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": 240,
   "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 = \"city_data.csv\"\n",
    "ride_data_to_load = \"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": 241,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Latoya</td>\n",
       "      <td>2019-03-11 12:26:48</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1994999424437</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Paulville</td>\n",
       "      <td>2019-02-27 11:17:56</td>\n",
       "      <td>43.25</td>\n",
       "      <td>793208410091</td>\n",
       "      <td>44</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simpsonburgh</td>\n",
       "      <td>2019-04-26 00:43:24</td>\n",
       "      <td>35.98</td>\n",
       "      <td>111953927754</td>\n",
       "      <td>21</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Karenland</td>\n",
       "      <td>2019-01-08 03:28:48</td>\n",
       "      <td>35.09</td>\n",
       "      <td>7995623208694</td>\n",
       "      <td>4</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>North Jasmine</td>\n",
       "      <td>2019-03-09 06:26:29</td>\n",
       "      <td>42.81</td>\n",
       "      <td>5327642267789</td>\n",
       "      <td>33</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>New Kimberlyborough</td>\n",
       "      <td>2019-02-07 01:10:11</td>\n",
       "      <td>9.34</td>\n",
       "      <td>7992781920789</td>\n",
       "      <td>33</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>2019-01-12 01:45:27</td>\n",
       "      <td>26.86</td>\n",
       "      <td>4820052223501</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-03-02 18:03:20</td>\n",
       "      <td>44.26</td>\n",
       "      <td>2494064867705</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>2019-01-10 12:17:31</td>\n",
       "      <td>12.74</td>\n",
       "      <td>2108723916092</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Roberthaven</td>\n",
       "      <td>2019-05-04 14:48:56</td>\n",
       "      <td>25.66</td>\n",
       "      <td>1026197778354</td>\n",
       "      <td>47</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   city                 date   fare        ride_id  \\\n",
       "0    Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1    South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2    Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3            Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4            South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "5          South Latoya  2019-03-11 12:26:48   9.52  1994999424437   \n",
       "6         New Paulville  2019-02-27 11:17:56  43.25   793208410091   \n",
       "7          Simpsonburgh  2019-04-26 00:43:24  35.98   111953927754   \n",
       "8       South Karenland  2019-01-08 03:28:48  35.09  7995623208694   \n",
       "9         North Jasmine  2019-03-09 06:26:29  42.81  5327642267789   \n",
       "10  New Kimberlyborough  2019-02-07 01:10:11   9.34  7992781920789   \n",
       "11          West Angela  2019-01-12 01:45:27  26.86  4820052223501   \n",
       "12           Rodneyfort  2019-03-02 18:03:20  44.26  2494064867705   \n",
       "13          West Angela  2019-01-10 12:17:31  12.74  2108723916092   \n",
       "14          Roberthaven  2019-05-04 14:48:56  25.66  1026197778354   \n",
       "\n",
       "    driver_count   type  \n",
       "0              5  Urban  \n",
       "1             72  Urban  \n",
       "2             57  Urban  \n",
       "3             34  Urban  \n",
       "4             46  Urban  \n",
       "5             10  Urban  \n",
       "6             44  Urban  \n",
       "7             21  Urban  \n",
       "8              4  Urban  \n",
       "9             33  Urban  \n",
       "10            33  Urban  \n",
       "11            48  Urban  \n",
       "12            34  Urban  \n",
       "13            48  Urban  \n",
       "14            47  Urban  "
      ]
     },
     "execution_count": 241,
     "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(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable 1: Get a Summary DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural        125\n",
       "Suburban     625\n",
       "Urban       1625\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  1. Get the total rides for each city type\n",
    "\n",
    "# Urban\n",
    "#urban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Urban\"]\n",
    "\n",
    "#urban_ride_count = urban_cities_df.count()[\"ride_id\"]\n",
    "#print(urban_ride_count)\n",
    "\n",
    "#Suburban\n",
    "#suburban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Suburban\"]\n",
    "\n",
    "#suburban_ride_count = suburban_cities_df.count()[\"ride_id\"]\n",
    "#suburban_ride_count\n",
    "\n",
    "#Rural\n",
    "\n",
    "#rural_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Rural\"]\n",
    "\n",
    "#rural_ride_count = rural_cities_df.count()[\"ride_id\"]\n",
    "#rural_ride_count\n",
    "\n",
    "# Woah that's a complicated way to do it, let's make it easy\n",
    "\n",
    "rides_by_type = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\n",
    "\n",
    "rides_by_type\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural         78\n",
       "Suburban     490\n",
       "Urban       2405\n",
       "Name: driver_count, dtype: int64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Get the total drivers for each city type\n",
    "\n",
    "#urban_cities_df = pyber_data_df[pyber_data_df[\"type\"] == \"Urban\"]\n",
    "\n",
    "#drivers_by_type = pyber_data_df.groupby([\"type\"]).count()[\"driver_count\"]\n",
    "#drivers_by_type\n",
    "\n",
    "#pyber data dataframe contains duplicates\n",
    "#to get rid of dupes, use city data\n",
    "\n",
    "drivers_by_type = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\n",
    "drivers_by_type\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  3. Get the total amount of fares for each city type\n",
    "\n",
    "sum_fares_by_type = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\n",
    "sum_fares_by_type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  4. Get the average fare per ride for each city type. \n",
    "mean_fares_by_type = pyber_data_df.groupby([\"type\"]).mean()[\"fare\"]\n",
    "mean_fares_by_type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "Rural       55.486282\n",
       "Suburban    39.502714\n",
       "Urban       16.571468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. Get the average fare per driver for each city type. \n",
    "\n",
    "average_driver_fare = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"] / city_data_df.groupby([\"type\"]).sum()[\"driver_count\"] \n",
    "\n",
    "average_driver_fare\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  6. Create a PyBer summary DataFrame. \n",
    "\n",
    "pyber_summary_df = pd.DataFrame({\"Drivers per City Type\" : drivers_by_type, \"Number of Rides per City Type\" : rides_by_type, \"Total Fares per City Type\" : sum_fares_by_type, \"Average Fare per City Type\" : mean_fares_by_type, \"Average Fare per Driver per City Type\" : average_driver_fare})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  7. Cleaning up the DataFrame. Delete the index name\n",
    "pyber_summary_df.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "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></th>\n",
       "      <th>Drivers per City Type</th>\n",
       "      <th>Number of Rides per City Type</th>\n",
       "      <th>Total Fares per City Type</th>\n",
       "      <th>Average Fare per City Type</th>\n",
       "      <th>Average Fare per Driver per City Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>78</td>\n",
       "      <td>125</td>\n",
       "      <td>$4327.93</td>\n",
       "      <td>$34.62</td>\n",
       "      <td>$55.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>490</td>\n",
       "      <td>625</td>\n",
       "      <td>$19356.33</td>\n",
       "      <td>$30.97</td>\n",
       "      <td>$39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>2405</td>\n",
       "      <td>1625</td>\n",
       "      <td>$39854.38</td>\n",
       "      <td>$24.53</td>\n",
       "      <td>$16.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Drivers per City Type  Number of Rides per City Type  \\\n",
       "Rural                        78                            125   \n",
       "Suburban                    490                            625   \n",
       "Urban                      2405                           1625   \n",
       "\n",
       "         Total Fares per City Type Average Fare per City Type  \\\n",
       "Rural                     $4327.93                     $34.62   \n",
       "Suburban                 $19356.33                     $30.97   \n",
       "Urban                    $39854.38                     $24.53   \n",
       "\n",
       "         Average Fare per Driver per City Type  \n",
       "Rural                                   $55.49  \n",
       "Suburban                                $39.50  \n",
       "Urban                                   $16.57  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  8. Format the columns.\n",
    "\n",
    "pyber_summary_df[\"Total Fares per City Type\"] = pyber_summary_df[\"Total Fares per City Type\"].map(\"${:.2f}\".format)\n",
    "\n",
    "\n",
    "\n",
    "pyber_summary_df[\"Average Fare per City Type\"] = pyber_summary_df[\"Average Fare per City Type\"].map(\"${:.2f}\".format)\n",
    "pyber_summary_df[\"Average Fare per Driver per City Type\"] = pyber_summary_df[\"Average Fare per Driver per City Type\"].map(\"${:.2f}\".format)\n",
    "\n",
    "pyber_summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverable 2.  Create a multiple line plot that shows the total weekly of the fares for each type of city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>city</th>\n",
       "      <th>date</th>\n",
       "      <th>fare</th>\n",
       "      <th>ride_id</th>\n",
       "      <th>driver_count</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Jonathanshire</td>\n",
       "      <td>2019-01-14 10:14:22</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5739410935873</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Michelleport</td>\n",
       "      <td>2019-03-04 18:24:09</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2343912425577</td>\n",
       "      <td>72</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Port Samanthamouth</td>\n",
       "      <td>2019-02-24 04:29:00</td>\n",
       "      <td>33.44</td>\n",
       "      <td>2005065760003</td>\n",
       "      <td>57</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-02-10 23:22:03</td>\n",
       "      <td>23.44</td>\n",
       "      <td>5149245426178</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Jack</td>\n",
       "      <td>2019-03-06 04:28:35</td>\n",
       "      <td>34.58</td>\n",
       "      <td>3908451377344</td>\n",
       "      <td>46</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South Latoya</td>\n",
       "      <td>2019-03-11 12:26:48</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1994999424437</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Paulville</td>\n",
       "      <td>2019-02-27 11:17:56</td>\n",
       "      <td>43.25</td>\n",
       "      <td>793208410091</td>\n",
       "      <td>44</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Simpsonburgh</td>\n",
       "      <td>2019-04-26 00:43:24</td>\n",
       "      <td>35.98</td>\n",
       "      <td>111953927754</td>\n",
       "      <td>21</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Karenland</td>\n",
       "      <td>2019-01-08 03:28:48</td>\n",
       "      <td>35.09</td>\n",
       "      <td>7995623208694</td>\n",
       "      <td>4</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>North Jasmine</td>\n",
       "      <td>2019-03-09 06:26:29</td>\n",
       "      <td>42.81</td>\n",
       "      <td>5327642267789</td>\n",
       "      <td>33</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>New Kimberlyborough</td>\n",
       "      <td>2019-02-07 01:10:11</td>\n",
       "      <td>9.34</td>\n",
       "      <td>7992781920789</td>\n",
       "      <td>33</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>2019-01-12 01:45:27</td>\n",
       "      <td>26.86</td>\n",
       "      <td>4820052223501</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rodneyfort</td>\n",
       "      <td>2019-03-02 18:03:20</td>\n",
       "      <td>44.26</td>\n",
       "      <td>2494064867705</td>\n",
       "      <td>34</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>West Angela</td>\n",
       "      <td>2019-01-10 12:17:31</td>\n",
       "      <td>12.74</td>\n",
       "      <td>2108723916092</td>\n",
       "      <td>48</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Roberthaven</td>\n",
       "      <td>2019-05-04 14:48:56</td>\n",
       "      <td>25.66</td>\n",
       "      <td>1026197778354</td>\n",
       "      <td>47</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   city                 date   fare        ride_id  \\\n",
       "0    Lake Jonathanshire  2019-01-14 10:14:22  13.83  5739410935873   \n",
       "1    South Michelleport  2019-03-04 18:24:09  30.24  2343912425577   \n",
       "2    Port Samanthamouth  2019-02-24 04:29:00  33.44  2005065760003   \n",
       "3            Rodneyfort  2019-02-10 23:22:03  23.44  5149245426178   \n",
       "4            South Jack  2019-03-06 04:28:35  34.58  3908451377344   \n",
       "5          South Latoya  2019-03-11 12:26:48   9.52  1994999424437   \n",
       "6         New Paulville  2019-02-27 11:17:56  43.25   793208410091   \n",
       "7          Simpsonburgh  2019-04-26 00:43:24  35.98   111953927754   \n",
       "8       South Karenland  2019-01-08 03:28:48  35.09  7995623208694   \n",
       "9         North Jasmine  2019-03-09 06:26:29  42.81  5327642267789   \n",
       "10  New Kimberlyborough  2019-02-07 01:10:11   9.34  7992781920789   \n",
       "11          West Angela  2019-01-12 01:45:27  26.86  4820052223501   \n",
       "12           Rodneyfort  2019-03-02 18:03:20  44.26  2494064867705   \n",
       "13          West Angela  2019-01-10 12:17:31  12.74  2108723916092   \n",
       "14          Roberthaven  2019-05-04 14:48:56  25.66  1026197778354   \n",
       "\n",
       "    driver_count   type  \n",
       "0              5  Urban  \n",
       "1             72  Urban  \n",
       "2             57  Urban  \n",
       "3             34  Urban  \n",
       "4             46  Urban  \n",
       "5             10  Urban  \n",
       "6             44  Urban  \n",
       "7             21  Urban  \n",
       "8              4  Urban  \n",
       "9             33  Urban  \n",
       "10            33  Urban  \n",
       "11            48  Urban  \n",
       "12            34  Urban  \n",
       "13            48  Urban  \n",
       "14            47  Urban  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Read the merged DataFrame\n",
    "\n",
    "\n",
    "pyber_data_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2019-01-01 00:08:16    37.91\n",
       "2019-01-01 00:46:46    47.74\n",
       "2019-01-01 02:07:24    24.07\n",
       "2019-01-01 03:46:50     7.57\n",
       "2019-01-01 05:23:21    10.75\n",
       "                       ...  \n",
       "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": 232,
     "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",
    "\n",
    "fare_by_date = pyber_data_df.groupby([\"date\"]).sum()[\"fare\"]\n",
    "fare_by_date\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Reset the index on the DataFrame you created in #1. This is needed to use the 'pivot()' function.\n",
    "# df = df.reset_index()\n",
    "\n",
    "fare_by_date = fare_by_date.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>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>2019-01-01 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": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. Create a pivot table with the 'date' as the index, the columns ='type', and values='fare' \n",
    "# to get the total fares for each type of city by the date. \n",
    "\n",
    "fares_index_date = pd.pivot_table(pyber_data_df, values = \"fare\", index = \"date\", columns = \"type\")\n",
    "fares_index_date.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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]\n"
     ]
    }
   ],
   "source": [
    "# 5. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\n",
    "\n",
    "#create a variable for the date range to obtain the indecies\n",
    "#then loc in the dataframe with those indicies\n",
    "\n",
    "date_range = (fares_index_date.index > '2019-01-01') & (fares_index_date.index <= '2019-04-29')\n",
    "\n",
    "print(fares_index_date.loc[date_range])\n",
    "\n",
    "fares_index_date = fares_index_date.loc[date_range]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 8.\n",
    "# df.index = pd.to_datetime(df.index)\n",
    "\n",
    "fares_index_date.index = pd.to_datetime(fares_index_date.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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",
    "\n",
    "fares_index_date.info()\n",
    "\n",
    "#fares_index_date.index.info() is more correct/direct I think? but it gives an error\n",
    "\n",
    "#that error actually has the dtype as DatetimeIndex which is what it is\n",
    "\n",
    "#an n-dimensional array with datetimes in it\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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": 238,
     "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",
    "\n",
    "\n",
    "weekly_fares = fares_index_date.resample('W').sum()\n",
    "weekly_fares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 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",
    "\n",
    "x_axis = weekly_fares.index\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (30,12))\n",
    "ax.plot(weekly_fares)\n",
    "ax.set_ylim([0,2750])\n",
    "#ax.set_xticks(weekly_fares.index[::5])\n",
    "#ax.set_xlim([\"2019-01-06\", \"2019-05-12\"])\n",
    "ax.set_ylabel(\"Fares ($ USD)\", size = 28)\n",
    "ax.set_xlabel(\"Month\", size = 28)\n",
    "ax.set_title(\"Total Fare by City Type\", size = 34)\n",
    "\n",
    "ax.set_xticks(x_axis[::5])\n",
    "ax.set_xticklabels(['January', \"Feb\", \"March\", \"April\"])\n",
    "ax.tick_params(axis='both', which='major', labelsize=25)\n",
    "\n",
    "#fig.legend()\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "#ax.locator_params(axis = x_axis, nbins = 4)\n",
    "#plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=2))\n",
    "\n",
    "#import matplotlib.dates as mdates\n",
    "#x_axis.set_major_locator(locator)\n",
    "\n",
    "fig.legend(weekly_fares,loc = \"center\", title = \"type\", fontsize = \"large\", title_fontsize = \"large\", bbox_to_anchor=(0.27, 0.25, 0.5, 0.5))\n",
    "\n",
    "plt.savefig(\"Pyber_fare_summary.png\")\n",
    "\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": []
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}