Code/Flights_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "161b2a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a0c5820",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v7/cd7r3hf1229b9tnytv_qr8hm0000gn/T/ipykernel_81766/2436519184.py:1: DtypeWarning: Columns (3,4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  fact = pd.read_csv('flights_fact.csv', usecols=['AIRLINE', 'ORIGIN_AIRPORT', 'DESTINATION_AIRPORT',\n"
     ]
    }
   ],
   "source": [
    "fact = pd.read_csv('flights_fact.csv', usecols=['AIRLINE', 'ORIGIN_AIRPORT', 'DESTINATION_AIRPORT',\n",
    "                                               'scheduledDeparture_key', 'scheduledArrival_key' ,'dateKey',\n",
    "                                               'on_time', 'delayed', 'early', 'TAIL_NUMBER', 'CANCELLED', 'DIVERTED'],\n",
    "                  dtype={'scheduledArrival_key' : 'str',\n",
    "                        'scheduledDeparture_key' : 'str',\n",
    "                        'dateKey': 'str'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7dac7b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep only the top 10 airports and airlines to make the dataset smaller\n",
    "top_10_airports = ['ATL', 'LAX', 'ORD', 'DFW', 'DEN', 'JFK', 'SFO', 'SEA', 'MCO', 'LAS']\n",
    "fact = fact[(fact['ORIGIN_AIRPORT'].isin(top_10_airports)) & (fact['DESTINATION_AIRPORT'].isin(top_10_airports))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2130aaba",
   "metadata": {},
   "outputs": [
    {
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       "      <td>UA</td>\n",
       "      <td>N87527</td>\n",
       "      <td>LAX</td>\n",
       "      <td>ORD</td>\n",
       "      <td>2355</td>\n",
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       "      <td>LAX</td>\n",
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       "      <td>JFK</td>\n",
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       "<p>367755 rows × 10 columns</p>\n",
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       "        AIRLINE TAIL_NUMBER ORIGIN_AIRPORT DESTINATION_AIRPORT  \\\n",
       "8            AA      N3LAAA            SFO                 DFW   \n",
       "9            DL      N826DN            LAS                 ATL   \n",
       "10           DL      N958DN            DEN                 ATL   \n",
       "21           NK      N632NK            LAS                 DFW   \n",
       "66           UA      N36476            SEA                 DEN   \n",
       "...         ...         ...            ...                 ...   \n",
       "4869825      UA      N87527            LAX                 ORD   \n",
       "4869830      AS      N570AS            LAX                 SEA   \n",
       "4869834      F9      N701FR            DEN                 MCO   \n",
       "4869842      AA      N3LYAA            LAX                 ORD   \n",
       "4869844      B6      N607JB            DEN                 JFK   \n",
       "\n",
       "        scheduledDeparture_key scheduledArrival_key   dateKey  early  on_time  \\\n",
       "8                         0030                 0545  20150101      1        0   \n",
       "9                         0030                 0711  20150101      1        0   \n",
       "10                        0030                 0523  20150101      1        0   \n",
       "21                        0103                 0530  20150101      0        1   \n",
       "66                        0530                 0907  20150101      0        1   \n",
       "...                        ...                  ...       ...    ...      ...   \n",
       "4869825                   2355                 0555  20151231      1        0   \n",
       "4869830                   2355                 0244  20151231      1        0   \n",
       "4869834                   2359                 0525  20151231      1        0   \n",
       "4869842                   2359                 0603  20151231      1        0   \n",
       "4869844                   2359                 0530  20151231      1        0   \n",
       "\n",
       "         delayed  \n",
       "8              0  \n",
       "9              0  \n",
       "10             0  \n",
       "21             0  \n",
       "66             0  \n",
       "...          ...  \n",
       "4869825        0  \n",
       "4869830        0  \n",
       "4869834        0  \n",
       "4869842        0  \n",
       "4869844        0  \n",
       "\n",
       "[367755 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b60842f3",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
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       "      <td>SFO</td>\n",
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       "      <td>N483AS</td>\n",
       "      <td>SEA</td>\n",
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      "text/plain": [
       "        AIRLINE TAIL_NUMBER ORIGIN_AIRPORT DESTINATION_AIRPORT  \\\n",
       "1498231      AA      N3JSAA            SFO                 ORD   \n",
       "4129581      WN      N7814B            LAX                 LAS   \n",
       "4434494      UA      N474UA            DFW                 DEN   \n",
       "2724428      F9      N924FR            ATL                 LAS   \n",
       "4157048      AS      N483AS            SEA                 LAX   \n",
       "\n",
       "        scheduledDeparture_key scheduledArrival_key   dateKey  early  on_time  \\\n",
       "1498231                   0930                 1552  20150427      0        1   \n",
       "4129581                   1835                 1935  20151104      0        0   \n",
       "4434494                   0550                 0701  20151128      1        0   \n",
       "2724428                   2040                 2155  20150725      1        0   \n",
       "4157048                   1805                 2051  20151106      1        0   \n",
       "\n",
       "         delayed  \n",
       "1498231        0  \n",
       "4129581        1  \n",
       "4434494        0  \n",
       "2724428        0  \n",
       "4157048        0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dbffeda6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fact = fact.sample(frac=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a29819e",
   "metadata": {},
   "outputs": [],
   "source": [
    "departure_time = pd.read_csv('departureTime_dim.csv', dtype={'scheduledDeparture_key':'str'})\n",
    "arrival_time = pd.read_csv('arrivalTime_dim.csv', dtype={'scheduledArrival_key':'str'})\n",
    "departure_date = pd.read_csv('date_dim.csv', dtype={'dateKey':'str'}, usecols=['MONTH', 'DAY', 'dateKey'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8f49215",
   "metadata": {},
   "outputs": [],
   "source": [
    "fact = pd.merge(fact, departure_time, left_on='scheduledDeparture_key', right_on='scheduledDeparture_key')\n",
    "fact = pd.merge(fact, arrival_time, left_on='scheduledArrival_key', right_on='scheduledArrival_key')\n",
    "fact = pd.merge(fact, departure_date, left_on='dateKey', right_on='dateKey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f70f0cff",
   "metadata": {},
   "outputs": [
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       "      <td>AS</td>\n",
       "      <td>N527AS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>LAX</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AIRLINE TAIL_NUMBER ORIGIN_AIRPORT DESTINATION_AIRPORT  \\\n",
       "228932      AS      N644AS            SEA                 DEN   \n",
       "290023      NK      N612NK            LAS                 DFW   \n",
       "25113       UA      N14242            ORD                 DEN   \n",
       "5461        F9      N906FR            DEN                 ATL   \n",
       "302593      AS      N527AS            SEA                 LAX   \n",
       "\n",
       "       scheduledDeparture_key scheduledArrival_key   dateKey  early  on_time  \\\n",
       "228932                   0830                 1209  20150726      1        0   \n",
       "290023                   2035                 0112  20150829      0        0   \n",
       "25113                    1735                 1912  20150622      0        0   \n",
       "5461                     1405                 1858  20150509      0        1   \n",
       "302593                   2055                 2333  20151125      1        0   \n",
       "\n",
       "        delayed  Hour_x  Minute_x  Hour_y  Minute_y  MONTH  DAY  \n",
       "228932        0       8        30      12         9      7   26  \n",
       "290023        1      20        35       1        12      8   29  \n",
       "25113         1      17        35      19        12      6   22  \n",
       "5461          0      14         5      18        58      5    9  \n",
       "302593        0      20        55      23        33     11   25  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "621655a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "fact.drop(columns=['TAIL_NUMBER', 'scheduledDeparture_key', 'scheduledArrival_key', 'dateKey',\n",
    "                  'DESTINATION_AIRPORT'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "df1a7bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fact['early'] = fact['early']*0\n",
    "fact['on_time'] = fact['on_time']*1\n",
    "fact['delayed'] = fact['delayed']*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "70e80351",
   "metadata": {},
   "outputs": [],
   "source": [
    "fact['target'] = fact['early'] + fact['on_time'] + fact['delayed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "b571a470",
   "metadata": {},
   "outputs": [],
   "source": [
    "fact = fact[fact['target'] <= 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "e84eaf80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.458876\n",
       "0    0.389223\n",
       "2    0.151901\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact['target'].value_counts() / fact['target'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "38c7525b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fact.drop(columns=['early', 'on_time', 'delayed'], inplace=True)\n",
    "# 'AIRLINE', 'ORIGIN_AIRPORT', 'DESTINATION_AIRPORT'\n",
    "fact = pd.get_dummies(fact ,columns=['AIRLINE', 'ORIGIN_AIRPORT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "b7d7820c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hour_x</th>\n",
       "      <th>Minute_x</th>\n",
       "      <th>Hour_y</th>\n",
       "      <th>Minute_y</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>target</th>\n",
       "      <th>AIRLINE_AA</th>\n",
       "      <th>AIRLINE_AS</th>\n",
       "      <th>AIRLINE_B6</th>\n",
       "      <th>...</th>\n",
       "      <th>ORIGIN_AIRPORT_ATL</th>\n",
       "      <th>ORIGIN_AIRPORT_DEN</th>\n",
       "      <th>ORIGIN_AIRPORT_DFW</th>\n",
       "      <th>ORIGIN_AIRPORT_JFK</th>\n",
       "      <th>ORIGIN_AIRPORT_LAS</th>\n",
       "      <th>ORIGIN_AIRPORT_LAX</th>\n",
       "      <th>ORIGIN_AIRPORT_MCO</th>\n",
       "      <th>ORIGIN_AIRPORT_ORD</th>\n",
       "      <th>ORIGIN_AIRPORT_SEA</th>\n",
       "      <th>ORIGIN_AIRPORT_SFO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>265746</th>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>53</td>\n",
       "      <td>11</td>\n",
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       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Hour_x  Minute_x  Hour_y  Minute_y  MONTH  DAY  target  AIRLINE_AA  \\\n",
       "210002      18         6      21         3      7    2       1           0   \n",
       "241198      11        50      13        36      3    6       0           0   \n",
       "161235       9        40      17        20      6   16       0           0   \n",
       "265746      13        45      20        53     11   14       0           0   \n",
       "63558        6        25       8        24      9   30       1           0   \n",
       "\n",
       "        AIRLINE_AS  AIRLINE_B6  ...  ORIGIN_AIRPORT_ATL  ORIGIN_AIRPORT_DEN  \\\n",
       "210002           0           0  ...                   0                   1   \n",
       "241198           0           1  ...                   0                   0   \n",
       "161235           0           0  ...                   0                   0   \n",
       "265746           0           0  ...                   0                   0   \n",
       "63558            0           0  ...                   0                   0   \n",
       "\n",
       "        ORIGIN_AIRPORT_DFW  ORIGIN_AIRPORT_JFK  ORIGIN_AIRPORT_LAS  \\\n",
       "210002                   0                   0                   0   \n",
       "241198                   0                   0                   1   \n",
       "161235                   0                   0                   1   \n",
       "265746                   0                   0                   0   \n",
       "63558                    1                   0                   0   \n",
       "\n",
       "        ORIGIN_AIRPORT_LAX  ORIGIN_AIRPORT_MCO  ORIGIN_AIRPORT_ORD  \\\n",
       "210002                   0                   0                   0   \n",
       "241198                   0                   0                   0   \n",
       "161235                   0                   0                   0   \n",
       "265746                   1                   0                   0   \n",
       "63558                    0                   0                   0   \n",
       "\n",
       "        ORIGIN_AIRPORT_SEA  ORIGIN_AIRPORT_SFO  \n",
       "210002                   0                   0  \n",
       "241198                   0                   0  \n",
       "161235                   0                   0  \n",
       "265746                   0                   0  \n",
       "63558                    0                   0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "1fa835d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = fact['target']\n",
    "fact.drop(columns='target', inplace=True)\n",
    "X = fact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "36e9264e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "58aa45cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn import svm\n",
    "# classifier = svm.SVC(kernel='rbf',\n",
    "#                     decision_function_shape='ovr')\n",
    "# classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "7deba932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn import svm\n",
    "# from sklearn import metrics\n",
    "# classifier = svm.SVC(kernel='rbf' , verbose=2)\n",
    "# classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "ed64212b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred = classifier.predict(X_test)\n",
    "# print(metrics.classification_report(y_test, y_pred, \n",
    "#                                     zero_division=1)) # silence warning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "e18f5915",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "e3f58b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "# from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "# from sklearn import metrics\n",
    "# from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# neigh = GaussianNB()\n",
    "# neigh.fit(X_train, y_train)\n",
    "# y_pred = neigh.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "dd65bc58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Confusion Matrix\n",
    "# confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "# confusion_matrix\n",
    "# #Calculating Model Accuracy\n",
    "# accuracy = accuracy_score(y_test, y_pred)*100\n",
    "# print('Accuracy of the model:' + str(round(accuracy, 2)) + ' %.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "b9821a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "d2ad0636",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "3d3367e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# # Create the model\n",
    "# model = LogisticRegression(multi_class='ovr', C=1)\n",
    "\n",
    "# # Train the model\n",
    "# model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# # Make predictions\n",
    "# y_pred = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "2fb29a40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.40      0.44     35574\n",
      "           1       0.49      0.72      0.58     41475\n",
      "           2       0.00      0.00      0.00     13906\n",
      "\n",
      "    accuracy                           0.49     90955\n",
      "   macro avg       0.33      0.38      0.34     90955\n",
      "weighted avg       0.41      0.49      0.44     90955\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dimitrisbouris/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/dimitrisbouris/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/dimitrisbouris/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd255418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIRLINE\n",
       "AA    0\n",
       "AS    0\n",
       "B6    0\n",
       "DL    0\n",
       "EV    0\n",
       "F9    0\n",
       "HA    0\n",
       "MQ    0\n",
       "NK    0\n",
       "OO    0\n",
       "UA    0\n",
       "US    0\n",
       "VX    0\n",
       "WN    0\n",
       "Name: CANCELLED, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact.groupby(by='AIRLINE')['CANCELLED'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "097cc0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fact['DIVERTED'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97df0fa6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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