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\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 3,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Latoya</td>\n",
       "      <td>2019-03-11 12:26:48</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1994999424437</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Paulville</td>\n",
       "      <td>2019-02-27 11:17:56</td>\n",
       "      <td>43.25</td>\n",
       "      <td>793208410091</td>\n",
       "      <td>44</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simpsonburgh</td>\n",
       "      <td>2019-04-26 00:43:24</td>\n",
       "      <td>35.98</td>\n",
       "      <td>111953927754</td>\n",
       "      <td>21</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Karenland</td>\n",
       "      <td>2019-01-08 03:28:48</td>\n",
       "      <td>35.09</td>\n",
       "      <td>7995623208694</td>\n",
       "      <td>4</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>North Jasmine</td>\n",
       "      <td>2019-03-09 06:26:29</td>\n",
       "      <td>42.81</td>\n",
       "      <td>5327642267789</td>\n",
       "      <td>33</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",
       "5        South Latoya  2019-03-11 12:26:48   9.52  1994999424437   \n",
       "6       New Paulville  2019-02-27 11:17:56  43.25   793208410091   \n",
       "7        Simpsonburgh  2019-04-26 00:43:24  35.98   111953927754   \n",
       "8     South Karenland  2019-01-08 03:28:48  35.09  7995623208694   \n",
       "9       North Jasmine  2019-03-09 06:26:29  42.81  5327642267789   \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",
       "5            10  Urban  \n",
       "6            44  Urban  \n",
       "7            21  Urban  \n",
       "8             4  Urban  \n",
       "9            33  Urban  "
      ]
     },
     "execution_count": 3,
     "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(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable 1: Get a Summary DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "total_rides_by_citytype = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "#total_rides_urban = pyber_data_df[pyber_data_df[\"type\"]==\"Urban\"].sum()\n",
    "#total_rides_urban\n",
    "total_rides_by_citytype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         537\n",
       "Suburban     8570\n",
       "Urban       59602\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Get the total drivers for each city type\n",
    "total_drivers_by_citytype = pyber_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "total_drivers_by_citytype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "total_fares_by_citytype = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "total_fares_by_citytype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       34.623440\n",
       "Suburban    30.970128\n",
       "Urban       24.525772\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "average_fares_by_citytype = pyber_data_df.groupby([\"type\"]).mean()[\"fare\"]\n",
    "average_fares_by_citytype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       34.623440\n",
       "Suburban    30.970128\n",
       "Urban       24.525772\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Alternative method get the average fare per ride for each city type. \n",
    "average_fares_per_ride= total_fares_by_citytype/total_rides_by_citytype\n",
    "average_fares_per_ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       8.059460\n",
       "Suburban    2.258615\n",
       "Urban       0.668675\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Get the average fare per driver for each city type. \n",
    "average_fares_perDriver= total_fares_by_citytype/total_drivers_by_citytype\n",
    "average_fares_perDriver"
   ]
  },
  {
   "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",
       "    <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>537</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</td>\n",
       "      <td>8.059460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>8570</td>\n",
       "      <td>19356.33</td>\n",
       "      <td>30.970128</td>\n",
       "      <td>2.258615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>59602</td>\n",
       "      <td>39854.38</td>\n",
       "      <td>24.525772</td>\n",
       "      <td>0.668675</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            537      4327.93              34.623440   \n",
       "Suburban          625           8570     19356.33              30.970128   \n",
       "Urban            1625          59602     39854.38              24.525772   \n",
       "\n",
       "          Average Fare per Driver  \n",
       "type                               \n",
       "Rural                    8.059460  \n",
       "Suburban                 2.258615  \n",
       "Urban                    0.668675  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "pyber_summary_df=pd.DataFrame(\n",
    "    {\"Total Rides\": total_rides_by_citytype,\n",
    "     \"Total Drivers\": total_drivers_by_citytype,\n",
    "     \"Total Fares\": total_fares_by_citytype,\n",
    "     \"Average Fare per Ride\": average_fares_per_ride,\n",
    "     \"Average Fare per Driver\": average_fares_perDriver   \n",
    "})\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>537</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</td>\n",
       "      <td>8.059460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>8570</td>\n",
       "      <td>19356.33</td>\n",
       "      <td>30.970128</td>\n",
       "      <td>2.258615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>59602</td>\n",
       "      <td>39854.38</td>\n",
       "      <td>24.525772</td>\n",
       "      <td>0.668675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total Rides  Total Drivers  Total Fares  Average Fare per Ride  \\\n",
       "Rural             125            537      4327.93              34.623440   \n",
       "Suburban          625           8570     19356.33              30.970128   \n",
       "Urban            1625          59602     39854.38              24.525772   \n",
       "\n",
       "          Average Fare per Driver  \n",
       "Rural                    8.059460  \n",
       "Suburban                 2.258615  \n",
       "Urban                    0.668675  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "pyber_summary_df.index.name = None\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>537</td>\n",
       "      <td>$4,327.93</td>\n",
       "      <td>$34.62</td>\n",
       "      <td>$8.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>8570</td>\n",
       "      <td>$19,356.33</td>\n",
       "      <td>$30.97</td>\n",
       "      <td>$2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>59602</td>\n",
       "      <td>$39,854.38</td>\n",
       "      <td>$24.53</td>\n",
       "      <td>$0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total Rides  Total Drivers Total Fares Average Fare per Ride  \\\n",
       "Rural             125            537   $4,327.93                $34.62   \n",
       "Suburban          625           8570  $19,356.33                $30.97   \n",
       "Urban            1625          59602  $39,854.38                $24.53   \n",
       "\n",
       "         Average Fare per Driver  \n",
       "Rural                      $8.06  \n",
       "Suburban                   $2.26  \n",
       "Urban                      $0.67  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "pyber_summary_df[\"Total Fares\"]=pyber_summary_df[\"Total Fares\"].map(\"${:,.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)\n",
    "\n",
    "pyber_summary_df\n"
   ]
  },
  {
   "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": 13,
   "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></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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0. not 1. Read the merged DataFrame\n",
    "pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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></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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 not 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",
    "new_pyber_data_df = pd.DataFrame(pyber_data_df.groupby([\"type\", \"date\"]).sum()[\"fare\"])\n",
    "new_pyber_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\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>type</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-01 09:45:36</td>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-02 11:18:32</td>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-03 19:51:01</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-04 03:31:26</td>\n",
       "      <td>24.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-06 07:38:40</td>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type                 date   fare\n",
       "0  Rural  2019-01-01 09:45:36  43.69\n",
       "1  Rural  2019-01-02 11:18:32  52.12\n",
       "2  Rural  2019-01-03 19:51:01  19.90\n",
       "3  Rural  2019-01-04 03:31:26  24.88\n",
       "4  Rural  2019-01-06 07:38:40  47.33"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 not 3. Reset the index on the DataFrame you created in #1. This is needed to use the 'pivot()' function.\n",
    "new_pyber_data_df = new_pyber_data_df.reset_index()\n",
    "new_pyber_data_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "      <th>2019-01-01 00:08:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.91</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2019-01-01 02:07:24</th>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2019-05-08 04:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.99</td>\n",
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       "    <tr>\n",
       "      <th>2019-05-08 04:39:49</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.45</td>\n",
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       "    <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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3 not 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",
    "new_pyber_pivot_df=new_pyber_data_df.pivot(index='date', columns='type', values='fare')\n",
    "new_pyber_pivot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "      <th>2019-01-01 02:07:24</th>\n",
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       "      <th>2019-01-01 03:46:50</th>\n",
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       "      <td>NaN</td>\n",
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       "    <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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4 not 5. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\n",
    "new_pyber_pivot_locdates_df = new_pyber_pivot_df.loc[(new_pyber_pivot_df.index > \"2019-01-01\") & (new_pyber_pivot_df.index <= \"2019-04-29\")]\n",
    "new_pyber_pivot_locdates_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5 not 6. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 8.\n",
    "new_pyber_pivot_locdates_df.index = pd.to_datetime(new_pyber_pivot_locdates_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "# 6 not 7. Check that the datatype for the index is datetime using df.info()\n",
    "new_pyber_pivot_locdates_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>187.92</td>\n",
       "      <td>721.60</td>\n",
       "      <td>1661.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>67.65</td>\n",
       "      <td>1105.13</td>\n",
       "      <td>2050.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>306.00</td>\n",
       "      <td>1218.20</td>\n",
       "      <td>1939.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>179.69</td>\n",
       "      <td>1203.28</td>\n",
       "      <td>2129.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>333.08</td>\n",
       "      <td>1042.79</td>\n",
       "      <td>2086.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10</th>\n",
       "      <td>115.80</td>\n",
       "      <td>974.34</td>\n",
       "      <td>2162.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-17</th>\n",
       "      <td>95.82</td>\n",
       "      <td>1045.50</td>\n",
       "      <td>2235.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24</th>\n",
       "      <td>419.06</td>\n",
       "      <td>1412.74</td>\n",
       "      <td>2466.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>175.14</td>\n",
       "      <td>858.46</td>\n",
       "      <td>2218.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-10</th>\n",
       "      <td>303.94</td>\n",
       "      <td>925.27</td>\n",
       "      <td>2470.93</td>\n",
       "    </tr>\n",
       "    <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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7 not 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_df = new_pyber_pivot_locdates_df.resample(\"W\").sum()\n",
    "Weekly_fares_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 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",
    "ax = Weekly_fares_df.plot(figsize=(16, 6))\n",
    "lgnd = plt.legend(loc=\"center\", title=\"type\")\n",
    "#remove index name from the dataframe\n",
    "Weekly_fares_df.index.name = None\n",
    "# Import the style from Matplotlib.\n",
    "from matplotlib import style\n",
    "# Use the graph style fivethirtyeight.\n",
    "style.use('fivethirtyeight')\n",
    "ax.set_ylabel(\"Fare($USD)\")\n",
    "ax.set_title(\"Total Fare by City Type\")\n",
    "#ax.set_xticks(x_label)\n",
    "plt.savefig(\"Resources/PyBer_fare_summary.png\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .9 Alternative way to plot (pyplot method)\n",
    "Weekly_fares_df.plot(figsize = (16, 6))\n",
    "style.use('fivethirtyeight')\n",
    "plt.ylabel(\"Fare($)\")\n",
    "plt.title(\"Total Fare by City Type\")\n",
    "plt.savefig(\"Resources/PyBer_fare_summary2.png\")\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
}