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",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>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",
    "ride_count = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "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",
    "driver_count = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "driver_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "fare_total = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "average_fare_ride = fare_total / ride_count\n"
   ]
  },
  {
   "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",
    "average_fare_driver = fare_total / driver_count\n",
    "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\": ride_count,\n",
    "          \"Total Drivers\": driver_count,\n",
    "          \"Total Fares\": fare_total,\n",
    "          \"Average Fare per Ride\": average_fare_ride,\n",
    "          \"Average Fare per Driver\": average_fare_driver})\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "pyber_summary_df.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>$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": {
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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": 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": [],
   "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",
    "fare_sum_df = pyber_data_df.groupby([\"type\", \"date\"]).sum()[\"fare\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Reset the index on the DataFrame you created in #1. This is needed to use the 'pivot()' function.\n",
    "fare_sum_df = fare_sum_df.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "  </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",
    "fare_sum_df = fare_sum_df.pivot(index=\"date\", columns=\"type\", values=\"fare\")\n",
    "fare_sum_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\n",
    "jan_apr_df = fare_sum_df.loc['2019-01-01':'2019-04-29']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 8.\n",
    "jan_apr_df.index = pd.to_datetime(jan_apr_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
    "jan_apr_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "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",
    "jan_apr_df_weekly = jan_apr_df.resample(\"W\").sum()\n",
    "jan_apr_df_weekly.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 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",
    "\n",
    "jan_apr_df_weekly.plot(figsize=(14, 6))\n",
    "plt.legend(mode=\"Expanded\", bbox_to_anchor=(1, 1))\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Fare ($USD)\", fontsize=12)\n",
    "plt.title(\"Total Fare by City Type\", fontsize=12)\n",
    "\n",
    "plt.savefig(\"analysis/PyBer_fare_summary.png\",  bbox_inches=\"tight\")\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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}