PyBer_Challenge_FINAL.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": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Files to Load \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": 349,
   "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",
       "    <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": 349,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable 1: Get a Summary DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "total_rides_by_type = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "total_rides_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         78\n",
       "Suburban     490\n",
       "Urban       2405\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Get the total drivers for each city type\n",
    "total_drivers_by_type = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]     \n",
    "total_drivers_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "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": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "total_fares_by_type = pyber_data_df.groupby([\"type\"]).sum()[(\"fare\")]\n",
    "total_fares_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       34.623440\n",
       "Suburban    30.970128\n",
       "Urban       24.525772\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "avg_ridefares_by_type = pyber_data_df.groupby([\"type\"]).mean()[(\"fare\")]\n",
    "avg_ridefares_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       55.486282\n",
       "Suburban    39.502714\n",
       "Urban       16.571468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Get the average fare per driver for each city type. \n",
    "avg_driverfares_by_type = total_fares_by_type/total_drivers_by_type    \n",
    "avg_driverfares_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>78</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</td>\n",
       "      <td>55.486282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>19356.33</td>\n",
       "      <td>30.970128</td>\n",
       "      <td>39.502714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>2405</td>\n",
       "      <td>39854.38</td>\n",
       "      <td>24.525772</td>\n",
       "      <td>16.571468</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             78      4327.93              34.623440   \n",
       "Suburban          625            490     19356.33              30.970128   \n",
       "Urban            1625           2405     39854.38              24.525772   \n",
       "\n",
       "          Average Fare per Driver  \n",
       "type                               \n",
       "Rural                   55.486282  \n",
       "Suburban                39.502714  \n",
       "Urban                   16.571468  "
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "pyber_summary_df = pd.DataFrame()\n",
    "pyber_summary_df[\"Total Rides\"] = total_rides_by_type\n",
    "pyber_summary_df[\"Total Drivers\"] = total_drivers_by_type\n",
    "pyber_summary_df[\"Total Fares\"] = total_fares_by_type\n",
    "pyber_summary_df[\"Average Fare per Ride\"] = avg_ridefares_by_type\n",
    "pyber_summary_df[\"Average Fare per Driver\"] = avg_driverfares_by_type\n",
    "\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "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>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>78</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</td>\n",
       "      <td>55.486282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>19356.33</td>\n",
       "      <td>30.970128</td>\n",
       "      <td>39.502714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>2405</td>\n",
       "      <td>39854.38</td>\n",
       "      <td>24.525772</td>\n",
       "      <td>16.571468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total Rides  Total Drivers  Total Fares  Average Fare per Ride  \\\n",
       "Rural             125             78      4327.93              34.623440   \n",
       "Suburban          625            490     19356.33              30.970128   \n",
       "Urban            1625           2405     39854.38              24.525772   \n",
       "\n",
       "          Average Fare per Driver  \n",
       "Rural                   55.486282  \n",
       "Suburban                39.502714  \n",
       "Urban                   16.571468  "
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "pyber_summary_df.index.name = None\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total Rides                  int64\n",
       "Total Drivers                int64\n",
       "Total Fares                float64\n",
       "Average Fare per Ride      float64\n",
       "Average Fare per Driver    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyber_summary_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "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>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>78</td>\n",
       "      <td>$4,327.93</td>\n",
       "      <td>$34.62</td>\n",
       "      <td>$55.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>$19,356.33</td>\n",
       "      <td>$30.97</td>\n",
       "      <td>$39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1,625</td>\n",
       "      <td>2,405</td>\n",
       "      <td>$39,854.38</td>\n",
       "      <td>$24.53</td>\n",
       "      <td>$16.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Total Rides Total Drivers Total Fares Average Fare per Ride  \\\n",
       "Rural            125            78   $4,327.93                $34.62   \n",
       "Suburban         625           490  $19,356.33                $30.97   \n",
       "Urban          1,625         2,405  $39,854.38                $24.53   \n",
       "\n",
       "         Average Fare per Driver  \n",
       "Rural                     $55.49  \n",
       "Suburban                  $39.50  \n",
       "Urban                     $16.57  "
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns\n",
    "pyber_summary_df[\"Total Rides\"] = pyber_summary_df[\"Total Rides\"].map(\"{:,}\".format)\n",
    "pyber_summary_df[\"Total Drivers\"] = pyber_summary_df[\"Total Drivers\"].map(\"{:,}\".format)\n",
    "pyber_summary_df[\"Total Fares\"] = pyber_summary_df[\"Total Fares\"].map(\"${:,.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",
    "\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": 359,
   "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": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Read the merged DataFrame (?)\n",
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "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></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": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Using groupby() to create a new DataFrame showing the sum of the fares for each date where the indices are the city type and date.\n",
    "faresum_by_type_and_date = pyber_data_df.groupby([\"type\", \"date\"]).sum()[[\"fare\"]]\n",
    "faresum_by_type_and_date\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>index</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>2370</td>\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>2371</td>\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>2372</td>\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>2373</td>\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>2374</td>\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 × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                city                 date   fare        ride_id  \\\n",
       "0         0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1         1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2         2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3         3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4         4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "...     ...                 ...                  ...    ...            ...   \n",
       "2370   2370         Michaelberg  2019-04-29 17:04:39  13.38  8550365057598   \n",
       "2371   2371     Lake Latoyabury  2019-01-30 00:05:47  20.76  9018727594352   \n",
       "2372   2372         North Jaime  2019-02-10 21:03:50  11.11  2781339863778   \n",
       "2373   2373        West Heather  2019-05-07 19:22:15  44.94  4256853490277   \n",
       "2374   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 7 columns]"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. 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_data_df = pyber_data_df.reset_index()\n",
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "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",
       "    <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": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. 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",
    "faresum_type_and_date_pivot = pyber_data_df.pivot(index=\"date\", columns=\"type\")[\"fare\"]\n",
    "faresum_type_and_date_pivot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "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>2019-04-28 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": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\n",
    "faresum_date_pivot_df = faresum_type_and_date_pivot.loc['2019-01-01':'2019-04-29']\n",
    "faresum_date_pivot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 8.\n",
    "# df.index = pd.to_datetime(df.index)\n",
    "faresum_date_pivot_df.index = pd.to_datetime(faresum_date_pivot_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "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": [
    "# 7. Check that the datatype for the index is datetime using df.info()\n",
    "faresum_date_pivot_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "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",
       "    <tr>\n",
       "      <th>2019-03-17</th>\n",
       "      <td>163.39</td>\n",
       "      <td>906.20</td>\n",
       "      <td>2044.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-24</th>\n",
       "      <td>189.76</td>\n",
       "      <td>1122.20</td>\n",
       "      <td>2368.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>199.42</td>\n",
       "      <td>1045.06</td>\n",
       "      <td>1942.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-07</th>\n",
       "      <td>501.24</td>\n",
       "      <td>1010.73</td>\n",
       "      <td>2356.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-14</th>\n",
       "      <td>269.79</td>\n",
       "      <td>784.82</td>\n",
       "      <td>2390.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-21</th>\n",
       "      <td>214.14</td>\n",
       "      <td>1149.27</td>\n",
       "      <td>2303.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28</th>\n",
       "      <td>191.85</td>\n",
       "      <td>1357.75</td>\n",
       "      <td>2238.29</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\n",
       "2019-03-17  163.39    906.20  2044.42\n",
       "2019-03-24  189.76   1122.20  2368.37\n",
       "2019-03-31  199.42   1045.06  1942.77\n",
       "2019-04-07  501.24   1010.73  2356.70\n",
       "2019-04-14  269.79    784.82  2390.72\n",
       "2019-04-21  214.14   1149.27  2303.80\n",
       "2019-04-28  191.85   1357.75  2238.29"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8. Create a new DataFrame using the \"resample()\" function by week 'W' and get the sum of the fares for each week.\n",
    "fares_per_week_df = faresum_date_pivot_df.resample(\"W\").sum()\n",
    "fares_per_week_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 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",
    "\n",
    "# Use the graph style fivethirtyeight.\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# Plot it! (see what it looks like by default...)\n",
    "fares_per_week_df.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adding titles, formating & save file... (trying to match image sample in Mod 5 Chall instructions - \n",
    "# holding back from making colors match previous schemes (?))\n",
    "\n",
    "fares_per_week_df.plot(figsize = (20, 7))\n",
    "\n",
    "plt.title(\"Total Fares by City Type\")\n",
    "plt.ylabel(\"Fare ($USD)\")\n",
    "plt.xlabel(\"Month\")\n",
    "\n",
    "# Save figure\n",
    "plt.savefig(\"resources/PyBer_fare_summary.png\")\n",
    "\n",
    "# Show figure\n",
    "plt.show()\n"
   ]
  },
  {
   "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
}