lab/5/data science/2e/BoardingData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2c11156c-2afb-40c0-9a1d-ac93057b7296",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerFirstName     1588941\n",
      "PassengerSecondName    1556752\n",
      "PassengerLastName      1851607\n",
      "PassengerSex            155169\n",
      "PassengerBirthDate     2172062\n",
      "PassengerDocument      2327209\n",
      "BookingCode            2095801\n",
      "TicketNumber           2869544\n",
      "Baggage                 155195\n",
      "FlightDate             1241176\n",
      "FlightTime             1396327\n",
      "FlightNumber           1551474\n",
      "CodeShare               155170\n",
      "Destination             155733\n",
      "Index                        0\n",
      "dtype: int64 18.379364013671875 MiB\n"
     ]
    },
    {
     "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>PassengerFirstName</th>\n",
       "      <th>PassengerSecondName</th>\n",
       "      <th>PassengerLastName</th>\n",
       "      <th>PassengerSex</th>\n",
       "      <th>PassengerBirthDate</th>\n",
       "      <th>PassengerDocument</th>\n",
       "      <th>BookingCode</th>\n",
       "      <th>TicketNumber</th>\n",
       "      <th>Baggage</th>\n",
       "      <th>FlightDate</th>\n",
       "      <th>FlightTime</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>CodeShare</th>\n",
       "      <th>Destination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SAVELII</td>\n",
       "      <td>VIKTOROVICH</td>\n",
       "      <td>RUSANOV</td>\n",
       "      <td>Male</td>\n",
       "      <td>03/10/1983</td>\n",
       "      <td>2879 096860</td>\n",
       "      <td>FRNINO</td>\n",
       "      <td>6625956945991971</td>\n",
       "      <td>Transit</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>06:05</td>\n",
       "      <td>SU1369</td>\n",
       "      <td>Own</td>\n",
       "      <td>Moscow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LEV</td>\n",
       "      <td>MARKOVICH</td>\n",
       "      <td>ISAEV</td>\n",
       "      <td>Male</td>\n",
       "      <td>12/13/1975</td>\n",
       "      <td>1788 173211</td>\n",
       "      <td>Not presented</td>\n",
       "      <td>1643715499224676</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2017-03-18</td>\n",
       "      <td>22:10</td>\n",
       "      <td>SU1180</td>\n",
       "      <td>Own</td>\n",
       "      <td>Volgograd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NIKOLAI</td>\n",
       "      <td>I.</td>\n",
       "      <td>NIKOLSKII</td>\n",
       "      <td>Male</td>\n",
       "      <td>12/26/1990</td>\n",
       "      <td>4396 926588</td>\n",
       "      <td>VWNYGF</td>\n",
       "      <td>6247422701565929</td>\n",
       "      <td>Transit</td>\n",
       "      <td>2017-03-18</td>\n",
       "      <td>22:10</td>\n",
       "      <td>SU1180</td>\n",
       "      <td>Own</td>\n",
       "      <td>Volgograd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANATOLII</td>\n",
       "      <td>PETROVICH</td>\n",
       "      <td>SHILOV</td>\n",
       "      <td>Male</td>\n",
       "      <td>05/24/1997</td>\n",
       "      <td>2595 919752</td>\n",
       "      <td>WQFFUE</td>\n",
       "      <td>Not presented</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2017-03-18</td>\n",
       "      <td>22:10</td>\n",
       "      <td>SU1180</td>\n",
       "      <td>Own</td>\n",
       "      <td>Volgograd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MIROSLAVA</td>\n",
       "      <td>VIACHESLAVOVNA</td>\n",
       "      <td>SEMENOVA</td>\n",
       "      <td>Female</td>\n",
       "      <td>01/31/1976</td>\n",
       "      <td>6775 516990</td>\n",
       "      <td>Not presented</td>\n",
       "      <td>Not presented</td>\n",
       "      <td>Registered</td>\n",
       "      <td>2017-03-12</td>\n",
       "      <td>11:45</td>\n",
       "      <td>SU6284</td>\n",
       "      <td>Own</td>\n",
       "      <td>Moscow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerFirstName PassengerSecondName PassengerLastName PassengerSex  \\\n",
       "0            SAVELII         VIKTOROVICH           RUSANOV         Male   \n",
       "1                LEV           MARKOVICH             ISAEV         Male   \n",
       "2            NIKOLAI                  I.         NIKOLSKII         Male   \n",
       "3           ANATOLII           PETROVICH            SHILOV         Male   \n",
       "4          MIROSLAVA      VIACHESLAVOVNA          SEMENOVA       Female   \n",
       "\n",
       "  PassengerBirthDate PassengerDocument    BookingCode      TicketNumber  \\\n",
       "0         03/10/1983       2879 096860         FRNINO  6625956945991971   \n",
       "1         12/13/1975       1788 173211  Not presented  1643715499224676   \n",
       "2         12/26/1990       4396 926588         VWNYGF  6247422701565929   \n",
       "3         05/24/1997       2595 919752         WQFFUE     Not presented   \n",
       "4         01/31/1976       6775 516990  Not presented     Not presented   \n",
       "\n",
       "      Baggage FlightDate FlightTime FlightNumber CodeShare Destination  \n",
       "0     Transit 2017-03-22      06:05       SU1369       Own      Moscow  \n",
       "1  Registered 2017-03-18      22:10       SU1180       Own   Volgograd  \n",
       "2     Transit 2017-03-18      22:10       SU1180       Own   Volgograd  \n",
       "3  Registered 2017-03-18      22:10       SU1180       Own   Volgograd  \n",
       "4  Registered 2017-03-12      11:45       SU6284       Own      Moscow  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cudf\n",
    "\n",
    "df = cudf.read_csv('BoardingData.csv', sep=';', dtype={\n",
    "    'PassengerFirstName': 'object',\n",
    "    'PassengerSecondName': 'object',\n",
    "    'PassengerLastName': 'object',\n",
    "    'PassengerBirthDate': 'object',\n",
    "    'PassengerDocument': 'object',\n",
    "    'BookingCode': 'object',\n",
    "    'TicketNumber': 'object',\n",
    "    'Baggage': 'category',\n",
    "    'FlightDate': 'datetime64[s]',\n",
    "    'FlightTime': 'object',\n",
    "    'FlightNumber': 'object',\n",
    "    'PassengerSex': 'category',\n",
    "    'CodeShare': 'category',\n",
    "    'Destination\\r': 'category',\n",
    "})\n",
    "df.rename(columns={'Destination\\r': 'Destination'}, inplace=1)\n",
    "print(df.memory_usage(), df.memory_usage().sum() / 2**20, 'MiB')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "0a1045e8-74f9-4323-b2ec-42449adde616",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PassengerSecondName'] = '.' + df['PassengerSecondName'].str.slice(0, 1) + '.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2a748997-b20f-41d4-b8e2-0929c7d1dad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PassengerBirthDate_y'] = df['PassengerBirthDate'].str.slice(6, 10).astype('int16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "8f3b7ef1-86c0-428e-8f2c-72bb96c70629",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PassengerName'] = (df['PassengerFirstName'] +\\\n",
    "                       df['PassengerSecondName'] +\\\n",
    "                       df['PassengerLastName']).hash_values('murmur3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "505924e3-4eaa-42bb-83d4-e2bde09e14f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PassengerDocument'] = df['PassengerDocument'].str.replace(' ', '').astype('uint64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e658a327-2355-48d8-85e5-4e431785304e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hours = df['FlightTime'].str.slice(0, 2).astype('int32')\n",
    "minutes = df['FlightTime'].str.slice(3, 5).astype('int32')\n",
    "\n",
    "df['FlightDate_s'] = df['FlightDate'] + (hours * 3600 + minutes * 60).astype('timedelta64[s]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "7b32961b-5c6c-482c-9bc3-97f1a296a452",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BookingCode'] = (df['BookingCode'] != 'Not presented').astype('int8')\n",
    "df['TicketNumber'] = (df['TicketNumber'] != 'Not presented').astype('int8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "12a2369f-5c7f-4776-ba4f-da6a58cd0a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['FlightDate', 'FlightTime',\\\n",
    "                 'PassengerBirthDate', 'PassengerFirstName',\\\n",
    "                 'PassengerSecondName', 'PassengerLastName'], inplace=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "df8ba5f2-2ad3-4964-b0aa-07f49ab5dbd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerSex             155169\n",
      "PassengerDocument       1241176\n",
      "BookingCode              155147\n",
      "TicketNumber             155147\n",
      "Baggage                  155195\n",
      "FlightNumber            1551474\n",
      "CodeShare                155170\n",
      "Destination              155733\n",
      "PassengerBirthDate_y     310294\n",
      "PassengerName            620588\n",
      "FlightDate_s            1241176\n",
      "Index                         0\n",
      "dtype: int64 5.623120307922363 MiB\n"
     ]
    },
    {
     "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>PassengerSex</th>\n",
       "      <th>PassengerDocument</th>\n",
       "      <th>BookingCode</th>\n",
       "      <th>TicketNumber</th>\n",
       "      <th>Baggage</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>CodeShare</th>\n",
       "      <th>Destination</th>\n",
       "      <th>PassengerBirthDate_y</th>\n",
       "      <th>PassengerName</th>\n",
       "      <th>FlightDate_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>2879096860</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Transit</td>\n",
       "      <td>SU1369</td>\n",
       "      <td>Own</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>1983</td>\n",
       "      <td>702066535</td>\n",
       "      <td>2017-03-22 06:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>1788173211</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Registered</td>\n",
       "      <td>SU1180</td>\n",
       "      <td>Own</td>\n",
       "      <td>Volgograd</td>\n",
       "      <td>1975</td>\n",
       "      <td>1506391098</td>\n",
       "      <td>2017-03-18 22:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>4396926588</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Transit</td>\n",
       "      <td>SU1180</td>\n",
       "      <td>Own</td>\n",
       "      <td>Volgograd</td>\n",
       "      <td>1990</td>\n",
       "      <td>921530796</td>\n",
       "      <td>2017-03-18 22:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>2595919752</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Registered</td>\n",
       "      <td>SU1180</td>\n",
       "      <td>Own</td>\n",
       "      <td>Volgograd</td>\n",
       "      <td>1997</td>\n",
       "      <td>2477460736</td>\n",
       "      <td>2017-03-18 22:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>6775516990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Registered</td>\n",
       "      <td>SU6284</td>\n",
       "      <td>Own</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>1976</td>\n",
       "      <td>3539264187</td>\n",
       "      <td>2017-03-12 11:45:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerSex  PassengerDocument  BookingCode  TicketNumber     Baggage  \\\n",
       "0         Male         2879096860            1             1     Transit   \n",
       "1         Male         1788173211            0             1  Registered   \n",
       "2         Male         4396926588            1             1     Transit   \n",
       "3         Male         2595919752            1             0  Registered   \n",
       "4       Female         6775516990            0             0  Registered   \n",
       "\n",
       "  FlightNumber CodeShare Destination  PassengerBirthDate_y  PassengerName  \\\n",
       "0       SU1369       Own      Moscow                  1983      702066535   \n",
       "1       SU1180       Own   Volgograd                  1975     1506391098   \n",
       "2       SU1180       Own   Volgograd                  1990      921530796   \n",
       "3       SU1180       Own   Volgograd                  1997     2477460736   \n",
       "4       SU6284       Own      Moscow                  1976     3539264187   \n",
       "\n",
       "         FlightDate_s  \n",
       "0 2017-03-22 06:05:00  \n",
       "1 2017-03-18 22:10:00  \n",
       "2 2017-03-18 22:10:00  \n",
       "3 2017-03-18 22:10:00  \n",
       "4 2017-03-12 11:45:00  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.memory_usage(), df.memory_usage().sum() / 2 ** 20, 'MiB')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3c078b2c-ded7-44fd-aebe-7ce623cfa6dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerSex                 category\n",
       "PassengerDocument              uint64\n",
       "BookingCode                      int8\n",
       "TicketNumber                     int8\n",
       "Baggage                      category\n",
       "FlightNumber                   object\n",
       "CodeShare                    category\n",
       "Destination                  category\n",
       "PassengerBirthDate_y            int16\n",
       "PassengerName                  uint32\n",
       "FlightDate_s            datetime64[s]\n",
       "dtype: object"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "bfb512ae-e250-413a-8c43-290f586b32e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Moscow</td>\n",
       "      <td>55.750599</td>\n",
       "      <td>37.617496</td>\n",
       "      <td>17332000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>59.937500</td>\n",
       "      <td>30.308599</td>\n",
       "      <td>5597763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>Novosibirsk</td>\n",
       "      <td>55.049999</td>\n",
       "      <td>82.950005</td>\n",
       "      <td>1633851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Yekaterinburg</td>\n",
       "      <td>56.835598</td>\n",
       "      <td>60.612797</td>\n",
       "      <td>1468833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>Kazan</td>\n",
       "      <td>55.796402</td>\n",
       "      <td>49.108898</td>\n",
       "      <td>1259173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city        lat        lng  population\n",
       "18             Moscow  55.750599  37.617496    17332000\n",
       "108  Saint Petersburg  59.937500  30.308599     5597763\n",
       "475       Novosibirsk  55.049999  82.950005     1633851\n",
       "525     Yekaterinburg  56.835598  60.612797     1468833\n",
       "594             Kazan  55.796402  49.108898     1259173"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df = cudf.read_csv('worldcities.csv', dtype={\n",
    "    'lat': 'float32',\n",
    "    'lng': 'float32',\n",
    "    'population': 'uint32',\n",
    "})\n",
    "cities_df = cities_df[cities_df['country'] == 'Russia']\n",
    "cities_df.drop(columns=['city_ascii', 'country', 'iso2', 'iso3',\\\n",
    "                        'admin_name', 'capital', 'id'], inplace = 1)\n",
    "cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "97c13fe2-6308-4aee-bc82-9cfdafacb945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import datashader as ds\n",
    "import datashader.transfer_functions as tf\n",
    "from datashader.colors import inferno\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "canvas = ds.Canvas(\n",
    "    x_range=(19, 169),\n",
    "    y_range=(41, 82),\n",
    "    plot_width=800,\n",
    "    plot_height=400\n",
    ")\n",
    "\n",
    "agg = canvas.points(cities_df, 'lng', 'lat')\n",
    "spread = tf.spread(agg, px=3)\n",
    "img = tf.shade(spread, cmap=inferno, how='log')\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.imshow(img.to_pil(), origin='lower')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "2ee12a60-8440-4af2-aa18-81a5088ae347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_documents = df.groupby('PassengerName')['PassengerDocument'].nunique()\n",
    "unique_documents.head().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "46b5a759-95f6-45ef-8342-da1044629a84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerDocument\n",
       "1548113497    16\n",
       "7126372174    15\n",
       "8816864475    14\n",
       "5293500602    14\n",
       "6937933652    14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flight_count = df.groupby('PassengerDocument').size().sort_values(ascending=False)\n",
    "flight_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "cd4ddd40-245c-4649-8fdd-50eb85093ac6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48 22.92 2.08\n"
     ]
    }
   ],
   "source": [
    "unique_dest = df['Destination'].nunique()\n",
    "passenger_dest = df.groupby('PassengerDocument')['Destination'].nunique() / unique_dest * 100\n",
    "passenger_dest = passenger_dest.round(2)\n",
    "print(unique_dest, passenger_dest.max(), passenger_dest.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "cd5f7f62-dd65-46f8-83f4-b3bf64e84543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-01-01T22:50:00 2017-01-01T00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(df['FlightDate_s'].max(), df['FlightDate_s'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "63a5a4f0-cbe1-4ce5-8f34-9dc7fa3d16d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000 1970\n"
     ]
    }
   ],
   "source": [
    "birth_date = df['PassengerBirthDate_y'].sort_values(ascending=True)\n",
    "print(birth_date.max(), birth_date.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "1aca0e78-fdc5-477e-ac85-06923fd1d49f",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3524809427.py, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[140], line 7\u001b[0;36m\u001b[0m\n\u001b[0;31m    passenger_dt_trigger = avg_passenger < avg_total ? 1 - avg_passenger / avg_total : 0\u001b[0m\n\u001b[0m                                                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "df_sorted = df.sort_values(['PassengerDocument','FlightDate_s'])\n",
    "df_sorted['PrevFlight_s'] = df_sorted.groupby('PassengerDocument')['FlightDate_s'].shift(1)\n",
    "df_sorted['FlightDt_s'] = df_sorted['FlightDate_s'] - df_sorted['PrevFlight_s']\n",
    "avg_passenger = df_sorted.groupby('PassengerDocument')['FlightDt_s'].mean()\n",
    "avg_total = avg_passenger.mean()\n",
    "\n",
    "passenger_dt_trigger = avg_passenger < avg_total ? 1 - avg_passenger / avg_total : 0\n",
    "passenger_dt_trigger.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed4b01d4-1ff0-42f0-a105-14a6f07e912a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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