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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Richardfort</td>\n",
       "      <td>38</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Williamsstad</td>\n",
       "      <td>59</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Angela</td>\n",
       "      <td>67</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West Robert</td>\n",
       "      <td>39</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West Anthony</td>\n",
       "      <td>70</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Martinezhaven</td>\n",
       "      <td>25</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Karenberg</td>\n",
       "      <td>22</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Barajasview</td>\n",
       "      <td>26</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  driver_count   type\n",
       "0    Richardfort            38  Urban\n",
       "1   Williamsstad            59  Urban\n",
       "2    Port Angela            67  Urban\n",
       "3     Rodneyfort            34  Urban\n",
       "4    West Robert            39  Urban\n",
       "5   West Anthony            70  Urban\n",
       "6    West Angela            48  Urban\n",
       "7  Martinezhaven            25  Urban\n",
       "8      Karenberg            22  Urban\n",
       "9    Barajasview            26  Urban"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the City 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)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first ten rows of the city_data_df\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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": 5,
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable 1: Get a Summary DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "Rural        125\n",
      "Suburban     625\n",
      "Urban       1625\n",
      "Name: ride_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "# Used code from wanghen21, “Pyber_challenge.ipynb.” This simplified the code. Previously creating unnessary dataframes. \n",
    "total_rides_count = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "print(total_rides_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "Rural         537\n",
      "Suburban     8570\n",
      "Urban       59602\n",
      "Name: driver_count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 2. Get the total drivers for each city type.\n",
    "# Total driver count using Excel, urban: 2,405; suburban: 490; rural: 78\n",
    "total_drivers_count = pyber_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "print(total_drivers_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "Rural        4327.93\n",
      "Suburban    19356.33\n",
      "Urban       39854.38\n",
      "Name: fare, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "total_fare_sum = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "print(total_fare_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "Rural       34.623440\n",
      "Suburban    30.970128\n",
      "Urban       24.525772\n",
      "Name: fare, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#  4 Get the average fare per ride for each city type. \n",
    "avg_per_ride = pyber_data_df.groupby([\"type\"]).mean()[\"fare\"]\n",
    "print(avg_per_ride)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "Rural       8.059460\n",
      "Suburban    2.258615\n",
      "Urban       0.668675\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 5. Get the average fare per driver for each city type. \n",
    "avg_fare_per_driver = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"] / total_drivers_count\n",
    "print(avg_fare_per_driver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <th>type</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>&lt;class 'type'&gt;</td>\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>&lt;class 'type'&gt;</td>\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>&lt;class 'type'&gt;</td>\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",
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      ],
      "text/plain": [
       "                    Type  Total Rides  Total Drivers  Total Fares  \\\n",
       "type                                                                \n",
       "Rural     <class 'type'>          125            537      4327.93   \n",
       "Suburban  <class 'type'>          625           8570     19356.33   \n",
       "Urban     <class 'type'>         1625          59602     39854.38   \n",
       "\n",
       "          Average Fare per Ride  Average Fare per Driver  \n",
       "type                                                      \n",
       "Rural                 34.623440                 8.059460  \n",
       "Suburban              30.970128                 2.258615  \n",
       "Urban                 24.525772                 0.668675  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "pyber_summary_df = pd.DataFrame({\n",
    "    \"Type\": type,\n",
    "    \"Total Rides\": total_rides_count,\n",
    "    \"Total Drivers\": total_drivers_count,\n",
    "    \"Total Fares\": total_fare_sum,\n",
    "    \"Average Fare per Ride\": avg_per_ride,\n",
    "    \"Average Fare per Driver\": avg_fare_per_driver})\n",
    "\n",
    "# View the dataframe.\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "pyber_summary_df.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Type</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>&lt;class 'type'&gt;</td>\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>&lt;class 'type'&gt;</td>\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>&lt;class 'type'&gt;</td>\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": [
       "                    Type Total Rides Total Drivers Total Fares  \\\n",
       "Rural     <class 'type'>         125           537   $4,327.93   \n",
       "Suburban  <class 'type'>         625         8,570  $19,356.33   \n",
       "Urban     <class 'type'>       1,625        59,602  $39,854.38   \n",
       "\n",
       "         Average Fare per Ride Average Fare per Driver  \n",
       "Rural                   $34.62                   $8.06  \n",
       "Suburban                $30.97                   $2.26  \n",
       "Urban                   $24.53                   $0.67  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "pyber_summary_df[\"Total Rides\"] = pyber_summary_df[\"Total Rides\"].map(\"{:,}\".format)\n",
    "pyber_summary_df[\"Total Drivers\"] = pyber_summary_df[\"Total Drivers\"].map(\"{:,}\".format)\n",
    "pyber_summary_df[\"Total Fares\"] = pyber_summary_df[\"Total Fares\"].map(\"${:,.2f}\".format)\n",
    "pyber_summary_df[\"Average Fare per Ride\"] = pyber_summary_df[\"Average Fare per Ride\"].map(\"${:,.2f}\".format)\n",
    "pyber_summary_df[\"Average Fare per Driver\"] = pyber_summary_df[\"Average Fare per Driver\"].map(\"${:,.2f}\".format)\n",
    "pyber_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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-04-29 17:04:39</td>\n",
       "      <td>13.38</td>\n",
       "      <td>8550365057598</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-01-30 00:05:47</td>\n",
       "      <td>20.76</td>\n",
       "      <td>9018727594352</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>North Jaime</td>\n",
       "      <td>2019-02-10 21:03:50</td>\n",
       "      <td>11.11</td>\n",
       "      <td>2781339863778</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>West Heather</td>\n",
       "      <td>2019-05-07 19:22:15</td>\n",
       "      <td>44.94</td>\n",
       "      <td>4256853490277</td>\n",
       "      <td>4</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>Newtonview</td>\n",
       "      <td>2019-04-25 10:20:13</td>\n",
       "      <td>55.84</td>\n",
       "      <td>9990581345298</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 6 columns</p>\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",
       "2370         Michaelberg  2019-04-29 17:04:39  13.38  8550365057598   \n",
       "2371     Lake Latoyabury  2019-01-30 00:05:47  20.76  9018727594352   \n",
       "2372         North Jaime  2019-02-10 21:03:50  11.11  2781339863778   \n",
       "2373        West Heather  2019-05-07 19:22:15  44.94  4256853490277   \n",
       "2374          Newtonview  2019-04-25 10:20:13  55.84  9990581345298   \n",
       "\n",
       "      driver_count   type  \n",
       "0                5  Urban  \n",
       "1               72  Urban  \n",
       "2               57  Urban  \n",
       "3               34  Urban  \n",
       "4               46  Urban  \n",
       "...            ...    ...  \n",
       "2370             6  Rural  \n",
       "2371             2  Rural  \n",
       "2372             1  Rural  \n",
       "2373             4  Rural  \n",
       "2374             1  Rural  \n",
       "\n",
       "[2375 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Read the merged DataFrame\n",
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: fare, Length: 2375, dtype: float64"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {},
   "outputs": [],
   "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",
    "pyber_pivot = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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>2019-01-01 09:45:36</th>\n",
       "      <td>43.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 12:32:48</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.56</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 14:40:14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 14:42:25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 14:52:06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.15</td>\n",
       "      <td>NaN</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\n",
       "2019-01-01 09:45:36  43.69       NaN    NaN\n",
       "2019-01-01 12:32:48    NaN     25.56    NaN\n",
       "2019-01-01 14:40:14    NaN       NaN   5.42\n",
       "2019-01-01 14:42:25    NaN       NaN  12.31\n",
       "2019-01-01 14:52:06    NaN     31.15    NaN"
      ]
     },
     "execution_count": 25,
     "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",
    "pyber_pivot = pd.pivot_table(pyber_pivot, values='fare', index = 'date', columns ='type')\n",
    "\n",
    "pyber_pivot.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>2019-01-01 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>2019-05-08 04:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 04:39:49</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 07:29:01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 11:38:35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 13:10:18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 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",
       "2019-05-08 04:20:00    NaN       NaN  21.99\n",
       "2019-05-08 04:39:49    NaN       NaN  18.45\n",
       "2019-05-08 07:29:01    NaN       NaN  18.55\n",
       "2019-05-08 11:38:35    NaN       NaN  19.77\n",
       "2019-05-08 13:10:18    NaN       NaN  18.04\n",
       "\n",
       "[2375 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\n",
    "# Used code from wanghen21, “Pyber_challenge.ipynb.” This simplified the code. Previously using standard dataframe creation\n",
    "# and not incorporating pivot table and loc function\n",
    "challenge_pivot = pyber_pivot.loc[\"1/1/2019\":\"4/29/2019\"] \n",
    "challenge_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-01 00:08:16', '2019-01-01 00:46:46',\n",
       "               '2019-01-01 02:07:24', '2019-01-01 03:46:50',\n",
       "               '2019-01-01 05:23:21', '2019-01-01 09:45:36',\n",
       "               '2019-01-01 12:32:48', '2019-01-01 14:40:14',\n",
       "               '2019-01-01 14:42:25', '2019-01-01 14:52:06',\n",
       "               ...\n",
       "               '2019-05-07 20:05:15', '2019-05-07 20:22:19',\n",
       "               '2019-05-07 20:40:29', '2019-05-08 01:54:18',\n",
       "               '2019-05-08 02:31:25', '2019-05-08 04:20:00',\n",
       "               '2019-05-08 04:39:49', '2019-05-08 07:29:01',\n",
       "               '2019-05-08 11:38:35', '2019-05-08 13:10:18'],\n",
       "              dtype='datetime64[ns]', name='date', length=2375, freq=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 7.\n",
    "# pyber_df.index = pd.to_datetime(df.index)\n",
    "challenge_pivot.index = pd.to_datetime(challenge_pivot.index)\n",
    "challenge_pivot.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 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": [
    "# 6. Check that the datatype for the index is datetime using df.info()\n",
    "challenge_pivot.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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": 32,
     "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 = challenge_pivot.resample('W').sum()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 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 the style from Matplotlib.\n",
    "from matplotlib import style\n",
    "# Use the graph style fivethirtyeight.\n",
    "style.use('fivethirtyeight')\n",
    "df.plot(figsize = (15,5))\n",
    "plt.title(\"Total Fare By City Type\")\n",
    "plt.ylabel(\"Fare ($USD)\")\n",
    "plt.xlabel(\"Months\")\n",
    "\n",
    "plt.savefig(\"Resources/PyBer_fare_summary.png\",dpi = 300, bbox_inches='tight')"
   ]
  }
 ],
 "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}