhJ0wmHxK3rBGbYs/2VFgG1pDnjzcxQDO4DoZcbPcQjFlZXT4598MivQdtSVYBWOrWjnXEzG/JkbKkpLzLYJ9cvh4jni1u3alV9aWVnckAY1ZlxKuVqFhz8UaDuOhqAUDmt0ZTbQxuWsUtnrli0NtE1BTt9J6Y5OXtKNn8MLxRUV38Q/+WSTHqYOSuGwWAY4gbBVP3y+or5D0H/7/E3++/fBPDGyHU+MbMcb140k85fvDxxXSjFnypM8e04PHh8Ry1s3n82uTesOW+bmZWk8PDjikNfuLZkH8mxcPIfJF/bniZHt+PyxCVQ5/hzZqygtYfIF/Y9Yj58Y7SUtCx3PIRvy8pr0D8VfWK2NBwNtx9EStMKRkZZSAswHOpbk55btydqwpj7ri+vYlbF3P8udnyzhjmm/cdzQ0Xx8/yXs3LAagAUfTuaXaS8z/qGXuOPjX4lu2553bzuPiv1HbsHf89VKHvlx24FXfPfeALhcLj5/9DqGX3ILt32wgJyM5fz+9TsHzvvx9SROPPdSOh43oH4u+mC8+TlKgGwgeun27VkNYURjp7iiYkb8k082+ajmoBUOizS0k1TWzp2+WKn6W8i4/+jx2E4bS3z3XrTv0Ydz73yKyFYxbFu9BKUUiz59ldET/sXxZ11Mp17Hc2nye1SUFrMy9ciTIqPbdCAmvtOBV0hoKAClhXnsL9jDKZf+g47HDaDfqHHssVoj29cu5Y/FPzHmpkfq65I9OVw8R1zmnj0F+ysr9zWUMY0Rl1KuqCBobUCQC0dGWko2et5E25yM5bv3bLGvaoh6XU4nq2Z/TmVpCT0GnkJBzhaK83LpNeIvB/KEt2jJsYNHsnX1b0cs77WrR/DsOd1559Zz2bR0/oH0qDbtiYnvzMbFP+EoLyMrfRGdep+As6qKb565nQseeY2wiMj6uERv9J6U7ujiJX0D1vcsu6ioWXdXisrLP2/35JNBER4Q1MJhkQLEAiz5+u2f69PXkbtxDUmnteHxU6L59pk7ufqFL+nU+wSK8/XEx5i2HQ7KH922AyV5NU+KjInvxIWPvMbVz3/OVc9/QXxCH979x7lsWf4LACLClf/5lJ/ffpaXLhlIF9tJnHzBBH756AW69T+Z6LYdmHrjGCZf0J85U56sr8t2x1urI8v6X5pzPMf+ysrC8JCQWwNth78IC7QBDUAGejf1YwtytuzOXrd8cfcTh3vbOPmoiU+w8c/PllJeUsTaudP5MulGbn7rpz8ziBx8glKHprnRPsFG+wTbgfc9Bp5CwY6t/PLxixw7RK8VnDDoNO6c9merJW/bH/w+/V3++dnvvPuPsQy/9FZO/MslvH7NCLoNOJm+I8/zz8V650zgE/eEVLu9NNFm2wbELtm+PWtcv371WX+jZVN+/r0jp0wJmiHpoG9xWEOzXwCtgJAlX7210FFRXi9rRISFRxDfvRfd+g9h7D+foXOfgSz65BVi2nUEONDyqKakYA/R7Tp4K6pGjjl+KHmHCYb99pk7SLz7WURCyFm/goHn/p3IqBj6jhx3UDennjisn2NjXl5hSUVFYX0b0djYWlAwb+SUKR8E2g5/EvTCAZCRlrIVWAh0Ktu3t2LzsvlpDVGvcrmoqqygTddjiYnvxB+L5xw45qgoJyt9IT1OHOFTmTs3rCImvrPXY8tmfEhEy1ac8JdL0Ft0gLPKYf1ficvprOOV1Jqek9Idx3hJ31j9R3ZRUVZ9G9GY2F9ZWbxl797LA22Hv2kWwmHxLXo/kLBl376/rLykyK/7zP7wyiNsWbGQgh1Z5G5cww+vPsqW5WmcdN4ViAinXflP0j54nrVzvyH3j7V8lXQjES2jOSnxz+/UF49fzxePX3/g/cJPXmHdvBnkbdvIrk3r+OHVR8mYN5MRl912SP0le3cz962nGT/xFQBaxrSm43H9+eXjl9iRmc7aOdNJGHSqPy+5Jg7r59jQzPwcf+Tn33fBRx/tDrQd/qY5+DgAyEhLyes/atz3wHlOR+X2zAWz5px03pV/91f5xfm7+OKxCRTn59IiOo5OvU9gwqvf0efUcwA447oHcJSXMfM/d1O2r4Bjjh/GDW/MIjLqz43fC3O3H1Sm01HJ9y9NZN+eHMIjW9KxZ3+ue2UGfU9PPKT+756/j5HX3EPrTn8+8C9Nfo8vn7iR3/73BoPGXc3xZ13sr8s9HGcCH7knpNrtZYk2WxbQZvG2bVvG9+/fEHYEnG2FhQvOmDLlnSPnbHpIc9q8qP+ocdHA80ARUH7R41NuiGnX0VvT2lB3tk4cFJ7gmZhos50PXABkv/23v90VExnpbWuFoGF/ZWXJyh07ep/3/vtBucdPc+qqVEeTfgl0BEifNW222d3e7/SYlO5I8JK+AWs5we2FhUEfz7EpP//BYBUNaGbCYbEQyAdislYszNm6evGCQBsUhHjzc1QvWBMS7H6OP/Lz54ycMuXNQNtRnzQ74chIS6kEpgHxQMjCj19KK87ftf0Ipxl8w9u8lXJgMxDz69atWQ1uUQORVVCQ9fHy5RcF2o76ptkJh8Uq4Gegm8tZpX75+MXpTkdlRaCNCiJG15CeDsRuKyws3lde3iR2ZfeFPSUlBZ+kp1/08qJFQb/GarMUDregsD1A27ysDYXr5s2cFWCzgoljJqU7jvOSfiBybXuQxXPsr6ws+2L16uufS0tbGWhbGoJmKRwAGWkpZcCbQAwQvvL7T9bsybKvDrBZwcRh/RyZe/ZkNaAt9YrD6ayavnbtY4/Mnj0j0LY0FM1WOAAy0lKygP8B3QDmvztpVsX+4oKAGhU8ePNzVKBbHbG/BYmfw6WUSrXb356VmflyoG1pSJq1cFjMQU+C61xWXFj5+9dvf222VPALo2tITwdisouKSgrLyvIa0J56YVFW1g+frlx5b6rd3qy+M81eODLSUpzAu+hlBqO2rPglJyt94fzAWhUUdJmU7ujjJd3dz9Gk4znW5Oauev233y63WlLNimYvHAAZaSl7gbfQgWEhv0x7+Zf6XmqwmeDNz7ENvTF4SObu3VkNa47/+CMvL+vt338/39rustlhhONPVgE/AT1Qih9ff/xbs5nTUePNz1GJni0b9+vWrVlNccrDul27Nj+/YMG4D5cvb7bxP0Y4LKwh2s/R/o5jnI5K1+xXHvuiJH93doBNa8qMriF9BRCzs7i4tLC8vEnNHF2Rk7Nx0vz5V/9v1aoGWTq+sWKEww0rqvR1dHO6S0VpsePH1x//1Ox6X2c6Tkp3eFvyazO6u8L2wsKsBrXoKFi8bVvGc2lpV8zMyDjyQrFBjhEODzLSUkqBl4ECoEPJ3t1lc6Y+9bHZDa7O1OTncAGh65uAn0MpxZyNG5e/vHDh5al2+/JA29MYMMLhhYy0lCJgMnrX+3YFOVuK0z54/uP6WnIwyPHm53CgZ8vGNnY/h9Plcn6+atW8d5YuvTrVbjcOcwsjHDWQkZayBy0eYUBc7obV+b9+9tonTvet0gy1YdSkdIe3FZnTgehdJSVlBWVlNS/1HkAqqqoqpy5ZMvPbjIwbU+32zCOf0XwwwnEYrH1ZXkBvrxC1deWiHUu/ee9/ziprIU9DbWgPeNtKblP1H9sa4fochWVl+15YsODjBVu2/CPVbm909gUaIxxHICMt5Q+0z6M90GLDoh+2/PrpKx+abotPePNzZKOD7hqdn2Pljh0bH0pNfXF1bu59qXZ7kxr1aSiMcNSCjLSUNcAUoDNWdOm8d559r6K0uDCwljUZavJzZAJxC7OytqpG4OioqKoq/2DZsrRJ8+e/WFRePqm5BnfVBiMctSQjLWUx8CLQFojL3bgmf/Yrj71bWpgftMvD+ZEzavBzrASi80tLy/eWlQX0Pm4rLMx+5Icfpv+wYcOjwNTmGEbuC0Y4fCAjLWUV8CwQCcQX5m4rSZl8//t7c7LsATatsdMOONFL+oF4jm0FBVkNaVA1TpfLmWq3L3vo++/fztm37/5Uu31Rqt0e8NZPY6fBhENEskTkgXoqe7SIKBGJr4/y3clIS9kEPA1UAJ3LS4oqZ71w/+fbVi9ZWN91N3Fq8nNUAWHrAuDnKCgr2ztp/vzvP1y+/BkF/061203rsZbUSjhEpL2IvGH9+CtEZJeIzBWRvxz57OAjIy1lB1o8tgM9lMvF/PcmzV3941fT63NT6yaONz9HFZafY1FW1lZXA/k5lFJqWXZ2xv0pKdPW5Oben2q3f2v5XAy1pLYtjq+BYcCNQB9gHJCKboIGFBGJCES9GWkpBeg9WhYBx2KtIvbLxy+/X15SFHTrafqBMyalO7x939KBqIKysoq9paU769uIrQUFm5+aOzdl8oIFL5c6HI+k2u0bj3yWwZMjCoeItAZGAhOVUnOVUluVUkuVUpOVUv+z8hzSDRGR+SLymkdx0SIyTURKRCTXyzlKRC7xSDuobCvPHSIyXUT2o30O1ZwiIitFpFxElovIELfz2onIZyKSLSJlIrJORK73qGu+1bJ6VkTyRGS3iEwWEa/3KSMtpQK9lsenwDFA9NaVi3Z8+8wdU7Izlv+mlNmzxY3WwEle0g/4ObbW47yV3SUlOa8sWvT9Q6mpP2bs3v008E6q3b6/vuoLdmrT4iixXuNFpMVR1ncfsB4YDCQBz4pIXfYlTAK+B05AT0qrZjLwEHAy+gs5S0RaWcdaoGdljkMHJP0XmCoiZ3mUfRW6330qcCdwD3BZTYZkpKWojLSUH9Ctj5ZA18qy/c6f33r6x18/fe19f+9R28Tx5ufIARxA2LrcXL8HWhWVl+d/uHz5T3fNnDn7161bPwImptrtvxsH6NFxROFQSlUBE4CrgUIR+c16Cg+vQ31LlFLPKKU2KKWmovcYva8O5XyulHpHKbVZKeX+ZXtKKTVbKbUWuB4tFlda15GjlHpeKbXSOu8tYDpwhUfZGUqp/7Ns/AKYB3iKyyFYsR6PAMvRXZeoTUvnbf/2mTvezMlYvti0PgDvfg4nkAHELdq6dZtLKb8swVdaWVk8Y926tNu/+WZWqt3+BfBwqt3+eardbiYr+oFa+TiUUl8DXYDz0b6NU4HFIvKIj/V5Tkf+DajLDsTLjlS+UqoEWFNdvoiEisijIrJaRPJFpAS4GOjuUYbnSuc7gA61McqaHDcVHWnaCqv1Mfetp2f/+tnrH5SX7GvurY+Rk9IdoV7SVwJRReXllXn79+84mgoqqqrK523a9NudM2bM/GzVqplOpR5PtdvfMRGg/qXWu9UrpcrRK2T9BDwpIu8AT4jIZPQUac8An/A62KNqWU5d+qYPAPcDd6MFpQTtH/EUBU/vusKHYWtrQaAV/UeN24Ru7YwAdm36/edt2WuXTjn9mnvP6tL3pOEi3uKhgp5YdDd1qUf6FqrjOQoLszpER3fzpVClFLnFxVkLs7LWz8rMzC+vqvoDvSiT3XRJ6odaC4cXMqzzW6A3NupcfcDyhfRFe8zdOcXL+/Vu7z3L6ej+vhacgvZtICJRwPHo7hDA6cB3SqmPreOCHiEq9KH8WpORllLUf9S4KegfyfVA64rS4h1zpz75Q8Lg09cMPPeyMXEdu/Wsj7obOWdyqHDsQC9hEL42Nzfr5G7dTq9NQWUOR8ma3NyVMzIytm7Kz3cAecBnwMrmtup4Q3NE4RCRdugd3t9DN+OL0c7HB4G5Sql9IvIzcIOIzET/+B/Fe0vhFBF5GPgKvazctWhnZDU/A3eIyK/oCVDPAuU+XM9jIrIH/UX8P/SX8VPr2AbgMhE5Hf0F+yfaF+Epbn7Dan0s6z9q3B/o6xwG5GetWJiTtWLhx8cNG9P9hLP/dmZshy4J9WVDI+RM4Dn3hFS73Zlos60D+i7aunXbtYMHu0JCQry28lxKubYXFm5M27w5Y/aGDUVOHfuRDswH1luxIYZ6pjYtjhJgMbqJ3wsdbp2D/kE+beX5N5AAzLDyP4P2iXjyIjr0+FF0d+P/lFJfuR2/Hz28OR/YhRYnb0vP1cRE9DR4G7AOGKeUqu7WPI0WilSgDPgA+IS6+Vh8IiMtpbD/qHFvoGM+Lkffq/xNv/+8bdPvP3/Y59RzEgaMufDMmPjOnv6WYOT0SemOsImDwj1/4CuBwcUVFXl7SktzOkZHH+N+sKC0dNea3Nx109ety8ktLnYC+cBsYFmq3W420WpgpBFMSmxW9B81LhQYBPwd7V/JQ4sttpHn9ew/evyZMe06+tTHb4KMmDgofLF7QqLN1hV4Cth238iRY07s1GnI9qKiTet3796Utnnz7px9+yLQrdDfgF+AP0x3JHAY4QgQ/UeNCwOGAJcC8bgJSL9R5/fqN2rc6Oi2HboG0MT65JGJg8L/7Z6QaLOFAK8BBdEREWH7KyujlY6LAd3CnY32XQT9TvBNASMcAab/qHHhaJ/R34E2aB/RfoBuA4Z27HPqOQM7HNf/hIgWraIDaKa/+WnioPBzPBMTbbbbgNPQAroS7bvYBOSb0ZHGhRGORkL/UeMi0M7TS4A4tB8mD3CFhIaJ7fSxxyUMGnli2249+4aGhdVlqLsxsR9oM3FQ+EFD34k2Wyx6/tN24+Rs3BjhaGRYAtIXOAPtCxGgyHrRMqZ1RP8xF/Y/ZsDQgTHtOyc04XiQ0ycOCl8UaCMMdcMIRyOm/6hxsehRqLPQIzEK3ZUpA2jXvVec7fTEAfHde/eMie/cvYm1RB6fOCj86SNnMzRGjHA0AfqPGifoDbFPRotIa3SMSgFWnEtYRIvQhEGnde1iO+nYtt16JkS1bd8lNCw8IEsOHI7Ksv3lYRGRP4aEhn00cVD414G2x1A3jHA0MfqPGhcCHIeOkh2CDuMGLSCFaEEhJDRMuvQ9qX2n3id2a9vt2K6x8Z27RETFtAkLj4hsCDtdLqezsrSkqLxkX0HZvoKCvdmbc7LXLcvZtWldLPDvjLSUjIaww1A/GOFowlgtkfbowLYTrVcUukujgH3oEYoD8Q4tY1pHtO6SEBvXoWtcdNv2sa1ax8e2iImLbREdFxfZKjo2omVUbEhoWIRCKRRKr5el/7b+ufSK5Eq5nM6qiv3FheXFhQWlhfmFJXt3F+zbs6Nwb86WwoIdWcXK5WoBRKOHVRU6DmMj8FFGWkqj3ITJUDuMcAQRlpDEA12Bnuh1R3qgHazVEwhD0BP5KtxedVk2T4AI9Fyl6peLgycq5qPnDq1B7xe7MyMtxYyWBAFGOIIcK04kDt2lqf6/IzpqtQN6+LMVf/7oa8Jz+KZ6tCfXeuUAe9F+l0JgX0ZaitNf12FoXBjhMNB/1LhIIAYtINXdHJfb354vF1CckZZiFvhtphjhMBgMPmM2ZDIYDD5jhMNgMPiMEQ6DweAzRjgMBoPPGOEwGAw+Y4TDYDD4jBEOg8HgM0Y4DAaDzxjhMBgMPmOEw2Aw+IwRDoPB4DNGOAwGg88Y4TAYDD5jhMNgMPiMEQ6DweAzRjgMBoPPGOEwGAw+Y4TDYDD4jBEOg8HgM0Y4DAaDzxjhMBgMPmOEw2Aw+IwRDoPB4DNGOAwGg88Y4TAYDD7z/72LTHVsjeAnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the percentage of fares by city type pie chart.\n",
    "plt.pie(type_percents,\n",
    "    labels=[\"Rural\", \"Suburban\", \"Urban\"],\n",
    "    colors=[\"gold\", \"lightskyblue\", \"lightcoral\"],\n",
    "    explode=[0, 0, 0.1],\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, startangle=150)\n",
    "plt.title(\"% of Total Fares by City Type\")\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2375"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5.6.1 Ride %\n",
    "# To calculate the percentage of rides for each city type based on all the rides, we will:\n",
    "pyber_data_df.groupby([\"type\"])\n",
    "pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "pyber_data_df[\"ride_id\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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": 221,
     "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": 222,
   "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": 226,
   "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": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5.7.1 Driver % ---- Translates to sum the driver counts by type, then divide by total driver counts times 100 \n",
    "# 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": 227,
   "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": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable                        Type           Data/Info\n",
      "--------------------------------------------------------\n",
      "Drivers_array                   list           n=3\n",
      "ax                              AxesSubplot    AxesSubplot(0.125,0.125;0.775x0.755)\n",
      "city_data_df                    DataFrame                      city  dri<...>n\\n[120 rows x 3 columns]\n",
      "city_data_to_load               str            Resources/city_data.csv\n",
      "driver_percents                 Series         type\\nRural        2.6236<...>ver_count, dtype: float64\n",
      "drivers_count_array             list           n=3\n",
      "fares_mean_array                list           n=3\n",
      "fig                             Figure         Figure(720x432)\n",
      "lgnd                            Legend         Legend\n",
      "matplotlib                      module         <module 'matplotlib' from<...>matplotlib\\\\__init__.py'>\n",
      "mean_array                      list           n=3\n",
      "mean_drivers_array              list           n=3\n",
      "mean_rural_drivers_count        float          4.296\n",
      "mean_rural_fares                float          34.623440000000016\n",
      "mean_rural_ride_count           float          6.944444444444445\n",
      "mean_suburban_drivers_count     float          13.712\n",
      "mean_suburban_fares             float          30.97012800000002\n",
      "mean_urban_drivers_count        float          36.67815384615385\n",
      "mean_urban_fares                float          24.52577230769236\n",
      "mean_urban_ride_count           float          24.62121212121212\n",
      "median_drivers_array            list           n=3\n",
      "median_rural_drivers_count      float64        4.0\n",
      "median_rural_fares              float64        37.05\n",
      "median_rural_ride_count         float64        6.0\n",
      "median_suburban_drivers_count   float64        16.0\n",
      "median_suburban_fares           float64        30.75\n",
      "median_urban_drivers_count      float64        37.0\n",
      "median_urban_fares              float64        24.64\n",
      "median_urban_ride_count         float64        24.0\n",
      "mode_rural_drivers_count        ModeResult     ModeResult(mode=array([1]<...>nt64), count=array([32]))\n",
      "mode_rural_fares                ModeResult     ModeResult(mode=array([37.05]), count=array([2]))\n",
      "mode_rural_ride_count           ModeResult     ModeResult(mode=array([6]<...>int64), count=array([5]))\n",
      "mode_suburban_drivers_count     ModeResult     ModeResult(mode=array([20<...>nt64), count=array([79]))\n",
      "mode_suburban_fares             ModeResult     ModeResult(mode=array([17.99]), count=array([3]))\n",
      "mode_suburban_ride_count        ModeResult     ModeResult(mode=array([17<...>int64), count=array([7]))\n",
      "mode_urban_drivers_count        ModeResult     ModeResult(mode=array([39<...>nt64), count=array([86]))\n",
      "mode_urban_fares                ModeResult     ModeResult(mode=array([22.86]), count=array([5]))\n",
      "mode_urban_ride_count           ModeResult     ModeResult(mode=array([22<...>int64), count=array([7]))\n",
      "mpl                             module         <module 'matplotlib' from<...>matplotlib\\\\__init__.py'>\n",
      "np                              module         <module 'numpy' from 'C:\\<...>ges\\\\numpy\\\\__init__.py'>\n",
      "pd                              module         <module 'pandas' from 'C:<...>es\\\\pandas\\\\__init__.py'>\n",
      "plt                             module         <module 'matplotlib.pyplo<...>\\\\matplotlib\\\\pyplot.py'>\n",
      "pyber_data_df                   DataFrame                          city <...>\\n[2375 rows x 6 columns]\n",
      "ride_count_data                 list           n=3\n",
      "ride_data_df                    DataFrame                          city <...>\\n[2375 rows x 4 columns]\n",
      "ride_data_to_load               str            Resources/ride_data.csv\n",
      "ride_fare_array                 list           n=3\n",
      "ride_percents                   Series         type\\nRural        5.2631<...>: ride_id, dtype: float64\n",
      "rural_avg_fare                  Series         city\\nBradshawfurt       <...>ame: fare, dtype: float64\n",
      "rural_cities_df                 DataFrame                       city    <...>n\\n[125 rows x 6 columns]\n",
      "rural_driver_count              Series         city\\nBradshawfurt       <...>ver_count, dtype: float64\n",
      "rural_drivers                   Series         2250    9\\n2251    8\\n225<...>Length: 125, dtype: int64\n",
      "rural_fares                     Series         2250    43.22\\n2251    12<...>ngth: 125, dtype: float64\n",
      "rural_ride_count                Series         city\\nBradshawfurt       <...>me: ride_id, dtype: int64\n",
      "sts                             module         <module 'scipy.stats' fro<...>ipy\\\\stats\\\\__init__.py'>\n",
      "suburban_avg_fare               Series         city\\nBarronchester      <...>ame: fare, dtype: float64\n",
      "suburban_cities_df              DataFrame                       city    <...>n\\n[625 rows x 6 columns]\n",
      "suburban_driver_count           Series         city\\nBarronchester      <...>ver_count, dtype: float64\n",
      "suburban_drivers                Series         1625    11\\n1626    20\\n1<...>Length: 625, dtype: int64\n",
      "suburban_fares                  Series         1625    27.79\\n1626    18<...>ngth: 625, dtype: float64\n",
      "suburban_ride_count             Series         city\\nBarronchester      <...>me: ride_id, dtype: int64\n",
      "sum_fares_by_type               Series         type\\nRural        4327.9<...>ame: fare, dtype: float64\n",
      "total_fares                     float64        63538.64\n",
      "type_percents                   Series         type\\nRural        6.8114<...>ame: fare, dtype: float64\n",
      "urban_avg_fare                  Series         city\\nAmandaburgh        <...>ength: 66, dtype: float64\n",
      "urban_cities_df                 DataFrame                          city <...>\\n[1625 rows x 6 columns]\n",
      "urban_city_outlier              str            West Angela\n",
      "urban_driver_count              Series         city\\nAmandaburgh        <...>ength: 66, dtype: float64\n",
      "urban_drivers                   Series         0        5\\n1       72\\n2<...>ength: 1625, dtype: int64\n",
      "urban_fares                     Series         0       13.83\\n1       30<...>gth: 1625, dtype: float64\n",
      "urban_ride_count                Series         city\\nAmandaburgh        <...> Length: 66, dtype: int64\n",
      "x_labels                        list           n=3\n"
     ]
    }
   ],
   "source": [
    "whos"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}