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Delete tools/flights/test2.ipynb

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- "source": [
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- "import pandas as pd\n",
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- "data = pd.read_csv('/home/xj/toolAugEnv/code/toolConstraint/database/flights/Combined_Flights_2022.csv')"
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309
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310
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326
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327
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328
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329
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330
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- " FlightDate Airline Origin Dest \\\n",
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- "0 2022-04-04 Commutair Aka Champlain Enterprises, Inc. GJT DEN \n",
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- "1 2022-04-04 Commutair Aka Champlain Enterprises, Inc. HRL IAH \n",
338
- "2 2022-04-04 Commutair Aka Champlain Enterprises, Inc. DRO DEN \n",
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- "3 2022-04-04 Commutair Aka Champlain Enterprises, Inc. IAH GPT \n",
340
- "4 2022-04-04 Commutair Aka Champlain Enterprises, Inc. DRO DEN \n",
341
- "... ... ... ... ... \n",
342
- "4078313 2022-03-31 Republic Airlines MSY EWR \n",
343
- "4078314 2022-03-17 Republic Airlines CLT EWR \n",
344
- "4078315 2022-03-08 Republic Airlines ALB ORD \n",
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- "4078316 2022-03-25 Republic Airlines EWR PIT \n",
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- "4078317 2022-03-07 Republic Airlines EWR RDU \n",
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- "\n",
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- " Cancelled Diverted CRSDepTime DepTime DepDelayMinutes DepDelay \\\n",
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- "0 False False 1133 1123.0 0.0 -10.0 \n",
350
- "1 False False 732 728.0 0.0 -4.0 \n",
351
- "2 False False 1529 1514.0 0.0 -15.0 \n",
352
- "3 False False 1435 1430.0 0.0 -5.0 \n",
353
- "4 False False 1135 1135.0 0.0 0.0 \n",
354
- "... ... ... ... ... ... ... \n",
355
- "4078313 False True 1949 2014.0 25.0 25.0 \n",
356
- "4078314 True False 1733 1817.0 44.0 44.0 \n",
357
- "4078315 False False 1700 2318.0 378.0 378.0 \n",
358
- "4078316 False True 2129 2322.0 113.0 113.0 \n",
359
- "4078317 False True 1154 1148.0 0.0 -6.0 \n",
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- "\n",
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- " ... WheelsOff WheelsOn TaxiIn CRSArrTime ArrDelay ArrDel15 \\\n",
362
- "0 ... 1140.0 1220.0 8.0 1245 -17.0 0.0 \n",
363
- "1 ... 744.0 839.0 9.0 849 -1.0 0.0 \n",
364
- "2 ... 1535.0 1622.0 14.0 1639 -3.0 0.0 \n",
365
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366
- "4 ... 1154.0 1243.0 8.0 1245 6.0 0.0 \n",
367
- "... ... ... ... ... ... ... ... \n",
368
- "4078313 ... 2031.0 202.0 32.0 2354 NaN NaN \n",
369
- "4078314 ... NaN NaN NaN 1942 NaN NaN \n",
370
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371
- "4078316 ... 2347.0 933.0 6.0 2255 NaN NaN \n",
372
- "4078317 ... 1201.0 1552.0 4.0 1333 NaN NaN \n",
373
- "\n",
374
- " ArrivalDelayGroups ArrTimeBlk DistanceGroup DivAirportLandings \n",
375
- "0 -2.0 1200-1259 1 0 \n",
376
- "1 -1.0 0800-0859 2 0 \n",
377
- "2 -1.0 1600-1659 2 0 \n",
378
- "3 -2.0 1600-1659 2 0 \n",
379
- "4 0.0 1200-1259 2 0 \n",
380
- "... ... ... ... ... \n",
381
- "4078313 NaN 2300-2359 5 1 \n",
382
- "4078314 NaN 1900-1959 3 0 \n",
383
- "4078315 12.0 1800-1859 3 0 \n",
384
- "4078316 NaN 2200-2259 2 1 \n",
385
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386
- "\n",
387
- "[4078318 rows x 61 columns]"
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391
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- "execution_count": 6,
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- "id": "e572970c",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "str_data = data.astype(str)"
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- ]
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- {
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- "execution_count": 7,
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- "id": "27a7cd6b",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "column_names = ', '.join(data.columns.tolist())"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 8,
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- "id": "a5cf51cc",
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- {
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- "data": {
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- "'FlightDate, Airline, Origin, Dest, Cancelled, Diverted, CRSDepTime, DepTime, DepDelayMinutes, DepDelay, ArrTime, ArrDelayMinutes, AirTime, CRSElapsedTime, ActualElapsedTime, Distance, Year, Quarter, Month, DayofMonth, DayOfWeek, Marketing_Airline_Network, Operated_or_Branded_Code_Share_Partners, DOT_ID_Marketing_Airline, IATA_Code_Marketing_Airline, Flight_Number_Marketing_Airline, Operating_Airline, DOT_ID_Operating_Airline, IATA_Code_Operating_Airline, Tail_Number, Flight_Number_Operating_Airline, OriginAirportID, OriginAirportSeqID, OriginCityMarketID, OriginCityName, OriginState, OriginStateFips, OriginStateName, OriginWac, DestAirportID, DestAirportSeqID, DestCityMarketID, DestCityName, DestState, DestStateFips, DestStateName, DestWac, DepDel15, DepartureDelayGroups, DepTimeBlk, TaxiOut, WheelsOff, WheelsOn, TaxiIn, CRSArrTime, ArrDelay, ArrDel15, ArrivalDelayGroups, ArrTimeBlk, DistanceGroup, DivAirportLandings'"
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- "cell_type": "code",
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- "execution_count": 15,
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- "id": "061d3979",
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- "metadata": {},
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- "pd.options.display.max_rows = 300"
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- "name": "stdout",
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- "text": [
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- "FlightDate 2022-04-04\n",
463
- "Airline Commutair Aka Champlain Enterprises, Inc.\n",
464
- "Origin DEN\n",
465
- "Dest CPR\n",
466
- "Cancelled False\n",
467
- "Diverted False\n",
468
- "CRSDepTime 1540\n",
469
- "DepTime 1546.0\n",
470
- "DepDelayMinutes 6.0\n",
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- "DepDelay 6.0\n",
472
- "ArrTime 1650.0\n",
473
- "ArrDelayMinutes 0.0\n",
474
- "AirTime 45.0\n",
475
- "CRSElapsedTime 70.0\n",
476
- "ActualElapsedTime 64.0\n",
477
- "Distance 230.0\n",
478
- "Year 2022\n",
479
- "Quarter 2\n",
480
- "Month 4\n",
481
- "DayofMonth 4\n",
482
- "DayOfWeek 1\n",
483
- "Marketing_Airline_Network UA\n",
484
- "Operated_or_Branded_Code_Share_Partners UA_CODESHARE\n",
485
- "DOT_ID_Marketing_Airline 19977\n",
486
- "IATA_Code_Marketing_Airline UA\n",
487
- "Flight_Number_Marketing_Airline 4288\n",
488
- "Operating_Airline C5\n",
489
- "DOT_ID_Operating_Airline 20445\n",
490
- "IATA_Code_Operating_Airline C5\n",
491
- "Tail_Number N14177\n",
492
- "Flight_Number_Operating_Airline 4288\n",
493
- "OriginAirportID 11292\n",
494
- "OriginAirportSeqID 1129202\n",
495
- "OriginCityMarketID 30325\n",
496
- "OriginCityName Denver, CO\n",
497
- "OriginState CO\n",
498
- "OriginStateFips 8\n",
499
- "OriginStateName Colorado\n",
500
- "OriginWac 82\n",
501
- "DestAirportID 11122\n",
502
- "DestAirportSeqID 1112205\n",
503
- "DestCityMarketID 31122\n",
504
- "DestCityName Casper, WY\n",
505
- "DestState WY\n",
506
- "DestStateFips 56\n",
507
- "DestStateName Wyoming\n",
508
- "DestWac 88\n",
509
- "DepDel15 0.0\n",
510
- "DepartureDelayGroups 0.0\n",
511
- "DepTimeBlk 1500-1559\n",
512
- "TaxiOut 13.0\n",
513
- "WheelsOff 1559.0\n",
514
- "WheelsOn 1644.0\n",
515
- "TaxiIn 6.0\n",
516
- "CRSArrTime 1650\n",
517
- "ArrDelay 0.0\n",
518
- "ArrDel15 0.0\n",
519
- "ArrivalDelayGroups 0.0\n",
520
- "ArrTimeBlk 1600-1659\n",
521
- "DistanceGroup 1\n",
522
- "DivAirportLandings 0\n",
523
- "Name: 10, dtype: object\n"
524
- ]
525
- }
526
- ],
527
- "source": [
528
- "print(data.iloc[10],flush=True)"
529
- ]
530
- },
531
- {
532
- "cell_type": "code",
533
- "execution_count": 57,
534
- "id": "1a0f208d",
535
- "metadata": {},
536
- "outputs": [],
537
- "source": [
538
- "filter_data = data[['FlightDate','DepTime','ArrTime','Distance','OriginCityName','DestCityName']]"
539
- ]
540
- },
541
- {
542
- "cell_type": "code",
543
- "execution_count": 58,
544
- "id": "4cf6383d",
545
- "metadata": {},
546
- "outputs": [
547
- {
548
- "name": "stdout",
549
- "output_type": "stream",
550
- "text": [
551
- "FlightDate\n",
552
- "DepTime\n",
553
- "ArrTime\n",
554
- "Distance\n",
555
- "OriginCityName\n",
556
- "DestCityName\n"
557
- ]
558
- }
559
- ],
560
- "source": [
561
- "for unit in filter_data:\n",
562
- " print(unit)"
563
- ]
564
- },
565
- {
566
- "cell_type": "code",
567
- "execution_count": 63,
568
- "id": "ddbf175a",
569
- "metadata": {},
570
- "outputs": [
571
- {
572
- "data": {
573
- "application/vnd.jupyter.widget-view+json": {
574
- "model_id": "fc2986be7e664907986359e2bcf22671",
575
- "version_major": 2,
576
- "version_minor": 0
577
- },
578
- "text/plain": [
579
- "0it [00:00, ?it/s]"
580
- ]
581
- },
582
- "metadata": {},
583
- "output_type": "display_data"
584
- },
585
- {
586
- "name": "stderr",
587
- "output_type": "stream",
588
- "text": [
589
- "IOPub message rate exceeded.\n",
590
- "The notebook server will temporarily stop sending output\n",
591
- "to the client in order to avoid crashing it.\n",
592
- "To change this limit, set the config variable\n",
593
- "`--NotebookApp.iopub_msg_rate_limit`.\n",
594
- "\n",
595
- "Current values:\n",
596
- "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
597
- "NotebookApp.rate_limit_window=3.0 (secs)\n",
598
- "\n"
599
- ]
600
- },
601
- {
602
- "ename": "KeyboardInterrupt",
603
- "evalue": "",
604
- "output_type": "error",
605
- "traceback": [
606
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
607
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
608
- "Cell \u001b[0;32mIn[63], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mautonotebook\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, unit \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28menumerate\u001b[39m(filter_data\u001b[38;5;241m.\u001b[39miterrows())):\n\u001b[0;32m----> 3\u001b[0m \u001b[43mfilter_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mOriginCityName\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m unit[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOriginCityName\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 4\u001b[0m filter_data\u001b[38;5;241m.\u001b[39mloc[idx,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDestCityName\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m unit[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDestCityName\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 5\u001b[0m filter_data\u001b[38;5;241m.\u001b[39mloc[idx,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m((unit[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistance\u001b[39m\u001b[38;5;124m'\u001b[39m]) \u001b[38;5;241m*\u001b[39m random\u001b[38;5;241m.\u001b[39muniform(\u001b[38;5;241m0.2\u001b[39m,\u001b[38;5;241m0.5\u001b[39m))\n",
609
- "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/indexing.py:849\u001b[0m, in \u001b[0;36m_LocationIndexer.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_valid_setitem_indexer(key)\n\u001b[1;32m 848\u001b[0m iloc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miloc\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39miloc\n\u001b[0;32m--> 849\u001b[0m \u001b[43miloc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setitem_with_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
610
- "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/indexing.py:1835\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer\u001b[0;34m(self, indexer, value, name)\u001b[0m\n\u001b[1;32m 1832\u001b[0m \u001b[38;5;66;03m# align and set the values\u001b[39;00m\n\u001b[1;32m 1833\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m take_split_path:\n\u001b[1;32m 1834\u001b[0m \u001b[38;5;66;03m# We have to operate column-wise\u001b[39;00m\n\u001b[0;32m-> 1835\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setitem_with_indexer_split_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1836\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1837\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_single_block(indexer, value, name)\n",
611
- "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/indexing.py:1928\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_with_indexer_split_path\u001b[0;34m(self, indexer, value, name)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1926\u001b[0m \u001b[38;5;66;03m# scalar value\u001b[39;00m\n\u001b[1;32m 1927\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m loc \u001b[38;5;129;01min\u001b[39;00m ilocs:\n\u001b[0;32m-> 1928\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setitem_single_column\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpi\u001b[49m\u001b[43m)\u001b[49m\n",
612
- "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/indexing.py:2034\u001b[0m, in \u001b[0;36m_iLocIndexer._setitem_single_column\u001b[0;34m(self, loc, value, plane_indexer)\u001b[0m\n\u001b[1;32m 2030\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39misetitem(loc, value)\n\u001b[1;32m 2031\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2032\u001b[0m \u001b[38;5;66;03m# set value into the column (first attempting to operate inplace, then\u001b[39;00m\n\u001b[1;32m 2033\u001b[0m \u001b[38;5;66;03m# falling back to casting if necessary)\u001b[39;00m\n\u001b[0;32m-> 2034\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumn_setitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplane_indexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2036\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_clear_item_cache()\n",
613
- "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/internals/managers.py:1385\u001b[0m, in \u001b[0;36mBlockManager.column_setitem\u001b[0;34m(self, loc, idx, value, inplace_only)\u001b[0m\n\u001b[1;32m 1383\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1384\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m col_mgr\u001b[38;5;241m.\u001b[39msetitem((idx,), value)\n\u001b[0;32m-> 1385\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_block\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
614
- "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/internals/managers.py:1213\u001b[0m, in \u001b[0;36mBlockManager.iset\u001b[0;34m(self, loc, value, inplace)\u001b[0m\n\u001b[1;32m 1211\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iset_split_block(blkno_l, blk_locs, value_getitem(val_locs))\n\u001b[1;32m 1212\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1213\u001b[0m \u001b[43mblk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_inplace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblk_locs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue_getitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mval_locs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1214\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 1215\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
615
- "File \u001b[0;32m~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/internals/blocks.py:924\u001b[0m, in \u001b[0;36mBlock.set_inplace\u001b[0;34m(self, locs, values, copy)\u001b[0m\n\u001b[1;32m 922\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy:\n\u001b[1;32m 923\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m--> 924\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlocs\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m values\n",
616
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
617
- ]
618
- }
619
- ],
620
- "source": [
621
- "from tqdm.autonotebook import tqdm\n",
622
- "for idx, unit in tqdm(enumerate(filter_data.iterrows())):\n",
623
- " filter_data.loc[idx,'OriginCityName'] = unit[1]['OriginCityName'].split(',')[0]\n",
624
- " filter_data.loc[idx,'DestCityName'] = unit[1]['DestCityName'].split(',')[0]\n",
625
- " filter_data.loc[idx,'price'] = int((unit[1]['Distance']) * random.uniform(0.2,0.5))"
626
- ]
627
- },
628
- {
629
- "cell_type": "code",
630
- "execution_count": 62,
631
- "id": "0ac9a59d",
632
- "metadata": {},
633
- "outputs": [
634
- {
635
- "data": {
636
- "text/html": [
637
- "<div>\n",
638
- "<style scoped>\n",
639
- " .dataframe tbody tr th:only-of-type {\n",
640
- " vertical-align: middle;\n",
641
- " }\n",
642
- "\n",
643
- " .dataframe tbody tr th {\n",
644
- " vertical-align: top;\n",
645
- " }\n",
646
- "\n",
647
- " .dataframe thead th {\n",
648
- " text-align: right;\n",
649
- " }\n",
650
- "</style>\n",
651
- "<table border=\"1\" class=\"dataframe\">\n",
652
- " <thead>\n",
653
- " <tr style=\"text-align: right;\">\n",
654
- " <th></th>\n",
655
- " <th>FlightDate</th>\n",
656
- " <th>DepTime</th>\n",
657
- " <th>ArrTime</th>\n",
658
- " <th>Distance</th>\n",
659
- " <th>OriginCityName</th>\n",
660
- " <th>DestCityName</th>\n",
661
- " <th>price</th>\n",
662
- " </tr>\n",
663
- " </thead>\n",
664
- " <tbody>\n",
665
- " <tr>\n",
666
- " <th>0</th>\n",
667
- " <td>2022-04-04</td>\n",
668
- " <td>1123.0</td>\n",
669
- " <td>1228.0</td>\n",
670
- " <td>212.0</td>\n",
671
- " <td>Grand Junction</td>\n",
672
- " <td>Denver</td>\n",
673
- " <td>72.0</td>\n",
674
- " </tr>\n",
675
- " <tr>\n",
676
- " <th>1</th>\n",
677
- " <td>2022-04-04</td>\n",
678
- " <td>728.0</td>\n",
679
- " <td>848.0</td>\n",
680
- " <td>295.0</td>\n",
681
- " <td>Harlingen/San Benito</td>\n",
682
- " <td>Houston</td>\n",
683
- " <td>141.0</td>\n",
684
- " </tr>\n",
685
- " <tr>\n",
686
- " <th>2</th>\n",
687
- " <td>2022-04-04</td>\n",
688
- " <td>1514.0</td>\n",
689
- " <td>1636.0</td>\n",
690
- " <td>251.0</td>\n",
691
- " <td>Durango</td>\n",
692
- " <td>Denver</td>\n",
693
- " <td>114.0</td>\n",
694
- " </tr>\n",
695
- " <tr>\n",
696
- " <th>3</th>\n",
697
- " <td>2022-04-04</td>\n",
698
- " <td>1430.0</td>\n",
699
- " <td>1547.0</td>\n",
700
- " <td>376.0</td>\n",
701
- " <td>Houston</td>\n",
702
- " <td>Gulfport/Biloxi</td>\n",
703
- " <td>103.0</td>\n",
704
- " </tr>\n",
705
- " <tr>\n",
706
- " <th>4</th>\n",
707
- " <td>2022-04-04</td>\n",
708
- " <td>1135.0</td>\n",
709
- " <td>1251.0</td>\n",
710
- " <td>251.0</td>\n",
711
- " <td>Durango</td>\n",
712
- " <td>Denver</td>\n",
713
- " <td>118.0</td>\n",
714
- " </tr>\n",
715
- " <tr>\n",
716
- " <th>...</th>\n",
717
- " <td>...</td>\n",
718
- " <td>...</td>\n",
719
- " <td>...</td>\n",
720
- " <td>...</td>\n",
721
- " <td>...</td>\n",
722
- " <td>...</td>\n",
723
- " <td>...</td>\n",
724
- " </tr>\n",
725
- " <tr>\n",
726
- " <th>4078313</th>\n",
727
- " <td>2022-03-31</td>\n",
728
- " <td>2014.0</td>\n",
729
- " <td>234.0</td>\n",
730
- " <td>1167.0</td>\n",
731
- " <td>New Orleans, LA</td>\n",
732
- " <td>Newark, NJ</td>\n",
733
- " <td>NaN</td>\n",
734
- " </tr>\n",
735
- " <tr>\n",
736
- " <th>4078314</th>\n",
737
- " <td>2022-03-17</td>\n",
738
- " <td>1817.0</td>\n",
739
- " <td>NaN</td>\n",
740
- " <td>529.0</td>\n",
741
- " <td>Charlotte, NC</td>\n",
742
- " <td>Newark, NJ</td>\n",
743
- " <td>NaN</td>\n",
744
- " </tr>\n",
745
- " <tr>\n",
746
- " <th>4078315</th>\n",
747
- " <td>2022-03-08</td>\n",
748
- " <td>2318.0</td>\n",
749
- " <td>59.0</td>\n",
750
- " <td>723.0</td>\n",
751
- " <td>Albany, NY</td>\n",
752
- " <td>Chicago, IL</td>\n",
753
- " <td>NaN</td>\n",
754
- " </tr>\n",
755
- " <tr>\n",
756
- " <th>4078316</th>\n",
757
- " <td>2022-03-25</td>\n",
758
- " <td>2322.0</td>\n",
759
- " <td>939.0</td>\n",
760
- " <td>319.0</td>\n",
761
- " <td>Newark, NJ</td>\n",
762
- " <td>Pittsburgh, PA</td>\n",
763
- " <td>NaN</td>\n",
764
- " </tr>\n",
765
- " <tr>\n",
766
- " <th>4078317</th>\n",
767
- " <td>2022-03-07</td>\n",
768
- " <td>1148.0</td>\n",
769
- " <td>1556.0</td>\n",
770
- " <td>416.0</td>\n",
771
- " <td>Newark, NJ</td>\n",
772
- " <td>Raleigh/Durham, NC</td>\n",
773
- " <td>NaN</td>\n",
774
- " </tr>\n",
775
- " </tbody>\n",
776
- "</table>\n",
777
- "<p>4078318 rows × 7 columns</p>\n",
778
- "</div>"
779
- ],
780
- "text/plain": [
781
- " FlightDate DepTime ArrTime Distance OriginCityName \\\n",
782
- "0 2022-04-04 1123.0 1228.0 212.0 Grand Junction \n",
783
- "1 2022-04-04 728.0 848.0 295.0 Harlingen/San Benito \n",
784
- "2 2022-04-04 1514.0 1636.0 251.0 Durango \n",
785
- "3 2022-04-04 1430.0 1547.0 376.0 Houston \n",
786
- "4 2022-04-04 1135.0 1251.0 251.0 Durango \n",
787
- "... ... ... ... ... ... \n",
788
- "4078313 2022-03-31 2014.0 234.0 1167.0 New Orleans, LA \n",
789
- "4078314 2022-03-17 1817.0 NaN 529.0 Charlotte, NC \n",
790
- "4078315 2022-03-08 2318.0 59.0 723.0 Albany, NY \n",
791
- "4078316 2022-03-25 2322.0 939.0 319.0 Newark, NJ \n",
792
- "4078317 2022-03-07 1148.0 1556.0 416.0 Newark, NJ \n",
793
- "\n",
794
- " DestCityName price \n",
795
- "0 Denver 72.0 \n",
796
- "1 Houston 141.0 \n",
797
- "2 Denver 114.0 \n",
798
- "3 Gulfport/Biloxi 103.0 \n",
799
- "4 Denver 118.0 \n",
800
- "... ... ... \n",
801
- "4078313 Newark, NJ NaN \n",
802
- "4078314 Newark, NJ NaN \n",
803
- "4078315 Chicago, IL NaN \n",
804
- "4078316 Pittsburgh, PA NaN \n",
805
- "4078317 Raleigh/Durham, NC NaN \n",
806
- "\n",
807
- "[4078318 rows x 7 columns]"
808
- ]
809
- },
810
- "execution_count": 62,
811
- "metadata": {},
812
- "output_type": "execute_result"
813
- }
814
- ],
815
- "source": [
816
- "filter_data"
817
- ]
818
- },
819
- {
820
- "cell_type": "code",
821
- "execution_count": 43,
822
- "id": "10a2b0e3",
823
- "metadata": {},
824
- "outputs": [
825
- {
826
- "data": {
827
- "application/vnd.jupyter.widget-view+json": {
828
- "model_id": "b7bbe97af9df406fb0e56a6f8dfd8656",
829
- "version_major": 2,
830
- "version_minor": 0
831
- },
832
- "text/plain": [
833
- "0it [00:00, ?it/s]"
834
- ]
835
- },
836
- "metadata": {},
837
- "output_type": "display_data"
838
- },
839
- {
840
- "name": "stdout",
841
- "output_type": "stream",
842
- "text": [
843
- "(0, FlightDate 2022-04-04\n",
844
- "DepTime 1123.0\n",
845
- "ArrTime 1228.0\n",
846
- "Distance 212.0\n",
847
- "OriginCityName Grand Junction\n",
848
- "DestCityName Denver\n",
849
- "Name: 0, dtype: object)\n"
850
- ]
851
- }
852
- ],
853
- "source": [
854
- "import random\n",
855
- "for idx, unit in tqdm(enumerate(filter_data.iterrows())):\n",
856
- " filter_data.loc[idx,'price'] = eval(unit[1]['Distance']) * random.uniform(0.2,0.5))"
857
- ]
858
- },
859
- {
860
- "cell_type": "code",
861
- "execution_count": 44,
862
- "id": "1ca1f597",
863
- "metadata": {},
864
- "outputs": [
865
- {
866
- "name": "stderr",
867
- "output_type": "stream",
868
- "text": [
869
- "/tmp/ipykernel_1504075/2172656540.py:1: SettingWithCopyWarning: \n",
870
- "A value is trying to be set on a copy of a slice from a DataFrame.\n",
871
- "Try using .loc[row_indexer,col_indexer] = value instead\n",
872
- "\n",
873
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
874
- " filter_data['price'] = None\n"
875
- ]
876
- }
877
- ],
878
- "source": [
879
- "filter_data['price'] = None"
880
- ]
881
- },
882
- {
883
- "cell_type": "code",
884
- "execution_count": null,
885
- "id": "e32d2e3c",
886
- "metadata": {},
887
- "outputs": [],
888
- "source": [
889
- "import random\n",
890
- "random.uniform(0.2,0.5)"
891
- ]
892
- },
893
- {
894
- "cell_type": "code",
895
- "execution_count": null,
896
- "id": "cf4ecc5e",
897
- "metadata": {},
898
- "outputs": [],
899
- "source": []
900
- }
901
- ],
902
- "metadata": {
903
- "kernelspec": {
904
- "display_name": "Python 3 (ipykernel)",
905
- "language": "python",
906
- "name": "python3"
907
- },
908
- "language_info": {
909
- "codemirror_mode": {
910
- "name": "ipython",
911
- "version": 3
912
- },
913
- "file_extension": ".py",
914
- "mimetype": "text/x-python",
915
- "name": "python",
916
- "nbconvert_exporter": "python",
917
- "pygments_lexer": "ipython3",
918
- "version": "3.9.16"
919
- }
920
- },
921
- "nbformat": 4,
922
- "nbformat_minor": 5
923
- }