01-EDA-Flight_delay_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detailed EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [**Table of Content**](#table-of-content)\n",
    "  - [**Import**](#import)\n",
    "  - [**Importing airports database for location and timezone**](#importing-airports-database-for-location-and-timezone)\n",
    "  - [**Importing Training Dataset**](#importing-training-dataset)\n",
    "  - [**Importing Airports Database**](#importing-airports-database)\n",
    "  - [**Feature Engineering**](#feature-engineering)\n",
    "  - [**Basic Exploratory Data Analysis**](#basic-exploratory-data-analysis)\n",
    "  - [**Visualization**](#visualization)\n",
    "    - [**Plots with flights delays**](#plots-with-flights-delays)\n",
    "    - [**Plots with NO flights delays**](#plots-with-no-flights-delays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from models.flight_duration import *\n",
    "from models.get_countries import *\n",
    "from models.timesplit import *\n",
    "from models.get_geo_info import *\n",
    "from models.airport_dummies import *\n",
    "\n",
    "from models.code_plot import *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing airports database for location and timezone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icao</th>\n",
       "      <th>iata</th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>subd</th>\n",
       "      <th>country</th>\n",
       "      <th>elevation</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>tz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00AK</th>\n",
       "      <td>00AK</td>\n",
       "      <td></td>\n",
       "      <td>Lowell Field</td>\n",
       "      <td>Anchor Point</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>US</td>\n",
       "      <td>450.0</td>\n",
       "      <td>59.9492</td>\n",
       "      <td>-151.695999</td>\n",
       "      <td>America/Anchorage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00AL</th>\n",
       "      <td>00AL</td>\n",
       "      <td></td>\n",
       "      <td>Epps Airpark</td>\n",
       "      <td>Harvest</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>820.0</td>\n",
       "      <td>34.864799</td>\n",
       "      <td>-86.770302</td>\n",
       "      <td>America/Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00AZ</th>\n",
       "      <td>00AZ</td>\n",
       "      <td></td>\n",
       "      <td>Cordes Airport</td>\n",
       "      <td>Cordes</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>US</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>34.305599</td>\n",
       "      <td>-112.165001</td>\n",
       "      <td>America/Phoenix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00CA</th>\n",
       "      <td>00CA</td>\n",
       "      <td></td>\n",
       "      <td>Goldstone /Gts/ Airport</td>\n",
       "      <td>Barstow</td>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>35.350498</td>\n",
       "      <td>-116.888</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00CO</th>\n",
       "      <td>00CO</td>\n",
       "      <td></td>\n",
       "      <td>Cass Field</td>\n",
       "      <td>Briggsdale</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>US</td>\n",
       "      <td>4830.0</td>\n",
       "      <td>40.6222</td>\n",
       "      <td>-104.344002</td>\n",
       "      <td>America/Denver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      icao iata                     name          city        subd country  \\\n",
       "00AK  00AK                  Lowell Field  Anchor Point      Alaska      US   \n",
       "00AL  00AL                  Epps Airpark       Harvest     Alabama      US   \n",
       "00AZ  00AZ                Cordes Airport        Cordes     Arizona      US   \n",
       "00CA  00CA       Goldstone /Gts/ Airport       Barstow  California      US   \n",
       "00CO  00CO                    Cass Field    Briggsdale    Colorado      US   \n",
       "\n",
       "     elevation        lat         lon                   tz  \n",
       "00AK     450.0    59.9492 -151.695999    America/Anchorage  \n",
       "00AL     820.0  34.864799  -86.770302      America/Chicago  \n",
       "00AZ    3810.0  34.305599 -112.165001      America/Phoenix  \n",
       "00CA    3038.0  35.350498    -116.888  America/Los_Angeles  \n",
       "00CO    4830.0    40.6222 -104.344002       America/Denver  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing airports database for location and timezone\n",
    "# pip install airportsdata\n",
    "\n",
    "import airportsdata\n",
    "\n",
    "airports = airportsdata.load()  # key is ICAO code, the default\n",
    "df_1 = pd.DataFrame(airports).transpose()\n",
    "\n",
    "df_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>DATOP</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>STD</th>\n",
       "      <th>STA</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>AC</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_id_0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-03 10:30:00</td>\n",
       "      <td>2016-01-03 12.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_id_1</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>TU 0757</td>\n",
       "      <td>MXP</td>\n",
       "      <td>TUN</td>\n",
       "      <td>2016-01-13 15:05:00</td>\n",
       "      <td>2016-01-13 16.55.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 31BIMO</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_id_2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>TU 0214</td>\n",
       "      <td>TUN</td>\n",
       "      <td>IST</td>\n",
       "      <td>2016-01-16 04:10:00</td>\n",
       "      <td>2016-01-16 06.45.00</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32AIMN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID       DATOP     FLTID DEPSTN ARRSTN                  STD  \\\n",
       "0  train_id_0  2016-01-03  TU 0712     CMN    TUN  2016-01-03 10:30:00   \n",
       "1  train_id_1  2016-01-13  TU 0757     MXP    TUN  2016-01-13 15:05:00   \n",
       "2  train_id_2  2016-01-16  TU 0214     TUN    IST  2016-01-16 04:10:00   \n",
       "\n",
       "                   STA STATUS         AC  target  \n",
       "0  2016-01-03 12.55.00    ATA  TU 32AIMN   260.0  \n",
       "1  2016-01-13 16.55.00    ATA  TU 31BIMO    20.0  \n",
       "2  2016-01-16 06.45.00    ATA  TU 32AIMN     0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing Training data\n",
    "df = pd.read_csv('data/Train.csv')\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Airports Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icao</th>\n",
       "      <th>iata</th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>subd</th>\n",
       "      <th>country</th>\n",
       "      <th>elevation</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>tz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ICY</th>\n",
       "      <td>19AK</td>\n",
       "      <td>ICY</td>\n",
       "      <td>Icy Bay Airport</td>\n",
       "      <td>Icy Bay</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>US</td>\n",
       "      <td>50.0</td>\n",
       "      <td>59.969002</td>\n",
       "      <td>-141.662003</td>\n",
       "      <td>America/Anchorage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HGZ</th>\n",
       "      <td>2AK6</td>\n",
       "      <td>HGZ</td>\n",
       "      <td>Hog River Airport</td>\n",
       "      <td>Hogatza</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>US</td>\n",
       "      <td>534.0</td>\n",
       "      <td>66.216103</td>\n",
       "      <td>-155.669006</td>\n",
       "      <td>America/Anchorage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BYW</th>\n",
       "      <td>38WA</td>\n",
       "      <td>BYW</td>\n",
       "      <td>Blakely Island Airport</td>\n",
       "      <td>Blakely Island</td>\n",
       "      <td>Washington</td>\n",
       "      <td>US</td>\n",
       "      <td>66.0</td>\n",
       "      <td>48.578999</td>\n",
       "      <td>-122.825996</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     icao iata                    name            city        subd country  \\\n",
       "ICY  19AK  ICY         Icy Bay Airport         Icy Bay      Alaska      US   \n",
       "HGZ  2AK6  HGZ       Hog River Airport         Hogatza      Alaska      US   \n",
       "BYW  38WA  BYW  Blakely Island Airport  Blakely Island  Washington      US   \n",
       "\n",
       "    elevation        lat         lon                   tz  \n",
       "ICY      50.0  59.969002 -141.662003    America/Anchorage  \n",
       "HGZ     534.0  66.216103 -155.669006    America/Anchorage  \n",
       "BYW      66.0  48.578999 -122.825996  America/Los_Angeles  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing Airports database for 'IATA'\n",
    "\n",
    "airports = airportsdata.load('IATA')\n",
    "airports_df = pd.DataFrame(airports).transpose()\n",
    "\n",
    "airports_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature engineering\n",
    "\n",
    "# flight duration in minutes\n",
    "df = flight_duration(df,\"scheduled_time_dep\", \"scheduled_time_arr\")\n",
    "\n",
    "# Country names are included\n",
    "df = get_geo_info(df,airports_df)\n",
    "\n",
    "# Convert 'dep_date' datatype and extract months, year and days\n",
    "df_1 = timeSplit(df,\"dep_date\",df.ID)\n",
    "df = df_1.merge(df, on=\"ID\")\n",
    "\n",
    "# cleaning dataframe \n",
    "df = code_plot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic EDA \n",
    "\n",
    "def EDA(df):\n",
    "    print(\" \")\n",
    "    print(\"-----Data Shape-------------\")\n",
    "    print(df.shape)\n",
    "    print(\" \")\n",
    "    print(\"----------Data Types-----------\")\n",
    "    print(df.dtypes)\n",
    "    print(\" \")\n",
    "    print(\"----------INFO-----------------\")\n",
    "    print(df.info())\n",
    "    print(\" \")\n",
    "    print(\"-------Missing Values----------\")\n",
    "    print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "-----Data Shape-------------\n",
      "(107833, 19)\n",
      " \n",
      "----------Data Types-----------\n",
      "year                               int64\n",
      "month                              int64\n",
      "weekday                            int64\n",
      "FLTID                             object\n",
      "DEPSTN                            object\n",
      "ARRSTN                            object\n",
      "Flight_status                     object\n",
      "AC                                object\n",
      "target                           float64\n",
      "flight_duration                  float64\n",
      "dep_country                       object\n",
      "arr_country                       object\n",
      "day_name                          object\n",
      "National_International_Flight     object\n",
      "dep_hour                           int64\n",
      "arr_hour                           int64\n",
      "dep_AM/PM                         object\n",
      "arr_AM/PM                         object\n",
      "flight_ID                         object\n",
      "dtype: object\n",
      " \n",
      "----------INFO-----------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 107833 entries, 0 to 107832\n",
      "Data columns (total 19 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   year                           107833 non-null  int64  \n",
      " 1   month                          107833 non-null  int64  \n",
      " 2   weekday                        107833 non-null  int64  \n",
      " 3   FLTID                          107833 non-null  object \n",
      " 4   DEPSTN                         107833 non-null  object \n",
      " 5   ARRSTN                         107833 non-null  object \n",
      " 6   Flight_status                  107833 non-null  object \n",
      " 7   AC                             107833 non-null  object \n",
      " 8   target                         107833 non-null  float64\n",
      " 9   flight_duration                107833 non-null  float64\n",
      " 10  dep_country                    107833 non-null  object \n",
      " 11  arr_country                    107833 non-null  object \n",
      " 12  day_name                       107833 non-null  object \n",
      " 13  National_International_Flight  107833 non-null  object \n",
      " 14  dep_hour                       107833 non-null  int64  \n",
      " 15  arr_hour                       107833 non-null  int64  \n",
      " 16  dep_AM/PM                      107833 non-null  object \n",
      " 17  arr_AM/PM                      107833 non-null  object \n",
      " 18  flight_ID                      107833 non-null  object \n",
      "dtypes: float64(2), int64(5), object(12)\n",
      "memory usage: 16.5+ MB\n",
      "None\n",
      " \n",
      "-------Missing Values----------\n",
      "year                             0\n",
      "month                            0\n",
      "weekday                          0\n",
      "FLTID                            0\n",
      "DEPSTN                           0\n",
      "ARRSTN                           0\n",
      "Flight_status                    0\n",
      "AC                               0\n",
      "target                           0\n",
      "flight_duration                  0\n",
      "dep_country                      0\n",
      "arr_country                      0\n",
      "day_name                         0\n",
      "National_International_Flight    0\n",
      "dep_hour                         0\n",
      "arr_hour                         0\n",
      "dep_AM/PM                        0\n",
      "arr_AM/PM                        0\n",
      "flight_ID                        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "EDA(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>Flight_status</th>\n",
       "      <th>AC</th>\n",
       "      <th>target</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>dep_country</th>\n",
       "      <th>arr_country</th>\n",
       "      <th>day_name</th>\n",
       "      <th>National_International_Flight</th>\n",
       "      <th>dep_hour</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>dep_AM/PM</th>\n",
       "      <th>arr_AM/PM</th>\n",
       "      <th>flight_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>TU 9004</td>\n",
       "      <td>DJE</td>\n",
       "      <td>DJE</td>\n",
       "      <td>RTR</td>\n",
       "      <td>TU 736</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>National</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>TU 0744</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>RTR</td>\n",
       "      <td>TU 320</td>\n",
       "      <td>3.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>National</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>TU 0526</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>RTR</td>\n",
       "      <td>TU 736</td>\n",
       "      <td>17.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>National</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>TU 0712</td>\n",
       "      <td>CMN</td>\n",
       "      <td>CMN</td>\n",
       "      <td>RTR</td>\n",
       "      <td>TU 32A</td>\n",
       "      <td>10.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>MA</td>\n",
       "      <td>MA</td>\n",
       "      <td>Monday</td>\n",
       "      <td>National</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>PM</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>TU 9975</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 736</td>\n",
       "      <td>135.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>National</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107821</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>WKL 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>National</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>PM</td>\n",
       "      <td>PM</td>\n",
       "      <td>WKL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107825</th>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>WKL 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>National</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>PM</td>\n",
       "      <td>AM</td>\n",
       "      <td>WKL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107827</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>SGT 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>National</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>PM</td>\n",
       "      <td>AM</td>\n",
       "      <td>SGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107828</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>WKL 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 32A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>National</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>PM</td>\n",
       "      <td>AM</td>\n",
       "      <td>WKL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107830</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>SGT 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>National</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>SGT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5523 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  month  weekday      FLTID DEPSTN ARRSTN Flight_status      AC  \\\n",
       "298     2016      4        5   TU 9004     DJE    DJE           RTR  TU 736   \n",
       "418     2016      7        5   TU 0744     TUN    TUN           RTR  TU 320   \n",
       "461     2016      8        0   TU 0526     TUN    TUN           RTR  TU 736   \n",
       "1107    2016      4        0   TU 0712     CMN    CMN           RTR  TU 32A   \n",
       "1294    2016      6        3   TU 9975     TUN    TUN           ATA  TU 736   \n",
       "...      ...    ...      ...        ...    ...    ...           ...     ...   \n",
       "107821  2018      1        2  WKL 0000     TUN    TUN           SCH  TU 320   \n",
       "107825  2018      8        2  WKL 0000     TUN    TUN           SCH  TU 320   \n",
       "107827  2018     12        2  SGT 0000     TUN    TUN           SCH  TU 736   \n",
       "107828  2018      7        3  WKL 0000     TUN    TUN           SCH  TU 32A   \n",
       "107830  2018     11        2  SGT 0000     TUN    TUN           SCH  TU 736   \n",
       "\n",
       "        target  flight_duration dep_country arr_country   day_name  \\\n",
       "298       35.0             50.0          TN          TN   Saturday   \n",
       "418        3.0            155.0          TN          TN   Saturday   \n",
       "461       17.0            165.0          TN          TN     Monday   \n",
       "1107      10.0            145.0          MA          MA     Monday   \n",
       "1294     135.0             60.0          TN          TN   Thursday   \n",
       "...        ...              ...         ...         ...        ...   \n",
       "107821     0.0            180.0          TN          TN  Wednesday   \n",
       "107825     0.0            180.0          TN          TN  Wednesday   \n",
       "107827     0.0            120.0          TN          TN  Wednesday   \n",
       "107828     0.0            180.0          TN          TN   Thursday   \n",
       "107830     0.0            470.0          TN          TN  Wednesday   \n",
       "\n",
       "       National_International_Flight  dep_hour  arr_hour dep_AM/PM arr_AM/PM  \\\n",
       "298                         National         3         4        AM        AM   \n",
       "418                         National         7         9        AM        AM   \n",
       "461                         National         6         9        AM        AM   \n",
       "1107                        National        10        13        AM        PM   \n",
       "1294                        National        10        11        AM        AM   \n",
       "...                              ...       ...       ...       ...       ...   \n",
       "107821                      National        15        18        PM        PM   \n",
       "107825                      National        22         1        PM        AM   \n",
       "107827                      National        23         1        PM        AM   \n",
       "107828                      National        23         2        PM        AM   \n",
       "107830                      National         5        12        AM        AM   \n",
       "\n",
       "       flight_ID  \n",
       "298           TU  \n",
       "418           TU  \n",
       "461           TU  \n",
       "1107          TU  \n",
       "1294          TU  \n",
       "...          ...  \n",
       "107821       WKL  \n",
       "107825       WKL  \n",
       "107827       SGT  \n",
       "107828       WKL  \n",
       "107830       SGT  \n",
       "\n",
       "[5523 rows x 19 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for flights having same destination for departure and arrival flights\n",
    "\n",
    "df[(df['DEPSTN'] == df['ARRSTN'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>FLTID</th>\n",
       "      <th>DEPSTN</th>\n",
       "      <th>ARRSTN</th>\n",
       "      <th>Flight_status</th>\n",
       "      <th>AC</th>\n",
       "      <th>target</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>dep_country</th>\n",
       "      <th>arr_country</th>\n",
       "      <th>day_name</th>\n",
       "      <th>National_International_Flight</th>\n",
       "      <th>dep_hour</th>\n",
       "      <th>arr_hour</th>\n",
       "      <th>dep_AM/PM</th>\n",
       "      <th>arr_AM/PM</th>\n",
       "      <th>flight_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44399</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TU 9952</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>ATA</td>\n",
       "      <td>TU 32A</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>National</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>TU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66674</th>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>National</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>PM</td>\n",
       "      <td>PM</td>\n",
       "      <td>AOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68831</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>DJE</td>\n",
       "      <td>DJE</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>National</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>PM</td>\n",
       "      <td>PM</td>\n",
       "      <td>AOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68852</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 32A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>National</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>PM</td>\n",
       "      <td>PM</td>\n",
       "      <td>AOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103728</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>MIR</td>\n",
       "      <td>MIR</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>National</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>AOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103943</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 32A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>National</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>AOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105121</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>DJE</td>\n",
       "      <td>DJE</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 32A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>National</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>PM</td>\n",
       "      <td>PM</td>\n",
       "      <td>AOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105246</th>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 32A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>National</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>PM</td>\n",
       "      <td>PM</td>\n",
       "      <td>AOG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106409</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>TUN</td>\n",
       "      <td>TUN</td>\n",
       "      <td>SCH</td>\n",
       "      <td>TU 32A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>National</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>AM</td>\n",
       "      <td>AM</td>\n",
       "      <td>AOG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  month  weekday      FLTID DEPSTN ARRSTN Flight_status      AC  \\\n",
       "44399   2017      3        1   TU 9952     TUN    TUN           ATA  TU 32A   \n",
       "66674   2017     10        3  AOG 0000     TUN    TUN           SCH  TU 736   \n",
       "68831   2017      9        0  AOG 0000     DJE    DJE           SCH  TU 736   \n",
       "68852   2017      3        2  AOG 0000     TUN    TUN           SCH  TU 32A   \n",
       "103728  2018      7        1  AOG 0000     MIR    MIR           SCH  TU 736   \n",
       "103943  2018      5        0  AOG 0000     TUN    TUN           SCH  TU 32A   \n",
       "105121  2018      5        1  AOG 0000     DJE    DJE           SCH  TU 32A   \n",
       "105246  2018      8        4  AOG 0000     TUN    TUN           SCH  TU 32A   \n",
       "106409  2018     10        1  AOG 0000     TUN    TUN           SCH  TU 32A   \n",
       "\n",
       "        target  flight_duration dep_country arr_country   day_name  \\\n",
       "44399     11.0             25.0          TN          TN    Tuesday   \n",
       "66674      0.0             28.0          TN          TN   Thursday   \n",
       "68831      0.0             25.0          TN          TN     Monday   \n",
       "68852      0.0             26.0          TN          TN  Wednesday   \n",
       "103728     0.0             28.0          TN          TN    Tuesday   \n",
       "103943     0.0             18.0          TN          TN     Monday   \n",
       "105121     0.0             26.0          TN          TN    Tuesday   \n",
       "105246     0.0             20.0          TN          TN     Friday   \n",
       "106409     0.0             10.0          TN          TN    Tuesday   \n",
       "\n",
       "       National_International_Flight  dep_hour  arr_hour dep_AM/PM arr_AM/PM  \\\n",
       "44399                       National         5         5        AM        AM   \n",
       "66674                       National        13        14        PM        PM   \n",
       "68831                       National        14        14        PM        PM   \n",
       "68852                       National        15        15        PM        PM   \n",
       "103728                      National         6         6        AM        AM   \n",
       "103943                      National         8         8        AM        AM   \n",
       "105121                      National        14        15        PM        PM   \n",
       "105246                      National        14        15        PM        PM   \n",
       "106409                      National         8         8        AM        AM   \n",
       "\n",
       "       flight_ID  \n",
       "44399         TU  \n",
       "66674        AOG  \n",
       "68831        AOG  \n",
       "68852        AOG  \n",
       "103728       AOG  \n",
       "103943       AOG  \n",
       "105121       AOG  \n",
       "105246       AOG  \n",
       "106409       AOG  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking flights having flight duration less than 30 mins\n",
    "# and also checking for flights having same destination for departure and arrival flights\n",
    "\n",
    "df[(df['flight_duration']>0) & (df['flight_duration']<30) & (df['DEPSTN'] == df['ARRSTN'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of delayed flights in the dataset\n",
    "\n",
    "percenatge_delay = round(len(df[df['target']!=0])/len(df)*100, 2)\n",
    "percenatge_delay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots with flights delays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following plots are plotted for flights with delay\n",
    "\n",
    "- Number of International and National Flight delays in the dataset\n",
    "- Percentage of International flight delay in Dataset\n",
    "- Delayed Flight count per year, month and week days\n",
    "- Top-5 Flight IDs delay count\n",
    "- Top-5 Aircraft's delay count for International and national flights\n",
    "- Hourly Flight delay count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flights dataset with delays\n",
    "\n",
    "df_delay = df[df['target']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "International    66720\n",
       "National          2945\n",
       "Name: National_International_Flight, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No. of International and National Flight delays in the dataset\n",
    "\n",
    "df_delay.National_International_Flight.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.77262613938132"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %age of International flight in Dataset\n",
    "\n",
    "len(df_delay[df_delay['National_International_Flight'] == 'International'])/len(df_delay)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# International and National flight delay count\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "sns.countplot(x='National_International_Flight', data=df_delay , palette=['g'])\n",
    "\n",
    "plt.xlabel('Flight Status', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('International and National flight delay count', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    20422\n",
       "2017    24317\n",
       "2018    24926\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delayed Flight count per year\n",
    "\n",
    "df_delay.year.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>year</th>\n",
       "      <th>National_International_Flight</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>International</td>\n",
       "      <td>19491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>National</td>\n",
       "      <td>931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>International</td>\n",
       "      <td>23256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>National</td>\n",
       "      <td>1061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>International</td>\n",
       "      <td>23973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>National</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year National_International_Flight  count\n",
       "0  2016                 International  19491\n",
       "1  2016                      National    931\n",
       "2  2017                 International  23256\n",
       "3  2017                      National   1061\n",
       "4  2018                 International  23973\n",
       "5  2018                      National    953"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# National_International_Flight delays count per year\n",
    "\n",
    "df_delay.groupby(['year', 'National_International_Flight']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Delayed Flight count per year\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.countplot(x='year', data=df_delay , palette=['g', 'b', 'm'])\n",
    "\n",
    "#plt.legend(loc='upper right', title='Aircrafts Types')\n",
    "\n",
    "plt.xlabel('Delayed Flight years', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('Flight delay count per year', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>year</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>55.493096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>80.493646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>86.832344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     target\n",
       "0  2016  55.493096\n",
       "1  2017  80.493646\n",
       "2  2018  86.832344"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average Flight delays per year\n",
    "\n",
    "df_delay.groupby('year', as_index=False)['target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Flight delays per year\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "plot = sns.barplot(x='year', y='target', data=df_delay, ci=None, palette=['g', 'b', 'm'])\n",
    "\n",
    "plt.xlabel('Delayed Flight years', size=15, weight='bold')\n",
    "plt.ylabel('Delay', size=15, weight='bold')\n",
    "plt.title('Avg Flight delay per year', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     5545\n",
       "2     2899\n",
       "3     5838\n",
       "4     6224\n",
       "5     4285\n",
       "6     5662\n",
       "7     8420\n",
       "8     9238\n",
       "9     5228\n",
       "10    5783\n",
       "11    4729\n",
       "12    5814\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Per Month Delayed Flight count\n",
    "\n",
    "df_delay.month.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>month</th>\n",
       "      <th>National_International_Flight</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>International</td>\n",
       "      <td>5240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>National</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>International</td>\n",
       "      <td>2790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>National</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>International</td>\n",
       "      <td>5530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>National</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>International</td>\n",
       "      <td>5928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>National</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>International</td>\n",
       "      <td>4050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>National</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>International</td>\n",
       "      <td>5369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>National</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>International</td>\n",
       "      <td>8185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7</td>\n",
       "      <td>National</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>International</td>\n",
       "      <td>8999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>National</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>International</td>\n",
       "      <td>5019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9</td>\n",
       "      <td>National</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10</td>\n",
       "      <td>International</td>\n",
       "      <td>5524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>National</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11</td>\n",
       "      <td>International</td>\n",
       "      <td>4513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11</td>\n",
       "      <td>National</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12</td>\n",
       "      <td>International</td>\n",
       "      <td>5573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12</td>\n",
       "      <td>National</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month National_International_Flight  count\n",
       "0       1                 International   5240\n",
       "1       1                      National    305\n",
       "2       2                 International   2790\n",
       "3       2                      National    109\n",
       "4       3                 International   5530\n",
       "5       3                      National    308\n",
       "6       4                 International   5928\n",
       "7       4                      National    296\n",
       "8       5                 International   4050\n",
       "9       5                      National    235\n",
       "10      6                 International   5369\n",
       "11      6                      National    293\n",
       "12      7                 International   8185\n",
       "13      7                      National    235\n",
       "14      8                 International   8999\n",
       "15      8                      National    239\n",
       "16      9                 International   5019\n",
       "17      9                      National    209\n",
       "18     10                 International   5524\n",
       "19     10                      National    259\n",
       "20     11                 International   4513\n",
       "21     11                      National    216\n",
       "22     12                 International   5573\n",
       "23     12                      National    241"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# National_International_Flight delays count per month\n",
    "\n",
    "df_delay.groupby(['month', 'National_International_Flight']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per Month Delayed Flight count\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.countplot(x='month', data=df_delay, palette=['g'])\n",
    "\n",
    "#plt.legend(loc='upper right', title='Aircrafts Types')\n",
    "\n",
    "plt.xlabel('Flight Months', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('Flight delay count per month', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>index</th>\n",
       "      <th>day_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>11699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>11163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Friday</td>\n",
       "      <td>10969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monday</td>\n",
       "      <td>10406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>9935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>7756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>7737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  day_name\n",
       "0     Sunday     11699\n",
       "1   Saturday     11163\n",
       "2     Friday     10969\n",
       "3     Monday     10406\n",
       "4   Thursday      9935\n",
       "5    Tuesday      7756\n",
       "6  Wednesday      7737"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Week day Flight count delays\n",
    "\n",
    "df_delay.day_name.value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>day_name</th>\n",
       "      <th>National_International_Flight</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>International</td>\n",
       "      <td>10519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Friday</td>\n",
       "      <td>National</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Monday</td>\n",
       "      <td>International</td>\n",
       "      <td>10062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monday</td>\n",
       "      <td>National</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>International</td>\n",
       "      <td>10568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>National</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>International</td>\n",
       "      <td>11255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>National</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>International</td>\n",
       "      <td>9554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>National</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>International</td>\n",
       "      <td>7387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>National</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>International</td>\n",
       "      <td>7375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>National</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     day_name National_International_Flight  count\n",
       "0      Friday                 International  10519\n",
       "1      Friday                      National    450\n",
       "2      Monday                 International  10062\n",
       "3      Monday                      National    344\n",
       "4    Saturday                 International  10568\n",
       "5    Saturday                      National    595\n",
       "6      Sunday                 International  11255\n",
       "7      Sunday                      National    444\n",
       "8    Thursday                 International   9554\n",
       "9    Thursday                      National    381\n",
       "10    Tuesday                 International   7387\n",
       "11    Tuesday                      National    369\n",
       "12  Wednesday                 International   7375\n",
       "13  Wednesday                      National    362"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# National_International_Flight delays count per week day\n",
    "\n",
    "df_delay.groupby(['day_name', 'National_International_Flight']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Week day Flight count delays\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "day_order = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "sns.countplot(x = \"day_name\", data = df_delay, order = day_order, palette=['g'])\n",
    "\n",
    "plt.xlabel('Flight Weekday', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('Week Day Flight delay count', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>index</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TU 0613</td>\n",
       "      <td>1221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TU 0397</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TU 0722</td>\n",
       "      <td>913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TU 0216</td>\n",
       "      <td>895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TU 0717</td>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  count\n",
       "0  TU 0613    1221\n",
       "1  TU 0397    1034\n",
       "2  TU 0722     913\n",
       "3  TU 0216     895\n",
       "4  TU 0717     883"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delay count for top 5 flight IDs\n",
    "\n",
    "df_aircraft = df_delay.FLTID.value_counts().reset_index(name='count').head()\n",
    "df_aircraft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RRx6RJH322We2gHWziSKk60NMM/9wsH37drtAs2rVKgUHByswMFDR0dHy9fWVt7e3JOmnn36yez9SUlJsvy8uLi62691u/KPEjYErNjbWNuX6nco8j+mNAmAGPU8AkIOmTZuqYsWKOnbsmG3a6nbt2unUqVOaO3eupOtfvG6c4vlGb775puLi4lS+fHktW7ZMFy9elHR9Jrl/Dun7p3r16snb21uJiYlauXKlPD09VbNmTS1btsw2tCu7a5gKStmyZdW0aVNt2bJFf/75p/r166dOnTrp4MGDWrhwYZ7sI3OyBOn6F/PIyEhVqlTpjm4WHBMTo7///luGYSgpKUk7d+7UqlWrJF2fNGLgwIGSrk/YEB0dbbu2KzU1VU2aNFFSUpKWLFli295DDz2U6/6efvppzZw5UxkZGerXr59eeOEFJScnZ7mvUqbQ0FBt375d6enpev755/X888/Lzc1Ns2fPtt0jbPny5Tnuz9PTU66urkpPT9euXbv0wQcfqEKFCoqMjLSbSjw1NdVuGFunTp30/vvv2wKKh4fHTadEl64PhXvqqaf05ZdfKjExUb1791a3bt108eJFzZ49W9L135HGjRvLxcVF3bt318yZM5WamqqwsDD169dPnp6eWrZsmS20hYaG2nq8brxObN68efLz81NqaqrpGSvN8PT01JUrV3TixAmtXLlSDzzwABNHAMgR4QkAcuDq6qoPPvhAL7zwghITE233dspksVg0atQo1a9fP8u6Hh4eKlOmjObMmWPXXq5cOdssc7mxWq1677331LdvX6Wnp2cJJM2bN9dzzz13eweWR/79738rJiZGycnJ2rZtm23K9vr169sm0bhx2NWtqlSpktzd3ZWSkqJDhw5p5MiRGjZs2B2Fpw8++CDbdqvVqrfeektVqlSxtb311lvq06ePkpKStGLFCq1YscJundq1a6tTp0657u+FF17Qxo0bdeDAAR07dkxjx46VdP0Le8OGDe0mJZGuh5iNGzdqw4YNOnHiRJZpu/v27Zvr8bu7uyskJMQ2HXdmgMk8xsyhb3FxcXbBpFOnTvrggw9svS9BQUGm77WUeR78/vvv2rdvn21iikyTJk2yXeM2cOBA/frrr9q0aZPi4+PtevGk6380GD16tO1506ZNbROzxMfH2855X19f+fv753qzYbOqV6+umJgYXblyRaNHj1bTpk0JTwByxLA9AMiFv7+/1qxZowEDBti+zBcvXlxPPPGEvvjiixwDjNVq1eLFixUSEqLChQuraNGi6tChg5YtW2ZqQgXp+gx5S5YsUVBQkIoVKyY3NzdVqVJFI0aM0Jw5c7JMJlHQqlevrsWLF6tx48by8vJSyZIl1adPH3344Ye2Zf55LditKFy4sKZOnaqHH35YVqtVZcuWtQ37ygvu7u566KGH1KVLF3311Vfq0KGD3esBAQGKjIxU9+7dVbFiRXl4eMjDw0PVqlXTkCFD9MUXX9j13mTHw8NDX3zxhXr16qWSJUvK09NTzZo107Jly1SzZs0sy1ssFr3//vsaN26catasKS8vLxUtWlR16tTR22+/rZEjR970uCZOnKh+/frJ19dXHh4eeuSRRxQeHq7//ve/tmUypyfPVK5cOQUGBtqemxmyl6lYsWJatmyZ+vfvrwoVKshqtcrb21vNmjXTggUL9PTTT9uWdXV11Zw5c/TOO++oYcOGeuCBB+Th4SF/f3+NGjVK8+fPt7tO0MvLS/Pnz1fz5s3l5eWlBx98UF26dNHy5ctveu2XWePHj1dgYKCsVquKFy9uG1ILANmxGAzyBYA807JlS506dUpFixbNdgrze4VhGIqIiJCPj4+8vb3l7+9vC3PHjx+3TdPetWtXvfnmm44sFSYkJSUpKChIly5dUvHixfXjjz/eNBgCwP2IYXsAgFtmsVg0ffp02wxrXbt2VbNmzZSUlGR3494bb3QL5/Ptt9/KMAwtWrTIdi1dhw4dCE4AkAN6ngAgD90vPU+S9PHHH2v69Ok5vl61alWtWLHCbuIHOJe2bdvabhwrScWLF1dkZKTDZnEEAGdHzxMA4LYMGDBAZcqU0cqVK3Xo0CFdunRJVqtVvr6+CgoK0oABAwhOTi4gIEDx8fFyd3dXnTp19OqrrxKcACAX9DwBAAAAgAn3Tc/T1atXtX//fnl7e8vFxcXR5QAAAABwMhkZGUpMTFRAQEC2oyfum/C0f/9+9ezZ09FlAAAAAHByixcvzvY+jvdNeMq8N8jixYtVpkwZB1cDAAAAwNnExcWpZ8+eOd5X8L4JT5lD9cqUKaOHHnrIwdUAAAAAcFY5XeZTqIDrAAAAAIC7EuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeLoFV9NSHF0CChg/cwAAAGRydXQBdxMPq7sq9K/m6DJQgI5/csjRJQAAAMBJ0PMEAAAAACYUaHhKSkpShw4ddPLkSUnSl19+qQ4dOqhjx456/fXXlZqaKkmKjY1Vly5d1LZtW40ZM0bp6emSpNOnT6tnz54KDg7WoEGDdPny5YIsHwAAAMB9rMDC0969e9WjRw8dO3ZMknT06FHNnTtXy5Yt05o1a3Tt2jUtWbJEkjRixAiNGzdO69evl2EYioiIkCS98cYbCgsLU3R0tAICAjR79uyCKh8AAADAfa7AwlNERIQmTJggHx8fSZKbm5smTpyoIkWKyGKxqFq1ajp9+rROnTqlq1evKjAwUJIUGhqq6OhopaWlaefOnWrbtq1dOwAAAAAUhAKbMOLNN9+0e+7r6ytfX19J0rlz57R48WJNmTJFCQkJ8vb2ti3n7e2t+Ph4nT9/XkWKFJGrq6tdOwAAAAAUBIdPGBEfH68+ffqoS5cuatiwoQzDyLKMxWLJsR0AAAAACoJDw9ORI0fUo0cPde7cWS+99JIkqXTp0jp79qxtmcTERPn4+KhEiRJKSkpSRkaGXTsAAAAAFASHhaekpCT169dPr7zyivr27Wtr9/X1lbu7u2JiYiRJkZGRat68uaxWq+rXr6+oqCi7dgAAAAAoCA4LTytWrNDZs2c1b948derUSZ06ddIHH3wgSZo2bZqmTJmidu3aKTk5WeHh4ZKkCRMmKCIiQu3bt9euXbs0dOhQR5UPAAAA4D5jMbK7mOgedPLkSbVq1UobN27UQw89dNvbqdC/Wh5WBWd3/JNDji4BAAAABeRmmcHhE0YAAAAAwN2A8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCE+CkrqakOboEFDB+5gAAODdXRxcAIHse7lY9WG2oo8tAATp/6H1HlwAAAHJBzxMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAkFGp6SkpLUoUMHnTx5UpK0detWdezYUW3atNGMGTNsy8XGxqpLly5q27atxowZo/T0dEnS6dOn1bNnTwUHB2vQoEG6fPlyQZYPAAAA4D5WYOFp79696tGjh44dOyZJunr1qkaPHq3Zs2crKipK+/fv1+bNmyVJI0aM0Lhx47R+/XoZhqGIiAhJ0htvvKGwsDBFR0crICBAs2fPLqjyAQAAANznCiw8RUREaMKECfLx8ZEk7du3TxUqVFD58uXl6uqqjh07Kjo6WqdOndLVq1cVGBgoSQoNDVV0dLTS0tK0c+dOtW3b1q4dAAAAAApCgd0k980337R7npCQIG9vb9tzHx8fxcfHZ2n39vZWfHy8zp8/ryJFisjV1dWuHQAAAAAKgsMmjDAMI0ubxWK55XYAAAAAKAgOC0+lS5fW2bNnbc8TEhLk4+OTpT0xMVE+Pj4qUaKEkpKSlJGRYdcOAAAAAAXBYeGpTp06Onr0qI4fP66MjAytW7dOzZs3l6+vr9zd3RUTEyNJioyMVPPmzWW1WlW/fn1FRUXZtQMAAABAQSiwa57+yd3dXVOnTtWQIUOUkpKiFi1aKDg4WJI0bdo0jR07VpcvX1aNGjUUHh4uSZowYYJGjRqlOXPmqGzZspo+fbqjygcAAABwnynw8LRp0ybb48aNG2vNmjVZlvH399eKFSuytPv6+mrhwoX5Wh8AAAAAZMdhw/YAAAAA4G5CeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAJSakuboElDA+JkDwK1zdXQBAADHc3O36tnqAx1dBgpQROx/HV0CANx16HkCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAABQ4K6mMtvf/YafOe4FzLYHAAAKnIebVcXDBji6DBSgC0s+dnQJwB2j5wkAAAAATCA8AQAA4J6WkZbi6BJQwPLrZ86wPQAAANzTXKzuWjjuEUeXgQLU+z9H82W79DwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJjhFeFq9erVCQkIUEhKit99+W5IUGxurLl26qG3bthozZozS09MlSadPn1bPnj0VHBysQYMG6fLly44sHQAAAMB9wuHhKTk5WW+++aYWLlyo1atXa9euXdq6datGjBihcePGaf369TIMQxEREZKkN954Q2FhYYqOjlZAQIBmz57t4CMAAAAAcD9weHjKyMjQtWvXlJycrPT0dKWnp8vV1VVXr15VYGCgJCk0NFTR0dFKS0vTzp071bZtW7t2AAAAAMhvro4uoEiRInrllVfUrl07eXh4qEGDBrJarfL29rYt4+3trfj4eJ0/f15FihSRq6urXTsAAAAA5DeH9zwdPHhQK1eu1HfffactW7aoUKFC+umnn7IsZ7FYZBhGtu0AAAAAkN9Mh6fIyEht3749S/v//vc/LV++/LYL2LJlixo3bqySJUvKzc1NoaGh2r59u86ePWtbJjExUT4+PipRooSSkpKUkZFh1w4AAAAA+c10eBo1apQWLVqUpX3RokV65513brsAf39/bd26VVeuXJFhGNq0aZMaNGggd3d3xcTESLoe3Jo3by6r1ar69esrKirKrh0AAAAA8luu1zx9/PHHev/9923Pv/32W1WvXj3LcsWLF7/tApo1a6YDBw4oNDRUVqtVtWrVUv/+/dW6dWuNHTtWly9fVo0aNRQeHi5JmjBhgkaNGqU5c+aobNmymj59+m3vGwAAAADMyjU8Pf/884qIiNCpU6dyvObI1dVVzz///B0V0b9/f/Xv39+uzd/fXytWrMiyrK+vrxYuXHhH+wMAAACAW5VreHJzc1NkZKQuX76sJ554Qo8//rgmT55se91isahYsWLy8PDI90IBAAAAwJFuOlV50aJFVbRoUR08eLAg6gEAAAAAp2T6Pk/Jycn6/PPPtW/fPqWlpdkN4bNYLJo7d26+FAgAAAAAzsB0eBo7dqyioqK41xIAAACA+5Lp8LRp0yYVKlRIvXr1UpUqVeTqanpVAAAAALjrmU5AhQsXVkBAgF5//fX8rAcAAAAAnJLpm+T26tVLR44c0V9//ZWf9QAAAACAUzLd85SQkKD09HS1bt1a1apVk6enp+1aJyaMAAAAAHCvMx2elixZYnv8yy+/2L3GhBEAAAAA7nWmw9OUKVPysw4AAAAAcGqmw1Pnzp3zsw4AAAAAcGqmw9O4ceNyfM1isWjSpEl5UhAAAAAAOCPT4Wn58uWyWCx2N8nNfE54AgAAAHCvMx2eBg8ebHtsGIZSUlJ04MAB7d27V6+99lq+FAcAAAAAzuK2wtON+vTpo3379qlbt255VhQAAAAAOBvTN8nNSeHChbVhw4a8qAUAAAAAnNZtTxiRkZGhuLg4bdu2TQ8++GCeFwYAAAAAzuSOJozI1KtXrzwtCgAAAACczR1d8+Th4aFatWqpYcOGeVoUAAAAADibO54wAgAAAADuB7c0YcTBgwf1r3/9S48++qjq1aungQMH6tChQ/lVGwAAAAA4DdM9T7GxsQoLC1NycrKt7fvvv9eOHTu0dOlS+fn55UuBAAAAAOAMTPc8vffee0pOTlZ4eLjWrFmjNWvWqHfv3rpy5YqmT5+enzUCAAAAgMOZ7nnavXu3atSoodGjR9vaxowZo5iYGO3cuTNfigMAAAAAZ2G656lQoZwXze01AAAAALgXmE49gYGBio2N1dSpU3X48GEdPnxYU6ZMUWxsrOrWrZufNQIAAACAw5ketvfyyy9rx44dWrBggRYsWCBJMgxDrq6uGjJkSL4VCAAAAADOwHTPU+3atbV06VI1a9ZMXl5eKly4sBo3bqwFCxaodu3a+VkjAAAAADic6Z4nSapZs6Y+/fTT/KoFAAAAAJzWLc30EBkZqUWLFtmeDxo0SCtWrMjzogAAAADA2ZgOT19++aVef/11bdq0SZKUmpqqzZs3a9y4cVq2bFm+FQgAAAAAzsB0eFqwYIE8PDzUp08fSZLVatXs2bPl4eGhhQsX5luBAAAAAOAMTIenkydP6tFHH1WLFi0kSRaLRU888YQeffRRnTx5Mt8KBAAAAABnYDo8FS9eXLGxsTp79qytLS4uTvv371exYsXypTgAAAAAcBamZ9tr37695s+fr9atW8vPz08ZGRk6dOiQUlNTbUP5AAAAAOBeZTo8vfrqqzp16pS++eYb/fLLL7b2J598UsOGDcuP2gAAAADAaZgOT25ubpo5c6aOHj2qgwcPSpL8/PxUqVKlfCsOAAAAAJzFLd0kV5IeeeQRPfLII9m+9tJLL+m7777TgQMH7rgwAAAAAHAmt3STXDMMw8jrTQIAAACAw+V5eAIAAACAexHhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADAhFu+z1NuQkJCVL169bzcJAAAAAA4hVsKT1u2bNHDDz+shx9+WNOnT9fmzZvVpEkTDR8+XK6urmrfvn1+1QkAAAAADmU6PK1cuVJjx47Vf/7zHx09elSffPKJJOnQoUMqXLiwBg8enG9FAgAAAICjmb7maf78+XJzc1O5cuUUHR0tq9WqOXPm6IEHHtDatWvzs0YAAAAAcDjT4enEiROqX7++mjRpou3btysgIEBBQUGqVauW4uPj87NGAAAAAHA40+HJw8NDly9f1vHjx3X69GnVr19fknTq1CkVKVIk3woEAAAAAGdgOjzVrFlTe/fu1bPPPiuLxaJWrVpp9OjROnr0qBo2bHhHRWzatEmhoaEKDg7W5MmTJUlbt25Vx44d1aZNG82YMcO2bGxsrLp06aK2bdtqzJgxSk9Pv6N9AwAAAIAZpsPTiBEjVKpUKV26dEndunVTYGCgXFxcVLp0aQ0bNuy2Czhx4oQmTJig2bNna+3atTpw4IA2b96s0aNHa/bs2YqKitL+/fu1efNmWx3jxo3T+vXrZRiGIiIibnvfAAAAAGCW6fDk7++vH374QTt37tTEiRMlSX379lVUVJQeeuih2y7gm2++Ufv27VWmTBlZrVbNmDFDnp6eqlChgsqXLy9XV1d17NhR0dHROnXqlK5evarAwEBJUmhoqKKjo2973wAAAABglunw1KpVK02cONHu+qZHHnlEw4YNu6P7Ox0/flwZGRnq16+fnnrqKS1ZskQJCQny9va2LePj46P4+Pgs7d7e3kxWAQAAAKBA5Hqfp40bNyo2NlbS9Ykhdu7cqVmzZtleNwxD+/fvV1JS0m0XkJGRoV27dmnhwoXy8vLSiy++KE9PzyzLWSwWGYaRbTsAAAAA5Ldcw1PZsmU1ZMgQGYYhi8Wio0eP6qOPPrJbxjAM1alT57YLKFWqlBo3bqwSJUpIut7DFR0dLRcXF9syCQkJ8vHxUenSpXX27Flbe2Jionx8fG573wAAAABgVq7hqUaNGho5cqQOHz6sFStWqFy5cmrSpInt9UKFCunBBx/UM888c9sFBAUFaeTIkbp06ZIKFy6sH3/8UcHBwfrkk090/PhxPfTQQ1q3bp26dOkiX19fubu7KyYmRvXq1VNkZKSaN29+2/sGAAAAALNyDU+S1KdPH0lSmTJlVKlSpTu6vik7derU0QsvvKCwsDClpaWpadOm6tGjhypVqqQhQ4YoJSVFLVq0UHBwsCRp2rRpGjt2rC5fvqwaNWooPDw8T+sBAAAAgOzcNDxlGjx4sFJTU3Xw4EElJydnuf7o0Ucfve0iunbtqq5du9q1NW7cWGvWrMmyrL+/v1asWHHb+wIAAACA22E6PP3www8aMWKELl26lOU1i8WiAwcO5GlhAAAAAOBMTIent956SxcvXpSbm5tKlCihQoVMz3IOAAAAAHc90+Hp9OnTKleunFavXq2iRYvmZ00AAAAA4HRMh6e6desqPT2d4AQAAADgvpRreNq9e7ftcefOnTVx4kTNmDFDjRo1kru7u92ydzJhBAAAAAA4u1zDU1hYmCwWi13bJ598ok8++cSujQkjAAAAANzrcg1P5cqVK6g6AAAAAMCp5RqeNm3aVFB1AAAAAIBTY75xAAAAADDB9Gx71atXz/E1q9WqUqVK6cknn9Tw4cOzTCYBAAAAAHc70z1PJUqUkNVqlWEYWf6lpqbq9OnTWrhwoWbMmJGf9QIAAACAQ5gOT1OmTJHFYtH48eO1Y8cO7dy5U2PGjJGnp6fmzp2r//3vf3rggQe0fv36/KwXAAAAABzilsJT7dq1FRYWpmLFiqlo0aLq3bu3atSooXfeeUeVK1dW7dq1lZiYmJ/1AgAAAIBDmL7m6dSpU0pNTVVaWpqsVqskKTU1VXFxcTp79qxSUlJ05swZ22sAAAAAcC8xHZ7q1KmjXbt2KTQ0VC1atJBhGPrhhx90+vRpBQYGau3atTp8+LBq1aqVn/UCAAAAgEOYDk8TJkxQv379dPjwYf3++++SJMMwVKpUKU2cOFEbN26Uq6urBg4cmG/FAgAAAICjmA5PVapU0fr167V27Vr98ccfSk9PV7Vq1dShQwd5enrKMAyFhISoQoUK+VkvAAAAADiE6fAkSR4eHnrmmWeyfc3f3z9PCgIAAAAAZ5RreOrbt68effRRDR48WH379s1xOYvForlz5+Z5cQAAAADgLHINT1u3bpWXl5ftcU4sFkveVgUAAAAATibX8DRlyhSVLVvW9hgAAAAA7le5hqfOnTtn+xgAAAAA7je5hqf//ve/pjZisVg0YMCAPCkIAAAAAJxRruHp/fffv+n1TIZhEJ4AAAAA3PNMD9sDAAAAgPtZruHJ19dXlSpVUvv27QuqHgAAAABwSoVye3HWrFn6+uuv7drCw8P14Ycf5mtRAAAAAOBscu15kq5f03SjHTt2qFixYvlWEAAAAAA4o1x7ngAAAAAA1xGeAAAAAMCEmw7bu3jxonbv3n3TtkcffTRvKwMAAAAAJ3LT8LRr1y717NnT9txisWTbduDAgfypEAAAAACcQK7hqVy5cgVVBwAAAAA4tVzD06ZNmwqqDgAAAABwakwYAQAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwwWnC09tvv61Ro0ZJkmJjY9WlSxe1bdtWY8aMUXp6uiTp9OnT6tmzp4KDgzVo0CBdvnzZkSUDAAAAuI84RXjatm2bVq1aZXs+YsQIjRs3TuvXr5dhGIqIiJAkvfHGGwoLC1N0dLQCAgI0e/ZsR5UMAAAA4D7j8PB04cIFzZgxQwMHDpQknTp1SlevXlVgYKAkKTQ0VNHR0UpLS9POnTvVtm1bu3YAAAAAKAgOD0/jx4/XsGHDVKxYMUlSQkKCvL29ba97e3srPj5e58+fV5EiReTq6mrXDgAAAAAFwaHhafny5SpbtqwaN25sazMMI8tyFoslx3YAAAAAKAiujtx5VFSUEhMT1alTJ128eFFXrlyRxWLR2bNnbcskJibKx8dHJUqUUFJSkjIyMuTi4mJrBwAAAICC4NCep88//1zr1q3T6tWr9fLLL6tly5aaMmWK3N3dFRMTI0mKjIxU8+bNZbVaVb9+fUVFRdm1AwAAAEBBcPg1T9mZNm2apkyZonbt2ik5OVnh4eGSpAkTJigiIkLt27fXrl27NHToUMcWCgAAAOC+4dBhezcKDQ1VaGioJMnf318rVqzIsoyvr68WLlxY0KUBAAAAgHP2PAEAAACAsyE8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJThGeZs2apZCQEIWEhOidd96RJG3dulUdO3ZUmzZtNGPGDNuysbGx6tKli9q2basxY8YoPT3dUWUDAAAAuI84PDxt3bpVW7Zs0apVqxQZGalff/1V69at0+jRozV79mxFRUVp//792rx5syRpxIgRGjdunNavXy/DMBQREeHgIwAAAABwP3B4ePL29taoUaPk5uYmq9WqypUr69ixY6pQoYLKly8vV1dXdezYUdHR0Tp16pSuXr2qwMBASVJoaKiio6MdewAAAAAA7gsOD09Vq1a1haFjx44pKipKFotF3t7etmV8fHwUHx+vhIQEu3Zvb2/Fx8cXdMkAAAAA7kMOD0+ZDh8+rL59+2rkyJF6+OGHs7xusVhkGEa27QAAAACQ35wiPMXExOi5557T8OHD1blzZ5UuXVpnz561vZ6QkCAfH58s7YmJifLx8XFEyQAAAADuMw4PT2fOnNFLL72kadOmKSQkRJJUp04dHT16VMePH1dGRobWrVun5s2by9fXV+7u7oqJiZEkRUZGqnnz5o4sHwAAAMB9wtXRBcydO1cpKSmaOnWqra179+6aOnWqhgwZopSUFLVo0ULBwcGSpGnTpmns2LG6fPmyatSoofDwcEeVDgAAAOA+4vDwNHbsWI0dOzbb19asWZOlzd/fXytWrMjvsgAAAADAjsOH7QEAAADA3YDwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwIS7MjytXbtW7du3V+vWrbV48WJHlwMAAADgPuDq6AJuVXx8vGbMmKGvvvpKbm5u6t69uxo2bKgqVao4ujQAAAAA97C7Ljxt3bpVjRo1UvHixSVJbdu2VXR0tAYPHpzrehkZGZKkuLi4O9q/5eodrY67zMmTJx26/0K64tD9o2A5+nxLNZIdun8ULEefb5JUKIVz7n7i6HPuwmWH7h4F7HbPt8yskJkd/umuC08JCQny9va2Pffx8dG+fftuul5iYqIkqWfPnne0f3dZ7mh93F1atWrl0P2X4nS7rzj6fMP9xRnON++bL4J7iOPPOTcH7x8Fadb/7ux8S0xMVIUKFbK033XhyTCMLG0Wy82/YQYEBGjx4sXy9vaWi4tLfpQGAAAA4C6WkZGhxMREBQQEZPv6XReeSpcurV27dtmeJyQkyMfH56breXh4qH79+vlZGgAAAIC7XHY9Tpnuutn2mjRpom3btuncuXNKTk7Whg0b1Lx5c0eXBQAAAOAed1f2PA0bNkzh4eFKS0tT165dVbt2bUeXBQAAAOAeZzGyu4gIAAAAAGDnrhu2BwAAAACOQHgCAAAAABMITwAAAABgAuEJAAAAAEy462bbQ87eeOMN7d69W2lpafrzzz9VuXJlSVJ4eLgsFot27NihqVOn2pbfvn27Zs2apYULF9ptJzU1VWPGjNH+/fvl4eGhadOmqXLlyjIMQ7Nnz9Y333yj5ORkDRo0SE8//bRtvfj4eHXp0kVbtmyxtX3wwQdav369LBaLunbtqueffz5/3wQUmPw+3y5fvqxRo0bp2LFjcnFx0WuvvaYmTZro2rVrmjJlin788Ue5u7urV69eeuaZZ7R582ZNnz7dtt34+HjVqVNHH3/8ccG8IchX+X2+jR8/Xnv37rUtd+jQIc2YMUOtW7fWpEmTFBMTI8Mw9Mwzz+i5556TJM2aNUtff/21JKlFixZ67bXX8vldQEEpiM+30aNH648//pAkDRw4UCEhIbb1svv/dNOmTZo1a5auXLmiZs2aaezYsfn5FqCAOeozLjg4WFLWc2758uVatGiRbfmTJ0+qU6dOGj9+fL69B3cNA/ecEydOGEFBQXZtK1euNEaOHGnX9vPPPxu9evXKsv5nn31mjBs3zjAMw9ixY4fRtWtXwzAMIzIy0ggLCzNSUlKMhIQEo3HjxsbFixcNwzCM77//3mjTpo1RrVo123a2b99udO/e3UhLSzOSk5ONoKAg48iRI3l6rHC8/DrfZs6cabz77ruGYRjG77//bjRt2tQwDMOIiIgwnn/+eSMlJcVITk42QkNDjf/7v/+z22ZCQoLRqlUr4+jRo3lyjHAe+XW+3Wj58uVG3759jWvXrhkRERHGyy+/bGRkZBiXL1822rVrZ+zfv9/46aefjG7duhkpKSlGamqqER4ebmzYsCEPjxTOIL/Ot+nTpxtTp041DMMwzp49azRt2tRITEw0DCP7/0///PNPo1mzZsaZM2eM1NRUo0ePHsb333+fdwcKp1HQn3GGkf05d6NDhw4ZrVu3Nv7666/bOqZ7DcP2kMX333+vp556SpL02GOP6fz58zp9+rS+/vpr9e3bV25ubvL29taSJUvk4eEhSVqxYoVmzpxpt50GDRroiy++kKurq/766y9lZGTIy8urwI8Hzi2n823w4MEaOnSopOt/8XrggQckSQcOHFCrVq3k5uYmDw8PNWzYUBs3brTb5jvvvKPu3burYsWKBXkouAvkdL5lOn/+vD788ENNmjRJFotFVatW1eDBg1WoUCF5eXmpfPnyOnPmjLy9vTVq1Ci5ubnJarWqcuXKdtsBpJzPtwYNGqh3796SpJIlS6p48eI6e/aspOz/P/3mm2/Uvn17lSlTRlarVTNmzFCdOnUK9mBwV7jVzzgp+3PuRhMnTtSwYcNUokSJ/C3+LkF4QhYJCQny9va2Pff29lZcXJyOHz+uI0eOqFu3burcubMOHDggNzc3SdLMmTNVrVq1LNuyWq368MMPFRISosaNG6t06dIFdhy4O+R0vkmSq6ur+vXrp0GDBtmGfNaoUUMbN25UcnKyLl26pJ9++sn2pUOSjh07ph07dig8PLxgDwR3hdzON0maP3++QkJC5OvrK0kKDAxU1apVJUm7d+/Wvn379Nhjj6lq1aoKDAyUdP2ci4qKUosWLQruQHBXyOl8a9q0qcqVKydJioqKUmpqqqpUqSIp+/9Pjx8/royMDPXr109PPfWUlixZYvuDEnCjW/2Mk3L+DidJW7du1dWrV9WuXbv8K/ouwzVP94lChbLmZMMwbH91MLN+RkaGfvvtNy1atEhnz55Vjx49VKNGjZv+df/ll1/Wv/71Lw0cOFARERHq1q3b7RwC7iJ5cb5lmjt3rk6dOqXu3burbt266tKli44fP65nn31WpUuXVpMmTZSSkmJb/ssvv1RYWJgt2OPel1fn27Vr17Ry5UqtXLkyyzI7duzQq6++qmnTptl9aT18+LAGDBigkSNH0tN5n8jLz7evv/5ab731lj777DO5uub8lSwjI0O7du3SwoUL5eXlpRdffFGrVq1SaGjorR8A7joF8RmXk2XLlnG9+j/Q83SfKFasmC5dumTXdu7cuWz/cuXj46PExETb88TERPn4+KhUqVIKDg6W1WpV2bJlVadOHR04cCDHfR45ckSxsbGSJE9PT7Vp00a//fZbHh0RnFlenG87duxQQkKCJMnX11d169bV4cOHdfHiRfXu3Vtr167VZ599prS0ND388MO29Tdu3Kj27dvn05HBGeXF+SZJe/bsUcWKFbP0kG/YsEFDhw7Ve++9p6ZNm9raY2Ji9Nxzz2n48OHq3LlzXh4SnFhenW8LFy7U22+/rblz58rf3z/XfZYqVUqNGzdWiRIl5OHhoVatWmnfvn15cDS4G+T3Z1xOUlNTtXPnTrVs2fIOqr/3EJ7uE4GBgdq3b5/+/PNPSdd/IVatWqXGjRtnWbZFixZavXq1JGnXrl1yd3dXuXLlFBQUpK+//lqGYej8+fPat2+fqlevnuM+T548qbFjxyo1NVWpqanauHGj6tWrlz8HCKeSF+fb999/r08++UTS9WEI+/fvV61atbRnzx6NHz9ehmEoLi5O33zzjZ588klJ1/8zuXr1qsqXL19ARwpnkBfnmyT98ssvWT6j9u3bp4kTJ2revHlq2LChrf3MmTN66aWXNG3aNLtZ0nDvy4vz7dtvv9X8+fO1dOlS+fn53XSfQUFB2rJliy5duqSMjAz9+OOPqlmzZt4eGJxWfn7G5ea3335TxYoVuV79Hxi2d58oUaKE/vOf/2jo0KHKyMhQamqq2rRpk+0Qut69e2v8+PEKCQmRm5ub3nnnHUnSc889p3fffVcdOnRQRkaGXnzxRT3yyCM57rNFixbau3evnn76abm4uKhNmzZ8ybhP5MX59uKLL2rMmDHq2LGjXFxcNHr0aPn6+qpcuXLavHmzOnToIEkaM2aMHnroIUnXA3uZMmUK7kDhFPLifJOkEydOZPkiO2fOHGVkZGjkyJG2tpdfflnbtm1TSkqK3dTB3bt3V48ePfLhCOFM8uJ8+/DDD5WSkqKBAwfalp08ebJq1aqV7T7r1KmjF154QWFhYUpLS1PTpk3VpUuX/DlAOJ38/IzLzYkTJ/g/NRsWwzAMRxcBAAAAAM6OYXsAAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAATuXy5cuqW7eu/Pz89Morr2S7jJ+fn/z8/BQXF1fA1f1/S5YsUfPmzRUQEGC7SW5SUpI+//xzh9UEAMhf3OcJAOBUvv76a125ckWStHHjRp07d04lSpSwW6ZVq1aSJHd39wKvT5KSk5P15ptvKj09XTVr1lT16tV1/Phx9ejRQx4eHnr++ecdUhcAIH8RngAATmXlypWSJDc3N6Wmpmr16tVZwsjs2bMdUZrN+fPnlZ6erkKFCmn58uVycXHRrl279Ndff8nX19ehtQEA8g/D9gAATuPYsWPavXu33NzcNGjQIEnS8uXLsyz3z2F7LVu2lJ+fn+bPn69mzZqpefPmunDhglJSUjR16lQ9/vjjqlWrltq1a6dly5bZtjNq1Cj5+fnp/fffV/v27fXYY49py5Ytunjxol577TU1bdpUAQEBevzxx/Wf//xHqamp2r59u4KCgiRJ165dU40aNTRq1Cj17NlTknTq1Cn5+flp+/btSklJ0ZQpUxQUFKRatWqpWbNmGjNmjJKSkvL7rQQA5APCEwDAaWT2OjVr1kzPPPOMChUqpCNHjigmJsbU+tOmTVPlypVVo0YNFS9eXP/+97/1+eefKy0tTY899pjOnDmjCRMm6KuvvrJb77///a+8vLxUunRpBQYG6vXXX9fq1atVtGhRNWrUSElJSVq0aJGWLl2qBx98UE2bNpUkWSwWtWrVSo0aNVL9+vUlSR4eHmrVqpUefPBBzZw5U/Pnz5ckNW7cWK6urlqxYoXGjRuXR+8YAKAgMWwPAOAUMjIyFBkZKUkKDg6Wt7e3GjRooJ9//lkrVqxQvXr1brqN3r17a+TIkZKkP/74Qxs2bJCnp6dWr16t0qVL67vvvtOCBQuy9PwEBARoxYoVtufBwcGqWrWqXnrpJbm5uWnu3Ll655139Mcff6hPnz6aNGmSWrVqpUKFCtmGED700EPq2bOnSpYsaWs7fvy4JOm5555Tz549dfHiRUVGRsrPz++O3y8AQMGj5wkA4BS2bNmihIQEubm52SaE6NChg6Trk0iYGeqW2fsjSQcPHpR0fYhf6dKlJUlBQUGaP3++wsPD7db7ZzALDg6Wn5+fJk+erKefflrvvvuuJCk1NfWWjqlnz55yd3fXW2+9pccee0yvvfaaXFxcFBgYeEvbAQA4B3qeAABOIXPIXmpqapYwk5ycrLVr16pHjx65bqNo0aJZ2tLT022Pr127pvT0dLm5ueW4XkZGhnr37q1ffvlFoaGh6t+/v/7880/NmDFDhmHc0jE1atRI33zzjdavX69t27Zpz5492rJlixYtWqS1a9fK09PzlrYHAHAsep4AAA53/vx5bdq0SZJUqVIl+fv72/55e3tLyn7iiH8qVOj//7eWOTTu0KFDOn36tCTphx9+UGBgoAYOHJjjegcOHNAvv/yi4sWLa/LkyWrfvr1Onjxpet/Xrl2ztc2YMUMTJkxQw4YNNWfOHP30008qWbKkTpw4oT/++OOm2wQAOBd6ngAADrd27VqlpaWpdOnSWrdunVxcXGyv7dmzR927d9evv/6q2NhYVa9e3dQ2K1eurCeffFLffvutOnfurJo1a2rPnj3KyMhQy5Ytc1yvTJkyslqtunDhgp5++mlZrVb9+uuvkmS7/1R2Mu9FdebMGYWHh+vFF19UcnKyvvvuO+3atUuBgYGKj4/XX3/9JR8fH1WuXNnUcQAAnAc9TwAAh8uc/e6ZZ56xC06SVLduXQUEBEiSIiIibmm706ZNU58+fWS1WrVz506VKVNG48eP17PPPpvjOt7e3nrnnXdUoUIF/fnnn0pLS9Prr78ui8Wi3bt3KyMjI9v1KlasqLCwMBUpUkS//fabrl69qpEjR2ro0KEqWbKkduzYob/++kutW7fW/Pnz5eHhcUvHAgBwPItxqwO4AQAAAOA+RM8TAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACY8P8A5afTqBeGVwAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Delay count for top 5 flight IDs\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "plot = sns.barplot(x='index', y='count', data=df_aircraft, ci=None, palette=['g', 'b', 'm', 'c', 'y'])\n",
    "\n",
    "plt.xlabel('Aircrafts', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('Top-5 Flight IDs delay count', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AC      National_International_Flight\n",
       "TU 32A  International                    23070\n",
       "TU 320  International                    20336\n",
       "TU 736  International                    11026\n",
       "TU 31B  International                     4500\n",
       "TU 332  International                     4053\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top-5 Aircrafts delay count for International and national flights\n",
    "\n",
    "df_delay.groupby(['AC', 'National_International_Flight']).size().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Database is created for Top-5 Aircrafts delay count for International and national flights\n",
    "\n",
    "df_air = df_delay.query(\"AC == 'TU 32A' | AC == 'TU 320' | AC == 'TU 736' | AC == 'TU 31B' | AC == 'TU 332'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top-5 Aircrafts delay count for International and national flights\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "sns.countplot(x='National_International_Flight', hue='AC', data=df_air, palette=['g', 'b', 'm', 'c', 'y'])\n",
    "\n",
    "plt.legend(loc='upper right', title='Aircrafts Types')\n",
    "\n",
    "plt.xlabel('Aircrafts Flying Destinations', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('Top-5 delayed Aircrafts count', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top-5 Aircrafts Avg delay for International and national flights\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(18,7))\n",
    "\n",
    "plot = sns.barplot(x='National_International_Flight', y='target', hue='AC', data=df_air, ci=None, palette=['g', 'b', 'y', 'm'])\n",
    "\n",
    "plt.legend(loc='upper center', title='Aircrafts Types')\n",
    "\n",
    "#plt.margins(0)\n",
    "\n",
    "plt.xlabel('Aircrafts Flying Destinations', size=15, weight='bold')\n",
    "plt.ylabel('Avg_Delay', size=15, weight='bold')\n",
    "plt.title('Top-5 Aircrafts Average Flight Delay', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hourly Flight delay count for International and National flights\n",
    "\n",
    "df_hr_dep = df_delay.groupby(['dep_hour', 'National_International_Flight']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hourly Flight delay count\n",
    "\n",
    "#palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "sns.lineplot(x='dep_hour', y='count', data=df_hr_dep, ci=None, color='g')\n",
    "\n",
    "plt.margins(0)\n",
    "\n",
    "plt.xlabel('Flight Hours', size=15, weight='bold')\n",
    "plt.ylabel('Flight_Count', size=15, weight='bold')\n",
    "plt.title('Hourly Flight delay count', size=20, weight='bold')\n",
    "plot.set_xticks(range(0,26,6))\n",
    "plt.ylim(0, 4000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dep_hour\n",
       "0     115.942600\n",
       "1     115.781977\n",
       "2     112.512397\n",
       "3      72.573171\n",
       "4      92.870670\n",
       "5      70.954598\n",
       "6      44.309309\n",
       "7      45.169237\n",
       "8      58.493801\n",
       "9      59.145919\n",
       "10     57.491123\n",
       "11     68.459128\n",
       "12     72.019886\n",
       "13     72.496197\n",
       "14     83.073831\n",
       "15     81.330889\n",
       "16     94.444652\n",
       "17     95.851097\n",
       "18     85.932232\n",
       "19    112.803770\n",
       "20     94.629542\n",
       "21    120.520243\n",
       "22    112.481694\n",
       "23    117.615925\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hourly Average Flight Delay\n",
    "\n",
    "df_delay.groupby(['dep_hour'])['target'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hourly Average Flight Delay\n",
    "\n",
    "#palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "plot = sns.lineplot(x='dep_hour', y='target', data=df_delay, ci=None, color='g')\n",
    "\n",
    "plt.margins(0)\n",
    "\n",
    "plt.xlabel('Flight Hours', size=15, weight='bold')\n",
    "plt.ylabel('Avg_Delay', size=15, weight='bold')\n",
    "plt.title('Hourly Average Flight Delay', size=20, weight='bold')\n",
    "plot.set_xticks(range(0,25,6))\n",
    "plt.ylim(0, 150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots with NO flights delays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following plots are plotted for flights with NO delay\n",
    "\n",
    "- Number of International and National Flight with NO delays in the dataset\n",
    "- Top-5 Flight IDs count with NO delays\n",
    "- Top-5 Aircraft's count for International and national flights with NO delays\n",
    "- Hourly Flight count with NO delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flights dataset with no delays\n",
    "df_no_delay = df[df['target']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.39547262897258"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %age of NO delayed flights in Dataset\n",
    "\n",
    "len(df_no_delay)/len(df)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "International    22212\n",
       "National         15956\n",
       "Name: National_International_Flight, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No. of International and National Flight with NO delays\n",
    "\n",
    "df_no_delay.National_International_Flight.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# International and National NO flight delays count\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "sns.countplot(x='National_International_Flight', data=df_no_delay , palette=['g'])\n",
    "\n",
    "plt.xlabel('Flight Status', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('International and National NO flight delays count', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>index</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WKL 0000</td>\n",
       "      <td>3105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AOG 0000</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UG 0002</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UG 0003</td>\n",
       "      <td>915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UG 0009</td>\n",
       "      <td>885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  count\n",
       "0  WKL 0000    3105\n",
       "1  AOG 0000    1103\n",
       "2   UG 0002     951\n",
       "3   UG 0003     915\n",
       "4   UG 0009     885"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 flight IDs with NO delays\n",
    "\n",
    "df_aircraft_1 = df_no_delay.FLTID.value_counts().reset_index(name='count').head()\n",
    "df_aircraft_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9e/fq119/1YULFyTJ7j7A9u3bmzOUrlmzRnXr1rXrhQoNDZUkubq6qk2bNlq9erU2btyoZs2aqUaNGmrUqJGaNWumRx991K4NAshdhCcAue6vv/4yX2d3j5Knp6fOnj2r5ORkJSYm2v26XKRIEXPoUIbMF3cZFzxvvfWWeVN+hn79+ql///6SZHfxJF2/0KlWrZqOHDkiwzAUGxtrKTzdyWx72cl8sZgxtCuDlamYH3zwQbv3GedpGMZdqN2tyzzMMONC+dlnn5XNZtPnn3+uM2fO6MSJEzpx4oS+//57ubm5qXfv3nb3K/1dxuQLkv13n52//vrLPPfMQwgzZG5/mdvljWS+Tys7f28zmS9i7+Q7aNGihQ4ePKgdO3bo6NGj5gQpr776qjZt2qT4+Hjt37/fDGoBAQF2wft23ag9Scr2obc3M3jwYP3yyy+Kj4/XwoULFRAQkGWdcuXKma8PHz6c4/4yLy9fvvwN18v89+rvbSa7v1eZe79yktFzdCO7du3SqFGjzKGUbm5uql27tuLi4rIEw7p166pq1ar6888/tWbNGg0cONAcslelShXVqlXLXHfixIny8PDQ0qVLlZCQoP3792v//v2aN2+eHnroIb3zzjs59jwCuHsITwByXeZQkHnih7+XFSpUKMuwnStXrujq1at2v05nni3rZoHn8uXLOnLkiM6ePatatWrZ3fCd+WKmSJEiFs/m7sr8S3zGr9MZbna/k3S9NyW/SElJMYdKubi42F38hYWFqXv37vrjjz/066+/aufOndqyZYsSExP18ccfq27dutleWEv2PQx/DzwZvY+VKlWSq6urSpYsKZvNZgbiv8vc/jLaZeae0Mw9A5Ju+jDZ3Pr8W7RooU8//VRJSUn69NNPJUnu7u7y9vZWjRo1tH//fs2dO9dsM3drcpC7fT4eHh7q37+/JkyYoGvXrmn9+vVZ1vHy8tKDDz6oS5cu6dixY9q7d2+2jxOIiYkxe6ddXFxy7DXM/Dytv/+9ym7mwMxtrEmTJnr22Wez3W/lypVveMxLly6pd+/eunLliipXrqxRo0apYcOGcnV1VUBAQLa9au3bt9f777+v6Oho/fzzz4qMjJT0/xNFZChcuLBGjBihN998U9u3b9e2bdu0Y8cO7dy5U+fOndO///1vrVu3LssPMADuvvzzf10A/1iZhxMtXLjQbtmGDRvMWcQaN26c7fYZN1lL13shfv31V0lSwYIFValSJUnSunXrdPDgQbs//fv31/79+9WtWze98cYbmjlzpt1+MoZ3eXp63vawuztVqVIl8+L9559/tuvpWLZs2V05RuYL4tzskVq0aJF5gRgYGCg3NzddvXpV06dP17///W+NGTNGderU0YsvvqgZM2aY9wFJynGGuKJFi5qh948//tC5c+fMZf/9738VGhqqBg0amPe11KlTR9L16d8zTwN97do1u4kLMibYyBzYMweu8+fPZxv2b0fGd2z1869Xr57ZQ7JixQq7+mb8N+MeJhcXFzVv3txyHW6lHndDjx49bjgMTrrePp9++mnz/YgRI7KE5JSUFA0ZMkRpaWmSrg8F/XsvWWaZ7+dav369uZ0ku6n8M1SuXNmcZv/s2bMKCAhQ69at1bp1a8XFxWnbtm26ePFijj2fW7duNe/DCgoKkp+fn1xdXRUbG2vXZjNr3769+fcz4z43m82mkJAQc52oqCi9//776tu3r5YtW6ZmzZppwIABmj9/vrp16ybp+iyPmZ9nByD30PMEINc1a9ZMlSpVUnR0tDlt9RNPPKFTp06Zs6LZbDb16dMn2+3HjRuns2fPqkKFCvr222/NITZBQUFZhvT9XaNGjeTu7q64uDgtWbJEhQoVUu3atfXtt9+a0zFndw9TXilTpoyaNWumTZs26fjx4/rXv/6l9u3b68CBA+b9EHcq46JQun6BFx4ersqVK9/Rw4J37typK1euyDAMxcfHa/v27eZzhNzc3PTyyy9Luv6LeUREhHlvV0pKipo2bar4+HgtWLDA3F9OQ7AkqUuXLnr//feVkpKisLAwvfDCC0pPTzd7ZdLT0/XYY49Juj5McPDgwZKk3r1768UXX9RDDz2k5cuXm4G5adOm5vk//PDD5nEWL14sPz8/FSlSRB988MFNh+1ZlRHQEhISNH/+fBUtWlTt27e/4fpOTk4KCAjQ0qVLzeFyGaHJ19dXc+bMMct9fX1zvP/n73WQrp/nww8/nCdtv0CBAnrrrbf07LPP3jC09enTR2vWrFF0dLQOHjyo0NBQPfPMM6pUqZJiYmK0cOFCHT16VNL1HriMSVJupEyZMnr00Ue1bds2RUVFqXfv3urYsaMOHz6sWbNmZVnf1dVVwcHBWrJkiaKiovSvf/1LnTp1UmxsrD7++GOlpKSoZMmSdqHm7zJ/B/Pnz1epUqVkGIbmzp1rhre/P3rA09NTfn5+2rx5sxncGzRoYPf3oWjRopo9e7ZSU1O1ZcsWxcTEqHLlyjp9+rRWr15trpd5+COA3EN4ApDrnJ2d9eGHH+rFF19UXFyc+WynDDabTUOGDDGfW5NZwYIFVbp06SzPOClbtuxNL6Ck67/Kv//+++rVq5fS0tKyBBJ/f389//zzt3did8l//vMf7dy5U4mJidqyZYs5JbePj485TMnq83WyU7lyZbm5uSk5OVmHDh3S4MGDNWDAgDsKTzeaGtnFxUXjx483p6mWpPHjx+u5555TfHy8Fi9enGXa6nr16uUYJCTp+eef13//+19t27ZN0dHRWaaN7t+/v3nMDh06aM+ePVqwYIEuX76sKVOm2K1bpUoVTZo0yXxfo0YN1a5dW3/88YeuXr2qV155RdL1+38aN25sTtZwJ2rWrKnDhw/LMAyNGTNGlSpVuuk5P/7443YPM84IT40bN5aTk5MZnqze65K5NyZjBr0nnnjils7jdvn4+KhDhw52D+rNrGjRopo1a5b69u2rgwcPKjY2Nts2Vr58eX366ad2k4bcyOjRo/XMM8/o0qVL+t///mfOTFipUiUZhqFjx47ZrZ/x9zA6Olpbt261m+gm4yG9Oc0G2KhRI/NHoosXL5o9SdL1vxepqanZPmy3Q4cOds/0ypgoIoO7u7veeustjRw50hzm+nc9e/a86TOoANwdDNsDkCdq1Kih5cuXq0+fPubFfPHixfX4449r7ty5NwwwLi4umj9/voKDg1WkSBEVK1ZMISEh+vbbby1NqCBd/2V+wYIFatGihR544AG5urqqatWqGjRokGbMmJFlMom8VrNmTc2fP19NmjRR4cKFVapUKT333HP66KOPzHVuNoVzTooUKaKJEyfq4YcflouLi8qUKWPp4tMqNzc3lS9fXp07d9bSpUuz/Dpfp04dhYeHq1u3bqpUqZIKFiyoggULqnr16urfv7/mzp1709nCXF1d9eWXX2rgwIGqVq2aeX9T48aN9fHHH+vVV1+1W3/UqFH67LPP9Pjjj6tkyZJydXVV5cqV9corr2jhwoV25+/k5KSZM2cqKChIxYoVU7FixdSuXTstXrz4rl2QDhgwQM2aNZObm5seeOCBmz7PSLreY5vRs+ru7m7eb1OsWDG7IGT1fqf27dura9euKlasmNkDezuTQNyuQYMG5XiPYrly5bR06VKNHTtWzZo1U8mSJeXs7KzixYvLx8dHQ4cO1Q8//GB5Wu4qVaro22+/VcuWLVW4cGEVL15cXbp00TfffJNtT13JkiW1cOFC9erVSxUrVpSrq6tKlSqlgIAAzZ0796YTxbi5uWnWrFkKDAxUqVKlVLRoUdWuXVvDhw83J0Q5e/aseV9ThjZt2pj/Brm4uGQbaLt27ar58+erVatWKlu2rFxcXFSsWDH5+Pjo3Xff1bBhwyx9JgDunM1w1JRMAJCDli1b6tSpUypWrFi2U5j/UxiGoYULF8rDw0Pu7u6qUaOGeSF17Ngxc5r2Ll26aNy4cY6sKoBcsH37dnOCiscff1yfffaZg2sEICcM2wMAB7LZbJoyZYp5g3yXLl3UvHlzxcfH2z24N/PzigDc2y5duqTt27crPj7eroe5U6dODqwVACsITwDgYL169TLvy8nunqBq1aopKCjIEVUDkAuSkpKyDDWtX7++3QOfAeRPhCcAcLA+ffqodOnSWrJkiQ4dOqTLly/LxcVF5cqVU4sWLdSnTx+7GfMA3Nvc3d1Vvnx5xcbGqkSJEvL399fAgQPz1XPbAGSPe54AAAAAwIL7pucpKSlJ+/btk7u7uwoUKODo6gAAAADIZ9LT0xUXF6c6depkO+rjvglP+/btU1hYmKOrAQAAACCfmz9/frbPn7xvwlPGMz3mz5+v0qVLO7g2AAAAAPKbs2fPKiws7IbPQ7xvwlPGUL3SpUurfPnyDq4NAAAAgPzqRrf5MK0LAAAAAFhAeAIAAAAACwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALCA8AQAAAAAFhCebkFSarKjq4A8xncOAACADM6OrsC9pKCLmyr2ru7oaiAPHZt5yNFVAAAAQD5BzxMAAAAAWEB4AgAAAAALCE8AAAAAYAHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABbkaXj68MMPFRQUpODgYM2ePVuStHnzZoWGhqpt27aaOnWquW5kZKQ6d+6sdu3aafjw4UpLS5MknT59WmFhYQoMDFTfvn2VkJCQl6cAAAAA4D6VZ+Fp27Zt+vXXX7V8+XItWbJE8+bN04EDBzRs2DBNnz5dq1at0r59+7RhwwZJ0qBBgzRy5EitXr1ahmFo4cKFkqS3335b3bt3V0REhOrUqaPp06fn1SkAAAAAuI/lWXh69NFHNXfuXDk7O+v8+fNKT0/X5cuXVbFiRVWoUEHOzs4KDQ1VRESETp06paSkJHl7e0uSOnXqpIiICKWmpmr79u1q166dXTkAAAAA5LY8Hbbn4uKijz76SMHBwWrSpIliY2Pl7u5uLvfw8FBMTEyWcnd3d8XExOjixYsqWrSonJ2d7coBAAAAILfl+YQRr732mrZs2aIzZ84oOjo6y3KbzSbDMG6pHAAAAAByW56FpyNHjigyMlKSVKhQIbVt21Zbt27VuXPnzHViY2Pl4eEhT09Pu/K4uDh5eHioZMmSio+PV3p6ul05AAAAAOS2PAtPJ0+e1IgRI5SSkqKUlBStXbtW3bp1U1RUlI4dO6b09HStXLlS/v7+KleunNzc3LRz505JUnh4uPz9/eXi4iIfHx+tWrXKrhwAAAAAcptzXh0oICBAe/bsUYcOHVSgQAG1bdtWwcHBKlmypPr376/k5GQFBAQoMDBQkjR58mSNGDFCCQkJqlWrlnr27ClJGjVqlIYMGaIZM2aoTJkymjJlSl6dAgAAAID7mM3I7kaif6CTJ0+qVatWWrt2rcqXL3/b+6nYu/pdrBXyu2MzDzm6CgAAAMgjN8sMeT5hBAAAAADciwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALCA8AQAAAAAFhCeAAAAAMACwhMAAAAAWEB4AgAAAAALCE8AAAAAYAHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABYQngAAAADAAsITAAAAAFhAeAIAAAAACwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALCA8AQAAAAAFhCeAAAAAMACwhMAAAAAWEB4AgAAAAALCE8AAAAAYAHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABYQngAAAADAAsITAAAAAFhAeAIAAAAAC/I0PH3yyScKDg5WcHCwJk2aJEkaOnSo2rZtq/bt26t9+/b66aefJEmbN29WaGio2rZtq6lTp5r7iIyMVOfOndWuXTsNHz5caWlpeXkKAAAAAO5TeRaeNm/erE2bNun7779XeHi4/vjjD/3000/at2+fvv76ay1btkzLli1TmzZtlJSUpGHDhmn69OlatWqV9u3bpw0bNkiSBg0apJEjR2r16tUyDEMLFy7Mq1MAAAAAcB/Ls/Dk7u6uIUOGyNXVVS4uLqpSpYpOnz6t06dPa+TIkQoNDdVHH32ka9euae/evapYsaIqVKggZ2dnhYaGKiIiQqdOnVJSUpK8vb0lSZ06dVJERERenQIAAACA+5hzXh2oWrVq5uvo6GitWrVKCxYs0LZt2zRmzBgVLlxYffr00eLFi1W4cGG5u7ub63t4eCgmJkaxsbF25e7u7oqJicmrUwAAAABwH8vzCSMOHz6sXr16afDgwapcubKmTZumUqVKqVChQurRo4c2bNggwzCybGez2W5YDgAAAAC5LU/D086dO/X8889r4MCB6tixow4ePKjVq1ebyw3DkLOzszw9PXXu3DmzPDY2Vh4eHlnK4+Li5OHhkZenAAAAAOA+lWfh6cyZM3r11Vc1efJkBQcHS7oelsaPH69Lly4pNTVV3333ndq0aaP69esrKipKx44dU3p6ulauXCl/f3+VK1dObm5u2rlzpyQpPDxc/v7+eXUKAAAAAO5jeXbP05dffqnk5GRNnDjRLOvWrZt69+6tZ555RmlpaWrbtq1CQkIkSRMnTlT//v2VnJysgIAABQYGSpImT56sESNGKCEhQbVq1VLPnj3z6hQAAAAA3MdsRnY3Ev0DnTx5Uq1atdLatWtVvnz5295Pxd7V72KtkN8dm3nI0VUAAABAHrlZZsjzCSMAAAAA4F5EeAIAAAAACwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALCA8AQAAAAAFhCeAAAAAMACwhMAAAAAWEB4AgAAAAALCE8AAAAAYAHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABYQngAAAADAAsITAAAAAFhAeAIAAAAACwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALCA8AQAAAAAFhCeAAAAAMACwhMAAAAAWEB4AgAAAAALCE8AAAAAYAHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABYQngAAAADAAsITAAAAAFiQp+Hpk08+UXBwsIKDgzVp0iRJ0ubNmxUaGqq2bdtq6tSp5rqRkZHq3Lmz2rVrp+HDhystLU2SdPr0aYWFhSkwMFB9+/ZVQkJCXp4CAAAAgPuU5fAUHh6urVu3Zin/4YcftGjRoptuv3nzZm3atEnff/+9wsPD9ccff2jlypUaNmyYpk+frlWrVmnfvn3asGGDJGnQoEEaOXKkVq9eLcMwtHDhQknS22+/re7duysiIkJ16tTR9OnTrZ4CAAAAANw2y+FpyJAh+vrrr7OUf/3112YvUk7c3d01ZMgQubq6ysXFRVWqVFF0dLQqVqyoChUqyNnZWaGhoYqIiNCpU6eUlJQkb29vSVKnTp0UERGh1NRUbd++Xe3atbMrBwAAAIDc5pzTws8++0wffPCB+f7nn39WzZo1s6xXvHjxmx6oWrVq5uvo6GitWrVKPXr0kLu7u1nu4eGhmJgYxcbG2pW7u7srJiZGFy9eVNGiReXs7GxXDgAAAAC5LceepxdeeEFly5aVYRiy2WwyDCPLnwIFCuiFF16wfMDDhw+rV69eGjx4sB5++OEsyzOOcyvlAAAAAJDbcux5cnV1VXh4uBISEvT444/rscce09ixY83lNptNDzzwgAoWLGjpYDt37tRrr72mYcOGKTg4WNu2bdO5c+fM5bGxsfLw8JCnp6ddeVxcnDw8PFSyZEnFx8crPT1dBQoUMMsBAAAAILflGJ4kqVixYipWrJgOHDhwRwc6c+aMXn31VU2dOlVNmjSRJNWvX19RUVE6duyYypcvr5UrV6pz584qV66c3NzctHPnTjVq1Ejh4eHy9/eXi4uLfHx8tGrVKoWGhprlAAAAAJDbbhqeMiQmJmr27Nnau3evUlNT7YbQ2Ww2ffnllzlu/+WXXyo5OVkTJ040y7p166aJEyeqf//+Sk5OVkBAgAIDAyVJkydP1ogRI5SQkKBatWqpZ8+ekqRRo0ZpyJAhmjFjhsqUKaMpU6bc0gkDAAAAwO2wGdndSJSNgQMHatWqVTe87ygyMvKuV+5uOnnypFq1aqW1a9eqfPnyt72fir2r38VaIb87NvOQo6sAAACAPHKzzGC552ndunVycnLSs88+q6pVq5oz3gEAAADA/cByAipSpIjq1KmjoUOH5mZ9AAAAACBfsvyQ3GeffVZHjhzR+fPnc7M+AAAAAJAvWe55io2NVVpamtq0aaPq1aurUKFC5jOWrEwYAQAAAAD3MsvhacGCBebr3377zW4ZD6oFAAAA8E9nOTxNmDAhN+sBAAAAAPma5fDUsWPH3KwHAAAAAORrlsPTyJEjb7jMZrNpzJgxd6VCAAAAAJAfWQ5PixYtks1ms3tIbsZ7whMAAACAfzrL4alfv37ma8MwlJycrP3792vPnj168803c6VyAAAAAJBf3FZ4yuy5557T3r179fTTT9+1SgEAAABAfmP5Ibk3UqRIEa1Zs+Zu1AUAAAAA8q3bnjAiPT1dZ8+e1ZYtW1SiRIm7XjEAAAAAyE/uaMKIDM8+++xdrRQAAAAA5Dd3dM9TwYIFVbduXfn6+t7VSgEAAABAfnPHE0YAAAAAwP3gliaMOHDggF566SU1bNhQjRo10ssvv6xDhw7lVt0AAAAAIN+w3PMUGRmp7t27KzEx0Sz75ZdftG3bNn3zzTfy8vLKlQoCAAAAQH5guefp/fffV2Jionr27Knly5dr+fLl6tGjh65evaopU6bkZh0BAAAAwOEs9zzt2rVLtWrV0rBhw8yy4cOHa+fOndq+fXuuVA4AAAAA8gvLPU9OTjdeNadlAAAAAPBPYDn1eHt7KzIyUhMnTtThw4d1+PBhTZgwQZGRkWrQoEFu1hEAAAAAHM7ysL3XXntN27Zt01dffaWvvvpKkmQYhpydndW/f/9cqyAAAAAA5AeWe57q1aunb775Rs2bN1fhwoVVpEgRNWnSRF999ZXq1auXm3UEAAAAAIez3PMkSbVr19bnn3+eW3UBAAAAgHzrlmZ6CA8P19dff22+79u3rxYvXnzXKwUAAAAA+Y3l8PTdd99p6NChWrdunSQpJSVFGzZs0MiRI/Xtt9/mWgUBAAAAID+wHJ6++uorFSxYUM8995wkycXFRdOnT1fBggU1b968XKsgAAAAAOQHlsPTyZMn1bBhQwUEBEiSbDabHn/8cTVs2FAnT57MtQoCAAAAQH5gOTwVL15ckZGROnfunFl29uxZ7du3Tw888ECuVA4AAAAA8gvLs+0FBQVpzpw5atOmjby8vJSenq5Dhw4pJSXFHMoHAAAAAP9UlsPTv//9b506dUo//fSTfvvtN7O8devWGjBgQG7UDQAAAADyDcvhydXVVR9//LGioqJ04MABSZKXl5cqV66ca5UDAAAAgPzilh6SK0mPPPKIHnnkkWyXvfrqq1q/fr32799/xxUDAAAAgPzklh6Sa4VhGHd7lwAAAADgcHc9PAEAAADAPxHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABYQngAAAADAglt+zlNOgoODVbNmzbu5SwAAAADIF24pPG3atEkPP/ywHn74YU2ZMkUbNmxQ06ZNNXDgQDk7OysoKCi36gkAAAAADmU5PC1ZskQjRozQO++8o6ioKM2cOVOSdOjQIRUpUkT9+vXLtUoCAAAAgKNZvudpzpw5cnV1VdmyZRURESEXFxfNmDFDDz74oFasWJGbdQQAAAAAh7Mcnk6cOCEfHx81bdpUW7duVZ06ddSiRQvVrVtXMTExuVlHAAAAAHA4y+GpYMGCSkhI0LFjx3T69Gn5+PhIkk6dOqWiRYvmWgUBAAAAID+wHJ5q166tPXv2qGvXrrLZbGrVqpWGDRumqKgo+fr65mYdAQAAAMDhLIenQYMG6aGHHtLly5f19NNPy9vbWwUKFJCnp6cGDBiQm3UEAAAAAIezPNtejRo1tHHjRiUkJJjD9Hr16qWhQ4eqcOHCuVZBAAAAAMgPLPc8tWrVSqNHj7a7v+mRRx7RgAEDbun5TvHx8QoJCdHJkyclSUOHDlXbtm3Vvn17tW/fXj/99JMkafPmzQoNDVXbtm01depUc/vIyEh17txZ7dq10/Dhw5WWlmb52AAAAABwu3LseVq7dq0iIyMlXZ8YYvv27frkk0/M5YZhaN++fYqPj7d0sD179mjEiBGKjo42y/bt26evv/5aHh4eZllSUpKGDRumefPmqUyZMurTp482bNiggIAADRo0SGPHjpW3t7eGDRumhQsXqnv37rdyzgAAAABwy3IMT2XKlFH//v1lGIZsNpuioqI0bdo0u3UMw1D9+vUtHWzhwoUaNWqU3nzzTUnS1atXdfr0aY0cOVKnT59WmzZt1K9fP+3du1cVK1ZUhQoVJEmhoaGKiIhQ1apVlZSUJG9vb0lSp06d9NFHHxGeAAAAAOS6HMNTrVq1NHjwYB0+fFiLFy9W2bJl1bRpU3O5k5OTSpQooaeeesrSwcaNG2f3/vz58/Lz89OYMWNUuHBh9enTR4sXL1bhwoXl7u5urufh4aGYmBjFxsbalbu7u/OMKQAAAAB54qYTRjz33HOSpNKlS6ty5cq3dH/TzVSoUMGuJ6tHjx4KDw9XYGBglnVtNpsMw8i2HAAAAABym+XZ9vr166eUlBQdOHBAiYmJWYJMw4YNb/ngBw8eVHR0tNq1ayfp+hBAZ2dneXp66ty5c+Z6sbGx8vDwyFIeFxdnd68UAAAAAOQWy+Fp48aNGjRokC5fvpxlmc1m0/79+2/54IZhaPz48fLz81PhwoX13XffqWPHjqpfv76ioqJ07NgxlS9fXitXrlTnzp1Vrlw5ubm5aefOnWrUqJHCw8Pl7+9/y8cFAAAAgFtlOTyNHz9ely5dkqurq0qWLCknJ8uznN9QjRo11Lt3bz3zzDNKS0tT27ZtFRISIkmaOHGi+vfvr+TkZAUEBJhD+SZPnqwRI0YoISFBtWrVUs+ePe+4HgAAAABwM5bD0+nTp1W2bFktW7ZMxYoVu6ODrlu3znwdFhamsLCwLOs0adJEy5cvz1Jeo0YNLV68+I6ODwAAAAC3ynJ4atCggdLS0u44OAEAAADAvSjH8LRr1y7zdceOHTV69GhNnTpVfn5+cnNzs1v3diaMAAAAAIB7RY7hqXv37lmmAp85c6ZmzpxpV3a7E0YAAAAAwL0ix/BUtmzZvKoHAAAAAORrOYanzBM7AAAAAMD97M7nGwcAAACA+4Dl2fZq1qx5w2UuLi566KGH1Lp1aw0cODDLZBIAAAAAcK+z3PNUsmRJubi4yDCMLH9SUlJ0+vRpzZs3T1OnTs3N+gIAAACAQ1gOTxMmTJDNZtNbb72lbdu2afv27Ro+fLgKFSqkL7/8Uj/88IMefPBBrV69OjfrCwAAAAAOcUvhqV69eurevbseeOABFStWTD169FCtWrU0adIkValSRfXq1VNcXFxu1hcAAAAAHMLyPU+nTp1SSkqKUlNT5eLiIklKSUnR2bNnde7cOSUnJ+vMmTPmMgAAAAD4J7EcnurXr68dO3aoU6dOCggIkGEY2rhxo06fPi1vb2+tWLFChw8fVt26dXOzvgAAAADgEJbD06hRo/Svf/1Lhw8f1p9//ilJMgxDDz30kEaPHq21a9fK2dlZL7/8cq5VFgAAAAAcxXJ4qlq1qlavXq0VK1bo6NGjSktLU/Xq1RUSEqJChQrJMAwFBwerYsWKuVlfAAAAAHAIy+FJkgoWLKinnnoq22U1atS4KxUCAAAAgPwox/DUq1cvNWzYUP369VOvXr1uuJ7NZtOXX3551ysHAAAAAPlFjuFp8+bNKly4sPn6Rmw2292tFQAAAADkMzmGpwkTJqhMmTLmawAAAAC4X+UYnjp27JjtawAAAAC43+QYnj799FNLO7HZbOrTp89dqRAAAAAA5Ec5hqcPPvjgpvczGYZBeAIAAADwj2d52B4AAAAA3M9yDE/lypVT5cqVFRQUlFf1AQAAAIB8ySmnhZ988ol+/PFHu7KePXvqo48+ytVKAQAAAEB+k2PPk3T9nqbMtm3bpgceeCDXKgQAAAAA+VGOPU8AAAAAgOsITwAAAABgwU2H7V26dEm7du26aVnDhg3vbs0AAAAAIB+5aXjasWOHwsLCzPc2my3bsv379+dODQEAAAAgH8gxPJUtWzav6gEAAAAA+VqO4WndunV5VQ8AAAAAyNeYMAIAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnIJ9KSk51dBWQx/jOAQDI35wdXQEA2Svo5qIS1d9wdDWQhy4e+sDRVQAAADmg5wkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYkKfhKT4+XiEhITp58qQkafPmzQoNDVXbtm01depUc73IyEh17txZ7dq10/Dhw5WWliZJOn36tMLCwhQYGKi+ffsqISEhL6sPAAAA4D6WZ+Fpz549euaZZxQdHS1JSkpK0rBhwzR9+nStWrVK+/bt04YNGyRJgwYN0siRI7V69WoZhqGFCxdKkt5++211795dERERqlOnjqZPn55X1QcAAABwn8uz8LRw4UKNGjVKHh4ekqS9e/eqYsWKqlChgpydnRUaGqqIiAidOnVKSUlJ8vb2liR16tRJERERSk1N1fbt29WuXTu7cgAAAADIC855daBx48bZvY+NjZW7u7v53sPDQzExMVnK3d3dFRMTo4sXL6po0aJydna2KwcAAACAvOCwCSMMw8hSZrPZbrkcAAAAAPKCw8KTp6enzp07Z76PjY2Vh4dHlvK4uDh5eHioZMmSio+PV3p6ul05AAAAAOQFh4Wn+vXrKyoqSseOHVN6erpWrlwpf39/lStXTm5ubtq5c6ckKTw8XP7+/nJxcZGPj49WrVplVw4AAAAAeSHP7nn6Ozc3N02cOFH9+/dXcnKyAgICFBgYKEmaPHmyRowYoYSEBNWqVUs9e/aUJI0aNUpDhgzRjBkzVKZMGU2ZMsVR1QcAAABwn8nz8LRu3TrzdZMmTbR8+fIs69SoUUOLFy/OUl6uXDnNmzcvV+sHAAAAANlx2LA9AED+kZKc6ugqII/xnQPArXPYsD0AQP7h6uairjVfdnQ1kIcWRn7q6CoAwD2HnicAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAyHNJKUxYcb/hO8c/ARNGAACAPFfQ1UXFu/dxdDWQh/5a8JmjqwDcMXqeAAAAAMACwhMAAAD+0dJTkx1dBeSx3PrOGbYHAACAf7QCLm6aN/IRR1cDeajHO1G5sl96ngAAAADAAsITAAAAAFhAeAIAAAAACwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALCA8AQAAAAAFhCeAAAAAMACwhMAAAAAWEB4AgAAAAALCE8AAAAAYAHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABYQngAAAADAAsITAAAAAFhAeAIAAAAACwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALDA2dEVkKSePXvq/Pnzcna+Xp0xY8bo+PHjmjFjhlJTU/X8888rLCxMkrR582ZNmDBBycnJeuKJJzRgwABHVh0AAADAfcLh4ckwDB09elS//PKLGZ5iYmI0YMAALV26VK6ururWrZt8fX1Vvnx5DRs2TPPmzVOZMmXUp08fbdiwQQEBAQ4+CwAAAAD/dA4PT0ePHpXNZtNLL72k8+fPq2vXripSpIj8/PxUvHhxSVK7du0UERGhRx99VBUrVlSFChUkSaGhoYqIiCA8AQAAAMh1Dr/n6fLly2rSpImmTZumOXPm6Ntvv9Xp06fl7u5uruPh4aGYmBjFxsZmWw4AAAAAuc3h4alBgwaaNGmSChcurJIlS6pLly766KOPsqxns9lkGEa25QAAAACQ2xwennbs2KEtW7aY7w3DULly5XTu3DmzLDY2Vh4eHvL09My2HAAAAABym8PD05UrVzRp0iQlJycrPj5e33//vd577z1t2bJFFy5cUGJiotasWSN/f3/Vr19fUVFROnbsmNLT07Vy5Ur5+/s7+hQAAAAA3AccPmFEixYttGfPHnXo0EHXrl1T9+7d1ahRIw0YMEA9e/ZUamqqunTponr16kmSJk6cqP79+ys5OVkBAQEKDAx08BkAAAAAuB84PDxJ0htvvKE33njDriw0NFShoaFZ1m3SpImWL1+eRzUDAAAAgOscPmwPAAAAAO4FhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALCA8AQAAAAAFhCeAAAAAMACwhMAAAAAWEB4AgAAAAALCE8AAAAAYAHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABYQngAAAADAAsITAAAAAFhAeAIAAAAACwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAAAAALCA8AQAAAAAFhCeAAAAAMACwhMAAAAAWEB4AgAAAAALCE8AAAAAYAHhCQAAAAAsIDwBAAAAgAWEJwAAAACwgPAEAAAAABYQngAAAADAAsITAAAAAFhAeAIAAAAACwhPAAAAAGAB4QkAAAAALCA8AQAAAIAFhCcAAAAAsOCeDE8rVqxQUFCQ2rRpo/nz5zu6OgAAAADuA86OrsCtiomJ0dSpU7V06VK5urqqW7du8vX1VdWqVR1dNQAAAAD/YPdceNq8ebP8/PxUvHhxSVK7du0UERGhfv365bhdenq6JOns2bN3dHxb0h1tjnvMyZMnHXp8J1116PGRtxzd3lKMRIceH3nL0e1NkpySaXP3E0e3ub8SHHp45LHbbW8ZWSEjO/zdPReeYmNj5e7ubr738PDQ3r17b7pdXFycJCksLOyOju8m2x1tj3tLq1atHHr8h2hu9xVHtzfcX/JDe3O/+Sr4B3F8m3N18PGRlz754c7aW1xcnCpWrJil/J4LT4ZhZCmz2W5+hVmnTh3Nnz9f7u7uKlCgQG5UDQAAAMA9LD09XXFxcapTp062y++58OTp6akdO3aY72NjY+Xh4XHT7QoWLCgfH5/crBoAAACAe1x2PU4Z7rnZ9po2baotW7bowoULSkxM1Jo1a+Tv7+/oagEAAAD4h7sne54GDBignj17KjU1VV26dFG9evUcXS0AAAAA/3A2I7ubiAAAAAAAdu65YXsAAAAA4AiEJwAAAACwgPAEAAAAABYQngAAAADAAsLTPeqFF17QTz/9ZL5/99131aBBA6WkpJhlzZs314kTJ9SyZUudPHnSLP/iiy8UHBysuLg4DRkyREuXLs3xWJGRkercubPatWun4cOHKy0tTZJ0+vRphYWFKTAwUH379lVCQoIk6fLly+rdu7eeeOIJhYWFKS4uTpKUkpKiQYMG6YknnlDHjh115MiRu/Z5IO8cOnRIXl5eWr16tV358uXL9eSTTyo4OFihoaGaNm2a0tPTzeVXr17Vu+++q7Zt2yooKEjBwcFatGhRtse41TZkGIbeffddBQYGKigoSDt37jT3NWvWLAUGBqpdu3Zas2bN3f44kAdOnjypli1bZin38vIyXzuifaWnp2vUqFEKCQlRcHCw5syZc5fPHI6SX9tcWlqaRo4cqZCQEIWGhmrFihV3+9ThAPm1vV27dk3jxo1TYGCg2rdvf8Pj3XcM3JM+++wzY+LEieb7J5980njuueeMzZs3G4ZhGNHR0UabNm0MwzCMFi1aGCdOnDAMwzBmz55thISEGOfPnzcMwzAGDx5sLFmyJMdjBQcHG7t37zYMwzCGDh1qzJ8/3zAMw+jdu7excuVKwzAM45NPPjEmTZpkGIZhvP3228Znn31mGIZhfP/998brr79uGIZhfPHFF8bIkSMNwzCMbdu2GV26dLmjzwCOMWHCBKN///7G888/b5YtWbLECAkJMY4dO2YYhmFcuXLFePXVV42hQ4ea6/Tq1csYOXKkkZSUZBiGYcTExBghISHG+vXrsxzjVtvQjz/+aLz00ktGenq6cfToUaN169ZGamqqsWfPHqN9+/ZGUlKSce7cOaNVq1bGxYsX7/ZHglx24sQJo0WLFlnKq1evbr52RPtauHCh8dprrxnp6elGQkKC8cQTTxj79u27m6cOB8mvbW7p0qXGgAEDDMMwjPPnzxuPPvqoceXKlbt23nCM/NreFi5caLzwwgtGcnKykZiYaHTq1Mn4/fff7+ap35PoebpH+fn5affu3ZKkmJgYubq6KjAwUJs2bZIk7dixQ02bNrXbZu7cuQoPD9dXX32lkiVLWjrOqVOnlJSUJG9vb0lSp06dFBERodTUVG3fvl3t2rWzK5ekX375RaGhoZKkkJAQbdy4Uampqfrll1/05JNPSpIaN26sixcv6vTp03f2QSBPpaWlafny5RowYID279+v48ePS5I++eQTjRgxQg8//LAkqWjRoho3bpxWrlypU6dOadeuXTp8+LBGjhwpNzc3SZKHh4fGjBljvs/sVtvQhg0bFBQUJCcnJz3yyCMqW7asdu/erY0bN6pNmzZyc3NTqVKl9Oijj+qXX37Jg08KeclR7atatWrq16+fnJycVLhwYVWoUEFnzpzJuxOHwziqzXXs2FGTJk2SJMXGxsrFxUUuLi55dNZwFEe1t/3796tVq1ZydXVVwYIF5evrq7Vr1+bdiedThKd7VO3atXX8+HElJydr06ZNatasmZo1a2YXnpo3b26uv2DBAo0fP149evSwHJyk6/84u7u7m+/d3d0VExOjixcvqmjRonJ2drYr//s2zs7OKlq0qC5cuJDtvs6ePXv7HwLy3C+//KKyZcvqkUceUevWrfXtt9/qwoULOnXqVJaHVT/44IOqWrWq/vjjD+3Zs0fe3t5Z/iffoEEDNWnSJMtxbrUNxcbGysPDw3I5/lkc1b68vb1VrVo1Sdcvbvbu3avGjRvnxikin3FUm8vYfvjw4erSpYu6du2a7cUz/lkc1d5q1aqltWvXKjExUZcvX9b//vc/nTt3LpfO8t5BeLpHFShQQPXr19fvv/+uTZs2qXnz5qpQoYKSkpJ06dIl7d69W76+vub6W7du1aeffqpJkybdUm+Pkc0zlG022w3Lb8TJKfumdqNy5E9Lly5VSEiIJCkoKEjff/+92RYy39+UITU1Ndv9zJ07V+3bt1dQUJD69etn6dg5taHs2mNO5bi3ZPedGYZxw39z8qp9Zdi2bZtee+01TZ48WQ8++KCl4yF/y+9tbty4cfrvf/+rNWvWmD+a4t6VX9tb586dVatWLXXt2lX//ve/1bRpU3o6RXi6pzVp0sT8tTNjWF2TJk20du1aFS9eXMWKFTPXfe+99/T444+rW7duGjhwYLYXutnx9PS0+5UhLi5OHh4eKlmypOLj4839ZJRL17uSM7ZJS0tTfHy8ihcvLg8PD/Omxb9vg/zv/Pnz2rhxo2bNmqWWLVtqxIgRunz5srZs2aKHH37YHEaa4cKFCzpx4oTq1KmjunXrau/evWZ76dmzp5YtW6ZRo0bp0qVLWY51q23I09Pzlspxb3nggQd05coVu7Lz58+bQcVR7UuS1qxZozfeeEPvv/++mjVrdndPHA6TX9vcvn37FB0dLUkqUaKEHnvsMR08ePCunjvyXn5tb5cuXVKPHj20YsUKffHFF0pNTTWH59/PCE/3MD8/Py1btkzVq1c3h881a9ZMs2fPzvI/cVdXV0lSv379lJycrGnTplk6Rrly5eTm5mbOvBIeHi5/f3+5uLjIx8dHq1atsiuXpICAAIWHh0uSVq1aJR8fH7m4uCggIEDLli2TdH1YoZubm8qWLXtnHwLyzPLly+Xn56eNGzdq3bp1Wr9+vV5++WV99913euONNzR+/HidOHFCkpSQkKARI0YoKChIZcuWVaNGjVS1alW98847SkpKkiQlJyfrv//9b7a/ht1qG/L399eKFSuUnp6uY8eOKTo6WnXr1pW/v7/WrFmjxMREXbhwQb/++mu2QxyQvxUtWlQVK1a0m+Hxu+++M79LR7WvvXv3avTo0Zo1a5ZdTz/uffm1ze3Zs0fvvfeerl27pvj4eG3atEkNGzbM5U8DuS2/trfdu3frrbfekmEYOnv2rH766Se1bt06lz+N/M9mZNdXh3tGs2bN9MorrygsLEySdOXKFfn5+WnevHnmP6gtW7bU3LlzVb58eUnSkSNH1KVLF82cOVNLlizRDz/8YIYvSfr888/l4+Njvj9w4IBGjBihhIQE1apVSxMmTJCrq6tOnTqlIUOG6Pz58ypTpoymTJmiBx98UH/99ZeGDBmiEydOqFixYpo8ebLKly+v5ORkvfXWW9q3b59cXV01duxY1a5dOw8/LdyJ0NBQDRgwwG461fPnz6tly5ZaunSpDh8+rJkzZyolJUXp6ekKDg7Wyy+/bLat5ORkTZ8+XevWrZOTk5NSU1PVtGlT9e7dO0tv0K22IcMwNGnSJG3cuFGSNHToUPOev1mzZmnJkiVKS0tT37591aFDh7z5wHBXRUVFafTo0bp48aJSU1Pl5eWlt956y7yH0xHtq2/fvtq1a5dKly5t7vu1115Tq1at8u6DQa7Jj20uPT1db7/9tnbu3CknJyeFhYWpW7duef7Z4O7Lj+3NMAyNHj1aO3bskCS9/vrratu2bd5+MPkQ4QkAAAAALGDYHgAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACwgPAEAAACABYQnAEC+kpCQoAYNGsjLy0uvv/56tut4eXnJy8tLZ8+ezePa/b8FCxbI399fderUUceOHSVJ8fHxmj17tsPqBADIXc43XwUAgLzz448/6urVq5KktWvX6sKFC+azTjJkPEvJzc0tz+snSYmJiRo3bpzS0tJUu3Zt1axZU8eOHdMzzzyjggUL6oUXXnBIvQAAuYvwBADIV5YsWSJJcnV1VUpKipYtW5YljEyfPt0RVTNdvHhRaWlpcnJy0qJFi1SgQAHt2LFD58+fV7ly5RxaNwBA7mHYHgAg34iOjtauXbvk6uqqvn37SpIWLVqUZb2/D9tr2bKlvLy8NGfOHDVv3lz+/v7666+/lJycrIkTJ+qxxx5T3bp19cQTT+jbb7819zNkyBB5eXnpgw8+UFBQkBo3bqxNmzbp0qVLevPNN9WsWTPVqVNHjz32mN555x2lpKRo69atatGihSTp2rVrqlWrloYMGaKwsDBJ0qlTp+Tl5aWtW7cqOTlZEyZMUIsWLVS3bl01b95cw4cPV3x8fG5/lACAXEB4AgDkGxm9Ts2bN9dTTz0lJycnHTlyRDt37rS0/eTJk1WlShXVqlVLxYsX13/+8x/Nnj1bqampaty4sc6cOaNRo0Zp6dKldtt9+umnKly4sDw9PeXt7a2hQ4dq2bJlKlasmPz8/BQfH6+vv/5a33zzjUqUKKFmzZpJkmw2m1q1aiU/Pz/5+PhIkgoWLKhWrVqpRIkS+vjjjzVnzhxJUpMmTeTs7KzFixdr5MiRd+kTAwDkJYbtAQDyhfT0dIWHh0uSAgMD5e7urkcffVS//vqrFi9erEaNGt10Hz169NDgwYMlSUePHtWaNWtUqFAhLVu2TJ6enlq/fr2++uqrLD0/derU0eLFi833gYGBqlatml599VW5urrqyy+/1KRJk3T06FE999xzGjNmjFq1aiUnJydzCGH58uUVFhamUqVKmWXHjh2TJD3//PMKCwvTpUuXFB4eLi8vrzv+vAAAeY+eJwBAvrBp0ybFxsbK1dXVnBAiJCRE0vVJJKwMdcvo/ZGkAwcOSLo+xM/T01OS1KJFC82ZM0c9e/a02+7vwSwwMFBeXl4aO3asOnTooPfee0+SlJKSckvnFBYWJjc3N40fP16NGzfWm2++qQIFCsjb2/uW9gMAyB/oeQIA5AsZQ/ZSUlKyhJnExEStWLFCzzzzTI77KFasWJaytLQ08/W1a9eUlpYmV1fXG26Xnp6uHj166LffflOnTp3Uu3dvHT9+XFOnTpVhGLd0Tn5+fvrpp5+0evVqbdmyRbt379amTZv09ddfa8WKFSpUqNAt7Q8A4Fj0PAEAHO7ixYtat26dJKly5cqqUaOG+cfd3V1S9hNH/J2T0///by1jaNyhQ4d0+vRpSdLGjRvl7e2tl19++Ybb7d+/X7/99puKFy+usWPHKigoSCdPnrR87GvXrpllU6dO1ahRo+Tr66sZM2bof//7n0qVKqUTJ07o6NGjN90nACB/oecJAOBwK1asUGpqqjw9PbVy5UoVKFDAXLZ7925169ZNf/zxhyIjI1WzZk1L+6xSpYpat26tn3/+WR07dlTt2rW1e/dupaenq2XLljfcrnTp0nJxcdFff/2lDh06yMXFRX/88Yckmc+fyk7Gs6jOnDmjnj176pVXXlFiYqLWr1+vHTt2yNvbWzExMTp//rw8PDxUpUoVS+cBAMg/6HkCADhcxux3Tz31lF1wkqQGDRqoTp06kqSFCxfe0n4nT56s5557Ti4uLtq+fbtKly6tt956S127dr3hNu7u7po0aZIqVqyo48ePKzU1VUOHDpXNZtOuXbuUnp6e7XaVKlVS9+7dVbRoUR08eFBJSUkaPHiw3njjDZUqVUrbtm3T+fPn1aZNG82ZM0cFCxa8pXMBADiezbjVAdwAAAAAcB+i5wkAAAAALCA8AQAAAIAFhCcAAAAAsIDwBAAAAAAWEJ4AAAAAwALCEwAAAABYQHgCAAAAAAsITwAAAABgAeEJAAAAACz4P2Q5TVONckpPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 5 flight IDs with NO delays\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "plot = sns.barplot(x='index', y='count', data=df_aircraft_1, ci=None, palette=['g', 'b', 'm', 'c', 'y'])\n",
    "\n",
    "plt.xlabel('Aircrafts', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('Top-5 Flight IDs count with NO delays', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AC      National_International_Flight\n",
       "TU 32A  International                    5845\n",
       "TU 736  International                    5594\n",
       "TU 320  International                    4893\n",
       "UG AT7  National                         4505\n",
       "TU 736  National                         3106\n",
       "TU 32A  National                         2879\n",
       "TU CR9  National                         2179\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top-5 Aircrafts delay count for International and national flights\n",
    "\n",
    "df_no_delay.groupby(['AC', 'National_International_Flight']).size().sort_values(ascending=False).head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Database is created for Top-5 Aircrafts for International and national flights with NO delays\n",
    "\n",
    "df_air_1 = df_no_delay.query(\"AC == 'TU 32A' | AC == 'TU 320' | AC == 'TU 736' | AC == 'UG AT7' | AC == 'TU CR9'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top-5 Aircrafts count for International and national flights with NO delays\n",
    "\n",
    "# palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "sns.countplot(x='National_International_Flight', hue='AC', data=df_air_1, palette=['g', 'b', 'm', 'c', 'y'])\n",
    "\n",
    "plt.legend(loc='upper center', title='Aircrafts Types')\n",
    "\n",
    "plt.xlabel('Aircrafts Flying Destinations', size=15, weight='bold')\n",
    "plt.ylabel('Flights_count', size=15, weight='bold')\n",
    "plt.title('Top-5 Aircrafts count with NO delays', size=20, weight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hourly Flight count for International and National flights with NO delays\n",
    "\n",
    "df_hr_dep_1 = df_no_delay.groupby(['dep_hour', 'National_International_Flight']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hourly Flight count with NO delays\n",
    "\n",
    "#palette=['g', 'b', 'm', 'c', 'y']\n",
    "\n",
    "sns.set_theme(style='white')\n",
    "sns.set_color_codes(\"dark\")\n",
    "\n",
    "plt.subplots(figsize=(14,7))\n",
    "\n",
    "sns.lineplot(x='dep_hour', y='count', data=df_hr_dep_1, ci=None, color='g')\n",
    "\n",
    "plt.margins(0)\n",
    "\n",
    "plt.xlabel('Flight Hours', size=15, weight='bold')\n",
    "plt.ylabel('Flight_Count', size=15, weight='bold')\n",
    "plt.title('Hourly Flight count for NO delays', size=20, weight='bold')\n",
    "plot.set_xticks(range(0,26,6))\n",
    "plt.ylim(0, 2000);"
   ]
  }
 ],
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