Uber case study-yatin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import header files\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the csv file and store it in Dataframe df\n",
    "df=pd.read_csv('Uber Request Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Request id</th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>11/7/2016 11:51</td>\n",
       "      <td>11/7/2016 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>867</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>11/7/2016 17:57</td>\n",
       "      <td>11/7/2016 18:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1807</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>12/7/2016 9:17</td>\n",
       "      <td>12/7/2016 9:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2532</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>12/7/2016 21:08</td>\n",
       "      <td>12/7/2016 22:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3112</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>13-07-2016 08:33:16</td>\n",
       "      <td>13-07-2016 09:25:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status    Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed      11/7/2016 11:51   \n",
       "1         867      Airport        1.0  Trip Completed      11/7/2016 17:57   \n",
       "2        1807         City        1.0  Trip Completed       12/7/2016 9:17   \n",
       "3        2532      Airport        1.0  Trip Completed      12/7/2016 21:08   \n",
       "4        3112         City        1.0  Trip Completed  13-07-2016 08:33:16   \n",
       "\n",
       "        Drop timestamp  \n",
       "0      11/7/2016 13:00  \n",
       "1      11/7/2016 18:47  \n",
       "2       12/7/2016 9:58  \n",
       "3      12/7/2016 22:03  \n",
       "4  13-07-2016 09:25:47  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6745 entries, 0 to 6744\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Request id         6745 non-null   int64  \n",
      " 1   Pickup point       6745 non-null   object \n",
      " 2   Driver id          4095 non-null   float64\n",
      " 3   Status             6745 non-null   object \n",
      " 4   Request timestamp  6745 non-null   object \n",
      " 5   Drop timestamp     2831 non-null   object \n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 316.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#Check the datatypes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Request id              0\n",
       "Pickup point            0\n",
       "Driver id            2650\n",
       "Status                  0\n",
       "Request timestamp       0\n",
       "Drop timestamp       3914\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for NA values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We do not need to deal with NA values due to below reasons:\n",
    "#Driver id N/A implies that there were No Cars Available and Hence there is no Driver ID.\n",
    "#Drop timestamp N/A implies that the trip was not completed due to Cabs not available or Cancellation.\n",
    "#Hence we proceed further without dealing with these NA values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Cleaning\n",
    "#Changing the date to correct format and select dayfirst=True\n",
    "df[\"Request timestamp\"] = pd.to_datetime(df[\"Request timestamp\"], dayfirst=True) #Dayfirst=true will parse date with the day first\n",
    "df[\"Drop timestamp\"] = pd.to_datetime(df[\"Drop timestamp\"], dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Request id</th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 11:51:00</td>\n",
       "      <td>2016-07-11 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>867</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 17:57:00</td>\n",
       "      <td>2016-07-11 18:47:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1807</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 09:17:00</td>\n",
       "      <td>2016-07-12 09:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2532</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 21:08:00</td>\n",
       "      <td>2016-07-12 22:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3112</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13 08:33:16</td>\n",
       "      <td>2016-07-13 09:25:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status   Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed 2016-07-11 11:51:00   \n",
       "1         867      Airport        1.0  Trip Completed 2016-07-11 17:57:00   \n",
       "2        1807         City        1.0  Trip Completed 2016-07-12 09:17:00   \n",
       "3        2532      Airport        1.0  Trip Completed 2016-07-12 21:08:00   \n",
       "4        3112         City        1.0  Trip Completed 2016-07-13 08:33:16   \n",
       "\n",
       "       Drop timestamp  \n",
       "0 2016-07-11 13:00:00  \n",
       "1 2016-07-11 18:47:00  \n",
       "2 2016-07-12 09:58:00  \n",
       "3 2016-07-12 22:03:00  \n",
       "4 2016-07-13 09:25:47  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the data again\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract data and add additional columns to the dataframe\n",
    "\n",
    "df['Request Date'] = df[\"Request timestamp\"].dt.date\n",
    "df['Request Time'] = df[\"Request timestamp\"].dt.time\n",
    "df['Drop Date'] = df[\"Drop timestamp\"].dt.date\n",
    "df['Drop Time'] = df[\"Drop timestamp\"].dt.time\n",
    "df[\"Request hour\"] = df[\"Request timestamp\"].dt.hour"
   ]
  },
  {
   "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>Request id</th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "      <th>Request Date</th>\n",
       "      <th>Request Time</th>\n",
       "      <th>Drop Date</th>\n",
       "      <th>Drop Time</th>\n",
       "      <th>Request hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 11:51:00</td>\n",
       "      <td>2016-07-11 13:00:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>11:51:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>867</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 17:57:00</td>\n",
       "      <td>2016-07-11 18:47:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>17:57:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>18:47:00</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1807</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 09:17:00</td>\n",
       "      <td>2016-07-12 09:58:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>09:17:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>09:58:00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2532</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 21:08:00</td>\n",
       "      <td>2016-07-12 22:03:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>21:08:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>22:03:00</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3112</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13 08:33:16</td>\n",
       "      <td>2016-07-13 09:25:47</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>08:33:16</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>09:25:47</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status   Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed 2016-07-11 11:51:00   \n",
       "1         867      Airport        1.0  Trip Completed 2016-07-11 17:57:00   \n",
       "2        1807         City        1.0  Trip Completed 2016-07-12 09:17:00   \n",
       "3        2532      Airport        1.0  Trip Completed 2016-07-12 21:08:00   \n",
       "4        3112         City        1.0  Trip Completed 2016-07-13 08:33:16   \n",
       "\n",
       "       Drop timestamp Request Date Request Time   Drop Date Drop Time  \\\n",
       "0 2016-07-11 13:00:00   2016-07-11     11:51:00  2016-07-11  13:00:00   \n",
       "1 2016-07-11 18:47:00   2016-07-11     17:57:00  2016-07-11  18:47:00   \n",
       "2 2016-07-12 09:58:00   2016-07-12     09:17:00  2016-07-12  09:58:00   \n",
       "3 2016-07-12 22:03:00   2016-07-12     21:08:00  2016-07-12  22:03:00   \n",
       "4 2016-07-13 09:25:47   2016-07-13     08:33:16  2016-07-13  09:25:47   \n",
       "\n",
       "   Request hour  \n",
       "0            11  \n",
       "1            17  \n",
       "2             9  \n",
       "3            21  \n",
       "4             8  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the data again\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Based on the request hours, categorise the requests-\n",
    "#Early Morning time: 00:00:00 to 04:59:59\n",
    "#Morning time: 05:00:00 to 08:59:59\n",
    "#Late Morning time: 09:00:00 to 11:59:59\n",
    "#Afternoon time: 12:00:00 to 16:59:59\n",
    "#Evening time: 17:00:00 to 20:59:59\n",
    "#Night time: 21:00:00 to 23:59:59\n",
    "df[\"Time Slot\"] = df[\"Request hour\"].apply(lambda x: \"Early Morning\" if x<5 else \"Morning\" if x<9 else \"Late Morning\" if x<12 else \"Afternoon\" if x<17 else \"Evening\" if x<21 else \"Night\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Request id</th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
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       "      <th>Request hour</th>\n",
       "      <th>Time Slot</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 11:51:00</td>\n",
       "      <td>2016-07-11 13:00:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>11:51:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>Late Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>867</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 17:57:00</td>\n",
       "      <td>2016-07-11 18:47:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>17:57:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>18:47:00</td>\n",
       "      <td>17</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1807</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 09:17:00</td>\n",
       "      <td>2016-07-12 09:58:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>09:17:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>09:58:00</td>\n",
       "      <td>9</td>\n",
       "      <td>Late Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2532</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 21:08:00</td>\n",
       "      <td>2016-07-12 22:03:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>21:08:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>22:03:00</td>\n",
       "      <td>21</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3112</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13 08:33:16</td>\n",
       "      <td>2016-07-13 09:25:47</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>08:33:16</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>09:25:47</td>\n",
       "      <td>8</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3879</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13 21:57:28</td>\n",
       "      <td>2016-07-13 22:28:59</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>21:57:28</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>22:28:59</td>\n",
       "      <td>21</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4270</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-14 06:15:32</td>\n",
       "      <td>2016-07-14 07:13:15</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>06:15:32</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>07:13:15</td>\n",
       "      <td>6</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5510</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-15 05:11:52</td>\n",
       "      <td>2016-07-15 06:07:52</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>05:11:52</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>06:07:52</td>\n",
       "      <td>5</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6248</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-15 17:57:27</td>\n",
       "      <td>2016-07-15 18:50:51</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>17:57:27</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>18:50:51</td>\n",
       "      <td>17</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>267</td>\n",
       "      <td>City</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 06:46:00</td>\n",
       "      <td>2016-07-11 07:25:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>06:46:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>07:25:00</td>\n",
       "      <td>6</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status   Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed 2016-07-11 11:51:00   \n",
       "1         867      Airport        1.0  Trip Completed 2016-07-11 17:57:00   \n",
       "2        1807         City        1.0  Trip Completed 2016-07-12 09:17:00   \n",
       "3        2532      Airport        1.0  Trip Completed 2016-07-12 21:08:00   \n",
       "4        3112         City        1.0  Trip Completed 2016-07-13 08:33:16   \n",
       "5        3879      Airport        1.0  Trip Completed 2016-07-13 21:57:28   \n",
       "6        4270      Airport        1.0  Trip Completed 2016-07-14 06:15:32   \n",
       "7        5510      Airport        1.0  Trip Completed 2016-07-15 05:11:52   \n",
       "8        6248         City        1.0  Trip Completed 2016-07-15 17:57:27   \n",
       "9         267         City        2.0  Trip Completed 2016-07-11 06:46:00   \n",
       "\n",
       "       Drop timestamp Request Date Request Time   Drop Date Drop Time  \\\n",
       "0 2016-07-11 13:00:00   2016-07-11     11:51:00  2016-07-11  13:00:00   \n",
       "1 2016-07-11 18:47:00   2016-07-11     17:57:00  2016-07-11  18:47:00   \n",
       "2 2016-07-12 09:58:00   2016-07-12     09:17:00  2016-07-12  09:58:00   \n",
       "3 2016-07-12 22:03:00   2016-07-12     21:08:00  2016-07-12  22:03:00   \n",
       "4 2016-07-13 09:25:47   2016-07-13     08:33:16  2016-07-13  09:25:47   \n",
       "5 2016-07-13 22:28:59   2016-07-13     21:57:28  2016-07-13  22:28:59   \n",
       "6 2016-07-14 07:13:15   2016-07-14     06:15:32  2016-07-14  07:13:15   \n",
       "7 2016-07-15 06:07:52   2016-07-15     05:11:52  2016-07-15  06:07:52   \n",
       "8 2016-07-15 18:50:51   2016-07-15     17:57:27  2016-07-15  18:50:51   \n",
       "9 2016-07-11 07:25:00   2016-07-11     06:46:00  2016-07-11  07:25:00   \n",
       "\n",
       "   Request hour     Time Slot  \n",
       "0            11  Late Morning  \n",
       "1            17       Evening  \n",
       "2             9  Late Morning  \n",
       "3            21         Night  \n",
       "4             8       Morning  \n",
       "5            21         Night  \n",
       "6             6       Morning  \n",
       "7             5       Morning  \n",
       "8            17       Evening  \n",
       "9             6       Morning  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the data again\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Request id</th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "      <th>Request Date</th>\n",
       "      <th>Request Time</th>\n",
       "      <th>Drop Date</th>\n",
       "      <th>Drop Time</th>\n",
       "      <th>Request hour</th>\n",
       "      <th>Time Slot</th>\n",
       "      <th>Availability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 11:51:00</td>\n",
       "      <td>2016-07-11 13:00:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>11:51:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>Late Morning</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>867</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 17:57:00</td>\n",
       "      <td>2016-07-11 18:47:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>17:57:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>18:47:00</td>\n",
       "      <td>17</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1807</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 09:17:00</td>\n",
       "      <td>2016-07-12 09:58:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>09:17:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>09:58:00</td>\n",
       "      <td>9</td>\n",
       "      <td>Late Morning</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2532</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 21:08:00</td>\n",
       "      <td>2016-07-12 22:03:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>21:08:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>22:03:00</td>\n",
       "      <td>21</td>\n",
       "      <td>Night</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3112</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13 08:33:16</td>\n",
       "      <td>2016-07-13 09:25:47</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>08:33:16</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>09:25:47</td>\n",
       "      <td>8</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status   Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed 2016-07-11 11:51:00   \n",
       "1         867      Airport        1.0  Trip Completed 2016-07-11 17:57:00   \n",
       "2        1807         City        1.0  Trip Completed 2016-07-12 09:17:00   \n",
       "3        2532      Airport        1.0  Trip Completed 2016-07-12 21:08:00   \n",
       "4        3112         City        1.0  Trip Completed 2016-07-13 08:33:16   \n",
       "\n",
       "       Drop timestamp Request Date Request Time   Drop Date Drop Time  \\\n",
       "0 2016-07-11 13:00:00   2016-07-11     11:51:00  2016-07-11  13:00:00   \n",
       "1 2016-07-11 18:47:00   2016-07-11     17:57:00  2016-07-11  18:47:00   \n",
       "2 2016-07-12 09:58:00   2016-07-12     09:17:00  2016-07-12  09:58:00   \n",
       "3 2016-07-12 22:03:00   2016-07-12     21:08:00  2016-07-12  22:03:00   \n",
       "4 2016-07-13 09:25:47   2016-07-13     08:33:16  2016-07-13  09:25:47   \n",
       "\n",
       "   Request hour     Time Slot Availability  \n",
       "0            11  Late Morning            Y  \n",
       "1            17       Evening            Y  \n",
       "2             9  Late Morning            Y  \n",
       "3            21         Night            Y  \n",
       "4             8       Morning            Y  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Based on the status of the trip, categorise whether the cab is available or not.\n",
    "#If Status is 'Trip Completed', then Availability is Y otherwise N\n",
    "df[\"Availability\"] = df[\"Status\"].apply(lambda x: \"Y\" if x==\"Trip Completed\" else \"N\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Export the data to csv to create plots in Tableau if needed.\n",
    "df.to_csv (r'Uber Data-Export.csv', index = None, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x234a463bf08>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot an overall graph to show the Demand-Supply gap\n",
    "#A bar chart is chosen as it will clearly show the all the requests' status and can be interpreted easily.\n",
    "plt.title('Trip Status')\n",
    "sns.countplot(df['Status'])\n",
    "#We observe that for ~2500 requests, users were not able to get the cab due to 'No Cars Available'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculate the percentage of Not Completed Trips (Cancelled or Not Available):\n",
    "#Plotted a pie-chart in order to show the percentage clearly.\n",
    "df.groupby(['Availability']).size().plot(kind=\"pie\",figsize=(10, 10),table=True,autopct='%1.1f%%',title=\"Percentage of Trips Not Completed\")\n",
    "plt.ylabel(\"\")\n",
    "#58% of request could not get completed due to different reasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x234a50f0d48>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's see the demand supply gap for all the requests during each hour of the day.\n",
    "# A line chart For Demand-Supply will easily show the gap between demand and supply during different times of the day.\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "plt.xticks([i for i in range(0,24)])\n",
    "df.groupby(df['Request hour']).count()['Request id'].plot(ax=ax, label='Demand')\n",
    "df[df['Status']==\"Trip Completed\"].groupby(df[df['Status']==\"Trip Completed\"]['Request hour']).count()['Request id'].plot(ax=ax, label='Supply')\n",
    "plt.grid(True)\n",
    "plt.title(\"Demand Supply Gap\")\n",
    "plt.legend()\n",
    "#Majority of requests are between 17:00 to 21:00 and 05:00 to 09:00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x234a4f52c88>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's plot a graph to see whether the majority requests are from Airport or City\n",
    "plt.subplots(figsize=(15,10))\n",
    "timeorder = [\"Early Morning\", \"Morning\", \"Late Morning\", \"Afternoon\", \"Evening\", \"Night\"]\n",
    "plt.title(\"Airport vs City Requests during different time slots\")\n",
    "sns.countplot(x='Time Slot',hue='Pickup point', order=timeorder, data=df)\n",
    "\n",
    "#The huge demand in morning hours can be due to large number of departing flights during that time.\n",
    "#The huge demand in evening hours can be due to large number of incoming flights during tha time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#By carefully looking at the above plots, it is clear that-\n",
    "#Peak-hour requests in Morning (Between 05:00 to 09:00) are at City. This implies that there were a lot of outgoing flights early morning.\n",
    "#Peak-hour requests in Evening (Between 17:00 to 21:00) are at Airport. This implies that there were a lot of incoming flights during evening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's analyse further to see the major categories during morning hours.\n",
    "#A pie chart is good option here as it will clearly show the percentages of status.\n",
    "df[(df[\"Time Slot\"]==\"Morning\") & (df[\"Pickup point\"] == \"City\")].groupby(['Status']).size().plot(kind=\"pie\",figsize=(10, 10),table=True,autopct='%1.1f%%',title=\"Morning time slot\")\n",
    "plt.ylabel(\"\")\n",
    "#About 49% of requests were cancelled by the drivers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's analyse further to see the major categories during evening hours.\n",
    "#A pie chart is good option here as it will clearly show the percentages of status.\n",
    "df[(df[\"Time Slot\"]==\"Evening\") & (df[\"Pickup point\"] == \"Airport\")].groupby(['Status']).size().plot(kind=\"pie\",figsize=(10, 10),table=True,autopct='%1.1f%%',title=\"Evening time slot\")\n",
    "plt.ylabel(\"\")\n",
    "#About 73% of requests could not be fulfilled due to 'No Cars Available'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reasons for Demand-Supply gap could be as below:\n",
    "#Reasons for 49% cancellations during Morning hours-\n",
    "#Airport could have been too far from driver's location and he would not be making enough profit out of the ride. Hence, he would have cancelled the request.\n",
    "#There were no incoming flights during the morning hours. Hence there driver could not see any profits going to the airport.\n",
    "#Reasons for 73% Non-availability during Evening hours-\n",
    "#There were not many incoming flights scheduled during these hours. Hence not many drivers were available near Airport.\n",
    "#In previous trips during that day, not many customers went to Airport. Hence, cabs were not available.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Based on the above analysis, we can provide below recommendations:\n",
    "#Provide better incentives to the drivers for morning and evening rides from City to Airport and Airport to City respectively.\n",
    "#To avoid cancellation of rides by drives, provide 5% allowance for waiting idle at the airports.\n",
    "#Provide car-pool option more frequently during peak-time hours.\n",
    "#To avoid ‘No cabs available’ at the airports, increase the range for cabs search.\n",
    "#Penalize the drivers in case they cancel more than 5 rides in a single day.\n",
    "#Partner with other local companies in order to increase the availability of cabs during peak hours.\n"
   ]
  }
 ],
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