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",
    "# File to Load (Remember to change these)\n",
    "city_data_to_load = \"Resources/city_data.csv\"\n",
    "ride_data_to_load = \"Resources/ride_data.csv\"\n",
    "\n",
    "# Read the City and Ride Data\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "ride_data_df = pd.read_csv(ride_data_to_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 2,
     "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "city_type_ride_count = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "city_type_ride_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         78\n",
       "Suburban     490\n",
       "Urban       2405\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Get the total drivers for each city type\n",
    "city_type_driver_count = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "city_type_driver_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "city_type_fare_total = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "city_type_fare_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       34.623440\n",
       "Suburban    30.970128\n",
       "Urban       24.525772\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "city_type_average_fare_ride = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"] / pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "city_type_average_fare_ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       55.486282\n",
       "Suburban    39.502714\n",
       "Urban       16.571468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Get the average fare per driver for each city type. \n",
    "city_type_average_fare_driver = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"] / city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "city_type_average_fare_driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Rides</th>\n",
       "      <th>Total Drivers</th>\n",
       "      <th>Total Fares</th>\n",
       "      <th>Average Fare per Ride</th>\n",
       "      <th>Average Fare per Driver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "pyber_summary_df = pd.DataFrame({\n",
    "          \"Total Rides\" : city_type_ride_count,\n",
    "          \"Total Drivers\": city_type_driver_count,\n",
    "          \"Total Fares\": city_type_fare_total,\n",
    "          \"Average Fare per Ride\": city_type_average_fare_ride,\n",
    "          \"Average Fare per Driver\": city_type_average_fare_driver})\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Rides</th>\n",
       "      <th>Total Drivers</th>\n",
       "      <th>Total Fares</th>\n",
       "      <th>Average Fare per Ride</th>\n",
       "      <th>Average Fare per Driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>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": 9,
     "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": 10,
   "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></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                     $16.57  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns\n",
    "pyber_summary_df[\"Total Rides\"] = pyber_summary_df[\"Total Rides\"].map(\"{:,}\".format)\n",
    "\n",
    "pyber_summary_df[\"Total Drivers\"] = pyber_summary_df[\"Total Drivers\"].map(\"{:,}\".format)\n",
    "\n",
    "pyber_summary_df[\"Total Fares\"] = pyber_summary_df[\"Total Fares\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "pyber_summary_df[\"Average Fare per Ride\"] = pyber_summary_df[\"Average Fare per Ride\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "pyber_summary_df[\"Average Fare per Driver\"] = pyber_summary_df[\"Average Fare per Driver\"].map(\"${:,.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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Read the merged DataFrame\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. 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",
    "city_type_date_fare_df = pyber_data_df.groupby([\"type\", \"date\"]).sum()[\"fare\"]\n",
    "city_type_date_fare_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-01 09:45:36</td>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-02 11:18:32</td>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-03 19:51:01</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-04 03:31:26</td>\n",
       "      <td>24.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-06 07:38:40</td>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type                 date   fare\n",
       "0  Rural  2019-01-01 09:45:36  43.69\n",
       "1  Rural  2019-01-02 11:18:32  52.12\n",
       "2  Rural  2019-01-03 19:51:01  19.90\n",
       "3  Rural  2019-01-04 03:31:26  24.88\n",
       "4  Rural  2019-01-06 07:38:40  47.33"
      ]
     },
     "execution_count": 13,
     "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",
    "city_type_date_fare_df = city_type_date_fare_df.reset_index()\n",
    "city_type_date_fare_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:08:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:46:46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:07:24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:46:50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 05:23:21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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": 14,
     "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",
    "city_type_date_fare_pivot = city_type_date_fare_df.pivot(index=\"date\", columns=\"type\", values=\"fare\")\n",
    "city_type_date_fare_pivot.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-28 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",
       "</div>"
      ],
      "text/plain": [
       "type                 Rural  Suburban  Urban\n",
       "date                                       \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"
      ]
     },
     "execution_count": 15,
     "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",
    "city_type_date_fare_jan_apr_df = city_type_date_fare_pivot.loc['2019-01-01':'2019-04-29']\n",
    "city_type_date_fare_jan_apr_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
    "city_type_date_fare_jan_apr_df.index = pd.to_datetime(city_type_date_fare_jan_apr_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
    "city_type_date_fare_jan_apr_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "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",
    "city_type_weekly_fares_df = city_type_date_fare_jan_apr_df.resample('W').sum()\n",
    "city_type_weekly_fares_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 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",
    "# Plot resampled DataFrame\n",
    "city_type_weekly_fares = city_type_weekly_fares_df.plot(figsize = (20,6))\n",
    "# Add a title \n",
    "city_type_weekly_fares.set_title(\"Total Fare by City Type\")\n",
    "#  Add a x- and y-axis label\n",
    "city_type_weekly_fares.set_xlabel(\"\")\n",
    "city_type_weekly_fares.set_ylabel(\"Fare ($USD)\")\n",
    "# Save Figure\n",
    "plt.savefig(\"Analysis/PyBer_fare_summary.png\")\n",
    "# Display Figure\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}