cleaning_exploring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:80% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Importations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "import math\n",
    "from sklearn import preprocessing\n",
    "import pickle\n",
    "from dateutil import parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_progress_bar(n_tot, n_prog) :\n",
    "    from time import sleep\n",
    "    progress = math.ceil((n_prog + 1) * 100 / n_tot)\n",
    "    sys.stdout.write('\\r')\n",
    "    sys.stdout.write(\"[%-100s] %d%%\" %('='*progress, progress))\n",
    "    sys.stdout.flush()\n",
    "    sleep(0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Gathering 2016 data from monthly divided files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_data = ['MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'FL_DATE', 'TAIL_NUM', 'UNIQUE_CARRIER', 'ORIGIN', 'ORIGIN_AIRPORT_ID',\n",
    "                 'ORIGIN_CITY_NAME', 'DEST_AIRPORT_ID', 'CRS_DEP_TIME', 'CRS_ARR_TIME', 'ARR_DELAY_NEW', 'DISTANCE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016 = pd.DataFrame(columns=features_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting current path\n",
    "path = os.getcwd()\n",
    "data_files = [file for file in os.listdir(path + '/data') if file.startswith('2016_')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading 2016 full dataset\n"
     ]
    }
   ],
   "source": [
    "try :\n",
    "    data_2016 = pd.read_csv(path + \"/data/data_2016.csv\", sep=',')\n",
    "    print(\"Loading 2016 full dataset\")\n",
    "except :\n",
    "    for file in data_files :\n",
    "        print(\"Treating file : %s\" % file)\n",
    "        # Verifying data presence\n",
    "        # Initializing rows to skip\n",
    "        pb_entries = []\n",
    "        try_again = True\n",
    "        while try_again :\n",
    "            try :\n",
    "                data_month = pd.read_csv(path + \"/data/\" + file, sep=',', skiprows=pb_entries)\n",
    "                try_again = False\n",
    "            except FileNotFoundError :\n",
    "                print(\"Please check if the file %s is in the 'data' folder at the current location\" % file)\n",
    "            except pd.errors.ParserError as e:\n",
    "                error = str(e)\n",
    "                pb_entries.append(int(error.split(\"in line \",1)[1].split(\", \",1)[0]) - 1)\n",
    "                \n",
    "        # Select features\n",
    "        data_month = data_month.loc[:, features_data]\n",
    "        # Drop missing columns\n",
    "        data_month = data_month.dropna(axis='columns', how='all')\n",
    "        # Drop duplicates\n",
    "        data_month = data_month.drop_duplicates(keep='first')\n",
    "\n",
    "        # Save month data\n",
    "        data_2016 = data_2016.append(data_month)\n",
    "    \n",
    "    # Last duplicates check\n",
    "    data_2016 = data_2016.drop_duplicates(keep='first')\n",
    "\n",
    "    # Save data\n",
    "    print(\"Saving\")\n",
    "    data_2016.to_csv(\"data/data_2016.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1) Duplicates and missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial shape :  (5518859, 14)\n",
      "No duplicate\n"
     ]
    }
   ],
   "source": [
    "# Getting rid of the duplicates\n",
    "print(\"initial shape : \", data_2016.shape)\n",
    "dup = data_2016[data_2016.duplicated()].shape[0]\n",
    "if dup > 0 :\n",
    "    print(\"duplicates found : \", dup)\n",
    "    data_2016 = data_2016.drop_duplicates(keep='first')\n",
    "    print(\"Shape without duplicates: \", data_2016.shape)\n",
    "else :\n",
    "    print(\"No duplicate\")"
   ]
  },
  {
   "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>feature</th>\n",
       "      <th>missing values</th>\n",
       "      <th>missing values (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ARR_DELAY_NEW</td>\n",
       "      <td>78710</td>\n",
       "      <td>1.426201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TAIL_NUM</td>\n",
       "      <td>12728</td>\n",
       "      <td>0.230627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DISTANCE</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CRS_ARR_TIME</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MONTH</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DAY_OF_MONTH</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DAY_OF_WEEK</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FL_DATE</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UNIQUE_CARRIER</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ORIGIN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ORIGIN_AIRPORT_ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ORIGIN_CITY_NAME</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DEST_AIRPORT_ID</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CRS_DEP_TIME</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              feature  missing values  missing values (%)\n",
       "12      ARR_DELAY_NEW           78710            1.426201\n",
       "4            TAIL_NUM           12728            0.230627\n",
       "13           DISTANCE               3            0.000054\n",
       "11       CRS_ARR_TIME               2            0.000036\n",
       "0               MONTH               0            0.000000\n",
       "1        DAY_OF_MONTH               0            0.000000\n",
       "2         DAY_OF_WEEK               0            0.000000\n",
       "3             FL_DATE               0            0.000000\n",
       "5      UNIQUE_CARRIER               0            0.000000\n",
       "6              ORIGIN               0            0.000000\n",
       "7   ORIGIN_AIRPORT_ID               0            0.000000\n",
       "8    ORIGIN_CITY_NAME               0            0.000000\n",
       "9     DEST_AIRPORT_ID               0            0.000000\n",
       "10       CRS_DEP_TIME               0            0.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking for missing values\n",
    "missing_val_df = data_2016.isnull().sum(axis=0).reset_index()\n",
    "missing_val_df.columns = ['feature', 'missing values']\n",
    "missing_val_df['missing values (%)']=100 - ((data_2016.shape[0]-missing_val_df['missing values'])/data_2016.shape[0]*100)\n",
    "missing_val_df = missing_val_df.sort_values('missing values (%)', ascending=False)\n",
    "missing_val_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So many datapoints : we allow ourselves to delete these missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MONTH                0\n",
       "DAY_OF_MONTH         0\n",
       "DAY_OF_WEEK          0\n",
       "FL_DATE              0\n",
       "TAIL_NUM             0\n",
       "UNIQUE_CARRIER       0\n",
       "ORIGIN               0\n",
       "ORIGIN_AIRPORT_ID    0\n",
       "ORIGIN_CITY_NAME     0\n",
       "DEST_AIRPORT_ID      0\n",
       "CRS_DEP_TIME         0\n",
       "CRS_ARR_TIME         0\n",
       "ARR_DELAY_NEW        0\n",
       "DISTANCE             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete for missing values\n",
    "data_2016 = data_2016.dropna()\n",
    "data_2016.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2) Feature engineering"
   ]
  },
  {
   "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>MONTH</th>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>FL_DATE</th>\n",
       "      <th>TAIL_NUM</th>\n",
       "      <th>UNIQUE_CARRIER</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>ORIGIN_AIRPORT_ID</th>\n",
       "      <th>ORIGIN_CITY_NAME</th>\n",
       "      <th>DEST_AIRPORT_ID</th>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <th>CRS_ARR_TIME</th>\n",
       "      <th>ARR_DELAY_NEW</th>\n",
       "      <th>DISTANCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>N4YBAA</td>\n",
       "      <td>AA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>11298</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>11433.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>N434AA</td>\n",
       "      <td>AA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>11298</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>11433.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>N541AA</td>\n",
       "      <td>AA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>11298</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>11433.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>N489AA</td>\n",
       "      <td>AA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>11298</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>11433.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>N439AA</td>\n",
       "      <td>AA</td>\n",
       "      <td>DFW</td>\n",
       "      <td>11298</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>11433.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>986.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MONTH  DAY_OF_MONTH  DAY_OF_WEEK     FL_DATE TAIL_NUM UNIQUE_CARRIER  \\\n",
       "0      1             6            3  2016-01-06   N4YBAA             AA   \n",
       "1      1             7            4  2016-01-07   N434AA             AA   \n",
       "2      1             8            5  2016-01-08   N541AA             AA   \n",
       "3      1             9            6  2016-01-09   N489AA             AA   \n",
       "4      1            10            7  2016-01-10   N439AA             AA   \n",
       "\n",
       "  ORIGIN  ORIGIN_AIRPORT_ID       ORIGIN_CITY_NAME  DEST_AIRPORT_ID  \\\n",
       "0    DFW              11298  Dallas/Fort Worth, TX          11433.0   \n",
       "1    DFW              11298  Dallas/Fort Worth, TX          11433.0   \n",
       "2    DFW              11298  Dallas/Fort Worth, TX          11433.0   \n",
       "3    DFW              11298  Dallas/Fort Worth, TX          11433.0   \n",
       "4    DFW              11298  Dallas/Fort Worth, TX          11433.0   \n",
       "\n",
       "   CRS_DEP_TIME  CRS_ARR_TIME  ARR_DELAY_NEW  DISTANCE  \n",
       "0        1100.0        1438.0            0.0     986.0  \n",
       "1        1100.0        1438.0            0.0     986.0  \n",
       "2        1100.0        1438.0            7.0     986.0  \n",
       "3        1100.0        1438.0            0.0     986.0  \n",
       "4        1100.0        1438.0          113.0     986.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting time data\n",
      "Concatenating time data\n",
      "Parsing time data\n"
     ]
    }
   ],
   "source": [
    "# Build complete time format\n",
    "print(\"Getting time data\")\n",
    "date_df = data_2016.loc[:, ['FL_DATE', 'CRS_DEP_TIME']]\n",
    "date_df['HOUR'] = date_df.loc[:, 'CRS_DEP_TIME'].apply(lambda x : math.trunc(x / 100))\n",
    "date_df['MINUTES'] = date_df.loc[:, 'CRS_DEP_TIME'].apply(lambda x : int(x % 100))\n",
    "print(\"Concatenating time data\")\n",
    "date_df['DATETIME'] = date_df.loc[:, 'FL_DATE'] + \"T\" + date_df.loc[:, 'HOUR'].apply(str) + \":\" + date_df.loc[:, 'MINUTES'].apply(str)\n",
    "print(\"Parsing time data\")\n",
    "data_2016.loc[:, 'DATE_TIME'] = date_df.loc[:, 'DATETIME'].apply(parser.parse)\n",
    "\n",
    "# Remove full date\n",
    "data_2016 = data_2016.drop('FL_DATE', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting departure and arrival time in seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(date_time) :\n",
    "    \"\"\"\n",
    "    Routine converting time of day to number of seconds\n",
    "    \"\"\"\n",
    "    seconds = date_time.hour * 3600 + date_time.minute * 60 + date_time.second\n",
    "    return seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016.loc[:, 'TIME_SECONDS'] = data_2016.loc[:, 'DATE_TIME'].apply(func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016.loc[:, 'ARR_TIME_SECS'] = data_2016.loc[:, 'CRS_ARR_TIME'].apply(lambda x : (x/100)*3600 + x%100*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort data by date and time\n",
    "data_2016 = data_2016.sort_values(by=['DATE_TIME'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "categ_feat = ['MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'UNIQUE_CARRIER', 'TAIL_NUM', 'ORIGIN_AIRPORT_ID', 'DEST_AIRPORT_ID']\n",
    "num_feat = ['CRS_DEP_TIME', 'CRS_ARR_TIME', 'ARR_DELAY_NEW', 'DISTANCE', 'DATE_TIME']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016_explo = data_2016.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean delay as a function of the categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONTH\n",
      "DAY_OF_MONTH\n",
      "DAY_OF_WEEK\n",
      "UNIQUE_CARRIER\n",
      "TAIL_NUM\n",
      "ORIGIN_AIRPORT_ID\n",
      "DEST_AIRPORT_ID\n"
     ]
    }
   ],
   "source": [
    "# Initialize dict of feature(feature values, mean of delay for those values)\n",
    "mean_delays_categ = {}\n",
    "for feat in categ_feat :\n",
    "    print(feat)\n",
    "    # Get mean serie grouped by feature\n",
    "    feat_mean_serie = data_2016_explo['ARR_DELAY_NEW'].groupby(data_2016_explo[feat]).apply(lambda x : x.mean())\n",
    "    # Save values of features\n",
    "    mean_delays_categ[feat] = [feat_mean_serie.index.values.tolist()]\n",
    "    # Save means of values of features\n",
    "    mean_delays_categ[feat].append(feat_mean_serie.values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean delay as a function of the numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRS_DEP_TIME\n",
      "CRS_ARR_TIME\n",
      "ARR_DELAY_NEW\n",
      "DISTANCE\n",
      "DATE_TIME\n"
     ]
    }
   ],
   "source": [
    "# Initialize dict of feature(feature values, mean of delay for those values)\n",
    "mean_delays_num = {}\n",
    "for feat in num_feat :\n",
    "    print(feat)\n",
    "    # Get mean serie grouped by feature\n",
    "    feat_mean_serie = data_2016_explo['ARR_DELAY_NEW'].groupby(data_2016_explo[feat]).apply(lambda x : x.mean())\n",
    "    # Save values of features\n",
    "    mean_delays_num[feat] = [feat_mean_serie.index.values.tolist()]\n",
    "    # Save means of values of features\n",
    "    mean_delays_num[feat].append(feat_mean_serie.values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Airports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct dictionnary of airports names for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ap_df = data_2016_explo.loc[:,['ORIGIN_AIRPORT_ID','ORIGIN','ORIGIN_CITY_NAME']].drop_duplicates(subset=['ORIGIN_AIRPORT_ID'], keep='first')\n",
    "# Get airports initials\n",
    "airport_initials = ap_df.loc[:,'ORIGIN'].values\n",
    "# Get airports full names\n",
    "airport_names = ap_df.loc[:,'ORIGIN_CITY_NAME'].values\n",
    "# Get airports encoded values\n",
    "airport_number = ap_df.loc[:,'ORIGIN_AIRPORT_ID'].values\n",
    "# Save dict\n",
    "airport_dict = dict(zip(airport_number, airport_initials))\n",
    "airport_dict_name = dict(zip(airport_number, airport_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of different airports ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "311 origin airports and 310 destination airports\n"
     ]
    }
   ],
   "source": [
    "n_dep_ap = data_2016_explo.loc[:,'ORIGIN_AIRPORT_ID'].unique().shape[0]\n",
    "n_arr_ap = data_2016_explo.loc[:,'DEST_AIRPORT_ID'].unique().shape[0]\n",
    "print(\"%i origin airports and %i destination airports\" % (n_dep_ap, n_arr_ap))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most frequented airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get 10 biggest airports\n",
    "ap_flights_counts = data_2016_explo.loc[:,'ORIGIN'].value_counts().head(10)\n",
    "ap_numbers = data_2016_explo.loc[:,'ORIGIN_AIRPORT_ID'].value_counts().head(10).index.values\n",
    "ap_names = []\n",
    "for number in ap_numbers :\n",
    "    ap_names.append(airport_dict_name[number].split(\",\")[0])\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "sns.barplot(ap_flights_counts.index.values, ap_flights_counts.values, palette='PuOr')\n",
    "ax.set_xticklabels(ap_names, fontsize=10)\n",
    "ax.set_title(\"Most frequented airports in 2016\", loc=\"center\", fontsize=15)\n",
    "ax.set_ylabel(\"Flights in 2016\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Least frequented airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bqSRJkiRp7pkcSpIkSZJMDiVJkiRJJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJIlZTg6THJbk8iRn9YzbKMlxSc5rz3ds45PkQ0nOT3JGkgfOZqySJEmStDqZ7ZbDw4Gd+8a9CTi+qrYCjm+vAZ4IbNUe+wAfm6UYJUmSJGm1M6vJYVWdBFzZN3oX4Ig2fASwa8/4T1bnZGDDJJvOTqSSJEmStHqZD9cc3rmqLgVoz5u08ZsBl/SUW9rGSZIkSZLGbD4kh4NkknE1acFknyRLkixZtmzZDIclSZIkSaue+ZAcXjbRXbQ9X97GLwW26Cm3OfDbyRZQVQdV1aKqWrRgwYIZDVaSJEmSVkXzITn8CrBnG94T+HLP+Oe3u5Y+FPjjRPdTSZIkSdJ4rTWbK0tyJLAY2DjJUmA/4J3AUUleCPwaeGYr/g3gScD5wJ+BvWczVkmSJElancxqclhVuw+Y9NhJyhbw0pmNSJIkSZIE86NbqSRJkiRpjpkcSpIkSZJMDiVJkiRJJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAlYa64D0K3bCfved65DmDce8z8/n+sQJEmSpJVmy6EkSZIkyeRQkiRJkmRyKEmSJEnC5FCSJEmShDekkeaVi498wVyHMG8s3P2wuQ5BkiRptWLLoSRJkiTJ5FCSJEmSZHIoSZIkScLkUJIkSZKEyaEkSZIkiRW8W2mS2wB7ANsCNwGnA0dV1Y0zEJskSZIkaZYMbDlMcmSSA3pebwOcCxwE7Ar8E/BJ4PQkm890oJIkSZKkmTOsW+ljgO/1vD4A+CNw76raqqruBWwDFPCBmQtRkiRJkjTThnUrvQNwWc/rRwL/VFUXTYyoqguS7AccPjPhSZIkSZJmw7CWw/OBRT2v/wSsOWAZN40zKEmSJEnS7BqWHB4A/EeSndrrDwP/neTeEwXadYhvB74xcyFKkiRJkmbawG6lVXVQkrsBxyY5HzgTuBvwiyQT3U3vApwGvGrGI5UkSZIkzZih/3NYVf8O3B/4CrAA+C1wIXAJcCzwLGCHqlo2w3FKkiRJkmbQlP9zWFU/B94wC7FIkiRJkubI0JZDSZIkSdLqYdrJYZJHJfnuOIKRJEmSJM2NKbuVjmAB8OgxLEeSxurqn31mrkOYN25//+dOa/7rfn/R1IVWE+vd6R5zHYIkSTNiYHKY5PkjLuPBY4pFkiRJkjRHhrUcHg4UkBGWU9MNJMmrgX9pyzoT2BvYFPgssBFwKvC8qrp+uuuSJEmSJN3SsGsOfwccAtxuiseoLYwDJdkMeAWwqKq2A9YEngO8C3h/VW0F/AF44XTXJUmSJEn6e8OSwx8BD6qqa4c9gOvGFMtawHpJ1gLWBy4FdgKObtOPAHYd07okSZIkST2GJYefo/vD+6n8AnjbdIKoqt8A/wv8mi4p/CNwCnBVVd3Qii0FNpvOeiRJkiRJkxuYHFbVUVX1rKkWUFVnV9VbpxNEkjsCuwD3AO4K3BZ44mSrGzD/PkmWJFmybNmy6YQiSZIkSaulaf/P4Zg8DrioqpZV1d+ALwIPBzZs3UwBNgd+O9nMVXVQVS2qqkULFiyYnYglSZIkaRUy5f8cJrknsDOwDXBHuta7q4BzgG9V1QVjiOPXwEOTrE93DeNjgSXACcBudHcs3RP48hjWJUmSJEnqM7DlMMl6ST4JnEt3PeBjgLvTdf18DPAe4NwkRyRZdzpBVNWP6W48cyrd31isARwEvBF4TZLzgTsBh05nPZIkSZKkyQ1rOXw38ARgd+BLrbvnzZKsTXed4AGt7CumE0hV7Qfs1zf6QmCH6SxXkiRJkjS1Ycnhc4BXVNXnJ5vYksWjW5L4IaaZHEqStLpYdsXVcx3CvLFg49vPdQiSpGbYDWnWpfvj+alcBawznnAkSZIkSXNhWHL4HeAdSe42qECSLYC3t7KSJEmSpFupYd1KXwocC1yQ5Kd0dye9iu5upRvS3b30wW38y2Y4TkmSJEnSDBqYHFbVb5PcH3gW3V9Z3Jfuryyg6256Dt3NaI6uqhtmOlBJkiRJ0swZ+j+HVXUjcGR7SJIkSZJWUcOuOZQkSZIkrSaGJodJ1k/yvCSvTfLEJGtOUmbLJIfNXIiSJEmSpJk2sFtpkk2BHwALgT8D6wPnJnleVS3pKboA2BN4wQzGKUmSJEmaQcNaDv8H+CuwdVVtAGwP/A44KclusxGcJEmSJGl2DEsOdwL2q6rzAKrqjDbuAOCzSV49C/FJkiRJkmbBsLuV3hG4rHdEVRXwxiS/Aj6UZAvg8zMYnyRJkiRpFgxLDi8AdgC+1z+hqj6a5DLg/wGLZyY0SZIkSdJsGdat9DjgX5NMWqaqvgA8EdhyJgKTJEmSJM2eYS2H7wVOBDYArp6sQFWdmOShwEPGH5okSZIkabYMTA6r6nfA16daQFWdA5wzzqAkSZIkSbNrWLdSSZIkSdJqwuRQkiRJkmRyKEmSJEkyOZQkSZIkYXIoSZIkSWL4X1ncQpJ1gUcBmwPr9k2uqvrYOAOTJEmSJM2ekZLDJI8AvghsPKBIASaHkiRJknQrNWq30g8BFwAPANapqjX6HmvOXIiSJEmSpJk2arfSrYFnVNXPZjIYSZIkSdLcGLXl8AzgLjMZiCRJkiRp7oyaHL4YeHWSR89kMJIkSZKkuTFqt9LjgPWB7yb5G3B1f4Gq2mScgUmSJEmSZs+oyeFH6O5IKkmSJElaBY2UHFbV/jMchyRJkiRpDo16zaEkSZIkaRU2sOUwyVHAvlV1QRseqqqeNdbIJEmSJEmzZli30gXA2m14E7zmUJIkzUOnnnrxXIcwbzzwgQvnOgRJt2IDk8OqekzP8OJZiUaSJEmSNCe85lCSJEmSNH+SwyQbJjk6yTlJzk7ysCQbJTkuyXnt+Y5zHackSZIkrYrmTXIIfBD4VlVtA9wfOBt4E3B8VW0FHN9eS5IkSZLGbF4kh0luDzwKOBSgqq6vqquAXYAjWrEjgF3nJkJJkiRJWrXNi+QQ2BJYBnwiyWlJDklyW+DOVXUpQHveZLKZk+yTZEmSJcuWLZu9qCVJkiRpFbHSyWGSbZLsmuSuY4hjLeCBwMeq6gHAtaxAF9KqOqiqFlXVogULFowhHEmSJElavYyUHCY5MMnHe14/GzgT+CJwTpKHTzOOpcDSqvpxe300XbJ4WZJN2zo3BS6f5nokSZIkSZMYteVwZ+CkntdvB44E7gp8u71eaVX1O+CSJFu3UY8FfgF8BdizjdsT+PJ01iNJkiRJmtxaI5bbBLgEIMlWwL2AZ1TV75IcBHxuDLG8HPh0ktsAFwJ70yWvRyV5IfBr4JljWI8kSZIkqc+oyeGVwJ3b8OOA31XVWe11gDWnG0hVnQ4smmTSY6e7bEmSJEnScKMmh98E3pbkzsAbgKN6pm0HXDzmuCRJkiRJs2jUaw5fC5wMvIju2sP/7Jn2dLrrDiVJkiRJt1IjtRxW1R+BFwyY9sixRiRJkiRJmnWj/pXFhUnuP2DadkkuHG9YkiRJkqTZNGq30oXAOgOmrQ9sPpZoJEmSJElzYmC30iS3BzbsGXWXJHfrK7Yu8BzgNzMQmyRJkiRplgy75vDVwH5AtccxA8qF7oY1kiRJkqRbqWHJ4WeAJXTJ31eA1wHn9pW5Hji3qn49M+FJkiRJkmbDwOSwqs4DzgNI8hjg1Kr602wFJkmSJEmaPaP+lcX3JoaTrAXcZpIyfx5jXJIkSZKkWTTqX1ncPsmHk/wW+Avwp0kekiRJkqRbqZFaDoEDgacAhwC/oLvWUJIkSZK0ihg1OfxH4NVVdchMBiNJkiRJmhsjdSsFrgWWzmQgkiRJkqS5M2py+F7gJUlGLS9JkiRJuhUZ2K00ybv7Rt0fODfJCcBVfdOqqt447uAkSZIkSbNj2DWHz+x7fVMr//hJyhZgcihJkiRJt1IDk8OqusdsBiJJkiRJmjteQyhJkiRJGu2vLJI8f8jkm4CrgZ9V1a/GEpUkSZIkaVaN+j+Hh9NdVwiQnvG94yrJ14A9quqa8YQnSZIkSZoNo3YrfSBwPvAm4D7Axu15X+AC4HHAnsAjgXeNP0xJkiRJ0kwateXwvcBHq+qDPeOuBN6d5Hpgv6p6dJI7A68FXjrmOCVJkiRJM2jUlsOHAWcPmHY28OA2fApwp+kGJUmSJEmaXaMmh0uBvQZM27tNB7gj8PtpxiRJkiRJmmWjdit9C3Bkku2ArwLLgAXAU4Ftgee0co8H/m/cQUqSJEmSZtZIyWFVfT7JRcAbgecCdwF+B/wU2LuqTmnlXjJTgUqSJEmSZs6oLYdU1RLgmTMYiyRJkiRpjox6zaEkSZIkaRU2sOUwyVHAvlV1QRsepqrq2eMNTZIkSZI0W4Z1K10ArN2GNwFq5sORJEmSJM2FgclhVT2mZ3jxrEQjSZIkSZoTXnMoSZIkSRp6zeEK/S1FVX10+uFIkiRJkubCsGsOP7wCyylg2slhkjWBJcBvquopSe4BfBbYCDgVeF5VXT/d9UiSJEmSbmlgt9KqWmMFHmuOKZ5XAmf3vH4X8P6q2gr4A/DCMa1HkiRJktRj3lxzmGRz4MnAIe11gJ2Ao1uRI4Bd5yY6SZIkSVq1DUwOkxybZOu+cTslue0MxfIB4A3ATe31nYCrquqG9nopsNkMrVuSJEmSVmvDWg4fB9xh4kW7HvA4YOuBc6ykJE8BLq+qU3pHT1J00v9aTLJPkiVJlixbtmzc4UmSJEnSKm9Fu5VOlrCNw47A05JcTHcDmp3oWhI3TDJx05zNgd9ONnNVHVRVi6pq0YIFC2YoREmSJEladc2Law6rat+q2ryqFgLPAb5bVXsAJwC7tWJ7Al+eoxAlSZIkaZU2VXI4WTfOSbt2zpA3Aq9Jcj7dNYiHzuK6JUmSJGm1Mex/DgG+neSGvnHHTzKOqtpkHAFV1YnAiW34QmCHcSxXkiRJkjTYsOTwrbMWhSRJkiRpTg1MDqvK5FCSJEmSVhPz4oY0kiRJkqS5ZXIoSZIkSTI5lCRJkiSZHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJIl5khwm2SLJCUnOTvLzJK9s4zdKclyS89rzHec6VkmSJElaFc2L5BC4AXhtVd0HeCjw0iTbAm8Cjq+qrYDj22tJkiRJ0pjNi+Swqi6tqlPb8J+As4HNgF2AI1qxI4Bd5yZCSZIkSVq1zYvksFeShcADgB8Dd66qS6FLIIFN5i4ySZIkSVp1zavkMMkGwBeAV1XV1Ssw3z5JliRZsmzZspkLUJIkSZJWUfMmOUyyNl1i+Omq+mIbfVmSTdv0TYHLJ5u3qg6qqkVVtWjBggWzE7AkSZIkrULmRXKYJMChwNlV9b6eSV8B9mzDewJfnu3YJEmSJGl1sNZcB9DsCDwPODPJ6W3cm4F3AkcleSHwa+CZcxSfJEmSJK3S5kVyWFXfBzJg8mNnMxZJkiRJWh3Ni26lkiRJkqS5ZXIoSZIkSTI5lCRJkiSZHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnCX64BAAATkElEQVTC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJEwOJUmSJEmYHEqSJEmSMDmUJEmSJGFyKEmSJEnC5FCSJEmShMmhJEmSJAmTQ0mSJEkSJoeSJEmSJG4lyWGSnZOcm+T8JG+a63gkSZIkaVUz75PDJGsCHwGeCGwL7J5k27mNSpIkSZJWLWvNdQAj2AE4v6ouBEjyWWAX4BdzGpUkSdIq5rNHnDTXIcwbz9nzUdNexr+//LAxRLJqeMcBL5j2Mp6x/RvHEMmq4Yunv2tGljvvWw6BzYBLel4vbeMkSZIkSWOSqprrGIZK8kzgH6vqX9rr5wE7VNXLe8rsA+zTXm4NnDvrga64jYEr5jqIVYj1OT7W5XhZn+NlfY6X9Tk+1uV4WZ/jZX2Oz62lLu9eVQtWdKZbQ7fSpcAWPa83B37bW6CqDgIOms2gpivJkqpaNNdxrCqsz/GxLsfL+hwv63O8rM/xsS7Hy/ocL+tzfFb1urw1dCv9KbBVknskuQ3wHOArcxyTJEmSJK1S5n3LYVXdkORlwLeBNYHDqurncxyWJEmSJK1S5n1yCFBV3wC+MddxjNmtqhvsrYD1OT7W5XhZn+NlfY6X9Tk+1uV4WZ/jZX2Ozypdl/P+hjSSJEmSpJl3a7jmUJIkSZI0w0wOV0CSa/pe75Xkw7O4/kOSbDtb65uuJE9PUkm2GVLm8CS7TbGcE5MMvSvUVGVGWc+oRolnivkryad6Xq+VZFmSr40jvulIsmGSl/S8XjxKXCtSv0kWJjlrOnHOhCR3TvKZJBcmOSXJj5I8fSWWs1eSu85EjPNZkhuTnJ7k50l+luQ1SdZo0xYl+VAbvnm/mWT/JK9rw29L8riVXPe83KZG1VN3ZyX5fJL1V3I52yd50krMt1WSryW5oG37JySZ1r9/T3c/Oc11X9Mz/KQk5yW5W5IXJXn+mNbxlCSntW39F0n+bYrys3q8MF8keUvbJ5zRtvGHDCm70vuAVdFsHHMmefM4l7cC6x37cVB/fY1QfnGSh/e83nUcx9hJvpVks7YPPLdt96cn2a3/GGuK5UzM/7MkP02yfc+0byTZsA2v0PsehcnhrUhV/UtV/WKu41gBuwPfp7vDrJa7FtguyXrt9eOB38xhPL02BEbaca1KkgT4EnBSVW1ZVQ+i2243X4nF7QWsdskhcF1VbV9V96Xbpp8E7AdQVUuq6hXDZq6q/6yq78xCnPPRRN1tB1wPvGgll7M9Xb2PLMm6wNeBg6rqnm3bfzmw5QosY17evyDJY4EDgJ2r6tdV9fGq+uQYlrs23TVHT62q+wMPAE6c7nJXNUkeBjwFeGBV3Q94HHDJoPKr+T5grsxJcsj8OA5aDDy85/WuwLSSw/Z+NqqqifeyR9u3b19VR7Pix1h7tH3MR4H3TIysqidV1VXTiXUYk8MxSHK7JBe1HwyS3D7JxUnWTvKvLeP/WZIvTJwRbi0tH0ryw9ZSsVsbv7idLTg6yTlJPt0OXG8+E5tkzTb/WUnOTPLquXv3k0uyAbAj8EJ6ksN0PtzOtH4d2KRn2n+2ujoryUET77tn+hpJjkjyjiHrnbJuBq2n1e+7kvwkyS+TPLKNXy/JZ9uZz88B6/UvcyV8E3hyG94dOLInvh3adnFae966jd8ryZeSfLVtby9L1zpzWpKTk2zU8z7en+SkJGcneXCSL6Y7e/6OnvW8ptXBWUle1Ua/E7hnO8s1sSPaYLLtcSpJNkhyfJJT22exyyRltmzxP7h9du9pn80ZaWfik2za3stEy8ojV6yqR7ITcH1VfXxiRFX9qqoOSN/Z2nQtLIsn29ba93gR8OkW73qTbW9J7pPkJz3LXJjkjDZ8cZKN2/CiJCe24Udn+RnI05LcbgbqYSyq6nJgH+Bl7f1O2QKdntbnVgdv7dl2tmnjFyQ5ro0/MMmvJuoKWDPJwelaKY5NO+jI8H3wx9K1kl3Y6vew9p05fKbqZgT/B9wrfa2hSV6XZP82/Hf7qnR/9fQ24NltG3l2BuxL+uwB/Kiqbv6LqKo6q6oOb+vaKN1+54x0+5n7tfH7t+35WOCTGbKfbPW8pH02bx17jU2i7ScOBp5cVRf0xDzRUj1of79+kqMm3keSH+fvW0BvR3dDv98DVNVfq+rcNv9T2zynJflOkjtPEttI216S3dv2f1aSd/WMvybJf7Vt+uTJ1jFPbApcUVV/BaiqK6rqt5PtE2HSfcDE5/OTJPdq4yet3/bZHtY+1wuTDD0ZdWuX5O7pfl/PaM93a+Nv0YsnrVUpk/yOJnknsF4b9+lW7u+OC9q+6OxMsn+dppU9Dvpiuta585K8u69e/u57Mdk2k2Qh3Um4V7f3/2jgacB72ut7ZgWP35vFDD9RNNkx1ih+BGzW8z5vPk7oe/+vz/JjqLe2cW9P8sq+Ohr+/agqHyM+gBuB03sevwY+3KZ9Ati1De8DvLcN36ln/ncAL2/DhwOfp0vQtwXOb+MXA3+ka7FYo20Qj2jTTqQ78HwQcFzPcjec67qZpK7+GTi0Df+Q7swhwDOA4+j+luSuwFXAbm3aRj3zf4rurOzE+34o3Y7jLQPWN7RuWn2Psp6Jz+1JwHfa8Gvo/kIF4H7ADcCiadTNNW05RwPrtm1pMfC1Nv32wFpt+HHAF9rwXsD5dAcmC9p28qI27f3Aq3rex7va8CuB39L9SK8DLAXu1OrpTOC2wAbAz+nOfi8EzuqJdeD22Peebq7fnnFrAbdvwxu32DOxDmBr4DRg+57vzb+34XWAJcA9gNdOfO5tu7ndDGyvrwDeP2DaXrTveXv9tVYvg7a1E3u3jyHb2+nAlm34jT3v/WJg4za8CDixDX8V2LENbzCxjcyXB3DNJOP+ANy5b/u+uT6B/YHXTfIdvZjl+8qXAIe04Q8D+7bhnYFq29ZCuu/lxLZ0FPDPbXjYPvizbZvcBbga+Ae67fyUiWXNZt2178yXgRfz99/F1wH792xjk+2r+rfVSfclfet+H/DKIbEdAOzXhncCTu/57E4B1muvB+4nad8Buu/vicD9Zrg+/wZc2b+evu1tUB2+DjiwDW/HgP09cAhwOd3v0h7AGm38HVl+s79/6VnHzZ/NKNse3e/jr+n29WsB32X5MUaxfD/ybtq+Y7496PZTpwO/pGv5eHTv9tCGe/eJh3PLfcDEfv/5LN9/DKrf/emONdah2yf8Hlh7rutgmvU37Jjzq8CebfgFwJf667C9nti3TPo7Ss9+m+HHBZPuX6fx3qZzHHQhcIc236+ALYZ9L6bYZl7XE1N/3a3Q8Xub9iFgpzZ8InBuz+d3J/r261PU0Yks34e+CvjvnmkXs/w4YeIzfgJdj4a02L4GPKqt89RWZg3ggt73NtnDlsMVM9H1Z/uq2h74z55phwB7t+G96ZJF6JrN/y/JmXQ/IPftmedLVXVTdV1Fe8/8/aSqllbVTXQb1MK+OC4EtkxyQJKd6X5Y5pvd6X78aM+7t+FHAUdW1Y1V9Vu6H7wJj2lnd86kOwjprasD6b5Q/zXFekepm2Hr+WJ7PoXl9f4o4P8BVNUZwBlTxDCltpyFdPXS/zctdwA+n67V4P198Z1QVX+qqmV0SdtX2/gzueV28pWe8T+vqkurO3t7IbAF8AjgmKq6tqquoXvfg1rkptoeBwnw3+laxL5Dd9ZrYjtfQHcQ/M9VdXob9wTg+UlOB35MtyPdCvgpsHe6VpN/qKo/jbj+lZbkI+1s4U+HFBv1ezhoezsKeFYbfjbwuSnC+gHwvnbGb8OqumGkNzO3RmplHmCy7+IjaPuVqvoWXfI54aKebal3nmH74K9W94t5JnBZVZ3ZtvOfM/p2Pg7rte1+Cd0B4KEjzDNZ/fQbti+ZVJJjWqvBxPIfQXcAT1V9F7hTkju0aV+pquva8LD95LOSnEp3Mui+TLPr1gj+RpcovHCKclNtY2cxYH9fVf8CPBb4CV1CeVibtDnw7ba9vZ7BdT7VtvdguhNDy9p3/dN0dQxd1+OJlvhhn/+car8tD6I78bcM+FySvRj+G9zryJ7nh7XhYfX79epaca+gS9zna4vqqIYdcz4M+Ewb/hTddjvMKL+jw44LBu1fV9o0joOOr6o/VtVfgF8Ad2/jB30vRv1O9luZ4/cd6S6nmtDbrfT3I66316eTLKU7gXzAFGWf0B6nAacC2wBbVdXFwO+TPGBi+lSxmByOSVX9AFjYmqbXbD8q0J1heFlV/QPwVrozHRP+2jOcAeNvpO//KKvqD8D96c4qvJQuMZ03ktyJbod/SJKL6b6Mz05u7o5Yk8yzLt2Zxd1aXR3MLevqh3Q/KOv2z9trqroZYT0Tdd9f738X8xh8BfhferpSNG+nSwK3A546ID6Am3pe38Qt4/3rJGV6y63IQfvQ7XGIPeiSwAe1H7bLWP5e/kh37cmOPeVDd2ZuYkd6j6o6tqpOojso+g3wqYzphhJ9fg48cOJFVb2U7sBvAd0Z09595bqtzJTfwym2t8/RHTTfu1tcndfG967v5s++qt5Jd9ZzPeDkDLnR03yQZEu67eXylVzEZN/FYdvtoO30cKbeBw/6nsyW3oPAl1fV9QzY7noM2lf1GrYvmdC/7T+d7uz8Rm3UZHU+sT+8dsD4myW5B13y9Njqrjv7+oA4xukmuhMvD87wG26s6DZ2Cy2hez/d9VL/1EYfQNe68w/AvzH4vU5nH/23llj2xz7vtBPBJ1bVfsDL6H4Xhv0G32L2SYaH1e/K/latCibq5+b9Rjvmug3AiL+jK7N/na7pHgf1xjLoezHqd7Lf4azA8Xv7zbuk7b/HZQ+6HlSfAT4yRdkA/9PzW3Kvqpo40XgI3X59b5afyBrI5HC8Pkm3gX+iZ9ztgEvTXY+4xzhW0voZr1FVXwD+g54f9nliN+CTVXX3qlpYVVsAF9GdlToJeE6667U2BR7T5pn40l2R7nrF/jtfHkp3ZunzGXLzgxHqZqr1TOYk2meXZDu6rhDjcBjwtqo6s2/8HVh+YfZeY1pXv5OAXdNdX3Nb4Ol01zr9iW6bHYc7AJdX1d+SPIblZ/egO8O3K11L4XPbuG8DL87ya3fvneS2Se7elnMw3XYwE9v7d4F1k7y4Z9zEHSMvBrZPd83rFsAOLb5B21pvHQ7c3qq7DurGNm9vq+HFdGfbYfkBJ0nu2Q5G30XXwjRvk8MkC4CP0/0gj/PEyvdpra1JnkDXXWgqY98Hz5LLgE2S3CnJOnQ39phK//d3lH3JZ4AdkzytZ1zv3VJ793+L6a4hm6yVfNB+8vZ0SeQf010D9MQR3se0VdWf6epsjyRTtSD26t3GtqXr7nkL6a6nXtwzanu67m1wyzrfcwXD7vVj4NFJNk6yJl3ryvemsbxZl2TrJFv1jNqerpsdjPYb/Oye5x+14XHV763dD1l+P4c9WN5adTHLfz92ASZ+Twf9jv5t4jeXwccFM2k2joMGbTP9+8v+1yv62/FE4FtTlFnhY6yq+hvw78BDk9xnSNFvAy9o3yvS3TF14r4ex9BdivHgVm6o1emsymz4NF2/5N4zIP9Bt5P/FV33kXEceG8GfCLtNvHAvmNY5jjtTnfRba8vAM+lu35oJ7q6+CXtx66qrkpycBt/MV0XiFuoqve17kyfSrJH64LTb2jdjLKeSXysLfMMum6VP5mi/EiqainwwUkmvRs4IslruGW327GpqlPT3fhg4r0cUlWnAST5QevK8U26s/yjOjDJB9rwJXRn+76aZAldvZ3TF8O1SZ4CHJfkWrozWwuBU9sZz2V0CeRi4PVJ/kZ3ncLYWw6rqpLsCrw/yRvauq+l68rxA7qTG2fSXSt5aptt0LZ2OPDxJNfRdf0Ztr19ju4OZPfoGfdW4NDW4vHjnvGvakn2jXRdab45jbc8Eya6Rq5Nd/b6U3TXs43TW4Ejkzybbt9xKd2P7QZD5pmJffCMaydV3kYX+0X0fX8GOAF4U/sc/ocR9iVVdV37Hr6vfX8vo6vTiZtX7c/y/d+fGXxAPul+sqp+luQ0uhbKC+m+T7Oiqq5M1+X7pCRXjDjbR+nq7Ay67lln0PV06BXgDUkOBK6j21fs1abtT3cS8zfAydzyu70isV+aZF+6zzTAN6rqyyuzrDm0AXBAulvu30B33fk+dPcaGOU3eJ0kP6ZryJi4NGV/xlC/q4BXAIcleT3d79XEZU0HA19Od8Oz41neur+YyX9HDwLOSHJqVe0x2XFBupu3zIhZOg7an8m3ma8CR6e7Wd7L6bqTH5zu0o3dWPHfjp3bcgaqqt/3HmNV1euTnN56Vw2b77ok76XrhTHpya6qOrYljz9qHfWuobv/x+VVdX2SE4CrqurGKd7HzRdoagzS3bFol6p63lzHIkmrmtaCdmNV3ZDuNvkfm+pHVVoRrZVu7ar6S5J70h1g33vMXcU0hXSXpCxq1w9K81r7bfpBVc3Jf7tOpZ3EPhV4Zs8lLAPZcjgmSQ6ga1Je4T8gliSN5G7AUe2H7nrgX+c4Hq161gdOaF3JArzYxFDSMNXd8G++Jobb0t2o55hREkOw5VCSJEmShDekkSRJkiRhcihJkiRJwuRQkiRJkoTJoSRJkiQJk0NJkiRJEiaHkiRJkiTg/wMvt+cJXIPKuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get 10 smallest airports\n",
    "ap_flights_counts = data_2016_explo.loc[:,'ORIGIN'].value_counts().tail(10)\n",
    "ap_numbers = data_2016_explo.loc[:,'ORIGIN_AIRPORT_ID'].value_counts().tail(10).index.values\n",
    "ap_names = []\n",
    "for number in ap_numbers :\n",
    "    ap_names.append(airport_dict_name[number].split(\",\")[0])\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "sns.barplot(ap_flights_counts.index.values, ap_flights_counts.values, palette='PuOr')\n",
    "ax.set_xticklabels(ap_names, fontsize=10)\n",
    "ax.set_ylabel(\"Flights in 2016\", fontsize=15)\n",
    "ax.set_title(\"Least frequented airports in 2016\", loc=\"center\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some airports have very few flights over the year, some have a lot : we wont consider those with too few flights because of their poor statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting lower limit of airports flights\n",
    "flights_low = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of departure airports with 1000 or less flights per year : 88\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_dep_ap = data_2016_explo.loc[:,'ORIGIN_AIRPORT_ID'].value_counts()\n",
    "print(\"Number of departure airports with %i or less flights per year : %i\" % (flights_low, \n",
    "                                                                              freq_dep_ap[freq_dep_ap <= flights_low].shape[0]))\n",
    "# Saving small airports for later removal\n",
    "small_dep_ap = freq_dep_ap[freq_dep_ap < flights_low].index.values\n",
    "\n",
    "# Bar plot\n",
    "freq_dep_ap[freq_dep_ap >= flights_low].plot.bar()\n",
    "axes = plt.gca()\n",
    "axes.get_xaxis().set_ticks([])\n",
    "axes.set_xlabel(\"Airports\", fontsize=15)\n",
    "axes.set_ylabel(\"Departure flights\", fontsize=15)\n",
    "axes.set_title(\"Number of departure flights by airport\", fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "ax.hist(data_2016_explo.loc[:,'ORIGIN_AIRPORT_ID'].value_counts(), bins=100)\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel(\"Departures in 2016\", fontsize=15)\n",
    "ax.set_ylabel(\"Airports count\", fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of departure airports with 1000 or less flights per year : 87\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_arr_ap = data_2016_explo.loc[:,'DEST_AIRPORT_ID'].value_counts()\n",
    "print(\"Number of departure airports with %i or less flights per year : %i\" % (flights_low, \n",
    "                                                                              freq_arr_ap[freq_arr_ap <= flights_low].shape[0]))\n",
    "# Saving small airports for later removal\n",
    "small_arr_ap = freq_arr_ap[freq_arr_ap < flights_low].index.values\n",
    "\n",
    "# Bar plot\n",
    "freq_arr_ap[freq_arr_ap >= flights_low].plot.bar()\n",
    "axes = plt.gca()\n",
    "axes.get_xaxis().set_ticks([])\n",
    "axes.set_xlabel(\"Airports\", fontsize=15)\n",
    "axes.set_ylabel(\"Arrival flights\", fontsize=15)\n",
    "axes.set_title(\"Number of arrival flights by airport\", fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "ax.hist(data_2016_explo.loc[:,'DEST_AIRPORT_ID'].value_counts(), bins=100)\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel(\"Arrivals in 2016\", fontsize=15)\n",
    "ax.set_ylabel(\"Airports count\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Airports with too few departures or arrivals may lead to biased models because of there small statistics, we will keep the airports with at least 1000 departures or arrivals per year, what we may do because of the high amount of entries in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape before removing small airports : 5440148\n",
      "Shape after removing small airports : 5341242\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape before removing small airports : %i\" % data_2016_explo.shape[0])\n",
    "\n",
    "# Check for small airports\n",
    "small_dep = small_dep_ap\n",
    "small_arr = small_arr_ap\n",
    "\n",
    "# Loop\n",
    "small_ap = small_dep.shape[0] + small_arr.shape[0]\n",
    "while small_ap != 0 :\n",
    "    data_2016_explo = data_2016_explo[~data_2016_explo.loc[:, 'ORIGIN_AIRPORT_ID'].isin(small_dep)]\n",
    "    data_2016_explo = data_2016_explo[~data_2016_explo.loc[:, 'DEST_AIRPORT_ID'].isin(small_arr)]\n",
    "    # Check again\n",
    "    freq_dep_ap_temp = data_2016_explo.loc[:,'ORIGIN_AIRPORT_ID'].value_counts()\n",
    "    small_dep = freq_dep_ap_temp[freq_dep_ap_temp < flights_low].index.values\n",
    "    freq_arr_ap_temp = data_2016_explo.loc[:,'DEST_AIRPORT_ID'].value_counts()\n",
    "    small_arr = freq_arr_ap_temp[freq_arr_ap_temp < flights_low].index.values\n",
    "    # Loop\n",
    "    small_ap = small_dep.shape[0] + small_arr.shape[0]\n",
    "    \n",
    "print(\"Shape after removing small airports : %i\" % data_2016_explo.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Airlines and delays : inspired from https://www.kaggle.com/fabiendaniel/predicting-flight-delays-tutorial/notebook#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Airlines full names\n",
    "airlines_full_names = {'AA' : 'American Airlines', 'AS' : 'Alaska Airlines', 'B6' : 'Jetblue Airways Corporation',\n",
    "                       'DL' : 'Delta Air Lines, Inc.', 'EV' : 'ExpressJet Airlines', 'F9' : 'Frontier Airlines, Inc.',\n",
    "                       'HA' : 'Hawaiian Airlines Inc.', 'NK' : 'Spirit Airlines, Inc.', 'OO' : ' Skywest Airlines',\n",
    "                       'UA' : ' United Airlines, Inc.', 'VX' : 'Virgin America', 'WN' : 'Southwest Airlines'}\n",
    "# Construct dictionnary of airlines names for later use\n",
    "airlines_le = preprocessing.LabelEncoder()\n",
    "# Get airlines names\n",
    "airlines_names = data_2016_explo.loc[:,'UNIQUE_CARRIER'].value_counts().index.values\n",
    "# Get airlines encoded values\n",
    "airlines_number = airlines_le.fit_transform(airlines_names)\n",
    "# Save dict\n",
    "airlines_dict = dict(zip(airlines_number, airlines_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax = plt.subplots(figsize=(15,6))\n",
    "# Initialise flights DF\n",
    "flights_df = pd.DataFrame()\n",
    "# Total flights per airline\n",
    "flights_df['flights'] = data_2016_explo.loc[:,'UNIQUE_CARRIER'].value_counts()\n",
    "flights_df['flights_per'] = flights_df['flights'].apply(lambda x : x / flights_df['flights'].sum())\n",
    "flights_df['init'] = flights_df.index.values\n",
    "# Airline name and mean delay\n",
    "flights_df['names'] = flights_df['init'].apply(lambda x : airlines_full_names[x])\n",
    "flights_df['means'] = flights_df['init'].apply(lambda x : mean_delays_categ['UNIQUE_CARRIER'][1][mean_delays_categ['UNIQUE_CARRIER'][0].index(x)])\n",
    "# Explode for small airlines\n",
    "explode = [0.3 if flights_df['flights'].values[i] <= flights_df['flights'].sum()*4/100 else 0.0 for i in range(len(flights_df['flights'].values))]\n",
    "# Pie chart\n",
    "ax.pie(flights_df['flights'].values, labels=flights_df['names'].values, explode=explode, autopct='%.0f%%',\n",
    "       textprops={'fontsize': 13}, startangle=15, colors=sns.color_palette(\"PuOr\", 12))\n",
    "ax.axis('equal');  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax.set_title('2016 Total flights by airline', fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort by flights descending\n",
    "flights_df = flights_df.sort_values(by=['flights'], ascending=False)\n",
    "# Save color palette from flights sort\n",
    "flights_df['color'] = sns.color_palette(\"PuOr\", flights_df.shape[0])\n",
    "# Sort by flights ascending\n",
    "flights_df = flights_df.sort_values(by=['flights'], ascending=True)\n",
    "# Reindex for plotting\n",
    "flights_df.index = np.arange(0,12,1)\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "g = sns.barplot(flights_df['flights'].index.values, flights_df['means'].values, palette=flights_df['color'].values)\n",
    "ax.set_xticklabels(flights_df['names'].values, rotation=75, fontsize=12)\n",
    "ax.set_ylabel(\"Mean delay over 2016 (min)\", fontsize=15)\n",
    "ax.set_title(\"Mean delay by airline in 2016\", loc=\"center\", fontsize=15)\n",
    "# Add percentage of 2016 flights\n",
    "for index, row in flights_df.iterrows():\n",
    "    g.text(row.name, row.means+0.2, '{:.0%} {}'.format(row.flights_per, 'flights'), color='blue', ha='center');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears from those charts that on average some airlines present more delayed flights than others. However, some airlines are very small and account for much fewer flights than others.\n",
    "\n",
    "Those different statistics would lead to think that _**a regression model for each airline may be more suitable to this DataSet.**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean delay behaviour for 5 main arline in main airport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most frequented airport is : Atlanta, GA\n",
      "The most used airlines for Atlanta, GA in 2016 are : \n",
      "Delta Air Lines, Inc.(DL) with 245511 flights in 2016\n",
      "ExpressJet Airlines(EV)   with 48048  flights in 2016\n",
      "Southwest Airlines(WN)    with 42369  flights in 2016\n",
      "American Airlines(AA)     with 11383  flights in 2016\n",
      "Spirit Airlines, Inc.(NK) with 7138   flights in 2016\n"
     ]
    }
   ],
   "source": [
    "# Main airport ?\n",
    "main_ap = data_2016_explo.loc[:, 'ORIGIN_AIRPORT_ID'].value_counts().index.values[0]\n",
    "print(\"The most frequented airport is : %s\" % airport_dict_name[main_ap])\n",
    "# 5 most used airlines in this airport in 2016 ?\n",
    "main_airlines = data_2016_explo.loc[:, 'UNIQUE_CARRIER'][data_2016_explo.loc[:, 'ORIGIN_AIRPORT_ID'] == main_ap].value_counts().head()\n",
    "print(\"The most used airlines for %s in 2016 are : \" % airport_dict_name[main_ap])\n",
    "for airline, count in main_airlines.iteritems() :\n",
    "    print(\"%-25s with %-6i flights in 2016\" % (airlines_full_names[airline] + '({})'.format(airline), count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import calendar\n",
    "months = [month for month in calendar.month_name]\n",
    "months.pop(0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "# Seaborn style\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context(font_scale=1.5, rc={\"lines.linewidth\": 2.5})\n",
    "# Iterate over airlines\n",
    "for airline, count in main_airlines.iteritems() :\n",
    "    # Retrieve relevant data\n",
    "    data_airline_airport = data_2016_explo[(data_2016_explo.loc[:, 'ORIGIN_AIRPORT_ID'] == main_ap) & \n",
    "                                           (data_2016_explo.loc[:, 'UNIQUE_CARRIER'] == airline)]\n",
    "    mean_monthly_delay = data_airline_airport['ARR_DELAY_NEW'].groupby(data_airline_airport['MONTH']).apply(lambda x : x.mean())\n",
    "    # Plot monthly mean delay\n",
    "    ax.plot(months, mean_monthly_delay.values, label=airlines_full_names[airline])\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel('Month', fontsize=12)\n",
    "ax.set_ylabel('Mean delay (min) in %s' % airport_dict[main_ap], fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cum_sum_df = pd.DataFrame()\n",
    "# Iterate over airlines\n",
    "count_pos = -2\n",
    "for airline, count in main_airlines.iteritems() :\n",
    "    temp_cum_sum_df = pd.DataFrame()\n",
    "    # Retrieve relevant data\n",
    "    data_airline_airport = data_2016_explo[(data_2016_explo.loc[:, 'ORIGIN_AIRPORT_ID'] == main_ap) & \n",
    "                                           (data_2016_explo.loc[:, 'UNIQUE_CARRIER'] == airline)]\n",
    "    cum_monthly_delay = data_airline_airport['ARR_DELAY_NEW'].groupby(data_airline_airport['MONTH']).sum().cumsum()\n",
    "    temp_cum_sum_df['month'] = cum_monthly_delay.index.values\n",
    "    temp_cum_sum_df['cum_delay'] = cum_monthly_delay.apply(lambda x : x/60) \n",
    "    temp_cum_sum_df['airline'] = airline\n",
    "    temp_cum_sum_df['pos_text'] = count_pos / 5\n",
    "    \n",
    "    cum_sum_df = pd.concat((cum_sum_df, temp_cum_sum_df))\n",
    "    \n",
    "    count_pos += 1\n",
    "    \n",
    "# Replace NaN of January with 0\n",
    "cum_sum_df = cum_sum_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def autolabel(bps, label):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar displaying a label\n",
    "    \"\"\"\n",
    "    for bp in bps:\n",
    "        height = bp.get_height()\n",
    "        ax.text(bp.get_x() + bp.get_width()/2., 1.02*height, '%s' % label, ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "# Number of x abscisses\n",
    "N = cum_sum_df.month.unique().shape[0]\n",
    "\n",
    "ind = np.arange(N)  # the x locations for the months\n",
    "width = 0.15       # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "# Seaborn style\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context(font_scale=1.5, rc={\"lines.linewidth\": 2.5})\n",
    "\n",
    "mult_pos = 0\n",
    "for airline in cum_sum_df.airline.unique() :\n",
    "    # Plot monthly cumulated delay\n",
    "    bp = ax.bar(ind + (width*1.1) * mult_pos, cum_sum_df[cum_sum_df.airline==airline].cum_delay, width, label=airline)\n",
    "    # Loop\n",
    "    mult_pos += 1\n",
    "    # Insert airline name on bar\n",
    "    autolabel(bp, airline)\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xticks(ind + width / 2)\n",
    "ax.set_xticklabels(months, fontsize=13)\n",
    "ax.set_xlabel('Month', fontsize=12)\n",
    "ax.set_ylabel('Cumulated delay (h) in %s' % airport_dict[main_ap], fontsize=12)\n",
    "ax.set_ylim((0,25000));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the delays themselves, over the full year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median : 0.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    5.341242e+06\n",
       "mean     1.188756e+01\n",
       "std      3.827911e+01\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      0.000000e+00\n",
       "75%      6.000000e+00\n",
       "95%      6.600000e+01\n",
       "99%      1.720000e+02\n",
       "max      2.142000e+03\n",
       "Name: ARR_DELAY_NEW, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistics\n",
    "print(\"Median : %.2f\" % data_2016_explo.loc[:, 'ARR_DELAY_NEW'].median(axis=0))\n",
    "data_2016_explo.loc[:, 'ARR_DELAY_NEW'].describe(percentiles=[0.25, 0.5, 0.75, 0.95, 0.99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median is equal to zero, and the fourth quartile is at 7min of delay, therefore one can assume that most of the flights are on schedule ! That's a relief.\n",
    "In order to not bias our model we should not give too much credit to the high delays which are most likely due to rare airplanes damage, or weather."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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h4+OD4uJiODg4ICIiAm5ubnJthg0bhuTkZJSUlCAhIUHpqEBV9kkVSUlJMDQ0rDQXpNzu3bsxefJk9OrVC7GxsS99fVH0f/kqz7ei16LqnJT7X8IwICJ///03Tp48CVdX10rrzp49i/DwcLRr1w6+vr5Yv349/Pz8sHv3bgBQ+Q2honPnzsndPnnyJNq1awfg2YteSUmJ3Prs7Gy528q2q6enh2bNmsldgwB4do6zoaHha83Yb9++PQDgxIkTcstPnDghq1mdzM3NkZmZKTd57PTp06/UR8XHZ+XKlThx4gT69euHkJAQ/PLLL5BKpXITN5/n5OSEnJwc7NixA127doWWlhZ69uyJ9PR0/P777+jbt69K29WELl264OjRo3LL0tPTYWdnp/Ftv6pu3bohOjoaBw8ehK2tLRYsWICffvqpUjsDAwM0bdpUbrhfKpXK/Z+0a9cOly9fRvPmzdG6dWu0bt0aWlpaWLRoEW7evFmpzx9++AHTpk3D3LlzMWLECHTq1AnZ2dlyb2q9e/dGgwYNsHHjRmRlZWHw4MFq26dXUT4pVNEbdmJiImbPno1hw4Zh+fLlL/zQQOrHMFBLPXjwAHfv3sXdu3dx/fp1xMfH49NPP0XXrl0rfWoAng2Hbt68GVFRUbh27RrOnz+P5ORkWFpaAoDs+Pj58+dRVFSkch179uzBpk2bcOXKFSxZsgRnz57FJ598AgCwtrZGfn4+vv/+e9kEr+dn/pZv99KlSwovdjR58mRs2LABcXFxyM7Oxvbt27Fp0yaMGzfutd6sjI2N8eGHH2L+/PlITU3F5cuXER4ejszMTIwdO/aV+3sZLy8vFBQUICQkBJcvX0ZSUhKWL18OQPU32/Ln5ezZsygpKcGNGzcQEhKC9PR03LhxAwkJCSgqKoKVlZXC+xsYGMDS0hLbtm1D9+7dAQA9e/bEX3/9haKiIvTo0UOl7b6OkpIS3L17V+n6MWPG4NixY4iOjsbly5exfPlynD59Gh9//PFrba86GBkZYebMmUhJSVF6rNvHxwcbNmxAfHw8rly5gtDQUOTm5srWjxkzBoWFhQgICMA///yDs2fP4osvvkBWVhbatGlTqT8DAwPZ3+vFixexYMECnDx5Um5ovG7dunB1dcXq1avh4OCAd999V637pKpz587BzMys0vK8vDzMmTMHvXr1wrRp01BQUCB7/VI0x4nUj2Ggllq7di3s7e1hb28Pd3d3rFu3Dl5eXoiNjVU4maxNmzZYtWoV/vzzT7i5uWHs2LFo1qwZoqKiADz7tOLi4gI/P79Xmm3r6+uLxMREuLm54dChQ1izZo1sRnKPHj0wdepUrF27Fh9++CHS0tIwbdo0uft/+umn2Lp1K3x9fSv1PWrUKPj5+eGbb77Bhx9+iPXr1yMgIEAWNl5HaGgoHBwcMGvWLAwbNgynT5/GunXrKs19UIfGjRsjNjYWZ8+ehbu7O6KiouDl5QVA8VC1It27d0e3bt3g6emJ7du3IygoCD169MDMmTPh4uKC77//HuHh4UqHZYFnhwqePHkie+Pv3LkzGjZsiF69eik9HFNxu6/ju+++g729vdL15VenO3DgAIYMGYLffvtN7u/nv6xevXpo3LixwnXjxo3DtGnTsGzZMgwdOhQlJSX44IMPZOsNDQ2xfv165OXlYeTIkfjkk09gZGSE9evXK/y0HBERgcLCQgwdOhTjx4/HvXv3MHPmTFy6dAkPHz6UtRsyZAgePXqEoUOHqn2fVHX37t1Kk4GBZ4cPHjx4gNTUVNnrVvlPeHh4lbZJqpEIYj1AQlTDLl26hKKiIrmgsW/fPgQEBODkyZMamadA4pWcnIzZs2cjJSWFQ/BUCUcGiGrIzZs3MXbsWCQmJiI3NxdHjhxBdHQ0Bg0axCBAanP58mUkJiYiMjISHh4eDAKkEEcGiGrQpk2bsHHjRuTm5uLtt9/GwIED4efn99LT54hU9euvv2LWrFno2rUrli9fzsvxkkIMA0RERCLHwwREREQiJ8oDk48ePUJGRgYMDQ0VzqwnIiKqTcrKynD37l1YWFgovJiTKMNARkaG7Fv4iIiIxGLz5s0KL9wlyjBQ/uUomzdvRrNmzWq4GiIiIs26desWRo8erfTLwUQZBsoPDTRr1kyt38ZFRET0X6bs0DgnEBIREYkcwwAREZHIMQwQERGJHMMAERGRyDEMEBERiRzDABERkcgxDBAREYkcwwAREZHIMQwQERGJnCivQKgJsbGqtZswQbN1EBERvSqODBAREYkcwwAREZHIMQwQERGJHMMAERGRyDEMEBERiRzDABERkcgxDBAREYkcwwAREZHIMQwQERGJHMMAERGRyDEMEBERiRzDABERkcgxDBAREYkcwwAREZHIMQwQERGJHMMAERGRyFVrGMjPz4eTkxMuX76M7OxseHp6wsvLC/PmzYNUKgUArFy5Eh999BFGjRqFM2fOAIBa2hIREZFi1RYGSktLERwcjPr16wMAwsPDMWPGDGzZsgWCICApKQmZmZk4cuQI4uLiEBUVhZCQELW0JSIiIuWqLQxERERg1KhRaNKkCQAgMzMT3bp1AwA4Ojri8OHDOH78OOzt7SGRSNC8eXOUlZWhoKCgym2JiIhIuWoJA7t27YKBgQEcHBxkywRBgEQiAQDo6uqiqKgIxcXF0NPTk7UpX17VtkRERKRcnerYyM6dOyGRSJCWlobz58/D398fBQUFsvUlJSXQ19eHnp4eSkpK5JY3atQIWlpaVWpLREREylXLyMDmzZuxadMmbNy4ER06dEBERAQcHR2Rnp4OAEhJSYGdnR1sbW2RmpoKqVSK3NxcSKVSGBgYoGPHjlVqS0RERMpVy8iAIv7+/pg7dy6ioqLQtm1buLi4QFtbG3Z2dvDw8IBUKkVwcLBa2hIREZFyEkEQhJouorrl5OSgb9++SEpKQsuWLdXSZ2ysau0mTFDL5oiIiFT2svc9XnSIiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESuTnVtqKysDEFBQbh69Sq0tbURHh4OQRAQEBAAiUSC9u3bY968edDS0sLKlStx6NAh1KlTB4GBgbC0tER2dnaV2xIREVFl1fYOmZycDAD48ccfMW3aNISHhyM8PBwzZszAli1bIAgCkpKSkJmZiSNHjiAuLg5RUVEICQkBgCq3JSIiIsWqbWTggw8+QO/evQEAubm5aNy4MQ4dOoRu3boBABwdHfHnn3/CxMQE9vb2kEgkaN68OcrKylBQUIDMzMwqte3Xr1917SoREdEbpVrHzuvUqQN/f3+EhobCxcUFgiBAIpEAAHR1dVFUVITi4mLo6enJ7lO+vKptiYiISLFqP5AeERGBAwcOYO7cuXj8+LFseUlJCfT19aGnp4eSkhK55Y0aNZI75v86bYmIiEixagsD8fHx+OabbwAADRo0gEQigYWFBdLT0wEAKSkpsLOzg62tLVJTUyGVSpGbmwupVAoDAwN07NixSm2JiIhIMZXCwNixY1FYWFhpeUFBAYYNG6bShvr3749z585h9OjR8PX1RWBgIIKDg7FixQp4eHigtLQULi4usLCwgJ2dHTw8PDB16lQEBwcDAPz9/avUloiIiBSTCIIgKFpx4sQJXLt2DQAwe/ZszJkzR+74PABcunQJW7ZswYkTJzRfqRrl5OSgb9++SEpKQsuWLdXSZ2ysau0mTFDL5oiIiFT2svc9pWcTaGlpISgoCOVZITw8XG69RCKBrq4uPvvsMzWXTERERNVJaRiwtrZGRkYGAMDZ2Rk7duyAgYFBtRVGRERE1UOl6wz89ttvmq6DiIiIaohKYeDu3buIjo7GqVOn8OTJk0rrDxw4oPbCiIiIqHqoFAaCg4Nx4sQJuLu785x9IiKiWkalMHD8+HEsW7YM77//vqbrISIiomqm0nUGdHR00LRpU03XQkRERDVApTDg5eWF6OhoPHz4UNP1EBERUTVT6TDBiRMnkJ6ejq5du8LQ0BA6Ojpy6zmBkIiI6M2lUhiwtraGtbW1pmshIiKiGqBSGJgyZYqm6yAiIqIaolIYWLNmzQvXT5o0SS3FEBERUfVTKQxs375d7nZZWRny8/NRp04d2NraMgwQERG9wV77csTFxcWYPXs2unTpovaiiIiIqPqodGqhInp6epg2bRq+++47ddZDRERE1ey1wwAAlJSUoKioSF21EBERUQ147QmExcXF2LdvH7p37672ooiIiKj6vNYEQgCoW7cuunfvDj8/P7UXRURERNXntScQEhERUe2gUhgAnh0WSEhIwMWLF1GnTh20b98egwYNgp6enibrIyIiIg1TKQxcv34d3t7euH//PkxNTSGVSrFjxw7ExMRg8+bNaNGihabrJCIiIg1R6WyCr776CsbGxvjtt9+wY8cO7Nq1C0lJSWjTpg0iIyM1XSMRERFpkEphIC0tDQEBAXjnnXdkywwMDDBr1iykpaVprDgiIiLSPJXCQP369aGlVbmplpYWnj59qvaiiIiIqPqoFAa6d++OxYsXy11gqLCwEF9//TWvM0BERPSGU2kC4ZdffolRo0bByckJpqamAIDLly/DwMCAlyMmIiJ6w6kUBoyMjLBv3z7ZqYX16tWDh4cH3NzcoKOjo+kaiYiISINUvs7AmTNnYGxsDC8vLwDAwoULceLECfTo0UNjxREREZHmqTRnID4+HhMmTMCVK1dky+7fv49PPvkE+/fv11hxREREpHkqjQzExsZi3rx5GDFihGxZZGQk7OzsEBMTg4EDB2qsQCIiItIslcLAjRs3FB4O6NmzJxYuXKj2omqz2FjV2k2YoNk6iIiIyql0mMDY2Bi///57peV//vknjIyM1F4UERERVR+VRgZ8fX0RFBSEc+fOoXPnzgCAjIwMJCQkIDg4WKMFEhERkWapFAaGDBkCHR0dbNiwAfv370fdunXRtm1bLF26FB988IGmayQiIiINUvnUwkGDBmHQoEGarIWIiIhqgEpzBoiIiKj2YhggIiISOYYBIiIikWMYICIiEjmVw8CNGzdQXFwMADh69CgWLFiAPXv2aKwwIiIiqh4qhYGff/4ZLi4uOH36NK5du4YJEybg6NGjmD9/Pr7//nsNl0hERESapFIYiImJwWeffYZevXphz549aNq0KRISEhAZGYmtW7dqukYiIiLSIJXCwNWrVzFkyBAAwB9//IE+ffpAIpGgU6dOuHnzpkYLJCIiIs1SKQy88847yMvLQ15eHjIyMtCrVy8AwIULF9C4cWONFkhERESapdIVCD/88EP83//9H+rXr4+mTZuiZ8+eSExMRFhYGD766CNN10hEREQapFIYmDVrFpo3b45r167By8sL2trauHfvHkaPHo3JkydrukYiIiLSIJXCQExMDHx9fdGgQQPZMi8vLxQXFyMiIgKzZ8/WWIFERESkWUrnDBQUFCA3Nxe5ublYtWoVrly5Irtd/vPXX3/xbAIiIqI3nNKRgZSUFAQEBEAikQCAwrkBgiCgf//+mquOiIiINE5pGBgyZAiMjY0hlUoxZswYxMTE4K233pKtl0gk0NXVRbt27aqlUCIiItKMF84ZsLW1BQAkJSWhefPmslECIiIiqj1UmkDYvHlzJCQkICMjA48fP4YgCHLrQ0NDNVIcERERaZ5KYSA8PBwbN26EmZkZ9PX15dapMlpQWlqKwMBA3LhxA0+ePMHkyZMnEUC+AAAdQ0lEQVTRrl072ZyE9u3bY968edDS0sLKlStx6NAh1KlTB4GBgbC0tER2dnaV2xIREZFiKoWBPXv2IDw8XHZJ4leVkJCAt99+G4sXL8a///6LoUOH4r333sOMGTPQvXt3BAcHyw5FHDlyBHFxcbh58yamTp2KnTt3Ijw8vEpt+/Xr91p1ExERiYFKYaC0tBR2dnavvZEBAwbAxcVFdltbWxuZmZno1q0bAMDR0RF//vknTExMYG9vD4lEgubNm6OsrAwFBQVVbsswQEREpJxK4+f9+vXDTz/99Nob0dXVhZ6eHoqLizFt2jTMmDEDgiDIDjHo6uqiqKgIxcXF0NPTk7tfUVFRldsSERGRckpHBubOnSv7/fHjx1i+fDlSUlLQpk0baGtry7VVZQLhzZs38fnnn8PLywuDBw/G4sWLZetKSkqgr68PPT09lJSUyC1v1KiR3DH/12lLREREyikdGcjKypL95Ofnw87ODtra2rh+/brcuqysrJduJC8vDz4+Ppg1a5bs4kUdO3ZEeno6gGcXOLKzs4OtrS1SU1MhlUqRm5sLqVQKAwODKrclIiIi5ZSODGzcuFFtG1mzZg0KCwsRExODmJgYAMCcOXMQFhaGqKgotG3bFi4uLtDW1oadnR08PDwglUoRHBwMAPD398fcuXNfuy0REREpJxEqXjRAgaNHjyq+s0SCunXrolmzZmjatKnai9OUnJwc9O3bF0lJSWjZsqVa+oyNVUs3MhMmqLc/IiISr5e976l0NsG4ceMglUoBQHbBoYrXF+jatSuio6Px9ttvV7VmIiIiqkYqnU2waNEiNG/eHKtXr8bRo0dx9OhRxMbGwtjYGLNmzcLWrVvx5MkTuUmBRERE9GZQKQxER0cjLCwMvXv3RqNGjdCoUSM4ODggJCQEW7ZsgbW1NQIDA5GcnKzpeomIiEjNVAoDBQUFaNKkSaXl7777Lu7evSv7/cGDB+qtjoiIiDROpTBgY2ODJUuWyJ3XX1xcjGXLlsHS0hIA8Pvvv6N169aaqZKIiIg0RqUJhMHBwRg/fjycnJxgamoKqVSKK1euQF9fH2vXrkVaWhrCwsKwZMkSTddLREREaqZSGGjTpg0SExORmJiI8+fPQ1tbG56ennB1dYWOjg4aNmyIvXv3wtTUVNP1EhERkZqpFAYAoEGDBhg+fLjCdc2bN1dbQURERFS9lIYBFxcXbNu2DW+//Tb69+9f6boCzztw4IBGihOzV7mIES9QREREVaE0DAwePBj16tWT/f6iMEBERERvLqVhYMqUKbLfp06dWi3FEBERUfVTGgbWrFmjUgcSiQQTJ05UW0FERERUvZSGge3bt6vUAcMAERHRm01pGPjtt9+qsw4iIiKqIUqvQLh582Y8fvy4OmshIiKiGqA0DISFhaG4uFhu2dy5c1FQUKDxooiIiKj6KA0DgiBUWrZv3z657ycgIiKiN59KX1RUTlFAICIiojfbK4UBIiIiqn1eGAZ41UEiIqLa74VfVBQeHo769evLbpeWliIqKgp6enpy7UJDQzVTHREREWmc0jDQtWtX3Lp1S26ZjY0N8vLykJeXJ1vG0QMiIqI3m9IwsHHjxuqsg4iIiGoIJxASERGJHMMAERGRyDEMEBERiRzDABERkcgxDBAREYkcwwAREZHIMQwQERGJHMMAERGRyDEMEBERiRzDABERkcgxDBAREYncC7+1kN4MsbGqtZswQbN1EBHRm4kjA0RERCLHMEBERCRyDANEREQixzkDIsK5BUREpAhHBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRI5hgIiISOQYBoiIiESuWsPA6dOn4e3tDQDIzs6Gp6cnvLy8MG/ePEilUgDAypUr8dFHH2HUqFE4c+aM2toSERGRYtUWBtauXYugoCA8fvwYABAeHo4ZM2Zgy5YtEAQBSUlJyMzMxJEjRxAXF4eoqCiEhISopS0REREpV21hwNjYGCtWrJDdzszMRLdu3QAAjo6OOHz4MI4fPw57e3tIJBI0b94cZWVlKCgoqHJbIiIiUq7awoCLiwvq1Kkjuy0IAiQSCQBAV1cXRUVFKC4uhp6enqxN+fKqtiUiIiLlamwCoZbW/zZdUlICfX196OnpoaSkRG55o0aNqtyWiIiIlKuxMNCxY0ekp6cDAFJSUmBnZwdbW1ukpqZCKpUiNzcXUqkUBgYGVW5LREREytV5eRPN8Pf3x9y5cxEVFYW2bdvCxcUF2trasLOzg4eHB6RSKYKDg9XSloiIiJSTCIIg1HQR1S0nJwd9+/ZFUlISWrZsqZY+Y2PV0s1/woQJNV0BERGp08ve93jRISIiIpFjGCAiIhI5hgEiIiKRYxggIiISOYYBIiIikWMYICIiErkau84A/XepepokT0EkIqodODJAREQkcgwDREREIscwQEREJHIMA0RERCLHMEBERCRyDANEREQixzBAREQkcgwDREREIseLDtFr48WJiIhqB44MEBERiRzDABERkcgxDBAREYkcwwAREZHIcQIh/WdwQiIRUc1gGCCNU/VNnoiIagYPExAREYkcwwAREZHIMQwQERGJHMMAERGRyDEMEBERiRzDABERkcgxDBAREYkcwwAREZHIMQwQERGJHK9ASG+cV7miIS9dTET0chwZICIiEjmGASIiIpHjYQKq1fhNiEREL8cwQPQKGC6IqDbiYQIiIiKR48gAEV7tDAUiotqGIwNEREQixzBAREQkcjxMQKQBnGhIRG8ShgGiWoRXZySi18EwQFSDOIJARP8FDANEbwCe7UBEmsQJhERERCLHkQEikaqpQxQ8NEL038MwQEQvxDdvotqPYYCI1ILzGojeXAwDRPSfpO5w8SojFxwNIbFhGCAiUeDIBZFyDANERK/pvx4wOHJBqmIYICKqpf7rYQVgYPmvqJVhQCqVYv78+fjnn3+go6ODsLAwtG7duqbLIiKiCt6EwFJTqjMo1cowcPDgQTx58gTbtm3DqVOn8NVXX2H16tWy9WVlZQCAW7duqW2b9++rrSsiIiLk5Kivr/L3u/L3v4pqZRg4fvw4HBwcAADW1tbIyMiQW3/37l0AwOjRo6u9NiIiIlV8+636+7x7967CkfJaGQaKi4uhp6cnu62trY2nT5+iTp1nu2thYYHNmzfD0NAQ2traNVUmERFRtSgrK8Pdu3dhYWGhcH2tDAN6enooKSmR3ZZKpbIgAAD169eHnZ1dTZRGRERUI140d65WflGRra0tUlJSAACnTp2CmZlZDVdERET03yURBEGo6SLUrfxsggsXLkAQBCxatAimpqZq7782n61QWlqKwMBA3LhxA0+ePMHkyZPRrFkzTJo0CW3atAEAeHp6YtCgQTVbqBoNGTIEjRo1AgC0bNkSHh4eWLhwIbS1tWFvb48pU6bUcIXqs2vXLuzevRsA8PjxY5w/fx5LlixBZGQkjIyMAABTp05Ft27darLMKjt9+jS+/vprbNy4EdnZ2QgICIBEIkH79u0xb948aGlpYeXKlTh06BDq1KmDwMBAWFpa1nTZr+35/T1//jxCQ0Ohra0NHR0dREREoHHjxggLC8OJEyegq6sLAIiJiZH93b9pnt/fzMxMha9PteX5fX5f/fz8kJeXBwC4ceMGrKyssHTpUkyaNAn37t1D3bp1Ua9ePXz7KpMOBHplBw4cEPz9/QVBEISTJ08KkyZNquGK1G/Hjh1CWFiYIAiCUFBQIDg5OQnbt28X1q1bV8OVacajR48Ed3d3uWVubm5Cdna2IJVKhU8++UTIyMiooeo0a/78+cKPP/4oREVFCT///HNNl6M2sbGxgqurqzBixAhBEARh4sSJwl9//SUIgiDMnTtX+OWXX4SMjAzB29tbkEqlwo0bN4Rhw4bVZMlVUnF/R48eLZw7d04QBEHYunWrsGjRIkEQBGHUqFFCfn5+jdWpLhX3V9HrU215fivua7l79+4Jbm5uwu3btwVBEISBAwcKUqn0tbZRKw8TaNrLzlaoDQYMGIDp06fLbmtrayMjIwOHDh3C6NGjERgYiOLi4hqsUL3+/vtvPHz4ED4+Phg7diyOHj2KJ0+ewNjYGBKJBPb29khLS6vpMtXu7NmzuHTpEjw8PJCZmYmdO3fCy8sLX331FZ4+fVrT5VWJsbExVqxYIbudmZkpG+lwdHTE4cOHcfz4cdjb20MikaB58+YoKytDQUFBTZVcJRX3NyoqCh06dADwbPJYvXr1IJVKkZ2djeDgYIwaNQo7duyoqXKrrOL+Knp9qi3Pb8V9LbdixQqMGTMGTZo0QV5eHgoLCzFp0iR4enoiOTn5lbbBMPAalJ2tUJvo6upCT08PxcXFmDZtGmbMmAFLS0t8+eWX2Lx5M1q1aoVVq1bVdJlqU79+ffj6+mLdunUICQnB7Nmz0aBBA9l6XV1dFBUV1WCFmvHNN9/g888/BwD06tULc+fOxebNm/HgwQP8+OOPNVxd1bi4uMhNHBYEARKJBMD/ns+K/8tv8vNccX+bNGkCADhx4gQ2bdqEcePG4cGDBxgzZgwWL16Mb7/9Flu2bMHff/9dUyVXScX9VfT6VFue34r7CgD5+flIS0vDsGHDADw7tOvj44NVq1Zh5cqVCA8PR35+vsrbYBh4DS87W6G2uHnzJsaOHQt3d3cMHjwY/fr1k52W0q9fP5w7d66GK1QfExMTuLm5QSKRwMTEBI0aNcK9e/dk60tKSqCvr1+DFapfYWEhrly5gh49egAAhg8fjlatWkEikaBv37616vkFAC2t/73clT+fFf+XS0pK3tjj54okJiZi3rx5iI2NhYGBARo0aICxY8eiQYMG0NPTQ48ePd7YMFCRoten2vz8/vzzz3B1dZWdHt+4cWOMGjUKderUwbvvvosOHTrg6tWrKvfHMPAaxHC2Ql5eHnx8fDBr1ix89NFHAABfX1+cOXMGAJCWloZOnTrVZIlqtWPHDnz11VcAgNu3b+Phw4do2LAhrl27BkEQkJqaWutORz169Cjef/99AM8+Nbu5ucmuUlbbnl8A6NixI9LT0wEAKSkpsLOzg62tLVJTUyGVSpGbmwupVAoDA4MarlQ99uzZg02bNmHjxo1o1aoVACArKwteXl4oKytDaWkpTpw4UWueZ0WvT7X5+U1LS4Ojo6Ps9uHDhzFjxgwAz0LPxYsX0bZtW5X7q30fZ6tBv3798Oeff2LUqFGysxVqmzVr1qCwsBAxMTGIiYkBAAQEBGDRokWoW7cuGjdujNDQ0BquUn0++ugjzJ49G56enpBIJFi0aBG0tLTwf//3fygrK4O9vT2srKxquky1unr1Klq2bAkAkEgkCAsLw5QpU1C/fn2Ymppi5MiRNVyhevn7+2Pu3LmIiopC27Zt4eLiAm1tbdjZ2cHDwwNSqRTBwcE1XaZalJWVYeHChTAyMsLUqVMBAF27dsW0adMwePBgjBw5EnXr1oW7uzvat29fw9Wqx/z58xEaGir3+qSnp1crn1/g2f9vecgDACcnJ6SmpmLkyJHQ0tLCF1988UrBp1aeWkhERESq42ECIiIikWMYICIiEjmGASIiIpFjGCAiIhI5hgEiIiKRYxgg0fD29oa5ubnCn02bNgEAnJ2dZadSrlixAv369Xul/ufMmaN0vSAIiI+Pf6Wrgh07dgzm5ubIyclRqf2uXbvQsWNHlfun/1HnY5eeng5zc3PZdRuUmTNnzmtfEjgnJwfm5uY4duyYSu0vXryI4cOH17qrpZJ68DoDJCqurq4ICAiotPz5S5aW8/HxwejRo9W27RMnTsDf3x9JSUlq65PUZ9CgQXIXcdG0tLQ0nDlz5rWv12FkZITU1FS8/fbbKrVv3749OnTogLVr12Ly5MmvtU2qvRgGSFTq168PQ0NDldrq6urKvuZVHXhJj/+2+vXro379+tW2vWXLluHjjz+Wu0zyq9DW1lb5b7nc+PHjMWLECIwZM6bWXJaX1IOHCYiUqHiY4OrVq/Dx8YG1tTWcnZ0RHx8vd4lb4NmXWH355ZewsbFB9+7dsXDhQpSVlSEnJ0c2ytC3b1+F30AGPPv2xDFjxsDKygqurq7IzMyUWy+VSrFmzRr06dMH1tbWGD58OH7//Xel+/D333/j008/hZ2dHSwsLODi4oL4+HgAwK+//oqOHTvi7t27svaCIKBPnz744YcfKvWlaFi64jJvb29ERkZi6tSpsLS0hLOzM7Zu3SrXz7FjxzBmzBjY2Njg/fffR1hYGB4+fCjX34EDBzB06FBYWVnB3d0dBw8eVLqPjx8/Rnh4OPr06QMLCwv06NEDs2fPlvW5a9cuuLi4YP78+ejSpQu+/PJLpcvKDxP4+/vD29tbbjtnzpyBubk5srOzIZVKERMTg/79+8PCwgJ2dnaYOnWqyt+Id/LkSWRmZuKDDz6QLfP29kZ0dDT8/f1hbW0Ne3t7bN++HceOHYObmxusrKzg6emJa9euKX3slyxZglmzZsHW1haOjo4IDQ2VOyxgamqKFi1aYPv27SrVSeLBMECkggcPHmD8+PHQ0dHB9u3bERoaiujoaJSVlcm1+/nnn9G6dWskJCQgICAAGzduxN69e2FkZCSbixAXFwcfH59K27h//z7GjRuHxo0bY+fOnZgxYwbWrFkj12bJkiXYtWsXFixYgD179mDo0KGYMmWKXCB5vmYfHx80adIE27dvx549e9C1a1cEBQUhLy8PvXv3hr6+Pvbt2ye7z9GjR3H37l0MHjz4tR+rDRs2oFmzZti9ezd8fX0RGhqKn376CQBw+vRpjBs3Dp07d8aOHTsQHh6OpKQk+Pn5yfURGRkJPz8/xMXFwcjICP7+/njw4IHC7UVERCA5ORmLFy/Gzz//jODgYOzbtw/btm2TtcnKykJxcTHi4+MxceJEpcvKDRkyBMeOHcPt27dly/bu3QsbGxu0bt0a69evx4YNGxAUFIQDBw5gyZIlOH78OFavXq3SY/Tbb7/Bysqq0hD/2rVrYW5ujr1796Jv375YsGABQkJCEBQUhE2bNuH27duIiopS2u/69ethYmKCnTt3YuLEidi8ebPc8ws8u2ztb7/9plKdJB4MAyQq8fHxsLGxkft50aS/cvv370dhYSEWL14MMzMz2df9VmRjY4PPP/8crVq1wtChQ2FmZoaMjAxoa2vjrbfeAgAYGBgoPPywb98+lJaWYuHChWjXrh0++OADTJkyRba+pKQEGzZsQGBgIBwcHNC6dWuMGTMG7u7uiI2NrdTfw4cPMW7cOAQFBaFt27YwNTXFxIkTUVpaiqysLNStWxeurq5ISEiQ3WfPnj1wdHSs0pe5mJmZYc6cOTA1NcXo0aPh6uqKjRs3AgC+++47WFhYwN/fH6ampnBycsL8+fORnJyMixcvyvrw9fWFo6MjzMzMMH36dBQXF+PSpUsKt2dlZYXw8HDY2dmhZcuWGDRoECwtLXHhwgW5dp999hlatWoFU1PTFy4DgB49eqBZs2ZITEwE8Oxa//v378eQIUMAPPuWy4iICDg6OqJFixZwcnKCg4NDpW0qc/r0abRr167S8k6dOsHHxwetWrXCmDFjUFpainHjxqFbt27o3LkzBg4cKPc4VdShQwd89tlnMDExwejRo2Fubo5Tp07JtWnfvj1Onz6tUp0kHpwzQKLywQcf4IsvvpBbpsq8gHPnzsHU1FTuOGuXLl0qtTMxMZG7/dZbb+HRo0cq1Xbx4kWYmJjI1WNtbS37/fLly3jy5AmmT58ud5y5tLQUjRs3rtTfu+++Cy8vL8THx+P8+fPIysqSfV1t+YjGsGHDsHHjRly+fBktW7bEgQMHEB4erlK9ynTt2lXutpWVFX799VfZPjo5OcmtL/82yIsXL8LS0hKA/ONY/piXlpYq3J67uztSU1MRGRmJrKwsXLp0CdeuXZN9CRPw7IuYnr+tbNnz69zc3PDTTz9h/PjxSEtLw/379zFo0CAAz846OXnyJJYuXYqrV6/iypUruHz5ssrfbJmfnw9bW9tKy1u3bi37vUGDBgAAY2Nj2bL69evjyZMnSvtt06aN3G19ff1Kj5uBgQFKS0tx//59WUAlYhggUdHT05N7wVWVtrY2pFLpS9spmgym6sRBiURSqW3dunVlv+vo6AB4Npeh4j4o2u6dO3fg4eGBpk2bok+fPujduzeaNGmC4cOHy9p07NgR7733Hvbu3Qtzc3Noa2ujd+/eKtULoNJhEgCoU0f+ZUUqlUIikQAA6tWrV6l9+T4/f7/n97tiu4qCgoJw8OBBDB06FP3794efnx8WLFgg10ZLS0v2+L1o2fOGDh2KNWvWICsrCz/99BOcnZ2hr68PAFi9ejViY2MxbNgwODg4YOLEidiwYQNyc3OV9vc8Rc81UPmxK2+rKkX7U3E75c+Ztra2yv1S7ccwQKQCc3NzxMXFoaioSPZJ9VWHWl/2ot6hQwfs3r1b7hNbRkaGbH3r1q1Rt25d3L59W+4UuJUrV6KsrAzTp0+X6+/XX39FSUkJNm/eLHvh/+OPPwDIv0EMHToUO3bswPXr1zF48GCFb8TA/96gS0pKZMuysrIqtas46fHUqVOyiXnt2rXDyZMn5dYfP34cACoN1auiuLgYO3fuxPLly9G/f38AwNOnT3H9+nU0b978lft7Xps2bWBjY4N9+/bh4MGD+Prrr2XrfvjhB0ybNg3jx4+XLcvOzlb4Zq6IoaGhypMN1e3ff/9FgwYNFJ5OS+LFOQNEKnB1dYW+vj78/f1x4cIF/PXXX7Lzw1X95FY+/H/+/HkUFRVVWj9w4EC89dZb+PLLL3HhwgX88ccfiI6Olq1v0KABxo0bhyVLliAxMRHXr1/Hhg0bsGrVKrnvNS/3zjvvoLi4GAcOHMCNGzeQlJSEefPmAYDcULObmxuysrJkn66VadKkCVq0aIHvv/8eV65cwbFjx7Bs2bJK+5+WloY1a9bg6tWr2LBhA/bv3y+bMPnpp5/i7NmziIiIwJUrV/DHH38gJCQETk5OrxUG6tWrh4YNGyIpKQnXrl3DuXPnMHPmTNy8efOFw+mqGjJkCNatWwcdHR3Y29vLlhsYGCA1NRWXL1/GxYsXsWDBApw8eVLlbVpaWuLcuXNVru91ZGZmyg7HEJVjGCBSQb169bB27VoUFhZi+PDhCAwMxMiRIwEoHtJWpF27dnBxcYGfn5/cm3w5PT09/PDDD3j69ClGjBiBBQsW4NNPP5VrM2PGDHh6eiIyMhIDBw7E1q1bsWDBAgwbNqxSfwMHDsTHH3+MsLAwfPjhh1i+fDk+++wztG7dGmfPnpW1MzAwgL29PYyNjdGpUyel9UskEkRGRuL+/ftwc3PDvHnzMHPmzEqHKPr3748zZ87A3d0dW7duxeLFi+Hs7Azg2eTCNWvW4MiRI3Bzc8Ps2bPRr18/LF++XKXHsKK6deti2bJlyMzMhKurKz777DO89dZb8PHxkRtVeV2DBg3C06dP4erqKvepPyIiAoWFhRg6dCjGjx+Pe/fuYebMmbh06ZLslMYXcXZ2xt9//43CwsIq1/iqjhw5gr59+1b7dum/TSLwSihEL3Xjxg1cu3YNPXv2lC07deoUPDw8cOjQIRgZGdVgdVU3bNgwDB48WG7Y+3V4e3vD2NgYCxcuVFNltdeIESMwZMgQtV7l8mXOnz+PMWPGIDk5WTb/gQjgyACRSh49egQfHx9s3rwZOTk5OHPmDL766it07dr1jQ4CBw8exJIlS5CVlfXCQwSkftOnT8emTZsUTsLUlA0bNmD8+PEMAlQJwwCRCkxNTbFkyRJs27YNgwYNwoQJE2BiYqJwuP9Nsnr1atnFf1S9xj2ph729PaytrbFz585q2d6FCxfw999/Y8KECdWyPXqz8DABERGRyHFkgIiISOQYBoiIiESOYYCIiEjkGAaIiIhEjmGAiIhI5BgGiIiIRO7/Aa0EppzhqUvwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "sns.distplot(data_2016_explo.loc[:, 'ARR_DELAY_NEW'][(data_2016_explo.loc[:, 'ARR_DELAY_NEW'] > 0) & (data_2016_explo.loc[:, 'ARR_DELAY_NEW'] < 172)].values,\n",
    "             kde=False,\n",
    "             bins=40,\n",
    "             color='b')\n",
    "ax.set_xlabel(\"Flight delay upon arrival (min)\", fontsize=15)\n",
    "ax.set_ylabel(\"Flights count\", fontsize=15)\n",
    "ax.set_title(\"Distribution of flights with : 0 min < delay < 172 min\", fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1c8b0c0f0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_2016_explo.loc[:, 'ARR_DELAY_NEW'][data_2016_explo.loc[:, 'ARR_DELAY_NEW'] > 0].values,\n",
    "             kde=False,\n",
    "             color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this distribution and the statistics we choose to select the delays inferiors to 140 minutes (2h20minutes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape before removing long delays : 5341242\n",
      "Shape after removing long delays : 5258789\n"
     ]
    }
   ],
   "source": [
    "# Set threshold\n",
    "max_delay = 140\n",
    "print(\"Shape before removing long delays : %i\" % data_2016_explo.shape[0])\n",
    "data_2016_explo = data_2016_explo[data_2016_explo.loc[:, 'ARR_DELAY_NEW'] <= max_delay]\n",
    "print(\"Shape after removing long delays : %i\" % data_2016_explo.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temporality of delays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to look for temporal patterns in delays, we will look for one airline and one airport, preferably major ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observing Southwest Airlines airline delays for Chicago, IL airport\n"
     ]
    }
   ],
   "source": [
    "# Select airline\n",
    "airline_test = 'WN'\n",
    "delay_datetime_df = data_2016_explo[data_2016_explo['UNIQUE_CARRIER'] == airline_test]\n",
    "\n",
    "# Main airport for this airline\n",
    "airport_test = delay_datetime_df.loc[:, 'ORIGIN_AIRPORT_ID'].value_counts().index[0]\n",
    "\n",
    "print(\"Observing %s airline delays for %s airport\" % (airlines_full_names[airline_test], airport_dict_name[airport_test]))\n",
    "\n",
    "# Select main airport\n",
    "delay_datetime_df = delay_datetime_df[delay_datetime_df['ORIGIN_AIRPORT_ID'] == airport_test]\n",
    "\n",
    "# Sort delays by departure date\n",
    "delay_datetime_df = delay_datetime_df.sort_values(by=['DATE_TIME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_delay_plot(df, month=None, week=None, day=None) :\n",
    "    \"\"\"\n",
    "    Routine to plot delays as a function of departure time\n",
    "    No safeguards\n",
    "    \"\"\"\n",
    "    import calendar\n",
    "    \n",
    "    time_df = df.copy()\n",
    "    # Select time window\n",
    "    if (month == None) and (week == None) and (day == None) :\n",
    "        pass\n",
    "    if month is not None :\n",
    "        n_days_month = calendar.monthrange(2016, month)[1]\n",
    "        if month > 12 :\n",
    "            return \"ERROR : month must be <= 12\"\n",
    "        time_df = time_df[time_df['MONTH'] == month]\n",
    "    if week is not None :\n",
    "        if month is None :\n",
    "            return \"ERROR : must provide month with week\"\n",
    "        elif week > 5 :\n",
    "            return \"ERROR : week must be <= 5\"\n",
    "        # Day start\n",
    "        day_start = (week - 1) * 7 + 1\n",
    "        # Day end\n",
    "        day_end = week * 7\n",
    "        # End of month\n",
    "        if day_end > n_days_month : day_end = n_days_month\n",
    "        # Select days\n",
    "        time_df = time_df[time_df['DAY_OF_MONTH'].isin(range(day_start, day_end + 1))]\n",
    "    if day is not None :\n",
    "        if month is None :\n",
    "            return \"ERROR : must provide month with day\"\n",
    "        elif day > n_days_month :\n",
    "            return \"ERROR : week must be <= n_days_month\"\n",
    "        time_df = time_df[time_df['DAY_OF_MONTH'] == day]\n",
    "\n",
    "    # Plot everything\n",
    "    time = time_df.loc[:, 'DATE_TIME']\n",
    "    delays = time_df.loc[:, 'ARR_DELAY_NEW']\n",
    "    \n",
    "    fig = plt.figure(figsize=(20, 5))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.scatter(time.values, delays.values, s=6)   \n",
    "    ax.set_ylabel('Delay (min)', fontsize=15)\n",
    "    ax.set_xlabel('Departure date and time', fontsize=15)\n",
    "    ax.set_xlim((time.values.min(), time.values.max())); \n",
    "    \n",
    "    df_return = pd.DataFrame(columns=[\"TIME\", \"DELAYS\"])\n",
    "    df_return[\"TIME\"] = time\n",
    "    df_return[\"DELAYS\"] = delays\n",
    "    \n",
    "    return df_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_time = time_delay_plot(delay_datetime_df, month=1, week=1, day=None)\n",
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "df_time.loc[:, 'TIME'][df_time.DELAYS > 0].hist(color='r', bins=168, alpha=0.8)\n",
    "ax.set_xlabel(\"Departure date and time\", fontsize=15)\n",
    "ax.set_ylabel(\"Number of delayed flights upon arrival\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A time pattern appears quite clearly, at least during the day : delays tend to rise around noon and in the evening."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting mean delay as a function of categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x3240 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot mean delay by feature\n",
    "fig = plt.figure(figsize=(20,45))\n",
    "\n",
    "for feature, num in zip(categ_feat, range(1,len(categ_feat)-1)) :\n",
    "    x_list = list(mean_delays_categ[feature][0])\n",
    "    if isinstance(x_list[0], str) :\n",
    "        le = preprocessing.LabelEncoder()\n",
    "        x = le.fit_transform(x_list)\n",
    "    else :\n",
    "        x = x_list\n",
    "    y = mean_delays_categ[feature][1]\n",
    "    ax = fig.add_subplot(13,3,num)\n",
    "    ax.bar(x, y)\n",
    "    ax.set_title(feature)\n",
    "    ax.set_ylabel('Mean delay (min)', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flight delays seem to be more related to month and carrier rather to the day of month or day of week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar charts for airports\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "# Departure Airport\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax1.bar(mean_delays_categ['ORIGIN_AIRPORT_ID'][0],mean_delays_categ['ORIGIN_AIRPORT_ID'][1], width=20)\n",
    "ax1.set_ylabel(\"Mean delay (min)\")\n",
    "ax1.set_xlabel(\"Departure airports\")\n",
    "# Destination Airport\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.bar(mean_delays_categ['DEST_AIRPORT_ID'][0],mean_delays_categ['DEST_AIRPORT_ID'][1], width=20);\n",
    "ax2.set_ylabel(\"Mean delay (min)\")\n",
    "ax2.set_xlabel(\"Destination airports\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some airports presents mean delays above the average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting mean delay as a function of numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x3240 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot mean delay by feature\n",
    "fig = plt.figure(figsize=(20,45))\n",
    "\n",
    "for feature, num in zip(num_feat, range(1,len(num_feat)+1)) :\n",
    "    x_list = list(mean_delays_num[feature][0])\n",
    "    if isinstance(x_list[0], str) :\n",
    "        le = preprocessing.LabelEncoder()\n",
    "        x = le.fit_transform(x_list)\n",
    "    else :\n",
    "        x = x_list\n",
    "    y = mean_delays_num[feature][1]\n",
    "    ax = fig.add_subplot(13,3,num)\n",
    "    ax.scatter(x, y, s=3)\n",
    "    ax.set_title(feature) \n",
    "    ax.set_ylabel('Mean delay (min)', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bulk year data are difficult to read, let's look to distance and scheduled departure time for our selected company and airport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sub_df\n",
    "distance_delays = delay_datetime_df.loc[:, ['DISTANCE', 'ARR_DELAY_NEW']]\n",
    "# Distances\n",
    "distances = distance_delays['DISTANCE'].value_counts()\n",
    "# Remove too few flights\n",
    "distances_ok = distances[distances >= 10]\n",
    "# Select delays\n",
    "distance_delays = distance_delays[distance_delays['DISTANCE'].isin(distances_ok.index.values)]\n",
    "# Mean delay grouped by distance\n",
    "distance_delay = distance_delays['ARR_DELAY_NEW'].groupby(distance_delays['DISTANCE']).apply(lambda x : x.mean())\n",
    "\n",
    "# Plot\n",
    "fig = plt.figure(figsize=(20, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(distances_ok, distance_delay);    \n",
    "ax.set_ylabel('Mean delay (min)', fontsize=15)\n",
    "ax.set_xlabel('Flight distance', fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sizes = {} # clé : coordonnées ; valeur : nombre de points à ces coordonnées\n",
    "for (yt, yp) in zip(list(delay_datetime_df.loc[:, 'DISTANCE'].values), list(delay_datetime_df.loc[:, 'ARR_DELAY_NEW'].values)):\n",
    "    if (yt, yp) in sizes:\n",
    "        sizes[(yt, yp)] += 1\n",
    "    else:\n",
    "        sizes[(yt, yp)] = 1\n",
    "\n",
    "keys = sizes.keys()\n",
    "plt.scatter([k[0] for k in keys], # vraie valeur (abscisse)\n",
    "            [k[1] for k in keys], # valeur predite (ordonnee)\n",
    "            s=[sizes[k] for k in keys], # taille du marqueur\n",
    "            color='coral');\n",
    "\n",
    "ax.set_xlabel('Flight distance', fontsize=15)\n",
    "ax.set_ylabel('Flight delay (min)', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flight distance does not seem to be an impacting factor on delays for this company and this departure airport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Schedule departure time of day : WRONG PLOTS : Day Of Week Different from one month to another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2520 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 35))\n",
    "\n",
    "# January, each day of the week\n",
    "days_of_week = [1,2,3,4,5,6,7]\n",
    "days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "days_dict = dict(zip(days_of_week, days))\n",
    "\n",
    "for dow, num in zip(days_of_week, range(1, len(days_of_week) + 1)) :\n",
    "    \n",
    "    time_df = delay_datetime_df[(delay_datetime_df['MONTH'] == 1) & (delay_datetime_df['DAY_OF_WEEK'] == dow)]\n",
    "    # Sub_df\n",
    "    dep_time_delays = time_df.loc[:, ['CRS_DEP_TIME', 'ARR_DELAY_NEW']]\n",
    "    # Distances\n",
    "    dep_times = dep_time_delays['CRS_DEP_TIME'].value_counts()\n",
    "    # Mean delay grouped by distance\n",
    "    dep_time_delay = dep_time_delays['ARR_DELAY_NEW'].groupby(dep_time_delays['CRS_DEP_TIME']).apply(lambda x : x.mean())\n",
    "    \n",
    "    ax = fig.add_subplot(7, 1, num)\n",
    "    ax.bar(dep_time_delay.index.values, dep_time_delay.values, width=15);\n",
    "    # Day ?\n",
    "    day = days_dict[dow]\n",
    "    ax.set_title(day)\n",
    "    ax.set_ylabel('Delay (min)')\n",
    "    ax.set_xlabel('Flight departure time');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On most days of week the delay seems to rise as the scheduled departure time is late in the day, as one might have been expected delays tend to accumulate during the day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aircrafts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct dictionnary of aicrafts names encoded for later use\n",
    "aircrafts_le = preprocessing.LabelEncoder()\n",
    "# Get aircrafts names\n",
    "aircrafts_names = data_2016_explo.loc[:,'TAIL_NUM'].value_counts().index.values\n",
    "# Get aircrafts encoded values\n",
    "aircrafts_number = aircrafts_le.fit_transform(aircrafts_names)\n",
    "# Save dict\n",
    "aircrafts_num_dict = dict(zip(aircrafts_number, aircrafts_names))\n",
    "aircrafts_name_dict = dict(zip(aircrafts_names, aircrafts_number))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting aircrafts name into number\n",
    "delay_datetime_df.loc[:, 'AIRCRAFT_ID'] = delay_datetime_df.loc[:, 'TAIL_NUM'].apply(lambda x : aircrafts_name_dict[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sub_df\n",
    "aircrafts_delays = delay_datetime_df.loc[:, ['AIRCRAFT_ID', 'ARR_DELAY_NEW']]\n",
    "# Aircrafts\n",
    "aircrafts = aircrafts_delays['AIRCRAFT_ID'].value_counts()\n",
    "# Remove too few flights\n",
    "aircrafts_ok = aircrafts[aircrafts >= 10]\n",
    "# Select delays\n",
    "aircrafts_delays = aircrafts_delays[aircrafts_delays['AIRCRAFT_ID'].isin(aircrafts_ok.index.values)]\n",
    "# Mean delay grouped by aircraft\n",
    "aircrafts_delay = aircrafts_delays['ARR_DELAY_NEW'].groupby(aircrafts_delays['AIRCRAFT_ID']).apply(lambda x : x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sns.distplot(aircrafts_delay, kde=False, color='r')\n",
    "ax.set_xlabel('Aircraft mean delay upon arrival (min)', fontsize=15)\n",
    "ax.set_ylabel('Number of aircrafts', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this particular airline and airport, the distribution of delayed aircraft follows a normal law centered around 8 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting features that we won't use in the regression model :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Month : as we only have data over 1 year, the month feature is not usefull as we will predict the future based on the past.\n",
    "* Day of month and day of week : those did not seem to have a major impact over delays, at least on a global scale.\n",
    "* Origin : we will only use the origin airport id\n",
    "* Departure and Arrival time : we will keep the converted times in seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Exporting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_features = ['UNIQUE_CARRIER', 'ORIGIN', 'TIME_SECONDS', 'ARR_DELAY_NEW']\n",
    "# Saving all air data for API\n",
    "data_2016_explo.loc[:, keep_features].to_csv(path + '/data/air_data.csv', index=False)\n",
    "# with open(path + '/data/air_data.pkl', 'wb') as f :  \n",
    "#     pickle.dump(data_2016_explo.loc[:, keep_features], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving airlines and airports for API\n",
    "airlines = data_2016_explo.loc[:, 'UNIQUE_CARRIER'].unique()\n",
    "al_ap_df = pd.DataFrame()\n",
    "for airline in airlines :\n",
    "    al_ap_df = pd.concat((al_ap_df, pd.Series(data_2016_explo.loc[:, 'ORIGIN'][data_2016_explo.loc[:, 'UNIQUE_CARRIER'] == airline].unique())), axis=1)\n",
    "al_ap_df.columns = airlines\n",
    "# Save\n",
    "al_ap_df.to_csv(path + '/data/airlines_airports.csv', index=False)\n",
    "# with open(path + '/data/airlines_airports.pkl', 'wb') as f :  \n",
    "#     pickle.dump(al_ap_df, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.1.1) Exporting data for 2016, all airports, all airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016_final = data_2016_explo.drop(['MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'ORIGIN', 'TAIL_NUM', 'CRS_DEP_TIME', 'CRS_ARR_TIME', 'DISTANCE', 'DEST_AIRPORT_ID'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save data for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the dataframe with pickle : saving lots of time and keeping 'DATE_TIME' in date format and not string\n",
    "with open(path + '/data/data_airlines.pkl', 'wb') as f :  \n",
    "    pickle.dump(data_2016_final, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.1.2) Exporting data for 2016, all airports, all airlines, more features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_temp = data_2016_explo.drop(['MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'ORIGIN', 'CRS_DEP_TIME', 'CRS_ARR_TIME'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save data for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the dataframe with pickle : saving lots of time and keeping 'DATE_TIME' in date format and not string\n",
    "with open(path + '/data/data_more_features.pkl', 'wb') as f :  \n",
    "    pickle.dump(data_temp, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.2) Exporting data for 2016, one airport, all airlines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting data for the main airport we have at our disposal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "main airport : Atlanta, GA with 366245 flights in 2016\n"
     ]
    }
   ],
   "source": [
    "# Main airport by number of flights\n",
    "main_airport = data_2016_explo.loc[:, 'ORIGIN_AIRPORT_ID'].value_counts().index[0]\n",
    "flights_main_airport = data_2016_explo.loc[:, 'ORIGIN_AIRPORT_ID'].value_counts().values[0]\n",
    "print(\"main airport : %s with %i flights in 2016\" % (airport_dict_name[main_airport],flights_main_airport))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016_1_aiport = data_2016_explo[data_2016_explo.loc[:, 'ORIGIN_AIRPORT_ID'] == main_airport]\n",
    "data_2016_1_aiport = data_2016_1_aiport.drop(['MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'ORIGIN',\n",
    "                                              'CRS_DEP_TIME', 'CRS_ARR_TIME', 'ORIGIN_AIRPORT_ID',\n",
    "                                              'DEST_AIRPORT_ID', 'DATE_TIME'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save data for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the dataframe with pickle : saving lots of time and keeping 'DATE_TIME' in date format and not string\n",
    "with open(path + '/data/data_2016_1_aiport.pkl', 'wb') as f :  \n",
    "    pickle.dump(data_2016_1_aiport, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3.1) Exporting data for 2016, one airport, one airline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting data for the main airline and main airport for this airline we have at our disposal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Main airport : Chicago, IL with 80917 flights in 2016 for Southwest Airlines airline\n"
     ]
    }
   ],
   "source": [
    "# Select airline\n",
    "main_airline = 'WN'\n",
    "data_2016_1_airline_1_aiport = data_2016_explo[data_2016_explo['UNIQUE_CARRIER'] == main_airline]\n",
    "# Main airport for this airline by number of flights\n",
    "main_airport = data_2016_1_airline_1_aiport.loc[:, 'ORIGIN_AIRPORT_ID'].value_counts().index[0]\n",
    "flights_main_airport = data_2016_1_airline_1_aiport.loc[:, 'ORIGIN_AIRPORT_ID'].value_counts().values[0]\n",
    "print(\"Main airport : %s with %i flights in 2016 for %s airline\" % (airport_dict_name[main_airport], flights_main_airport, airlines_full_names[main_airline]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016_1_airline_1_aiport = data_2016_1_airline_1_aiport[data_2016_1_airline_1_aiport.loc[:, 'ORIGIN_AIRPORT_ID'] == main_airport]\n",
    "data_2016_1_airline_1_aiport = data_2016_1_airline_1_aiport.drop(['MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'ORIGIN',\n",
    "                                                                  'CRS_DEP_TIME', 'CRS_ARR_TIME', 'UNIQUE_CARRIER',\n",
    "                                                                  'ORIGIN_AIRPORT_ID', 'DEST_AIRPORT_ID', 'DATE_TIME'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save data for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the dataframe with pickle : saving lots of time and keeping 'DATE_TIME' in date format and not string\n",
    "with open(path + '/data/data_2016_1_airline_1_aiport.pkl', 'wb') as f :  \n",
    "    pickle.dump(data_2016_1_airline_1_aiport, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3.2) Exporting data for 2016, one airport, one airline with date_time for prophet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting data for the main airline and main airport for this airline we have at our disposal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Main airport : Chicago, IL with 80917 flights in 2016 for Southwest Airlines airline\n"
     ]
    }
   ],
   "source": [
    "# Select airline\n",
    "main_airline = 'WN'\n",
    "data_2016_1_airline_1_aiport_prophet = data_2016_explo[data_2016_explo['UNIQUE_CARRIER'] == main_airline]\n",
    "# Main airport for this airline by number of flights\n",
    "main_airport = data_2016_1_airline_1_aiport_prophet.loc[:, 'ORIGIN_AIRPORT_ID'].value_counts().index[0]\n",
    "flights_main_airport = data_2016_1_airline_1_aiport_prophet.loc[:, 'ORIGIN_AIRPORT_ID'].value_counts().values[0]\n",
    "print(\"Main airport : %s with %i flights in 2016 for %s airline\" % (airport_dict_name[main_airport], flights_main_airport, airlines_full_names[main_airline]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016_1_airline_1_aiport_prophet = data_2016_1_airline_1_aiport_prophet[data_2016_1_airline_1_aiport_prophet.loc[:, 'ORIGIN_AIRPORT_ID'] == main_airport]\n",
    "data_2016_1_airline_1_aiport_prophet = data_2016_1_airline_1_aiport_prophet.drop(['MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'ORIGIN',\n",
    "                                                                  'CRS_DEP_TIME', 'CRS_ARR_TIME', 'UNIQUE_CARRIER',\n",
    "                                                                  'ORIGIN_AIRPORT_ID', 'DEST_AIRPORT_ID'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save data for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the dataframe with pickle : saving lots of time and keeping 'DATE_TIME' in date format and not string\n",
    "with open(path + '/data/data_2016_1_airline_1_aiport_prophet.pkl', 'wb') as f :  \n",
    "    pickle.dump(data_2016_1_airline_1_aiport_prophet, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.4) Exporting data for 2016, all airports, one airline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting data for the main airline we have at our disposal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select airline\n",
    "main_airline = 'WN'\n",
    "data_2016_1_airline = data_2016_explo[data_2016_explo['UNIQUE_CARRIER'] == main_airline]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2016_1_airline = data_2016_1_airline.drop(['MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'ORIGIN',\n",
    "                                                'CRS_DEP_TIME', 'CRS_ARR_TIME', 'DATE_TIME',\n",
    "                                                'UNIQUE_CARRIER', 'DEST_AIRPORT_ID'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save data for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the dataframe with pickle : saving lots of time and keeping 'DATE_TIME' in date format and not string\n",
    "with open(path + '/data/data_2016_1_airline.pkl', 'wb') as f :  \n",
    "    pickle.dump(data_2016_1_airline, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6) Group data by aircraft, taking into account the aircraft's history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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