Uber Supply-Demand Gap Case Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uber Supply Demand Gap Case Study\n",
    "\n",
    "##### The objective of this case study is to identify the main cause of the problem i.e. The cancellation of rides and the unavailability of cars.\n",
    "\n",
    "\n",
    "## - Load the libraries, load the data onto the notebook and standardize it according to the need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the useful libraries\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": 47,
   "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",
       "    </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 08:33:00</td>\n",
       "      <td>2016-07-13 09:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6740</th>\n",
       "      <td>6745</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:49:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6741</th>\n",
       "      <td>6752</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:50:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6742</th>\n",
       "      <td>6751</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:52:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6743</th>\n",
       "      <td>6754</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:54:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6744</th>\n",
       "      <td>6753</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:55:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6745 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Request id Pickup point  Driver id             Status  \\\n",
       "0            619      Airport        1.0     Trip Completed   \n",
       "1            867      Airport        1.0     Trip Completed   \n",
       "2           1807         City        1.0     Trip Completed   \n",
       "3           2532      Airport        1.0     Trip Completed   \n",
       "4           3112         City        1.0     Trip Completed   \n",
       "...          ...          ...        ...                ...   \n",
       "6740        6745         City        NaN  No Cars Available   \n",
       "6741        6752      Airport        NaN  No Cars Available   \n",
       "6742        6751         City        NaN  No Cars Available   \n",
       "6743        6754         City        NaN  No Cars Available   \n",
       "6744        6753      Airport        NaN  No Cars Available   \n",
       "\n",
       "       Request timestamp      Drop timestamp  \n",
       "0    2016-07-11 11:51:00 2016-07-11 13:00:00  \n",
       "1    2016-07-11 17:57:00 2016-07-11 18:47:00  \n",
       "2    2016-07-12 09:17:00 2016-07-12 09:58:00  \n",
       "3    2016-07-12 21:08:00 2016-07-12 22:03:00  \n",
       "4    2016-07-13 08:33:00 2016-07-13 09:25:00  \n",
       "...                  ...                 ...  \n",
       "6740 2016-07-15 23:49:00                 NaT  \n",
       "6741 2016-07-15 23:50:00                 NaT  \n",
       "6742 2016-07-15 23:52:00                 NaT  \n",
       "6743 2016-07-15 23:54:00                 NaT  \n",
       "6744 2016-07-15 23:55:00                 NaT  \n",
       "\n",
       "[6745 rows x 6 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the data\n",
    "uber = pd.read_excel('Uber Request Data.xlsx')\n",
    "uber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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   datetime64[ns]\n",
      " 5   Drop timestamp     2831 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(1), int64(1), object(2)\n",
      "memory usage: 316.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# identifying the datatypes\n",
    "uber.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6745.000000</td>\n",
       "      <td>6745</td>\n",
       "      <td>4095.000000</td>\n",
       "      <td>6745</td>\n",
       "      <td>6745</td>\n",
       "      <td>2831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4016</td>\n",
       "      <td>2282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-15 19:19:00</td>\n",
       "      <td>2016-07-13 08:53:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2831</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-11 00:00:00</td>\n",
       "      <td>2016-07-11 00:51:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-15 23:59:00</td>\n",
       "      <td>2016-07-16 01:09:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3384.644922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149.501343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1955.099667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.051994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1691.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3387.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5080.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6766.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Request id Pickup point    Driver id          Status  \\\n",
       "count   6745.000000         6745  4095.000000            6745   \n",
       "unique          NaN            2          NaN               3   \n",
       "top             NaN         City          NaN  Trip Completed   \n",
       "freq            NaN         3507          NaN            2831   \n",
       "first           NaN          NaN          NaN             NaN   \n",
       "last            NaN          NaN          NaN             NaN   \n",
       "mean    3384.644922          NaN   149.501343             NaN   \n",
       "std     1955.099667          NaN    86.051994             NaN   \n",
       "min        1.000000          NaN     1.000000             NaN   \n",
       "25%     1691.000000          NaN    75.000000             NaN   \n",
       "50%     3387.000000          NaN   149.000000             NaN   \n",
       "75%     5080.000000          NaN   224.000000             NaN   \n",
       "max     6766.000000          NaN   300.000000             NaN   \n",
       "\n",
       "          Request timestamp       Drop timestamp  \n",
       "count                  6745                 2831  \n",
       "unique                 4016                 2282  \n",
       "top     2016-07-15 19:19:00  2016-07-13 08:53:00  \n",
       "freq                      8                    5  \n",
       "first   2016-07-11 00:00:00  2016-07-11 00:51:00  \n",
       "last    2016-07-15 23:59:00  2016-07-16 01:09:00  \n",
       "mean                    NaN                  NaN  \n",
       "std                     NaN                  NaN  \n",
       "min                     NaN                  NaN  \n",
       "25%                     NaN                  NaN  \n",
       "50%                     NaN                  NaN  \n",
       "75%                     NaN                  NaN  \n",
       "max                     NaN                  NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6745, 6)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning - Checking duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
       "    </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 08:33:00</td>\n",
       "      <td>2016-07-13 09:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6740</th>\n",
       "      <td>6745</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:49:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6741</th>\n",
       "      <td>6752</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:50:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6742</th>\n",
       "      <td>6751</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:52:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6743</th>\n",
       "      <td>6754</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:54:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6744</th>\n",
       "      <td>6753</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:55:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6745 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Request id Pickup point  Driver id             Status  \\\n",
       "0            619      Airport        1.0     Trip Completed   \n",
       "1            867      Airport        1.0     Trip Completed   \n",
       "2           1807         City        1.0     Trip Completed   \n",
       "3           2532      Airport        1.0     Trip Completed   \n",
       "4           3112         City        1.0     Trip Completed   \n",
       "...          ...          ...        ...                ...   \n",
       "6740        6745         City        NaN  No Cars Available   \n",
       "6741        6752      Airport        NaN  No Cars Available   \n",
       "6742        6751         City        NaN  No Cars Available   \n",
       "6743        6754         City        NaN  No Cars Available   \n",
       "6744        6753      Airport        NaN  No Cars Available   \n",
       "\n",
       "       Request timestamp      Drop timestamp  \n",
       "0    2016-07-11 11:51:00 2016-07-11 13:00:00  \n",
       "1    2016-07-11 17:57:00 2016-07-11 18:47:00  \n",
       "2    2016-07-12 09:17:00 2016-07-12 09:58:00  \n",
       "3    2016-07-12 21:08:00 2016-07-12 22:03:00  \n",
       "4    2016-07-13 08:33:00 2016-07-13 09:25:00  \n",
       "...                  ...                 ...  \n",
       "6740 2016-07-15 23:49:00                 NaT  \n",
       "6741 2016-07-15 23:50:00                 NaT  \n",
       "6742 2016-07-15 23:52:00                 NaT  \n",
       "6743 2016-07-15 23:54:00                 NaT  \n",
       "6744 2016-07-15 23:55:00                 NaT  \n",
       "\n",
       "[6745 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6745, 6)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No rows get dropped i.e. no duplicate data\n",
    "uber.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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",
       "    </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 08:33:00</td>\n",
       "      <td>2016-07-13 09:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6740</th>\n",
       "      <td>6745</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:49:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6741</th>\n",
       "      <td>6752</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:50:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6742</th>\n",
       "      <td>6751</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:52:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6743</th>\n",
       "      <td>6754</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:54:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6744</th>\n",
       "      <td>6753</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:55:00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6745 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Request id Pickup point  Driver id             Status  \\\n",
       "0            619      Airport        1.0     Trip Completed   \n",
       "1            867      Airport        1.0     Trip Completed   \n",
       "2           1807         City        1.0     Trip Completed   \n",
       "3           2532      Airport        1.0     Trip Completed   \n",
       "4           3112         City        1.0     Trip Completed   \n",
       "...          ...          ...        ...                ...   \n",
       "6740        6745         City        NaN  No Cars Available   \n",
       "6741        6752      Airport        NaN  No Cars Available   \n",
       "6742        6751         City        NaN  No Cars Available   \n",
       "6743        6754         City        NaN  No Cars Available   \n",
       "6744        6753      Airport        NaN  No Cars Available   \n",
       "\n",
       "       Request timestamp      Drop timestamp  \n",
       "0    2016-07-11 11:51:00 2016-07-11 13:00:00  \n",
       "1    2016-07-11 17:57:00 2016-07-11 18:47:00  \n",
       "2    2016-07-12 09:17:00 2016-07-12 09:58:00  \n",
       "3    2016-07-12 21:08:00 2016-07-12 22:03:00  \n",
       "4    2016-07-13 08:33:00 2016-07-13 09:25:00  \n",
       "...                  ...                 ...  \n",
       "6740 2016-07-15 23:49:00                 NaT  \n",
       "6741 2016-07-15 23:50:00                 NaT  \n",
       "6742 2016-07-15 23:52:00                 NaT  \n",
       "6743 2016-07-15 23:54:00                 NaT  \n",
       "6744 2016-07-15 23:55:00                 NaT  \n",
       "\n",
       "[6745 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# changing the Request timestamp and Drop timestamp datatype from object to datetime\n",
    "uber['Request timestamp'] = pd.to_datetime(uber['Request timestamp'])\n",
    "uber['Drop timestamp'] = pd.to_datetime(uber['Drop timestamp'])\n",
    "uber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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   datetime64[ns]\n",
      " 5   Drop timestamp     2831 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(1), int64(1), object(2)\n",
      "memory usage: 316.3+ KB\n"
     ]
    }
   ],
   "source": [
    "uber.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data has only two columns (Driver id and Drop timestamp) with null values which are not required to be cleaned because we need to perform analytics on for these problematic values only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### -------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "## -  Deriving or Extracting the useful data from the given dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Hour from Request timestamp\n",
    "def timestamp(t):\n",
    "    return t.hour\n",
    "uber['Pickup Hour'] = uber['Request timestamp'].apply(timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Hour from Drop timestamp\n",
    "def timestamp1(t1):\n",
    "    return t1.hour\n",
    "uber['Drop Hour'] = uber['Drop timestamp'].apply(timestamp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the journey time \n",
    "uber['Journey Time'] = uber['Drop timestamp']-uber['Request timestamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Pickup Hour</th>\n",
       "      <th>Drop Hour</th>\n",
       "      <th>Journey Time</th>\n",
       "      <th>Request Date</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>11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>01:09:00</td>\n",
       "      <td>2016-07-11</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>17</td>\n",
       "      <td>18.0</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>2016-07-11</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>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>00:41:00</td>\n",
       "      <td>2016-07-12</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>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>00:55:00</td>\n",
       "      <td>2016-07-12</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 08:33:00</td>\n",
       "      <td>2016-07-13 09:25:00</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>00:52:00</td>\n",
       "      <td>2016-07-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6740</th>\n",
       "      <td>6745</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:49:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6741</th>\n",
       "      <td>6752</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:50:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6742</th>\n",
       "      <td>6751</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:52:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6743</th>\n",
       "      <td>6754</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:54:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6744</th>\n",
       "      <td>6753</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:55:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6745 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Request id Pickup point  Driver id             Status  \\\n",
       "0            619      Airport        1.0     Trip Completed   \n",
       "1            867      Airport        1.0     Trip Completed   \n",
       "2           1807         City        1.0     Trip Completed   \n",
       "3           2532      Airport        1.0     Trip Completed   \n",
       "4           3112         City        1.0     Trip Completed   \n",
       "...          ...          ...        ...                ...   \n",
       "6740        6745         City        NaN  No Cars Available   \n",
       "6741        6752      Airport        NaN  No Cars Available   \n",
       "6742        6751         City        NaN  No Cars Available   \n",
       "6743        6754         City        NaN  No Cars Available   \n",
       "6744        6753      Airport        NaN  No Cars Available   \n",
       "\n",
       "       Request timestamp      Drop timestamp  Pickup Hour  Drop Hour  \\\n",
       "0    2016-07-11 11:51:00 2016-07-11 13:00:00           11       13.0   \n",
       "1    2016-07-11 17:57:00 2016-07-11 18:47:00           17       18.0   \n",
       "2    2016-07-12 09:17:00 2016-07-12 09:58:00            9        9.0   \n",
       "3    2016-07-12 21:08:00 2016-07-12 22:03:00           21       22.0   \n",
       "4    2016-07-13 08:33:00 2016-07-13 09:25:00            8        9.0   \n",
       "...                  ...                 ...          ...        ...   \n",
       "6740 2016-07-15 23:49:00                 NaT           23        NaN   \n",
       "6741 2016-07-15 23:50:00                 NaT           23        NaN   \n",
       "6742 2016-07-15 23:52:00                 NaT           23        NaN   \n",
       "6743 2016-07-15 23:54:00                 NaT           23        NaN   \n",
       "6744 2016-07-15 23:55:00                 NaT           23        NaN   \n",
       "\n",
       "     Journey Time Request Date  \n",
       "0        01:09:00   2016-07-11  \n",
       "1        00:50:00   2016-07-11  \n",
       "2        00:41:00   2016-07-12  \n",
       "3        00:55:00   2016-07-12  \n",
       "4        00:52:00   2016-07-13  \n",
       "...           ...          ...  \n",
       "6740          NaT   2016-07-15  \n",
       "6741          NaT   2016-07-15  \n",
       "6742          NaT   2016-07-15  \n",
       "6743          NaT   2016-07-15  \n",
       "6744          NaT   2016-07-15  \n",
       "\n",
       "[6745 rows x 10 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber['Request Date'] = uber['Request timestamp'].dt.date\n",
    "uber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6745 entries, 0 to 6744\n",
      "Data columns (total 10 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   datetime64[ns] \n",
      " 5   Drop timestamp     2831 non-null   datetime64[ns] \n",
      " 6   Pickup Hour        6745 non-null   int64          \n",
      " 7   Drop Hour          2831 non-null   float64        \n",
      " 8   Journey Time       2831 non-null   timedelta64[ns]\n",
      " 9   Request Date       6745 non-null   object         \n",
      "dtypes: datetime64[ns](2), float64(2), int64(2), object(3), timedelta64[ns](1)\n",
      "memory usage: 527.1+ KB\n"
     ]
    }
   ],
   "source": [
    "uber.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting timeslots for the day and dividing them into 8 parts\n",
    "\n",
    "### Time Distribution\n",
    "- 1 : `Midnight (00:00 - 02:59)`\n",
    "- 2 : `Early Morning (03:00 - 05:59)`\n",
    "- 3 : `Morning (06:00 - 08:59)`\n",
    "- 4 : `Early Afternoon (09:00 - 11:59)`\n",
    "- 5 : `Afternoon (12:00 - 14:59)`\n",
    "- 6 : `Early evening (15:00 - 17:59)`\n",
    "- 7 : `Late evening (18:00 - 20:59)`\n",
    "- 8 : `Night (21:00 - 23:59)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "uber.loc[(uber['Pickup Hour'] >= 0) & (uber['Pickup Hour'] < 3),'Pickup Timeslots'] = 1\n",
    "uber.loc[(uber['Pickup Hour'] >= 3) & (uber['Pickup Hour'] < 6),'Pickup Timeslots'] = 2\n",
    "uber.loc[(uber['Pickup Hour'] >= 6) & (uber['Pickup Hour'] < 9),'Pickup Timeslots'] = 3\n",
    "uber.loc[(uber['Pickup Hour'] >= 9) & (uber['Pickup Hour'] < 12),'Pickup Timeslots'] = 4\n",
    "uber.loc[(uber['Pickup Hour'] >= 12) & (uber['Pickup Hour'] < 15),'Pickup Timeslots'] = 5\n",
    "uber.loc[(uber['Pickup Hour'] >= 15) & (uber['Pickup Hour'] < 18),'Pickup Timeslots'] = 6\n",
    "uber.loc[(uber['Pickup Hour'] >= 18) & (uber['Pickup Hour'] < 21),'Pickup Timeslots'] = 7\n",
    "uber.loc[(uber['Pickup Hour'] >= 21) & (uber['Pickup Hour'] <= 23),'Pickup Timeslots'] = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Pickup Hour</th>\n",
       "      <th>Drop Hour</th>\n",
       "      <th>Journey Time</th>\n",
       "      <th>Request Date</th>\n",
       "      <th>Pickup Timeslots</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>11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>01:09:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>4</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>17</td>\n",
       "      <td>18.0</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>6</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>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>00:41:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>4</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>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>00:55:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>8</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 08:33:00</td>\n",
       "      <td>2016-07-13 09:25:00</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>00:52:00</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6740</th>\n",
       "      <td>6745</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:49:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6741</th>\n",
       "      <td>6752</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:50:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6742</th>\n",
       "      <td>6751</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:52:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6743</th>\n",
       "      <td>6754</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:54:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6744</th>\n",
       "      <td>6753</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:55:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6745 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Request id Pickup point  Driver id             Status  \\\n",
       "0            619      Airport        1.0     Trip Completed   \n",
       "1            867      Airport        1.0     Trip Completed   \n",
       "2           1807         City        1.0     Trip Completed   \n",
       "3           2532      Airport        1.0     Trip Completed   \n",
       "4           3112         City        1.0     Trip Completed   \n",
       "...          ...          ...        ...                ...   \n",
       "6740        6745         City        NaN  No Cars Available   \n",
       "6741        6752      Airport        NaN  No Cars Available   \n",
       "6742        6751         City        NaN  No Cars Available   \n",
       "6743        6754         City        NaN  No Cars Available   \n",
       "6744        6753      Airport        NaN  No Cars Available   \n",
       "\n",
       "       Request timestamp      Drop timestamp  Pickup Hour  Drop Hour  \\\n",
       "0    2016-07-11 11:51:00 2016-07-11 13:00:00           11       13.0   \n",
       "1    2016-07-11 17:57:00 2016-07-11 18:47:00           17       18.0   \n",
       "2    2016-07-12 09:17:00 2016-07-12 09:58:00            9        9.0   \n",
       "3    2016-07-12 21:08:00 2016-07-12 22:03:00           21       22.0   \n",
       "4    2016-07-13 08:33:00 2016-07-13 09:25:00            8        9.0   \n",
       "...                  ...                 ...          ...        ...   \n",
       "6740 2016-07-15 23:49:00                 NaT           23        NaN   \n",
       "6741 2016-07-15 23:50:00                 NaT           23        NaN   \n",
       "6742 2016-07-15 23:52:00                 NaT           23        NaN   \n",
       "6743 2016-07-15 23:54:00                 NaT           23        NaN   \n",
       "6744 2016-07-15 23:55:00                 NaT           23        NaN   \n",
       "\n",
       "     Journey Time Request Date  Pickup Timeslots  \n",
       "0        01:09:00   2016-07-11                 4  \n",
       "1        00:50:00   2016-07-11                 6  \n",
       "2        00:41:00   2016-07-12                 4  \n",
       "3        00:55:00   2016-07-12                 8  \n",
       "4        00:52:00   2016-07-13                 3  \n",
       "...           ...          ...               ...  \n",
       "6740          NaT   2016-07-15                 8  \n",
       "6741          NaT   2016-07-15                 8  \n",
       "6742          NaT   2016-07-15                 8  \n",
       "6743          NaT   2016-07-15                 8  \n",
       "6744          NaT   2016-07-15                 8  \n",
       "\n",
       "[6745 rows x 11 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting the datatype of Pickup Timeslots from object to int\n",
    "uber['Pickup Timeslots'] = uber['Pickup Timeslots'].astype(int)\n",
    "uber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>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>Pickup Hour</th>\n",
       "      <th>Drop Hour</th>\n",
       "      <th>Journey Time</th>\n",
       "      <th>Request Date</th>\n",
       "      <th>Pickup Timeslots</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2831</th>\n",
       "      <td>2905</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-13 06:08:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>4805</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-14 17:07:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2833</th>\n",
       "      <td>5202</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-14 20:51:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2834</th>\n",
       "      <td>5927</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-15 10:12:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2835</th>\n",
       "      <td>2347</td>\n",
       "      <td>Airport</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-12 19:14:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6740</th>\n",
       "      <td>6745</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:49:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6741</th>\n",
       "      <td>6752</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:50:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6742</th>\n",
       "      <td>6751</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:52:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6743</th>\n",
       "      <td>6754</td>\n",
       "      <td>City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:54:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6744</th>\n",
       "      <td>6753</td>\n",
       "      <td>Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Cars Available</td>\n",
       "      <td>2016-07-15 23:55:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3914 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Request id Pickup point  Driver id             Status  \\\n",
       "2831        2905         City        1.0          Cancelled   \n",
       "2832        4805         City        1.0          Cancelled   \n",
       "2833        5202      Airport        1.0          Cancelled   \n",
       "2834        5927         City        1.0          Cancelled   \n",
       "2835        2347      Airport        2.0          Cancelled   \n",
       "...          ...          ...        ...                ...   \n",
       "6740        6745         City        NaN  No Cars Available   \n",
       "6741        6752      Airport        NaN  No Cars Available   \n",
       "6742        6751         City        NaN  No Cars Available   \n",
       "6743        6754         City        NaN  No Cars Available   \n",
       "6744        6753      Airport        NaN  No Cars Available   \n",
       "\n",
       "       Request timestamp Drop timestamp  Pickup Hour  Drop Hour Journey Time  \\\n",
       "2831 2016-07-13 06:08:00            NaT            6        NaN          NaT   \n",
       "2832 2016-07-14 17:07:00            NaT           17        NaN          NaT   \n",
       "2833 2016-07-14 20:51:00            NaT           20        NaN          NaT   \n",
       "2834 2016-07-15 10:12:00            NaT           10        NaN          NaT   \n",
       "2835 2016-07-12 19:14:00            NaT           19        NaN          NaT   \n",
       "...                  ...            ...          ...        ...          ...   \n",
       "6740 2016-07-15 23:49:00            NaT           23        NaN          NaT   \n",
       "6741 2016-07-15 23:50:00            NaT           23        NaN          NaT   \n",
       "6742 2016-07-15 23:52:00            NaT           23        NaN          NaT   \n",
       "6743 2016-07-15 23:54:00            NaT           23        NaN          NaT   \n",
       "6744 2016-07-15 23:55:00            NaT           23        NaN          NaT   \n",
       "\n",
       "     Request Date  Pickup Timeslots  \n",
       "2831   2016-07-13                 3  \n",
       "2832   2016-07-14                 6  \n",
       "2833   2016-07-14                 7  \n",
       "2834   2016-07-15                 4  \n",
       "2835   2016-07-12                 7  \n",
       "...           ...               ...  \n",
       "6740   2016-07-15                 8  \n",
       "6741   2016-07-15                 8  \n",
       "6742   2016-07-15                 8  \n",
       "6743   2016-07-15                 8  \n",
       "6744   2016-07-15                 8  \n",
       "\n",
       "[3914 rows x 11 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copying problematic data to another dataframe\n",
    "problem = uber[uber['Status'] != 'Trip Completed']\n",
    "problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating two datasets for different pickup points\n",
    "uber_air = uber[uber['Pickup point']=='Airport']\n",
    "uber_city = uber[uber['Pickup point']=='City']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## - Analysis of data to identify some trends that are causing problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pickup point\n",
       "Airport    3238\n",
       "City       3507\n",
       "Name: Pickup point, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of people whose pickup point is either airport or city\n",
    "count = uber.groupby('Pickup point')['Pickup point'].count()\n",
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis\n",
    "### This plot shows the frequency of people wanting to start their journey from Airport or City "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "sns.countplot(x='Pickup point',data=uber).set_title('Count of people travelling from Airport and City')\n",
    "plt.savefig('a.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Segmented Univariate Analysis\n",
    "### This plot will show the status of the requests made on both pickup points "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "#Status of Airport and city\n",
    "sns.countplot(x='Status',hue='Pickup point',data=uber)\n",
    "plt.savefig('b.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pickup point  Status           \n",
       "Airport       Cancelled             198\n",
       "              No Cars Available    1713\n",
       "              Trip Completed       1327\n",
       "City          Cancelled            1066\n",
       "              No Cars Available     937\n",
       "              Trip Completed       1504\n",
       "Name: Pickup point, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of rides with their status at both the pickup points\n",
    "c1 = uber.groupby(['Pickup point','Status'])['Pickup point'].count()\n",
    "c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pickup point\n",
       "Airport    1911\n",
       "City       2003\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2 = problem.groupby('Pickup point')['Status'].count()\n",
    "c2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Segmented Univariate Analysis\n",
    "### This plot shows the frequency of the problematic ride status at both the pickup points\n",
    "\n",
    "In this we can see that most of the requests with 'No cars available' status are at the Airport with very less 'Cancelled' requests but at the city, both the problematic requests are approximately equal in numbers.\n",
    "Although, both the Pickup points have high problematic requests, but the city (1911) have a slightly higher than that of Airport (2003). Also cancellation is a bigger problem than the unavailability of cabs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "sns.countplot(x='Pickup point',hue='Status',data=problem)\n",
    "plt.savefig('c.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pickup Timeslots  Status           \n",
       "1                 Cancelled             12\n",
       "                  No Cars Available    169\n",
       "                  Trip Completed       102\n",
       "2                 Cancelled            229\n",
       "                  No Cars Available    214\n",
       "                  Trip Completed       297\n",
       "3                 Cancelled            492\n",
       "                  No Cars Available    239\n",
       "                  Trip Completed       496\n",
       "4                 Cancelled            252\n",
       "                  No Cars Available    189\n",
       "                  Trip Completed       404\n",
       "5                 Cancelled             48\n",
       "                  No Cars Available    134\n",
       "                  Trip Completed       298\n",
       "6                 Cancelled             78\n",
       "                  No Cars Available    326\n",
       "                  Trip Completed       344\n",
       "7                 Cancelled             89\n",
       "                  No Cars Available    895\n",
       "                  Trip Completed       491\n",
       "8                 Cancelled             64\n",
       "                  No Cars Available    484\n",
       "                  Trip Completed       399\n",
       "Name: Pickup Timeslots, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of the status of the requests at different timeslots of the day\n",
    "c3=uber.groupby(['Pickup Timeslots','Status'])['Pickup Timeslots'].count()\n",
    "c3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pickup Timeslots\n",
       "1    181\n",
       "2    443\n",
       "3    731\n",
       "4    441\n",
       "5    182\n",
       "6    404\n",
       "7    984\n",
       "8    548\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of problematic requests at different timeslots\n",
    "c4 = problem.groupby('Pickup Timeslots')['Status'].count()\n",
    "c4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Derived metrices\n",
    "### The plots shows us the frequency at different timeslots of the day\n",
    "\n",
    "- The first piechart shows us the total ampunt of requests coming in the 8 different timeslots of the day. We can see that the slot number 3 and 7 `(i.e. Morning and Late evening)` get the highest number of requests comparatively to the other slots.\n",
    "- Second plot shows the number of requests made in different timeslots od the day with their status. As we can see that the morning slots of 2, 3 and 4 `(from 3:00 to 12:00)` have a higher cancellation rate, but the evening slots of 6, 7 and 8 `(from 15:00 to 00:00)` have a hogher number of unavailable cabs.\n",
    "\n",
    "### Time Distribution\n",
    "- 1 : `Midnight (00:00 - 02:59)`\n",
    "- 2 : `Early Morning (03:00 - 05:59)`\n",
    "- 3 : `Morning (06:00 - 08:59)`\n",
    "- 4 : `Early Afternoon (09:00 - 11:59)`\n",
    "- 5 : `Afternoon (12:00 - 14:59)`\n",
    "- 6 : `Early evening (15:00 - 17:59)`\n",
    "- 7 : `Late evening (18:00 - 20:59)`\n",
    "- 8 : `Night (21:00 - 23:59)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#subplots\n",
    "plt.figure(figsize=(15,5))\n",
    "#subplot 1\n",
    "# plot to show the amount of requests at 8 different timeslots of the day.\n",
    "plt.subplot(1,2,1)\n",
    "uber.groupby('Pickup Timeslots').size().plot(kind='pie',stacked=True,table=True,subplots=True)\n",
    "plt.ylabel(\"\")\n",
    "#subplot 2\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='Pickup Timeslots', hue='Status',data=uber).set_title('Status of requests made in a patrticular time slot')\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('d.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Request Date  Status           \n",
       "2016-07-11    Cancelled            262\n",
       "              No Cars Available    504\n",
       "              Trip Completed       601\n",
       "2016-07-12    Cancelled            240\n",
       "              No Cars Available    505\n",
       "              Trip Completed       562\n",
       "2016-07-13    Cancelled            270\n",
       "              No Cars Available    490\n",
       "              Trip Completed       577\n",
       "2016-07-14    Cancelled            252\n",
       "              No Cars Available    571\n",
       "              Trip Completed       530\n",
       "2016-07-15    Cancelled            240\n",
       "              No Cars Available    580\n",
       "              Trip Completed       561\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_status = uber.groupby(['Request Date','Status'])['Status'].count()\n",
    "date_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This plot shows the count of all the requests with their status\n",
    "\n",
    "The pie chart shows the number of the total requests came on all the 5 consecutive dates of the July month. Every date got approximately same amount of requests `(i.e. nearly 1300)`\n",
    "\n",
    "The second plot shows the data for five consecutive days of July month. We can see that every status for all the five days are in approximately same amount so this analysis doesn't have much significance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#subplots\n",
    "plt.figure(figsize=(15,5))\n",
    "#subplot 1\n",
    "plt.subplot(1,2,1)\n",
    "uber.groupby('Request Date').size().plot(kind='pie',stacked=True,table=True,subplots=True)\n",
    "plt.ylabel(\"\")\n",
    "#subplot 2\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='Request Date',hue='Status',data=uber).set_title('Status of all the requests made on individual dates')\n",
    "plt.show()\n",
    "plt.savefig('e.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This plot shows the count of requests at different pickup points at every hour.\n",
    "\n",
    "This is an important analysis as we can derive that most of the requests come from City in the morning and most of the requests at night come from the Airport location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.countplot(x='Pickup Hour',hue='Pickup point',data=uber).set_title('Count of requests for every hour at different pickup points')\n",
    "plt.savefig('f.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count of requests at different pickup locations for every hour of the day\n",
    "\n",
    "As we know that most of the morning requests come from the `City` location and most of the night requests come from the `Airport` location. So it was clear that the morning problems would appear at the `City` and the night problems would appear at the `Airport` as shown in the below analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1276.12x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.catplot(x='Pickup Hour',hue='Status',col='Pickup point',kind='count',data=uber,aspect=1.6)\n",
    "plt.savefig('j.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# - Supply-Demand Gap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supply Demand Gap in this analysis means the difference between the numbers of requests made by travellers `(i.e. Supply)` and the count of drivers or cars available `(i.e. Demand)` at the pickup point and at a particular time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for the supply demand gap at the Airport\n",
    "\n",
    "This plot shows the difference between the numbers of requests made by travellers and the number of drivers available at the `Airport` on a particular time of the day.\n",
    "\n",
    "We can see that from `17:00 to 22:00`, the Supply-Demand gap is pretty high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Pickup Hour</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Request id</th>\n",
       "      <th>SD Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>53</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>42</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>72</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>89</td>\n",
       "      <td>92</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>89</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>69</td>\n",
       "      <td>73</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>82</td>\n",
       "      <td>89</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>54</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>73</td>\n",
       "      <td>87</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>65</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "      <td>61</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>93</td>\n",
       "      <td>308</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>96</td>\n",
       "      <td>405</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>98</td>\n",
       "      <td>366</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>103</td>\n",
       "      <td>378</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>89</td>\n",
       "      <td>343</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>83</td>\n",
       "      <td>183</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>62</td>\n",
       "      <td>98</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pickup Hour  Driver id  Request id  SD Gap\n",
       "0             0         23          53      30\n",
       "1             1         13          42      29\n",
       "2             2         16          41      25\n",
       "3             3         15          45      30\n",
       "4             4         38          72      34\n",
       "5             5         89          92       3\n",
       "6             6         85          89       4\n",
       "7             7         80          83       3\n",
       "8             8         69          73       4\n",
       "9             9         82          89       7\n",
       "10           10         62          75      13\n",
       "11           11         54          64      10\n",
       "12           12         73          87      14\n",
       "13           13         44          65      21\n",
       "14           14         43          50       7\n",
       "15           15         63          76      13\n",
       "16           16         52          61       9\n",
       "17           17         93         308     215\n",
       "18           18         96         405     309\n",
       "19           19         98         366     268\n",
       "20           20        103         378     275\n",
       "21           21         89         343     254\n",
       "22           22         83         183     100\n",
       "23           23         62          98      36"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_air_1 = uber_air.groupby('Pickup Hour',as_index=False).agg({'Driver id':'count','Request id':'count'})\n",
    "uber_air_1['SD Gap'] = uber_air_1['Request id'] - uber_air_1['Driver id']\n",
    "uber_air_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "# Plotting barplot\n",
    "uber_air_2 = uber_air_1[['Driver id','Request id']]\n",
    "uber_air_2.plot.bar(rot=0)\n",
    "plt.savefig('g.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for the supply demand gap at the City\n",
    "\n",
    "This plot shows the difference between the numbers of requests made by travellers and the number of drivers available at the `City` on a particular time of the day.\n",
    "\n",
    "We can see that from `05:00 to 09:00`, the Supply-Demand gap is pretty high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Pickup Hour</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Request id</th>\n",
       "      <th>SD Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>46</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>43</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>58</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>47</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>131</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>272</td>\n",
       "      <td>353</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>227</td>\n",
       "      <td>309</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>263</td>\n",
       "      <td>323</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>264</td>\n",
       "      <td>350</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>266</td>\n",
       "      <td>342</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>116</td>\n",
       "      <td>168</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>107</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>97</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>63</td>\n",
       "      <td>95</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>56</td>\n",
       "      <td>86</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>60</td>\n",
       "      <td>95</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>61</td>\n",
       "      <td>98</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>93</td>\n",
       "      <td>110</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>92</td>\n",
       "      <td>105</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>92</td>\n",
       "      <td>107</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>99</td>\n",
       "      <td>114</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>95</td>\n",
       "      <td>106</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>83</td>\n",
       "      <td>121</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>51</td>\n",
       "      <td>96</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pickup Hour  Driver id  Request id  SD Gap\n",
       "0             0         20          46      26\n",
       "1             1         16          43      27\n",
       "2             2         26          58      32\n",
       "3             3         21          47      26\n",
       "4             4         91         131      40\n",
       "5             5        272         353      81\n",
       "6             6        227         309      82\n",
       "7             7        263         323      60\n",
       "8             8        264         350      86\n",
       "9             9        266         342      76\n",
       "10           10        116         168      52\n",
       "11           11         76         107      31\n",
       "12           12         67          97      30\n",
       "13           13         63          95      32\n",
       "14           14         56          86      30\n",
       "15           15         60          95      35\n",
       "16           16         61          98      37\n",
       "17           17         93         110      17\n",
       "18           18         92         105      13\n",
       "19           19         92         107      15\n",
       "20           20         99         114      15\n",
       "21           21         95         106      11\n",
       "22           22         83         121      38\n",
       "23           23         51          96      45"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_city_1 = uber_city.groupby('Pickup Hour',as_index=False).agg({'Driver id':'count','Request id':'count'})\n",
    "uber_city_1['SD Gap'] = uber_city_1['Request id'] - uber_city_1['Driver id']\n",
    "uber_city_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "# Plotting barplot\n",
    "uber_city_2 = uber_city_1[['Driver id','Request id']]\n",
    "uber_city_2.plot.bar(rot=0)\n",
    "plt.savefig('h.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This plot shows the demand and supply gap at Airport and City\n",
    "\n",
    "- Here `0` means `Airport` and `1` means `City`.\n",
    "\n",
    "We can see that the demand supply gap at `Airport` is much higher than that at the `City`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "uber_1 = uber.groupby('Pickup point',as_index=False).agg({'Driver id':'count','Request id':'count'})\n",
    "uber_2 = uber_1[['Driver id','Request id']]\n",
    "uber_2.plot.bar(rot=0)\n",
    "plt.savefig('i.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}