Uber Supply Demand Gap Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1569,
   "id": "b8d7908e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# supress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1570,
   "id": "16a6e9ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abb0d0d",
   "metadata": {},
   "source": [
    "### Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1571,
   "id": "92f6ea1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#uber_data = pd.read_csv('Uber Request Data.csv', parse_dates=['Request timestamp', 'Drop timestamp'])\n",
    "uber_data = pd.read_csv('Uber Request Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1572,
   "id": "54a7fd4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Request id</th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>11/7/2016 11:51</td>\n",
       "      <td>11/7/2016 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>867</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>11/7/2016 17:57</td>\n",
       "      <td>11/7/2016 18:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1807</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>12/7/2016 9:17</td>\n",
       "      <td>12/7/2016 9:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2532</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>12/7/2016 21:08</td>\n",
       "      <td>12/7/2016 22:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3112</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>13-07-2016 08:33:16</td>\n",
       "      <td>13-07-2016 09:25:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status    Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed      11/7/2016 11:51   \n",
       "1         867      Airport        1.0  Trip Completed      11/7/2016 17:57   \n",
       "2        1807         City        1.0  Trip Completed       12/7/2016 9:17   \n",
       "3        2532      Airport        1.0  Trip Completed      12/7/2016 21:08   \n",
       "4        3112         City        1.0  Trip Completed  13-07-2016 08:33:16   \n",
       "\n",
       "        Drop timestamp  \n",
       "0      11/7/2016 13:00  \n",
       "1      11/7/2016 18:47  \n",
       "2       12/7/2016 9:58  \n",
       "3      12/7/2016 22:03  \n",
       "4  13-07-2016 09:25:47  "
      ]
     },
     "execution_count": 1572,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c3d6ad",
   "metadata": {},
   "source": [
    "### Inspecting the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1573,
   "id": "f223aba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6745, 6)"
      ]
     },
     "execution_count": 1573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a4cc65",
   "metadata": {},
   "source": [
    "There are 6745 rows and 6 columns in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1574,
   "id": "e85155c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6745 entries, 0 to 6744\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Request id         6745 non-null   int64  \n",
      " 1   Pickup point       6745 non-null   object \n",
      " 2   Driver id          4095 non-null   float64\n",
      " 3   Status             6745 non-null   object \n",
      " 4   Request timestamp  6745 non-null   object \n",
      " 5   Drop timestamp     2831 non-null   object \n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 316.3+ KB\n"
     ]
    }
   ],
   "source": [
    "uber_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1575,
   "id": "2166004f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Request id</th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>11/7/2016 11:51</td>\n",
       "      <td>11/7/2016 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>867</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>11/7/2016 17:57</td>\n",
       "      <td>11/7/2016 18:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1807</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>12/7/2016 9:17</td>\n",
       "      <td>12/7/2016 9:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2532</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>12/7/2016 21:08</td>\n",
       "      <td>12/7/2016 22:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3112</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>13-07-2016 08:33:16</td>\n",
       "      <td>13-07-2016 09:25:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status    Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed      11/7/2016 11:51   \n",
       "1         867      Airport        1.0  Trip Completed      11/7/2016 17:57   \n",
       "2        1807         City        1.0  Trip Completed       12/7/2016 9:17   \n",
       "3        2532      Airport        1.0  Trip Completed      12/7/2016 21:08   \n",
       "4        3112         City        1.0  Trip Completed  13-07-2016 08:33:16   \n",
       "\n",
       "        Drop timestamp  \n",
       "0      11/7/2016 13:00  \n",
       "1      11/7/2016 18:47  \n",
       "2       12/7/2016 9:58  \n",
       "3      12/7/2016 22:03  \n",
       "4  13-07-2016 09:25:47  "
      ]
     },
     "execution_count": 1575,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1576,
   "id": "4618f29c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Two date formats are found in the date columns\n",
    "# Handling the two date formats by parsing it seperately \n",
    "uber_data['Request timestamp1'] = pd.to_datetime(uber_data['Request timestamp'], format='%d/%m/%Y %H:%M', errors='coerce')\n",
    "mask = uber_data['Request timestamp1'].isnull()\n",
    "uber_data.loc[mask, 'Request timestamp1'] = pd.to_datetime(uber_data[mask]['Request timestamp'], format='%d-%m-%Y %H:%M:%S',\n",
    "                                             errors='coerce')\n",
    "\n",
    "uber_data['Drop timestamp1'] = pd.to_datetime(uber_data['Drop timestamp'], format='%d/%m/%Y %H:%M', errors='coerce')\n",
    "mask = uber_data['Drop timestamp1'].isnull()\n",
    "uber_data.loc[mask, 'Drop timestamp1'] = pd.to_datetime(uber_data[mask]['Drop timestamp'], format='%d-%m-%Y %H:%M:%S',\n",
    "                                             errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1577,
   "id": "44c90b45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Request id</th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "      <th>Request timestamp1</th>\n",
       "      <th>Drop timestamp1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>11/7/2016 11:51</td>\n",
       "      <td>11/7/2016 13:00</td>\n",
       "      <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>11/7/2016 17:57</td>\n",
       "      <td>11/7/2016 18:47</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>12/7/2016 9:17</td>\n",
       "      <td>12/7/2016 9:58</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>12/7/2016 21:08</td>\n",
       "      <td>12/7/2016 22:03</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>13-07-2016 08:33:16</td>\n",
       "      <td>13-07-2016 09:25:47</td>\n",
       "      <td>2016-07-13 08:33:16</td>\n",
       "      <td>2016-07-13 09:25:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3879</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>13-07-2016 21:57:28</td>\n",
       "      <td>13-07-2016 22:28:59</td>\n",
       "      <td>2016-07-13 21:57:28</td>\n",
       "      <td>2016-07-13 22:28:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4270</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>14-07-2016 06:15:32</td>\n",
       "      <td>14-07-2016 07:13:15</td>\n",
       "      <td>2016-07-14 06:15:32</td>\n",
       "      <td>2016-07-14 07:13:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5510</td>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>15-07-2016 05:11:52</td>\n",
       "      <td>15-07-2016 06:07:52</td>\n",
       "      <td>2016-07-15 05:11:52</td>\n",
       "      <td>2016-07-15 06:07:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6248</td>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>15-07-2016 17:57:27</td>\n",
       "      <td>15-07-2016 18:50:51</td>\n",
       "      <td>2016-07-15 17:57:27</td>\n",
       "      <td>2016-07-15 18:50:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>267</td>\n",
       "      <td>City</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>11/7/2016 6:46</td>\n",
       "      <td>11/7/2016 7:25</td>\n",
       "      <td>2016-07-11 06:46:00</td>\n",
       "      <td>2016-07-11 07:25:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status    Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed      11/7/2016 11:51   \n",
       "1         867      Airport        1.0  Trip Completed      11/7/2016 17:57   \n",
       "2        1807         City        1.0  Trip Completed       12/7/2016 9:17   \n",
       "3        2532      Airport        1.0  Trip Completed      12/7/2016 21:08   \n",
       "4        3112         City        1.0  Trip Completed  13-07-2016 08:33:16   \n",
       "5        3879      Airport        1.0  Trip Completed  13-07-2016 21:57:28   \n",
       "6        4270      Airport        1.0  Trip Completed  14-07-2016 06:15:32   \n",
       "7        5510      Airport        1.0  Trip Completed  15-07-2016 05:11:52   \n",
       "8        6248         City        1.0  Trip Completed  15-07-2016 17:57:27   \n",
       "9         267         City        2.0  Trip Completed       11/7/2016 6:46   \n",
       "\n",
       "        Drop timestamp  Request timestamp1     Drop timestamp1  \n",
       "0      11/7/2016 13:00 2016-07-11 11:51:00 2016-07-11 13:00:00  \n",
       "1      11/7/2016 18:47 2016-07-11 17:57:00 2016-07-11 18:47:00  \n",
       "2       12/7/2016 9:58 2016-07-12 09:17:00 2016-07-12 09:58:00  \n",
       "3      12/7/2016 22:03 2016-07-12 21:08:00 2016-07-12 22:03:00  \n",
       "4  13-07-2016 09:25:47 2016-07-13 08:33:16 2016-07-13 09:25:47  \n",
       "5  13-07-2016 22:28:59 2016-07-13 21:57:28 2016-07-13 22:28:59  \n",
       "6  14-07-2016 07:13:15 2016-07-14 06:15:32 2016-07-14 07:13:15  \n",
       "7  15-07-2016 06:07:52 2016-07-15 05:11:52 2016-07-15 06:07:52  \n",
       "8  15-07-2016 18:50:51 2016-07-15 17:57:27 2016-07-15 18:50:51  \n",
       "9       11/7/2016 7:25 2016-07-11 06:46:00 2016-07-11 07:25:00  "
      ]
     },
     "execution_count": 1577,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1578,
   "id": "2c2f1f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting unnecessary columns \n",
    "\n",
    "uber_data= uber_data.drop(['Request timestamp', 'Drop timestamp'], axis =1)\n",
    "\n",
    "uber_data = uber_data.rename(columns = {'Request timestamp1' : 'Request timestamp'})\n",
    "uber_data = uber_data.rename(columns = {'Drop timestamp1' : 'Drop timestamp'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1579,
   "id": "17ee4937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-11 19:02:00    0.088955\n",
       "2016-07-11 17:57:00    0.088955\n",
       "2016-07-11 08:37:00    0.088955\n",
       "2016-07-11 09:40:00    0.088955\n",
       "2016-07-12 21:42:00    0.074129\n",
       "                         ...   \n",
       "2016-07-15 18:08:33    0.014826\n",
       "2016-07-15 04:50:54    0.014826\n",
       "2016-07-14 18:42:23    0.014826\n",
       "2016-07-13 19:26:50    0.014826\n",
       "2016-07-15 23:55:03    0.014826\n",
       "Name: Request timestamp, Length: 5618, dtype: float64"
      ]
     },
     "execution_count": 1579,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data['Request timestamp'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1580,
   "id": "c6112669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-11 13:00:00    0.141293\n",
       "2016-07-12 21:53:00    0.141293\n",
       "2016-07-12 21:04:00    0.141293\n",
       "2016-07-12 06:22:00    0.141293\n",
       "2016-07-11 18:39:00    0.105970\n",
       "                         ...   \n",
       "2016-07-12 05:40:00    0.035323\n",
       "2016-07-12 22:42:00    0.035323\n",
       "2016-07-13 13:09:40    0.035323\n",
       "2016-07-13 23:22:35    0.035323\n",
       "2016-07-15 09:56:07    0.035323\n",
       "Name: Drop timestamp, Length: 2598, dtype: float64"
      ]
     },
     "execution_count": 1580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data['Drop timestamp'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1581,
   "id": "3b8cc8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correcting data types\n",
    "\n",
    "uber_data['Request timestamp']=pd.to_datetime(uber_data['Request timestamp'])\n",
    "uber_data['Drop timestamp']=pd.to_datetime(uber_data['Drop timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1582,
   "id": "c2f03dac",
   "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_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1583,
   "id": "dc0b389b",
   "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 08:33:16</td>\n",
       "      <td>2016-07-13 09:25:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Request id Pickup point  Driver id          Status   Request timestamp  \\\n",
       "0         619      Airport        1.0  Trip Completed 2016-07-11 11:51:00   \n",
       "1         867      Airport        1.0  Trip Completed 2016-07-11 17:57:00   \n",
       "2        1807         City        1.0  Trip Completed 2016-07-12 09:17:00   \n",
       "3        2532      Airport        1.0  Trip Completed 2016-07-12 21:08:00   \n",
       "4        3112         City        1.0  Trip Completed 2016-07-13 08:33:16   \n",
       "\n",
       "       Drop timestamp  \n",
       "0 2016-07-11 13:00:00  \n",
       "1 2016-07-11 18:47:00  \n",
       "2 2016-07-12 09:58:00  \n",
       "3 2016-07-12 22:03:00  \n",
       "4 2016-07-13 09:25:47  "
      ]
     },
     "execution_count": 1583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1584,
   "id": "a6bc9c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting unnecessary column - driver id\n",
    "\n",
    "#uber_data= uber_data.drop(['Driver id'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1585,
   "id": "8819623a",
   "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>Driver id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6745.000000</td>\n",
       "      <td>4095.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3384.644922</td>\n",
       "      <td>149.501343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1955.099667</td>\n",
       "      <td>86.051994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1691.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3387.000000</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5080.000000</td>\n",
       "      <td>224.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6766.000000</td>\n",
       "      <td>300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Request id    Driver id\n",
       "count  6745.000000  4095.000000\n",
       "mean   3384.644922   149.501343\n",
       "std    1955.099667    86.051994\n",
       "min       1.000000     1.000000\n",
       "25%    1691.000000    75.000000\n",
       "50%    3387.000000   149.000000\n",
       "75%    5080.000000   224.000000\n",
       "max    6766.000000   300.000000"
      ]
     },
     "execution_count": 1585,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1586,
   "id": "168f788d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6745, 6)"
      ]
     },
     "execution_count": 1586,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1587,
   "id": "96e2447f",
   "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": 1587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing number of null values in each column\n",
    "\n",
    "uber_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e218755",
   "metadata": {},
   "source": [
    "Drop timestamp and Driver id columns has null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1588,
   "id": "0c7a839c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City       3507\n",
      "Airport    3238\n",
      "Name: Pickup point, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# unique values in dataframe for each column\n",
    "\n",
    "print(uber_data['Pickup point'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1589,
   "id": "ad616e41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "619     1\n",
      "1132    1\n",
      "1161    1\n",
      "1157    1\n",
      "1160    1\n",
      "Name: Request id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(uber_data['Request id'].value_counts().head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1590,
   "id": "ddb36a9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trip Completed       2831\n",
      "No Cars Available    2650\n",
      "Cancelled            1264\n",
      "Name: Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(uber_data['Status'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1591,
   "id": "6a4fc80f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-07-11 19:02:00    6\n",
      "2016-07-11 17:57:00    6\n",
      "2016-07-11 08:37:00    6\n",
      "2016-07-11 09:40:00    6\n",
      "2016-07-12 21:42:00    5\n",
      "Name: Request timestamp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(uber_data['Request timestamp'].value_counts().head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1592,
   "id": "62c9b334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-07-11 13:00:00    4\n",
      "2016-07-12 21:53:00    4\n",
      "2016-07-12 21:04:00    4\n",
      "2016-07-12 06:22:00    4\n",
      "2016-07-11 18:39:00    3\n",
      "Name: Drop timestamp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(uber_data['Drop timestamp'].value_counts().head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54705e66",
   "metadata": {},
   "source": [
    "### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1593,
   "id": "b75a3150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Status\n",
       "Cancelled            1264\n",
       "No Cars Available    2650\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1593,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspecting null values in drop  time column\n",
    "# 3914 null values in the column\n",
    "uber_data[uber_data['Drop timestamp'].isnull()].groupby('Status').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5f6f72",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "'Drop timestamp' is empty when the status of the trip is either cancelled or no cars are available.\n",
    "This is a valid reason for null values as the trips did not occur\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1594,
   "id": "4b0d094a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Status\n",
       "No Cars Available    2650\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1594,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data[uber_data['Driver id'].isnull()].groupby('Status').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f1f22e",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "The driver id is null as there is no car available. Hence, there are no missing values in the data. \n",
    "The null values are valid\n",
    "    </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1595,
   "id": "2d49e425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6745\n",
      "6745\n"
     ]
    }
   ],
   "source": [
    "# Checking if the request ID's are unique\n",
    "\n",
    "print(len(uber_data['Request id'].unique()))\n",
    "print(uber_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1596,
   "id": "94d5920d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1596,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking existence of duplicate rows\n",
    "# If sum = 0, there are no duplicate rows\n",
    "\n",
    "sum(uber_data.duplicated(subset=\"Request id\"))== 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c26a75a",
   "metadata": {},
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1597,
   "id": "62dd9957",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorizing the drop hour and request hour columns\n",
    "\n",
    "# Late Night: 12 AM to 4 AM\n",
    "# Early Morning: 4 AM to 8 AM\n",
    "# Late Morning: 8 AM to 12 PM\n",
    "# Afternoon: 12 PM to 4 PM\n",
    "# Evening: 4 PM to 8 PM\n",
    "# Night: 8 PM to 12 AM\n",
    "\n",
    "trip_time_label = [\"Late night\", \"Early morning\",\"Late morning\", \"Afternoon\",\"Evening\", \"Night\"]\n",
    "bins = [1,4,8,12,16,20,24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1598,
   "id": "51c8f435",
   "metadata": {},
   "outputs": [],
   "source": [
    "uber_data[\"Request date\"] = uber_data[\"Request timestamp\"].dt.date\n",
    "uber_data[\"Request hour\"] = uber_data[\"Request timestamp\"].dt.hour\n",
    "uber_data[\"Request day\"] = uber_data[\"Request timestamp\"].dt.day_name()\n",
    "uber_data['Request hour timeslot'] = pd.cut(uber_data['Request hour'], bins, labels =trip_time_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1599,
   "id": "49cd94a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "uber_data[\"Drop date\"] = uber_data[\"Drop timestamp\"].dt.date\n",
    "uber_data[\"Drop hour\"] = uber_data[\"Drop timestamp\"].dt.hour\n",
    "uber_data[\"Drop day\"] = uber_data[\"Drop timestamp\"].dt.day_name()\n",
    "uber_data['Drop hour timeslot'] = pd.cut(uber_data['Drop hour'], bins, labels =trip_time_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1600,
   "id": "dfed6e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting Request id as index of the dataframe\n",
    "uber_data = uber_data.set_index(['Request id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1601,
   "id": "21b5a8a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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 point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "      <th>Request date</th>\n",
       "      <th>Request hour</th>\n",
       "      <th>Request day</th>\n",
       "      <th>Request hour timeslot</th>\n",
       "      <th>Drop date</th>\n",
       "      <th>Drop hour</th>\n",
       "      <th>Drop day</th>\n",
       "      <th>Drop hour timeslot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Request id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 11:51:00</td>\n",
       "      <td>2016-07-11 13:00:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>11</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 17:57:00</td>\n",
       "      <td>2016-07-11 18:47:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>17</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1807</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 09:17:00</td>\n",
       "      <td>2016-07-12 09:58:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>9</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Late morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 21:08:00</td>\n",
       "      <td>2016-07-12 22:03:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>21</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Night</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3112</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13 08:33:16</td>\n",
       "      <td>2016-07-13 09:25:47</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>8</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Late morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pickup point  Driver id          Status   Request timestamp  \\\n",
       "Request id                                                               \n",
       "619             Airport        1.0  Trip Completed 2016-07-11 11:51:00   \n",
       "867             Airport        1.0  Trip Completed 2016-07-11 17:57:00   \n",
       "1807               City        1.0  Trip Completed 2016-07-12 09:17:00   \n",
       "2532            Airport        1.0  Trip Completed 2016-07-12 21:08:00   \n",
       "3112               City        1.0  Trip Completed 2016-07-13 08:33:16   \n",
       "\n",
       "                Drop timestamp Request date  Request hour Request day  \\\n",
       "Request id                                                              \n",
       "619        2016-07-11 13:00:00   2016-07-11            11      Monday   \n",
       "867        2016-07-11 18:47:00   2016-07-11            17      Monday   \n",
       "1807       2016-07-12 09:58:00   2016-07-12             9     Tuesday   \n",
       "2532       2016-07-12 22:03:00   2016-07-12            21     Tuesday   \n",
       "3112       2016-07-13 09:25:47   2016-07-13             8   Wednesday   \n",
       "\n",
       "           Request hour timeslot   Drop date  Drop hour   Drop day  \\\n",
       "Request id                                                           \n",
       "619                 Late morning  2016-07-11       13.0     Monday   \n",
       "867                      Evening  2016-07-11       18.0     Monday   \n",
       "1807                Late morning  2016-07-12        9.0    Tuesday   \n",
       "2532                       Night  2016-07-12       22.0    Tuesday   \n",
       "3112               Early morning  2016-07-13        9.0  Wednesday   \n",
       "\n",
       "           Drop hour timeslot  \n",
       "Request id                     \n",
       "619                 Afternoon  \n",
       "867                   Evening  \n",
       "1807             Late morning  \n",
       "2532                    Night  \n",
       "3112             Late morning  "
      ]
     },
     "execution_count": 1601,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1602,
   "id": "b6b9e952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-15    20.474426\n",
       "2016-07-11    20.266864\n",
       "2016-07-14    20.059303\n",
       "2016-07-13    19.822090\n",
       "2016-07-12    19.377317\n",
       "Name: Request date, dtype: float64"
      ]
     },
     "execution_count": 1602,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data['Request date'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1603,
   "id": "7b1ab2ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-11    20.558107\n",
       "2016-07-13    20.098905\n",
       "2016-07-12    20.063582\n",
       "2016-07-15    19.286471\n",
       "2016-07-14    18.933239\n",
       "2016-07-16     1.059696\n",
       "Name: Drop date, dtype: float64"
      ]
     },
     "execution_count": 1603,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data['Drop date'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1604,
   "id": "fb33ad25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-11    20.558107\n",
       "2016-07-13    20.098905\n",
       "2016-07-12    20.063582\n",
       "2016-07-15    19.286471\n",
       "2016-07-14    18.933239\n",
       "2016-07-16     1.059696\n",
       "Name: Drop date, dtype: float64"
      ]
     },
     "execution_count": 1604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data['Drop date'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1605,
   "id": "a1a85622",
   "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>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "      <th>Request date</th>\n",
       "      <th>Request hour</th>\n",
       "      <th>Request day</th>\n",
       "      <th>Request hour timeslot</th>\n",
       "      <th>Drop date</th>\n",
       "      <th>Drop hour</th>\n",
       "      <th>Drop day</th>\n",
       "      <th>Drop hour timeslot</th>\n",
       "      <th>Trip duration_min</th>\n",
       "      <th>Trip duration_hr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Request id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 11:51:00</td>\n",
       "      <td>2016-07-11 13:00:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>11</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11 17:57:00</td>\n",
       "      <td>2016-07-11 18:47:00</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>17</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1807</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 09:17:00</td>\n",
       "      <td>2016-07-12 09:58:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>9</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12 21:08:00</td>\n",
       "      <td>2016-07-12 22:03:00</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>21</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Night</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Night</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3112</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13 08:33:16</td>\n",
       "      <td>2016-07-13 09:25:47</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>8</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pickup point  Driver id          Status   Request timestamp  \\\n",
       "Request id                                                               \n",
       "619             Airport        1.0  Trip Completed 2016-07-11 11:51:00   \n",
       "867             Airport        1.0  Trip Completed 2016-07-11 17:57:00   \n",
       "1807               City        1.0  Trip Completed 2016-07-12 09:17:00   \n",
       "2532            Airport        1.0  Trip Completed 2016-07-12 21:08:00   \n",
       "3112               City        1.0  Trip Completed 2016-07-13 08:33:16   \n",
       "\n",
       "                Drop timestamp Request date  Request hour Request day  \\\n",
       "Request id                                                              \n",
       "619        2016-07-11 13:00:00   2016-07-11            11      Monday   \n",
       "867        2016-07-11 18:47:00   2016-07-11            17      Monday   \n",
       "1807       2016-07-12 09:58:00   2016-07-12             9     Tuesday   \n",
       "2532       2016-07-12 22:03:00   2016-07-12            21     Tuesday   \n",
       "3112       2016-07-13 09:25:47   2016-07-13             8   Wednesday   \n",
       "\n",
       "           Request hour timeslot   Drop date  Drop hour   Drop day  \\\n",
       "Request id                                                           \n",
       "619                 Late morning  2016-07-11       13.0     Monday   \n",
       "867                      Evening  2016-07-11       18.0     Monday   \n",
       "1807                Late morning  2016-07-12        9.0    Tuesday   \n",
       "2532                       Night  2016-07-12       22.0    Tuesday   \n",
       "3112               Early morning  2016-07-13        9.0  Wednesday   \n",
       "\n",
       "           Drop hour timeslot  Trip duration_min  Trip duration_hr  \n",
       "Request id                                                          \n",
       "619                 Afternoon               69.0               1.0  \n",
       "867                   Evening               50.0               0.0  \n",
       "1807             Late morning               41.0               0.0  \n",
       "2532                    Night               55.0               0.0  \n",
       "3112             Late morning               52.0               0.0  "
      ]
     },
     "execution_count": 1605,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing trip duration for completed trips\n",
    "\n",
    "uber_data['Trip duration_min'] = abs(uber_data['Drop timestamp'] - uber_data['Request timestamp']).astype('timedelta64[m]')\n",
    "uber_data['Trip duration_hr'] = abs(uber_data['Drop timestamp'] - uber_data['Request timestamp']).astype('timedelta64[h]')\n",
    "uber_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1606,
   "id": "6df8b548",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "      <th>Request date</th>\n",
       "      <th>Request hour</th>\n",
       "      <th>Request day</th>\n",
       "      <th>Request hour timeslot</th>\n",
       "      <th>Drop date</th>\n",
       "      <th>Drop hour</th>\n",
       "      <th>Drop day</th>\n",
       "      <th>Drop hour timeslot</th>\n",
       "      <th>Trip duration_min</th>\n",
       "      <th>Trip duration_hr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Request id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2905</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-13 06:08:41</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>6</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-14 17:07:58</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>17</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5202</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-14 20:51:37</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>20</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5927</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-15 10:12:40</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>10</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\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>2016-07-12</td>\n",
       "      <td>19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pickup point  Driver id     Status   Request timestamp  \\\n",
       "Request id                                                          \n",
       "2905               City        1.0  Cancelled 2016-07-13 06:08:41   \n",
       "4805               City        1.0  Cancelled 2016-07-14 17:07:58   \n",
       "5202            Airport        1.0  Cancelled 2016-07-14 20:51:37   \n",
       "5927               City        1.0  Cancelled 2016-07-15 10:12:40   \n",
       "2347            Airport        2.0  Cancelled 2016-07-12 19:14:00   \n",
       "\n",
       "           Drop timestamp Request date  Request hour Request day  \\\n",
       "Request id                                                         \n",
       "2905                  NaT   2016-07-13             6   Wednesday   \n",
       "4805                  NaT   2016-07-14            17    Thursday   \n",
       "5202                  NaT   2016-07-14            20    Thursday   \n",
       "5927                  NaT   2016-07-15            10      Friday   \n",
       "2347                  NaT   2016-07-12            19     Tuesday   \n",
       "\n",
       "           Request hour timeslot Drop date  Drop hour Drop day  \\\n",
       "Request id                                                       \n",
       "2905               Early morning       NaT        NaN      NaN   \n",
       "4805                     Evening       NaT        NaN      NaN   \n",
       "5202                     Evening       NaT        NaN      NaN   \n",
       "5927                Late morning       NaT        NaN      NaN   \n",
       "2347                     Evening       NaT        NaN      NaN   \n",
       "\n",
       "           Drop hour timeslot  Trip duration_min  Trip duration_hr  \n",
       "Request id                                                          \n",
       "2905                      NaN                NaN               NaN  \n",
       "4805                      NaN                NaN               NaN  \n",
       "5202                      NaN                NaN               NaN  \n",
       "5927                      NaN                NaN               NaN  \n",
       "2347                      NaN                NaN               NaN  "
      ]
     },
     "execution_count": 1606,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data[uber_data['Status'] == \"Cancelled\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1607,
   "id": "d54ab27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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 point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request timestamp</th>\n",
       "      <th>Drop timestamp</th>\n",
       "      <th>Request date</th>\n",
       "      <th>Request hour</th>\n",
       "      <th>Request day</th>\n",
       "      <th>Request hour timeslot</th>\n",
       "      <th>Drop date</th>\n",
       "      <th>Drop hour</th>\n",
       "      <th>Drop day</th>\n",
       "      <th>Drop hour timeslot</th>\n",
       "      <th>Trip duration_min</th>\n",
       "      <th>Trip duration_hr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Request id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2905</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-13 06:08:41</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>6</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-14 17:07:58</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>17</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5202</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-14 20:51:37</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>20</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5927</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2016-07-15 10:12:40</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>10</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\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>2016-07-12</td>\n",
       "      <td>19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pickup point  Driver id     Status   Request timestamp  \\\n",
       "Request id                                                          \n",
       "2905               City        1.0  Cancelled 2016-07-13 06:08:41   \n",
       "4805               City        1.0  Cancelled 2016-07-14 17:07:58   \n",
       "5202            Airport        1.0  Cancelled 2016-07-14 20:51:37   \n",
       "5927               City        1.0  Cancelled 2016-07-15 10:12:40   \n",
       "2347            Airport        2.0  Cancelled 2016-07-12 19:14:00   \n",
       "\n",
       "           Drop timestamp Request date  Request hour Request day  \\\n",
       "Request id                                                         \n",
       "2905                  NaT   2016-07-13             6   Wednesday   \n",
       "4805                  NaT   2016-07-14            17    Thursday   \n",
       "5202                  NaT   2016-07-14            20    Thursday   \n",
       "5927                  NaT   2016-07-15            10      Friday   \n",
       "2347                  NaT   2016-07-12            19     Tuesday   \n",
       "\n",
       "           Request hour timeslot Drop date  Drop hour Drop day  \\\n",
       "Request id                                                       \n",
       "2905               Early morning       NaN        NaN      NaN   \n",
       "4805                     Evening       NaN        NaN      NaN   \n",
       "5202                     Evening       NaN        NaN      NaN   \n",
       "5927                Late morning       NaN        NaN      NaN   \n",
       "2347                     Evening       NaN        NaN      NaN   \n",
       "\n",
       "           Drop hour timeslot  Trip duration_min  Trip duration_hr  \n",
       "Request id                                                          \n",
       "2905                      NaN                NaN               NaN  \n",
       "4805                      NaN                NaN               NaN  \n",
       "5202                      NaN                NaN               NaN  \n",
       "5927                      NaN                NaN               NaN  \n",
       "2347                      NaN                NaN               NaN  "
      ]
     },
     "execution_count": 1607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the drop columns and trip duration to NaN when status is cancelled and no cars available \n",
    "\n",
    "uber_data.loc[uber_data['Status'] == \"No Cars Available\",['Drop date','Drop hour', 'Drop day', 'Drop hour timeslot', 'Trip duration_min', 'Trip duration_hr']] = np.nan\n",
    "uber_data.loc[uber_data['Status'] == \"Cancelled\",['Drop date','Drop hour','Drop day', 'Drop hour timeslot', 'Trip duration_min', 'Trip duration_hr']] = np.nan\n",
    "\n",
    "uber_data[uber_data['Status'] == \"Cancelled\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1608,
   "id": "ff2be01e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max:  83.0\n",
      "Min:  20.0\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values of trip duration in minutes to bin the values\n",
    "\n",
    "print(\"Max: \" ,uber_data['Trip duration_min'].max())\n",
    "print(\"Min: \" ,uber_data['Trip duration_min'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1609,
   "id": "526e401c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binning trip duration in minutes, bin for every 15 mins \n",
    "\n",
    "uber_data['trip duration slot'] = pd.cut(x=uber_data['Trip duration_min'], bins=[1, 15, 30, 45, 60, 75, 90] , labels=['1 to 15', '16 to 30', '31 to 45', '46 to 60', '61 to 75', '75-90'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1610,
   "id": "63214721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46 to 60    940\n",
       "31 to 45    859\n",
       "61 to 75    797\n",
       "16 to 30    147\n",
       "75-90        88\n",
       "1 to 15       0\n",
       "Name: trip duration slot, dtype: int64"
      ]
     },
     "execution_count": 1610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data['trip duration slot'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1611,
   "id": "3dc52960",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2831\n",
      "Trip Completed       2831\n",
      "No Cars Available    2650\n",
      "Cancelled            1264\n",
      "Name: Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking if the column is binned by comparing the counts\n",
    "\n",
    "print(uber_data['trip duration slot'].value_counts().sum())\n",
    "print(uber_data.Status.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1612,
   "id": "bade895b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting timestamp columns\n",
    "uber_data.drop(['Request timestamp', 'Drop timestamp'], axis =1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1613,
   "id": "f7cc56f5",
   "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>Pickup point</th>\n",
       "      <th>Driver id</th>\n",
       "      <th>Status</th>\n",
       "      <th>Request date</th>\n",
       "      <th>Request hour</th>\n",
       "      <th>Request day</th>\n",
       "      <th>Request hour timeslot</th>\n",
       "      <th>Drop date</th>\n",
       "      <th>Drop hour</th>\n",
       "      <th>Drop day</th>\n",
       "      <th>Drop hour timeslot</th>\n",
       "      <th>Trip duration_min</th>\n",
       "      <th>Trip duration_hr</th>\n",
       "      <th>trip duration slot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Request id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>11</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61 to 75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>17</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46 to 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1807</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>9</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31 to 45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>21</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Night</td>\n",
       "      <td>2016-07-12</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Night</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46 to 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3112</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>8</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Late morning</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46 to 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>21</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Night</td>\n",
       "      <td>2016-07-13</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Night</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31 to 45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4270</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>6</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>2016-07-14</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46 to 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5510</th>\n",
       "      <td>Airport</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>5</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46 to 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6248</th>\n",
       "      <td>City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>17</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Evening</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46 to 60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>City</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Trip Completed</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>6</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Early morning</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31 to 45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pickup point  Driver id          Status Request date  Request hour  \\\n",
       "Request id                                                                      \n",
       "619             Airport        1.0  Trip Completed   2016-07-11            11   \n",
       "867             Airport        1.0  Trip Completed   2016-07-11            17   \n",
       "1807               City        1.0  Trip Completed   2016-07-12             9   \n",
       "2532            Airport        1.0  Trip Completed   2016-07-12            21   \n",
       "3112               City        1.0  Trip Completed   2016-07-13             8   \n",
       "3879            Airport        1.0  Trip Completed   2016-07-13            21   \n",
       "4270            Airport        1.0  Trip Completed   2016-07-14             6   \n",
       "5510            Airport        1.0  Trip Completed   2016-07-15             5   \n",
       "6248               City        1.0  Trip Completed   2016-07-15            17   \n",
       "267                City        2.0  Trip Completed   2016-07-11             6   \n",
       "\n",
       "           Request day Request hour timeslot   Drop date  Drop hour  \\\n",
       "Request id                                                            \n",
       "619             Monday          Late morning  2016-07-11       13.0   \n",
       "867             Monday               Evening  2016-07-11       18.0   \n",
       "1807           Tuesday          Late morning  2016-07-12        9.0   \n",
       "2532           Tuesday                 Night  2016-07-12       22.0   \n",
       "3112         Wednesday         Early morning  2016-07-13        9.0   \n",
       "3879         Wednesday                 Night  2016-07-13       22.0   \n",
       "4270          Thursday         Early morning  2016-07-14        7.0   \n",
       "5510            Friday         Early morning  2016-07-15        6.0   \n",
       "6248            Friday               Evening  2016-07-15       18.0   \n",
       "267             Monday         Early morning  2016-07-11        7.0   \n",
       "\n",
       "             Drop day Drop hour timeslot  Trip duration_min  Trip duration_hr  \\\n",
       "Request id                                                                      \n",
       "619            Monday          Afternoon               69.0               1.0   \n",
       "867            Monday            Evening               50.0               0.0   \n",
       "1807          Tuesday       Late morning               41.0               0.0   \n",
       "2532          Tuesday              Night               55.0               0.0   \n",
       "3112        Wednesday       Late morning               52.0               0.0   \n",
       "3879        Wednesday              Night               31.0               0.0   \n",
       "4270         Thursday      Early morning               57.0               0.0   \n",
       "5510           Friday      Early morning               56.0               0.0   \n",
       "6248           Friday            Evening               53.0               0.0   \n",
       "267            Monday      Early morning               39.0               0.0   \n",
       "\n",
       "           trip duration slot  \n",
       "Request id                     \n",
       "619                  61 to 75  \n",
       "867                  46 to 60  \n",
       "1807                 31 to 45  \n",
       "2532                 46 to 60  \n",
       "3112                 46 to 60  \n",
       "3879                 31 to 45  \n",
       "4270                 46 to 60  \n",
       "5510                 46 to 60  \n",
       "6248                 46 to 60  \n",
       "267                  31 to 45  "
      ]
     },
     "execution_count": 1613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db64202",
   "metadata": {},
   "source": [
    "### Data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e772a07",
   "metadata": {},
   "source": [
    "<span style=\"color:red\">Univariate analysis</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1614,
   "id": "13bb76b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to plot countplots for univariate analysis\n",
    "\n",
    "def univariate_analysis(col):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    ax = sns.countplot(data=uber_data,x=col)\n",
    "\n",
    "    plt.title(\"Summary of trips, countplot of {}\".format(col))\n",
    "    plt.xlabel(\"{} of trip\".format(col))\n",
    "    plt.ylabel(\"Number of trips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1615,
   "id": "1744a1fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "univariate_analysis(\"Status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1616,
   "id": "86054ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trip Completed       41.971831\n",
       "No Cars Available    39.288362\n",
       "Cancelled            18.739807\n",
       "Name: Status, dtype: float64"
      ]
     },
     "execution_count": 1616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data.Status.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8126557",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "    \n",
    "1. No cars available status is more than cancelled status. \n",
    "2. Trip completed ~ 42%. 58% of the trip requests are not being met. \n",
    "\n",
    "Hence, there is a huge supply deficit in Uber\n",
    "    \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1617,
   "id": "8c170fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 1617,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_data['Driver id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aedef2e8",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "Only 300 cabs/drivers are available to service all the trip requests \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1618,
   "id": "c1b6902a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "univariate_analysis(\"Pickup point\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5485c68",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "City is more preferred than the airports as the passenger pickup point\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1619,
   "id": "bb742506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "univariate_analysis(\"Request day\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1620,
   "id": "251540e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "univariate_analysis(\"Drop day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01880f50",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "Passenger requests for an Uber are the highest on Wednesdays.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1621,
   "id": "270f5d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "univariate_analysis(\"Request hour timeslot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1622,
   "id": "7f3a55d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "univariate_analysis(\"Drop hour timeslot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a38bb0",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "Ubers are requested the highest during evenings and early mornings as is evident from the request and drop hour timeslot distribution\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1623,
   "id": "b330996e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "univariate_analysis(\"Trip duration_hr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1624,
   "id": "12f43bd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "univariate_analysis(\"trip duration slot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1625,
   "id": "0303fd08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.countplot(data=uber_data, y=\"Trip duration_min\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f509f7",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "1. Highest number of trips last for 49 mins and 58 mins\n",
    "2. Lowest trip time is 20 mins and highest trip time is 83 mins\n",
    "3. Most of the trips last between 30 to 75 mins\n",
    "4. Nobody books cabs for trips that last for less than 15 mins. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8ce8f9b",
   "metadata": {},
   "source": [
    "<span style=\"color:red\">Bivariate analysis</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1626,
   "id": "761ad741",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to plt countplots for bivariate analysis\n",
    "\n",
    "def bivariate_analysis(df, col1, col2):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    ax = sns.countplot(data=df,x=col1, hue=col2)\n",
    "\n",
    "    plt.title(\"Summary of trips, countplot of {}\".format(col1))\n",
    "    plt.xlabel(\"{} of trip\".format(col1))\n",
    "    plt.ylabel(\"Number of trips\")\n",
    "\n",
    "    #for p in ax.patches:\n",
    "        #ax.annotate(p.get_height(), (p.get_x() + p.get_width()/2 , p.get_height()+50) , ha=\"center\", va=\"center\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1627,
   "id": "5ba7dc0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(uber_data ,\"Pickup point\", \"Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69466c2c",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "1. There is a huge demand for cabs at the Airport which is not being met due to insufficient number of cabs. Very few cabs are cancelled at the airport. The demand must be addressed immediately\n",
    "2. Huge number of trips are cancelled when pick up is from the city\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1628,
   "id": "068a21a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(uber_data, \"Request hour\", \"Pickup point\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90b8120",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "1. Large number of requests are seen in the day at the city(5 AM to 10 AM) and at nights at the airport(5 PM to 9 PM)\n",
    "2. This suggests that during the daytime, passengers travel to the airport from the city.\n",
    "3. This also suggests that more passengers land in the evening and travel from airport to the city\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1629,
   "id": "5e376420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(uber_data, \"Request hour\", \"Status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1630,
   "id": "f8e9f012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dOnXCYrHg4eHBnDlzrFP1ffv2pUGDBhQqVIiqVaumqc5ixYrh7OxMy5YtmTx58kP2Kin2pYODA4MHD6Zv377Wkd+xY8cmGrUrVqwYw4YNIzAwELPZjIuLC59++ilubm6J4lq+fHmSkTiRh2UyUpojEBGbZxgGn3/+ORcuXGDEiBGZ3v6/7zRMq4EDBxIcHIyrq2sGRZZ299/x+yTy8/NjyJAhVKlSJatDEZH/QCNsIo+xV155BS8vL2bNmpXVoaRZdHQ0Pj4+NpGsiYg8LjTCJiIiImLjdNOBiIiIiI1TwiYiIiJi45SwiYiIiNi4J/6mgxs3bmOx6DI9ERERsV12diZy5cqe4vonPmGzWAwlbCIiIvJY05SoiIiIiI1TwiYiIiJi45SwiYiIiNi4J/4atuSYzQncuBFOQkJc6hvLE8PBwYlcuTyxt38qT3sREXmMPZV/uW7cCMfFJRvZs+fTC3mfEoZhcPt2BDduhJMnT/6sDkdEROShPJVTogkJcWTP7q5k7SliMpnInt1do6oiIvJYeioTNkDJ2lNIx1xERB5XT23CJiIiIvK4UML2GDpy5DA9e75Fp05t6NDhdfr0eZ/Tp/8EoFev97h582aqdaR1OxEREcl6T+VNB4+zuLg4Bgz4kE8+mUnJkqUA2Lw5hL5932f58nXs27c3TfWkdTsRERHJekrYHjMxMTFERUURHX3HWla/fiOyZ8/OhAmjAXj//bf46KOpnDp1ksWLvyA+Pp4bN67TqJEf3bu/w9ixIxJt99573Rk9egKlSr0AQMuW/owePYFixUowZcpHHD78G/b2DhQoUJDBg4eRLVu2zN9xEZGnhHsOZ5ydnDK0jdi4OCJuxWZoG5K+lLA9Ztzd3XnnnZ706dMTD488lC1blgoVKlGvXgNq1KhNSMh6pk2bQ44cORg9ehhBQcMpXPhZrl4Np0ULP1q1eoPBg4dZt8uZM2eKbR09epiDBw/w5ZfLMZlMzJo1jT//PMlLL5XLvB0WEXnKODs50fmLDzK0jQVvTgWUsD1OlLA9htq0aU/Tpq9x8OAv/PbbLyxZspAlSxby+eeLrNuYTCYmTJhMaOgOvvtuE2fP/oVhGMTERAM509RO0aLFsLOzo0ePTrz8sg916tTlhRdezJidEhERkRTppoPHzKFDv/LVV4vIli071avX5N13P2Dx4mWYTCb27dtj3S46OpouXdrxxx/HKVGiFO+++wEODg4YhpGkTpPJlKg8Pj4eADc3NxYs+Jr33vsQOzs7hg0bzKpVyzN+J0VERCQRjbA9ZnLmzMXChfMoU6Ys5cqVB+Datavcvh3F888Xw97enoSEBMLDw7h9+zbdu7+Lo6MjmzZtJC4uDovFAmDd7m6dOTl+/BilS5fhl1/2c+3aVQB27drB118vZsqUWZQv7w3A8eO/Z/5Oi4iIPOWUsD1mnn22COPGfcxnn83kypUrODs7kT27K4MGDePZZ/+POnVeITCwB6NHT6BatRq0bdsSJydHihYtxv/9X1HOnz9HwYKFrNuNHTuRd955n0mTxrF27SpKlixFyZKlAahatRp79oTSsWNrnnkmG25ubgwYEJzFPSAiIvL0MRnJzZE9Qa5di8JiSbyLly+fJV++IlkUkWQlHXsRsXWenm6ZctNBeHhkhrYhD8fOzkTu3K4pr8/EWERERETkEShhExEREbFxSthEREREbJwSNhEREREbp4RNRERExMYpYRMRERGxcXoOG+Dm7oKLs2O61xsTG09kREy61ysiIiJPFyVsgIuzI237L0n3er+a2I5IHpywffzxBA4f/o2EhHjOnz/H//1fUQBatWpDkyZNrdtdvRrO+PGjmDRpWprb//vvM8ycOZXLly9hGAZFixbjww/7PfCF7/9VjRqV2Llzf4rrL168wMKF8xg0aGia65w3bw4AXbu+9Z/jExEReRwpYctiffoMAODSpYv07PkWCxZ8lex2efJ4PlSydvVqOD17vk2/foOpUaMWhmGwePEXDB7cl1mz5qZL7I/i8uVLXLhwPsvaFxEReRwpYbNhLVv688ILL3Ly5AmGDBnJ0KGDWLFiPWPGDMfZ2Zljx37n9u3bdO7clYYNmyT67OrVK6hYsTI1atQC7r7gvV27TuTPX4CEhAQSEhKYMGE0p079gZ2dHW3atKdRIz9CQtYTGrqTiIhbXL0aTrNmAVy+fJlfftmHu3sOJk2axvXr1xg4sDfPPvt//PXXafLly8fQoaNwd89hbf/OnTt88skETp/+E4vFQrt2HXn11YZMnTqJixcv8PHHE+jTZwCLFy/ghx++w2y2UKVKVd55531MJhNffbWIdetWkyNHTtzc3Chdukym9r2IiIgt0U0HNq5q1Wp8/fUqcuXySFR+4cJ55sz5gmnTZjNz5lTrC9vvOXnyBCVKlExUZm9vz6uvNsTBwYH58+eQI0cOFi9extSpnzJ//uecOnUSgGPHjjJu3CQ++WQG06dPpmrVaixc+A0AP/+8G4A//zzFa6+15Msvl1GkyHPMn/9ZorYWLpxHyZKlmT//S2bO/IxFi+Zz4cJ5PvigLyVLlqZPnwHs2RPKiRPH+PzzRXzxxRLCw8PZsuVbjh//nY0b1zF//hKmTJlFePiVdO1TERGRx41G2GzcCy+8mGx548b+ODg44OWVl5deKsehQ7/i61vPut5kMuHk5JxivQcO7GfgwCEA5MyZk5o1a3Hw4AGyZ8/OSy+VI3t2V7Jnv/tOs4oVKwOQL19+IiPvvnuucOFn8fauBECjRn6MGBGUqP79+38mNjaGjRvXARATE8Nff50mW7Zsibb5/fcjdO3aAYDY2Bjy5s3HtWvXqFq1unVbX996mM3mNPaYiIjIk0cJm41zdk4+6bK3/+fQGYYl0TJAqVIvcPz474nKLBYLwcED6Nt3IIZhSbTOMMBsTgDA0THxHbMODklPk9Tat1jMDBkyipIlSwFw/fo13N1zcOjQr4m2ef31N2jTpj0AkZGR2Nvbs3btKsC4ry17JWwiIvJU05ToY2rbtu8wDIPLly/x++9HKFeufKL1TZu+xu7du9i9eycAhmGwYMFcbty4jodHbry9K7Nx41oAbt68yY4dP1KhQqU0t3/u3FlOnjwBwMaN66latVqi9d7elVmzZgUAV69epVOnNwgLu4y9vYM1+fL2rszmzSHcuXOHhIQEBg3qw48/bqVSpcrs2rWDqKgoYmNj+emnHx6hh0RERJ4cGmHj7vPSvprYLkPqzSixsTF07dqB+Pg4+vULIkeOnInW586dh0mTpjFr1lRmz56OxWKhRIlSjBv3MQBvvtmNjz+eQMeOrbFYLHTs2IWSJUvx558n09S+m5s78+bN4fz58zz/fDHr9Oo9Xbp05+OPJ9Chw+tYLBbeffd9ChYshKurK1FRkYwaNYQhQ0Zx6tQf9OjRGYvFTJUq1WjUyA+TyUSrVm/QrVtH3NzcyJs3f7r0mYiIyOPKZBiGkfpmj69r16KwWBLv4uXLZ8mXr0gWRfTfjRkznAoVKtK4sX+WtH/vESQrVqzPkvb/i8f92IvIk8/T043OX3yQoW0seHMq4eGRGdqGPBw7OxO5c7umvD4TYxERERGRR6Ap0cdQUNDwLG0/f/4Cj+XomoiIyONKI2wiIiIiNi5DE7aoqCj8/Pw4f/7uq4hCQ0Px9/enfv36TJ482brdsWPHCAgIoEGDBgQFBZGQcPfxEhcvXqRdu3Y0bNiQd955h9u3b2dkuCIiIiI2KcMStt9++4033niDM2fOAHcfnDp48GBmzZpFSEgIR44cYfv27QD069ePoUOHsnnzZgzDYNmyZQCMGDGCtm3bsmnTJl588UVmzZqVUeGKiIiI2KwMS9iWLVvGsGHD8PLyAuDQoUMUKVKEwoUL4+DggL+/P5s2beLChQvExMRQvnx5AAICAti0aRPx8fHs27ePBg0aJCoXERERedpk2E0HY8aMSbR85coVPD09rcteXl6EhYUlKff09CQsLIwbN27g6upqfcr+vfKMkCuHEw4PeI3To0qIi+XGrbhUt7t9O4pPP53Jr78ewN7eATc3NwIDe1nfEpBRQkLWc/DgAYKChtOypT/Tp88hf/4CafpsVj9aRERE5GmSaXeJWiwWTCaTddkwDEwmU4rl9/5/v38vp0VyzzS5csUOB4d/BhcdnJw5MLHbQ9edmor95+LgkPDAbSwWC/36fUjFipVYvPgbHBwcOHBgH337vs8336xI8kDc9GRnZ8JkMln7wt4+cb88iMlkws7OlObtbYWdnR2enm5ZHYaISJbTd+HjJdMStnz58hEeHm5dDg8Px8vLK0n51atX8fLywsPDg8jISMxmM/b29tbtH1ZyD861WCwkJFhS+ET6Sq2d/ft/5vLlS7z5Zg/AjoQEC+XKVWTw4KHExSUwZsxITp/+k+vXr1OsWDGGDx/D9evXGTy4L0WLPs8ff5zAwyM3o0aNx909B1u2bGLRonmAidKlX2DAgGDi4uL45JMJnD79JxaLhXbtOvLqqw2xWAwMw7DGaDZbiI2NZ9asqRw8eACz2ULjxn60bt0OwzCYMWMyu3btJE+ePFgsFsqX9860fkwvFotFD4sUEZuWWYmUvgtti808OLdcuXL89ddfnD17FrPZzIYNG6hVqxYFCxbE2dmZAwcOALB27Vpq1aqFo6MjlSpVIiQkBIA1a9ZQq1atzAo30/zxxwmKFy+BnV3iQ+HjU4OzZ8/g4ODInDlfsHTpaiIjI9m9excAp06dpHXrdixevAxXV1e2bPmW8PArTJ/+CZ98MoMvv1yGxWImNHQnCxfOo2TJ0syf/yUzZ37GokXzuXDhfLLxrF+/GoD585fw+ecL2bFjO7/9dpAff9zKH3+c4MsvlzFq1AQuXDiXsR0jIiIiVpk2wubs7Mz48ePp2bMnsbGx1K5dm4YNGwIwadIkgoODiYqKokyZMnTs2BGAYcOGMXDgQGbPnk3+/Pn55JNPMivcTGNnZ8Iphevnypf3xt09BytXLuPvv89w/vw5oqOjAciVy4MSJe5e41a0aDEiIiI4cuQQL71UDi+vvAAMGTIKgIUL5xEbG8PGjeuAu3fs/vXX6WTb3L//Z06e/IMDB/YDEB19hz//PMWZM6epXdsXBwcHcuXKRdWq1dOvE0REROSBMjxh27Ztm/VnHx8f1q1bl2SbUqVKsWLFiiTlBQsWZPHixRkaX1YrVeoFVq9ekeSavTlzZvLCC2WYN+8zWrVqQ+PGTbl58yb3Xv3q5OSUqB7DMHBwcOD+y/xu3LgBgMViZsiQUdabGK5fv/a/6dNvk8RjNt99UXvt2nUBuHnzJs888wyzZk3l/rfO2tvbp8v+i4iISOoeryvGn0DlylUgVy4P5s//DLPZDMDevbsJCVnH3r27qVu3Hk2aNMXV1ZWDBw9gsZhTrKt06TIcPXqEa9euAjB9+ifs3Lkdb+/KrFlzNyG+evUqnTq9QVjY5WTrqFixEuvWrSEhIYE7d+7w7rtdOXr0MJUqvcy2bd8RFxdHREQEe/fuTueeEBERkZToXaLcffxGxf5zM6Te1JhMJsaP/4Tp0z+mY8fWODg4kCNHTj76aCr29g6MGBHE999vxsHBkZdeKsvFixepWDH5uvLk8eSDD/rQu3dPLBYzL75YlsaN/YmJiebjjyfQocPrWCx3R9AKFizEb78dTFJH8+YtOX/+HG++2Raz2Uzjxv54e1cC4Nix3+nYsTUeHrn5v/8r+p/6RkRERNLOZBiGkfpmj6/k7hK9fPks+fIVyaKIJCvp2IuIrfP0dKPzFx9kaBsL3pyqu0RtjM3cJSoiIiIij0YJm4iIiIiNU8ImIiIiYuOUsImIiIjYOCVsIiIiIjZOCZuIiIiIjdNz2AD3HM44/+vNAekhNi6OiFupP4tNRERE5EGUsAHOTk4Z8sybBW9OBR6csF26dJFWrZoyefIMKleuai1v2dKf6dPnkD9/gTS1FR8fzxdffM7Onduxs7PHycmJ7t3foXLlKv9lF5K1YsU3zJgxhZUrN5A7d55HqmP8+FE0b96CO3fuMH/+Z8yY8VmK244ZM5wKFSrSuLF/ovKQkPUcPHiAoKDhjxSDiIjI40JTojbAwcGBCRPGcOfO7UeuY8yY4Vy9Gs7nny9iwYKvGDAgmFGjhqb4kvf/IiRkPTVr1rG+TP5RDBw4hFKlXkjHqERERJ5cSthsQJ48nlSuXIXp06cku37Rovm0b9+Kjh1bM336ZOs7R+85f/4cP/30A7169cfZ2RmA558vxvDhY3BxcQHuvky+R4/OtGkTQGBgD65fvwaAn189+vR5n86d2xIWdpnAwB507dqB7t07cuTI4SSxnDp1koiICNq168T69WuwWCwAdOnSjuPHjwFgNpt57bXG3LhxnW3bvqdHj8506vQGbdu24PDh3wAIDOzBL7/sT1T3wYMHeOedrnTp0o5WrZqxY8eP1nWhoTvo0qU9HTu2ZuvW75LEdezYUetne/V6j4sXLzy400VERB4jSthsRGDgh/z882727duTqHz37l3s3PkTc+cuZv78JVy4cI41a1Ym2ubkyRMULvwszzzzTKJyb+9K5M9fgPPnz/H332f49NP5fPPNKvLmzcfmzd8CcPPmTdq168iCBV+xceM6qlWrwbx5i+na9W0OHfo1SZwbN66jbt16lCpVGnt7e+tL4Bs0aMz3328G4Jdf9lGsWHFy5MjJ2rUrmThxCgsXfk3bth1ZvHhBin2wcuVSBg4cwvz5Sxg4MJjPP59tXRcTE8Nnny3gk09mMG3aJOsL7uHudPD48aMZNmwM8+cvoU2b9kyYMCb1ThcREXlM6Bo2G5E9uysDBgQzYcIYFi36xlp+4MA+6tVrYB0pa9KkKd9+u5EWLV63bmNnZ4fTA26aKFSoMIGBvVi/fg1//32Wo0cPU7BgIev6MmVeBKBSpZcJCurPH3+coFq1GonaAEhISOC7777lk09mAODrW4+1a1fi41OdevUa8PbbXXjvvQ/47rvN1K/fCDs7O8aO/Yhdu3bw999nOXjwAHZ2Kf8bYciQUYSG7uCHH77n6NHDREdHW9c1auSHg4MDefJ4UqZMWX7//Yh13blzZ7l48TwDB/a2lt2+/ejTyyIiIrZGCZsNefnlqkmmRg3DkmgbwwCzOSFRWcmSL3DmzBliY2Nwdnaxli9b9hUeHrkpVOhZhg8Pok2btvj6voK9vR2GYVi3u/eZsmXL8+WXywgN3cnWrVsICVnPlCmzrNvt2vUTkZGRDB7cD7ibwN24cZ0rV8Lw8spL4cJFOHjwAPv3/0zv3gO4c+cO3bt3on79RpQrV4Hnny/GypXLUtz/997rjrd3RSpUqEjFipUZMSLYus7e3j5Rnzg4/HPqms0WChQoyIIFX/1v2cyNG9dT7mgREZHHjKZEbcy9qdF7U37e3pX5/vvNxMbGkJCQQEjIOry9KyX6TL58+ahWrTqTJ39EbOzdu1L/+OM4S5YspGjR5/n11wNUqFCR5s1bUrjws4SG7rRee3a/WbOmsnnztzRq5EevXgP4448TidZv3Lie7t3fYcWK9axYsZ41a77lpZfKsX79GgAaNmzMjBmT8fauhIuLC+fO/Y3JZKJjxy54e1di+/Yfkm0XICLiFufOnaVr17epWrU6O3ZsT7Tt999vxjAMLl++xPHjxyhd+kXruiJF/o+IiAh+++3g/+Jcx/DhQQ/Z8yIiIrZLI2zcfV7a3UdwpH+9D+ve1Gjv3oEAVK9ek5MnT9C1a0fM5gRefrkqLVq0TvK5QYOGMXv2NN58sy2Ojk64uLgwZMgoihYthpubO4MH96Njx7ufK1myNJcuXUxSR4sWrRkxIpiQkPXY2dkRHDzCuu769WscPLifwYOHJfpMmzbt+fjj8XTu3I1atXz56KNxvPNOTwCKFStOsWIlaNu2JXZ2Jl5+2SfZ6+IA3N1z4OfXjA4dXsfBwQFv78rExMRYp0WfeSYbXbu2JyEhgX79BpMzZ07rZ52cnBg1ajxTp04iLi6ObNmyJ4pdRETkcWcy7p8bewJduxaFxZJ4Fy9fPku+fEWyKCLJSjr2ImLrPD3dMuTZoPdb8OZUwsMjM7QNeTh2diZy53ZNeX0mxiIiIiIij0AJm4iIiIiNe2oTtid8JliSoWMuIiKPq6cyYXNwcOL27Qj9AX+KGIbB7dsRODik/Lw6ERERW/VU3iWaK5cnN26EExV1M6tDkUzk4OBErlyeWR2GiIjIQ3sqEzZ7ewfy5Mmf1WGIiIiIpMlTOSUqIiIi8jhRwiYiIiJi45SwiYiIiNg4JWwiIiIiNk4Jm4iIiIiNU8ImIiIiYuOUsImIiIjYOCVsIiIiIjZOCZuIiIiIjVPCJiIiImLjlLCJiIiI2DglbCIiIiI2LksStrVr19KkSROaNGnChAkTAAgNDcXf35/69eszefJk67bHjh0jICCABg0aEBQUREJCQlaELCIiIpJlMj1hi46OZsyYMSxevJi1a9eyf/9+tm3bxuDBg5k1axYhISEcOXKE7du3A9CvXz+GDh3K5s2bMQyDZcuWZXbIIiIiIlkq0xM2s9mMxWIhOjqahIQEEhIScHV1pUiRIhQuXBgHBwf8/f3ZtGkTFy5cICYmhvLlywMQEBDApk2bMjtkERERkSzlkNkNurq68sEHH9CoUSOeeeYZKleuzJUrV/D09LRu4+XlRVhYWJJyT09PwsLCHqq93Lld0y12ERGRJ4Wnp1tWhyAPIdMTtuPHj7Ny5Up++OEH3Nzc6Nu3L2fOnMFkMlm3MQwDk8mExWJJtvxhXLsWhcVipFv8IiIiGSmzEqnw8MhMaUfSxs7O9MBBpkyfEt25cyc+Pj7kzp0bJycnAgIC2Lt3L+Hh4dZtwsPD8fLyIl++fInKr169ipeXV2aHLCIiIpKlMj1hK1WqFKGhody5cwfDMNi2bRvlypXjr7/+4uzZs5jNZjZs2ECtWrUoWLAgzs7OHDhwALh7d2mtWrUyO2QRERGRLJXpU6I1atTg999/JyAgAEdHR1566SV69uxJ9erV6dmzJ7GxsdSuXZuGDRsCMGnSJIKDg4mKiqJMmTJ07Ngxs0MWERERyVImwzCe6Au8dA2biIg8Tjw93ej8xQcZ2saCN6fqGjYbY3PXsImIiIjIw1HCJiIiImLjlLCJiIiI2LhUE7bo6Gh+/fVXAL7++msGDx7MxYsXMzouEREREfmfVBO2QYMGsXXrVg4dOsTcuXPJnz8/Q4YMyYzYRERERIQ0JGznzp2jT58+/PDDD7z22mv07NmTmzdvZkJoIiIiIgJpSNgSEhKAu28oqFq1KmazmTt37mR4YCIiIiJyV6oPzq1QoQKNGzfG3t4eb29vOnXqRLVq1TIjNhEREREhDQnbkCFDOHjwIKVKlcLOzo6uXbvq9VAiIiIimSjVhM3e3p7bt28zZcoU7O3t8fX1xc5OTwMRERERySypZl7Tp09n/PjxuLm54eLiwrBhw1i0aFFmxCYiIiIipGGEbd26daxatQo3NzcAunTpQps2bfQSdhEREZFMkuoIW86cOcmePbt12d3dnWzZsmVoUCIiIiLyj1RH2CpWrMi7775L69atsbe3Z926dRQoUIAtW7YAUL9+/QwPUkRERORplmrCdvToUQDmz5+fqHzx4sWYTCYlbCIiIiIZLNWEbfHixZkRh4iIiIikIMWEbcyYMQQFBfH2228nu/7TTz/NsKBERERE5B8pJmw+Pj4ANGjQINOCEREREZGkUkzY6tatC8CaNWtYuHBhpgUkIiIiIoml+liPyMhIvexdREREJAuletPBM888g6+vLyVLlkz0/DVdwyYiIiKSOVJN2Fq2bJkZcYiIiIhIClJN2M6ePcuHH36YqGz06NG89tprGRWTPAXc3F1wcXbMsPpjYuOJjIjJsPpFREQyU4oJ27Rp04iIiCAkJISoqChreXx8PDt37iQ4ODhTApQnk4uzI237L8mw+r+a2I5IlLCJiMiTIcWErVy5chw+fBg7Ozty5sxpLbe3t2fSpEmZEZuIiIiI8ICErXbt2tSuXZtatWpRtmzZzIxJ5D+zJMTj6emWoW0kxMVy41ZchrYhIiICabiGTcmaPI7sHBw5MLFbhrZRsf9cQAmbiIhkvFSfwyYiIiIiWSvFhO37778HIC5OIwgiIiIiWSnFhG3q1KkAtG7dOtOCEREREZGkUryGLXv27DRo0ICwsDD8/f2TrF+/fn2GBiYiIiIid6WYsM2dO5djx44RFBTEkCFDMjMmERGRR6KHcsuTKsWEzdXVlcqVKzNnzhy8vLw4evQoCQkJlC1bFldX18yMUUREJE30UG55UqX6WI/IyEg6dOhAnjx5MJvNhIWF8emnn+Lt7Z0Z8YmIiIg89VJN2CZMmMCkSZOoWrUqALt372b8+PEsW7Ysw4MTERERkTQ8h+327dvWZA3Ax8eH6OjoDA1KRERERP6RasJmMpm4cOGCdfn8+fPY29tnaFAiIiIi8o9Up0Tfe+89WrdujY+PDyaTiZ07dzJs2LD/1Oi2bduYMWMG0dHRVK9eneDgYEJDQxk3bhyxsbE0atSIXr16AVjvVL19+zaVKlVixIgRODikGraIiIjIEyPVEbZ69eqxaNEiKlSoQNmyZVm8eDENGjR45AbPnTvHsGHDmDVrFuvWreP3339n+/btDB48mFmzZhESEsKRI0fYvn07AP369WPo0KFs3rwZwzB07ZyIiIg8ddI0VFW0aFGKFi2aLg1+9913NG7cmHz58gEwefJkzp49S5EiRShcuDAA/v7+bNq0iWLFihETE0P58uUBCAgIYNq0abRt2zZdYhERERF5HGT63OLZs2dxdHTk7bff5tKlS9SpU4fixYvj6elp3cbLy4uwsDCuXLmSqNzT05OwsLCHai93bj0zTjKOp6dbVocgIpnsSfm9f1L242mR6Qmb2Wxm//79LF68mGzZsvHOO+/g4uKCyWSybmMYBiaTCYvFkmz5w7h2LQqLxUi3+CV9PClfFOHhkVkdgojcJzO+WzL69z6zvh/1/WVb7OxMDxxkSvUatv79+6drQHny5MHHxwcPDw9cXFyoV68eoaGhhIeHW7cJDw/Hy8uLfPnyJSq/evUqXl5e6RqPiIiIiK1LNWE7duwYhpF+I1S+vr7s3LmTiIgIzGYzO3bsoGHDhvz111+cPXsWs9nMhg0bqFWrFgULFsTZ2ZkDBw4AsHbtWmrVqpVusYiIiIg8DlKdEvXy8qJJkyaUK1eO7NmzW8uDg4MfqcFy5crRrVs32rZtS3x8PNWrV+eNN96gaNGi9OzZk9jYWGrXrk3Dhg0BmDRpEsHBwURFRVGmTBk6duz4SO2KiIiIPK5STdgqVKhAhQoV0rXRli1b0rJly0RlPj4+rFu3Lsm2pUqVYsWKFenavoiIiMjjJNWELTAwkJiYGM6ePUvx4sWJjY3lmWeeyYzYRERERIQ0XMP222+/Ua9ePd566y2uXLlCnTp1+OWXXzIjNhEREREhDQnbhAkTWLBgATlz5iRfvnxMnDiRMWPGZEZsIiIiIkIaEraYmBiKFStmXa5duzZmszlDgxIRERGRf6SasDk4OHDr1i3rA2tPnz6d4UGJiIiIyD9SvengnXfeoX379oSHh9O7d2927drFyJEjMyM2ERERESENCZuvry9FixZl165dWCwW3nvvPZ5//vnMiE1ERERESMOUKEBCQgIWiwUHBwccHDL99aMiIiIiT7VUE7aVK1fSsWNHDh8+zP79+2nXrh2bN2/OjNhEREREhDRMiS5YsIDVq1dbX7p+8eJF3nrrLRo0aJDhwYmIiIhIGkbYHB0drckaQIECBXB0dMzQoERERETkHymOsB09ehSAkiVLMnLkSFq3bo29vT2rVq3C29s70wIUERERedqlmLD17Nkz0fKPP/5o/dlkMhEcHJxhQYlI+nNzd8HFOeNGx2Ni44mMiMmw+kVEnmYpJmzbtm3LzDhEJIO5ODvStv+SDKv/q4ntiEQJm4hIRkj1poPw8HBWr17NzZs3E5X3798/o2ISERERkfuketPBO++8w6FDhzAMI9F/IiIiIpI5Uh1hi4+PZ8aMGZkRi4iIiIgkI9URtjJlyvDHH39kRiwiIiIikoxUR9i8vb1p3rw5np6eiV5LtXXr1gwNTERERETuSjVhmzdvHpMmTeLZZ5/NjHhERERE5F9STdjc3d1p3LhxZsQiIiIiIslINWGrWrUqEyZMoH79+jg5OVnLy5Qpk6GBiYiIiGQG9xzOON+X42SE2Lg4Im7FPvLnU03Y1q9fD8DmzZutZSaTSdewiYiIyBPB2cmJzl98kKFtLHhzKpCBCZveeCAiIiKStVJN2L744otky9988810D0ZEREREkko1Ybv/GWxxcXHs27cPHx+fDA1KRERERP6RasI2bty4RMthYWEEBQVlWEAiIiIikliqbzr4t7x583LhwoWMiEVEREREkvFQ17AZhsGRI0fInTt3hgYlIiIiIv94qGvYAPLnz0///v0zLCARERERSeyhr2ETERERkcyVYsI2aNCgFD9kMpkYO3ZshgQkIiIiIomlmLAVL148SdmNGzdYuHAhBQsWzNCgREREROQfKSZsXbp0SbQcGhrKgAED8Pf3Jzg4OMMDExEREZG7Ur2GLSEhgY8//pjVq1czYsQIGjRokBlxiYiIiMj/PDBhO3PmDL179yZ79uysWbOGfPnyZVZcIiIiIvI/KT44d+XKlbz++uu8+uqrLF68WMmaiIiISBZJMWELCgoiKiqKzz77DG9vb+t/FSpUwNvbO10anzBhAgMHDgTuXiPn7+9P/fr1mTx5snWbY8eOERAQQIMGDQgKCiIhISFd2hYRERF5XKQ4Jbp169YMbXj37t2sXr2aOnXqEBMTw+DBg1m8eDH58+fnrbfeYvv27dSuXZt+/foxevRoypcvz+DBg1m2bBlt27bN0NhEREREbEmKCVtGPrrj5s2bTJ48mbfffpvjx49z6NAhihQpQuHChQHw9/dn06ZNFCtWjJiYGMqXLw9AQEAA06ZNU8ImIiIiT5VU7xLNCEOHDqVXr15cunQJgCtXruDp6Wld7+XlRVhYWJJyT09PwsLCHqqt3Lld0ydokWR4erpldQg2Rf0hT4Mn5Tx/UvbjcfJf+jzTE7bly5eTP39+fHx8WLVqFQAWiwWTyWTdxjAMTCZTiuUP49q1KCwWI32Cl3TzpHxRhIdHZnUIaZYZff449Yc8mZ6E8zyzvh/Taz/c3F1wcXZMl7qSExMbT2RETIbVD7bR53Z2pgcOMmV6whYSEkJ4eDjNmjXj1q1b3LlzhwsXLmBvb2/dJjw8HC8vL/Lly0d4eLi1/OrVq3h5eWV2yCIiIpICF2dH2vZfkmH1fzWxHZFkbML2OMj0hO2LL76w/rxq1Sp+/vlnRowYQf369Tl79iyFChViw4YNtGjRgoIFC+Ls7MyBAweoWLEia9eupVatWpkdsoiIiEiWypJr2P7N2dmZ8ePH07NnT2JjY6lduzYNGzYEYNKkSQQHBxMVFUWZMmXo2LFjFkcrIiIikrmyNGELCAggICAAAB8fH9atW5dkm1KlSrFixYrMDk1ERETEZqT44FwRERERsQ02MSUqIvIkehLunhMR26CETUQkg+juORFJL5oSFREREbFxSthEREREbJwSNhEREREbp4RNRERExMYpYRMRERGxcUrYRERERGycEjYRERERG6eETURERMTGKWETERERsXF604HII4pLiMfT0y1D24iNiyPiVmyGtiEiIrZPCZvII3JycKTzFx9kaBsL3pwKKGETEXnaaUpURERExMYpYRMRERGxcUrYRERERGycEjYRERERG6eETURERMTGKWETERERsXFK2ERERERsnBI2ERERERunhE1ERETExilhExEREbFxejWViKQLSya8WzUhLpYbt+IytA0REVukhE1E0oWdgyMHJnbL0DYq9p8LKGETkaePpkRFREREbJwSNhEREREbp4RNRERExMYpYRMRERGxcUrYRERERGycEjYRERERG6eETURERMTG6TlsIvLYiMuEh/PGxsURcSs2Q9sQEXlYSthE5LHh5OBI5y8+yNA2Frw5FVDCJiK2RVOiIiIiIjZOCZuIiIiIjcuShG3GjBk0adKEJk2aMHHiRABCQ0Px9/enfv36TJ482brtsWPHCAgIoEGDBgQFBZGQkJAVIYuIiIhkmUxP2EJDQ9m5cyerV69mzZo1HD16lA0bNjB48GBmzZpFSEgIR44cYfv27QD069ePoUOHsnnzZgzDYNmyZZkdsoiIiEiWyvSEzdPTk4EDB+Lk5ISjoyPPP/88Z86coUiRIhQuXBgHBwf8/f3ZtGkTFy5cICYmhvLlywMQEBDApk2bMjtkERERkSyV6XeJFi9e3PrzmTNn+Pbbb2nfvj2enp7Wci8vL8LCwrhy5Uqick9PT8LCwh6qvdy5Xf970CJZKKMfYyFJPU59/jjF+qR4Uvr8cdqPxynWB/kv+5Flj/U4efIkb731Fv3798fe3p4zZ85Y1xmGgclkwmKxYDKZkpQ/jGvXorBYjPQKW9LJk/LLlxnCwyPTpR71edo9Tn2eXrE+KZ6EPs+s31Wd5/+whT63szM9cJApS246OHDgAJ07d6ZPnz689tpr5MuXj/DwcOv68PBwvLy8kpRfvXoVLy+vrAhZREREJMtk+gjbpUuXeO+995g8eTI+Pj4AlCtXjr/++ouzZ89SqFAhNmzYQIsWLShYsCDOzs4cOHCAihUrsnbtWmrVqpXZIT8W3HM44+zklKFt6AnwIiIiWSPTE7Z58+YRGxvL+PHjrWVt2rRh/Pjx9OzZk9jYWGrXrk3Dhg0BmDRpEsHBwURFRVGmTBk6duyY2SE/FpydnPQEeBFJd/rHoIhtyPSELTg4mODg4GTXrVu3LklZqVKlWLFiRUaHJSIiydA/BkVsg950ICIiImLjlLCJiIiI2DglbCIiIiI2TgmbiIiIiI1TwiYiIiJi45SwiYiIiNi4LHs1lS1xc3fBxdkxw+qPiY0nMiImw+oXERGRJ5sSNsDF2ZG2/ZdkWP1fTWxHJErYRERE5NFoSlRERETExilhExEREbFxSthEREREbJwSNhEREREbp5sORERE0siSEI+np1tWhyFPISVsIiIiaWTn4MiBid0ytI2K/edmaP3yeNKUqIiIiIiNU8ImIiIiYuOUsImIiIjYOCVsIiIiIjZOCZuIiIiIjVPCJiIiImLjlLCJiIiI2DglbCIiIiI2TgmbiIiIiI1TwiYiIiJi4/RqKhGRx5Teayny9FDCJiLymNJ7LUWeHkrYRERExGZpJPkuJWyZQCebiIjIo9FI8l1K2DKBTjYRERH5L3SXqIiIiIiNU8ImIiIiYuOUsImIiIjYOCVsIiIiIjZOCZuIiIiIjVPCJiIiImLjlLCJiIiI2DglbCIiIiI27rFI2NavX0/jxo2pX78+S5YsyepwRERERDKVzb/pICwsjMmTJ7Nq1SqcnJxo06YNVapUoVixYlkdmoiIiEimsPmELTQ0lKpVq5IzZ04AGjRowKZNmwgMDEzT5+3sTGnaLk+u7I8aYpo4uefO0PoB8rh6ZHgbae3PtFCfp436PDH1eWLq86TU52mjPk8sq/s8teNhMgzDSO+A0tOcOXO4c+cOvXr1AmD58uUcOnSIUaNGZXFkIiIiIpnD5q9hs1gsmEz/ZJ2GYSRaFhEREXnS2XzCli9fPsLDw63L4eHheHl5ZWFEIiIiIpnL5hO2atWqsXv3bq5fv050dDRbtmyhVq1aWR2WiIiISKax+ZsO8ubNS69evejYsSPx8fG0bNmSsmXLZnVYIiIiIpnG5m86EBEREXna2fyUqIiIiMjTTgmbiIiIiI1TwiYiIiJi45SwiYiIiNg4JWwiIiIiNk4J2yPau3cvHTp0eKjPPOz2KQkKCuLw4cMprj9//jx169ZNdt2gQYO4cOFCusSRXs6fP8+LL75Is2bNEv23ZMmSh6ojpX3OKt27dycsLCyrw3igrDyP09PUqVPZunVrVoeR7v744w9KlizJ5s2brWXbt2+nZs2a9OnTh2XLlrFhw4YsjPDJlR7fSw9y+PBhgoKC0qWup8H58+cpWbIku3btSlRet27dNPXlwIEDWbVqVZLyQ4cO8dFHH6VrrBnF5p/D9iT5+eef06WeMWPGPPJn9+7dy3vvvZcucaQnLy8v1q5dm9VhpKvPP/88q0PIEOl1HqenDz74IKtDyBArV66kYcOGLF26lAYNGgCwadMmAgMDad26NQMHDuTll1/O4iifXBn5vfTSSy/x0ksvZUjdTypHR0eGDBnCunXrcHV1tZbnypXrkf8unjp1imvXrqVXiBlKCVs6S0hIYPjw4Zw8eZKrV69SsmRJPvnkEyZNmgRAq1atWL58OT/99BPTpk0jISGBQoUKMWrUKHLlypWorrp169K0aVN27txJdHQ0EyZM4MUXX6RDhw4EBgZSpUoVPv74YzZv3kyuXLnw9PSkbt26vPzyy8TExNCrVy9OnjyJu7s7M2fOZPny5Vy5coUePXqwZMmSJO3Zqi+//JK1a9cSHR2No6MjH3/8MUWLFqVu3bqULVuWY8eOWf+FFBUVxSuvvMLWrVtxdXXl/Pnz9OjRg5CQEGt958+f57333qNo0aKcOnWKF154gQoVKrB69Wpu3brFzJkzef755/n1118ZM2YMsbGx5MqVi5EjR1KkSBE6dOhAjhw5OHnyJFOmTKF79+40aNCAAwcOYG9vz5QpUyhcuDB169Zl0aJF/Pzzz+zYsYNbt25x7tw5qlevzvDhwwGSPX4BAQFZ0c2JpPd53KRJE3bt2oWDgwPvvvsu8+fP5+zZswwYMIDGjRtz9epVgoKCuHjxIg4ODvTq1YtatWoxffp0fv31Vy5dukT79u359ttveemllzhw4ADXr18nODiY2rVrWxOXl19+mcDAQIoXL86xY8fInTs3U6dOJWfOnISEhDBt2jSyZctG6dKlMZvNjB8/Piu6N03i4+NZv349S5YsoU2bNvz999/s3buXrVu3snv3buzs7Ni2bRt79uzB09OT0qVLM3ToUC5fvozJZKJPnz5Uq1YtzX2Y0jGIjo4mODiYEydOYDKZ6Nq1K82bN2fVqlUpntdPskWLFnH27FmGDBkCwPjx48mXLx+tWrVi5MiRnDx5ErPZTPfu3fHz80uxn/bu3cuMGTNYvHgxHTp0SPaYXL58mb59+3Lr1i1KlCjBvn37+Omnn7K4B7KOl5cX1apVY8KECYwaNcpa/vPPPzNo0CAWL17MH3/8wcCBAzGbzVSqVImffvqJ7777DoAff/yRr776imvXrvH222/TqFEjpk2bxp07d5g9ezbvvPNOVu1ammhKNJ0dPHgQR0dHli5dynfffUdkZCTbt28nODgYgOXLl3P9+nU+/vhj5s2bx5o1a6hRo4b1D+G/5cyZkxUrVtCmTRvmzJmTaN22bds4cOAAGzZs4LPPPuP333+3rrt+/TpvvvkmGzZsIE+ePISEhNCjRw+8vLz47LPPbC5Zu3LlSpKphxMnThAVFcX333/P4sWL2bBhA3Xq1Ek0JVGrVi02b96Mh4cHAK6urtSpU4dNmzYBsGbNGpo3b56kvRMnTtC9e3fWrl3LL7/8woULF1i6dCl+fn4sXbqUuLg4evfubf3XXJs2bejdu7f18/emqUqXLk14eDg+Pj6sWbOGypUrJztlcvDgQaZNm8a6dev44YcfOHHixAOPX1ZL7/M4T548rFq1iueff57PPvuM+fPn89FHH/HZZ58BMGrUKKpWrcr69euZNm0agwcP5urVqwDExcUREhJC27ZtgbuJzNKlSxk0aBBTp05N0tbx48et5767uzvr16/n+vXrjB07loULF7JixQpu3bqVEd2WrrZv306BAgV47rnnqFevHkuXLqVVq1bUrVuX999/P9HPNWvWZMyYMbRo0YJVq1Yxe/Zshg4dSlRUFJC2PkzpGEyfPp1cuXKxYcMGFi5cyPTp0zl+/DiQ/Hn9JEnue+mFF17gu+++w2w2YxgGW7ZsoUmTJsyePZsyZcqwatUqlixZwqeffsq5c+eAtPVTcsdkzJgxNGrUiPXr19OwYUObv8QiMwwcOJCdO3cmmRq9f/0HH3zA2rVrKVy4MGaz2bouLi6O5cuXM2fOHCZPnoy7uzvvv/8+devWtflkDTTClu4qV65Mzpw5WbJkCadPn+bMmTPcuXMn0Ta//fYbly5domPHjgBYLBZy5MiRbH01a9YEoHjx4mzZsiXRutDQUBo1aoSTkxNOTk7Uq1fPus7Ly8v6Cq9ixYpx48aNdNvHjPCgqYePP/6YjRs3cubMGXbs2EHp0qWt68qVK5dk+xYtWjB9+nRatmxp/SPzb3ny5OGFF14AIF++fPj4+ABQoEABzp8/z5kzZ3B3d7f2YaNGjRg6dCiRkZEASV6Pdv9x2r9/f5L2KlSoYB3CL1y4MLdu3Xrg8ctq6X0e33v/b4ECBfDy8sLBwYECBQoQEREBwJ49exg9ejRwt3/KlSvHb7/9Bjy4r2/evJmkrdy5c1uPbfHixbl16xb79++nQoUK5M2bF4DmzZvz/fffP3S/ZKaVK1fi5+cHQOPGjenbt+8Dp35DQ0M5ffo006ZNA+6Okt5LGNLShykdgz179jB27FgAPDw8eOWVV/j5559xdXVN9rx+kqT0vVSqVCn27t2Lo6Mjzz33HJ6enoSGhhITE8PKlSsBuHPnDidPngSS//3/t+SOya5duxg3bhwAr776Ku7u7um+j48bV1dXRo0aZf3H9P1u3rzJhQsXqF27NnD3b8GiRYus61955RVMJhPFixe3+b+JyVHCls62bt3KtGnT6NixIwEBAdy4cYN/v/3LbDbj7e3Np59+CkBsbCy3b99Otj5nZ2cATCZTknV2dnZYLJZkP+fg8M+hNZlMSWJ4XFy6dIkOHTrQvn17atWqRZ48eTh27Jh1/b3+uV/lypW5cuUKW7ZsoVChQtY/0vdzcnJKtGxvb59oObl+NQzD+q81FxeXROvuP07J9fX9cd7b5kHHL6ul93ns6Oho/fn+c/Oef9ed1r5OzuPW18m5du0aO3bs4OjRoyxatAjDMIiIiLBO7STHYrGwcOFCcubMCdwdHcqdOzfff/99mvowpWPwoGOTXF8/DZo1a0ZISAiOjo74+/sDd/v/o48+okyZMgBcvXqVHDlysH79+jT1U3LHxN7e/qnp04dRo0YN69To/VLrr3vf8yl9d9g6TYmms927d9OoUSNatGiBu7s7e/futX652dvbk5CQQLly5fj111/566+/AJg1axYTJ0586LaqVavGli1biIuLIyoqih9//DHVE9He3j7RELGtO3z4MEWKFKFz58689NJLfP/996nGbzKZaN68OaNHj37k68GKFi3KzZs3OXToEAAhISEUKFDA+scwPTzK8cssmXkeA1StWpUVK1YAcO7cOX755RfKly+fLvsC4O3tzeHDh7ly5QqGYRASEmIzfZ2ctWvXUrVqVX766Se2bdvGDz/8wNtvv80333yTaLv7f5+rVq3KV199Bdy9kNrf35/o6Og0t5nSMbi//Pr162zduvWpv9HhlVdeYd++fezatYtXX30VuNt/X3/9NXA3WW7atCmXLl36T+34+Piwfv164O4U+b0RaflnavTKlSvWMjc3NwoXLsz27dsBrH33IPe+zx4HGmH7D+5Ns9zj7+9Pu3bt6Nu3Lxs3bsTR0RFvb2/Onz8P3P0lb9asGatWrWLs2LF8+OGHWCwW8ubN+0i3FdepU4eDBw/y2muvkSNHDry8vJIdcfr3Z3r06MHcuXMpXLjwQ7eZUe5dK3K/ypUr06tXL77++msaN26MYRhUrlzZOs3wIE2aNGH+/PmPPM3o5OTE5MmTGTVqFNHR0eTIkYPJkyc/Ul0peZTjlxGy+jyGu4+qGTp0qPW2+9GjR+Pl5fXfd+5/PDw8CA4OpkuXLjg5OVGoUCGbnl5avXo1vXr1SlTWrl075s6dS548eaxl1apV45NPPsHNzY3g4GCGDh1qHfGZOHFiojvpUpPSMXjvvfcYPnw4/v7+mM1m3n77bcqUKfPEXa+WnJS+l4KDg/H29iYuLo7s2bMDEBgYyPDhw/Hz88NsNtOvXz+effbZZC+RSKugoCAGDBjAsmXLKFWqlE2fs5nt3tRo165dE5VPnDiRwYMHM2XKFEqWLJlkdPnfypYty4wZM5g0aRJ9+/bNyJD/M5Oh8dbH1sGDBzlz5gyvvfYa8fHxtG7dmrFjx1KqVKmsDi1LWSwWvv76a/766y/rRfK2SMcv89y4cYPFixcTGBiInZ0do0ePtt7xK2KrFi1aRLVq1ShWrBhHjx5lyJAhyT5LTP4xY8YMXn/9dby8vNiyZQvr169n+vTpWR1WutAI22PsueeeY8aMGXzxxRcYhkHz5s31x567/9K9dOkS8+bNy+pQHkjHL/PkzJmTiIgI/Pz8sLe3p0yZMrz++utZHZbIAxUpUoTevXtjZ2eHs7NzokdZSPIKFChAly5dcHBwwN3d/T89t9TWaIRNRERExMbppgMRERERG6eETURERMTGKWETERERsXFK2ETEqmTJkvj7+9OsWTOaN29OgwYNaNGiBYcPH87q0KwOHTrE0KFDk11Xt27dTIv13Llz9OzZE4CwsDDatGmT4W126NDB+tq1tMT1MGbPnk2dOnUYNGhQovLIyEjr2yyS06xZMz0fTCQT6C5REUlk4cKF1nezAsybN4/Ro0ezdOnSLIzqH6dOnbKJdypevHjR+tDgvHnzJnmobVa5P66HsWLFCiZNmkSlSpUSld+6deuBSXBKr5QTkfSlETYRSVFCQgKXLl1K9I7Q2bNn89prr9GsWTPeffdda/J08uRJWrdujb+/P7169eL1119n7969nD9/PtGDef+9vHz5cgICAmjevDmdO3fmzz//BO4+0Ldly5YEBAQQEBDA5s2buXTpEtOmTWP//v1JRoLuWbp0KQEBAdSpUyfRw46XLl2Kn58fTZs2pUuXLtakZuDAgYkeAXP/ct26dfnwww9p1KhRotdCmc1mgoOD+fvvv+natWuifZo+fTr9+vWjY8eONGrUiL59+7J8+XLatWtH7dq12bBhQ6p9uWXLFl577TUCAgJo1aoV+/btS7Kf33//Pc2bN6dp06a88cYbHDp0KElc/3b58mXefvtt/P398fPzY+7cuQB8+OGHhIWFERQUREhISKLPDBo0iJiYGJo1a4bZbObFF1/kgw8+oEGDBhw+fJiSJUty/fp1Vq1aRY8ePejWrRuNGzfmzTfftInEWuSJYYiI/E+JEiUMPz8/w8/Pz6hevbpRt25dY9SoUcbVq1cNwzCM1atXGx9++KERHx9vGIZhfPPNN0a3bt0MwzAMPz8/Y9myZYZhGMa+ffuMkiVLGnv27DHOnTtnlC9f3trG/ct79+412rZta9y5c8cwDMPYsWOH0bBhQ8MwDKNjx47Ghg0bDMMwjGPHjhnDhw83DMMwVq5cafTo0SPZ+H19fY2RI0cahmEYV65cMV588UXj4sWLRmhoqFGvXj3j2rVr1joaNWpkWCwWY8CAAcbcuXOtddy/7Ovra8yYMSPZtvbs2WM0adIkyT5NmzbN8PX1NSIiIozo6GijcuXKxrhx4wzDMIzvvvvOqF+/fqp9+corrxgHDx609sn06dMNwzCM9u3bG99++61x6tQpo1q1asbff/9tGIZhhIaGGtWrVzciIyMTxfVv7dq1M+bPn28YhmFEREQY/v7+1j729fU1Dh06lOQz/z5+JUqUMFavXp1o+dq1a8bKlSuN8uXLG6dPnzYMwzA++ugjo2fPnsnGISIPT1OiIpLIvSnRo0eP0qNHD6pUqULu3LkB+OGHHzh8+DAtWrQA7r5VIjo6muvXr3Pq1CmaN28OQKVKlShZsmSqbf3444+cPXs20fVfERER3Lx5k0aNGjFy5Ei2bdtGtWrV6N27d5ri9/PzA8DT05M8efJYX6TeuHFj61RvQEAAY8aMsb5u60H+PUWYFtWqVcPNzQ0ALy8vatasCcCzzz7LzZs3gZT7Eu6+Wi0wMJDatWtTvXp1unfvnqj+PXv2ULVqVevr5Xx8fPDw8ODIkSMpviP1zp07/PLLL8yfPx+4+97FgIAAfvrpJ5o0afJQ+5dSn1SvXp3nnnsOgNdffz3Ja51E5NEpYRORZJUpU4ZBgwYxcOBASpcuTaFChbBYLHTr1o22bdsCEBcXx61bt3BxccFkMmHc9xxuR0dHgCTl8fHx1p8tFgvNmjWjX79+1uUrV66QI0cO2rRpg6+vL7t27WLHjh3MmDEj1QvuARwc/vlau9e2xWJJsp1hGCQkJDwwPoBs2bKl2ua/OTk5pRjTPSn1JUCvXr1o0aIFu3btYtWqVcyfP9/6AvZ7n/13YnZvf+71e3LtGf96TrrFYnmkF1+n1Cf29vaJ6r5/WUT+G13DJiIp8vPzo2zZsowbNw6AGjVqsGLFCqKiogCYOnUq/fv3J1u2bFSsWNF6Y8Lx48c5fvw4AO7u7sTHx3Pq1CkANm7caK2/Ro0abNy4kStXrgDw9ddf06lTJwDatGnDsWPHCAgIYNSoUURERBAeHo69vf1DJxk1a9YkJCSE69evA7By5Upy5sxJkSJFyJUrF0eOHAHu3u35888/p6lOe3v7JMndw0ipLxMSEqhbty7R0dG88cYbDBs2jBMnThAXF2f9rI+PDzt37uTcuXMA7N69m0uXLlGuXLkU43J1daVcuXIsWbIEuHv355o1a6hWrdoD43RwcMBsNidJ9pKzZ88e63Vr33zzDb6+vmnrDBFJlUbYROSBhgwZQtOmTdmxYwetWrUiLCyM119/HZPJRP78+Rk/fjwAEydOJDg4mOXLl1OwYEHy5MkD3J1669evH927d8fDw4OGDRta665Rowbdu3enS5cumEwmXF1dmTFjBiaTib59+zJ27FimTJmCyWQiMDCQQoUKYTabmTlzJoGBgcyYMSNN+1C9enU6d+5Mp06dsFgseHh4MGfOHOzs7OjQoQN9+/alQYMGFCpUiKpVq6apzmLFiuHs7EzLli0T3dyQVin1pYODA4MHD6Zv3744ODhgMpkYO3ZsolG7YsWKMWzYMAIDAzGbzbi4uPDpp5/i5uaWKK7ly5cnGombNGkSI0eOZNWqVcTFxeHv709AQMAD4/T09KRs2bI0adLEmuylJG/evPTr14/w8HCKFSvGyJEjH7pfRCR5epeoiGQIPz8/hgwZQpUqVbI6FMkEq1atYvPmzcyZMyerQxF5ImlKVERERMTGaYRNRERExMZphE1ERETExilhExEREbFxSthEREREbJwSNhEREREbp4RNRERExMb9P9p8UUNy0CzUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(uber_data, \"Request hour timeslot\", \"Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19248d1f",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "1. There is a huge unavailabilty of cabs in the evening.\n",
    "2. Most of the cabs are cancelled in the morning.\n",
    "3. Unavailabilty of cabs is higher in the evening, night and late night timeslots\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1631,
   "id": "ea7fef54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FOH78aLltbtzIQGjorCrbpt4uSV2+fBkAMGbMGDx48ADvv/8+2rRpgxYtWqBZs2YAAC8vLyQlJaFVq1bIy8uDvb09AMDHxwcxMTEYMWKEvsojIiKiKmZqaopvvtmkmVYqb8Pffxhsbd9Cr17O6NXLuQarq7zg4Lk629y6dRNXr6ZX2Tb1FswePXoEBwcHzJ07F4WFhQgICMC4ceNgZWWlaWNtbY3MzEzcvn1ba76VlRUyMzMrtb0GDcyrrHYiopeduqgQRgrjmi5DixRroud3+7YRFIr/3XiTy5/8XHxeo0Y2aN68OW7cuIaLF8/j559/wqefxuDu3TtYvnwp0tOvQCaTYfDgoRg2bDhkMtn/X48an3wSCrlcgXnzFiAwcDLee28Y+vTpCwCYPHm8Zrpnz67w9x+Nw4cPIS8vD5MmfQgXl3dL1Fteu6+/XoMff0yCXC5H8+YtMHPmbDRo0FCzHVvbtxAYOAkODj3x559n8OjRI0yd+k84OjojImIxlEolPv54KqKjvyixXSMjI1hZ1anwuOotmHXs2BEdO3bUTA8dOhQxMTHo3LmzZp4QAjKZDGq1WusBwqfzK+Pu3Wx+VyYR0f9nZVUHJyPG1XQZWjoHrYVSmVXTZVAVUavVKCpSa6ZVqic/F5935kwqrl27hrZt38LJk8chxJPly5cvQ9OmzbB0aSSys7MxefIYdO/eA0II5OUVICQkCFZWVpg+PQiA7P+/fuJ/2ys+rVKpYGJiinXrvsPFixcwdep4/N//2cPCwkKr3rLapaT8ipSUQ1iz5lvUqlUL69atwoIFn+Df//5Msx2VSo2MjOvo2vUdTJs2CwcO7ENUVCR69eqNoKAwREVF4NNPP9fa9+LjVPy8NzKSlXsxSW/B7MSJEygsLISDgwOAJ4PYpEkTKJVKTRulUglra2vY2Nhozb9z5w6srSv+zg8iIiKqefn5+Rg9+sljSCpVEerVq4958xbh9ddttNqdOHEMU6Z8BAAwNzfHhg3/e6585coo5OTkYPPmnRW+SDNkyPsAgFatWqNly1b444/f0Lt3yatmpbU7ciQFHh5eqFWrFgDgvfeG49tv+6GwsFCrr0KhgINDTwBAmza2ePToUYVqqyy9BbOsrCzExMTgv//9LwoLC7Fjxw4sWLAA06ZNQ3p6Opo2bYqEhAQMGTIETZo0gampKU6ePInOnTsjLi4OTk5O+iqNiIiI9ODZZ8zKIpfLtUJXRsZ11K9fHwDg5uYBIYDlyxdj+fIoAE9eyyGK3RQrKip6Zn3/izNCqGFkJC9juyXbqdWqZ+7aPbkKJ4T2XThjY2MYGRlp6qnsazAqSm+fynRxcYGzszO8vb0xZMgQDBkyBB07dkR4eDgCAwPh4eGBli1bon///gCAyMhILFu2DP3790dOTg4CAgL0VRoRERHVoC5dumH37ngAQHZ2NqZNm4Jr164BANq1a49x4yYhI+M64uN3AADq17fAuXN/AgD+/vsyLl26oLW+pKQEAMD58+eQnp4Oe/tOpW63tHbdu/fA7t3xyM3NBQBs3boZ9vadYGJiUqF9kcsVJYLii9Dri8KmTZuGadOmac1zcHBAfHx8iba2trbYunWrPsshIiIiCZgxIwiRkcswapQv1Go1/PxGw9a2nWa5qakp5sz5BDNmTEXnzl0xatRYLFnyCfz9k9GixT9gZ9dRa32nT/+B+PgdUKsFFixYirp165a63dLaDRgwCLdvZ2L8+FEQQo0mTZph3rxFFd6XN954AyYmphg/PgCrV//nhV+6KxPPXqt7SfHhfyKi/+HD/6Rvt26lw8amRU2XgV69uiAh4SfNrdAXbVfVnh0nXQ//8yuZiIiIiCSC33lEREREL63k5BNV2q6m8YoZERERkUQwmBERERFJBIMZERERkUQwmBERERFJBB/+JyIioipRp64ZzEyr/ovq8/ILkfUor8rXK0UMZkRERFQlzEyNMSJoY5Wvd1PESGSh4sHs8uWLCAjwxeLFyzXfmTlz5kcIDp6Lhg2tqrS2Q4d+xbVr6fD19auS9fFWJhEREb1Sdu+Oh4tLX8TFbdfMi4yMqfJQBgDnzv2Jx48fV9n6eMWMiIiIXhlFRUX48cckrFy5BpMnj0FGxnU0adIUQ4d64bPPVuH330/ihx8S8PDhA/Ts6YQ7d5QwNTVFWtqTgDV69Fj07++JvLw8LF++GBcv/gUjIyP4+vrB3X0AEhN3afo3bdocZ86kAgBsbBrB03PgC9fPYEZERESvjJSUZNjY2KB58xZwdOyNuLjtmDLlI602SuVtfPfdFigUCixZMh8ZGdexatV63Lt3F2PH+qNr1+7YvHkj6tWrhw0bYvHgwQOMHz8KrVu3LdF/3bpVAFAloQzgrUwiIiJ6hSQmxqNvXzcAwLvv9kNi4i4UFhZqtWnTxhYKxf+uTXl4eEGhUMDa+nW8/bYdUlNP4eTJE/D0HAQAqF+/PhwdnfD77ydL7V+VeMWMiIiIXgn379/DkSMpOH/+HLZs+S+EEMjKeoSDB/drtTM1NdWalsv/F4eEUEMuV0AItVYbIQCVqqjU/lWJV8yIiIjolZCUlIjOnbthx45EbN26C9u2JSAgYAx27txWbr/9+/dCCIFbt27izz/PwM7OHp06dcXu3XEAgAcPHuDXXw+gY8cuJfrK5XKoVKoq2wdeMSMiIqIqkZdfiE0RI/Wy3or44YddmDDhQ615Pj7vY9Omb1G7tnmZ/fLz8zB2rD8KCwswa1Yo6tWrjw8+GIdPP12OgIBhUKvVCAgYg7ZtbXHp0gWtvvb2nbBkyXxYWlpi6FDfyu/cM2RCCPHCa5GAu3ezoVa/ErtCRPTCrKzq4GTEuJouQ0vnoLVQKrNqugyqIrdupcPGpkVNl/HCliyZj44dO8PDw0sv6392nIyMZGjQoOyQyFuZRERERBLBW5lERERksEJD59d0CVp4xYyIiIhIIhjMiIiIiCSCtzKJSO8s6plAYaK/9/48j6KCfNx/WFDTZRARaWEwIyK9U5iYSvITggCDGRFJC4MZERERVQl9XR2v6BXux4+z8dVXK3Hq1EnI5QrUqVMHU6dOhxBq7Ny5DcHBcxEfvwO1atVCv379q7zOqsBgRkT0AurUNYOZqXFNl0EkCfq6Ol6RK9xqtRozZ/4TnTp1wfr1m6BQKPDbbycwc+ZH+O67WAQHzwUAnD79Bzp27FzlNVYVBjMiohdgZmqMEUEba7qMEvTx9nUiKfvttxPIzLyFsWMnwsjoyWcbO3Xqgjlz5uHEiePYsWMLRo0ai+TkX3Dy5HHUqVMH4eGLEBsbh9q1zXHz5g3MmvVPfPfdlhrdD34qk4iIiF56f/11Hq1bt9GEsqccHHrBwsICANC1a3f06uWEceMmwdGxNxwceuHnn/cBAJKSdqN/f89qr/tZDGZERET00jMyksGkks+3eXoOxJ49iQCAvXuTGMyIiIiIqoKt7Vv4669zePYrwFetWllmH3v7TlAqlTh4cD8aNWqChg2t9F2mTnzGjIiIqArwfX01y86uIywsLPH116sxevQ4yOVyHD16GImJ8WjVqrWmnVwuh0qlAgDIZDK4u3tixYpITJ06rYYq18ZgRkREVAX4vr4nQfDJNqt+vbrIZDKEh/8bn332KQIChkGhUKBevfr417+ikZ2drWnXpUs3rFr1BczNzeHi0hd9+7rh+++/g6Nj7yqv+3kwmBEREVGVeHJ1ruau0NWvXx9z5y4qdVmnTl0AAH37uqFvXzcAT16xcfToYfTv7wETE5Nqq7M8DGZERERkkEJDZyEz8xY+/fTzmi5Fg8GMiIiIDNKyZZ/WdAkl8FOZRERERBLBYEZERETP5dlXU5A2IdQAZJXqw2BGRERElaZQmODx40cMZ6UQQqCoqBAPHtyBiYlZpfryGTMiIiKqNAsLK9y/r0R29oOaLkWSjIzkqFXLHObm9SrVj8GMiIiIKk0uV6Bhw0Y1XcYrh7cyiYiIiCSCV8yIXiF16prBzNS4pssgIqLnxGBG9AoxMzXGiKCNNV1GCZsiRtZ0CURELwXeyiQiIiKSCL0Hs+XLlyM4OBgAkJKSAi8vL7i6uiIqKkrTJi0tDT4+PnBzc0NoaCiKior0XRYRERGR5Og1mB0+fBg7duwAAOTl5WHOnDn44osvkJiYiDNnzuDgwYMAgFmzZmHevHnYs2cPhBCIjY3VZ1lEREREkqS3YPbgwQNERUVh0qRJAIDU1FS0aNECzZo1g0KhgJeXF5KSkpCRkYG8vDzY29sDAHx8fJCUlKSvsoiIiIgkS28P/8+bNw/Tp0/HzZs3AQC3b9+GlZWVZrm1tTUyMzNLzLeyskJmZmalt9eggfmLF01EBsXKqk5Nl2BwOObVj2P+ctFLMNuyZQsaNWoEBwcHbN++HQCgVqshk/3v+6KEEJDJZGXOr6y7d7OhVvNrIciw8Rdw5SiVWS+8Do555VTFmEuVVM+FV3nMX0ZGRrJyLybpJZglJiZCqVRi0KBBePjwIXJycpCRkQG5XK5po1QqYW1tDRsbGyiVSs38O3fuwNraWh9lEREREUmaXoLZ+vXrNT9v374dx44dw4IFC+Dq6or09HQ0bdoUCQkJGDJkCJo0aQJTU1OcPHkSnTt3RlxcHJycnPRRFhEREZGkVdsLZk1NTREeHo7AwEDk5+fD2dkZ/fv3BwBERkYiLCwM2dnZaN++PQICAqqrLCIiIiLJ0Hsw8/HxgY+PDwDAwcEB8fHxJdrY2tpi69at+i6FiIiISNL45n8iIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIvX+JORERUVWqU9cMZqbGNV0GkV4wmBER0UvFzNQYI4I21nQZJWyKGFnTJdArgLcyiYiIiCSCwYyIiIhIIhjMiIiIiCSCwYyIiIhIIhjMiIiIiCSCwYyIiIhIIhjMiIiIiCSCwYyIiIhIIhjMiIiIiCSCwYyIiIhIIhjMiIiIiCSCwYyIiIhIInQGs9zcXJw6dQoA8P3332POnDm4ceOGvusiIiIiMjg6g1lISAj27duH1NRUrF27Fo0aNcLcuXOrozYiIiIig6IzmF27dg0ff/wxfv75ZwwePBiBgYF48OBBNZRGREREZFh0BrOioiIAQHJyMt555x2oVCrk5OTovTAiIiIiQ6PQ1aBjx47w8PCAXC5Hp06dMGrUKPTo0aM6aiMiIiIyKDqD2dy5c/H777/D1tYWRkZGGDt2LJycnKqjNiIiIiKDojOYyeVyPH78GCtWrIBcLoeLiwuMjPiWDSIiIqKqpjNhffbZZwgPD0edOnVgZmaGTz75BN9++2111EZERERkUHReMYuPj8f27dtRp04dAMCYMWPg6+uLgIAAvRdHREREZEh0XjGrX78+ateurZmuW7cuXnvtNb0WRURERGSIdF4x69y5M6ZMmYJhw4ZBLpcjPj4ejRs3xo8//ggAcHV11XuRRERERIZAZzA7e/YsAODrr7/Wmr9hwwbIZDIGMyIiIqIqojOYbdiwoTrqICIiIjJ4ZQazJUuWIDQ0FJMmTSp1+VdffaW3ooiIiIgMUZnBzMHBAQDg5uZWbcUQERERGbIyg1mfPn0AADt37sR//vOfaiuIiIiIyFDpfF1GVlbWc39peXR0NDw8PODp6Yn169cDAFJSUuDl5QVXV1dERUVp2qalpcHHxwdubm4IDQ3VfHk6ERERkaHQ+fB/rVq14OLigrZt22q9v0zXM2bHjh3DkSNHEB8fj6KiInh4eMDBwQFz5szBhg0b0KhRI0ycOBEHDx6Es7MzZs2ahcWLF8Pe3h5z5sxBbGwsRowY8eJ7SERERPSS0BnMhg4d+lwr7tatG7799lsoFApkZmZCpVLh0aNHaNGiBZo1awYA8PLyQlJSElq1aoW8vDzY29sDAHx8fBATE8NgRkRERAZFZzBLT0/HtGnTtOYtXrwYgwcP1rlyY2NjxMTE4Ouvv0b//v1x+/ZtWFlZaZZbW1sjMzOzxHwrKytkZmZWYjeIiIiIXn5lBrOYmBg8evQIiYmJyM7O1swvLCxEcnIywsLCKrSBjz76COPHj8ekSZNw5coVyGQyzTIhBGQyGdRqdanzK6NBA/NKtScisrKqU9MlGByOefXjmL9cygxmdnZ2OH36NIyMjFC/fn3NfLlcjsjISJ0rvnTpEgoKCtCuXTvUqlULrq6uSEpKglwu17RRKpWwtraGjY0NlEqlZv6dO3dgbW1dqR25ezcbarWoVB+iVw1/AVeOUpn1wuvgmFcOx7z6VcWYU9UxMpKVezGpzGDm7OwMZ2dnODk5oUOHDpXe8PXr1xETE4Pvv/8eALBv3z74+voiIiIC6enpaNq0KRISEjBkyBA0adIEpqamOHnyJDp37oy4uDg4OTlVeptERERELzOdz5g9TygDngS71NRUeHt7Qy6Xw9XVFZ6enrC0tERgYCDy8/Ph7OyM/v37AwAiIyMRFhaG7OxstG/fHgEBAc+1XSIiIqKXlc5g9iICAwMRGBioNc/BwQHx8fEl2tra2mLr1q36LIeIiIhI0sp8wexPP/0EACgoKKi2YoiIiIgMWZnBLDo6GgAwbNiwaiuGiIiIyJCVeSuzdu3acHNzQ2ZmJry8vEos37Vrl14LIyIiIjI0ZQaztWvXIi0tDaGhoZg7d2511kRERERkkMoMZubm5ujatStWrVoFa2trnD17FkVFRejQoQPMzfkyVyIiIqKqpvNTmVlZWfD390fDhg2hUqmQmZmJr776Cp06daqO+oiIiIgMhs5gtnz5ckRGRuKdd94BABw+fBjh4eGIjY3Ve3FEREREhqTMT2U+9fjxY00oA568hyw3N1evRREREREZIp3BTCaTISMjQzN9/fp1re+7JCIiIqKqofNW5ocffohhw4bBwcEBMpkMycnJ+OSTT6qjNiIiIiKDojOY9e3bFy1btsSRI0egVqsxceJEvPnmm9VRGxEREZFBqdB3ZbZs2RItW7bUdy1EREREBk3nM2ZEREREVD0YzIiIiIgkQmcwCwoKqo46iIiIiAyezmCWlpYGIUR11EJERERk0HQ+/G9tbQ1PT0/Y2dmhdu3amvlhYWF6LYyIiIjI0OgMZh07dkTHjh2roxYiIiIig6YzmE2dOhV5eXlIT09H69atkZ+fj1q1alVHbUREREQGReczZn/88Qf69u2LiRMn4vbt2+jduzd+++236qiNiIiIyKDoDGbLly/HN998g/r168PGxgYRERFYsmRJddRGREREZFB0BrO8vDy0atVKM+3s7AyVSqXXooiIiIgMkc5gplAo8PDhQ8hkMgDA5cuX9V4UERERkSHS+fD/5MmT4efnB6VSiRkzZuDQoUNYuHBhddRGREREZFB0BjMXFxe0bNkShw4dglqtxocffog333yzOmojIiIiMigV+q7MoqIiqNVqKBQKKBQ6sxwRERERPQedwWzbtm0ICAjA6dOnceLECYwcORJ79uypjtqIiIiIDIrOy1/ffPMNduzYAWtrawDAjRs3MHHiRLi5uem9OCIiIiJDovOKmbGxsSaUAUDjxo1hbGys16KIiIiIDFGZV8zOnj0LAGjbti0WLlyIYcOGQS6XY/v27ejUqVO1FUhERERkKMoMZoGBgVrTBw4c0Pwsk8kQFhamt6KIiIiIDFGZwWz//v3VWQcRERGRwdP58L9SqcSOHTvw4MEDrflBQUH6qomIiIjIIOl8+H/y5MlITU2FEELrDxERERFVLZ1XzAoLC/H5559XRy1EREREBk3nFbP27dvjr7/+qo5aiIiIiAyazitmnTp1gre3N6ysrLS+jmnfvn16LYyIiIjI0OgMZuvWrUNkZCSaN29eHfUQERERGSydwaxu3brw8PCojlqIiIiIDJrOYPbOO+9g+fLlcHV1hYmJiWZ++/bt9VoYERERkaHRGcx27doFANizZ49mnkwm4zNmRERERFVMZzDjNwAQERERVQ+dwWz9+vWlzv/ggw+qvBgiIiIiQ6YzmBV/h1lBQQGOHz8OBweHCq38888/xw8//AAAcHZ2RlBQEFJSUrBs2TLk5+fD3d0d06dPBwCkpaUhNDQUjx8/RpcuXbBgwQKt13MQERERvep0Jp9ly5ZpTWdmZiI0NFTnilNSUpCcnIwdO3ZAJpNh3LhxSEhIQGRkJDZs2IBGjRph4sSJOHjwIJydnTFr1iwsXrwY9vb2mDNnDmJjYzFixIjn3zMiIiKil4zON/8/6/XXX0dGRobOdlZWVggODoaJiQmMjY3x5ptv4sqVK2jRogWaNWsGhUIBLy8vJCUlISMjA3l5ebC3twcA+Pj4ICkpqdI7Q0RERPQyq9QzZkIInDlzBg0aNNC54tatW2t+vnLlCn744Qf4+fnByspKM9/a2hqZmZm4ffu21nwrKytkZmZWeCeIiIiIXgWVesYMABo1aoSgoKAKb+DChQuYOHEigoKCIJfLceXKFc0yIQRkMhnUajVkMlmJ+ZXRoIF5pdoTEVlZ1anpEgwOx7z6ccxfLpV+xqwyTp48iY8++ghz5syBp6cnjh07BqVSqVmuVCphbW0NGxsbrfl37tyBtbV1pbZ192421Grx3LUSvQr4C7hylMqsF14Hx7xyOObVryrGnKqOkZGs3ItJZQazkJCQMjvJZDIsXbq03A3fvHkTH374IaKiojSf4rSzs8Pff/+N9PR0NG3aFAkJCRgyZAiaNGkCU1NTnDx5Ep07d0ZcXBycnJx07RsRERHRK6XMYFb8GbGn7t+/j//85z9o0qSJzhWvW7cO+fn5CA8P18zz9fVFeHg4AgMDkZ+fD2dnZ/Tv3x8AEBkZibCwMGRnZ6N9+/YICAh4nv0hIiIiemmVGczGjBmjNZ2SkoLZs2fDy8sLYWFhOlccFhZWZrv4+PgS82xtbbF161ad6yUiIiJ6Vel8xqyoqAiffvopduzYgQULFsDNza066iIiIiIyOOUGsytXrmDGjBmoXbs2du7cCRsbm+qqi4iIiMjglPmC2W3btuH9999Hv379sGHDBoYyIiIiIj0r84pZaGgojIyMsHr1aqxZs0Yz/+k7xn777bdqKZCIiIjIUJQZzPbt21eddRAREREZvDKDWUVeiUFEREREVafSX2JORERERPrBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBLBYEZEREQkEQxmRERERBKh12CWnZ2NAQMG4Pr16wCAlJQUeHl5wdXVFVFRUZp2aWlp8PHxgZubG0JDQ1FUVKTPsoiIiIgkSW/B7I8//sDw4cNx5coVAEBeXh7mzJmDL774AomJiThz5gwOHjwIAJg1axbmzZuHPXv2QAiB2NhYfZVFREREJFl6C2axsbH45JNPYG1tDQBITU1FixYt0KxZMygUCnh5eSEpKQkZGRnIy8uDvb09AMDHxwdJSUn6KouIiIhIshT6WvGSJUu0pm/fvg0rKyvNtLW1NTIzM0vMt7KyQmZmZqW316CB+fMXS0QGycqqTk2XYHA45tWPY/5y0Vswe5ZarYZMJtNMCyEgk8nKnF9Zd+9mQ60WVVIr0cuKv4ArR6nMeuF1cMwrh2Ne/apizKnqGBnJyr2YVG2fyrSxsYFSqdRMK5VKWFtbl5h/584dze1PIiIiIkNSbcHMzs4Of//9N9LT06FSqZCQkAAnJyc0adIEpqamOHnyJAAgLi4OTk5O1VUWERERkWRU261MU1NThIeHIzAwEPn5+XB2dkb//v0BAJGRkQgLC0N2djbat2+PgICA6iqLiIiISDL0Hsz279+v+dnBwQHx8fEl2tja2mLr1q36LoWIiIhI0vjmfyIiIiKJYDAjIiIikggGMyIiIiKJYDAjIiIikggGMyIiIiKJqLbXZRBJhUU9EyhMTGu6DC1FBfm4/7CgpssgIqIaxmBGBkdhYoqTEeNqugwtnYPWAmAwIyIydLyVSURERCQRDGZEREREEsFgRkRERCQRDGZEREREEsGH/0lv6tQ1g5mpcU2XQURE9NJgMCO9MTM1xoigjTVdRgmbIkbWdAlERESl4q1MIiIiIolgMCMiIiKSCAYzIiIiIolgMCMiIiKSCAYzIiIiIolgMCMiIiKSCAYzIiIiIolgMCMiIiKSCAYzIiIiIongm/+JiIjopWRRzwQKE9OaLkNLUUE+7j8seO7+DGZERET0UlKYmOJkxLiaLkNL56C1ABjMiIiISE/q1DWDmalxTZdhEBjMiIiIqFxmpsYYEbSxpssoYVPEyJouocrx4X8iIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiWAwIyIiIpIIBjMiIiIiiZBUMNu1axc8PDzg6uqKjRs31nQ5RERERNVKUdMFPJWZmYmoqChs374dJiYm8PX1Rffu3dGqVauaLo2IiIioWkgmmKWkpOCdd95B/fr1AQBubm5ISkrC1KlTK9TfyEimx+roeTW0qF3TJZTKpG6Dmi6hhKo6hznmFccxr34c8+rHMa9+5Y25ruMhE0KIqi7oeaxatQo5OTmYPn06AGDLli1ITU3FokWLargyIiIiouohmWfM1Go1ZLL/pUghhNY0ERER0atOMsHMxsYGSqVSM61UKmFtbV2DFRERERFVL8kEsx49euDw4cO4d+8ecnNz8eOPP8LJyammyyIiIiKqNpJ5+P/111/H9OnTERAQgMLCQgwdOhQdOnSo6bKIiIiIqo1kHv4nIiIiMnSSuZVJREREZOgYzIiIiIgkgsGMiIiISCIYzIiIiIgkgsGMiIiISCIYzKpAdnY2BgwYgOvXr5e63N/f/7nWu2LFCnz22Wea6WPHjqF79+4YNGgQBg0ahJCQkOda78uqtHH+/fff8f7778PT0xMzZsxAQUFBiX4hISHIyMio8HYuXrwIX19fDBw4EP7+/pq+BQUFmDVrFtzd3TF48GBcunTpxXdKwqKjo+Hh4QFPT0+sX79ea1lhYSFGjRqFo0ePltq3smP+1K1bt9CtWzfNMc7IyEDHjh015/zYsWMrvyMvoeXLlyM4OFgzrY/zPDs7Gx9//DG8vb3h7e2Ns2fPAjC883z//v3w8fGBu7s7Fi9erLWsqs9zHx8fzbns5uaGt956C3fu3DHY87y4LVu2aPZ/0KBB6Ny5MxYuXIiQkBC4urpq5u/du7fU/qtXr4abmxu8vLzw5ZdfauanpKTAy8sLrq6uiIqKqq7deTGCXsipU6fEgAEDRPv27cW1a9dKbdOmTZtKrfPRo0ciJCREdOjQQcTExGjmr1u3Tnz11VcvVO/LqrRxzsrKEj179hRpaWlCCCGmT58uNm7cWKKvi4tLmcemNH5+fuLgwYNCCCE2bdokZsyYIYQQYu3atWLu3LlCCCGOHTsm3nvvvRfaJyk7evSo8PX1FYWFhSI3N1e4uLiIS5cuCSGEuHTpkhg2bJh4++23xZEjR0rtX9kxF0IIlUolxowZI+zt7TV9k5KSNGNuKFJSUkT37t3F7NmzhRD6O8/nzJkj/vWvfwkhhDh48KAYOnSoEMKwzvOrV6+KXr16iZs3b4qCggIxfPhwceDAASGE/s7zp2bNmiW+/PJLIYRhnufl+euvv0S/fv3E3bt3xYABA0RmZma57Q8dOiQGDBggsrKyRFFRkZg4caLYs2ePyM3NFc7OzuLq1auisLBQjBkzRnN8pYxXzF5QbGwsPvnkkzK/Purp/8Dee+89AMDPP/+MQYMGwcvLC1OmTMGdO3dK9Nm3bx/+8Y9/4IMPPtCaf/r0aSQnJ8PLywuTJk3CzZs3q3hvpKu0cT506BDs7e1ha2sLAAgLC0O/fv20+q1evRq3b9/GhAkTcP/+fZw6dQrvvfceBg4ciFGjRiE9Pb3EttavXw8nJyeo1WrcuHEDdevWBQAcOHAAAwcOBAB07doV9+7dw40bN/S1yzWqW7du+Pbbb6FQKHD37l2oVCq89tprAICtW7di3LhxsLOzK7Xv84w5AKxduxY9evSAhYWFZt7p06fx119/YdCgQQgICMD58+erfmcl5MGDB4iKisKkSZM08/Rxngsh8OOPP2LChAkAACcnJyxduhSAYZ3ne/fuhYeHB2xsbGBsbIyoqCjNea2v8xwADh8+jHPnzmH8+PEADO8812X+/PmYPn06atWqhRs3bmDOnDnw8vJCTEwM1Gp1ifZ//vknevXqBXNzc8jlcjg6OuKnn35CamoqWrRogWbNmkGhUMDLywtJSUk1sEeVVNPJ8FVR3v+cnl4xu3PnjujVq5em3Zo1a0RgYGCZ64yJidG6YjZ37lyxZ88eIcSTKznDhg2rqvJfGsXHedWqVWLWrFli2rRpYuDAgWL+/PkiLy+vzD75+fnCxcVF/PHHH0IIIRITE4WPj0+p23n48KHo2bOn6Ny5s/jzzz+FEEK4urqKK1euaNr4+vqKkydPVvUuSkp0dLSws7MTs2fPFmq1WmuZn5+fzisJFR3z06dPiw8++ECoVCqtYxwTEyM2btwoVCqVOHDggHj33XdFfn5+Fe+ldAQGBoqUlBSxbds2zRUzfZznSqVS9OjRQ3z55Zfi/fffF/7+/porcoZ0ns+bN08sWrRITJw4UQwcOFD8+9//1ut5/pSvr6/Yv3+/ZtrQzvPyHDp0SDN2V69eFVOmTBGZmZkiJydH+Pv7i82bN5fok5KSIgYMGCDu378v8vLyxJgxY8QHH3wgdu3aJT7++GOtdX/wwQfVti/Pi1fMqlFqaio6dOiApk2bAgCGDRuGI0eOVLj/woUL4erqCgAYPnw4Ll68iKysLL3U+jJQqVRITk7GjBkzsH37duTm5mL16tVltr9y5Qrq1q2r+aovd3d3XL16tdQxrFu3LpKTk/Hvf/8bkydPhkqlghACMplM00YIASOjV/uv0EcffYTDhw/j5s2biI2NrXT/iox5bm4uFixYgMWLF5cYz8DAQIwYMQJGRkZwdnbGa6+9hsuXL7/YTknUli1b0KhRIzg4OGjN18d5rlKpcOfOHdSpUwebN2/GxIkT8eGHHwKAQZ3nKpUKhw8fxtKlS7F582akpqZix44dlV5PZX63XLhwAffv34eLi4tmniGd57r897//1dwtatasGVauXAlra2vUqlUL/v7+OHjwYIk+Dg4O8PHxgb+/P8aNG4fOnTvD2NgYarW6xLlcfFqqXs2/bRL17CVYIQSKiooq3PfLL7+ESqXSmi+Xy6usvpdNw4YNYWdnh2bNmkEul8Pd3R2pqallti/tErgQosSYJiYmQvz/bypzcnJCXl4eHj58iNdffx23b9/WtLtz506Zt7BfdpcuXUJaWhoAoFatWnB1dX2u2ysVGfMTJ07g7t27mDx5MgYNGqS5PXT58mVs2LAB9+/f1+qrUEjmK36rVGJiIg4dOoRBgwYhJiYG+/fvx9KlS/VynltYWEChUGDAgAEAgJ49eyInJwd37941qPO8YcOGcHBwgKWlJczMzNC3b99yx7YsFf3dAgA//fQTPDw8tOYZ0nlenoKCAhw/fhx9+vQBAJw/fx579uzRLH86Lvv27dN8GCA6OhrZ2dlwdXXFrl27sGHDBpiYmKBZs2awsbGBUqnU9FcqlS/FucxgVg3kcjmKiopgZ2eHP/74Q/OJs82bN6N79+4VWoeRkRH27t2rOUl37twJOzs7zXM/hqhXr144e/as5lm7n3/+Ge3bty/RTi6XQ6VSoWXLlnjw4IHmF29iYiIaN26M+vXra7X/+uuvNZ/8OXLkCCwsLGBpaQlnZ2fExcUBeBImTE1N0bhxYz3uYc25fv06wsLCUFBQgIKCAuzbtw+dO3eucP/KjLmjoyP279+PuLg4xMXFwdraGqtXr0bLli1x/PhxbN26FcCTTyWr1Wq0bNmySvdVKtavX4+EhATExcXho48+Qp8+fTBnzhy9nOcmJibo0aMHdu/eDQA4deoUatWqBQsLC4M6z11cXJCcnIxHjx5BpVLh119/LXVsy1LZ3y3Ak7Hu0qWL1jxDOs/Lc/78efzjH//Q/LsmhMDSpUvx8OFDFBYWYvPmzejXrx/effddze+Lf/7zn7h+/TqmTJmCoqIiZGVlYevWrXB3d4ednR3+/vtvpKenQ6VSISEhAU5OTjW8l7oZXiSvAe+++y4GDRqE7du3Y+HChZg6dSoKCwvRuHFjLFmypMLrWb58OebOnYuVK1fC0tISEREReqxa+ho1aoSFCxdi0qRJyM/PR7t27TB79uwS7Xr37o0JEyZg7dq1iIqKwqJFi5Cbm4t69eqV+vHp8PBwzTjXqVMHMTExAJ689mTevHnw9PSEiYnJKz3+zs7OSE1Nhbe3N+RyOVxdXeHp6Vnh/pUd87KEhoYiODgYcXFxMDU1xaeffvrK3lYri77O8yVLlmDevHnYtGkTFAoFoqKiYGRkZFDnuZ2dHcaNG4cRI0agsLAQPXv2xJAhQyrc/3nO82vXruH111/Xmsfz/Ilr167BxsZGM21ra4sJEyZg+PDhKCoqgqurq+Yqb3G2trZwdXXFwIEDoVKpMHr0aM1/JMPDwxEYGIj8/Hw4Ozujf//+1bY/z0smnt6zISIiIqIaZXiRnIiIiEiiGMyIiIiIJILBjIiIiEgiGMyIiIiIJILBjIiIiEgiGMyIqNLGjBmDe/fulbps/PjxuHjx4nOvOykpCf7+/s/dvzTF633R+ioiODgY69atK7dNVlYWAgICKr3u7du3o3fv3hg7dmyJZfo8LkRUPfgeMyKqtEOHDpW5bM2aNdVYScUUr1cq9T18+BCnT5+udL+dO3di+vTpGDRoUIllL9txIaKSGMyIqFJCQkIAAKNGjcLq1asxcuRIdOjQAefPn8eMGTOwbNkyREdHIycnB5GRkWjcuDEuX74MMzMzhIeH48033yyxzujoaOzatQv169dHixYtNPODg4PRunVrzdWh4tN9+vTR2q5CocCqVatQUFCAe/fuwdvbG9OmTSu13ujoaLz99tvYvHkzNmzYACMjIzRs2BBz587FG2+8geDgYJibm+P8+fO4desW2rZti+XLl6N27dpadZ84cQLh4eGar+SZOHEi3NzcSrSJiIhAbm4ujI2NMW3aNDg5OSEkJAR5eXmal08X/3q1rKwsLFiwAOfOnYNMJoOjoyNmzJiBiIgInD59GtevX8f9+/cxevRovR4XIqoB1f616UT00mvTpo24e/euEEIIFxcX8fnnn2uWubi4iNTUVHHkyBFha2srjh8/LoQQYtOmTWLw4MEl1rV3717h4eEhsrKyRGFhoZgwYYLw8/MTQggxe/ZssXbtWk3b4tPFt6tWq4Wfn5/4+++/hRBC3Lp1S7Rr105T47P1pqamipSUFNG3b1/N/G3btgl3d3ehVqvF7NmzxbBhw0R+fr4oKCgQ3t7eYuvWrSVqDwgIEAkJCUIIIdLS0sT8+fO16rx3755wcHAQp06dEkII8ddff4lu3bqJq1evimvXrgl7e/tSxzcoKEgsWrRIqNVqkZ+fL8aMGSNWrVolhBDCz89P/PDDD3o/LkRUM/iMGRG9sGe/++8pW1tbzbIhQ4YgLS1N68uaAeDw4cPo168fzM3NoVAoKvWVOE/XLZPJ8NVXX+Hs2bP4/PPPER4eDiEEcnNzy+z766+/wsPDA5aWlgAAHx8fZGZmar7L1tHRESYmJjA2NkabNm3w8OHDEutwd3fHwoUL8fHHH+Ps2bOYMWOG1vLU1FQ0b94cdnZ2AIDWrVujU6dOOHbsWLn79csvv8DPzw8ymQwmJibw9fXFL7/8UuFxeepFjgsR1QwGMyJ6YU+/dPhZxW/PlTdPFPtmuOLLZTKZ1rLCwsJSt5uTk4PBgwfj7NmzeOuttxAUFASFQqHV91lPbz8+W0dRUREAwMzMrMw6nvL19UV8fDx69uyJ5ORkDBw4EPn5+ZrlKpUKMpmszG2UV1vxfmq1Wmef0rzocSGi6sdgRkSVJpfLKxQUzp07h3PnzgEANm/ejI4dO6Ju3bpabZycnJCUlIRHjx5BrVYjLi5Os8zCwgJnzpwBAGRmZpZ5pSk9PR3Z2dmYNm0a+vTpg6NHj6KgoEATvkqr19HREYmJiZpPMW7btq3EM266+Pr6Ii0tDT4+Pli0aBEePXoEpVKpWW5vb4/Lly8jNTUVAHDhwgUcP34c3bp1g0KhgEqlKjXw9erVC9999x2EECgoKEBsbCx69Oihs56qPC5EVDP48D8RVVr//v3h7++Pzz77rNx2DRs2xIoVK5CRkQFLS0tERESUaOPs7Izz589jyJAhqFu3LmxtbTW31fz9/TFz5ky4ubmhadOmeOedd0rdTtu2bdG7d2+4u7vDxMQEbdq0QatWrZCeno7mzZuXWm/Pnj0xevRojBo1Cmq1GpaWlli1ahWMjCr+/9WZM2di6dKlWLFiBWQyGaZOnYqmTZtqlltaWiI6OhqLFi1CXl4eZDIZli1bhjfeeAMqlQodOnSAp6cnNm7cCAsLC02/sLAwLF68GF5eXigsLISjoyMmTZqks56qPC5EVDNkorxr/UREz+no0aNYtGgREhISaroUKobHhUjaeCuTiIiISCJ4xYyIiIhIInjFjIiIiEgiGMyIiIiIJILBjIiIiEgiGMyIiIiIJILBjIiIiEgi/h8V786OaT+1nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(uber_data, \"trip duration slot\", \"Pickup point\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1632,
   "id": "a7a4196d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(uber_data, \"Trip duration_hr\", \"Pickup point\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1633,
   "id": "98698c7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(uber_data, \"Trip duration_hr\", \"Request hour timeslot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1634,
   "id": "dfebce20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(uber_data, \"Request hour\", \"Trip duration_hr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fb81ed",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "1. Trips from city to Airport and vice versa last for less than 30 mins with few exceptions\n",
    "2. Trips from city to Airport take little longer than from Airport to the city\n",
    "3. Number of trip with more than 1 hr of travel time are higher in numbers during peak time\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1635,
   "id": "a4313ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.countplot(data=uber_data, y=\"Request date\", hue=\"Request hour\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0470a352",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "All the dates have the similar pattern of requests, no unusual patterns are observed.\n",
    "    </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abce6c6d",
   "metadata": {},
   "source": [
    "### Analysis of City and Airport as pickup points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1653,
   "id": "8216cfa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataframe based on pick up points to do individual analysis\n",
    "\n",
    "airport = uber_data[uber_data[\"Pickup point\"] == \"Airport\"]\n",
    "city = uber_data[uber_data[\"Pickup point\"] == \"City\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d151bba",
   "metadata": {},
   "source": [
    "<span style=\"color:red\">Bivariate analysis of city and airport -\"Request hour timeslot\" against \"Status\" </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1637,
   "id": "33f455d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p2rUrXbp0wWKx4O7uzpw5c6xD9QMGDKBJkyYULVqUmjVrpmudpUqVwtnZmbZt2zJ16tQH7FVS7UsHBweGDh3KgAEDrEd+x48fn+SoXalSpRgxYgRBQUGYzWZy5MjBJ598gqura5K6li5dmuxInMiDMhmpjRGIiM0zDIPPPvuM8+fPM2rUqCxv/99XGqbX4MGDCQkJwcXFJZMqS797r/h9HPn6+jJs2DBq1KiR3aWIyH+gI2wij7AXX3wRT09PPvroo+wuJd1iYmLw9va2ibAmIvKo0BE2ERERERuniw5EREREbJwCm4iIiIiNU2ATERERsXGP/UUH167dwmLRaXoiIiJiu+zsTOTNmyvV+Y99YLNYDAU2EREReaRpSFRERETEximwiYiIiNg4BTYRERERG/fYn8OWErM5kWvXIkhMjE97YXlsODg4kTevB/b2T+THXkREHmFP5G+ua9ciyJEjJ7lyFdQDeZ8QhmFw61YU165FkD9/oewuR0RE5IE8kUOiiYnx5MrlprD2BDGZTOTK5aajqiIi8kh6IgMboLD2BNI+FxGRR9UTG9hEREREHhUKbI+g3347TO/er9OlSwCdO79C//7v8NdffwLQt+/bXL9+Pc11pHc5ERERyX5P5EUHj7L4+Hjee68PH344m7JlywGwcWMoAwa8w9Kla9i7d0+61pPe5URERCT7KbA9YmJjY7l58yYxMbet0xo3bkauXLmYNGksAO+88zrvvz+dkydPsHDhFyQkJHDtWiTNmvnSs+ebjB8/Kslyb7/dk7FjJ1Gu3LMAtG3rx9ixkyhVqgzTpr3P4cO/Ym/vQOHCRRg6dAQ5c+bM+g0XEXlCuOV2xtnJKVPbiIuPJ+pGXKa2IRlLge0R4+bmxptv9qZ//964u+enYsWKVKlSjUaNmlCnTn1CQ9cyY8YccufOzdixIwgOHkmxYk9z5UoEbdr40q7dqwwdOsK6XJ48eVJt68iRwxw8uJ+vvlqKyWTio49m8OefJ3j++UpZt8EiIk8YZycnun7xbqa2Mf+16YAC26NEge0RFBDQiZYtX+bgwQP8+usBFi1awKJFC/jssy+ty5hMJiZNmkpY2Ha+/34Dp0//jWEYxMbGAHnS1U6JEqWws7OjV68uvPCCNw0aNOTZZ5/LnI0SERGRVOmig0fMoUO/8PXXX5IzZy5q167LW2+9y8KFSzCZTOzdu9u6XExMDN26deSPP45Rpkw53nrrXRwcHDAMI9k6TSZTkukJCQkAuLq6Mn/+N7z9dh/s7OwYMWIoK1YszfyNFBERkSR0hO0RkydPXhYsmEuFChWpVKkyAFevXuHWrZuULFkKe3t7EhMTiYgI59atW/Ts+RaOjo5s2LCe+Ph4LBYLgHW5O+vMw7FjRylfvgIHDuzj6tUrAOzcuZ1vvlnItGkfUbmyFwDHjv2e9RstIiLyhFNge8Q8/XRxJkz4gE8/nc3ly5dxdnYiVy4XhgwZwdNP/48GDV4kKKgXY8dOolatOnTo0BYnJ0dKlCjF//5XgnPnzlKkSFHrcuPHT+bNN99hypQJrF69grJly1G2bHkAatasxe7dYQQGtuepp3Li6urKe++FZHMPiIiIPHlMRkpjZI+Rq1dvYrEk3cRLl05TsGDxbKpIspP2vYjYOg8P1yy56CAiIjpT25AHY2dnIl8+l9TnZ2EtIiIiIvIQFNhEREREbJwCm4iIiIiNU2ATERERsXEKbCIiIiI2ToFNRERExMZl6n3Ybt68SUBAAJ988glFixYlLCyMCRMmEBcXR7Nmzejbty8AR48eJTg4mFu3blGtWjVGjRqFg4MDFy5cYODAgVy9epVnnnmGKVOmkCtXrgyv09UtBzmcHTN8vbFxCURHxWb4ekVEROTJkmmB7ddffyUkJIRTp04BEBsby9ChQ1m4cCGFChXi9ddfZ9u2bdSvX5+BAwcyduxYKleuzNChQ1myZAkdOnRg1KhRdOjQgRYtWjB79mw++ugjBg4cmOG15nB2pMOgRRm+3q8ndySa+we2Dz6YxOHDv5KYmMC5c2f53/9KANCuXQAtWrS0LnflSgQTJ45hypQZ6W7/zJlTzJ49nUuXLmIYBiVKlKJPn4H3feD7f1WnTjV27NiX6vwLF86zYMFchgwZnu51zp07B4Du3V//z/WJiIg8ijJtSHTJkiWMGDECT09PAA4dOkTx4sUpVqwYDg4O+Pn5sWHDBs6fP09sbCyVK1cGwN/fnw0bNpCQkMDevXtp0qRJkumPm/7932P+/K95//3p5M/vwfz5XzN//tdJwhpA/vweDxTWrlyJoHfvN/Dze5kFC75lwYJvKVGiJEOHDsjoTXggly5d5Pz5c9lag4iIyKMm046wjRs3Lsnry5cv4+HhYX3t6elJeHh4sukeHh6Eh4dz7do1XFxccHBwSDL9SdK2rR/PPvscJ04cZ9iw0QwfPoRly9YybtxInJ2dOXr0d27dukXXrt1p2rRFkveuXLmMqlWrU6dOPeDOA947duxCoUKFSUxMJDExkUmTxnLy5B/Y2dkRENCJZs18CQ1dS1jYDqKibnDlSgStWvlz6dIlDhzYi5tbbqZMmUFk5FUGD+7H00//j7///ouCBQsyfPgY3NxyW9u/ffs2H344ib/++hOLxULHjoG89FJTpk+fwoUL5/ngg0n07/8eCxfO54cfvsdstlCjRk3efPMdTCYTX3/9JWvWrCR37jy4urpSvnyFLO17ERERW5JlzxK1WCyYTCbra8MwMJlMqU6/+/97/ft1eqT0mIfLl+1wcMia6y3S2469vV2Ky9eqVZvx4ydx4cIF63yTycSFC+eZO3c+kZGRdOnSEW9vb/Lly29938mTx6la9YUk63NwsKNZs+YAzJz5EXny5OGbb5Zx/fo1unULpFy5ctjZmTh27AiLFi0hKiqKl1/2Zdq0WfTvP5C33urF/v17KFWqDH/+eZJ+/QZRtWo1pk//kC+++Iz+/QdZ21m4cB7lyz/LyJFjuHXrJj17vkbFihXp128Qn38+h/feG8KuXTs5ceIYX3zxFSaTiZEjh7F58wb+979nWL9+DV9++Q0mk4kePbpQocJzGbLP7Ozs8PBw/c/rERF51Om78NGSZYGtYMGCREREWF9HRETg6emZbPqVK1fw9PTE3d2d6OhozGYz9vb21uUfVErPErVYLCQmWh5+Yx5Aetsxmy0pLl+uXAUSEy1J5huGQbNmvoA97u4ePP98JQ4cOICPT6N73mnCwcEx1fb37dvL4MHDSEy04OKSmzp16rFv3z5y5crFc89Vwtk5Jx4eOQGoXLkaiYkWChQoyPXrNzCbLRQr9jSVKnmRmGihSZMWjBoVbG0rMdHCzz/vIS4ulrVrVwMQExPDiRMnyZkzJ4ZhkJhoYc+ePfz222G6dOkIQFxcLJ6eBYiIuELNmrVxcsoBgI9PI8xmc4bsM4vFoufniYhNy6ogpe9C25LWs0SzLLBVqlSJv//+m9OnT1O0aFHWrVtHmzZtKFKkCM7Ozuzfv5+qVauyevVq6tWrh6OjI9WqVSM0NBQ/Pz9WrVpFvXr1sqpcm+Hs7JzidHv7f3adYViSvAYoV+5Zjh37Pck0i8VCSMh7DBgwGMNIGn4MA8zmRAAcHZNeMXt3WPpB2rdYzAwbNoayZcsBEBl5FTe33Bw69EuSZV555VUCAjoBEB0djb29PatXrwD+Cdn29vaYzeYU+0FERORJkGX3YXN2dmbixIn07t2b5s2bU6JECZo2bQrAlClTmDBhAk2bNuX27dsEBgYCMGLECJYsWULz5s3Zt28fffr0yapybd7Wrd9jGAaXLl3k999/o1Klyknmt2z5Mrt27WTXrh3AnaHm+fM/59q1SNzd8+HlVZ316+8c/bp+/Trbt/9IlSrV0t3+2bOnOXHiOADr16+lZs1aSeZ7eVVn1aplwJ2jpl26vEp4+CXs7R2s4cvLqzobN4Zy+/ZtEhMTGTKkPz/+uIVq1aqzc+d2bt68SVxcHD/99MND9JCIiMjjI9OPsG3dutX6b29vb9asWZNsmXLlyrFs2bJk04sUKcLChQsztT64c7+0ryd3zJT1Zpa4uFi6d+9MQkI8AwcGkzt3niTz8+XLz5QpM/joo+l8/PFMLBYLZcqUY8KEDwB47bUefPDBJAID22OxWAgM7EbZsuX4888T6Wrf1dWNuXPncO7cOUqWLMXgwcOSzO/WrScffDCJzp1fwWKx8NZb71CkSFFcXFy4eTOaMWOGMWzYGE6e/INevbpisZipUaMWzZr5YjKZaNfuVXr0CMTV1ZUCBQplSJ+JiIg8qkyGYRhpL/boSukctkuXTlOwYPFsqui/GzduJFWqVKV5c79saf/ixQv07v06y5atzZb2/4tHfd+LyOPPw8OVrl+8m6ltzH9tus5hszFpncOmR1OJiIiI2Lgsu+hAMk5w8Mhsbb9QocKP5NE1ERGRR5WOsImIiIjYOAU2ERERERunwCYiIiJi4xTYRERERGycLjoA8uZ2wsEp5ScK/BeJ8XFcuxGf5nK3bt3kk09m88sv+7G3d8DV1ZWgoL7WpwRkltDQtRw8uJ/g4JG0bevHzJlzKFSocLrem923FhEREXmSKLABDk7O7J/cI8PXW3XQ58D9A5vFYmHAgHfx8qrGF198jYODAwcO7GPAgHf46qslyW6IKyIiIk8eBbZsduDAPsLDL9G9++vY2d0ZofbyqsbQocOxWCxMmjSWv/76k8jISEqVKsXIkeOIjIxk6NABlChRkj/+OI67ez7GjJmIm1tuNm3awJdfzgVMlC//LO+9F0J8fDwffjiJv/76E4vFQseOgbz0UtMU6zGbzXz00XQOHtyP2WyheXNf2rfviGEYzJo1lZ07d5A/f34sFgtVqlTNwp4SERF5cukctmz2xx/HKV26jDWs3eXtXYfTp0/h4ODInDlfsHjxSqKjo9m1aycAJ0+eoH37jixcuAQXFxc2bfqOiIjLzJz5IR9+OIuvvlqCxWImLGwHCxbMpWzZ8syb9xWzZ3/Kl1/O4/z5cynWs3btSgDmzVvEZ58tYPv2bfz660F+/HELf/xxnK++WsKYMZM4f/5s5naMiIiIWOkIWzazszPhlMr5c5Ure+Hmlpvly5dw5swpzp07S0xMDAB587pTpsydc9xKlChFVFQUv/12iOefr4SnZwEAhg0bA8CCBXOJi4tl/fo7z3GNjY3l77//SrHNfft+5sSJP9i/fx8AMTG3+fPPk5w69Rf16/vg4OBA3rx5qVmzdsZ1goiIiNyXAls2K1fuWVauXIZhGJhMJuv0OXNm8+yzFZg791PatQugefOWXL9+nbuPfnVyckqyHsMwcHBw4J5VcO3aNQAsFjPDho2xXsQQGXn1/4dPv0tWj9l850Ht9es3BOD69es89dRTfPTRdO596qy9vX2GbL+IiIikTUOi2axSpSrkzevOvHmfYjabAdizZxehoWvYs2cXDRs2okWLlri4uHDw4H4sFnOq6ypfvgJHjvzG1atXAJg580N27NiGl1d1Vq1aBsCVK1fo0uVVwsMvpbiOqlWrsWbNKhITE7l9+zZvvdWdI0cOU63aC2zd+j3x8fFERUWxZ8+uDO4JERERSY2OsHHn9ht3rujM+PWmxWQyMXHih8yc+QGBge1xcHAgd+48vP/+dOztHRg1KpjNmzfi4ODI889X5MKFC1RN5Vz//Pk9ePfd/vTr1xuLxcxzz1WkeXM/YmNj+OCDSXTu/AoWy50jaEWKFOXXXw8mW0fr1m05d+4sr73WAbPZTPPmfnh5VQPg6NHfCQxsj7t7Pv73vxL/qW9EREQk/UyGce9A1+Pn6tWbWCxJN/HSpdMULFg8myqS7KR9LyK2zsPDla5fvJupbcx/bToREdGZ2oY8GDs7E/nyuaQ+PwtrEREREZGHoMAmIiIiYuMU2ERERERsnAKbiIiIiI1TYBMRERGxcQpsIiIiIjZO92ED3HI74/yvJwdkhLj4eKJupH0vNhEREZH7UWADnJ2cMuWeN/Nfmw7cP7BdvHiBdu1aMnXqLKpXr2md3ratHzNnzqFQocLpaishIYEvvviMHTu2YWdnj5OTEz17vkn16jX+yyakaNmyb5k1axrLl68jX778D7WOiRPH0Lp1G27fvs28eZ8ya9anqS47btxIqlSpSvPmfkmmh4au5eDB/QQHj3yoGkRERB4VGhK1AQ4ODkyaNI7bt2899DrGjRvJlSsRfPbZl8yf/zXvvRfCmDHDU33I+38RGrqWunUbWB8m/zAGDx5GuXLPZmBVIiIijy8FNhuQP78H1avXYObMaSnO//LLeXTq1I7AwPbMnDnV+szRu86dO8tPP/1A376DcHZ2BqBkyVKMHDmOHDlyAHceJt+rV1cCAvwJCupFZORVAHx9G9G//zt07dqB8PBLBAX1onv3zvTsGchvvx1OVsvJkyeIioqiY8curF27CovFAkC3bh05duwoAGazmZdfbs61a5Fs3bqZXr260qXLq3To0IbDh38FICioFwcO7Euy7oMH9/Pmm93p1q0j7dq1Yvv2H63zwsK2061bJwID27Nly/fJ6jp69Ij1vX37vs2FC+fv3+kiIiKPEAU2GxEU1Ieff97F3r27k0zftWsnO3b8xOefL2TevEWcP3+WVauWJ1nmxInjFCv2NE899VSS6V5e1ShUqDDnzp3lzJlTfPLJPL79dgUFChRk48bvALh+/TodOwYyf/7XrF+/hlq16jB37kK6d3+DQ4d+SVbn+vVraNiwEeXKlcfe3t76EPgmTZqzefNGAA4c2EupUqXJnTsPq1cvZ/LkaSxY8A0dOgSycOH8VPtg+fLFDB48jHnzFjF4cAifffaxdV5sbCyffjqfDz+cxYwZU6wPuIc7w8ETJ45lxIhxzJu3iICATkyaNC7tThcREXlE6Bw2G5ErlwvvvRfCpEnj+PLLb63T9+/fS6NGTaxHylq0aMl3362nTZtXrMvY2dnhdJ+LJooWLUZQUF/Wrl3FmTOnOXLkMEWKFLXOr1DhOQCqVXuB4OBB/PHHcWrVqpOkDYDExES+//47PvxwFgA+Po1YvXo53t61adSoCW+80Y23336X77/fSOPGzbCzs2P8+PfZuXM7Z86c5uDB/djZpf43wrBhYwgL284PP2zmyJHDxMTEWOc1a+aLg4MD+fN7UKFCRX7//TfrvLNnT3PhwjkGD+5nnXbr1sMPL4uIiNgaBTYb8sILNZMNjRqGJckyhgFmc2KSaWXLPsupU6eIi4vF2TmHdfqSJV/j7p6PokWfZuTIYAICOuDj8yL29nYYhmFd7u57KlaszFdfLSEsbAdbtmwiNHQt06Z9ZF1u586fiI6OZujQgcCdAHftWiSXL4fj6VmAYsWKc/Dgfvbt+5l+/d7j9u3b9OzZhcaNm1GpUhVKlizF8uVLUt3+t9/uiZdXVapUqUrVqtUZNSrEOs/e3j5Jnzg4/PPRNZstFC5chPnzv/7/12auXYtMvaNFREQeMRoStTF3h0bvDvl5eVVn8+aNxMXFkpiYSGjoGry8qiV5T8GCBalVqzZTp75PXNydq1L/+OMYixYtoESJkvzyy36qVKlK69ZtKVbsacLCdljPPbvXRx9NZ+PG72jWzJe+fd/jjz+OJ5m/fv1aevZ8k2XL1rJs2VpWrfqO55+vxNq1qwBo2rQ5s2ZNxcurGjly5ODs2TOYTCYCA7vh5VWNbdt+SLFdgKioG5w9e5ru3d+gZs3abN++LcmymzdvxDAMLl26yLFjRylf/jnrvOLF/0dUVBS//nrw/+tcw8iRwQ/Y8yIiIrZLR9i4c7+0O7fgyPj1Pqi7Q6P9+gUBULt2XU6cOE737oGYzYm88EJN2rRpn+x9Q4aM4OOPZ/Daax1wdHQiR44cDBs2hhIlSuHq6sbQoQMJDLzzvrJly3Px4oVk62jTpj2jRoUQGroWOzs7QkJGWedFRl7l4MF9DB06Isl7AgI68cEHE+natQf16vnw/vsTePPN3gCUKlWaUqXK0KFDW+zsTLzwgneK58UBuLnlxte3FZ07v4KDgwNeXtWJjY21Dos+9VROunfvRGJiIgMHDiVPnjzW9zo5OTFmzESmT59CfHw8OXPmSlK7iIjIo85k3Ds29hi6evUmFkvSTbx06TQFCxbPpookO2nfi4it8/BwzZR7g95r/mvTiYiIztQ25MHY2ZnIl88l9flZWIuIiIiIPAQFNhEREREb98QGtsd8JFhSoH0uIiKPqicysDk4OHHrVpR+gT9BDMPg1q0oHBxSv1+diIiIrXoirxLNm9eDa9ciuHnzenaXIlnIwcGJvHk9srsMERGRB/ZEBjZ7ewfy5y+U3WWIiIiIpMsTOSQqIiIi8ihRYBMRERGxcdkS2FavXk2LFi1o0aIFkyZNAiAsLAw/Pz8aN27M1KlTrcsePXoUf39/mjRpQnBwMImJiamtVkREROSxlOWBLSYmhnHjxrFw4UJWr17Nvn372Lp1K0OHDuWjjz4iNDSU3377jW3btgEwcOBAhg8fzsaNd54luWRJ6g8PFxEREXkcZXlgM5vNWCwWYmJiSExMJDExERcXF4oXL06xYsVwcHDAz8+PDRs2cP78eWJjY6lcuTIA/v7+bNiwIatLFhEREclWWX6VqIuLC++++y7NmjXjqaeeonr16ly+fBkPj39ut+Dp6Ul4eHiy6R4eHoSHhz9Qe/d7LpeIiMiTysPDNbtLkAeQ5YHt2LFjLF++nB9++AFXV1cGDBjAqVOnMJlM1mUMw8BkMmGxWFKc/iBSevi7iIiIrcqqIKWHv9sWm3v4+44dO/D29iZfvnw4OTnh7+/Pnj17iIiIsC4TERGBp6cnBQsWTDL9ypUreHp6ZnXJIiIiItkqywNbuXLlCAsL4/bt2xiGwdatW6lUqRJ///03p0+fxmw2s27dOurVq0eRIkVwdnZm//79wJ2rS+vVq5fVJYuIiIhkqywfEq1Tpw6///47/v7+ODo68vzzz9O7d29q165N7969iYuLo379+jRt2hSAKVOmEBISws2bN6lQoQKBgYFZXbKIiIhItjIZj/kT0HUOm4iIPEo8PFzp+sW7mdrG/Nem6xw2G2Nz57CJiIiIyINRYBMRERGxcQpsIiIiIjZOgU1ERETEximwiYiIiNg4BTYRERERG6fAJiIiImLjFNhEREREbJwCm4iIiIiNU2ATERERsXEKbCIiIiI2ToFNRERExMYpsImIiIjYuDQDW0xMDL/88gsA33zzDUOHDuXChQuZXZeIiIiI/L80A9uQIUPYsmULhw4d4vPPP6dQoUIMGzYsK2oTEREREdIR2M6ePUv//v354YcfePnll+nduzfXr1/PgtJEREREBNIR2BITEwHYsWMHNWvWxGw2c/v27UwvTERERETucEhrgSpVqtC8eXPs7e3x8vKiS5cu1KpVKytqExERERHSEdiGDRvGwYMHKVeuHHZ2dnTv3p169eplRW0iIiIiQjoCm729Pbdu3WLatGnY29vj4+ODnZ3uBiIiIiKSVdJMXjNnzmTixIm4urqSI0cORowYwZdffpkVtYmIiIgI6TjCtmbNGlasWIGrqysA3bp1IyAggMDAwEwvTkRERETScYQtT5485MqVy/razc2NnDlzZmpRIiIiIvKPNI+wVa1albfeeov27dtjb2/PmjVrKFy4MJs2bQKgcePGmV6kiIiIyJMszcB25MgRAObNm5dk+sKFCzGZTApsIiIiIpkszcC2cOHCrKhDRERERFKRamAbN24cwcHBvPHGGynO/+STTzKtKBERERH5R6qBzdvbG4AmTZpkWTEiIiIiklyqga1hw4YArFq1igULFmRZQSIiIiKSVJq39YiOjtbD3kVERESyUZoXHTz11FP4+PhQtmzZJPdf0zlsIiIiIlkjzcDWtm3brKhDRERERFKRZmA7ffo0ffr0STJt7NixvPzyy5lVk4iIiIjcI9XANmPGDKKioggNDeXmzZvW6QkJCezYsYOQkJAsKVBERETkSZdqYKtUqRKHDx/Gzs6OPHnyWKfb29szZcqUrKhNRERERLhPYKtfvz7169enXr16VKxYMStrEhEREZF7pHlbD4U1ERERkeyVZmATERERkeyVamDbvHkzAPHx8VlWjIiIiIgkl2pgmz59OgDt27fP8Ea3bt2Kv78/zZo1Y+zYsQCEhYXh5+dH48aNmTp1qnXZo0eP4u/vT5MmTQgODiYxMTHD6xERERGxZaledJArVy6aNGlCeHg4fn5+yeavXbv2oRo8e/YsI0aMYOnSpeTLl48uXbqwbds2RowYwcKFCylUqBCvv/4627Zto379+gwcOJCxY8dSuXJlhg4dypIlS+jQocNDtS0iIiLyKEo1sH3++eccPXqU4OBghg0blmENfv/99zRv3pyCBQsCMHXqVE6fPk3x4sUpVqwYAH5+fmzYsIFSpUoRGxtL5cqVAfD392fGjBkKbCIiIvJESTWwubi4UL16debMmYOnpydHjhwhMTGRihUr4uLi8tANnj59GkdHR9544w0uXrxIgwYNKF26NB4eHtZlPD09CQ8P5/Lly0mme3h4EB4e/tBti4iIiDyK0nw0VXR0NJ07dyZ//vyYzWbCw8P55JNP8PLyeqgGzWYz+/btY+HCheTMmZM333yTHDlyYDKZrMsYhoHJZMJisaQ4/UHky/fw4VJERORx5eHhmt0lyANIM7BNmjSJKVOmULNmTQB27drFxIkTWbJkyUM1mD9/fry9vXF3dwegUaNGbNiwAXt7e+syEREReHp6UrBgQSIiIqzTr1y5gqen5wO1d/XqTSwW46FqFRERyWpZFaQiIqKzpB1JHzs7030PMqV5H7Zbt25ZwxqAt7c3MTExD12Qj48PO3bsICoqCrPZzPbt22natCl///03p0+fxmw2s27dOurVq0eRIkVwdnZm//79AKxevZp69eo9dNsiIiIij6I0j7CZTCbOnz9PkSJFADh37lySo2EPqlKlSvTo0YMOHTqQkJBA7dq1efXVVylRogS9e/cmLi6O+vXr07RpUwCmTJlCSEgIN2/epEKFCgQGBj502yIiIiKPIpNhGPcdL9y8eTMjR47E29sbk8nEjh07GDFiBE2aNMmqGv8TDYmKiMijxMPDla5fvJupbcx/bbqGRG1MWkOiaR5ha9SoESVKlGD37t1YLBZef/11SpYsmaFFioiIiEjq0gxsACVKlKBEiRKZXYuIiIiIpEAPfxcRERGxcQpsIiIiIjYuzcA2aNCgrKhDRERERFKRZmA7evQoaVxIKiIiIiKZKM2LDjw9PWnRogWVKlUiV65c1ukhISGZWpiIiIiI3JFmYKtSpQpVqlTJilpEREREJAVpBragoCBiY2M5ffo0pUuXJi4ujqeeeiorahMRERER0nEO26+//kqjRo14/fXXuXz5Mg0aNODAgQNZUZuIiIiIkI7ANmnSJObPn0+ePHkoWLAgkydPZty4cVlRm4iIiIiQjsAWGxtLqVKlrK/r16+P2WzO1KJERERE5B9pBjYHBwdu3LiByWQC4K+//sr0okRERETkH2ledPDmm2/SqVMnIiIi6NevHzt37mT06NFZUZuIiIiIkI7A5uPjQ4kSJdi5cycWi4W3336bkiVLZkVtIiIiIkI6nyWamJiIxWLBwcEBB4c0M56IiIiIZKA0A9vy5csJDAzk8OHD7Nu3j44dO7Jx48asqE1ERERESMeQ6Pz581m5ciWenp4AXLhwgddff50mTZpkenEiIiIiko4jbI6OjtawBlC4cGEcHR0ztSgRERER+UeqR9iOHDkCQNmyZRk9ejTt27fH3t6eFStW4OXllWUFioiIiDzpUg1svXv3TvL6xx9/tP7bZDIREhKSaUWJiIiIyD9SDWxbt27NyjpEREREJBVpXnQQERHBypUruX79epLpgwYNyqyaREREROQeaV508Oabb3Lo0CEMw0jyn4iIiIhkjTSPsCUkJDBr1qysqEVEREREUpDmEbYKFSrwxx9/ZEUtIiIiIpKCNI+weXl50bp1azw8PJI8lmrLli2ZWpiIiIiI3JFmYJs7dy5Tpkzh6aefzop6RERERORf0gxsbm5uNG/ePCtqEREREZEUpBnYatasyaRJk2jcuDFOTk7W6RUqVMjUwkRERETkjjQD29q1awHYuHGjdZrJZNI5bCIiIiJZJM3ApiceiIiIiGSvNAPbF198keL01157LcOLEREREZHk0gxs996DLT4+nr179+Lt7Z2pRYmIiIjIP9IMbBMmTEjyOjw8nODg4EwrSERERESSSvNJB/9WoEABzp8/nxm1iIiIiEgKHugcNsMw+O2338iXL1+mFiUiIiIi/3igc9gAChUqxKBBgzKtIBERERFJ6oHPYRMRERGRrJVqYBsyZEiqbzKZTIwfPz5TChIRERGRpFINbKVLl0427dq1ayxYsIAiRYpkSOOTJk3i2rVrTJw4kbCwMCZMmEBcXBzNmjWjb9++ABw9epTg4GBu3bpFtWrVGDVqFA4OaR4YFBEREXlspHqVaLdu3ZL8V65cOVatWoWfnx8rVqz4zw3v2rWLlStXAhAbG8vQoUP56KOPCA0N5bfffmPbtm0ADBw4kOHDh7Nx40YMw2DJkiX/uW0RERGRR0mat/VITExk0qRJ9OvXj5CQEMaNG8dTTz31nxq9fv06U6dO5Y033gDg0KFDFC9enGLFiuHg4ICfnx8bNmzg/PnzxMbGUrlyZQD8/f3ZsGHDf2pbRERE5FFz37HFU6dO0a9fP3LlysWqVasoWLBghjQ6fPhw+vbty8WLFwG4fPkyHh4e1vmenp6Eh4cnm+7h4UF4ePgDtZUvn0uG1CwiIvI48fBwze4S5AGkGtiWL1/OpEmTeO2113jzzTczrMGlS5dSqFAhvL29rUOrFosFk8lkXcYwDEwmU6rTH8TVqzexWIyMKV5ERCSTZVWQioiIzpJ2JH3s7Ez3PciUamALDg7Gzs6OTz/9lM8++8w6/W5oOnDgwEMVFBoaSkREBK1ateLGjRvcvn2b8+fPY29vb10mIiICT09PChYsSEREhHX6lStX8PT0fKh2RURERB5VqQa2LVu2ZEqD9z45YcWKFfz888+MGjWKxo0bc/r0aYoWLcq6deto06YNRYoUwdnZmf3791O1alVWr15NvXr1MqUuEREReTK55XbG2ckpU9uIi48n6kbcQ78/1cCWUbfuSA9nZ2cmTpxI7969iYuLo379+jRt2hSAKVOmEBISws2bN6lQoQKBgYFZVpeIiIg8/pydnOj6xbuZ2sb816YDmRDYsoK/vz/+/v4AeHt7s2bNmmTLlCtXjmXLlmV1aSIiIiI2I83beoiIiIhI9lJgExEREbFxCmwiIiIiNk6BTURERMTGKbCJiIiI2DgFNhEREREbp8AmIiIiYuMU2ERERERsnAKbiIiIiI1TYBMRERGxcQpsIiIiIjZOgU1ERETEximwiYiIiNg4BTYRERERG6fAJiIiImLjFNhEREREbJxDdhcgTyZXtxzkcHbMtPXHxiUQHRWbaesXEduk7xZ5XCmwSbbI4exIh0GLMm39X0/uSDT6UhV50ui7RR5XGhIVERERsXEKbCIiIiI2ToFNRERExMYpsImIiIjYOAU2ERERERunwCYiIiJi4xTYRERERGycApuIiIiIjVNgExEREbFxCmwiIiIiNk6BTURERMTGKbCJiIiI2DgFNhEREREbp8AmIiIiYuMU2ERERERsnEN2FyAiWcPVLQc5nB0zbf2xcQlER8Vm2vpFRJ5kCmwiT4gczo50GLQo09b/9eSORKPAJiKSGTQkKiIiImLjFNhEREREbJwCm4iIiIiNU2ATERERsXEKbCIiIiI2LlsC26xZs2jRogUtWrRg8uTJAISFheHn50fjxo2ZOnWqddmjR4/i7+9PkyZNCA4OJjExMTtKFhEREck2WR7YwsLC2LFjBytXrmTVqlUcOXKEdevWMXToUD766CNCQ0P57bff2LZtGwADBw5k+PDhbNy4EcMwWLJkSVaXLCIiIpKtsvw+bB4eHgwePBgnJycASpYsyalTpyhevDjFihUDwM/Pjw0bNlCqVCliY2OpXLkyAP7+/syYMYMOHTpkddkiIiKSAt2UO2tkeWArXbq09d+nTp3iu+++o1OnTnh4eFine3p6Eh4ezuXLl5NM9/DwIDw8/IHay5fP5b8XLY8kDw/X7C7hiaM+lyfB4/I5z8jtyOybcufwyLxAmJX+S59n25MOTpw4weuvv86gQYOwt7fn1KlT1nmGYWAymbBYLJhMpmTTH8TVqzexWIyMKlsySFZ84UVERGd6G48S9bk8CR6Hz3lWBcKM2g71efrdbzvs7Ez3PciULRcd7N+/n65du9K/f39efvllChYsSEREhHV+REQEnp6eyaZfuXIFT0/P7ChZREREJNtkeWC7ePEib7/9NlOmTKFFixYAVKpUib///pvTp09jNptZt24d9erVo0iRIjg7O7N//34AVq9eTb169bK6ZBEREZFsleVDonPnziUuLo6JEydapwUEBDBx4kR69+5NXFwc9evXp2nTpgBMmTKFkJAQbt68SYUKFQgMDMzqkkVERESyVZYHtpCQEEJCQlKct2bNmmTTypUrx7JlyzK7LBERERGbpScdiIiIiNg4BTYRERERG6fAJiIiImLjsu0+bCIijzvdAV5EMooCm4hIJsnh7Jjpd4CPRoFN5EmgIVERERERG6fAJiIiImLjFNhEREREbJwCm4iIiIiNU2ATERERsXEKbCIiIiI2ToFNRERExMYpsImIiIjYOAU2ERERERunwCYiIiJi4xTYRERERGycApuIiIiIjVNgExEREbFxCmwiIiIiNk6BTURERMTGKbCJiIiI2DiH7C5AMoZbbmecnZwytY24+HiibsRlahsiIiKSnALbY8LZyYmuX7ybqW3Mf206oMAmIiKS1RTYRERE0smSmICHh2t2lyFPIAU2ERGRdLJzcGT/5B6Z2kbVQZ9n6vrl0aSLDkRERERsnI6wAa5uOcjh7Jhp64+NSyA6KjbT1i8iIiKPNwU2IIezIx0GLcq09X89uSPRKLCJiIjIw9GQqIiIiIiNU2ATERERsXEaEhURkVTpptwitkGBTUREUqWbcovYBgU2EXlk6GiPiDypFNhE5JGhoz1J6a77Ik8OBTYRyRAKD1lPd90XeXIosIlIhlB4EJHMoD8G71BgExEREZulPwbv0H3YRERERGycApuIiIiIjXskhkTXrl3Lxx9/TGJiIl26dKFjx47ZXZLYuKw45yE+MR4nB91iQkREMp/NB7bw8HCmTp3KihUrcHJyIiAggBo1alCqVKnsLk1sWFad86BbTIiISFaw+cAWFhZGzZo1yZMnDwBNmjRhw4YNBAUFpev9dnamdC2XP2+uhy0xQ+v4L/K7uGd6Gxm5HZnd505u+TJ1/aA+/zf1eXLq8/RRnyelPk/qSejztPaHyTAMI6MLykhz5szh9u3b9O3bF4ClS5dy6NAhxowZk82ViYiIiGQNm7/owGKxYDL9kzoNw0jyWkRERORxZ/OBrWDBgkRERFhfR0RE4OnpmY0ViYiIiGQtmw9stWrVYteuXURGRhITE8OmTZuoV69edpclIiIikmVs/qKDAgUK0LdvXwIDA0lISKBt27ZUrFgxu8sSERERyTI2f9GBiIiIyJPO5odERURERJ50CmwiIiIiNk6BTURERMTGKbCJiIiI2DgFNhEREREbp8D2kPbs2UPnzp0f6D0PunxqgoODOXz4cKrzz507R8OGDVOcN2TIEM6fP58hdWSUc+fO8dxzz9GqVask/y1atOiB1pHaNmeXnj17Eh4ent1l3Fd2fo4z0vTp09myZUt2l5Hh/vjjD8qWLcvGjRut07Zt20bdunXp378/S5YsYd26ddlY4eMrI76X7ufw4cMEBwdnyLqeBOfOnaNs2bLs3LkzyfSGDRumqy8HDx7MihUrkk0/dOgQ77//fobWmlls/j5sj5Off/45Q9Yzbty4h37vnj17ePvttzOkjozk6enJ6tWrs7uMDPXZZ59ldwmZIqM+xxnp3Xffze4SMsXy5ctp2rQpixcvpkmTJgBs2LCBoKAg2rdvz+DBg3nhhReyucrHV2Z+Lz3//PM8//zzmbLux5WjoyPDhg1jzZo1uLi4WKfnzZv3oX8vnjx5kqtXr2ZUiZlKgS2DJSYmMnLkSE6cOMGVK1coW7YsH374IVOmTAGgXbt2LF26lJ9++okZM2aQmJhI0aJFGTNmDHnz5k2yroYNG9KyZUt27NhBTEwMkyZN4rnnnqNz584EBQVRo0YNPvjgAzZu3EjevHnx8PCgYcOGvPDCC8TGxtK3b19OnDiBm5sbs2fPZunSpVy+fJlevXqxaNGiZO3Zqq+++orVq1cTExODo6MjH3zwASVKlKBhw4ZUrFiRo0ePWv9CunnzJi+++CJbtmzBxcWFc+fO0atXL0JDQ63rO3fuHG+//TYlSpTg5MmTPPvss1SpUoWVK1dy48YNZs+eTcmSJfnll18YN24ccXFx5M2bl9GjR1O8eHE6d+5M7ty5OXHiBNOmTaNnz540adKE/fv3Y29vz7Rp0yhWrBgNGzbkyy+/5Oeff2b79u3cuHGDs2fPUrt2bUaOHAmQ4v7z9/fPjm5OIqM/xy1atGDnzp04ODjw1ltvMW/ePE6fPs17771H8+bNuXLlCsHBwVy4cAEHBwf69u1LvXr1mDlzJr/88gsXL16kU6dOfPfddzz//PPs37+fyMhIQkJCqF+/vjW4vPDCCwQFBVG6dGmOHj1Kvnz5mD59Onny5CE0NJQZM2aQM2dOypcvj9lsZuLEidnRvemSkJDA2rVrWbRoEQEBAZw5c4Y9e/awZcsWdu3ahZ2dHVu3bmX37t14eHhQvnx5hg8fzqVLlzCZTPTv359atWqluw9T2wcxMTGEhIRw/PhxTCYT3bt3p3Xr1qxYsSLVz/Xj7Msvv+T06dMMGzYMgIkTJ1KwYEHatWvH6NGjOXHiBGazmZ49e+Lr65tqP+3Zs4dZs2axcOFCOnfunOI+uXTpEgMGDODGjRuUKVOGvXv38tNPP2VzD2QfT09PatWqxaRJkxgzZox1+s8//8yQIUNYuHAhf/zxB4MHD8ZsNlOtWjV++uknvv/+ewB+/PFHvv76a65evcobb7xBs2bNmDFjBrdv3+bjjz/mzTffzK5NSxcNiWawgwcP4ujoyOLFi/n++++Jjo5m27ZthISEALB06VIiIyP54IMPmDt3LqtWraJOnTrWX4T/lidPHpYtW0ZAQABz5sxJMm/r1q3s37+fdevW8emnn/L7779b50VGRvLaa6+xbt068ufPT2hoKL169cLT05NPP/3U5sLa5cuXkw09HD9+nJs3b7J582YWLlzIunXraNCgQZIhiXr16rFx40bc3d0BcHFxoUGDBmzYsAGAVatW0bp162TtHT9+nJ49e7J69WoOHDjA+fPnWbx4Mb6+vixevJj4+Hj69etn/WsuICCAfv36Wd9/d5iqfPnyRERE4O3tzapVq6hevXqKQyYHDx5kxowZrFmzhh9++IHjx4/fd/9lt4z+HOfPn58VK1ZQsmRJPv30U+bNm8f777/Pp59+CsCYMWOoWbMma9euZcaMGQwdOpQrV64AEB8fT2hoKB06dADuBJnFixczZMgQpk+fnqytY8eOWT/7bm5urF27lsjISMaPH8+CBQtYtmwZN27cyIxuy1Dbtm2jcOHCPPPMMzRq1IjFixfTrl07GjZsyDvvvJPk33Xr1mXcuHG0adOGFStW8PHHHzN8+HBu3rwJpK8PU9sHM2fOJG/evKxbt44FCxYwc+ZMjh07BqT8uX6cpPS99Oyzz/L9999jNpsxDINNmzbRokULPv74YypUqMCKFStYtGgRn3zyCWfPngXS108p7ZNx48bRrFkz1q5dS9OmTW3+FIusMHjwYHbs2JFsaPTe+e+++y6rV6+mWLFimM1m67z4+HiWLl3KnDlzmDp1Km5ubrzzzjs0bNjQ5sMa6AhbhqtevTp58uRh0aJF/PXXX5w6dYrbt28nWebXX3/l4sWLBAYGAmCxWMidO3eK66tbty4ApUuXZtOmTUnmhYWF0axZM5ycnHBycqJRo0bWeZ6entZHeJUqVYpr165l2DZmhvsNPXzwwQesX7+eU6dOsX37dsqXL2+dV6lSpWTLt2nThpkzZ9K2bVvrL5l/y58/P88++ywABQsWxNvbG4DChQtz7tw5Tp06hZubm7UPmzVrxvDhw4mOjgZI9ni0e/fTvn37krVXpUoV6yH8YsWKcePGjfvuv+yW0Z/ju8//LVy4MJ6enjg4OFC4cGGioqIA2L17N2PHjgXu9E+lSpX49ddfgfv39fXr15O1lS9fPuu+LV26NDdu3GDfvn1UqVKFAgUKANC6dWs2b978wP2SlZYvX46vry8AzZs3Z8CAAfcd+g0LC+Ovv/5ixowZwJ2jpHcDQ3r6MLV9sHv3bsaPHw+Au7s7L774Ij///DMuLi4pfq4fJ6l9L5UrV449e/bg6OjIM888g4eHB2FhYcTGxrJ8+XIAbt++zYkTJ4CUf/7/LaV9snPnTiZMmADASy+9hJubW4Zv46PGxcWFMWPGWP+Yvtf169c5f/489evXB+78Lvjyyy+t81988UVMJhOlS5e2+d+JKVFgy2BbtmxhxowZBAYG4u/vz7Vr1/j307/MZjNeXl588sknAMTFxXHr1q0U1+fs7AyAyWRKNs/Ozg6LxZLi+xwc/tm1JpMpWQ2PiosXL9K5c2c6depEvXr1yJ8/P0ePHrXOv9s/96pevTqXL19m06ZNFC1a1PpL+l5OTk5JXtvb2yd5nVK/GoZh/WstR44cSebdu59S6ut767y7zP32X3bL6M+xo6Oj9d/3fjbv+ve609vXKXnU+jolV69eZfv27Rw5coQvv/wSwzCIioqyDu2kxGKxsGDBAvLkyQPcOTqUL18+Nm/enK4+TG0f3G/fpNTXT4JWrVoRGhqKo6Mjfn5+wJ3+f//996lQoQIAV65cIXfu3KxduzZd/ZTSPrG3t39i+vRB1KlTxzo0eq+0+uvu93xq3x22TkOiGWzXrl00a9aMNm3a4Obmxp49e6xfbvb29iQmJlKpUiV++eUX/v77bwA++ugjJk+e/MBt1apVi02bNhEfH8/Nmzf58ccf0/wg2tvbJzlEbOsOHz5M8eLF6dq1K88//zybN29Os36TyUTr1q0ZO3bsQ58PVqJECa5fv86hQ4cACA0NpXDhwtZfhhnhYfZfVsnKzzFAzZo1WbZsGQBnz57lwIEDVK5cOUO2BcDLy4vDhw9z+fJlDMMgNDTUZvo6JatXr6ZmzZr89NNPbN26lR9++IE33niDb7/9Nsly9/4816xZk6+//hq4cyK1n58fMTEx6W4ztX1w7/TIyEi2bNnyxF/o8OKLL7J371527tzJSy+9BNzpv2+++Qa4E5ZbtmzJxYsX/1M73t7erF27FrgzRH73iLT8MzR6+fJl6zRXV1eKFSvGtm3bAKx9dz93v88eBTrC9h/cHWa5y8/Pj44dOzJgwADWr1+Po6MjXl5enDt3DrjzQ96qVStWrFjB+PHj6dOnDxaLhQIFCjzUZcUNGjTg4MGDvPzyy+TOnRtPT88Ujzj9+z29evXi888/p1ixYg/cZma5e67IvapXr07fvn355ptvaN68OYZhUL16desww/20aNGCefPmPfQwo5OTE1OnTmXMmDHExMSQO3dupk6d+lDrSs3D7L/MkN2fY7hzq5rhw4dbL7sfO3Ysnp6e/33j/p+7uzshISF069YNJycnihYtatPDSytXrqRv375JpnXs2JHPP/+c/PnzW6fVqlWLDz/8EFdXV0JCQhg+fLj1iM/kyZOTXEmXltT2wdtvv83IkSPx8/PDbDbzxhtvUKFChcfufLWUpPa9FBISgpeXF/Hx8eTKlQuAoKAgRo4cia+vL2azmYEDB/L000+neIpEegUHB/Pee++xZMkSypUrZ9Of2ax2d2i0e/fuSaZPnjyZoUOHMm3aNMqWLZvs6PK/VaxYkVmzZjFlyhQGDBiQmSX/ZyZDx1sfWQcPHuTUqVO8/PLLJCQk0L59e8aPH0+5cuWyu7RsZbFY+Oabb/j777+tJ8nbIu2/rHPt2jUWLlxIUFAQdnZ2jB071nrFr4it+vLLL6lVqxalSpXiyJEjDBs2LMV7ick/Zs2axSuvvIKnpyebNm1i7dq1zJw5M7vLyhA6wvYIe+aZZ5g1axZffPEFhmHQunVr/bLnzl+6Fy9eZO7cudldyn1p/2WdPHnyEBUVha+vL/b29lSoUIFXXnklu8sSua/ixYvTr18/7OzscHZ2TnIrC0lZ4cKF6datGw4ODri5uf2n+5baGh1hExEREbFxuuhARERExMYpsImIiIjYOAU2ERERERunwCYiVmXLlsXPz49WrVrRunVrmjRpQps2bTh8+HB2l2Z16NAhhg8fnuK8hg0bZlmtZ8+epXfv3gCEh4cTEBCQ6W127tzZ+ti19NT1ID7++GMaNGjAkCFDkkyPjo62Ps0iJa1atdL9wUSygK4SFZEkFixYYH02K8DcuXMZO3Ysixcvzsaq/nHy5EmbeKbihQsXrDcNLlCgQLKb2maXe+t6EMuWLWPKlClUq1YtyfQbN27cNwSn9kg5EclYOsImIqlKTEzk4sWLSZ4R+vHHH/Pyyy/TqlUr3nrrLWt4OnHiBO3bt8fPz4++ffvyyiuvsGfPHs6dO5fkxrz/fr106VL8/f1p3bo1Xbt25c8//wTu3NC3bdu2+Pv74+/vz8aNG7l48SIzZsxg3759yY4E3bV48WL8/f1p0KBBkpsdL168GF9fX1q2bEm3bt2soWbw4MFJbgFz7+uGDRvSp08fmjVrluSxUGazmZCQEM6cOUP37t2TbNPMmTMZOHAggYGBNGvWjAEDBrB06VI6duxI/fr1WbduXZp9uWnTJl5++WX8/f1p164de/fuTbadmzdvpnXr1rRs2ZJXX32VQ4cOJavr3y5dusQbb7yBn58fvr6+fP755wD06dOH8PBwgoODCQ0NTfKeIUOGEBsbS6tWrTCbzTz33HO8++67NGnShMOHD1O2bFkiIyNZsWIFvXr1okePHjRv3pzXXnvNJoK1yGPDEBH5f2XKlDF8fX0NX19fo3bt2kbDhg2NMWPGGFeuXDEMwzBWrlxp9OnTx0hISDAMwzC+/fZbo0ePHoZhGIavr6+xZMkSwzAMY+/evUbZsmWN3bt3G2fPnjUqV65sbePe13v27DE6dOhg3L592zAMw9i+fbvRtGlTwzAMIzAw0Fi3bp1hGIZx9OhRY+TIkYZhGMby5cuNXr16pVi/j4+PMXr0aMMwDOPy5cvGc889Z1y4cMEICwszGjVqZFy9etW6jmbNmhkWi8V47733jM8//9y6jntf+/j4GLNmzUqxrd27dxstWrRItk0zZswwfHx8jKioKCMmJsaoXr26MWHCBMMwDOP77783GjdunGZfvvjii8bBgwetfTJz5kzDMAyjU6dOxnfffWecPHnSqFWrlnHmzBnDMAwjLCzMqF27thEdHZ2krn/r2LGjMW/ePMMwDCMqKsrw8/Oz9rGPj49x6NChZO/59/4rU6aMsXLlyiSvr169aixfvtyoXLmy8ddffxmGYRjvv/++0bt37xTrEJEHpyFREUni7pDokSNH6NWrFzVq1CBfvnwA/PDDDxw+fJg2bdoAd54qERMTQ2RkJCdPnqR169YAVKtWjbJly6bZ1o8//sjp06eTnP8VFRXF9evXadasGaNHj2br1q3UqlWLfv36pat+X19fADw8PMifP7/1QerNmze3DvX6+/szbtw46+O27uffQ4TpUatWLVxdXQHw9PSkbt26ADz99NNcv34dSL0v4c6j1YKCgqhfvz61a9emZ8+eSda/e/duatasaX28nLe3N+7u7vz222+pPiP19u3bHDhwgHnz5gF3nrvo7+/PTz/9RIsWLR5o+1Lrk9q1a/PMM88A8MorryR7rJOIPDwFNhFJUYUKFRgyZAiDBw+mfPnyFC1aFIvFQo8ePejQoQMA8fHx3Lhxgxw5cmAymTDuuQ+3o6MjQLLpCQkJ1n9bLBZatWrFwIEDra8vX75M7ty5CQgIwMfHh507d7J9+3ZmzZqV5gn3AA4O/3yt3W3bYrEkW84wDBITE+9bH0DOnDnTbPPfnJycUq3prtT6EqBv3760adOGnTt3smLFCubNm2d9APvd9/47mN3dnrv9nlJ7xr/uk26xWB7qwdep9Ym9vX2Sdd/7WkT+G53DJiKp8vX1pWLFikyYMAGAOnXqsGzZMm7evAnA9OnTGTRoEDlz5qRq1arWCxOOHTvGsWPHAHBzcyMhIYGTJ08CsH79euv669Spw/r167l8+TIA33zzDV26dAEgICCAo0eP4u/vz5gxY4iKiiIiIgJ7e/sHDhl169YlNDSUyMhIAJYvX06ePHkoXrw4efPm5bfffgPuXO35888/p2ud9vb2ycLdg0itLxMTE2nYsCExMTG8+uqrjBgxguPHjxMfH299r7e3Nzt27ODs2bMA7Nq1i4sXL1KpUqVU63JxcaFSpUosWrQIuHP156pVq6hVq9Z963RwcMBsNicLeynZvXu39by1b7/9Fh8fn/R1hoikSUfYROS+hg0bRsuWLdm+fTvt2rUjPDycV155BZPJRKFChZg4cSIAkydPJiQkhKVLl1KkSBHy588P3Bl6GzhwID179sTd3Z2mTZta112nTh169uxJt27dMJlMuLi4MGvWLEwmEwMGDGD8+PFMmzYNk8lEUFAQRYsWxWw2M3v2bIKCgpg1a1a6tqF27dp07dqVLl26YLFYcHd3Z86cOdjZ2dG5c2cGDBhAkyZNKFq0KDVr1kzXOkuVKoWzszNt27ZNcnFDeqXWlw4ODgwdOpQBAwbg4OCAyWRi/PjxSY7alSpVihEjRhAUFITZbCZHjhx88sknuLq6Jqlr6dKlSY7ETZkyhdGjR7NixQri4+Px8/PD39//vnV6eHhQsWJFWrRoYQ17qSlQoAADBw4kIiKCUqVKMXr06AfuFxFJmZ4lKiKZwtfXl2HDhlGjRo3sLkWywIoVK9i4cSNz5szJ7lJEHksaEhURERGxcTrCJiIiImLjdIRNRERExMYpsImIiIjYOAU2ERERERunwCYiIiJi4xTYRERERGzc/wEAt0VOfaXacgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(airport, \"Request hour timeslot\", \"Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3d1f9d",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "1. Cabs are highly unavailable for passengers in the evening and at night time slots\n",
    "2. Very few cabs were cancelled at when passengers were picked up from the airport\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1638,
   "id": "979edcd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bivariate_analysis(city, \"Request hour timeslot\", \"Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8aa3a9b",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "1. Higher number of cabs were cancelled in the morning timeslot.\n",
    "2. Higher number of cabs were unavailable in the mornings too\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c912da",
   "metadata": {},
   "source": [
    "<span style=\"color:red\">Bivariate analysis of city and airport -\"trip duration slot\" against \"Request hour timeslot\" </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1639,
   "id": "2fb74f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(data=airport, hue=\"trip duration slot\", x=\"Request hour timeslot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071d6815",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "Peak time - early morning timeslot at the city and evening timeslot at the airport\n",
    "\n",
    "Trip from airport lasts for more than an hour the most in the early morning and evening timeslots.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1640,
   "id": "8111be61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(data=city, hue=\"trip duration slot\", x=\"Request hour timeslot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cb3e4d",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "Peak time - early morning timeslot at the city and evening timeslot at the airport\n",
    "\n",
    "Trip from city lasts for more than an hour the most in the early morning and evening timeslots.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1654,
   "id": "a7b3dcd9",
   "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>City</th>\n",
       "      <th>Airport</th>\n",
       "      <th>City_percent</th>\n",
       "      <th>Airport_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trip Completed</th>\n",
       "      <td>1504</td>\n",
       "      <td>1327</td>\n",
       "      <td>42.89</td>\n",
       "      <td>40.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cancelled</th>\n",
       "      <td>1066</td>\n",
       "      <td>198</td>\n",
       "      <td>30.40</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Cars Available</th>\n",
       "      <td>937</td>\n",
       "      <td>1713</td>\n",
       "      <td>26.72</td>\n",
       "      <td>52.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City  Airport  City_percent  Airport_percent\n",
       "Trip Completed     1504     1327         42.89            40.98\n",
       "Cancelled          1066      198         30.40             6.11\n",
       "No Cars Available   937     1713         26.72            52.90"
      ]
     },
     "execution_count": 1654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a dataframe to compare airport and city trip status information\n",
    "\n",
    "temp = pd.DataFrame(city['Status'].value_counts())\n",
    "temp = temp.rename(columns = {'Status' : 'City'})\n",
    "temp['Airport'] = airport.Status.value_counts()\n",
    "temp_city_total = temp.City.sum()\n",
    "temp_airport_total = temp.Airport.sum()\n",
    "\n",
    "temp['City_percent'] = round(temp.City/temp_city_total*100, 2)\n",
    "temp['Airport_percent']= round(temp.Airport/temp_airport_total*100,2)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1642,
   "id": "2a514bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "ax1.pie(temp.City, labels=temp.index.values, autopct='%1.1f%%')\n",
    "ax1.set_title(\"Status pie chart for City\", fontsize = 20)\n",
    "\n",
    "ax2.pie(temp.Airport, labels=temp.index.values, autopct='%1.1f%%')\n",
    "ax2.set_title(\"Status pie chart for Airport\", fontsize = 20)\n",
    "\n",
    "plt.subplots_adjust(left=0.1,\n",
    "                    bottom=0.1, \n",
    "                    right=2, \n",
    "                    top=0.9, \n",
    "                    wspace=0.4, \n",
    "                    hspace=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56365261",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "1. Only ~ 40% of the trips are completed and 60% are not serviced. Demand exists and this must be exploited.\n",
    "2. Drivers cancel trip to the airport from the city in the morning. There is some idle time involved and there is huge demand for cabs in the evening. Idle time at the airport could be cause of unavailabilty of drivers in the evening.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee0c2e3",
   "metadata": {},
   "source": [
    "#### Pickup point - Airport "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b2bbad",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "There are more inbound flights in the evenings. Airports have highest number of requests to travel to the city in the evening but not enough cabs are available to service the requests. \n",
    "\n",
    "<b>Comparing status in the evening at airport and the city</b>: \n",
    "Very few requests have cancelled or no cars available status in the city at the same time and the trip completion is relatively higher in this timeslot - More cars may be in the city during this timeslot\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d745cd",
   "metadata": {},
   "source": [
    "#### Pickup point - City "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ef67a3b",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "There are more outbound flights in the mornings. City has highest number of requests in the morning to travel to the airports but most of the trips were cancelled by the drivers.\n",
    "\n",
    "<b>Comparing status in the morning at airport and the city</b>: \n",
    "Very few requests have cancelled or no cars available status in the airport at the same time and the trip completion is relatively higher in this timeslot - More cars may be in the airport during this timeslot\n",
    "    \n",
    "Trips could have also been cancelled as it takes longer time from the city to the airport and they prefer to earn more with shorter trips within the city.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30fc1e15",
   "metadata": {},
   "source": [
    "### Supply Demand Analysis at the city"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d4f45e",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "The ‘gap’ refers to the difference between the supply and demand for cabs. In other words, it means a consumer-need that supply has not yet met.\n",
    "\n",
    "A gap in the market represents an opportunity for it to widen its customer base. Market penetration can achieved by identifying a gap in the market and filling it.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1643,
   "id": "f888e0c3",
   "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>Demand</th>\n",
       "      <th>Supply</th>\n",
       "      <th>Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Early morning</th>\n",
       "      <td>1335</td>\n",
       "      <td>373</td>\n",
       "      <td>962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late morning</th>\n",
       "      <td>714</td>\n",
       "      <td>286</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evening</th>\n",
       "      <td>436</td>\n",
       "      <td>330</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afternoon</th>\n",
       "      <td>374</td>\n",
       "      <td>208</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Night</th>\n",
       "      <td>323</td>\n",
       "      <td>196</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late night</th>\n",
       "      <td>236</td>\n",
       "      <td>82</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Demand  Supply  Gap\n",
       "Early morning    1335     373  962\n",
       "Late morning      714     286  428\n",
       "Evening           436     330  106\n",
       "Afternoon         374     208  166\n",
       "Night             323     196  127\n",
       "Late night        236      82  154"
      ]
     },
     "execution_count": 1643,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall demand through the day at different timeslots at the city\n",
    "\n",
    "city_demand_supply = pd.DataFrame(city['Request hour timeslot'].value_counts())\n",
    "city_demand_supply = city_demand_supply.rename(columns = {'Request hour timeslot' : 'Demand'})\n",
    "\n",
    "trips_completed = city[city['Status'] == 'Trip Completed']\n",
    "city_demand_supply['Supply'] = pd.DataFrame(trips_completed.groupby('Request hour timeslot')['Status'].count())\n",
    "\n",
    "city_demand_supply['Gap'] = city_demand_supply['Demand'] -city_demand_supply['Supply']\n",
    "city_demand_supply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1644,
   "id": "24fb8367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Supply-Demand Gap in the City')"
      ]
     },
     "execution_count": 1644,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(data=city_demand_supply)\n",
    "plt.title(\"Supply-Demand Gap in the City\", fontsize = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1645,
   "id": "3287882e",
   "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>Demand</th>\n",
       "      <th>Supply</th>\n",
       "      <th>Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>353</td>\n",
       "      <td>100</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>350</td>\n",
       "      <td>88</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>342</td>\n",
       "      <td>99</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>323</td>\n",
       "      <td>99</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>309</td>\n",
       "      <td>86</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>168</td>\n",
       "      <td>63</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>131</td>\n",
       "      <td>42</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>121</td>\n",
       "      <td>74</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>114</td>\n",
       "      <td>87</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>110</td>\n",
       "      <td>77</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Demand  Supply  Gap\n",
       "5      353     100  253\n",
       "8      350      88  262\n",
       "9      342      99  243\n",
       "7      323      99  224\n",
       "6      309      86  223\n",
       "10     168      63  105\n",
       "4      131      42   89\n",
       "22     121      74   47\n",
       "20     114      87   27\n",
       "17     110      77   33"
      ]
     },
     "execution_count": 1645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall demand through the day at different hours at the city\n",
    "\n",
    "city_demand_supply = pd.DataFrame(city['Request hour'].value_counts())\n",
    "city_demand_supply = city_demand_supply.rename(columns = {'Request hour' : 'Demand'})\n",
    "\n",
    "trips_completed = city[city['Status'] == 'Trip Completed']\n",
    "city_demand_supply['Supply'] = pd.DataFrame(trips_completed.groupby('Request hour')['Status'].count())\n",
    "\n",
    "city_demand_supply['Gap'] = city_demand_supply['Demand'] -city_demand_supply['Supply']\n",
    "city_demand_supply.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1646,
   "id": "2962577f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Supply-Demand Gap in the City')"
      ]
     },
     "execution_count": 1646,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(data=city_demand_supply)\n",
    "plt.title(\"Supply-Demand Gap in the City\", fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2142ba48",
   "metadata": {},
   "source": [
    "### Supply Demand Analysis at the airport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1647,
   "id": "8f5a5f59",
   "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>Demand</th>\n",
       "      <th>Supply</th>\n",
       "      <th>Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Evening</th>\n",
       "      <td>1457</td>\n",
       "      <td>312</td>\n",
       "      <td>1145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Night</th>\n",
       "      <td>624</td>\n",
       "      <td>203</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Early morning</th>\n",
       "      <td>337</td>\n",
       "      <td>308</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late morning</th>\n",
       "      <td>315</td>\n",
       "      <td>239</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afternoon</th>\n",
       "      <td>252</td>\n",
       "      <td>162</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late night</th>\n",
       "      <td>158</td>\n",
       "      <td>67</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Demand  Supply   Gap\n",
       "Evening          1457     312  1145\n",
       "Night             624     203   421\n",
       "Early morning     337     308    29\n",
       "Late morning      315     239    76\n",
       "Afternoon         252     162    90\n",
       "Late night        158      67    91"
      ]
     },
     "execution_count": 1647,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall demand through the day at different timeslots at the airport\n",
    "\n",
    "airport_demand_supply = pd.DataFrame(airport['Request hour timeslot'].value_counts())\n",
    "airport_demand_supply = airport_demand_supply.rename(columns = {'Request hour timeslot' : 'Demand'})\n",
    "\n",
    "trips_completed = airport[airport['Status'] == 'Trip Completed']\n",
    "airport_demand_supply['Supply'] = pd.DataFrame(trips_completed.groupby('Request hour timeslot')['Status'].count())\n",
    "\n",
    "airport_demand_supply['Gap'] = airport_demand_supply['Demand'] -airport_demand_supply['Supply']\n",
    "airport_demand_supply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1648,
   "id": "505c0fe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Supply-Demand Gap at the Airport')"
      ]
     },
     "execution_count": 1648,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(data=airport_demand_supply)\n",
    "plt.title(\"Supply-Demand Gap at the Airport\", fontsize = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1649,
   "id": "8ccfe5b4",
   "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>Demand</th>\n",
       "      <th>Supply</th>\n",
       "      <th>Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>405</td>\n",
       "      <td>81</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>378</td>\n",
       "      <td>74</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>366</td>\n",
       "      <td>83</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>343</td>\n",
       "      <td>61</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>308</td>\n",
       "      <td>74</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>183</td>\n",
       "      <td>80</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>98</td>\n",
       "      <td>62</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>89</td>\n",
       "      <td>81</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>89</td>\n",
       "      <td>74</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Demand  Supply  Gap\n",
       "18     405      81  324\n",
       "20     378      74  304\n",
       "19     366      83  283\n",
       "21     343      61  282\n",
       "17     308      74  234\n",
       "22     183      80  103\n",
       "23      98      62   36\n",
       "5       92      85    7\n",
       "6       89      81    8\n",
       "9       89      74   15"
      ]
     },
     "execution_count": 1649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall demand through the day at different hours at the airport\n",
    "\n",
    "airport_demand_supply = pd.DataFrame(airport['Request hour'].value_counts())\n",
    "airport_demand_supply = airport_demand_supply.rename(columns = {'Request hour' : 'Demand'})\n",
    "\n",
    "trips_completed = airport[airport['Status'] == 'Trip Completed']\n",
    "airport_demand_supply['Supply'] = pd.DataFrame(trips_completed.groupby('Request hour')['Status'].count())\n",
    "\n",
    "airport_demand_supply['Gap'] = airport_demand_supply['Demand'] -airport_demand_supply['Supply']\n",
    "airport_demand_supply.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1650,
   "id": "6825f1c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Supply-Demand Gap at the Airport')"
      ]
     },
     "execution_count": 1650,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(data=airport_demand_supply)\n",
    "plt.title(\"Supply-Demand Gap at the Airport\", fontsize = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1651,
   "id": "7f7f961b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Demand    3238\n",
       "Supply    1327\n",
       "Gap       1911\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Demand supply and gap at the airport\n",
    "\n",
    "airport_demand_supply.apply(lambda x: x.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1652,
   "id": "59c80374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Demand    3507\n",
       "Supply    1504\n",
       "Gap       2003\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Demand supply and gap at the city\n",
    "\n",
    "city_demand_supply.apply(lambda x: x.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3066ea",
   "metadata": {},
   "source": [
    "### Analyis of gap\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Observation:</b> \n",
    "\n",
    "- Only 300 cabs are available over the whole day currently to service all requests. \n",
    "- “Passengers” generate the demand, “Drivers” supply the demand. We observe a supply of more than 1300 and a gap of more than 2000.\n",
    "- Highest demand is seen at the airport in the evening timeslot and the gap is > 1100\n",
    "- Gap range more than 200 per hour and less than 350 per hour is observed at peak timeslot.\n",
    "- At the city - Highest gap is observed from 5 AM to 9 AM.\n",
    "- At the airport - Highest gap is observed from 5 PM to 9 PM\n",
    "\n",
    "\n",
    "More cabs must be made available at these peak timeslots. \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0daca3",
   "metadata": {},
   "source": [
    "### Recommendations to bridge the supply demand gaps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92092869",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "\n",
    "1. <b>Increase the number of cabs</b> to capture the incomplete requests at the city in the morning and at the airports in the evening.\n",
    "2. <b>Introduce surge pricing/dynamic pricing</b> for trips during peak hours. Some drivers move to the surge region to earn extra (increases supply). Some passengers may opt to wait to get a ride (reduces demand)\n",
    "3. <b>Introduce car pooling service</b>. Passengers can split the cost of the ride with somebody else. This could be an alternative option if passenger does not want to pay an exorbitant surge price to travel.\n",
    "4. <b>Set a threshold to the number of trip cancellations</b> in a day and impose penalty if cancellations exceed the threshold.\n",
    "5. <b>Monitor the inbound and outbound flight schedule</b> to plan ahead on the number of cabs that must be available to minimize the incomplete requests.\n",
    "6. <b>Increase market share</b> by marketing offers and campaigns to encourage passengers to plan trips in the timeslots where requests are low\n",
    "    \n",
    "7. <b>Trip cancellations</b> from the city may be due to idle time at the airport or returning empty on the return trip. \n",
    "    - Uber coul provide <b>incentive or bonus to the drivers who complete the Airport <-> City trips</b> or \n",
    "    - <b>pay a base price for the idle time</b> to bring down trip cancellations from the city and non-availability of cabs at the airport. \n",
    "</div>"
   ]
  }
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