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 check 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",
       "</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": [
    "## Challenge Deliverable 1. Generate a Ride-Sharing DataFrame by City Type"
   ]
  },
  {
   "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",
    "\n",
    "total_ride_count = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "total_ride_count.head()"
   ]
  },
  {
   "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",
    "\n",
    "total_driver_count = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "total_driver_count.head()"
   ]
  },
  {
   "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",
    "\n",
    "total_fare_count = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "total_fare_count"
   ]
  },
  {
   "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",
    "\n",
    "avg_fare_ride = total_fare_count / (total_ride_count)\n",
    "avg_fare_ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       55.486282\n",
       "Suburban    39.502714\n",
       "Urban       16.571468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Get the average fare per driver for each city type.\n",
    "\n",
    "avg_fare_driver = total_fare_count / (total_driver_count)\n",
    "avg_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",
    "\n",
    "PyBer_summary_df = pd.DataFrame(\n",
    "    {\n",
    "        \"Total Rides\": total_ride_count,\n",
    "        \"Total Drivers\": total_driver_count,\n",
    "        \"Total Fares\": total_fare_count,\n",
    "        \"Average Fare per Ride\": avg_fare_ride,\n",
    "        \"Average Fare per Driver\": avg_fare_driver,\n",
    "    }\n",
    ")\n",
    "PyBer_summary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Rides</th>\n",
       "      <th>Total Drivers</th>\n",
       "      <th>Total Fares</th>\n",
       "      <th>Average Fare per Ride</th>\n",
       "      <th>Average Fare per Driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>78</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</td>\n",
       "      <td>55.486282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>19356.33</td>\n",
       "      <td>30.970128</td>\n",
       "      <td>39.502714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>2405</td>\n",
       "      <td>39854.38</td>\n",
       "      <td>24.525772</td>\n",
       "      <td>16.571468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total Rides  Total Drivers  Total Fares  Average Fare per Ride  \\\n",
       "Rural             125             78      4327.93              34.623440   \n",
       "Suburban          625            490     19356.33              30.970128   \n",
       "Urban            1625           2405     39854.38              24.525772   \n",
       "\n",
       "          Average Fare per Driver  \n",
       "Rural                   55.486282  \n",
       "Suburban                39.502714  \n",
       "Urban                   16.571468  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "\n",
    "PyBer_summary_df.index.name = None\n",
    "PyBer_summary_df.head()"
   ]
  },
  {
   "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>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                     $16.57  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "# https://stackoverflow.com/questions/20937538/how-to-display-pandas-dataframe-of-floats-using-a-format-string-for-columns\n",
    "# I used the .map method from this stackoverflow question\n",
    "\n",
    "PyBer_summary_df[\"Total Fares\"] = PyBer_summary_df[\"Total Fares\"].map(\"${:,.2f}\".format)\n",
    "PyBer_summary_df[\"Average Fare per Ride\"] = PyBer_summary_df[\n",
    "    \"Average Fare per Ride\"\n",
    "].map(\"${:.2f}\".format)\n",
    "PyBer_summary_df[\"Average Fare per Driver\"] = PyBer_summary_df[\n",
    "    \"Average Fare per Driver\"\n",
    "].map(\"${:.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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    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]\n"
     ]
    }
   ],
   "source": [
    "# Print the merged DataFrame for reference.\n",
    "\n",
    "print(pyber_data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type   date               \n",
       "Rural  2019-01-01 09:45:36    43.69\n",
       "       2019-01-02 11:18:32    52.12\n",
       "       2019-01-03 19:51:01    19.90\n",
       "       2019-01-04 03:31:26    24.88\n",
       "       2019-01-06 07:38:40    47.33\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 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",
    "\n",
    "sum_of_fares_per_date = pyber_data_df.groupby([\"type\", \"date\"]).sum()[\"fare\"]\n",
    "sum_of_fares_per_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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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>index</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>2370</td>\n",
       "      <td>Michaelberg</td>\n",
       "      <td>2019-04-29 17:04:39</td>\n",
       "      <td>13.38</td>\n",
       "      <td>8550365057598</td>\n",
       "      <td>6</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>2371</td>\n",
       "      <td>Lake Latoyabury</td>\n",
       "      <td>2019-01-30 00:05:47</td>\n",
       "      <td>20.76</td>\n",
       "      <td>9018727594352</td>\n",
       "      <td>2</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>2372</td>\n",
       "      <td>North Jaime</td>\n",
       "      <td>2019-02-10 21:03:50</td>\n",
       "      <td>11.11</td>\n",
       "      <td>2781339863778</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>2373</td>\n",
       "      <td>West Heather</td>\n",
       "      <td>2019-05-07 19:22:15</td>\n",
       "      <td>44.94</td>\n",
       "      <td>4256853490277</td>\n",
       "      <td>4</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>2374</td>\n",
       "      <td>Newtonview</td>\n",
       "      <td>2019-04-25 10:20:13</td>\n",
       "      <td>55.84</td>\n",
       "      <td>9990581345298</td>\n",
       "      <td>1</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                city                 date   fare        ride_id  \\\n",
       "0         0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1         1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2         2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3         3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4         4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "...     ...                 ...                  ...    ...            ...   \n",
       "2370   2370         Michaelberg  2019-04-29 17:04:39  13.38  8550365057598   \n",
       "2371   2371     Lake Latoyabury  2019-01-30 00:05:47  20.76  9018727594352   \n",
       "2372   2372         North Jaime  2019-02-10 21:03:50  11.11  2781339863778   \n",
       "2373   2373        West Heather  2019-05-07 19:22:15  44.94  4256853490277   \n",
       "2374   2374          Newtonview  2019-04-25 10:20:13  55.84  9990581345298   \n",
       "\n",
       "      driver_count   type  \n",
       "0                5  Urban  \n",
       "1               72  Urban  \n",
       "2               57  Urban  \n",
       "3               34  Urban  \n",
       "4               46  Urban  \n",
       "...            ...    ...  \n",
       "2370             6  Rural  \n",
       "2371             2  Rural  \n",
       "2372             1  Rural  \n",
       "2373             4  Rural  \n",
       "2374             1  Rural  \n",
       "\n",
       "[2375 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Reset the index on the DataFrame you created in #1. This is needed to use the 'pivot()' function.\n",
    "# df = df.reset_index()\n",
    "\n",
    "pyber_data_df = pyber_data_df.reset_index()\n",
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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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       "      <th>2019-01-01 00:08:16</th>\n",
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       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "type                 Rural  Suburban  Urban\n",
       "date                                       \n",
       "2019-01-01 00:08:16    0.0      0.00  37.91\n",
       "2019-01-01 00:46:46    0.0     47.74   0.00\n",
       "2019-01-01 02:07:24    0.0     24.07   0.00\n",
       "2019-01-01 03:46:50    0.0      0.00   7.57\n",
       "2019-01-01 05:23:21    0.0      0.00  10.75"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. 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",
    "\n",
    "pyber_data_df = pyber_data_df.pivot(index=\"date\", columns=\"type\", values=\"fare\").fillna(\n",
    "    0\n",
    ")\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>date</th>\n",
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      ],
      "text/plain": [
       "type                 Rural  Suburban  Urban\n",
       "date                                       \n",
       "2019-01-01 00:08:16    0.0      0.00  37.91\n",
       "2019-01-01 00:46:46    0.0     47.74   0.00\n",
       "2019-01-01 02:07:24    0.0     24.07   0.00\n",
       "2019-01-01 03:46:50    0.0      0.00   7.57\n",
       "2019-01-01 05:23:21    0.0      0.00  10.75"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-28'.\n",
    "\n",
    "pyber_date_data_df = pyber_data_df.loc[\"2019-01-01\":\"2019-04-28\"]\n",
    "pyber_date_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "      <th>date</th>\n",
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      ],
      "text/plain": [
       "type                 Rural  Suburban  Urban\n",
       "date                                       \n",
       "2019-01-01 00:08:16    0.0      0.00  37.91\n",
       "2019-01-01 00:46:46    0.0     47.74   0.00\n",
       "2019-01-01 02:07:24    0.0     24.07   0.00\n",
       "2019-01-01 03:46:50    0.0      0.00   7.57\n",
       "2019-01-01 05:23:21    0.0      0.00  10.75"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. 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",
    "\n",
    "pyber_date_data_df.index = pd.to_datetime(pyber_date_data_df.index)\n",
    "pyber_date_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2177 entries, 2019-01-01 00:08:16 to 2019-04-27 23:52:44\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Rural     2177 non-null   float64\n",
      " 1   Suburban  2177 non-null   float64\n",
      " 2   Urban     2177 non-null   float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 68.0 KB\n"
     ]
    }
   ],
   "source": [
    "# 6. Check that the datatype for the index is datetime using df.info()\n",
    "\n",
    "pyber_date_data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <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>1169.04</td>\n",
       "      <td>1909.51</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   1169.04  1909.51"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7. Create a new DataFrame using the \"resample()\" function by week 'W' and get the sum of the fares for each week.\n",
    "\n",
    "pyber_date_data_per_week_df = pyber_date_data_df.resample(\"W\").sum()\n",
    "pyber_date_data_per_week_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8. Using the object-oriented interface method, plot the resample DataFrame using the df.plot() function.\n",
    "\n",
    "# Import the style from Matplotlib.\n",
    "from matplotlib import style\n",
    "\n",
    "# Use the graph style fivethirtyeight.\n",
    "style.use(\"fivethirtyeight\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax = pyber_date_data_per_week_df[\"Rural\"].plot()\n",
    "ax = pyber_date_data_per_week_df[\"Urban\"].plot()\n",
    "ax = pyber_date_data_per_week_df[\"Suburban\"].plot()\n",
    "\n",
    "\n",
    "ax.set_title(\"Total Fare by City Type\", fontsize=15)\n",
    "ax.set_ylabel(\"Fare ($USD)\", fontsize=10)\n",
    "ax.set_xlabel(\"Date\", fontsize=10)\n",
    "ax.legend(\n",
    "    fontsize=\"8\",\n",
    "    mode=\"Expanded\",\n",
    "    title=\"Type of City\",\n",
    "    ncol=1,\n",
    "    loc=\"lower left\",\n",
    "    bbox_to_anchor=(0.8, -0.5),\n",
    ")\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Analysis/PyBer_fare_summary.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}