03.EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 03.EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I have exploratory data analysis on two subsetted datasets, Holidays and Washington Metro area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Libraries and packages\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import datetime\n",
    "import requests\n",
    "import json\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "import time\n",
    "from selenium import webdriver\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Holidays dataframe dimensions(shape)  :  (7084008, 31)\n",
      "Washington dataframe dimensions(shape):  (2204853, 31)\n"
     ]
    }
   ],
   "source": [
    "# Read dataset\n",
    "holidays   = pd.read_csv('./assets/holidays.csv', low_memory=False) \n",
    "washington = pd.read_csv('./assets/washington.csv', low_memory=False)\n",
    "\n",
    "print(\"Holidays dataframe dimensions(shape)  : \", holidays.shape)\n",
    "print(\"Washington dataframe dimensions(shape): \", washington.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Functions for the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Examine the null values in the datasets\n",
    "# def null_check(df):\n",
    "#     null = pd.DataFrame(df.dtypes).T.rename(index={0:'column type'})\n",
    "#     null = null.append(pd.DataFrame(df.isnull().sum()).T.rename(index={0:'null values (#)'}))\n",
    "#     null = null.append(pd.DataFrame(df.isnull().sum()/df.shape[0]*100).T.rename(index={0:'null values (%)'}))\n",
    "#     return null.T.sort_values(by='null values (%)', ascending=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# counting null values in dataframe\n",
    "\n",
    "def isnull_counter(df):\n",
    "    foobar = df.isnull().sum(axis=0).reset_index()\n",
    "    foobar.columns = ['variable', 'missing values']\n",
    "    foobar['filling factor (%)']=(df.shape[0]-foobar['missing values'])/df.shape[0]*100\n",
    "    return foobar.sort_values('filling factor (%)').reset_index(drop = True).round(4)\n",
    "\n",
    "# counting categorical columns' value counts\n",
    "def value_counter(df):\n",
    "    foobar = df.value_counts().reset_index()\n",
    "    foobar.columns = ['value', 'counts']\n",
    "    foobar['(%)'] = df.value_counts(normalize=True).reset_index(drop=True).round(4)\n",
    "    return foobar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>missing values</th>\n",
       "      <th>filling factor (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CancellationCode</td>\n",
       "      <td>6954019</td>\n",
       "      <td>1.8350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LateAircraftDelay</td>\n",
       "      <td>5727735</td>\n",
       "      <td>19.1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NASDelay</td>\n",
       "      <td>5727735</td>\n",
       "      <td>19.1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WeatherDelay</td>\n",
       "      <td>5727735</td>\n",
       "      <td>19.1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CarrierDelay</td>\n",
       "      <td>5727735</td>\n",
       "      <td>19.1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SecurityDelay</td>\n",
       "      <td>5727735</td>\n",
       "      <td>19.1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AirTime</td>\n",
       "      <td>145679</td>\n",
       "      <td>97.9436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ActualElapsedTime</td>\n",
       "      <td>145679</td>\n",
       "      <td>97.9436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ArrDelay</td>\n",
       "      <td>145679</td>\n",
       "      <td>97.9436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ArrTime</td>\n",
       "      <td>134024</td>\n",
       "      <td>98.1081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WheelsOn</td>\n",
       "      <td>134024</td>\n",
       "      <td>98.1081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TaxiIn</td>\n",
       "      <td>134024</td>\n",
       "      <td>98.1081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TaxiOut</td>\n",
       "      <td>128641</td>\n",
       "      <td>98.1841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WheelsOff</td>\n",
       "      <td>128641</td>\n",
       "      <td>98.1841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DepDelay</td>\n",
       "      <td>125784</td>\n",
       "      <td>98.2244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DepTime</td>\n",
       "      <td>125780</td>\n",
       "      <td>98.2245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TailNum</td>\n",
       "      <td>20754</td>\n",
       "      <td>99.7070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CRSElapsedTime</td>\n",
       "      <td>8</td>\n",
       "      <td>99.9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Month</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DayofMonth</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DayOfWeek</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Distance</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Carrier</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Diverted</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Cancelled</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Origin</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Dest</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CRSArrTime</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CRSDepTime</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>FlightNum</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Year</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             variable  missing values  filling factor (%)\n",
       "0    CancellationCode         6954019              1.8350\n",
       "1   LateAircraftDelay         5727735             19.1456\n",
       "2            NASDelay         5727735             19.1456\n",
       "3        WeatherDelay         5727735             19.1456\n",
       "4        CarrierDelay         5727735             19.1456\n",
       "5       SecurityDelay         5727735             19.1456\n",
       "6             AirTime          145679             97.9436\n",
       "7   ActualElapsedTime          145679             97.9436\n",
       "8            ArrDelay          145679             97.9436\n",
       "9             ArrTime          134024             98.1081\n",
       "10           WheelsOn          134024             98.1081\n",
       "11             TaxiIn          134024             98.1081\n",
       "12            TaxiOut          128641             98.1841\n",
       "13          WheelsOff          128641             98.1841\n",
       "14           DepDelay          125784             98.2244\n",
       "15            DepTime          125780             98.2245\n",
       "16            TailNum           20754             99.7070\n",
       "17     CRSElapsedTime               8             99.9999\n",
       "18              Month               0            100.0000\n",
       "19         DayofMonth               0            100.0000\n",
       "20          DayOfWeek               0            100.0000\n",
       "21           Distance               0            100.0000\n",
       "22            Carrier               0            100.0000\n",
       "23           Diverted               0            100.0000\n",
       "24          Cancelled               0            100.0000\n",
       "25             Origin               0            100.0000\n",
       "26               Dest               0            100.0000\n",
       "27         CRSArrTime               0            100.0000\n",
       "28         CRSDepTime               0            100.0000\n",
       "29          FlightNum               0            100.0000\n",
       "30               Year               0            100.0000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isnull_counter(holidays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>missing values</th>\n",
       "      <th>filling factor (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CancellationCode</td>\n",
       "      <td>2161504</td>\n",
       "      <td>1.9661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LateAircraftDelay</td>\n",
       "      <td>1784905</td>\n",
       "      <td>19.0465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NASDelay</td>\n",
       "      <td>1784905</td>\n",
       "      <td>19.0465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WeatherDelay</td>\n",
       "      <td>1784905</td>\n",
       "      <td>19.0465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CarrierDelay</td>\n",
       "      <td>1784905</td>\n",
       "      <td>19.0465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SecurityDelay</td>\n",
       "      <td>1784905</td>\n",
       "      <td>19.0465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AirTime</td>\n",
       "      <td>48161</td>\n",
       "      <td>97.8157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ActualElapsedTime</td>\n",
       "      <td>48161</td>\n",
       "      <td>97.8157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ArrDelay</td>\n",
       "      <td>48161</td>\n",
       "      <td>97.8157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ArrTime</td>\n",
       "      <td>44131</td>\n",
       "      <td>97.9985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WheelsOn</td>\n",
       "      <td>44131</td>\n",
       "      <td>97.9985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TaxiIn</td>\n",
       "      <td>44131</td>\n",
       "      <td>97.9985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TaxiOut</td>\n",
       "      <td>43187</td>\n",
       "      <td>98.0413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WheelsOff</td>\n",
       "      <td>43187</td>\n",
       "      <td>98.0413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DepDelay</td>\n",
       "      <td>41835</td>\n",
       "      <td>98.1026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DepTime</td>\n",
       "      <td>41833</td>\n",
       "      <td>98.1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TailNum</td>\n",
       "      <td>11651</td>\n",
       "      <td>99.4716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CRSElapsedTime</td>\n",
       "      <td>2</td>\n",
       "      <td>99.9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Month</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DayofMonth</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DayOfWeek</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Distance</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Carrier</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Diverted</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Cancelled</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Origin</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Dest</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CRSArrTime</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CRSDepTime</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>FlightNum</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Year</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             variable  missing values  filling factor (%)\n",
       "0    CancellationCode         2161504              1.9661\n",
       "1   LateAircraftDelay         1784905             19.0465\n",
       "2            NASDelay         1784905             19.0465\n",
       "3        WeatherDelay         1784905             19.0465\n",
       "4        CarrierDelay         1784905             19.0465\n",
       "5       SecurityDelay         1784905             19.0465\n",
       "6             AirTime           48161             97.8157\n",
       "7   ActualElapsedTime           48161             97.8157\n",
       "8            ArrDelay           48161             97.8157\n",
       "9             ArrTime           44131             97.9985\n",
       "10           WheelsOn           44131             97.9985\n",
       "11             TaxiIn           44131             97.9985\n",
       "12            TaxiOut           43187             98.0413\n",
       "13          WheelsOff           43187             98.0413\n",
       "14           DepDelay           41835             98.1026\n",
       "15            DepTime           41833             98.1027\n",
       "16            TailNum           11651             99.4716\n",
       "17     CRSElapsedTime               2             99.9999\n",
       "18              Month               0            100.0000\n",
       "19         DayofMonth               0            100.0000\n",
       "20          DayOfWeek               0            100.0000\n",
       "21           Distance               0            100.0000\n",
       "22            Carrier               0            100.0000\n",
       "23           Diverted               0            100.0000\n",
       "24          Cancelled               0            100.0000\n",
       "25             Origin               0            100.0000\n",
       "26               Dest               0            100.0000\n",
       "27         CRSArrTime               0            100.0000\n",
       "28         CRSDepTime               0            100.0000\n",
       "29          FlightNum               0            100.0000\n",
       "30               Year               0            100.0000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isnull_counter(washington)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# currently all the departure/arrival time were set only 4 digits interger\n",
    "# between 0001 to 2400. Therefore, convert it into datetime function\n",
    "\n",
    "def time_converter(hr):\n",
    "    if pd.isnull(hr):\n",
    "        return np.nan\n",
    "    else:\n",
    "        if hr == 2400: hr = 0\n",
    "        # set 4 digits \n",
    "        hr = \"{0:04d}\".format(int(hr)) \n",
    "        hour = datetime.time(int(hr[0:2]), int(hr[2:4]))\n",
    "        return hour\n",
    "\n",
    "# change columns name\n",
    "mapping ={\n",
    "    'Year': 'year',\n",
    "    'Month': 'month',\n",
    "    'DayofMonth': 'day',\n",
    "    'DayOfWeek': 'dayofweek',\n",
    "    'Carrier': 'carrier',\n",
    "    'TailNum': 'tail_no',\n",
    "    'FlightNum': 'flight_no',\n",
    "    'Origin': 'origin',\n",
    "    'Dest': 'dest',\n",
    "    'CRSDepTime': 'sched_dep',\n",
    "    'DepTime': 'dep_time',\n",
    "    'DepDelay': 'dep_delay',\n",
    "    'TaxiOut': 'taxi_out',\n",
    "    'WheelsOff': 'wheels_off',\n",
    "    'WheelsOn': 'wheels_on',\n",
    "    'TaxiIn': 'taxi_in',\n",
    "    'CRSArrTime': 'sched_arr',\n",
    "    'ArrTime': 'arr_time',\n",
    "    'ArrDelay': 'arr_delay',\n",
    "    'Cancelled': 'cancelled',\n",
    "    'CancellationCode': 'cancel_code',\n",
    "    'Diverted': 'diverted',\n",
    "    'CRSElapsedTime': 'sched_elapsed',\n",
    "    'ActualElapsedTime': 'elapsed_time',\n",
    "    'AirTime': 'air_time',\n",
    "    'Distance': 'distance',\n",
    "    'CarrierDelay': 'carrier_delay',\n",
    "    'WeatherDelay': 'weather_delay',\n",
    "    'NASDelay': 'system_delay',\n",
    "    'SecurityDelay': 'security_delay',\n",
    "    'LateAircraftDelay': 'late_aircraft_delay'\n",
    "}\n",
    "\n",
    "day_map = {\n",
    "    '1': 'Mon', '2': 'Tue', '3': 'Wed', '4': 'Thu',\n",
    "    '5': 'Fri', '6': 'Sat', '7': 'Sun'\n",
    "}\n",
    "\n",
    "def preprocessing(df):\n",
    "    # easier to work with lowercase columns\n",
    "    df = df.rename(columns=mapping)\n",
    "    \n",
    "    # convert it to datetime\n",
    "    df['date'] = pd.to_datetime(df[['year', 'month', 'day']])\n",
    "    \n",
    "    # apply time converter to dep/arr time\n",
    "    times = ['sched_dep', 'dep_time', 'sched_arr', 'arr_time']\n",
    "    for x in times:\n",
    "        df[x] = df[x].apply(time_converter)\n",
    "    \n",
    "    # apply dayofweek\n",
    "    df['dayofweek'] = df['dayofweek'].astype(str).map(day_map)\n",
    "    \n",
    "    # fill tail_no\n",
    "    df['tail_no'] = df['tail_no'].fillna('na')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "holidays   = preprocessing(holidays)\n",
    "washington = preprocessing(washington)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's find some basic data analysis and plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### value_counter(df.cancelled and df.diverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6954019</td>\n",
       "      <td>0.9817</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>129989</td>\n",
       "      <td>0.0183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value   counts     (%)\n",
       "0    0.0  6954019  0.9817\n",
       "1    1.0   129989  0.0183"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counter(holidays.cancelled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129989"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counter(holidays.cancelled).iloc[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>counts</th>\n",
       "      <th>(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2161504</td>\n",
       "      <td>0.9803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>43349</td>\n",
       "      <td>0.0197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value   counts     (%)\n",
       "0    0.0  2161504  0.9803\n",
       "1    1.0    43349  0.0197"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counter(washington.cancelled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>value</th>\n",
       "      <th>counts</th>\n",
       "      <th>(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7068318</td>\n",
       "      <td>0.9978</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15690</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value   counts     (%)\n",
       "0    0.0  7068318  0.9978\n",
       "1    1.0    15690  0.0022"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counter(holidays.diverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
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       "      <th>(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2200041</td>\n",
       "      <td>0.9978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4812</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value   counts     (%)\n",
       "0    0.0  2200041  0.9978\n",
       "1    1.0     4812  0.0022"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counter(washington.diverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inspired and courtesy of Chris Albon- https://chrisalbon.com/python/data_visualization/matplotlib_pie_chart/\n",
    "\n",
    "# colors = [\"#E13F29\", \"#D69A80\", \"#D63B59\"]\n",
    "\n",
    "plt.pie(\n",
    "    holidays.cancelled.value_counts(),\n",
    "#     colors=colors,\n",
    "    labels=['Operated', 'Cancelled'],\n",
    "    explode=(0.15, 0),\n",
    "    startangle=90,\n",
    "    autopct='%1.1f%%'\n",
    ")\n",
    "\n",
    "\n",
    "# View the plot\n",
    "plt.tight_layout()\n",
    "plt.title(\"Holiday Seasons flight cancellation (2013-2017)\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inspired and courtesy of Chris Albon- https://chrisalbon.com/python/data_visualization/matplotlib_pie_chart/\n",
    "\n",
    "plt.pie(\n",
    "    washington.cancelled.value_counts(),\n",
    "    labels=['Operated', 'Cancelled'],\n",
    "    explode=(0.15, 0.1),\n",
    "    startangle=90,\n",
    "    autopct='%1.1f%%'\n",
    ")\n",
    "\n",
    "# counts = value_counter(washington.cancelled).iloc[1,1]\n",
    "\n",
    "# View the plot\n",
    "plt.tight_layout()\n",
    "plt.title(\"Washington Metro Area flight cancellation(In/Outbounds) (2013-2017)\")\n",
    "# plt.text\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables_to_remove = ['taxi_out', 'taxi_in', 'wheels_on', 'wheels_off', 'system_delay',\n",
    "                       'security_delay', 'carrier_delay', 'late_aircraft_delay',\n",
    "                       'weather_delay', 'diverted', 'cancelled', 'cancel_code', 'air_time',\n",
    "                       'year', 'month', 'day']\n",
    "df_holi = holidays.drop(variables_to_remove, axis=1)\n",
    "df_was = washington.drop(variables_to_remove, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>date</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>carrier</th>\n",
       "      <th>flight_no</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>sched_dep</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>sched_arr</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>sched_elapsed</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>tail_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-17</td>\n",
       "      <td>Sun</td>\n",
       "      <td>DL</td>\n",
       "      <td>1138</td>\n",
       "      <td>ATL</td>\n",
       "      <td>DCA</td>\n",
       "      <td>07:20:00</td>\n",
       "      <td>07:19:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>09:00:00</td>\n",
       "      <td>08:43:00</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>N906DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-17</td>\n",
       "      <td>Sun</td>\n",
       "      <td>DL</td>\n",
       "      <td>1139</td>\n",
       "      <td>DCA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>10:53:00</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>12:53:00</td>\n",
       "      <td>12:52:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>N906DA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-17</td>\n",
       "      <td>Sun</td>\n",
       "      <td>DL</td>\n",
       "      <td>1140</td>\n",
       "      <td>ATL</td>\n",
       "      <td>MSY</td>\n",
       "      <td>23:30:00</td>\n",
       "      <td>23:28:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>00:01:00</td>\n",
       "      <td>23:51:00</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>N663DN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-11-17</td>\n",
       "      <td>Sun</td>\n",
       "      <td>DL</td>\n",
       "      <td>1142</td>\n",
       "      <td>ATL</td>\n",
       "      <td>HOU</td>\n",
       "      <td>13:30:00</td>\n",
       "      <td>13:29:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>14:46:00</td>\n",
       "      <td>14:41:00</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>N968DL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-11-17</td>\n",
       "      <td>Sun</td>\n",
       "      <td>DL</td>\n",
       "      <td>1142</td>\n",
       "      <td>HOU</td>\n",
       "      <td>ATL</td>\n",
       "      <td>15:30:00</td>\n",
       "      <td>15:25:00</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>18:27:00</td>\n",
       "      <td>18:10:00</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>N968DL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date dayofweek carrier  flight_no origin dest sched_dep  dep_time  \\\n",
       "0 2013-11-17       Sun      DL       1138    ATL  DCA  07:20:00  07:19:00   \n",
       "1 2013-11-17       Sun      DL       1139    DCA  ATL  11:00:00  10:53:00   \n",
       "2 2013-11-17       Sun      DL       1140    ATL  MSY  23:30:00  23:28:00   \n",
       "3 2013-11-17       Sun      DL       1142    ATL  HOU  13:30:00  13:29:00   \n",
       "4 2013-11-17       Sun      DL       1142    HOU  ATL  15:30:00  15:25:00   \n",
       "\n",
       "   dep_delay sched_arr  arr_time  arr_delay  sched_elapsed  elapsed_time  \\\n",
       "0       -1.0  09:00:00  08:43:00      -17.0          100.0          84.0   \n",
       "1       -7.0  12:53:00  12:52:00       -1.0          113.0         119.0   \n",
       "2       -2.0  00:01:00  23:51:00      -10.0           91.0          83.0   \n",
       "3       -1.0  14:46:00  14:41:00       -5.0          136.0         132.0   \n",
       "4       -5.0  18:27:00  18:10:00      -17.0          117.0         105.0   \n",
       "\n",
       "   distance tail_no  \n",
       "0     547.0  N906DA  \n",
       "1     547.0  N906DA  \n",
       "2     425.0  N663DN  \n",
       "3     696.0  N968DL  \n",
       "4     696.0  N968DL  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_holi = df_holi[['date', 'dayofweek', 'carrier', 'flight_no', 'origin', 'dest',\n",
    "         'sched_dep', 'dep_time', 'dep_delay', 'sched_arr', 'arr_time',\n",
    "         'arr_delay', 'sched_elapsed', 'elapsed_time', 'distance', 'tail_no']]\n",
    "df_holi[:5]"
   ]
  },
  {
   "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>date</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>carrier</th>\n",
       "      <th>flight_no</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>sched_dep</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>sched_arr</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>sched_elapsed</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>tail_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-09-17</td>\n",
       "      <td>Tue</td>\n",
       "      <td>DL</td>\n",
       "      <td>2125</td>\n",
       "      <td>BWI</td>\n",
       "      <td>ATL</td>\n",
       "      <td>12:40:00</td>\n",
       "      <td>12:37:00</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>14:33:00</td>\n",
       "      <td>14:42:00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>N948DL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-09-17</td>\n",
       "      <td>Tue</td>\n",
       "      <td>DL</td>\n",
       "      <td>2137</td>\n",
       "      <td>BWI</td>\n",
       "      <td>DTW</td>\n",
       "      <td>07:40:00</td>\n",
       "      <td>07:38:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>09:13:00</td>\n",
       "      <td>09:04:00</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>N958DL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-09-17</td>\n",
       "      <td>Tue</td>\n",
       "      <td>DL</td>\n",
       "      <td>2145</td>\n",
       "      <td>DCA</td>\n",
       "      <td>DTW</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>19:32:00</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20:35:00</td>\n",
       "      <td>21:01:00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>N913DE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-09-17</td>\n",
       "      <td>Tue</td>\n",
       "      <td>DL</td>\n",
       "      <td>2163</td>\n",
       "      <td>DCA</td>\n",
       "      <td>MSP</td>\n",
       "      <td>06:30:00</td>\n",
       "      <td>06:49:00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>08:04:00</td>\n",
       "      <td>08:33:00</td>\n",
       "      <td>29.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>N938DN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-09-17</td>\n",
       "      <td>Tue</td>\n",
       "      <td>DL</td>\n",
       "      <td>2239</td>\n",
       "      <td>DCA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>17:15:00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18:50:00</td>\n",
       "      <td>19:24:00</td>\n",
       "      <td>34.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>N953DN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date dayofweek carrier  flight_no origin dest sched_dep  dep_time  \\\n",
       "0 2013-09-17       Tue      DL       2125    BWI  ATL  12:40:00  12:37:00   \n",
       "1 2013-09-17       Tue      DL       2137    BWI  DTW  07:40:00  07:38:00   \n",
       "2 2013-09-17       Tue      DL       2145    DCA  DTW  19:00:00  19:32:00   \n",
       "3 2013-09-17       Tue      DL       2163    DCA  MSP  06:30:00  06:49:00   \n",
       "4 2013-09-17       Tue      DL       2239    DCA  ATL  17:00:00  17:15:00   \n",
       "\n",
       "   dep_delay sched_arr  arr_time  arr_delay  sched_elapsed  elapsed_time  \\\n",
       "0       -3.0  14:33:00  14:42:00        9.0          113.0         125.0   \n",
       "1       -2.0  09:13:00  09:04:00       -9.0           93.0          86.0   \n",
       "2       32.0  20:35:00  21:01:00       26.0           95.0          89.0   \n",
       "3       19.0  08:04:00  08:33:00       29.0          154.0         164.0   \n",
       "4       15.0  18:50:00  19:24:00       34.0          110.0         129.0   \n",
       "\n",
       "   distance tail_no  \n",
       "0     577.0  N948DL  \n",
       "1     409.0  N958DL  \n",
       "2     405.0  N913DE  \n",
       "3     931.0  N938DN  \n",
       "4     547.0  N953DN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_was = df_was[['date', 'dayofweek', 'carrier', 'flight_no', 'origin', 'dest',\n",
    "         'sched_dep', 'dep_time', 'dep_delay', 'sched_arr', 'arr_time',\n",
    "         'arr_delay', 'sched_elapsed', 'elapsed_time', 'distance', 'tail_no']]\n",
    "df_was[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aircraft dataframe clean and merge with the main dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>age(year)</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>shipped</th>\n",
       "      <th>tailno</th>\n",
       "      <th>type_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N028AA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11 years</td>\n",
       "      <td>Bombardier CRJ-900LR</td>\n",
       "      <td>AGE (Aug 2006)</td>\n",
       "      <td>N806SK</td>\n",
       "      <td>CRJ9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13 years</td>\n",
       "      <td>Boeing 737-8FH</td>\n",
       "      <td>AGE (Feb 2005)</td>\n",
       "      <td>N549AS</td>\n",
       "      <td>B738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7 years</td>\n",
       "      <td>Boeing 737-890</td>\n",
       "      <td>AGE (Jan 2011)</td>\n",
       "      <td>N534AS</td>\n",
       "      <td>B738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18 years</td>\n",
       "      <td>Airbus A319-114</td>\n",
       "      <td>AGE (Feb 2000)</td>\n",
       "      <td>N314NB</td>\n",
       "      <td>A319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age(year)              aircraft         shipped  tailno type_code\n",
       "0       NaN                   NaN             NaN  N028AA       NaN\n",
       "1  11 years  Bombardier CRJ-900LR  AGE (Aug 2006)  N806SK      CRJ9\n",
       "2  13 years        Boeing 737-8FH  AGE (Feb 2005)  N549AS      B738\n",
       "3   7 years        Boeing 737-890  AGE (Jan 2011)  N534AS      B738\n",
       "4  18 years       Airbus A319-114  AGE (Feb 2000)  N314NB      A319"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aircrafts=pd.read_csv('./assets/aircrafts.csv', low_memory=False)\n",
    "aircrafts.head(5)"
   ]
  },
  {
   "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>variable</th>\n",
       "      <th>missing values</th>\n",
       "      <th>filling factor (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shipped</td>\n",
       "      <td>1238</td>\n",
       "      <td>80.8329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age(year)</td>\n",
       "      <td>592</td>\n",
       "      <td>90.8345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aircraft</td>\n",
       "      <td>589</td>\n",
       "      <td>90.8809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>type_code</td>\n",
       "      <td>589</td>\n",
       "      <td>90.8809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tailno</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    variable  missing values  filling factor (%)\n",
       "0    shipped            1238             80.8329\n",
       "1  age(year)             592             90.8345\n",
       "2   aircraft             589             90.8809\n",
       "3  type_code             589             90.8809\n",
       "4     tailno               0            100.0000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isnull_counter(aircrafts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6459, 5)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aircrafts.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### fuctions for the aircraft dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_years(years):\n",
    "    if type(years) != str:\n",
    "        return(years)\n",
    "    if \"year\" in years.lower():\n",
    "        return(float(''.join([char for char in years if char.isdigit()])))\n",
    "    elif \"brand new\" in years.lower():\n",
    "        return(0)\n",
    "    elif years == \"-\":\n",
    "        return(np.nan)\n",
    "    else:\n",
    "        return(years)\n",
    "    \n",
    "def clean_aircrafts(aircrafts):\n",
    "    if type(aircrafts) != str:\n",
    "        return(aircrafts)\n",
    "    if \"age (\" in aircrafts.lower():\n",
    "        return aircrafts.replace(\"AGE (\", \"\")[:-1]\n",
    "\n",
    "def find_manufacture(aircraft):\n",
    "    if aircraft == \"-\":\n",
    "        return \"Unknown\"\n",
    "    elif type(aircraft) == str:\n",
    "        return aircraft.split()[0]\n",
    "    else:\n",
    "        return \"Unknown\"\n",
    "\n",
    "top_manufacturers = aircrafts.aircraft.apply(find_manufacture).value_counts().index[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "aircrafts['age(year)']=aircrafts['age(year)'].apply(clean_years).fillna(\"Unknown\")\n",
    "aircrafts['shipped']  =pd.to_datetime(aircrafts['shipped'].apply(clean_aircrafts))\n",
    "aircrafts['aircraft'] =aircrafts.aircraft.apply(find_manufacture).apply(lambda x: \"Other\" if x not in top_manufacturers else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age(year)</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>shipped</th>\n",
       "      <th>tailno</th>\n",
       "      <th>type_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaT</td>\n",
       "      <td>N028AA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>Bombardier</td>\n",
       "      <td>2006-08-01</td>\n",
       "      <td>N806SK</td>\n",
       "      <td>CRJ9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>Boeing</td>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>N549AS</td>\n",
       "      <td>B738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Boeing</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>N534AS</td>\n",
       "      <td>B738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>Airbus</td>\n",
       "      <td>2000-02-01</td>\n",
       "      <td>N314NB</td>\n",
       "      <td>A319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age(year)    aircraft    shipped  tailno type_code\n",
       "0   Unknown     Unknown        NaT  N028AA       NaN\n",
       "1        11  Bombardier 2006-08-01  N806SK      CRJ9\n",
       "2        13      Boeing 2005-02-01  N549AS      B738\n",
       "3         7      Boeing 2011-01-01  N534AS      B738\n",
       "4        18      Airbus 2000-02-01  N314NB      A319"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aircrafts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>missing values</th>\n",
       "      <th>filling factor (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shipped</td>\n",
       "      <td>1238</td>\n",
       "      <td>80.8329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>type_code</td>\n",
       "      <td>589</td>\n",
       "      <td>90.8809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age(year)</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aircraft</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tailno</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    variable  missing values  filling factor (%)\n",
       "0    shipped            1238             80.8329\n",
       "1  type_code             589             90.8809\n",
       "2  age(year)               0            100.0000\n",
       "3   aircraft               0            100.0000\n",
       "4     tailno               0            100.0000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isnull_counter(aircrafts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's merge aircraft data with main dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "holiday dataframe dimension   :  (7084008, 16)\n",
      "Washington dataframe dimension:  (2204853, 16)\n",
      "aircraft dataframe dimension  :  (6459, 5)\n"
     ]
    }
   ],
   "source": [
    "print(\"holiday dataframe dimension   : \", df_holi.shape)\n",
    "print(\"Washington dataframe dimension: \", df_was.shape)\n",
    "print(\"aircraft dataframe dimension  : \", aircrafts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_holi tail_no counts :  6459\n",
      "aircrafts tailno counts:  5826\n"
     ]
    }
   ],
   "source": [
    "print(\"df_holi tail_no counts : \", df_holi.tail_no.nunique())\n",
    "print(\"aircrafts tailno counts: \", aircrafts.tailno.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7084245, 21)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.merge(df_holi, aircrafts.drop_duplicates(), how='left', left_on=\"tail_no\", right_on=\"tailno\")\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2204975, 21)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.merge(df_was, aircrafts.drop_duplicates(), how='left', left_on=\"tail_no\", right_on=\"tailno\")\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Save it to pickle and move to the next notebook for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_pickle('./assets/holidays.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_pickle('./assets/washington.pkl')"
   ]
  }
 ],
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