PyBer_Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pyber Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Loading and Reading CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# File to Load (Remember to change these)\n",
    "city_data_to_load = \"Resources/city_data.csv\"\n",
    "ride_data_to_load = \"Resources/ride_data.csv\"\n",
    "\n",
    "# Read the City and Ride Data\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "ride_data_df = pd.read_csv(ride_data_to_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 105,
     "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 data table for preview\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2375"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pyber_data_df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable 1: Get a Summary DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "city_ride_id = pyber_data_df.groupby(['type'])['ride_id'].count()\n",
    "city_ride_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         537\n",
       "Suburban     8570\n",
       "Urban       59602\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Get the total drivers for each city type\n",
    "city_driver_count = pyber_data_df.groupby(['type'])['driver_count'].sum()\n",
    "city_driver_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "city_fare_sum = pyber_data_df.groupby(['type'])['fare'].sum()\n",
    "city_fare_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       4.647746e+12\n",
       "Suburban    4.971015e+12\n",
       "Urban       4.873485e+12\n",
       "Name: ride_id, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_rides_sum = pyber_data_df.groupby(['type'])['ride_id'].mean()\n",
    "city_rides_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         537\n",
       "Suburban     8570\n",
       "Urban       59602\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_driver_sum = pyber_data_df.groupby(['type'])['driver_count'].sum()\n",
    "city_driver_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       34.623440\n",
       "Suburban    30.970128\n",
       "Urban       24.525772\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "average_fare_per_ride = city_fare_sum / city_ride_id\n",
    "average_fare_per_ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       8.059460\n",
       "Suburban    2.258615\n",
       "Urban       0.668675\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Get the average fare per driver for each city type. \n",
    "average_fare_per_driver = city_fare_sum / city_driver_count\n",
    "average_fare_per_driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Rides</th>\n",
       "      <th>Total_Drivers</th>\n",
       "      <th>Total_Fares</th>\n",
       "      <th>Average_Fare_per_Ride</th>\n",
       "      <th>Average_Fare_per_Driver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>537</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</td>\n",
       "      <td>8.059460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>8570</td>\n",
       "      <td>19356.33</td>\n",
       "      <td>30.970128</td>\n",
       "      <td>2.258615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>59602</td>\n",
       "      <td>39854.38</td>\n",
       "      <td>24.525772</td>\n",
       "      <td>0.668675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total_Rides  Total_Drivers  Total_Fares  Average_Fare_per_Ride  \\\n",
       "type                                                                       \n",
       "Rural             125            537      4327.93              34.623440   \n",
       "Suburban          625           8570     19356.33              30.970128   \n",
       "Urban            1625          59602     39854.38              24.525772   \n",
       "\n",
       "          Average_Fare_per_Driver  \n",
       "type                               \n",
       "Rural                    8.059460  \n",
       "Suburban                 2.258615  \n",
       "Urban                    0.668675  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "summary_df = pd.DataFrame({\n",
    "        'Total_Rides': city_ride_id,\n",
    "        'Total_Drivers': city_driver_sum,\n",
    "        'Total_Fares': city_fare_sum,\n",
    "        'Average_Fare_per_Ride': average_fare_per_ride,\n",
    "        'Average_Fare_per_Driver': average_fare_per_driver})\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "summary_df.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Rides</th>\n",
       "      <th>Total_Drivers</th>\n",
       "      <th>Total_Fares</th>\n",
       "      <th>Average_Fare_per_Ride</th>\n",
       "      <th>Average_Fare_per_Driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>537</td>\n",
       "      <td>$4,327.93</td>\n",
       "      <td>$34.62</td>\n",
       "      <td>$8.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>8570</td>\n",
       "      <td>$19,356.33</td>\n",
       "      <td>$30.97</td>\n",
       "      <td>$2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>59602</td>\n",
       "      <td>$39,854.38</td>\n",
       "      <td>$24.53</td>\n",
       "      <td>$0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total_Rides  Total_Drivers Total_Fares Average_Fare_per_Ride  \\\n",
       "Rural             125            537   $4,327.93                $34.62   \n",
       "Suburban          625           8570  $19,356.33                $30.97   \n",
       "Urban            1625          59602  $39,854.38                $24.53   \n",
       "\n",
       "         Average_Fare_per_Driver  \n",
       "Rural                      $8.06  \n",
       "Suburban                   $2.26  \n",
       "Urban                      $0.67  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "# Format the Total School Budget and the Per Student Budget columns.\n",
    "summary_df[\"Total_Fares\"] = summary_df[\"Total_Fares\"].map(\"${:,.2f}\".format)\n",
    "summary_df[\"Average_Fare_per_Ride\"] = summary_df[\"Average_Fare_per_Ride\"].map(\"${:,.2f}\".format)\n",
    "summary_df[\"Average_Fare_per_Driver\"] = summary_df[\"Average_Fare_per_Driver\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Rides</th>\n",
       "      <th>Total_Drivers</th>\n",
       "      <th>Total_Fares</th>\n",
       "      <th>Average_Fare_per_Ride</th>\n",
       "      <th>Average_Fare_per_Driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>537</td>\n",
       "      <td>$4,327.93</td>\n",
       "      <td>$34.62</td>\n",
       "      <td>$8.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>8,570</td>\n",
       "      <td>$19,356.33</td>\n",
       "      <td>$30.97</td>\n",
       "      <td>$2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1,625</td>\n",
       "      <td>59,602</td>\n",
       "      <td>$39,854.38</td>\n",
       "      <td>$24.53</td>\n",
       "      <td>$0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Total_Rides Total_Drivers Total_Fares Average_Fare_per_Ride  \\\n",
       "Rural            125           537   $4,327.93                $34.62   \n",
       "Suburban         625         8,570  $19,356.33                $30.97   \n",
       "Urban          1,625        59,602  $39,854.38                $24.53   \n",
       "\n",
       "         Average_Fare_per_Driver  \n",
       "Rural                      $8.06  \n",
       "Suburban                   $2.26  \n",
       "Urban                      $0.67  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df[\"Total_Rides\"] = summary_df[\"Total_Rides\"].map(\"{:,}\".format)\n",
    "summary_df[\"Total_Drivers\"] = summary_df[\"Total_Drivers\"].map(\"{:,}\".format)\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable 2.  Create a multiple line plot that shows the total weekly of the fares for each type of city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Read the merged DataFrame\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Rural</th>\n",
       "      <th>2019-01-01 09:45:36</th>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02 11:18:32</th>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03 19:51:01</th>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 03:31:26</th>\n",
       "      <td>24.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06 07:38:40</th>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Urban</th>\n",
       "      <th>2019-05-08 04:20:00</th>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 04:39:49</th>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 07:29:01</th>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 11:38:35</th>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 13:10:18</th>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            fare\n",
       "type  date                      \n",
       "Rural 2019-01-01 09:45:36  43.69\n",
       "      2019-01-02 11:18:32  52.12\n",
       "      2019-01-03 19:51:01  19.90\n",
       "      2019-01-04 03:31:26  24.88\n",
       "      2019-01-06 07:38:40  47.33\n",
       "...                          ...\n",
       "Urban 2019-05-08 04:20:00  21.99\n",
       "      2019-05-08 04:39:49  18.45\n",
       "      2019-05-08 07:29:01  18.55\n",
       "      2019-05-08 11:38:35  19.77\n",
       "      2019-05-08 13:10:18  18.04\n",
       "\n",
       "[2375 rows x 1 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Using groupby() to create a new DataFrame showing the sum of the fares \n",
    "#  for each date where the indices are the city type and date.\n",
    "df = pyber_data_df.groupby(['type','date']).sum()[['fare']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-01 09:45:36</td>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-02 11:18:32</td>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-03 19:51:01</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-04 03:31:26</td>\n",
       "      <td>24.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-06 07:38:40</td>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 04:20:00</td>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 04:39:49</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 07:29:01</td>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 11:38:35</td>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 13:10:18</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       type                 date   fare\n",
       "0     Rural  2019-01-01 09:45:36  43.69\n",
       "1     Rural  2019-01-02 11:18:32  52.12\n",
       "2     Rural  2019-01-03 19:51:01  19.90\n",
       "3     Rural  2019-01-04 03:31:26  24.88\n",
       "4     Rural  2019-01-06 07:38:40  47.33\n",
       "...     ...                  ...    ...\n",
       "2370  Urban  2019-05-08 04:20:00  21.99\n",
       "2371  Urban  2019-05-08 04:39:49  18.45\n",
       "2372  Urban  2019-05-08 07:29:01  18.55\n",
       "2373  Urban  2019-05-08 11:38:35  19.77\n",
       "2374  Urban  2019-05-08 13:10:18  18.04\n",
       "\n",
       "[2375 rows x 3 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Reset the index on the DataFrame you created in #1. This is needed to use the 'pivot()' function.\n",
    "df = df.reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:08:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:46:46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:07:24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:46:50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 05:23:21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type                 Rural  Suburban  Urban\n",
       "date                                       \n",
       "2019-01-01 00:08:16    NaN       NaN  37.91\n",
       "2019-01-01 00:46:46    NaN     47.74    NaN\n",
       "2019-01-01 02:07:24    NaN     24.07    NaN\n",
       "2019-01-01 03:46:50    NaN       NaN   7.57\n",
       "2019-01-01 05:23:21    NaN       NaN  10.75"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Create a pivot table with the 'date' as the index, the columns ='type', and values='fare' \n",
    "# to get the total fares for each type of city by the date. \n",
    "df = df.pivot(index='date', columns='type', values='fare')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2375 entries, 2019-01-01 00:08:16 to 2019-05-08 13:10:18\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Rural     125 non-null    float64\n",
      " 1   Suburban  625 non-null    float64\n",
      " 2   Urban     1625 non-null   float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 74.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\n",
    "df_date = df.loc['2019-01-01':'2019-04-29']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2196 entries, 2019-01-01 00:08:16 to 2019-04-28 19:35:03\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Rural     114 non-null    float64\n",
      " 1   Suburban  573 non-null    float64\n",
      " 2   Urban     1509 non-null   float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 68.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:08:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:46:46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:07:24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:46:50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 05:23:21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 14:28:36</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 16:29:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 17:26:52</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 17:38:09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>34.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 19:35:03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.96</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "type                 Rural  Suburban  Urban\n",
       "date                                       \n",
       "2019-01-01 00:08:16    NaN       NaN  37.91\n",
       "2019-01-01 00:46:46    NaN     47.74    NaN\n",
       "2019-01-01 02:07:24    NaN     24.07    NaN\n",
       "2019-01-01 03:46:50    NaN       NaN   7.57\n",
       "2019-01-01 05:23:21    NaN       NaN  10.75\n",
       "...                    ...       ...    ...\n",
       "2019-04-28 14:28:36    NaN       NaN  11.46\n",
       "2019-04-28 16:29:16    NaN       NaN  36.42\n",
       "2019-04-28 17:26:52    NaN       NaN  31.43\n",
       "2019-04-28 17:38:09    NaN     34.87    NaN\n",
       "2019-04-28 19:35:03    NaN     16.96    NaN\n",
       "\n",
       "[2196 rows x 3 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 8.\n",
    "df_date.index = pd.to_datetime(df_date.index)\n",
    "df_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2196 entries, 2019-01-01 00:08:16 to 2019-04-28 19:35:03\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Rural     114 non-null    float64\n",
      " 1   Suburban  573 non-null    float64\n",
      " 2   Urban     1509 non-null   float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 68.6 KB\n"
     ]
    }
   ],
   "source": [
    "# 6. Check that the datatype for the index is datetime using df.info()\n",
    "df_date.info()"
   ]
  },
  {
   "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>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>187.92</td>\n",
       "      <td>721.60</td>\n",
       "      <td>1661.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>67.65</td>\n",
       "      <td>1105.13</td>\n",
       "      <td>2050.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>306.00</td>\n",
       "      <td>1218.20</td>\n",
       "      <td>1939.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>179.69</td>\n",
       "      <td>1203.28</td>\n",
       "      <td>2129.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>333.08</td>\n",
       "      <td>1042.79</td>\n",
       "      <td>2086.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10</th>\n",
       "      <td>115.80</td>\n",
       "      <td>974.34</td>\n",
       "      <td>2162.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-17</th>\n",
       "      <td>95.82</td>\n",
       "      <td>1045.50</td>\n",
       "      <td>2235.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24</th>\n",
       "      <td>419.06</td>\n",
       "      <td>1412.74</td>\n",
       "      <td>2466.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>175.14</td>\n",
       "      <td>858.46</td>\n",
       "      <td>2218.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-10</th>\n",
       "      <td>303.94</td>\n",
       "      <td>925.27</td>\n",
       "      <td>2470.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type         Rural  Suburban    Urban\n",
       "date                                 \n",
       "2019-01-06  187.92    721.60  1661.68\n",
       "2019-01-13   67.65   1105.13  2050.43\n",
       "2019-01-20  306.00   1218.20  1939.02\n",
       "2019-01-27  179.69   1203.28  2129.51\n",
       "2019-02-03  333.08   1042.79  2086.94\n",
       "2019-02-10  115.80    974.34  2162.64\n",
       "2019-02-17   95.82   1045.50  2235.07\n",
       "2019-02-24  419.06   1412.74  2466.29\n",
       "2019-03-03  175.14    858.46  2218.20\n",
       "2019-03-10  303.94    925.27  2470.93"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7. Create a new DataFrame using the \"resample()\" function by week 'W' and get the sum of the fares for each week.\n",
    "df_date = df_date.resample('W').sum()\n",
    "df_date.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8. Using the object-oriented interface method, plot the resample DataFrame using the df.plot() function. \n",
    "\n",
    "# Import style and mdates from Matplotlib.\n",
    "from matplotlib import style\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# Use the graph style fivethirtyeight\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# Plot the figure\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 8))\n",
    "\n",
    "ax.plot(df_date.Rural, label=\"Rural\")\n",
    "\n",
    "ax.plot(df_date.Suburban, label=\"Suburban\")\n",
    "\n",
    "ax.plot(df_date.Urban, label=\"Urban\")\n",
    "\n",
    "# Incorporate the other graph properties\n",
    "plt.title(\"Total Fare by City Type\", fontsize=20)\n",
    "plt.ylabel(\"Fare ($)\", fontsize=20)\n",
    "plt.xticks()\n",
    "\n",
    "\n",
    "# Create a legend\n",
    "lgnd = plt.legend(fontsize=\"12\", mode=\"Expanded\",\n",
    "        scatterpoints=1, loc=\"center\", title=\"type\")\n",
    "lgnd.legendHandles[0]._sizes = [75]\n",
    "lgnd.legendHandles[1]._sizes = [75]\n",
    "lgnd.legendHandles[2]._sizes = [75]\n",
    "lgnd.get_title().set_fontsize(12)\n",
    "\n",
    "# Implement DateFormatter for better scope of time\n",
    "date_form = mdates.DateFormatter(\"%m-%d\")\n",
    "ax.xaxis.set_major_formatter(date_form)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"analysis/PyBer_fare_summary.png\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}