PyBer_Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pyber Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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",
    "# step. 8 Import the style from Matplotlib.\n",
    "from matplotlib import style\n",
    "# Use the graph style fivethirtyeight.\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# File to Load \n",
    "city_data_to_load = \"resources/city_data.csv\"\n",
    "ride_data_to_load = \"resources/ride_data.csv\"\n",
    "\n",
    "# Read the City and Ride Data\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "ride_data_df = pd.read_csv(ride_data_to_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data into a single dataset\n",
    "pyber_data_df = pd.merge(ride_data_df, city_data_df, how=\"left\", on=[\"city\", \"city\"])\n",
    "\n",
    "# Display the data table for preview\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable 1: Get a Summary DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "# use groupby() and count() on \"ride_id\" column.\n",
    "total_rides_by_type = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "total_rides_by_type\n"
   ]
  },
  {
   "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",
    "# use groupby() and sum() on the \"driver_count\" column.\n",
    "total_drivers_by_type = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "total_drivers_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        4327.93\n",
       "Suburban    19356.33\n",
       "Urban       39854.38\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "# use groupby() and sum() on the \"fare\" column.\n",
    "total_fares_by_type = pyber_data_df.groupby(\"type\").sum()[\"fare\"]\n",
    "total_fares_by_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       34.623440\n",
       "Suburban    30.970128\n",
       "Urban       24.525772\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "# average_fare_per_ride = total_fares_by_type / total_rides_by_type\n",
    "average_fare_per_ride = total_fares_by_type / total_rides_by_type\n",
    "average_fare_per_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",
    "# average_fare_per_driver = total_fares_by_type / total_drivers_by_type\n",
    "average_fare_per_driver = total_fares_by_type / total_drivers_by_type\n",
    "average_fare_per_driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\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>Total Rides</th>\n",
       "      <th>Total Drivers</th>\n",
       "      <th>Total Fares</th>\n",
       "      <th>Average Fare per Ride</th>\n",
       "      <th>Average Fare per Driver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>78</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</td>\n",
       "      <td>55.486282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>19356.33</td>\n",
       "      <td>30.970128</td>\n",
       "      <td>39.502714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1625</td>\n",
       "      <td>2405</td>\n",
       "      <td>39854.38</td>\n",
       "      <td>24.525772</td>\n",
       "      <td>16.571468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total Rides  Total Drivers  Total Fares  Average Fare per Ride  \\\n",
       "type                                                                       \n",
       "Rural             125             78      4327.93              34.623440   \n",
       "Suburban          625            490     19356.33              30.970128   \n",
       "Urban            1625           2405     39854.38              24.525772   \n",
       "\n",
       "          Average Fare per Driver  \n",
       "type                               \n",
       "Rural                   55.486282  \n",
       "Suburban                39.502714  \n",
       "Urban                   16.571468  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "pyber_summary_df = pd.DataFrame({\n",
    "    \"Total Rides\" : total_rides_by_type,\n",
    "    \"Total Drivers\" : total_drivers_by_type,\n",
    "    \"Total Fares\" : total_fares_by_type,\n",
    "    \"Average Fare per Ride\" : average_fare_per_ride,\n",
    "    \"Average Fare per Driver\": average_fare_per_driver\n",
    "})\n",
    "\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "# removing the \"type\" from the top left corner.\n",
    "pyber_summary_df.index.name = None\n",
    "# pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Total Rides</th>\n",
       "      <th>Total Drivers</th>\n",
       "      <th>Total Fares</th>\n",
       "      <th>Average Fare per Ride</th>\n",
       "      <th>Average Fare per Driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>78</td>\n",
       "      <td>$4,327.93</td>\n",
       "      <td>$34.62</td>\n",
       "      <td>$55.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>625</td>\n",
       "      <td>490</td>\n",
       "      <td>$19,356.33</td>\n",
       "      <td>$30.97</td>\n",
       "      <td>$39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>1,625</td>\n",
       "      <td>2,405</td>\n",
       "      <td>$39,854.38</td>\n",
       "      <td>$24.53</td>\n",
       "      <td>$16.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Total Rides Total Drivers Total Fares Average Fare per Ride  \\\n",
       "Rural            125            78   $4,327.93                $34.62   \n",
       "Suburban         625           490  $19,356.33                $30.97   \n",
       "Urban          1,625         2,405  $39,854.38                $24.53   \n",
       "\n",
       "         Average Fare per Driver  \n",
       "Rural                     $55.49  \n",
       "Suburban                  $39.50  \n",
       "Urban                     $16.57  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "# adding \"$\" to total fare and both average columns and adding \",\" to all the total columns.\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",
    "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\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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "        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>...</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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the merged DataFrame\n",
    "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",
       "                              ...  \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": 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",
    "# use groupby([\"type\",\"date\"]) and sum() on \"fare\" column.\n",
    "sum_fares_by_type_df = pyber_data_df.groupby([\"type\",\"date\"]).sum()[\"fare\"]\n",
    "sum_fares_by_type_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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": 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",
    "sum_fares_by_type_df = sum_fares_by_type_df.reset_index()\n",
    "sum_fares_by_type_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
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       "      <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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 04:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 04:39:49</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 07:29:01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 11:38:35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 13:10:18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "type                 Rural  Suburban  Urban\n",
       "date                                       \n",
       "2019-01-01 00:08:16    NaN       NaN  37.91\n",
       "2019-01-01 00:46:46    NaN     47.74    NaN\n",
       "2019-01-01 02:07:24    NaN     24.07    NaN\n",
       "2019-01-01 03:46:50    NaN       NaN   7.57\n",
       "2019-01-01 05:23:21    NaN       NaN  10.75\n",
       "...                    ...       ...    ...\n",
       "2019-05-08 04:20:00    NaN       NaN  21.99\n",
       "2019-05-08 04:39:49    NaN       NaN  18.45\n",
       "2019-05-08 07:29:01    NaN       NaN  18.55\n",
       "2019-05-08 11:38:35    NaN       NaN  19.77\n",
       "2019-05-08 13:10:18    NaN       NaN  18.04\n",
       "\n",
       "[2375 rows x 3 columns]"
      ]
     },
     "execution_count": 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",
    "sum_fares_by_type_pivot = sum_fares_by_type_df.pivot(index=\"date\", columns =\"type\", values=\"fare\")\n",
    "sum_fares_by_type_pivot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:08:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:46:46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:07:24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:46:50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 05:23:21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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": 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':'2019-04-29'.\n",
    "fares_jan_apr_df = sum_fares_by_type_pivot.loc['2019-01-01':'2019-04-29']\n",
    "fares_jan_apr_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <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",
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       "      <td>NaN</td>\n",
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       "    <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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-28 16:29:16</th>\n",
       "      <td>NaN</td>\n",
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       "    </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": 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",
    "fares_jan_apr_df.index = pd.to_datetime(fares_jan_apr_df.index)\n",
    "fares_jan_apr_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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. Check that the datatype for the index is datetime using df.info()\n",
    "fares_jan_apr_df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 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",
    "fares_weekly_df = fares_jan_apr_df.resample(\"W\").sum()\n",
    "fares_weekly_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 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",
    "# copied and moved to first cell so all imports are together.\n",
    "ax = fares_weekly_df.plot(figsize= (20, 5))\n",
    "# title\n",
    "ax.set_title(\"Total Fare by City Type\")\n",
    "# xlabel\n",
    "ax.set_xlabel(\"Months\")\n",
    "# ylabel\n",
    "ax.set_ylabel(\"Fare ($USD)\")\n",
    "# save figure as\n",
    "plt.savefig(\"analysis/Fig8.png\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda list -n pythondata matplotlib\n",
    "# checking which version of matplotlib is running."
   ]
  },
  {
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}