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",
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "# File to Load (Remember to change these)\n",
    "city_data_to_load = \"Resources/city_data.csv\"\n",
    "ride_data_to_load = \"Resources/ride_data.csv\"\n",
    "\n",
    "# Read the City and Ride Data\n",
    "city_data_df = pd.read_csv(city_data_to_load)\n",
    "ride_data_df = pd.read_csv(ride_data_to_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city                 date   fare        ride_id  \\\n",
       "0  Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1  South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2  Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3          Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4          South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "\n",
       "   driver_count   type  \n",
       "0             5  Urban  \n",
       "1            72  Urban  \n",
       "2            57  Urban  \n",
       "3            34  Urban  \n",
       "4            46  Urban  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data into a single dataset\n",
    "pyber_data_df = pd.merge(ride_data_df, city_data_df, how=\"left\", on=[\"city\", \"city\"])\n",
    "\n",
    "# Display the data table for preview\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deliverable 1: Get a Summary DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "total_rides_per_city = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "total_rides_per_city"
   ]
  },
  {
   "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",
    "total_drivers_per_city = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "total_drivers_per_city"
   ]
  },
  {
   "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",
    "total_fares_per_city = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "total_fares_per_city"
   ]
  },
  {
   "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",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "#ALTERNATE code: avg_fare_per_ride = total_fares_per_city / total_rides_per_city\n",
    "\n",
    "avg_fare_per_ride = pyber_data_df.groupby(['type']).mean()['fare']\n",
    "avg_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",
    "#ALTERNATE code: avg_fare_per_driver = total_fares_per_city / total_drivers_per_city\n",
    "\n",
    "avg_fare_per_driver = pyber_data_df.groupby(['type']).sum()[\"fare\"] / total_drivers_per_city\n",
    "avg_fare_per_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",
    "pyber_summary_df = pd.DataFrame({\"Total Rides\": total_rides_per_city,\n",
    "                                 \"Total Drivers\": total_drivers_per_city,\n",
    "                                 \"Total Fares\": total_fares_per_city,  \n",
    "                                 \"Average Fare per Ride\": avg_fare_per_ride,\n",
    "                                 \"Average Fare per Driver\": avg_fare_per_driver})\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Rides</th>\n",
       "      <th>Total Drivers</th>\n",
       "      <th>Total Fares</th>\n",
       "      <th>Average Fare per Ride</th>\n",
       "      <th>Average Fare per Driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>125</td>\n",
       "      <td>78</td>\n",
       "      <td>4327.93</td>\n",
       "      <td>34.623440</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",
    "pyber_summary_df.index.name = None\n",
    "pyber_summary_df"
   ]
  },
  {
   "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>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                     $16.57  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "\n",
    "# Format to include a thousands separator in the 'Total Rides' and 'Total Drivers' columns \n",
    "pyber_summary_df[\"Total Rides\"] = pyber_summary_df[\"Total Rides\"].map(\"{:,}\".format)\n",
    "pyber_summary_df[\"Total Drivers\"] = pyber_summary_df[\"Total Drivers\"].map(\"{:,}\".format)\n",
    "\n",
    "# Format to include a USD ($) sign and round amount to 2 decimal places in the 'Total Fares', 'Average Fare per Ride' and 'Average Fare per Driver' column                                \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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Rural</th>\n",
       "      <th>2019-01-01 09:45:36</th>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02 11:18:32</th>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03 19:51:01</th>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04 03:31:26</th>\n",
       "      <td>24.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06 07:38:40</th>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Urban</th>\n",
       "      <th>2019-05-08 04:20:00</th>\n",
       "      <td>21.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 04:39:49</th>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 07:29:01</th>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 11:38:35</th>\n",
       "      <td>19.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08 13:10:18</th>\n",
       "      <td>18.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2375 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            fare\n",
       "type  date                      \n",
       "Rural 2019-01-01 09:45:36  43.69\n",
       "      2019-01-02 11:18:32  52.12\n",
       "      2019-01-03 19:51:01  19.90\n",
       "      2019-01-04 03:31:26  24.88\n",
       "      2019-01-06 07:38:40  47.33\n",
       "...                          ...\n",
       "Urban 2019-05-08 04:20:00  21.99\n",
       "      2019-05-08 04:39:49  18.45\n",
       "      2019-05-08 07:29:01  18.55\n",
       "      2019-05-08 11:38:35  19.77\n",
       "      2019-05-08 13:10:18  18.04\n",
       "\n",
       "[2375 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9. 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",
    "sum_fares_df = pyber_data_df.groupby([\"type\", \"date\"]).sum()[[\"fare\"]]\n",
    "sum_fares_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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": [
    "# 10. 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_df = sum_fares_df.reset_index()\n",
    "sum_fares_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:08:16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:46:46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:07:24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:46:50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 05:23:21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 09:45:36</th>\n",
       "      <td>43.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 12:32:48</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.56</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 14:40:14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 14:42:25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 14:52:06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type                 Rural  Suburban  Urban\n",
       "date                                       \n",
       "2019-01-01 00:08:16    NaN       NaN  37.91\n",
       "2019-01-01 00:46:46    NaN     47.74    NaN\n",
       "2019-01-01 02:07:24    NaN     24.07    NaN\n",
       "2019-01-01 03:46:50    NaN       NaN   7.57\n",
       "2019-01-01 05:23:21    NaN       NaN  10.75\n",
       "2019-01-01 09:45:36  43.69       NaN    NaN\n",
       "2019-01-01 12:32:48    NaN     25.56    NaN\n",
       "2019-01-01 14:40:14    NaN       NaN   5.42\n",
       "2019-01-01 14:42:25    NaN       NaN  12.31\n",
       "2019-01-01 14:52:06    NaN     31.15    NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 11. 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_pivot_df = sum_fares_df.pivot(index = 'date', columns = 'type', values='fare')\n",
    "sum_fares_pivot_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
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       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
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       "  <tbody>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:46:46</th>\n",
       "      <td>NaN</td>\n",
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       "    </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",
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       "    <tr>\n",
       "      <th>2019-01-01 05:23:21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 12. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\n",
    "jan_april_2019_df = sum_fares_pivot_df.loc[\"2019-01-01\":\"2019-04-29\"]\n",
    "jan_april_2019_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 13. 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",
    "jan_april_2019_df.index = pd.to_datetime(jan_april_2019_df.index)"
   ]
  },
  {
   "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": [
    "# 14. Check that the datatype for the index is datetime using df.info()\n",
    "jan_april_2019_df.info()"
   ]
  },
  {
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type         Rural  Suburban    Urban\n",
       "date                                 \n",
       "2019-01-06  187.92    721.60  1661.68\n",
       "2019-01-13   67.65   1105.13  2050.43\n",
       "2019-01-20  306.00   1218.20  1939.02\n",
       "2019-01-27  179.69   1203.28  2129.51\n",
       "2019-02-03  333.08   1042.79  2086.94\n",
       "2019-02-10  115.80    974.34  2162.64\n",
       "2019-02-17   95.82   1045.50  2235.07\n",
       "2019-02-24  419.06   1412.74  2466.29\n",
       "2019-03-03  175.14    858.46  2218.20\n",
       "2019-03-10  303.94    925.27  2470.93"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 15. Create a new DataFrame using the \"resample()\" function by week 'W' and get the sum of the fares for each week.\n",
    "fares_per_week_df = jan_april_2019_df.resample('W').sum()\n",
    "fares_per_week_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 16. 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",
    "#Create the chart\n",
    "fares_per_week_df.plot(figsize=(20, 8))\n",
    "\n",
    "# Incorporate the other graph properties\n",
    "plt.title(\"Total Fare by City Type\", fontsize=18)\n",
    "plt.ylabel(\"Fare ($USD)\", fontsize=15)\n",
    "plt.xlabel(\"Month\", fontsize=15)\n",
    "plt.grid(True)\n",
    "\n",
    "# Create a legend\n",
    "lgnd = plt.legend(fontsize=\"12\", mode=\"Expanded\", scatterpoints=1, loc=\"best\", title=\"City Types\")\n",
    "lgnd.get_title().set_fontsize(12)\n",
    "\n",
    "# Save figure.\n",
    "plt.savefig(\"Analysis/PyBer_fare_summary.png\")\n",
    "\n",
    "# Show Figure\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}