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\"\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": {
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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",
       "  </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 = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "total_rides"
   ]
  },
  {
   "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 = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "total_drivers"
   ]
  },
  {
   "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 = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "total_fares"
   ]
  },
  {
   "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",
    "average_fare_per_ride = pyber_data_df.groupby([\"type\"]).mean()[\"fare\"]\n",
    "average_fare_per_ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "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/total_drivers\n",
    "average_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(\n",
    "    {\"Total Rides\":total_rides, \n",
    "      \"Total Drivers\":total_drivers,\n",
    "      \"Total Fares\": total_fares,\n",
    "      \"Average Fare per Ride\": average_fare_per_ride,\n",
    "      \"Average Fare per Driver\": average_fare_per_driver})\n",
    "\n",
    "pyber_summary_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "pyber_summary_df.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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                     $16.57  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "pyber_summary_df[\"Total Rides\"] = pyber_summary_df[\"Total Rides\"].map(\"{:,.0f}\".format)\n",
    "pyber_summary_df[\"Total Drivers\"] = pyber_summary_df[\"Total Drivers\"].map(\"{:,.0f}\".format)\n",
    "pyber_summary_df[\"Average Fare per Ride\"] = pyber_summary_df[\"Average Fare per Ride\"].map(\"${:,.2f}\".format)\n",
    "pyber_summary_df[\"Average Fare per Driver\"] = pyber_summary_df[\"Average Fare per Driver\"].map(\"${:,.2f}\".format)\n",
    "pyber_summary_df[\"Total Fares\"] = pyber_summary_df[\"Total Fares\"].map(\"${:,.2f}\".format)\n",
    "pyber_summary_df\n",
    "\n",
    "\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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       "<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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Read the merged DataFrame\n",
    "pyber_data_df.head()"
   ]
  },
  {
   "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",
       "       2019-01-08 06:19:45    19.39\n",
       "       2019-01-09 15:30:35    31.84\n",
       "       2019-01-11 04:39:27    16.42\n",
       "       2019-01-14 07:09:17    18.05\n",
       "       2019-01-14 15:58:48    54.10\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Using groupby() to create a new DataFrame showing the sum of the fares \n",
    "#  for each date where the indices are the city type and date.\n",
    "sum_fares_by_type = pyber_data_df.groupby([\"type\",\"date\"]).sum()[\"fare\"]\n",
    "sum_fares_by_type.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "\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>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>5</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-08 06:19:45</td>\n",
       "      <td>19.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-09 15:30:35</td>\n",
       "      <td>31.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-11 04:39:27</td>\n",
       "      <td>16.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-14 07:09:17</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rural</td>\n",
       "      <td>2019-01-14 15:58:48</td>\n",
       "      <td>54.10</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\n",
       "5  Rural  2019-01-08 06:19:45  19.39\n",
       "6  Rural  2019-01-09 15:30:35  31.84\n",
       "7  Rural  2019-01-11 04:39:27  16.42\n",
       "8  Rural  2019-01-14 07:09:17  18.05\n",
       "9  Rural  2019-01-14 15:58:48  54.10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Reset the index on the DataFrame you created in #1. This is needed to use the 'pivot()' function.\n",
    "sum_fares_by_type = sum_fares_by_type.reset_index()\n",
    "sum_fares_by_type.head(10)"
   ]
  },
  {
   "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",
       "      <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",
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       "    <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": [
    "# 4. Create a pivot table with the 'date' as the index, the columns ='type', and values='fare' to get the total fares for each type of city by the date. \n",
    "pivot = sum_fares_by_type.pivot(index = 'date', columns = 'type', values = 'fare')\n",
    "pivot.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburban</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\n",
    "pivot_dates_df = pivot.loc['2019-01-01':'2019-04-29']\n",
    "pivot_dates_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 8.\n",
    "pivot_dates_df.index = pd.to_datetime(pivot_dates_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": [
    "# 7. Check that the datatype for the index is datetime using df.info()\n",
    "pivot_dates_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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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>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": [
    "# 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 = pivot_dates_df.resample('w').sum()\n",
    "weekly_fares_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 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",
    "ax = weekly_fares_df.plot(figsize = (20,6))\n",
    "ax.set_title(\"Total Fare by City Tpe\")\n",
    "ax.set_xlabel(\"Months\")\n",
    "ax.set_ylabel(\"Fare ($USD)\")\n",
    "plt.savefig('analysis/PyBer_fare_summary.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
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