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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Matplotlib inline magic command\n",
    "%matplotlib inline\n",
    "\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": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\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",
       "  </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": 47,
     "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "rides_city_type = pyber_data_df.groupby(\"type\").count()[\"ride_id\"]\n",
    "rides_city_type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         78\n",
       "Suburban     490\n",
       "Urban       2405\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Get the total drivers for each city type\n",
    "drivers_city_type = city_data_df.groupby(\"type\").sum()[\"driver_count\"]\n",
    "drivers_city_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "fares_city_type = pyber_data_df.groupby(\"type\").sum()[\"fare\"]\n",
    "fares_city_type\n",
    "type(fares_city_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       34.623440\n",
       "Suburban    30.970128\n",
       "Urban       24.525772\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "avg_fare_ride = fares_city_type/rides_city_type\n",
    "avg_fare_ride\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       55.486282\n",
       "Suburban    39.502714\n",
       "Urban       16.571468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Get the average fare per driver for each city type. \n",
    "avg_fare_driver = fares_city_type/drivers_city_type\n",
    "avg_fare_driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "pyber_summary_df = pd.DataFrame({\n",
    "    \"Total Rides\" : rides_city_type,\n",
    "    \"Total Drivers\" : drivers_city_type,\n",
    "    \"Total Fares\" : fares_city_type,\n",
    "    \"Average Fare per Ride\" : avg_fare_ride,\n",
    "    \"Average Fare per Driver\" : avg_fare_driver})\n",
    "\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "pyber_summary_df.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>$34.62</td>\n",
       "      <td>$55.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>$19356.33</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>$39854.38</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    $4327.93                $34.62   \n",
       "Suburban         625           490   $19356.33                $30.97   \n",
       "Urban          1,625         2,405   $39854.38                $24.53   \n",
       "\n",
       "         Average Fare per Driver  \n",
       "Rural                     $55.49  \n",
       "Suburban                  $39.50  \n",
       "Urban                     $16.57  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "\n",
    "pyber_summary_df[\"Total Fares\"] = pyber_summary_df[\"Total Fares\"].map(\"${:.2f}\".format)\n",
    "pyber_summary_df[\"Average Fare per Ride\"] = pyber_summary_df[\"Average Fare per Ride\"].map(\"${:.2f}\".format)\n",
    "pyber_summary_df[\"Average Fare per Driver\"] = pyber_summary_df[\"Average Fare per Driver\"].map(\"${:.2f}\".format)\n",
    "pyber_summary_df[\"Total Rides\"] = pyber_summary_df[\"Total Rides\"].map(\"{:,}\".format)\n",
    "pyber_summary_df[\"Total Drivers\"] = pyber_summary_df[\"Total Drivers\"].map(\"{:,}\".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": 56,
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Read the merged DataFrame\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type   date               \n",
       "Rural  2019-01-01 09:45:36    43.69\n",
       "       2019-01-02 11:18:32    52.12\n",
       "       2019-01-03 19:51:01    19.90\n",
       "       2019-01-04 03:31:26    24.88\n",
       "       2019-01-06 07:38:40    47.33\n",
       "                              ...  \n",
       "Urban  2019-05-08 04:20:00    21.99\n",
       "       2019-05-08 04:39:49    18.45\n",
       "       2019-05-08 07:29:01    18.55\n",
       "       2019-05-08 11:38:35    19.77\n",
       "       2019-05-08 13:10:18    18.04\n",
       "Name: fare, Length: 2375, dtype: float64"
      ]
     },
     "execution_count": 57,
     "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_df = pyber_data_df.groupby([\"type\",\"date\"]).sum()[\"fare\"]\n",
    "fares_by_date_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "      <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": 58,
     "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_df = fares_by_date_df.reset_index()\n",
    "fares_by_date_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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",
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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": 59,
     "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_by_date_pivot = fares_by_date_df.pivot(index=\"date\", columns=\"type\", values=\"fare\")\n",
    "fares_by_date_pivot.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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": 60,
     "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",
    "new_range_df = fares_by_date_pivot.loc[\"2019-01-01\":\"2019-04-29\"]\n",
    "new_range_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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",
    "new_range_df.index = pd.to_datetime(new_range_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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",
    "new_range_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>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": 63,
     "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",
    "fare_by_week = new_range_df.resample(\"W\").sum()\n",
    "fare_by_week.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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",
    "fare_by_week.plot()\n",
    "\n",
    "plt.title(\"Total Fare by City Type\", fontsize=12)\n",
    "plt.ylabel(\"Fare ($USD)\", fontsize=10)\n",
    "plt.xlabel(\"\")\n",
    "\n",
    "# Moving the legend on purpose to the center so it matches the picture provided in the challenge:\n",
    "lgnd = plt.legend(fontsize=\"10\", mode=\"Expanded\",\n",
    "         loc=\"center\")\n",
    "\n",
    "# Chart looks too small.. Adjusting size:\n",
    "plt.gcf().set_size_inches((10, 6))\n",
    "\n",
    "# Saving chart as png:\n",
    "plt.savefig(\"analysis/Pyber_fare_summary.png\")\n",
    "\n",
    "# Display chart:\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}