Airlines Data Analysis (SQL).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Database Connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "connection = sqlite3.connect('travel.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor = connection.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tables in Database:\n",
      "['aircrafts_data', 'airports_data', 'boarding_passes', 'bookings', 'flights', 'seats', 'ticket_flights', 'tickets']\n"
     ]
    }
   ],
   "source": [
    "cursor.execute(\"\"\"Select name from sqlite_master where type = 'table';\"\"\")\n",
    "print(\"Tables in Database:\")\n",
    "table_list = []\n",
    "for tables in cursor.fetchall():\n",
    "    table_list.append(tables[0])\n",
    "print(table_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>aircraft_code</th>\n",
       "      <th>model</th>\n",
       "      <th>range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>773</td>\n",
       "      <td>{\"en\": \"Boeing 777-300\", \"ru\": \"Боинг 777-300\"}</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>763</td>\n",
       "      <td>{\"en\": \"Boeing 767-300\", \"ru\": \"Боинг 767-300\"}</td>\n",
       "      <td>7900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SU9</td>\n",
       "      <td>{\"en\": \"Sukhoi Superjet-100\", \"ru\": \"Сухой Суп...</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>320</td>\n",
       "      <td>{\"en\": \"Airbus A320-200\", \"ru\": \"Аэробус A320-...</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>{\"en\": \"Airbus A321-200\", \"ru\": \"Аэробус A321-...</td>\n",
       "      <td>5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>319</td>\n",
       "      <td>{\"en\": \"Airbus A319-100\", \"ru\": \"Аэробус A319-...</td>\n",
       "      <td>6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>733</td>\n",
       "      <td>{\"en\": \"Boeing 737-300\", \"ru\": \"Боинг 737-300\"}</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CN1</td>\n",
       "      <td>{\"en\": \"Cessna 208 Caravan\", \"ru\": \"Сессна 208...</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CR2</td>\n",
       "      <td>{\"en\": \"Bombardier CRJ-200\", \"ru\": \"Бомбардье ...</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code                                              model  range\n",
       "0           773    {\"en\": \"Boeing 777-300\", \"ru\": \"Боинг 777-300\"}  11100\n",
       "1           763    {\"en\": \"Boeing 767-300\", \"ru\": \"Боинг 767-300\"}   7900\n",
       "2           SU9  {\"en\": \"Sukhoi Superjet-100\", \"ru\": \"Сухой Суп...   3000\n",
       "3           320  {\"en\": \"Airbus A320-200\", \"ru\": \"Аэробус A320-...   5700\n",
       "4           321  {\"en\": \"Airbus A321-200\", \"ru\": \"Аэробус A321-...   5600\n",
       "5           319  {\"en\": \"Airbus A319-100\", \"ru\": \"Аэробус A319-...   6700\n",
       "6           733    {\"en\": \"Boeing 737-300\", \"ru\": \"Боинг 737-300\"}   4200\n",
       "7           CN1  {\"en\": \"Cessna 208 Caravan\", \"ru\": \"Сессна 208...   1200\n",
       "8           CR2  {\"en\": \"Bombardier CRJ-200\", \"ru\": \"Бомбардье ...   2700"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aircrafts_data = pd.read_sql_query(\"select * from aircrafts_data\", connection)\n",
    "aircrafts_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>airport_code</th>\n",
       "      <th>airport_name</th>\n",
       "      <th>city</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YKS</td>\n",
       "      <td>{\"en\": \"Yakutsk Airport\", \"ru\": \"Якутск\"}</td>\n",
       "      <td>{\"en\": \"Yakutsk\", \"ru\": \"Якутск\"}</td>\n",
       "      <td>(129.77099609375,62.0932998657226562)</td>\n",
       "      <td>Asia/Yakutsk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MJZ</td>\n",
       "      <td>{\"en\": \"Mirny Airport\", \"ru\": \"Мирный\"}</td>\n",
       "      <td>{\"en\": \"Mirnyj\", \"ru\": \"Мирный\"}</td>\n",
       "      <td>(114.03900146484375,62.534698486328125)</td>\n",
       "      <td>Asia/Yakutsk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KHV</td>\n",
       "      <td>{\"en\": \"Khabarovsk-Novy Airport\", \"ru\": \"Хабар...</td>\n",
       "      <td>{\"en\": \"Khabarovsk\", \"ru\": \"Хабаровск\"}</td>\n",
       "      <td>(135.18800354004,48.5279998779300001)</td>\n",
       "      <td>Asia/Vladivostok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PKC</td>\n",
       "      <td>{\"en\": \"Yelizovo Airport\", \"ru\": \"Елизово\"}</td>\n",
       "      <td>{\"en\": \"Petropavlovsk\", \"ru\": \"Петропавловск-К...</td>\n",
       "      <td>(158.453994750976562,53.1679000854492188)</td>\n",
       "      <td>Asia/Kamchatka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UUS</td>\n",
       "      <td>{\"en\": \"Yuzhno-Sakhalinsk Airport\", \"ru\": \"Хом...</td>\n",
       "      <td>{\"en\": \"Yuzhno-Sakhalinsk\", \"ru\": \"Южно-Сахали...</td>\n",
       "      <td>(142.718002319335938,46.8886985778808594)</td>\n",
       "      <td>Asia/Sakhalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>MMK</td>\n",
       "      <td>{\"en\": \"Murmansk Airport\", \"ru\": \"Мурманск\"}</td>\n",
       "      <td>{\"en\": \"Murmansk\", \"ru\": \"Мурманск\"}</td>\n",
       "      <td>(32.7508010864257812,68.7817001342773438)</td>\n",
       "      <td>Europe/Moscow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>ABA</td>\n",
       "      <td>{\"en\": \"Abakan Airport\", \"ru\": \"Абакан\"}</td>\n",
       "      <td>{\"en\": \"Abakan\", \"ru\": \"Абакан\"}</td>\n",
       "      <td>(91.3850021362304688,53.7400016784667969)</td>\n",
       "      <td>Asia/Krasnoyarsk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>BAX</td>\n",
       "      <td>{\"en\": \"Barnaul Airport\", \"ru\": \"Барнаул\"}</td>\n",
       "      <td>{\"en\": \"Barnaul\", \"ru\": \"Барнаул\"}</td>\n",
       "      <td>(83.5384979248046875,53.363800048828125)</td>\n",
       "      <td>Asia/Krasnoyarsk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>AAQ</td>\n",
       "      <td>{\"en\": \"Anapa Vityazevo Airport\", \"ru\": \"Витяз...</td>\n",
       "      <td>{\"en\": \"Anapa\", \"ru\": \"Анапа\"}</td>\n",
       "      <td>(37.3473014831539984,45.002101898192997)</td>\n",
       "      <td>Europe/Moscow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>CNN</td>\n",
       "      <td>{\"en\": \"Chulman Airport\", \"ru\": \"Чульман\"}</td>\n",
       "      <td>{\"en\": \"Neryungri\", \"ru\": \"Нерюнгри\"}</td>\n",
       "      <td>(124.914001464839998,56.9138984680179973)</td>\n",
       "      <td>Asia/Yakutsk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    airport_code                                       airport_name  \\\n",
       "0            YKS          {\"en\": \"Yakutsk Airport\", \"ru\": \"Якутск\"}   \n",
       "1            MJZ            {\"en\": \"Mirny Airport\", \"ru\": \"Мирный\"}   \n",
       "2            KHV  {\"en\": \"Khabarovsk-Novy Airport\", \"ru\": \"Хабар...   \n",
       "3            PKC        {\"en\": \"Yelizovo Airport\", \"ru\": \"Елизово\"}   \n",
       "4            UUS  {\"en\": \"Yuzhno-Sakhalinsk Airport\", \"ru\": \"Хом...   \n",
       "..           ...                                                ...   \n",
       "99           MMK       {\"en\": \"Murmansk Airport\", \"ru\": \"Мурманск\"}   \n",
       "100          ABA           {\"en\": \"Abakan Airport\", \"ru\": \"Абакан\"}   \n",
       "101          BAX         {\"en\": \"Barnaul Airport\", \"ru\": \"Барнаул\"}   \n",
       "102          AAQ  {\"en\": \"Anapa Vityazevo Airport\", \"ru\": \"Витяз...   \n",
       "103          CNN         {\"en\": \"Chulman Airport\", \"ru\": \"Чульман\"}   \n",
       "\n",
       "                                                  city  \\\n",
       "0                    {\"en\": \"Yakutsk\", \"ru\": \"Якутск\"}   \n",
       "1                     {\"en\": \"Mirnyj\", \"ru\": \"Мирный\"}   \n",
       "2              {\"en\": \"Khabarovsk\", \"ru\": \"Хабаровск\"}   \n",
       "3    {\"en\": \"Petropavlovsk\", \"ru\": \"Петропавловск-К...   \n",
       "4    {\"en\": \"Yuzhno-Sakhalinsk\", \"ru\": \"Южно-Сахали...   \n",
       "..                                                 ...   \n",
       "99                {\"en\": \"Murmansk\", \"ru\": \"Мурманск\"}   \n",
       "100                   {\"en\": \"Abakan\", \"ru\": \"Абакан\"}   \n",
       "101                 {\"en\": \"Barnaul\", \"ru\": \"Барнаул\"}   \n",
       "102                     {\"en\": \"Anapa\", \"ru\": \"Анапа\"}   \n",
       "103              {\"en\": \"Neryungri\", \"ru\": \"Нерюнгри\"}   \n",
       "\n",
       "                                   coordinates          timezone  \n",
       "0        (129.77099609375,62.0932998657226562)      Asia/Yakutsk  \n",
       "1      (114.03900146484375,62.534698486328125)      Asia/Yakutsk  \n",
       "2        (135.18800354004,48.5279998779300001)  Asia/Vladivostok  \n",
       "3    (158.453994750976562,53.1679000854492188)    Asia/Kamchatka  \n",
       "4    (142.718002319335938,46.8886985778808594)     Asia/Sakhalin  \n",
       "..                                         ...               ...  \n",
       "99   (32.7508010864257812,68.7817001342773438)     Europe/Moscow  \n",
       "100  (91.3850021362304688,53.7400016784667969)  Asia/Krasnoyarsk  \n",
       "101   (83.5384979248046875,53.363800048828125)  Asia/Krasnoyarsk  \n",
       "102   (37.3473014831539984,45.002101898192997)     Europe/Moscow  \n",
       "103  (124.914001464839998,56.9138984680179973)      Asia/Yakutsk  \n",
       "\n",
       "[104 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airports_data = pd.read_sql_query(\"select * from airports_data\", connection)\n",
    "airports_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>ticket_no</th>\n",
       "      <th>flight_id</th>\n",
       "      <th>boarding_no</th>\n",
       "      <th>seat_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005435212351</td>\n",
       "      <td>30625</td>\n",
       "      <td>1</td>\n",
       "      <td>2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005435212386</td>\n",
       "      <td>30625</td>\n",
       "      <td>2</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005435212381</td>\n",
       "      <td>30625</td>\n",
       "      <td>3</td>\n",
       "      <td>4H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005432211370</td>\n",
       "      <td>30625</td>\n",
       "      <td>4</td>\n",
       "      <td>5D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005435212357</td>\n",
       "      <td>30625</td>\n",
       "      <td>5</td>\n",
       "      <td>11A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579681</th>\n",
       "      <td>0005434302871</td>\n",
       "      <td>19945</td>\n",
       "      <td>85</td>\n",
       "      <td>20F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579682</th>\n",
       "      <td>0005432892791</td>\n",
       "      <td>19945</td>\n",
       "      <td>86</td>\n",
       "      <td>21C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579683</th>\n",
       "      <td>0005434302869</td>\n",
       "      <td>19945</td>\n",
       "      <td>87</td>\n",
       "      <td>20E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579684</th>\n",
       "      <td>0005432802476</td>\n",
       "      <td>19945</td>\n",
       "      <td>88</td>\n",
       "      <td>21F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579685</th>\n",
       "      <td>0005432802482</td>\n",
       "      <td>19945</td>\n",
       "      <td>89</td>\n",
       "      <td>21E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>579686 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ticket_no  flight_id  boarding_no seat_no\n",
       "0       0005435212351      30625            1      2D\n",
       "1       0005435212386      30625            2      3G\n",
       "2       0005435212381      30625            3      4H\n",
       "3       0005432211370      30625            4      5D\n",
       "4       0005435212357      30625            5     11A\n",
       "...               ...        ...          ...     ...\n",
       "579681  0005434302871      19945           85     20F\n",
       "579682  0005432892791      19945           86     21C\n",
       "579683  0005434302869      19945           87     20E\n",
       "579684  0005432802476      19945           88     21F\n",
       "579685  0005432802482      19945           89     21E\n",
       "\n",
       "[579686 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boarding_passes = pd.read_sql_query(\"select * from boarding_passes\", connection)\n",
    "boarding_passes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>book_ref</th>\n",
       "      <th>book_date</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00000F</td>\n",
       "      <td>2017-07-05 03:12:00+03</td>\n",
       "      <td>265700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000012</td>\n",
       "      <td>2017-07-14 09:02:00+03</td>\n",
       "      <td>37900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000068</td>\n",
       "      <td>2017-08-15 14:27:00+03</td>\n",
       "      <td>18100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000181</td>\n",
       "      <td>2017-08-10 13:28:00+03</td>\n",
       "      <td>131800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0002D8</td>\n",
       "      <td>2017-08-07 21:40:00+03</td>\n",
       "      <td>23600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262783</th>\n",
       "      <td>FFFEF3</td>\n",
       "      <td>2017-07-17 07:23:00+03</td>\n",
       "      <td>56000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262784</th>\n",
       "      <td>FFFF2C</td>\n",
       "      <td>2017-08-08 05:55:00+03</td>\n",
       "      <td>10800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262785</th>\n",
       "      <td>FFFF43</td>\n",
       "      <td>2017-07-20 20:42:00+03</td>\n",
       "      <td>78500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262786</th>\n",
       "      <td>FFFFA8</td>\n",
       "      <td>2017-08-08 04:45:00+03</td>\n",
       "      <td>28800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262787</th>\n",
       "      <td>FFFFF7</td>\n",
       "      <td>2017-07-01 22:12:00+03</td>\n",
       "      <td>73600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>262788 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       book_ref               book_date  total_amount\n",
       "0        00000F  2017-07-05 03:12:00+03        265700\n",
       "1        000012  2017-07-14 09:02:00+03         37900\n",
       "2        000068  2017-08-15 14:27:00+03         18100\n",
       "3        000181  2017-08-10 13:28:00+03        131800\n",
       "4        0002D8  2017-08-07 21:40:00+03         23600\n",
       "...         ...                     ...           ...\n",
       "262783   FFFEF3  2017-07-17 07:23:00+03         56000\n",
       "262784   FFFF2C  2017-08-08 05:55:00+03         10800\n",
       "262785   FFFF43  2017-07-20 20:42:00+03         78500\n",
       "262786   FFFFA8  2017-08-08 04:45:00+03         28800\n",
       "262787   FFFFF7  2017-07-01 22:12:00+03         73600\n",
       "\n",
       "[262788 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookings = pd.read_sql_query(\"select * from bookings\", connection)\n",
    "bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>flight_id</th>\n",
       "      <th>flight_no</th>\n",
       "      <th>scheduled_departure</th>\n",
       "      <th>scheduled_arrival</th>\n",
       "      <th>departure_airport</th>\n",
       "      <th>arrival_airport</th>\n",
       "      <th>status</th>\n",
       "      <th>aircraft_code</th>\n",
       "      <th>actual_departure</th>\n",
       "      <th>actual_arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1185</td>\n",
       "      <td>PG0134</td>\n",
       "      <td>2017-09-10 09:50:00+03</td>\n",
       "      <td>2017-09-10 14:55:00+03</td>\n",
       "      <td>DME</td>\n",
       "      <td>BTK</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>319</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3979</td>\n",
       "      <td>PG0052</td>\n",
       "      <td>2017-08-25 14:50:00+03</td>\n",
       "      <td>2017-08-25 17:35:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>HMA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4739</td>\n",
       "      <td>PG0561</td>\n",
       "      <td>2017-09-05 12:30:00+03</td>\n",
       "      <td>2017-09-05 14:15:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>AER</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5502</td>\n",
       "      <td>PG0529</td>\n",
       "      <td>2017-09-12 09:50:00+03</td>\n",
       "      <td>2017-09-12 11:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>UFA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6938</td>\n",
       "      <td>PG0461</td>\n",
       "      <td>2017-09-04 12:25:00+03</td>\n",
       "      <td>2017-09-04 13:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>ULV</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>SU9</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33116</th>\n",
       "      <td>33117</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-08-02 19:25:00+03</td>\n",
       "      <td>2017-08-02 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Arrived</td>\n",
       "      <td>CR2</td>\n",
       "      <td>2017-08-02 19:25:00+03</td>\n",
       "      <td>2017-08-02 20:10:00+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33117</th>\n",
       "      <td>33118</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-07-28 19:25:00+03</td>\n",
       "      <td>2017-07-28 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Arrived</td>\n",
       "      <td>CR2</td>\n",
       "      <td>2017-07-28 19:30:00+03</td>\n",
       "      <td>2017-07-28 20:15:00+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33118</th>\n",
       "      <td>33119</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-09-08 19:25:00+03</td>\n",
       "      <td>2017-09-08 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33119</th>\n",
       "      <td>33120</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-08-01 19:25:00+03</td>\n",
       "      <td>2017-08-01 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Arrived</td>\n",
       "      <td>CR2</td>\n",
       "      <td>2017-08-01 19:26:00+03</td>\n",
       "      <td>2017-08-01 20:12:00+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33120</th>\n",
       "      <td>33121</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-08-26 19:25:00+03</td>\n",
       "      <td>2017-08-26 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33121 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       flight_id flight_no     scheduled_departure       scheduled_arrival  \\\n",
       "0           1185    PG0134  2017-09-10 09:50:00+03  2017-09-10 14:55:00+03   \n",
       "1           3979    PG0052  2017-08-25 14:50:00+03  2017-08-25 17:35:00+03   \n",
       "2           4739    PG0561  2017-09-05 12:30:00+03  2017-09-05 14:15:00+03   \n",
       "3           5502    PG0529  2017-09-12 09:50:00+03  2017-09-12 11:20:00+03   \n",
       "4           6938    PG0461  2017-09-04 12:25:00+03  2017-09-04 13:20:00+03   \n",
       "...          ...       ...                     ...                     ...   \n",
       "33116      33117    PG0063  2017-08-02 19:25:00+03  2017-08-02 20:10:00+03   \n",
       "33117      33118    PG0063  2017-07-28 19:25:00+03  2017-07-28 20:10:00+03   \n",
       "33118      33119    PG0063  2017-09-08 19:25:00+03  2017-09-08 20:10:00+03   \n",
       "33119      33120    PG0063  2017-08-01 19:25:00+03  2017-08-01 20:10:00+03   \n",
       "33120      33121    PG0063  2017-08-26 19:25:00+03  2017-08-26 20:10:00+03   \n",
       "\n",
       "      departure_airport arrival_airport     status aircraft_code  \\\n",
       "0                   DME             BTK  Scheduled           319   \n",
       "1                   VKO             HMA  Scheduled           CR2   \n",
       "2                   VKO             AER  Scheduled           763   \n",
       "3                   SVO             UFA  Scheduled           763   \n",
       "4                   SVO             ULV  Scheduled           SU9   \n",
       "...                 ...             ...        ...           ...   \n",
       "33116               SKX             SVO    Arrived           CR2   \n",
       "33117               SKX             SVO    Arrived           CR2   \n",
       "33118               SKX             SVO  Scheduled           CR2   \n",
       "33119               SKX             SVO    Arrived           CR2   \n",
       "33120               SKX             SVO  Scheduled           CR2   \n",
       "\n",
       "             actual_departure          actual_arrival  \n",
       "0                          \\N                      \\N  \n",
       "1                          \\N                      \\N  \n",
       "2                          \\N                      \\N  \n",
       "3                          \\N                      \\N  \n",
       "4                          \\N                      \\N  \n",
       "...                       ...                     ...  \n",
       "33116  2017-08-02 19:25:00+03  2017-08-02 20:10:00+03  \n",
       "33117  2017-07-28 19:30:00+03  2017-07-28 20:15:00+03  \n",
       "33118                      \\N                      \\N  \n",
       "33119  2017-08-01 19:26:00+03  2017-08-01 20:12:00+03  \n",
       "33120                      \\N                      \\N  \n",
       "\n",
       "[33121 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights = pd.read_sql_query(\"select * from flights\", connection)\n",
    "flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>aircraft_code</th>\n",
       "      <th>seat_no</th>\n",
       "      <th>fare_conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>2A</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>319</td>\n",
       "      <td>2C</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319</td>\n",
       "      <td>2D</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>319</td>\n",
       "      <td>2F</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>319</td>\n",
       "      <td>3A</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>773</td>\n",
       "      <td>48H</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>773</td>\n",
       "      <td>48K</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>773</td>\n",
       "      <td>49A</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>773</td>\n",
       "      <td>49C</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>773</td>\n",
       "      <td>49D</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1339 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     aircraft_code seat_no fare_conditions\n",
       "0              319      2A        Business\n",
       "1              319      2C        Business\n",
       "2              319      2D        Business\n",
       "3              319      2F        Business\n",
       "4              319      3A        Business\n",
       "...            ...     ...             ...\n",
       "1334           773     48H         Economy\n",
       "1335           773     48K         Economy\n",
       "1336           773     49A         Economy\n",
       "1337           773     49C         Economy\n",
       "1338           773     49D         Economy\n",
       "\n",
       "[1339 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seats = pd.read_sql_query(\"select * from seats\", connection)\n",
    "seats"
   ]
  },
  {
   "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>ticket_no</th>\n",
       "      <th>flight_id</th>\n",
       "      <th>fare_conditions</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005432159776</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005435212351</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005435212386</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005435212381</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005432211370</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045721</th>\n",
       "      <td>0005435097522</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045722</th>\n",
       "      <td>0005435097521</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045723</th>\n",
       "      <td>0005435104384</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045724</th>\n",
       "      <td>0005435104352</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045725</th>\n",
       "      <td>0005435104389</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1045726 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ticket_no  flight_id fare_conditions  amount\n",
       "0        0005432159776      30625        Business   42100\n",
       "1        0005435212351      30625        Business   42100\n",
       "2        0005435212386      30625        Business   42100\n",
       "3        0005435212381      30625        Business   42100\n",
       "4        0005432211370      30625        Business   42100\n",
       "...                ...        ...             ...     ...\n",
       "1045721  0005435097522      32094         Economy    5200\n",
       "1045722  0005435097521      32094         Economy    5200\n",
       "1045723  0005435104384      32094         Economy    5200\n",
       "1045724  0005435104352      32094         Economy    5200\n",
       "1045725  0005435104389      32094         Economy    5200\n",
       "\n",
       "[1045726 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_flights = pd.read_sql_query(\"select * from ticket_flights\", connection)\n",
    "ticket_flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>ticket_no</th>\n",
       "      <th>book_ref</th>\n",
       "      <th>passenger_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005432000987</td>\n",
       "      <td>06B046</td>\n",
       "      <td>8149 604011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005432000988</td>\n",
       "      <td>06B046</td>\n",
       "      <td>8499 420203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005432000989</td>\n",
       "      <td>E170C3</td>\n",
       "      <td>1011 752484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005432000990</td>\n",
       "      <td>E170C3</td>\n",
       "      <td>4849 400049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005432000991</td>\n",
       "      <td>F313DD</td>\n",
       "      <td>6615 976589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366728</th>\n",
       "      <td>0005435999869</td>\n",
       "      <td>D730BA</td>\n",
       "      <td>0474 690760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366729</th>\n",
       "      <td>0005435999870</td>\n",
       "      <td>D730BA</td>\n",
       "      <td>6535 751108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366730</th>\n",
       "      <td>0005435999871</td>\n",
       "      <td>A1AD46</td>\n",
       "      <td>1596 156448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366731</th>\n",
       "      <td>0005435999872</td>\n",
       "      <td>7B6A53</td>\n",
       "      <td>9374 822707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366732</th>\n",
       "      <td>0005435999873</td>\n",
       "      <td>7B6A53</td>\n",
       "      <td>7380 075822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366733 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ticket_no book_ref passenger_id\n",
       "0       0005432000987   06B046  8149 604011\n",
       "1       0005432000988   06B046  8499 420203\n",
       "2       0005432000989   E170C3  1011 752484\n",
       "3       0005432000990   E170C3  4849 400049\n",
       "4       0005432000991   F313DD  6615 976589\n",
       "...               ...      ...          ...\n",
       "366728  0005435999869   D730BA  0474 690760\n",
       "366729  0005435999870   D730BA  6535 751108\n",
       "366730  0005435999871   A1AD46  1596 156448\n",
       "366731  0005435999872   7B6A53  9374 822707\n",
       "366732  0005435999873   7B6A53  7380 075822\n",
       "\n",
       "[366733 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets = pd.read_sql_query(\"select * from tickets\", connection)\n",
    "tickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Table: aircrafts_data\n",
      "('aircraft_code', 'character(3)')\n",
      "('model', 'jsonb')\n",
      "('range', 'INTEGER')\n",
      "\n",
      "Table: airports_data\n",
      "('airport_code', 'character(3)')\n",
      "('airport_name', 'jsonb')\n",
      "('city', 'jsonb')\n",
      "('coordinates', 'point')\n",
      "('timezone', 'TEXT')\n",
      "\n",
      "Table: boarding_passes\n",
      "('ticket_no', 'character(13)')\n",
      "('flight_id', 'INTEGER')\n",
      "('boarding_no', 'INTEGER')\n",
      "('seat_no', 'character varying(4)')\n",
      "\n",
      "Table: bookings\n",
      "('book_ref', 'character(6)')\n",
      "('book_date', 'timestamp with time zone')\n",
      "('total_amount', 'numeric(10,2)')\n",
      "\n",
      "Table: flights\n",
      "('flight_id', 'INTEGER')\n",
      "('flight_no', 'character(6)')\n",
      "('scheduled_departure', 'timestamp with time zone')\n",
      "('scheduled_arrival', 'timestamp with time zone')\n",
      "('departure_airport', 'character(3)')\n",
      "('arrival_airport', 'character(3)')\n",
      "('status', 'character varying(20)')\n",
      "('aircraft_code', 'character(3)')\n",
      "('actual_departure', 'timestamp with time zone')\n",
      "('actual_arrival', 'timestamp with time zone')\n",
      "\n",
      "Table: seats\n",
      "('aircraft_code', 'character(3)')\n",
      "('seat_no', 'character varying(4)')\n",
      "('fare_conditions', 'character varying(10)')\n",
      "\n",
      "Table: ticket_flights\n",
      "('ticket_no', 'character(13)')\n",
      "('flight_id', 'INTEGER')\n",
      "('fare_conditions', 'character varying(10)')\n",
      "('amount', 'numeric(10,2)')\n",
      "\n",
      "Table: tickets\n",
      "('ticket_no', 'character(13)')\n",
      "('book_ref', 'character(6)')\n",
      "('passenger_id', 'character varying(20)')\n"
     ]
    }
   ],
   "source": [
    "for table in table_list:\n",
    "    print(f\"\\nTable: {table}\")\n",
    "    column_info = connection.execute('PRAGMA table_info({})'.format(table))\n",
    "    for column in column_info.fetchall():\n",
    "        print(column[1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aircraft_code    0\n",
      "model            0\n",
      "range            0\n",
      "dtype: int64 \n",
      "\n",
      "airport_code    0\n",
      "airport_name    0\n",
      "city            0\n",
      "coordinates     0\n",
      "timezone        0\n",
      "dtype: int64 \n",
      "\n",
      "ticket_no      0\n",
      "flight_id      0\n",
      "boarding_no    0\n",
      "seat_no        0\n",
      "dtype: int64 \n",
      "\n",
      "book_ref        0\n",
      "book_date       0\n",
      "total_amount    0\n",
      "dtype: int64 \n",
      "\n",
      "flight_id              0\n",
      "flight_no              0\n",
      "scheduled_departure    0\n",
      "scheduled_arrival      0\n",
      "departure_airport      0\n",
      "arrival_airport        0\n",
      "status                 0\n",
      "aircraft_code          0\n",
      "actual_departure       0\n",
      "actual_arrival         0\n",
      "dtype: int64 \n",
      "\n",
      "aircraft_code      0\n",
      "seat_no            0\n",
      "fare_conditions    0\n",
      "dtype: int64 \n",
      "\n",
      "ticket_no          0\n",
      "flight_id          0\n",
      "fare_conditions    0\n",
      "amount             0\n",
      "dtype: int64 \n",
      "\n",
      "ticket_no       0\n",
      "book_ref        0\n",
      "passenger_id    0\n",
      "dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for table in table_list:\n",
    "    table_df = pd.read_sql_query(f\"select * from {table}\", connection)\n",
    "    print(table_df.isnull().sum(), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q: *How many planes have more than 100 seats?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>aircraft_code</th>\n",
       "      <th>seat_no</th>\n",
       "      <th>fare_conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>2A</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>319</td>\n",
       "      <td>2C</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319</td>\n",
       "      <td>2D</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>319</td>\n",
       "      <td>2F</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>319</td>\n",
       "      <td>3A</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>773</td>\n",
       "      <td>48H</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>773</td>\n",
       "      <td>48K</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>773</td>\n",
       "      <td>49A</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>773</td>\n",
       "      <td>49C</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>773</td>\n",
       "      <td>49D</td>\n",
       "      <td>Economy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1339 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     aircraft_code seat_no fare_conditions\n",
       "0              319      2A        Business\n",
       "1              319      2C        Business\n",
       "2              319      2D        Business\n",
       "3              319      2F        Business\n",
       "4              319      3A        Business\n",
       "...            ...     ...             ...\n",
       "1334           773     48H         Economy\n",
       "1335           773     48K         Economy\n",
       "1336           773     49A         Economy\n",
       "1337           773     49C         Economy\n",
       "1338           773     49D         Economy\n",
       "\n",
       "[1339 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>aircraft_code</th>\n",
       "      <th>num_seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>320</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>733</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>763</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>773</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  num_seats\n",
       "0           319        116\n",
       "1           320        140\n",
       "2           321        170\n",
       "3           733        130\n",
       "4           763        222\n",
       "5           773        402"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('''select aircraft_code, count(*) as num_seats from seats\n",
    "                    group by aircraft_code having num_seats > 100 \n",
    "                    ''', connection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A: *6 Planes have more than 100 seats*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q: *How the number of tickers booked and the amount earned changed with time?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Since tickets table contain all the information whereas in booking one person can book many tickets therefore will need both tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickets_and_bookings = pd.read_sql_query(''' \n",
    "                    select * from tickets \n",
    "                    join bookings \n",
    "                    on tickets.book_ref = bookings.book_ref\n",
    "                    order by book_date asc\n",
    "                    ''', connection)"
   ]
  },
  {
   "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>ticket_no</th>\n",
       "      <th>book_ref</th>\n",
       "      <th>passenger_id</th>\n",
       "      <th>book_ref</th>\n",
       "      <th>book_date</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005432628587</td>\n",
       "      <td>61EAC7</td>\n",
       "      <td>0093 271823</td>\n",
       "      <td>61EAC7</td>\n",
       "      <td>2017-06-21 14:05:00+03:00</td>\n",
       "      <td>52000</td>\n",
       "      <td>2017-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005432628588</td>\n",
       "      <td>61EAC7</td>\n",
       "      <td>1700 690956</td>\n",
       "      <td>61EAC7</td>\n",
       "      <td>2017-06-21 14:05:00+03:00</td>\n",
       "      <td>52000</td>\n",
       "      <td>2017-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005434876589</td>\n",
       "      <td>98A065</td>\n",
       "      <td>2496 629437</td>\n",
       "      <td>98A065</td>\n",
       "      <td>2017-06-21 15:48:00+03:00</td>\n",
       "      <td>211100</td>\n",
       "      <td>2017-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005434876590</td>\n",
       "      <td>98A065</td>\n",
       "      <td>4813 255607</td>\n",
       "      <td>98A065</td>\n",
       "      <td>2017-06-21 15:48:00+03:00</td>\n",
       "      <td>211100</td>\n",
       "      <td>2017-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005434876591</td>\n",
       "      <td>98A065</td>\n",
       "      <td>0849 378091</td>\n",
       "      <td>98A065</td>\n",
       "      <td>2017-06-21 15:48:00+03:00</td>\n",
       "      <td>211100</td>\n",
       "      <td>2017-06-21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366728</th>\n",
       "      <td>0005433193428</td>\n",
       "      <td>12165C</td>\n",
       "      <td>4912 642478</td>\n",
       "      <td>12165C</td>\n",
       "      <td>2017-08-15 18:00:00+03:00</td>\n",
       "      <td>187800</td>\n",
       "      <td>2017-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366729</th>\n",
       "      <td>0005433193429</td>\n",
       "      <td>12165C</td>\n",
       "      <td>8571 520185</td>\n",
       "      <td>12165C</td>\n",
       "      <td>2017-08-15 18:00:00+03:00</td>\n",
       "      <td>187800</td>\n",
       "      <td>2017-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366730</th>\n",
       "      <td>0005433717429</td>\n",
       "      <td>2AFB36</td>\n",
       "      <td>8107 392972</td>\n",
       "      <td>2AFB36</td>\n",
       "      <td>2017-08-15 18:00:00+03:00</td>\n",
       "      <td>21400</td>\n",
       "      <td>2017-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366731</th>\n",
       "      <td>0005434025524</td>\n",
       "      <td>21E611</td>\n",
       "      <td>8877 169201</td>\n",
       "      <td>21E611</td>\n",
       "      <td>2017-08-15 18:00:00+03:00</td>\n",
       "      <td>16400</td>\n",
       "      <td>2017-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366732</th>\n",
       "      <td>0005434640028</td>\n",
       "      <td>53C497</td>\n",
       "      <td>0350 227964</td>\n",
       "      <td>53C497</td>\n",
       "      <td>2017-08-15 18:00:00+03:00</td>\n",
       "      <td>67600</td>\n",
       "      <td>2017-08-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366733 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ticket_no book_ref passenger_id book_ref  \\\n",
       "0       0005432628587   61EAC7  0093 271823   61EAC7   \n",
       "1       0005432628588   61EAC7  1700 690956   61EAC7   \n",
       "2       0005434876589   98A065  2496 629437   98A065   \n",
       "3       0005434876590   98A065  4813 255607   98A065   \n",
       "4       0005434876591   98A065  0849 378091   98A065   \n",
       "...               ...      ...          ...      ...   \n",
       "366728  0005433193428   12165C  4912 642478   12165C   \n",
       "366729  0005433193429   12165C  8571 520185   12165C   \n",
       "366730  0005433717429   2AFB36  8107 392972   2AFB36   \n",
       "366731  0005434025524   21E611  8877 169201   21E611   \n",
       "366732  0005434640028   53C497  0350 227964   53C497   \n",
       "\n",
       "                       book_date  total_amount        date  \n",
       "0      2017-06-21 14:05:00+03:00         52000  2017-06-21  \n",
       "1      2017-06-21 14:05:00+03:00         52000  2017-06-21  \n",
       "2      2017-06-21 15:48:00+03:00        211100  2017-06-21  \n",
       "3      2017-06-21 15:48:00+03:00        211100  2017-06-21  \n",
       "4      2017-06-21 15:48:00+03:00        211100  2017-06-21  \n",
       "...                          ...           ...         ...  \n",
       "366728 2017-08-15 18:00:00+03:00        187800  2017-08-15  \n",
       "366729 2017-08-15 18:00:00+03:00        187800  2017-08-15  \n",
       "366730 2017-08-15 18:00:00+03:00         21400  2017-08-15  \n",
       "366731 2017-08-15 18:00:00+03:00         16400  2017-08-15  \n",
       "366732 2017-08-15 18:00:00+03:00         67600  2017-08-15  \n",
       "\n",
       "[366733 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets_and_bookings[\"book_date\"] = pd.to_datetime(tickets_and_bookings[\"book_date\"])\n",
    "tickets_and_bookings[\"date\"] = tickets_and_bookings[\"book_date\"].dt.date\n",
    "tickets_and_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = tickets_and_bookings.groupby(\"date\")[[\"date\"]].count()\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(x.index, x[\"date\"], marker=\"p\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Tickets\")\n",
    "plt.grid('b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bookings[\"book_date\"] = pd.to_datetime(bookings[\"book_date\"])\n",
    "bookings[\"date\"] = bookings[\"book_date\"].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-21</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-23</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-24</th>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-25</th>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-26</th>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-27</th>\n",
       "      <td>1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-28</th>\n",
       "      <td>1678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-29</th>\n",
       "      <td>2765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>3772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date\n",
       "date            \n",
       "2017-06-21     6\n",
       "2017-06-22    12\n",
       "2017-06-23    28\n",
       "2017-06-24   106\n",
       "2017-06-25   266\n",
       "2017-06-26   499\n",
       "2017-06-27  1028\n",
       "2017-06-28  1678\n",
       "2017-06-29  2765\n",
       "2017-06-30  3772"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = bookings.groupby(\"date\")[\"total_amount\"].sum().reset_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>date</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-21</td>\n",
       "      <td>441900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-22</td>\n",
       "      <td>775300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>1822000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-24</td>\n",
       "      <td>5977000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-25</td>\n",
       "      <td>15305400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  total_amount\n",
       "0  2017-06-21        441900\n",
       "1  2017-06-22        775300\n",
       "2  2017-06-23       1822000\n",
       "3  2017-06-24       5977000\n",
       "4  2017-06-25      15305400"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(x[\"date\"], x[\"total_amount\"], marker=\"p\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Total Amount\")\n",
    "plt.grid('b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q: *Average charges of each aircraft with diifferent fare conditions*"
   ]
  },
  {
   "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>flight_id</th>\n",
       "      <th>flight_no</th>\n",
       "      <th>scheduled_departure</th>\n",
       "      <th>scheduled_arrival</th>\n",
       "      <th>departure_airport</th>\n",
       "      <th>arrival_airport</th>\n",
       "      <th>status</th>\n",
       "      <th>aircraft_code</th>\n",
       "      <th>actual_departure</th>\n",
       "      <th>actual_arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1185</td>\n",
       "      <td>PG0134</td>\n",
       "      <td>2017-09-10 09:50:00+03</td>\n",
       "      <td>2017-09-10 14:55:00+03</td>\n",
       "      <td>DME</td>\n",
       "      <td>BTK</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>319</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3979</td>\n",
       "      <td>PG0052</td>\n",
       "      <td>2017-08-25 14:50:00+03</td>\n",
       "      <td>2017-08-25 17:35:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>HMA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4739</td>\n",
       "      <td>PG0561</td>\n",
       "      <td>2017-09-05 12:30:00+03</td>\n",
       "      <td>2017-09-05 14:15:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>AER</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5502</td>\n",
       "      <td>PG0529</td>\n",
       "      <td>2017-09-12 09:50:00+03</td>\n",
       "      <td>2017-09-12 11:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>UFA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6938</td>\n",
       "      <td>PG0461</td>\n",
       "      <td>2017-09-04 12:25:00+03</td>\n",
       "      <td>2017-09-04 13:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>ULV</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>SU9</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33116</th>\n",
       "      <td>33117</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-08-02 19:25:00+03</td>\n",
       "      <td>2017-08-02 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Arrived</td>\n",
       "      <td>CR2</td>\n",
       "      <td>2017-08-02 19:25:00+03</td>\n",
       "      <td>2017-08-02 20:10:00+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33117</th>\n",
       "      <td>33118</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-07-28 19:25:00+03</td>\n",
       "      <td>2017-07-28 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Arrived</td>\n",
       "      <td>CR2</td>\n",
       "      <td>2017-07-28 19:30:00+03</td>\n",
       "      <td>2017-07-28 20:15:00+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33118</th>\n",
       "      <td>33119</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-09-08 19:25:00+03</td>\n",
       "      <td>2017-09-08 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33119</th>\n",
       "      <td>33120</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-08-01 19:25:00+03</td>\n",
       "      <td>2017-08-01 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Arrived</td>\n",
       "      <td>CR2</td>\n",
       "      <td>2017-08-01 19:26:00+03</td>\n",
       "      <td>2017-08-01 20:12:00+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33120</th>\n",
       "      <td>33121</td>\n",
       "      <td>PG0063</td>\n",
       "      <td>2017-08-26 19:25:00+03</td>\n",
       "      <td>2017-08-26 20:10:00+03</td>\n",
       "      <td>SKX</td>\n",
       "      <td>SVO</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33121 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       flight_id flight_no     scheduled_departure       scheduled_arrival  \\\n",
       "0           1185    PG0134  2017-09-10 09:50:00+03  2017-09-10 14:55:00+03   \n",
       "1           3979    PG0052  2017-08-25 14:50:00+03  2017-08-25 17:35:00+03   \n",
       "2           4739    PG0561  2017-09-05 12:30:00+03  2017-09-05 14:15:00+03   \n",
       "3           5502    PG0529  2017-09-12 09:50:00+03  2017-09-12 11:20:00+03   \n",
       "4           6938    PG0461  2017-09-04 12:25:00+03  2017-09-04 13:20:00+03   \n",
       "...          ...       ...                     ...                     ...   \n",
       "33116      33117    PG0063  2017-08-02 19:25:00+03  2017-08-02 20:10:00+03   \n",
       "33117      33118    PG0063  2017-07-28 19:25:00+03  2017-07-28 20:10:00+03   \n",
       "33118      33119    PG0063  2017-09-08 19:25:00+03  2017-09-08 20:10:00+03   \n",
       "33119      33120    PG0063  2017-08-01 19:25:00+03  2017-08-01 20:10:00+03   \n",
       "33120      33121    PG0063  2017-08-26 19:25:00+03  2017-08-26 20:10:00+03   \n",
       "\n",
       "      departure_airport arrival_airport     status aircraft_code  \\\n",
       "0                   DME             BTK  Scheduled           319   \n",
       "1                   VKO             HMA  Scheduled           CR2   \n",
       "2                   VKO             AER  Scheduled           763   \n",
       "3                   SVO             UFA  Scheduled           763   \n",
       "4                   SVO             ULV  Scheduled           SU9   \n",
       "...                 ...             ...        ...           ...   \n",
       "33116               SKX             SVO    Arrived           CR2   \n",
       "33117               SKX             SVO    Arrived           CR2   \n",
       "33118               SKX             SVO  Scheduled           CR2   \n",
       "33119               SKX             SVO    Arrived           CR2   \n",
       "33120               SKX             SVO  Scheduled           CR2   \n",
       "\n",
       "             actual_departure          actual_arrival  \n",
       "0                          \\N                      \\N  \n",
       "1                          \\N                      \\N  \n",
       "2                          \\N                      \\N  \n",
       "3                          \\N                      \\N  \n",
       "4                          \\N                      \\N  \n",
       "...                       ...                     ...  \n",
       "33116  2017-08-02 19:25:00+03  2017-08-02 20:10:00+03  \n",
       "33117  2017-07-28 19:30:00+03  2017-07-28 20:15:00+03  \n",
       "33118                      \\N                      \\N  \n",
       "33119  2017-08-01 19:26:00+03  2017-08-01 20:12:00+03  \n",
       "33120                      \\N                      \\N  \n",
       "\n",
       "[33121 rows x 10 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights"
   ]
  },
  {
   "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>ticket_no</th>\n",
       "      <th>flight_id</th>\n",
       "      <th>fare_conditions</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005432159776</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005435212351</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005435212386</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005435212381</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005432211370</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045721</th>\n",
       "      <td>0005435097522</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045722</th>\n",
       "      <td>0005435097521</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045723</th>\n",
       "      <td>0005435104384</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045724</th>\n",
       "      <td>0005435104352</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045725</th>\n",
       "      <td>0005435104389</td>\n",
       "      <td>32094</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1045726 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ticket_no  flight_id fare_conditions  amount\n",
       "0        0005432159776      30625        Business   42100\n",
       "1        0005435212351      30625        Business   42100\n",
       "2        0005435212386      30625        Business   42100\n",
       "3        0005435212381      30625        Business   42100\n",
       "4        0005432211370      30625        Business   42100\n",
       "...                ...        ...             ...     ...\n",
       "1045721  0005435097522      32094         Economy    5200\n",
       "1045722  0005435097521      32094         Economy    5200\n",
       "1045723  0005435104384      32094         Economy    5200\n",
       "1045724  0005435104352      32094         Economy    5200\n",
       "1045725  0005435104389      32094         Economy    5200\n",
       "\n",
       "[1045726 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_and_ticket = pd.read_sql_query(''' \n",
    "select aircraft_code, fare_conditions, avg(amount)\n",
    "from ticket_flights \n",
    "join flights \n",
    "on flights.flight_id = ticket_flights.flight_id\n",
    "group by aircraft_code, fare_conditions\n",
    "''', connection)"
   ]
  },
  {
   "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>aircraft_code</th>\n",
       "      <th>fare_conditions</th>\n",
       "      <th>avg(amount)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>Business</td>\n",
       "      <td>113550.557703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>319</td>\n",
       "      <td>Economy</td>\n",
       "      <td>38311.402347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321</td>\n",
       "      <td>Business</td>\n",
       "      <td>34435.662664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>321</td>\n",
       "      <td>Economy</td>\n",
       "      <td>11534.974764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>733</td>\n",
       "      <td>Business</td>\n",
       "      <td>41865.626175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>733</td>\n",
       "      <td>Economy</td>\n",
       "      <td>13985.152000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>763</td>\n",
       "      <td>Business</td>\n",
       "      <td>82839.842866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>763</td>\n",
       "      <td>Economy</td>\n",
       "      <td>27594.721829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>773</td>\n",
       "      <td>Business</td>\n",
       "      <td>57779.909435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>773</td>\n",
       "      <td>Comfort</td>\n",
       "      <td>32740.552889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>773</td>\n",
       "      <td>Economy</td>\n",
       "      <td>19265.225693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CN1</td>\n",
       "      <td>Economy</td>\n",
       "      <td>6568.552345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CR2</td>\n",
       "      <td>Economy</td>\n",
       "      <td>13207.661102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SU9</td>\n",
       "      <td>Business</td>\n",
       "      <td>33487.849829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SU9</td>\n",
       "      <td>Economy</td>\n",
       "      <td>11220.183400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aircraft_code fare_conditions    avg(amount)\n",
       "0            319        Business  113550.557703\n",
       "1            319         Economy   38311.402347\n",
       "2            321        Business   34435.662664\n",
       "3            321         Economy   11534.974764\n",
       "4            733        Business   41865.626175\n",
       "5            733         Economy   13985.152000\n",
       "6            763        Business   82839.842866\n",
       "7            763         Economy   27594.721829\n",
       "8            773        Business   57779.909435\n",
       "9            773         Comfort   32740.552889\n",
       "10           773         Economy   19265.225693\n",
       "11           CN1         Economy    6568.552345\n",
       "12           CR2         Economy   13207.661102\n",
       "13           SU9        Business   33487.849829\n",
       "14           SU9         Economy   11220.183400"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_and_ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='aircraft_code', ylabel='avg(amount)'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=flights_and_ticket, x='aircraft_code', y='avg(amount)', hue='fare_conditions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing occupancy rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q: *For each aircraft calculate the total revenue per year and average revenue per ticket*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>ticket_no</th>\n",
       "      <th>flight_id</th>\n",
       "      <th>fare_conditions</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005432159776</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005435212351</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005435212386</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005435212381</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005432211370</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ticket_no  flight_id fare_conditions  amount\n",
       "0  0005432159776      30625        Business   42100\n",
       "1  0005435212351      30625        Business   42100\n",
       "2  0005435212386      30625        Business   42100\n",
       "3  0005435212381      30625        Business   42100\n",
       "4  0005432211370      30625        Business   42100"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_flights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>flight_id</th>\n",
       "      <th>flight_no</th>\n",
       "      <th>scheduled_departure</th>\n",
       "      <th>scheduled_arrival</th>\n",
       "      <th>departure_airport</th>\n",
       "      <th>arrival_airport</th>\n",
       "      <th>status</th>\n",
       "      <th>aircraft_code</th>\n",
       "      <th>actual_departure</th>\n",
       "      <th>actual_arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1185</td>\n",
       "      <td>PG0134</td>\n",
       "      <td>2017-09-10 09:50:00+03</td>\n",
       "      <td>2017-09-10 14:55:00+03</td>\n",
       "      <td>DME</td>\n",
       "      <td>BTK</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>319</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3979</td>\n",
       "      <td>PG0052</td>\n",
       "      <td>2017-08-25 14:50:00+03</td>\n",
       "      <td>2017-08-25 17:35:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>HMA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4739</td>\n",
       "      <td>PG0561</td>\n",
       "      <td>2017-09-05 12:30:00+03</td>\n",
       "      <td>2017-09-05 14:15:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>AER</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5502</td>\n",
       "      <td>PG0529</td>\n",
       "      <td>2017-09-12 09:50:00+03</td>\n",
       "      <td>2017-09-12 11:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>UFA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6938</td>\n",
       "      <td>PG0461</td>\n",
       "      <td>2017-09-04 12:25:00+03</td>\n",
       "      <td>2017-09-04 13:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>ULV</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>SU9</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   flight_id flight_no     scheduled_departure       scheduled_arrival  \\\n",
       "0       1185    PG0134  2017-09-10 09:50:00+03  2017-09-10 14:55:00+03   \n",
       "1       3979    PG0052  2017-08-25 14:50:00+03  2017-08-25 17:35:00+03   \n",
       "2       4739    PG0561  2017-09-05 12:30:00+03  2017-09-05 14:15:00+03   \n",
       "3       5502    PG0529  2017-09-12 09:50:00+03  2017-09-12 11:20:00+03   \n",
       "4       6938    PG0461  2017-09-04 12:25:00+03  2017-09-04 13:20:00+03   \n",
       "\n",
       "  departure_airport arrival_airport     status aircraft_code actual_departure  \\\n",
       "0               DME             BTK  Scheduled           319               \\N   \n",
       "1               VKO             HMA  Scheduled           CR2               \\N   \n",
       "2               VKO             AER  Scheduled           763               \\N   \n",
       "3               SVO             UFA  Scheduled           763               \\N   \n",
       "4               SVO             ULV  Scheduled           SU9               \\N   \n",
       "\n",
       "  actual_arrival  \n",
       "0             \\N  \n",
       "1             \\N  \n",
       "2             \\N  \n",
       "3             \\N  \n",
       "4             \\N  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickets_and_flights = pd.read_sql_query('''\n",
    "select aircraft_code, count(*) as ticket_count, sum(amount) as total_revenue, sum(amount)/count(*) as average_revenue_per_ticket from ticket_flights\n",
    "join flights\n",
    "on ticket_flights.flight_id = flights.flight_id\n",
    "group by aircraft_code\n",
    "''', connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>aircraft_code</th>\n",
       "      <th>ticket_count</th>\n",
       "      <th>total_revenue</th>\n",
       "      <th>average_revenue_per_ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>52853</td>\n",
       "      <td>2706163100</td>\n",
       "      <td>51201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321</td>\n",
       "      <td>107129</td>\n",
       "      <td>1638164100</td>\n",
       "      <td>15291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733</td>\n",
       "      <td>86102</td>\n",
       "      <td>1426552100</td>\n",
       "      <td>16568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763</td>\n",
       "      <td>124774</td>\n",
       "      <td>4371277100</td>\n",
       "      <td>35033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773</td>\n",
       "      <td>144376</td>\n",
       "      <td>3431205500</td>\n",
       "      <td>23765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CN1</td>\n",
       "      <td>14672</td>\n",
       "      <td>96373800</td>\n",
       "      <td>6568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CR2</td>\n",
       "      <td>150122</td>\n",
       "      <td>1982760500</td>\n",
       "      <td>13207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SU9</td>\n",
       "      <td>365698</td>\n",
       "      <td>5114484700</td>\n",
       "      <td>13985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  ticket_count  total_revenue  average_revenue_per_ticket\n",
       "0           319         52853     2706163100                       51201\n",
       "1           321        107129     1638164100                       15291\n",
       "2           733         86102     1426552100                       16568\n",
       "3           763        124774     4371277100                       35033\n",
       "4           773        144376     3431205500                       23765\n",
       "5           CN1         14672       96373800                        6568\n",
       "6           CR2        150122     1982760500                       13207\n",
       "7           SU9        365698     5114484700                       13985"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets_and_flights.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q: *Average occupancy per aircraft*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>ticket_no</th>\n",
       "      <th>flight_id</th>\n",
       "      <th>boarding_no</th>\n",
       "      <th>seat_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005435212351</td>\n",
       "      <td>30625</td>\n",
       "      <td>1</td>\n",
       "      <td>2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005435212386</td>\n",
       "      <td>30625</td>\n",
       "      <td>2</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005435212381</td>\n",
       "      <td>30625</td>\n",
       "      <td>3</td>\n",
       "      <td>4H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005432211370</td>\n",
       "      <td>30625</td>\n",
       "      <td>4</td>\n",
       "      <td>5D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005435212357</td>\n",
       "      <td>30625</td>\n",
       "      <td>5</td>\n",
       "      <td>11A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ticket_no  flight_id  boarding_no seat_no\n",
       "0  0005435212351      30625            1      2D\n",
       "1  0005435212386      30625            2      3G\n",
       "2  0005435212381      30625            3      4H\n",
       "3  0005432211370      30625            4      5D\n",
       "4  0005435212357      30625            5     11A"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boarding_passes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>flight_id</th>\n",
       "      <th>flight_no</th>\n",
       "      <th>scheduled_departure</th>\n",
       "      <th>scheduled_arrival</th>\n",
       "      <th>departure_airport</th>\n",
       "      <th>arrival_airport</th>\n",
       "      <th>status</th>\n",
       "      <th>aircraft_code</th>\n",
       "      <th>actual_departure</th>\n",
       "      <th>actual_arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1185</td>\n",
       "      <td>PG0134</td>\n",
       "      <td>2017-09-10 09:50:00+03</td>\n",
       "      <td>2017-09-10 14:55:00+03</td>\n",
       "      <td>DME</td>\n",
       "      <td>BTK</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>319</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3979</td>\n",
       "      <td>PG0052</td>\n",
       "      <td>2017-08-25 14:50:00+03</td>\n",
       "      <td>2017-08-25 17:35:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>HMA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4739</td>\n",
       "      <td>PG0561</td>\n",
       "      <td>2017-09-05 12:30:00+03</td>\n",
       "      <td>2017-09-05 14:15:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>AER</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5502</td>\n",
       "      <td>PG0529</td>\n",
       "      <td>2017-09-12 09:50:00+03</td>\n",
       "      <td>2017-09-12 11:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>UFA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6938</td>\n",
       "      <td>PG0461</td>\n",
       "      <td>2017-09-04 12:25:00+03</td>\n",
       "      <td>2017-09-04 13:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>ULV</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>SU9</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   flight_id flight_no     scheduled_departure       scheduled_arrival  \\\n",
       "0       1185    PG0134  2017-09-10 09:50:00+03  2017-09-10 14:55:00+03   \n",
       "1       3979    PG0052  2017-08-25 14:50:00+03  2017-08-25 17:35:00+03   \n",
       "2       4739    PG0561  2017-09-05 12:30:00+03  2017-09-05 14:15:00+03   \n",
       "3       5502    PG0529  2017-09-12 09:50:00+03  2017-09-12 11:20:00+03   \n",
       "4       6938    PG0461  2017-09-04 12:25:00+03  2017-09-04 13:20:00+03   \n",
       "\n",
       "  departure_airport arrival_airport     status aircraft_code actual_departure  \\\n",
       "0               DME             BTK  Scheduled           319               \\N   \n",
       "1               VKO             HMA  Scheduled           CR2               \\N   \n",
       "2               VKO             AER  Scheduled           763               \\N   \n",
       "3               SVO             UFA  Scheduled           763               \\N   \n",
       "4               SVO             ULV  Scheduled           SU9               \\N   \n",
       "\n",
       "  actual_arrival  \n",
       "0             \\N  \n",
       "1             \\N  \n",
       "2             \\N  \n",
       "3             \\N  \n",
       "4             \\N  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>aircraft_code</th>\n",
       "      <th>flight_id</th>\n",
       "      <th>seat_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>1162</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>319</td>\n",
       "      <td>1166</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319</td>\n",
       "      <td>1167</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>319</td>\n",
       "      <td>1168</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>319</td>\n",
       "      <td>1170</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11513</th>\n",
       "      <td>SU9</td>\n",
       "      <td>32925</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11514</th>\n",
       "      <td>SU9</td>\n",
       "      <td>32928</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11515</th>\n",
       "      <td>SU9</td>\n",
       "      <td>32931</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11516</th>\n",
       "      <td>SU9</td>\n",
       "      <td>32933</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11517</th>\n",
       "      <td>SU9</td>\n",
       "      <td>32937</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11518 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      aircraft_code  flight_id  seat_counts\n",
       "0               319       1162           51\n",
       "1               319       1166           54\n",
       "2               319       1167           57\n",
       "3               319       1168           60\n",
       "4               319       1170           58\n",
       "...             ...        ...          ...\n",
       "11513           SU9      32925           12\n",
       "11514           SU9      32928           25\n",
       "11515           SU9      32931           12\n",
       "11516           SU9      32933           16\n",
       "11517           SU9      32937            6\n",
       "\n",
       "[11518 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('''\n",
    "select aircraft_code, flights.flight_id, count(*) as seat_counts from boarding_passes\n",
    "join flights on\n",
    "boarding_passes.flight_id = flights.flight_id\n",
    "group by aircraft_code, flights.flight_id\n",
    "''', connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>aircraft_code</th>\n",
       "      <th>num_seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>320</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>733</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>763</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>773</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CN1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CR2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SU9</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  num_seats\n",
       "0           319        116\n",
       "1           320        140\n",
       "2           321        170\n",
       "3           733        130\n",
       "4           763        222\n",
       "5           773        402\n",
       "6           CN1         12\n",
       "7           CR2         50\n",
       "8           SU9         97"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(\"select aircraft_code, count(*) as num_seats from seats group by aircraft_code;\", connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging both tables\n",
    "occupancy_rate = pd.read_sql_query('''\n",
    "select a.aircraft_code, avg(a.seat_counts) as booked_seats, b.num_seats, avg(a.seat_counts)/b.num_seats as occupancy_rate from\n",
    "(select aircraft_code, flights.flight_id, count(*) as seat_counts from boarding_passes\n",
    "join flights on\n",
    "boarding_passes.flight_id = flights.flight_id\n",
    "group by aircraft_code, flights.flight_id) \n",
    "as a\n",
    "inner join\n",
    "(select aircraft_code, count(*) as num_seats from seats group by aircraft_code) \n",
    "as b\n",
    "on a.aircraft_code = b.aircraft_code group by a.aircraft_code;\n",
    "''', connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "occupancy_rate['inc_occupancy_rate'] = occupancy_rate['occupancy_rate'] + occupancy_rate['occupancy_rate'] * 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>aircraft_code</th>\n",
       "      <th>booked_seats</th>\n",
       "      <th>num_seats</th>\n",
       "      <th>occupancy_rate</th>\n",
       "      <th>inc_occupancy_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>53.583181</td>\n",
       "      <td>116</td>\n",
       "      <td>0.461924</td>\n",
       "      <td>0.508116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321</td>\n",
       "      <td>88.809231</td>\n",
       "      <td>170</td>\n",
       "      <td>0.522407</td>\n",
       "      <td>0.574648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733</td>\n",
       "      <td>80.255462</td>\n",
       "      <td>130</td>\n",
       "      <td>0.617350</td>\n",
       "      <td>0.679085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763</td>\n",
       "      <td>113.937294</td>\n",
       "      <td>222</td>\n",
       "      <td>0.513231</td>\n",
       "      <td>0.564554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773</td>\n",
       "      <td>264.925806</td>\n",
       "      <td>402</td>\n",
       "      <td>0.659019</td>\n",
       "      <td>0.724921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CN1</td>\n",
       "      <td>6.004431</td>\n",
       "      <td>12</td>\n",
       "      <td>0.500369</td>\n",
       "      <td>0.550406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CR2</td>\n",
       "      <td>21.482847</td>\n",
       "      <td>50</td>\n",
       "      <td>0.429657</td>\n",
       "      <td>0.472623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SU9</td>\n",
       "      <td>56.812113</td>\n",
       "      <td>97</td>\n",
       "      <td>0.585692</td>\n",
       "      <td>0.644261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  booked_seats  num_seats  occupancy_rate  inc_occupancy_rate\n",
       "0           319     53.583181        116        0.461924            0.508116\n",
       "1           321     88.809231        170        0.522407            0.574648\n",
       "2           733     80.255462        130        0.617350            0.679085\n",
       "3           763    113.937294        222        0.513231            0.564554\n",
       "4           773    264.925806        402        0.659019            0.724921\n",
       "5           CN1      6.004431         12        0.500369            0.550406\n",
       "6           CR2     21.482847         50        0.429657            0.472623\n",
       "7           SU9     56.812113         97        0.585692            0.644261"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupancy_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>aircraft_code</th>\n",
       "      <th>total_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>2706163100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321</td>\n",
       "      <td>1638164100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733</td>\n",
       "      <td>1426552100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763</td>\n",
       "      <td>4371277100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773</td>\n",
       "      <td>3431205500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CN1</td>\n",
       "      <td>96373800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CR2</td>\n",
       "      <td>1982760500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SU9</td>\n",
       "      <td>5114484700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  total_revenue\n",
       "0           319     2706163100\n",
       "1           321     1638164100\n",
       "2           733     1426552100\n",
       "3           763     4371277100\n",
       "4           773     3431205500\n",
       "5           CN1       96373800\n",
       "6           CR2     1982760500\n",
       "7           SU9     5114484700"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_revenue = pd.read_sql_query('''\n",
    "SELECT aircraft_code, sum(amount) as total_revenue FROM ticket_flights\n",
    "JOIN flights\n",
    "ON ticket_flights.flight_id = flights.flight_id\n",
    "GROUP BY aircraft_code\n",
    "''', connection)\n",
    "total_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>aircraft_code</th>\n",
       "      <th>booked_seats</th>\n",
       "      <th>num_seats</th>\n",
       "      <th>occupancy_rate</th>\n",
       "      <th>inc_occupancy_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>53.583181</td>\n",
       "      <td>116</td>\n",
       "      <td>0.461924</td>\n",
       "      <td>0.508116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321</td>\n",
       "      <td>88.809231</td>\n",
       "      <td>170</td>\n",
       "      <td>0.522407</td>\n",
       "      <td>0.574648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733</td>\n",
       "      <td>80.255462</td>\n",
       "      <td>130</td>\n",
       "      <td>0.617350</td>\n",
       "      <td>0.679085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763</td>\n",
       "      <td>113.937294</td>\n",
       "      <td>222</td>\n",
       "      <td>0.513231</td>\n",
       "      <td>0.564554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773</td>\n",
       "      <td>264.925806</td>\n",
       "      <td>402</td>\n",
       "      <td>0.659019</td>\n",
       "      <td>0.724921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CN1</td>\n",
       "      <td>6.004431</td>\n",
       "      <td>12</td>\n",
       "      <td>0.500369</td>\n",
       "      <td>0.550406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CR2</td>\n",
       "      <td>21.482847</td>\n",
       "      <td>50</td>\n",
       "      <td>0.429657</td>\n",
       "      <td>0.472623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SU9</td>\n",
       "      <td>56.812113</td>\n",
       "      <td>97</td>\n",
       "      <td>0.585692</td>\n",
       "      <td>0.644261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  booked_seats  num_seats  occupancy_rate  inc_occupancy_rate\n",
       "0           319     53.583181        116        0.461924            0.508116\n",
       "1           321     88.809231        170        0.522407            0.574648\n",
       "2           733     80.255462        130        0.617350            0.679085\n",
       "3           763    113.937294        222        0.513231            0.564554\n",
       "4           773    264.925806        402        0.659019            0.724921\n",
       "5           CN1      6.004431         12        0.500369            0.550406\n",
       "6           CR2     21.482847         50        0.429657            0.472623\n",
       "7           SU9     56.812113         97        0.585692            0.644261"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupancy_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "occupancy_rate[\"inc_total_annual_turnover\"] = (total_revenue[\"total_revenue\"]/occupancy_rate[\"occupancy_rate\"]) * occupancy_rate[\"inc_occupancy_rate\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.float_format\",str)"
   ]
  },
  {
   "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>aircraft_code</th>\n",
       "      <th>booked_seats</th>\n",
       "      <th>num_seats</th>\n",
       "      <th>occupancy_rate</th>\n",
       "      <th>inc_occupancy_rate</th>\n",
       "      <th>inc_total_annual_turnover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>53.58318098720292</td>\n",
       "      <td>116</td>\n",
       "      <td>0.46192397402761143</td>\n",
       "      <td>0.5081163714303726</td>\n",
       "      <td>2976779410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321</td>\n",
       "      <td>88.80923076923077</td>\n",
       "      <td>170</td>\n",
       "      <td>0.5224072398190045</td>\n",
       "      <td>0.574647963800905</td>\n",
       "      <td>1801980510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733</td>\n",
       "      <td>80.25546218487395</td>\n",
       "      <td>130</td>\n",
       "      <td>0.617349709114415</td>\n",
       "      <td>0.6790846800258565</td>\n",
       "      <td>1569207310.0000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763</td>\n",
       "      <td>113.93729372937294</td>\n",
       "      <td>222</td>\n",
       "      <td>0.5132310528350132</td>\n",
       "      <td>0.5645541581185146</td>\n",
       "      <td>4808404810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773</td>\n",
       "      <td>264.9258064516129</td>\n",
       "      <td>402</td>\n",
       "      <td>0.659019419033863</td>\n",
       "      <td>0.7249213609372492</td>\n",
       "      <td>3774326050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CN1</td>\n",
       "      <td>6.004431314623338</td>\n",
       "      <td>12</td>\n",
       "      <td>0.5003692762186115</td>\n",
       "      <td>0.5504062038404727</td>\n",
       "      <td>106011180.00000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CR2</td>\n",
       "      <td>21.48284690220174</td>\n",
       "      <td>50</td>\n",
       "      <td>0.42965693804403476</td>\n",
       "      <td>0.4726226318484382</td>\n",
       "      <td>2181036550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SU9</td>\n",
       "      <td>56.81211267605634</td>\n",
       "      <td>97</td>\n",
       "      <td>0.5856918832583128</td>\n",
       "      <td>0.644261071584144</td>\n",
       "      <td>5625933169.999999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code       booked_seats  num_seats      occupancy_rate  \\\n",
       "0           319  53.58318098720292        116 0.46192397402761143   \n",
       "1           321  88.80923076923077        170  0.5224072398190045   \n",
       "2           733  80.25546218487395        130   0.617349709114415   \n",
       "3           763 113.93729372937294        222  0.5132310528350132   \n",
       "4           773  264.9258064516129        402   0.659019419033863   \n",
       "5           CN1  6.004431314623338         12  0.5003692762186115   \n",
       "6           CR2  21.48284690220174         50 0.42965693804403476   \n",
       "7           SU9  56.81211267605634         97  0.5856918832583128   \n",
       "\n",
       "   inc_occupancy_rate  inc_total_annual_turnover  \n",
       "0  0.5081163714303726               2976779410.0  \n",
       "1   0.574647963800905               1801980510.0  \n",
       "2  0.6790846800258565         1569207310.0000002  \n",
       "3  0.5645541581185146               4808404810.0  \n",
       "4  0.7249213609372492               3774326050.0  \n",
       "5  0.5504062038404727         106011180.00000001  \n",
       "6  0.4726226318484382               2181036550.0  \n",
       "7   0.644261071584144          5625933169.999999  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupancy_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}