Flight_delay _data preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "9bd0fd97-17af-4725-ad49-2bfffd651080",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from feature_engine.encoding import RareLabelEncoder\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "520941f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2024_1.csv',\n",
       " '2024_2.csv',\n",
       " '2024_3.csv',\n",
       " '2024_4.csv',\n",
       " '2024_5.csv',\n",
       " '2024_6.csv',\n",
       " '2024_7.csv',\n",
       " '2024_8.csv',\n",
       " '2024_9.csv']"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_names = [f'2024_{i}.csv' for i in range(1, 10)]\n",
    "file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "9573a99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframes = []\n",
    "\n",
    "# combine the 10 csv \n",
    "for file_name in file_names:\n",
    "    df = pd.read_csv(file_name)\n",
    "    dataframes.append(df)\n",
    "\n",
    "df = pd.concat(dataframes, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "50abad38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# eliminate the dupilicated rows and null value \n",
    "df = df.drop_duplicates()\n",
    "df = df[df['CANCELLED']==0]\n",
    "df = df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "9795db89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YEAR', 'MONTH', 'DAY_OF_MONTH', 'FL_DATE', 'MKT_UNIQUE_CARRIER',\n",
       "       'MKT_CARRIER_FL_NUM', 'ORIGIN_CITY_NAME', 'DEST_CITY_NAME',\n",
       "       'CRS_DEP_TIME', 'DEP_TIME', 'DEP_DELAY', 'CRS_ARR_TIME', 'ARR_TIME',\n",
       "       'ARR_DELAY', 'CANCELLED', 'CRS_ELAPSED_TIME', 'ACTUAL_ELAPSED_TIME',\n",
       "       'AIR_TIME', 'DISTANCE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "b884a7c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>4136066</th>\n",
       "      <th>3129337</th>\n",
       "      <th>1833691</th>\n",
       "      <th>2426834</th>\n",
       "      <th>2368827</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL_DATE</th>\n",
       "      <td>7/21/2024 12:00:00 AM</td>\n",
       "      <td>6/5/2024 12:00:00 AM</td>\n",
       "      <td>4/4/2024 12:00:00 AM</td>\n",
       "      <td>5/3/2024 12:00:00 AM</td>\n",
       "      <td>4/30/2024 12:00:00 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_UNIQUE_CARRIER</th>\n",
       "      <td>UA</td>\n",
       "      <td>F9</td>\n",
       "      <td>DL</td>\n",
       "      <td>AA</td>\n",
       "      <td>AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_CARRIER_FL_NUM</th>\n",
       "      <td>5865.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>2342.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>764.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORIGIN_CITY_NAME</th>\n",
       "      <td>Cedar Rapids/Iowa City, IA</td>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Memphis, TN</td>\n",
       "      <td>Charlotte, NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEST_CITY_NAME</th>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>Austin, TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <td>1510</td>\n",
       "      <td>700</td>\n",
       "      <td>2046</td>\n",
       "      <td>520</td>\n",
       "      <td>2050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_TIME</th>\n",
       "      <td>1510.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>2047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_DELAY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_ARR_TIME</th>\n",
       "      <td>1631</td>\n",
       "      <td>901</td>\n",
       "      <td>2337</td>\n",
       "      <td>708</td>\n",
       "      <td>2256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR_TIME</th>\n",
       "      <td>1611.0</td>\n",
       "      <td>831.0</td>\n",
       "      <td>2337.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>2235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR_DELAY</th>\n",
       "      <td>-20.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>-21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CANCELLED</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_ELAPSED_TIME</th>\n",
       "      <td>81.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <td>61.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIR_TIME</th>\n",
       "      <td>40.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISTANCE</th>\n",
       "      <td>196.0</td>\n",
       "      <td>1476.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>1032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlightDelay</th>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>-18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        4136066               3129337  \\\n",
       "YEAR                                       2024                  2024   \n",
       "MONTH                                         7                     6   \n",
       "DAY_OF_MONTH                                 21                     5   \n",
       "FL_DATE                   7/21/2024 12:00:00 AM  6/5/2024 12:00:00 AM   \n",
       "MKT_UNIQUE_CARRIER                           UA                    F9   \n",
       "MKT_CARRIER_FL_NUM                       5865.0                 685.0   \n",
       "ORIGIN_CITY_NAME     Cedar Rapids/Iowa City, IA        Washington, DC   \n",
       "DEST_CITY_NAME                      Chicago, IL            Denver, CO   \n",
       "CRS_DEP_TIME                               1510                   700   \n",
       "DEP_TIME                                 1510.0                 650.0   \n",
       "DEP_DELAY                                   0.0                 -10.0   \n",
       "CRS_ARR_TIME                               1631                   901   \n",
       "ARR_TIME                                 1611.0                 831.0   \n",
       "ARR_DELAY                                 -20.0                 -30.0   \n",
       "CANCELLED                                   0.0                   0.0   \n",
       "CRS_ELAPSED_TIME                           81.0                 241.0   \n",
       "ACTUAL_ELAPSED_TIME                        61.0                 221.0   \n",
       "AIR_TIME                                   40.0                 199.0   \n",
       "DISTANCE                                  196.0                1476.0   \n",
       "FlightDelay                               -20.0                 -20.0   \n",
       "\n",
       "                                  1833691                2426834  \\\n",
       "YEAR                                 2024                   2024   \n",
       "MONTH                                   4                      5   \n",
       "DAY_OF_MONTH                            4                      3   \n",
       "FL_DATE              4/4/2024 12:00:00 AM   5/3/2024 12:00:00 AM   \n",
       "MKT_UNIQUE_CARRIER                     DL                     AA   \n",
       "MKT_CARRIER_FL_NUM                 2342.0                 1940.0   \n",
       "ORIGIN_CITY_NAME              Chicago, IL            Memphis, TN   \n",
       "DEST_CITY_NAME                Atlanta, GA  Dallas/Fort Worth, TX   \n",
       "CRS_DEP_TIME                         2046                    520   \n",
       "DEP_TIME                           2049.0                  514.0   \n",
       "DEP_DELAY                             3.0                   -6.0   \n",
       "CRS_ARR_TIME                         2337                    708   \n",
       "ARR_TIME                           2337.0                  812.0   \n",
       "ARR_DELAY                             0.0                   64.0   \n",
       "CANCELLED                             0.0                    0.0   \n",
       "CRS_ELAPSED_TIME                    111.0                  108.0   \n",
       "ACTUAL_ELAPSED_TIME                 108.0                  178.0   \n",
       "AIR_TIME                             75.0                   77.0   \n",
       "DISTANCE                            606.0                  431.0   \n",
       "FlightDelay                          -3.0                   70.0   \n",
       "\n",
       "                                   2368827  \n",
       "YEAR                                  2024  \n",
       "MONTH                                    4  \n",
       "DAY_OF_MONTH                            30  \n",
       "FL_DATE              4/30/2024 12:00:00 AM  \n",
       "MKT_UNIQUE_CARRIER                      AA  \n",
       "MKT_CARRIER_FL_NUM                   764.0  \n",
       "ORIGIN_CITY_NAME             Charlotte, NC  \n",
       "DEST_CITY_NAME                  Austin, TX  \n",
       "CRS_DEP_TIME                          2050  \n",
       "DEP_TIME                            2047.0  \n",
       "DEP_DELAY                             -3.0  \n",
       "CRS_ARR_TIME                          2256  \n",
       "ARR_TIME                            2235.0  \n",
       "ARR_DELAY                            -21.0  \n",
       "CANCELLED                              0.0  \n",
       "CRS_ELAPSED_TIME                     186.0  \n",
       "ACTUAL_ELAPSED_TIME                  168.0  \n",
       "AIR_TIME                             141.0  \n",
       "DISTANCE                            1032.0  \n",
       "FlightDelay                          -18.0  "
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FlightDelay'] = df['ARR_DELAY'] - df['DEP_DELAY']\n",
    "df.sample(5).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "63378ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the 95% percentile and eliminate extream value \n",
    "q95 = df['DEP_DELAY'].quantile(0.95)\n",
    "df = df[df['DEP_DELAY'] <= q95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "c59fc4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the 95% percentile and eliminate extream value \n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "df['DEP_DELAY'].plot(kind='hist', bins=50, color='blue', alpha=0.7)\n",
    "\n",
    "plt.title('Distribution of Departure Delay (Filtered)')\n",
    "plt.xlabel('Departure Delay (minutes)')\n",
    "plt.ylabel('Number of Flights')\n",
    "plt.grid(True, linestyle='--', alpha=0.6)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "6e6c36ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.114065673136494"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_flights = df['DEP_DELAY'].count()  #\n",
    "flights_less_than_zero = df[df['DEP_DELAY'] < 0].count()['DEP_DELAY']  \n",
    "\n",
    "percentage_less_than_zero = (flights_less_than_zero / total_flights) * 100\n",
    "percentage_less_than_zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "8188ce68",
   "metadata": {},
   "outputs": [],
   "source": [
    "q95 = df['FlightDelay'].quantile(0.95)\n",
    "df = df[df['FlightDelay'] <= q95]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "03be6eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['FlightDelay'] = pd.to_numeric(df['FlightDelay'], errors='coerce')  # \n",
    "plt.figure(figsize=(12, 6))\n",
    "df['FlightDelay'].dropna().plot(kind='hist', bins=50, color='blue', alpha=0.7)\n",
    "\n",
    "plt.title('Distribution of Flight Delay')\n",
    "plt.xlabel('Flight Delay (minutes)')\n",
    "plt.ylabel('Number of Flights')\n",
    "plt.grid(True, linestyle='--', alpha=0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "2f00bc40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.51317131431333"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "total_flights_flightdelay = df['FlightDelay'].count()  # total flight \n",
    "flights_flightdelay_less_than_zero = df[df['FlightDelay'] < 0].count()['FlightDelay']  # FlightDelay <0\n",
    "\n",
    "percentage_flightdelay_less_than_zero = (flights_flightdelay_less_than_zero / total_flights_flightdelay) * 100\n",
    "percentage_flightdelay_less_than_zero\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "b182607b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ARR_DELAY'] = pd.to_numeric(df['ARR_DELAY'], errors='coerce')  \n",
    "plt.figure(figsize=(12, 6))\n",
    "df['ARR_DELAY'].dropna().plot(kind='hist', bins=50, color='blue', alpha=0.7)\n",
    "\n",
    "plt.title('Distribution of ARR Delay')\n",
    "plt.xlabel('ARR Delay (minutes)')\n",
    "plt.ylabel('Number of Flights')\n",
    "plt.grid(True, linestyle='--', alpha=0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "b1b2b77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.20537403714539"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the arr_delay <0 percentage \n",
    "total_flights_flightdelay = df['ARR_DELAY'].count() \n",
    "flights_flightdelay_less_than_zero = df[df['ARR_DELAY'] < 0].count()['FlightDelay']  \n",
    "\n",
    "percentage_flightdelay_less_than_zero = (flights_flightdelay_less_than_zero / total_flights_flightdelay) * 100\n",
    "percentage_flightdelay_less_than_zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "aa37aa6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# \n",
    "df['DEP_DELAY'] = pd.to_numeric(df['DEP_DELAY'], errors='coerce')\n",
    "df['DISTANCE'] = pd.to_numeric(df['DISTANCE'], errors='coerce')\n",
    "df['FlightDelay'] = pd.to_numeric(df['FlightDelay'], errors='coerce')\n",
    "\n",
    "# draw heatmap of the correlation \n",
    "plt.figure(figsize=(8, 6))\n",
    "correlation_matrix = df[['DEP_DELAY', 'FlightDelay', 'DISTANCE']].corr()\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', cbar=True)\n",
    "plt.title('Correlation Matrix for DEP_DELAY, FlightDelay, and DISTANCE')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "4b162c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# carriers average delay \n",
    "plt.figure(figsize=(10, 6))\n",
    "delay_matrix_airline.sort_values(by='DEP_DELAY', ascending=False).plot(\n",
    "    kind='bar', legend=False, color='blue', alpha=0.7\n",
    ")\n",
    "plt.title('Average Departure Delay by Airline')\n",
    "plt.xlabel('Airline')\n",
    "plt.ylabel('Average Departure Delay (minutes)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6)\n",
    "plt.show()\n",
    "\n",
    "# 出发地前20个城市的平均延误柱状图\n",
    "top20_origins = delay_matrix_origin.nlargest(20, 'DEP_DELAY')\n",
    "plt.figure(figsize=(10, 6))\n",
    "top20_origins.sort_values(by='DEP_DELAY', ascending=True).plot(\n",
    "    kind='barh', legend=False, color='green', alpha=0.7\n",
    ")\n",
    "plt.title('Average Departure Delay by Origin City (Top 20)')\n",
    "plt.xlabel('Average Departure Delay (minutes)')\n",
    "plt.ylabel('Origin City')\n",
    "plt.grid(axis='x', linestyle='--', alpha=0.6)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "8b14f1af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        MKT_UNIQUE_CARRIER  MKT_CARRIER_FL_NUM flight_code\n",
      "0                       AA                1002      AA1002\n",
      "1                       AA                1007      AA1007\n",
      "2                       AA                1009      AA1009\n",
      "3                       AA                1010      AA1010\n",
      "4                       AA                1011      AA1011\n",
      "...                    ...                 ...         ...\n",
      "5644139                 WN                 996       WN996\n",
      "5644140                 WN                 997       WN997\n",
      "5644141                 WN                 997       WN997\n",
      "5644142                 WN                 998       WN998\n",
      "5644143                 WN                 999       WN999\n",
      "\n",
      "[4769152 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df['MKT_CARRIER_FL_NUM'] = df['MKT_CARRIER_FL_NUM'].fillna(0).astype(int)  \n",
    "\n",
    "# combine MKT_UNIQUE_CARRIER and MKT_CARRIER_FL_NUM  to flight_code\n",
    "df['flight_code'] = df['MKT_UNIQUE_CARRIER'] + df['MKT_CARRIER_FL_NUM'].astype(str)\n",
    "\n",
    "print(df[['MKT_UNIQUE_CARRIER', 'MKT_CARRIER_FL_NUM', 'flight_code']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "cbf2dddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/50/sdcn29lj1mxdbpljy43k22y00000gn/T/ipykernel_58914/978500603.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  df['FL_DATE'] = pd.to_datetime(df['FL_DATE'])\n"
     ]
    }
   ],
   "source": [
    "df['FL_DATE'] = pd.to_datetime(df['FL_DATE'])\n",
    "df['Day'] = df['FL_DATE'].dt.day_name()\n",
    "\n",
    "df['DepHour'] = df['DEP_TIME'] // 100\n",
    "df['DepMinute'] = df['DEP_TIME'] % 100\n",
    "# More precise time representation (hour + fraction of the hour)\n",
    "df['DepFraction'] = df['DepHour'] + df['DepMinute'] / 60\n",
    "df['DepTime_sin'] = np.sin(2 * np.pi * df['DepFraction'] / 24)\n",
    "df['DepTime_cos'] = np.cos(2 * np.pi * df['DepFraction'] / 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "b97ebd3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CRS_DepHour'] = df['CRS_DEP_TIME'] // 100\n",
    "df['CRS_DepMinute'] = df['CRS_DEP_TIME'] % 100\n",
    "# More precise time representation (hour + fraction of the hour)\n",
    "df['CRS_DepFraction'] = df['CRS_DepHour'] + df['CRS_DepMinute'] / 60\n",
    "df['CRS_DepTime_sin'] = np.sin(2 * np.pi * df['CRS_DepFraction'] / 24)\n",
    "df['CRS_DepTime_cos'] = np.cos(2 * np.pi * df['CRS_DepFraction'] / 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "fa39137b",
   "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>3383688</th>\n",
       "      <th>679258</th>\n",
       "      <th>317848</th>\n",
       "      <th>5642411</th>\n",
       "      <th>5407233</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL_DATE</th>\n",
       "      <td>2024-06-17 00:00:00</td>\n",
       "      <td>2024-02-06 00:00:00</td>\n",
       "      <td>2024-01-17 00:00:00</td>\n",
       "      <td>2024-09-30 00:00:00</td>\n",
       "      <td>2024-09-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_UNIQUE_CARRIER</th>\n",
       "      <td>AA</td>\n",
       "      <td>AA</td>\n",
       "      <td>WN</td>\n",
       "      <td>WN</td>\n",
       "      <td>UA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_CARRIER_FL_NUM</th>\n",
       "      <td>5807</td>\n",
       "      <td>303</td>\n",
       "      <td>20</td>\n",
       "      <td>3372</td>\n",
       "      <td>3641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORIGIN_CITY_NAME</th>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>Newark, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEST_CITY_NAME</th>\n",
       "      <td>Charlotte, NC</td>\n",
       "      <td>Spokane, WA</td>\n",
       "      <td>Pensacola, FL</td>\n",
       "      <td>Austin, TX</td>\n",
       "      <td>Columbus, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <td>1326</td>\n",
       "      <td>1856</td>\n",
       "      <td>1250</td>\n",
       "      <td>800</td>\n",
       "      <td>905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_TIME</th>\n",
       "      <td>1314.0</td>\n",
       "      <td>1933.0</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>857.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_DELAY</th>\n",
       "      <td>-12.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_ARR_TIME</th>\n",
       "      <td>1612</td>\n",
       "      <td>2042</td>\n",
       "      <td>1425</td>\n",
       "      <td>900</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR_TIME</th>\n",
       "      <td>1559.0</td>\n",
       "      <td>2115.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>1047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR_DELAY</th>\n",
       "      <td>-13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CANCELLED</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_ELAPSED_TIME</th>\n",
       "      <td>106.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <td>105.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIR_TIME</th>\n",
       "      <td>64.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISTANCE</th>\n",
       "      <td>372.0</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlightDelay</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_code</th>\n",
       "      <td>AA5807</td>\n",
       "      <td>AA303</td>\n",
       "      <td>WN20</td>\n",
       "      <td>WN3372</td>\n",
       "      <td>UA3641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepHour</th>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepMinute</th>\n",
       "      <td>14.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepFraction</th>\n",
       "      <td>13.233333</td>\n",
       "      <td>19.55</td>\n",
       "      <td>12.9</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepTime_sin</th>\n",
       "      <td>-0.317305</td>\n",
       "      <td>-0.918791</td>\n",
       "      <td>-0.233445</td>\n",
       "      <td>0.801254</td>\n",
       "      <td>0.716302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepTime_cos</th>\n",
       "      <td>-0.948324</td>\n",
       "      <td>0.394744</td>\n",
       "      <td>-0.97237</td>\n",
       "      <td>-0.598325</td>\n",
       "      <td>-0.69779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepHour</th>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepMinute</th>\n",
       "      <td>26</td>\n",
       "      <td>56</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepFraction</th>\n",
       "      <td>13.433333</td>\n",
       "      <td>18.933333</td>\n",
       "      <td>12.833333</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepTime_sin</th>\n",
       "      <td>-0.366501</td>\n",
       "      <td>-0.970296</td>\n",
       "      <td>-0.21644</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.691513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepTime_cos</th>\n",
       "      <td>-0.930418</td>\n",
       "      <td>0.241922</td>\n",
       "      <td>-0.976296</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.722364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 3383688                679258   \\\n",
       "YEAR                                2024                   2024   \n",
       "MONTH                                  6                      2   \n",
       "DAY_OF_MONTH                          17                      6   \n",
       "FL_DATE              2024-06-17 00:00:00    2024-02-06 00:00:00   \n",
       "MKT_UNIQUE_CARRIER                    AA                     AA   \n",
       "MKT_CARRIER_FL_NUM                  5807                    303   \n",
       "ORIGIN_CITY_NAME          Montgomery, AL  Dallas/Fort Worth, TX   \n",
       "DEST_CITY_NAME             Charlotte, NC            Spokane, WA   \n",
       "CRS_DEP_TIME                        1326                   1856   \n",
       "DEP_TIME                          1314.0                 1933.0   \n",
       "DEP_DELAY                          -12.0                   37.0   \n",
       "CRS_ARR_TIME                        1612                   2042   \n",
       "ARR_TIME                          1559.0                 2115.0   \n",
       "ARR_DELAY                          -13.0                   33.0   \n",
       "CANCELLED                            0.0                    0.0   \n",
       "CRS_ELAPSED_TIME                   106.0                  226.0   \n",
       "ACTUAL_ELAPSED_TIME                105.0                  222.0   \n",
       "AIR_TIME                            64.0                  201.0   \n",
       "DISTANCE                           372.0                 1477.0   \n",
       "FlightDelay                         -1.0                   -4.0   \n",
       "flight_code                       AA5807                  AA303   \n",
       "Day                               Monday                Tuesday   \n",
       "DepHour                             13.0                   19.0   \n",
       "DepMinute                           14.0                   33.0   \n",
       "DepFraction                    13.233333                  19.55   \n",
       "DepTime_sin                    -0.317305              -0.918791   \n",
       "DepTime_cos                    -0.948324               0.394744   \n",
       "CRS_DepHour                           13                     18   \n",
       "CRS_DepMinute                         26                     56   \n",
       "CRS_DepFraction                13.433333              18.933333   \n",
       "CRS_DepTime_sin                -0.366501              -0.970296   \n",
       "CRS_DepTime_cos                -0.930418               0.241922   \n",
       "\n",
       "                                 317848               5642411  \\\n",
       "YEAR                                2024                 2024   \n",
       "MONTH                                  1                    9   \n",
       "DAY_OF_MONTH                          17                   30   \n",
       "FL_DATE              2024-01-17 00:00:00  2024-09-30 00:00:00   \n",
       "MKT_UNIQUE_CARRIER                    WN                   WN   \n",
       "MKT_CARRIER_FL_NUM                    20                 3372   \n",
       "ORIGIN_CITY_NAME              Dallas, TX           Dallas, TX   \n",
       "DEST_CITY_NAME             Pensacola, FL           Austin, TX   \n",
       "CRS_DEP_TIME                        1250                  800   \n",
       "DEP_TIME                          1254.0                827.0   \n",
       "DEP_DELAY                            4.0                 27.0   \n",
       "CRS_ARR_TIME                        1425                  900   \n",
       "ARR_TIME                          1428.0                923.0   \n",
       "ARR_DELAY                            3.0                 23.0   \n",
       "CANCELLED                            0.0                  0.0   \n",
       "CRS_ELAPSED_TIME                    95.0                 60.0   \n",
       "ACTUAL_ELAPSED_TIME                 94.0                 56.0   \n",
       "AIR_TIME                            78.0                 36.0   \n",
       "DISTANCE                           592.0                189.0   \n",
       "FlightDelay                         -1.0                 -4.0   \n",
       "flight_code                         WN20               WN3372   \n",
       "Day                            Wednesday               Monday   \n",
       "DepHour                             12.0                  8.0   \n",
       "DepMinute                           54.0                 27.0   \n",
       "DepFraction                         12.9                 8.45   \n",
       "DepTime_sin                    -0.233445             0.801254   \n",
       "DepTime_cos                     -0.97237            -0.598325   \n",
       "CRS_DepHour                           12                    8   \n",
       "CRS_DepMinute                         50                    0   \n",
       "CRS_DepFraction                12.833333                  8.0   \n",
       "CRS_DepTime_sin                 -0.21644             0.866025   \n",
       "CRS_DepTime_cos                -0.976296                 -0.5   \n",
       "\n",
       "                                 5407233  \n",
       "YEAR                                2024  \n",
       "MONTH                                  9  \n",
       "DAY_OF_MONTH                          19  \n",
       "FL_DATE              2024-09-19 00:00:00  \n",
       "MKT_UNIQUE_CARRIER                    UA  \n",
       "MKT_CARRIER_FL_NUM                  3641  \n",
       "ORIGIN_CITY_NAME              Newark, NJ  \n",
       "DEST_CITY_NAME              Columbus, OH  \n",
       "CRS_DEP_TIME                         905  \n",
       "DEP_TIME                           857.0  \n",
       "DEP_DELAY                           -8.0  \n",
       "CRS_ARR_TIME                        1103  \n",
       "ARR_TIME                          1047.0  \n",
       "ARR_DELAY                          -16.0  \n",
       "CANCELLED                            0.0  \n",
       "CRS_ELAPSED_TIME                   118.0  \n",
       "ACTUAL_ELAPSED_TIME                110.0  \n",
       "AIR_TIME                            65.0  \n",
       "DISTANCE                           463.0  \n",
       "FlightDelay                         -8.0  \n",
       "flight_code                       UA3641  \n",
       "Day                             Thursday  \n",
       "DepHour                              8.0  \n",
       "DepMinute                           57.0  \n",
       "DepFraction                         8.95  \n",
       "DepTime_sin                     0.716302  \n",
       "DepTime_cos                     -0.69779  \n",
       "CRS_DepHour                            9  \n",
       "CRS_DepMinute                          5  \n",
       "CRS_DepFraction                 9.083333  \n",
       "CRS_DepTime_sin                 0.691513  \n",
       "CRS_DepTime_cos                -0.722364  "
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "a7cf906d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MKT_UNIQUE_CARRIER\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:97: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
      "  return is_numeric(pd.to_numeric(column, errors=\"ignore\"))\n",
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:101: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_datetime without passing `errors` and catch exceptions explicitly instead\n",
      "  return is_datetime(pd.to_datetime(column, errors=\"ignore\", utc=True))\n",
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:101: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  return is_datetime(pd.to_datetime(column, errors=\"ignore\", utc=True))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ORIGIN_CITY_NAME\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:97: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
      "  return is_numeric(pd.to_numeric(column, errors=\"ignore\"))\n",
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:101: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_datetime without passing `errors` and catch exceptions explicitly instead\n",
      "  return is_datetime(pd.to_datetime(column, errors=\"ignore\", utc=True))\n",
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:101: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  return is_datetime(pd.to_datetime(column, errors=\"ignore\", utc=True))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEST_CITY_NAME\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:97: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
      "  return is_numeric(pd.to_numeric(column, errors=\"ignore\"))\n",
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:101: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_datetime without passing `errors` and catch exceptions explicitly instead\n",
      "  return is_datetime(pd.to_datetime(column, errors=\"ignore\", utc=True))\n",
      "/Users/zhuhongjie/opt/anaconda3/lib/python3.9/site-packages/feature_engine/variable_manipulation.py:101: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  return is_datetime(pd.to_datetime(column, errors=\"ignore\", utc=True))\n"
     ]
    }
   ],
   "source": [
    "for col in ['MKT_UNIQUE_CARRIER', 'ORIGIN_CITY_NAME', 'DEST_CITY_NAME']:\n",
    "    print(col)\n",
    "    df[col] = df[col].fillna('None')\n",
    "    encoder = RareLabelEncoder(n_categories=1, max_n_categories=200, replace_with='Other', tol=0.0001)\n",
    "    new_col =col+'_new'\n",
    "    df[new_col] = encoder.fit_transform(df[[col]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "b7912c8c",
   "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>1801348</th>\n",
       "      <th>5299515</th>\n",
       "      <th>5073654</th>\n",
       "      <th>5186738</th>\n",
       "      <th>355414</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL_DATE</th>\n",
       "      <td>2024-04-02 00:00:00</td>\n",
       "      <td>2024-09-14 00:00:00</td>\n",
       "      <td>2024-09-03 00:00:00</td>\n",
       "      <td>2024-09-09 00:00:00</td>\n",
       "      <td>2024-01-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_UNIQUE_CARRIER</th>\n",
       "      <td>WN</td>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>AA</td>\n",
       "      <td>UA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_CARRIER_FL_NUM</th>\n",
       "      <td>1388</td>\n",
       "      <td>5758</td>\n",
       "      <td>5341</td>\n",
       "      <td>2970</td>\n",
       "      <td>5439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORIGIN_CITY_NAME</th>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Chicago, IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEST_CITY_NAME</th>\n",
       "      <td>Reno, NV</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>Rochester, NY</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>Roanoke, VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <td>855</td>\n",
       "      <td>709</td>\n",
       "      <td>950</td>\n",
       "      <td>530</td>\n",
       "      <td>1410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_TIME</th>\n",
       "      <td>856.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>1519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_DELAY</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_ARR_TIME</th>\n",
       "      <td>1020</td>\n",
       "      <td>929</td>\n",
       "      <td>1118</td>\n",
       "      <td>831</td>\n",
       "      <td>1705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR_TIME</th>\n",
       "      <td>953.0</td>\n",
       "      <td>918.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>1803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR_DELAY</th>\n",
       "      <td>-27.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CANCELLED</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_ELAPSED_TIME</th>\n",
       "      <td>145.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <td>117.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIR_TIME</th>\n",
       "      <td>103.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISTANCE</th>\n",
       "      <td>804.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlightDelay</th>\n",
       "      <td>-28.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_code</th>\n",
       "      <td>WN1388</td>\n",
       "      <td>DL5758</td>\n",
       "      <td>DL5341</td>\n",
       "      <td>AA2970</td>\n",
       "      <td>UA5439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepHour</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepMinute</th>\n",
       "      <td>56.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepFraction</th>\n",
       "      <td>8.933333</td>\n",
       "      <td>7.066667</td>\n",
       "      <td>9.633333</td>\n",
       "      <td>5.3</td>\n",
       "      <td>15.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepTime_sin</th>\n",
       "      <td>0.71934</td>\n",
       "      <td>0.961262</td>\n",
       "      <td>0.580703</td>\n",
       "      <td>0.983255</td>\n",
       "      <td>-0.763232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepTime_cos</th>\n",
       "      <td>-0.694658</td>\n",
       "      <td>-0.275637</td>\n",
       "      <td>-0.814116</td>\n",
       "      <td>0.182236</td>\n",
       "      <td>-0.646124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepHour</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepMinute</th>\n",
       "      <td>55</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepFraction</th>\n",
       "      <td>8.916667</td>\n",
       "      <td>7.15</td>\n",
       "      <td>9.833333</td>\n",
       "      <td>5.5</td>\n",
       "      <td>14.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepTime_sin</th>\n",
       "      <td>0.722364</td>\n",
       "      <td>0.95502</td>\n",
       "      <td>0.5373</td>\n",
       "      <td>0.991445</td>\n",
       "      <td>-0.5373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepTime_cos</th>\n",
       "      <td>-0.691513</td>\n",
       "      <td>-0.296542</td>\n",
       "      <td>-0.843391</td>\n",
       "      <td>0.130526</td>\n",
       "      <td>-0.843391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISTANCE_new</th>\n",
       "      <td>802_956</td>\n",
       "      <td>802_956</td>\n",
       "      <td>224_325</td>\n",
       "      <td>645_802</td>\n",
       "      <td>417_541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_DELAY_new</th>\n",
       "      <td>-1_3</td>\n",
       "      <td>-7_-5</td>\n",
       "      <td>-96_-9</td>\n",
       "      <td>-96_-9</td>\n",
       "      <td>28_89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlightDelay_new</th>\n",
       "      <td>-92_-20</td>\n",
       "      <td>-8_-6</td>\n",
       "      <td>-13_-10</td>\n",
       "      <td>-10_-8</td>\n",
       "      <td>-13_-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_UNIQUE_CARRIER_new</th>\n",
       "      <td>WN</td>\n",
       "      <td>DL</td>\n",
       "      <td>DL</td>\n",
       "      <td>AA</td>\n",
       "      <td>UA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORIGIN_CITY_NAME_new</th>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Chicago, IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEST_CITY_NAME_new</th>\n",
       "      <td>Reno, NV</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>Rochester, NY</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>Roanoke, VA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    1801348              5299515  \\\n",
       "YEAR                                   2024                 2024   \n",
       "MONTH                                     4                    9   \n",
       "DAY_OF_MONTH                              2                   14   \n",
       "FL_DATE                 2024-04-02 00:00:00  2024-09-14 00:00:00   \n",
       "MKT_UNIQUE_CARRIER                       WN                   DL   \n",
       "MKT_CARRIER_FL_NUM                     1388                 5758   \n",
       "ORIGIN_CITY_NAME                 Denver, CO     Jacksonville, FL   \n",
       "DEST_CITY_NAME                     Reno, NV         New York, NY   \n",
       "CRS_DEP_TIME                            855                  709   \n",
       "DEP_TIME                              856.0                704.0   \n",
       "DEP_DELAY                               1.0                 -5.0   \n",
       "CRS_ARR_TIME                           1020                  929   \n",
       "ARR_TIME                              953.0                918.0   \n",
       "ARR_DELAY                             -27.0                -11.0   \n",
       "CANCELLED                               0.0                  0.0   \n",
       "CRS_ELAPSED_TIME                      145.0                140.0   \n",
       "ACTUAL_ELAPSED_TIME                   117.0                134.0   \n",
       "AIR_TIME                              103.0                115.0   \n",
       "DISTANCE                              804.0                828.0   \n",
       "FlightDelay                           -28.0                 -6.0   \n",
       "flight_code                          WN1388               DL5758   \n",
       "Day                                 Tuesday             Saturday   \n",
       "DepHour                                 8.0                  7.0   \n",
       "DepMinute                              56.0                  4.0   \n",
       "DepFraction                        8.933333             7.066667   \n",
       "DepTime_sin                         0.71934             0.961262   \n",
       "DepTime_cos                       -0.694658            -0.275637   \n",
       "CRS_DepHour                               8                    7   \n",
       "CRS_DepMinute                            55                    9   \n",
       "CRS_DepFraction                    8.916667                 7.15   \n",
       "CRS_DepTime_sin                    0.722364              0.95502   \n",
       "CRS_DepTime_cos                   -0.691513            -0.296542   \n",
       "DISTANCE_new                        802_956              802_956   \n",
       "DEP_DELAY_new                          -1_3                -7_-5   \n",
       "FlightDelay_new                     -92_-20                -8_-6   \n",
       "MKT_UNIQUE_CARRIER_new                   WN                   DL   \n",
       "ORIGIN_CITY_NAME_new             Denver, CO     Jacksonville, FL   \n",
       "DEST_CITY_NAME_new                 Reno, NV         New York, NY   \n",
       "\n",
       "                                    5073654              5186738  \\\n",
       "YEAR                                   2024                 2024   \n",
       "MONTH                                     9                    9   \n",
       "DAY_OF_MONTH                              3                    9   \n",
       "FL_DATE                 2024-09-03 00:00:00  2024-09-09 00:00:00   \n",
       "MKT_UNIQUE_CARRIER                       DL                   AA   \n",
       "MKT_CARRIER_FL_NUM                     5341                 2970   \n",
       "ORIGIN_CITY_NAME               New York, NY          Chicago, IL   \n",
       "DEST_CITY_NAME                Rochester, NY     Philadelphia, PA   \n",
       "CRS_DEP_TIME                            950                  530   \n",
       "DEP_TIME                              938.0                518.0   \n",
       "DEP_DELAY                             -12.0                -12.0   \n",
       "CRS_ARR_TIME                           1118                  831   \n",
       "ARR_TIME                             1054.0                810.0   \n",
       "ARR_DELAY                             -24.0                -21.0   \n",
       "CANCELLED                               0.0                  0.0   \n",
       "CRS_ELAPSED_TIME                       88.0                121.0   \n",
       "ACTUAL_ELAPSED_TIME                    76.0                112.0   \n",
       "AIR_TIME                               51.0                 90.0   \n",
       "DISTANCE                              264.0                678.0   \n",
       "FlightDelay                           -12.0                 -9.0   \n",
       "flight_code                          DL5341               AA2970   \n",
       "Day                                 Tuesday               Monday   \n",
       "DepHour                                 9.0                  5.0   \n",
       "DepMinute                              38.0                 18.0   \n",
       "DepFraction                        9.633333                  5.3   \n",
       "DepTime_sin                        0.580703             0.983255   \n",
       "DepTime_cos                       -0.814116             0.182236   \n",
       "CRS_DepHour                               9                    5   \n",
       "CRS_DepMinute                            50                   30   \n",
       "CRS_DepFraction                    9.833333                  5.5   \n",
       "CRS_DepTime_sin                      0.5373             0.991445   \n",
       "CRS_DepTime_cos                   -0.843391             0.130526   \n",
       "DISTANCE_new                        224_325              645_802   \n",
       "DEP_DELAY_new                        -96_-9               -96_-9   \n",
       "FlightDelay_new                     -13_-10               -10_-8   \n",
       "MKT_UNIQUE_CARRIER_new                   DL                   AA   \n",
       "ORIGIN_CITY_NAME_new           New York, NY          Chicago, IL   \n",
       "DEST_CITY_NAME_new            Rochester, NY     Philadelphia, PA   \n",
       "\n",
       "                                    355414   \n",
       "YEAR                                   2024  \n",
       "MONTH                                     1  \n",
       "DAY_OF_MONTH                             19  \n",
       "FL_DATE                 2024-01-19 00:00:00  \n",
       "MKT_UNIQUE_CARRIER                       UA  \n",
       "MKT_CARRIER_FL_NUM                     5439  \n",
       "ORIGIN_CITY_NAME                Chicago, IL  \n",
       "DEST_CITY_NAME                  Roanoke, VA  \n",
       "CRS_DEP_TIME                           1410  \n",
       "DEP_TIME                             1519.0  \n",
       "DEP_DELAY                              69.0  \n",
       "CRS_ARR_TIME                           1705  \n",
       "ARR_TIME                             1803.0  \n",
       "ARR_DELAY                              58.0  \n",
       "CANCELLED                               0.0  \n",
       "CRS_ELAPSED_TIME                      115.0  \n",
       "ACTUAL_ELAPSED_TIME                   104.0  \n",
       "AIR_TIME                               74.0  \n",
       "DISTANCE                              531.0  \n",
       "FlightDelay                           -11.0  \n",
       "flight_code                          UA5439  \n",
       "Day                                  Friday  \n",
       "DepHour                                15.0  \n",
       "DepMinute                              19.0  \n",
       "DepFraction                       15.316667  \n",
       "DepTime_sin                       -0.763232  \n",
       "DepTime_cos                       -0.646124  \n",
       "CRS_DepHour                              14  \n",
       "CRS_DepMinute                            10  \n",
       "CRS_DepFraction                   14.166667  \n",
       "CRS_DepTime_sin                     -0.5373  \n",
       "CRS_DepTime_cos                   -0.843391  \n",
       "DISTANCE_new                        417_541  \n",
       "DEP_DELAY_new                         28_89  \n",
       "FlightDelay_new                     -13_-10  \n",
       "MKT_UNIQUE_CARRIER_new                   UA  \n",
       "ORIGIN_CITY_NAME_new            Chicago, IL  \n",
       "DEST_CITY_NAME_new              Roanoke, VA  "
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "3d0adc66-c7d0-47b3-b10a-3e4312e64857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of 'Other': 0\n",
      "Number of unique values: 350\n"
     ]
    }
   ],
   "source": [
    "df['ORIGIN_CITY_NAME'].value_counts()\n",
    "counts = df['ORIGIN_CITY_NAME'].value_counts()\n",
    "other_count = counts.get('Other', 0) \n",
    "print(f\"Number of 'Other': {other_count}\")\n",
    "unique_count = df['ORIGIN_CITY_NAME'].nunique()\n",
    "print(f\"Number of unique values: {unique_count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "dde534d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of 'Other': 0\n",
      "Number of unique values: 350\n"
     ]
    }
   ],
   "source": [
    "counts = df['DEST_CITY_NAME'].value_counts()\n",
    "other_count = counts.get('Other', 0) \n",
    "print(f\"Number of 'Other': {other_count}\")\n",
    "unique_count = df['DEST_CITY_NAME'].nunique()\n",
    "print(f\"Number of unique values: {unique_count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "7441d17c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DISTANCE\n",
      "337.0     24072\n",
      "399.0     23335\n",
      "224.0     21876\n",
      "296.0     21022\n",
      "594.0     19315\n",
      "          ...  \n",
      "1687.0        2\n",
      "105.0         1\n",
      "2154.0        1\n",
      "2062.0        1\n",
      "2390.0        1\n",
      "Name: count, Length: 1607, dtype: int64\n",
      "DEP_DELAY\n",
      "-5.0     363462\n",
      "-4.0     338554\n",
      "-3.0     321620\n",
      "-6.0     303597\n",
      "-2.0     289886\n",
      "          ...  \n",
      "-96.0         1\n",
      "-62.0         1\n",
      "-56.0         1\n",
      "-48.0         1\n",
      "-52.0         1\n",
      "Name: count, Length: 143, dtype: int64\n",
      "FlightDelay\n",
      "-8.0     216924\n",
      "-7.0     215374\n",
      "-9.0     215317\n",
      "-10.0    210266\n",
      "-6.0     209898\n",
      "          ...  \n",
      "-87.0         1\n",
      "-91.0         1\n",
      "-92.0         1\n",
      "-85.0         1\n",
      "-84.0         1\n",
      "Name: count, Length: 99, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in ['DISTANCE', 'DEP_DELAY', 'FlightDelay']:\n",
    "    print(df[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "286e6bc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('2024_clean.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6c337e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    Madison, WI\n",
       "1                  Charlotte, NC\n",
       "2               Philadelphia, PA\n",
       "3          Dallas/Fort Worth, TX\n",
       "4                  Las Vegas, NV\n",
       "                   ...          \n",
       "5644139            Nashville, TN\n",
       "5644140            Baltimore, MD\n",
       "5644141                Greer, SC\n",
       "5644142          San Antonio, TX\n",
       "5644143               Denver, CO\n",
       "Name: ORIGIN_CITY_NAME, Length: 5539508, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ORIGIN_CITY_NAME']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b088cb52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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