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: driver_count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "ride_count_per_type = pyber_data_df.groupby('type').count()['driver_count']\n",
    "ride_count_per_type"
   ]
  },
  {
   "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_per_type = city_data_df.groupby('type').sum()['driver_count']\n",
    "driver_count_per_type"
   ]
  },
  {
   "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",
    "sum_fares_per_type = pyber_data_df.groupby('type').sum()['fare']\n",
    "sum_fares_per_type"
   ]
  },
  {
   "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",
    "avg_fare_per_type = sum_fares_per_type / ride_count_per_type\n",
    "avg_fare_per_type"
   ]
  },
  {
   "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",
    "avg_fare_per_driver = sum_fares_per_type / driver_count_per_type\n",
    "avg_fare_per_driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "pyber_summary_df = pd.DataFrame({\n",
    "    'Total Rides' : ride_count_per_type,\n",
    "    'Total Drivers' : driver_count_per_type,\n",
    "    'Total Fares' : sum_fares_per_type,\n",
    "    'Average Fare per Ride' : avg_fare_per_type,\n",
    "    'Average Fare per Driver' : avg_fare_per_driver\n",
    "})"
   ]
  },
  {
   "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": [],
   "source": [
    "#  8. Format the columns.\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>1625</td>\n",
       "      <td>2405</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            1625           2405  $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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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></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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Read the merged DataFrame\n",
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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": 13,
     "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",
    "fares_by_date_type = pd.DataFrame(pyber_data_df.groupby(['type','date']).sum()['fare'])\n",
    "fares_by_date_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-01 09:45:36</td>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-02 11:18:32</td>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-03 19:51:01</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-04 03:31:26</td>\n",
       "      <td>24.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-06 07:38:40</td>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 04:20:00</td>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 04:39:49</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 07:29:01</td>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 11:38:35</td>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>Urban</td>\n",
       "      <td>2019-05-08 13:10:18</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       type                 date   fare\n",
       "0     Rural  2019-01-01 09:45:36  43.69\n",
       "1     Rural  2019-01-02 11:18:32  52.12\n",
       "2     Rural  2019-01-03 19:51:01  19.90\n",
       "3     Rural  2019-01-04 03:31:26  24.88\n",
       "4     Rural  2019-01-06 07:38:40  47.33\n",
       "...     ...                  ...    ...\n",
       "2370  Urban  2019-05-08 04:20:00  21.99\n",
       "2371  Urban  2019-05-08 04:39:49  18.45\n",
       "2372  Urban  2019-05-08 07:29:01  18.55\n",
       "2373  Urban  2019-05-08 11:38:35  19.77\n",
       "2374  Urban  2019-05-08 13:10:18  18.04\n",
       "\n",
       "[2375 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "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",
    "fares_by_date_type = fares_by_date_type.reset_index()\n",
    "fares_by_date_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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": 17,
     "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",
    "fares_pivot = fares_by_date_type.pivot(index='date', columns='type', values='fare')\n",
    "fares_pivot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </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": 21,
     "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",
    "fares_pivot_by_date = fares_pivot.loc['2019-01-01':'2019-04-29']\n",
    "fares_pivot_by_date\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 8.\n",
    "fares_pivot_by_date.index = pd.to_datetime(fares_pivot_by_date.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
    "fares_pivot_by_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>2019-01-06</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": 39,
     "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",
    "weekly_fares_by_type = fares_pivot_by_date.resample('W').sum()\n",
    "weekly_fares_by_type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 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",
    "weekly_fares_by_type.plot(figsize=(13,6))\n",
    "plt.title('Total Fare by City Type')\n",
    "plt.ylabel('Fare in $USD')\n",
    "plt.xlabel('Date')\n",
    "\n",
    "plt.savefig('Analysis/Fares_by_City_Type')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "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": "3.8.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}