{ "cells": [ { "cell_type": "code", "execution_count": 8, "id": "041c9721", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "data = pd.read_csv('/home/xj/toolAugEnv/code/toolConstraint/database/flights/Combined_Flights_2022.csv')\n", "# df.to_csv('/home/xj/toolAugEnv/code/toolConstraint/database/flights/clean_Flights_2022.csv')" ] }, { "cell_type": "code", "execution_count": 9, "id": "03d0f39e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FlightDateAirlineOriginDestCancelledDivertedCRSDepTimeDepTimeDepDelayMinutesDepDelay...WheelsOffWheelsOnTaxiInCRSArrTimeArrDelayArrDel15ArrivalDelayGroupsArrTimeBlkDistanceGroupDivAirportLandings
02022-04-04Commutair Aka Champlain Enterprises, Inc.GJTDENFalseFalse11331123.00.0-10.0...1140.01220.08.01245-17.00.0-2.01200-125910
12022-04-04Commutair Aka Champlain Enterprises, Inc.HRLIAHFalseFalse732728.00.0-4.0...744.0839.09.0849-1.00.0-1.00800-085920
22022-04-04Commutair Aka Champlain Enterprises, Inc.DRODENFalseFalse15291514.00.0-15.0...1535.01622.014.01639-3.00.0-1.01600-165920
32022-04-04Commutair Aka Champlain Enterprises, Inc.IAHGPTFalseFalse14351430.00.0-5.0...1446.01543.04.01605-18.00.0-2.01600-165920
42022-04-04Commutair Aka Champlain Enterprises, Inc.DRODENFalseFalse11351135.00.00.0...1154.01243.08.012456.00.00.01200-125920
..................................................................
40783132022-03-31Republic AirlinesMSYEWRFalseTrue19492014.025.025.0...2031.0202.032.02354NaNNaNNaN2300-235951
40783142022-03-17Republic AirlinesCLTEWRTrueFalse17331817.044.044.0...NaNNaNNaN1942NaNNaNNaN1900-195930
40783152022-03-08Republic AirlinesALBORDFalseFalse17002318.0378.0378.0...2337.052.07.01838381.01.012.01800-185930
40783162022-03-25Republic AirlinesEWRPITFalseTrue21292322.0113.0113.0...2347.0933.06.02255NaNNaNNaN2200-225921
40783172022-03-07Republic AirlinesEWRRDUFalseTrue11541148.00.0-6.0...1201.01552.04.01333NaNNaNNaN1300-135921
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4078318 rows × 61 columns

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" ], "text/plain": [ " FlightDate Airline Origin Dest \n", "0 2022-04-04 Commutair Aka Champlain Enterprises, Inc. GJT DEN \\\n", "1 2022-04-04 Commutair Aka Champlain Enterprises, Inc. HRL IAH \n", "2 2022-04-04 Commutair Aka Champlain Enterprises, Inc. DRO DEN \n", "3 2022-04-04 Commutair Aka Champlain Enterprises, Inc. IAH GPT \n", "4 2022-04-04 Commutair Aka Champlain Enterprises, Inc. DRO DEN \n", "... ... ... ... ... \n", "4078313 2022-03-31 Republic Airlines MSY EWR \n", "4078314 2022-03-17 Republic Airlines CLT EWR \n", "4078315 2022-03-08 Republic Airlines ALB ORD \n", "4078316 2022-03-25 Republic Airlines EWR PIT \n", "4078317 2022-03-07 Republic Airlines EWR RDU \n", "\n", " Cancelled Diverted CRSDepTime DepTime DepDelayMinutes DepDelay \n", "0 False False 1133 1123.0 0.0 -10.0 \\\n", "1 False False 732 728.0 0.0 -4.0 \n", "2 False False 1529 1514.0 0.0 -15.0 \n", "3 False False 1435 1430.0 0.0 -5.0 \n", "4 False False 1135 1135.0 0.0 0.0 \n", "... ... ... ... ... ... ... \n", "4078313 False True 1949 2014.0 25.0 25.0 \n", "4078314 True False 1733 1817.0 44.0 44.0 \n", "4078315 False False 1700 2318.0 378.0 378.0 \n", "4078316 False True 2129 2322.0 113.0 113.0 \n", "4078317 False True 1154 1148.0 0.0 -6.0 \n", "\n", " ... WheelsOff WheelsOn TaxiIn CRSArrTime ArrDelay ArrDel15 \n", "0 ... 1140.0 1220.0 8.0 1245 -17.0 0.0 \\\n", "1 ... 744.0 839.0 9.0 849 -1.0 0.0 \n", "2 ... 1535.0 1622.0 14.0 1639 -3.0 0.0 \n", "3 ... 1446.0 1543.0 4.0 1605 -18.0 0.0 \n", "4 ... 1154.0 1243.0 8.0 1245 6.0 0.0 \n", "... ... ... ... ... ... ... ... \n", "4078313 ... 2031.0 202.0 32.0 2354 NaN NaN \n", "4078314 ... NaN NaN NaN 1942 NaN NaN \n", "4078315 ... 2337.0 52.0 7.0 1838 381.0 1.0 \n", "4078316 ... 2347.0 933.0 6.0 2255 NaN NaN \n", "4078317 ... 1201.0 1552.0 4.0 1333 NaN NaN \n", "\n", " ArrivalDelayGroups ArrTimeBlk DistanceGroup DivAirportLandings \n", "0 -2.0 1200-1259 1 0 \n", "1 -1.0 0800-0859 2 0 \n", "2 -1.0 1600-1659 2 0 \n", "3 -2.0 1600-1659 2 0 \n", "4 0.0 1200-1259 2 0 \n", "... ... ... ... ... \n", "4078313 NaN 2300-2359 5 1 \n", "4078314 NaN 1900-1959 3 0 \n", "4078315 12.0 1800-1859 3 0 \n", "4078316 NaN 2200-2259 2 1 \n", "4078317 NaN 1300-1359 2 1 \n", "\n", "[4078318 rows x 61 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 10, "id": "036418f5", "metadata": {}, "outputs": [], "source": [ "data_dict = data.to_dict(orient = 'split')" ] }, { "cell_type": "code", "execution_count": 11, "id": "371a85fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4078318\n" ] } ], "source": [ "print(len(data_dict['data']))" ] }, { "cell_type": "code", "execution_count": 12, "id": "64d46483", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FlightDate 0\n", "DepTime 7\n", "ArrTime 10\n", "ActualElapsedTime 14\n", "Distance 15\n", "OriginCityName 34\n", "DestCityName 42\n" ] } ], "source": [ "for idx,unit in enumerate(data_dict['columns']):\n", " if unit in ['FlightDate','DepTime','ArrTime','ActualElapsedTime','Distance','OriginCityName','DestCityName']:\n", " print(unit, str(idx))" ] }, { "cell_type": "code", "execution_count": 27, "id": "81047adf", "metadata": {}, "outputs": [], "source": [ "import math\n", "def convert_to_hhmm(time_float):\n", " \"\"\"\n", " Convert a float time to hh:mm format\n", " :param time_float: Time as a float. Example: 757.0\n", " :return: Time in hh:mm format. Example: \"07:57\"\n", " \"\"\"\n", " try:\n", " hours = int(time_float // 100)\n", " minutes = int(time_float % 100)\n", " return \"{:02d}:{:02d}\".format(hours, minutes)\n", " except:\n", " return time_float\n", "\n", "def minutes_to_hours_minutes(minutes):\n", " # Check for NaN and handle it\n", " if math.isnan(minutes):\n", " return \"NaN\"\n", " \n", " # Ensure minutes is an integer or rounded to the nearest integer\n", " minutes = round(minutes)\n", " \n", " hours = minutes // 60\n", " remaining_minutes = minutes % 60\n", " return f\"{hours} hours {remaining_minutes} minutes\"" ] }, { "cell_type": "code", "execution_count": 33, "id": "ee34cbde", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "15392917a93840e6b66e7e457d404721", "version_major": 2, "version_minor": 0 }, "text/plain": [ "0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tqdm.autonotebook import tqdm\n", "import random\n", "new_data = []\n", "for idx, unit in tqdm(enumerate(data_dict['data'])):\n", " tmp_dict = {k:\"\" for k in ['FlightDate','DepTime','ArrTime','ActualElapsedTime','Distance','OriginCityName','DestCityName','Price']}\n", " tmp_dict['FlightDate'] = unit[0]\n", " tmp_dict['DepTime'] = convert_to_hhmm(unit[7])\n", " tmp_dict['ArrTime'] = convert_to_hhmm(unit[10])\n", " tmp_dict['ActualElapsedTime'] = minutes_to_hours_minutes(unit[14])\n", " tmp_dict['Distance'] = unit[15]\n", " tmp_dict['OriginCityName'] = unit[34].split(',')[0].split('\\\\')[0]\n", " tmp_dict['DestCityName'] = unit[42].split(',')[0].split('\\\\')[0]\n", " tmp_dict['Price'] = int((unit[15]) * random.uniform(0.2,0.5))\n", " new_data.append(tmp_dict)" ] }, { "cell_type": "code", "execution_count": 34, "id": "aee3f422", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'FlightDate': '2022-04-04',\n", " 'DepTime': '09:27',\n", " 'ArrTime': '11:19',\n", " 'ActualElapsedTime': '1 hours 52 minutes',\n", " 'Distance': 466.0,\n", " 'OriginCityName': 'Chicago',\n", " 'DestCityName': 'Lincoln',\n", " 'Price': 119}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_data[200]" ] }, { "cell_type": "code", "execution_count": 35, "id": "bfb243c0", "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(new_data)" ] }, { "cell_type": "code", "execution_count": 40, "id": "f152a150", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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34883942022-03-0107:2415:154 hours 51 minutes2422.0SeattleNew York566
35093822022-03-0122:2906:074 hours 38 minutes2422.0SeattleNew York692
37360562022-03-0123:3307:164 hours 43 minutes2422.0SeattleNew York510
37362602022-03-0114:3722:054 hours 28 minutes2422.0SeattleNew York1135
37363132022-03-0109:1117:175 hours 6 minutes2422.0SeattleNew York627
37768582022-03-0121:0104:324 hours 31 minutes2422.0SeattleNew York981
37785652022-03-0113:1821:084 hours 50 minutes2422.0SeattleNew York869
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" ], "text/plain": [ " FlightDate DepTime ArrTime ActualElapsedTime Distance \n", "3488394 2022-03-01 07:24 15:15 4 hours 51 minutes 2422.0 \\\n", "3509382 2022-03-01 22:29 06:07 4 hours 38 minutes 2422.0 \n", "3736056 2022-03-01 23:33 07:16 4 hours 43 minutes 2422.0 \n", "3736260 2022-03-01 14:37 22:05 4 hours 28 minutes 2422.0 \n", "3736313 2022-03-01 09:11 17:17 5 hours 6 minutes 2422.0 \n", "3776858 2022-03-01 21:01 04:32 4 hours 31 minutes 2422.0 \n", "3778565 2022-03-01 13:18 21:08 4 hours 50 minutes 2422.0 \n", "\n", " OriginCityName DestCityName Price \n", "3488394 Seattle New York 566 \n", "3509382 Seattle New York 692 \n", "3736056 Seattle New York 510 \n", "3736260 Seattle New York 1135 \n", "3736313 Seattle New York 627 \n", "3776858 Seattle New York 981 \n", "3778565 Seattle New York 869 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['OriginCityName']=='Seattle') & (df['DestCityName']=='New York')& (df['FlightDate']=='2022-03-01')]" ] }, { "cell_type": "code", "execution_count": 37, "id": "af7e3411", "metadata": {}, "outputs": [], "source": [ "df.to_csv('/home/xj/toolAugEnv/code/toolConstraint/database/flights/clean_Flights_2022_2.csv')" ] }, { "cell_type": "code", "execution_count": 10, "id": "461e83ef", "metadata": {}, "outputs": [], "source": [ "x = df[df['OriginCityName']=='Los Angeles']" ] }, { "cell_type": "code", "execution_count": 14, "id": "ed4e2107", "metadata": {}, "outputs": [], "source": [ "x = x[x['DestCityName']=='New York']" ] }, { "cell_type": "code", "execution_count": 15, "id": "56c918e3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1653452022-04-0107:5716:282475.0Los AngelesNew York1121
1654052022-04-0106:0714:252475.0Los AngelesNew York572
1655762022-04-0122:5807:152475.0Los AngelesNew York929
1656362022-04-0113:2821:362475.0Los AngelesNew York950
1658682022-04-0106:3914:462475.0Los AngelesNew York965
1661372022-04-0116:1000:232475.0Los AngelesNew York794
1662582022-04-0110:5719:132475.0Los AngelesNew York578
1664332022-04-0112:3520:542475.0Los AngelesNew York984
1667612022-04-0107:5216:052475.0Los AngelesNew York1165
2613062022-04-0111:2420:022475.0Los AngelesNew York1201
2613692022-04-0113:5722:192475.0Los AngelesNew York501
2793212022-04-0115:1623:262475.0Los AngelesNew York1162
2793222022-04-0100:5609:182475.0Los AngelesNew York1169
2795452022-04-0100:3808:492475.0Los AngelesNew York919
2809122022-04-0109:0117:352475.0Los AngelesNew York757
2817492022-04-0106:5215:222475.0Los AngelesNew York992
2819632022-04-0117:1301:272475.0Los AngelesNew York981
2824572022-04-0113:2021:372475.0Los AngelesNew York980
2841032022-04-0113:1821:562475.0Los AngelesNew York899
2841332022-04-0107:4215:352475.0Los AngelesNew York1232
2842332022-04-0121:4305:432475.0Los AngelesNew York749
2860512022-04-0109:2617:352475.0Los AngelesNew York1116
2874212022-04-0116:5601:002475.0Los AngelesNew York1033
2886772022-04-0112:35NaN2475.0Los AngelesNew York760
3259342022-04-0123:2507:172475.0Los AngelesNew York588
3314052022-04-0106:0514:292475.0Los AngelesNew York746
5654442022-04-0122:5507:092475.0Los AngelesNew York654
5654462022-04-0111:3919:492475.0Los AngelesNew York616
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" ], "text/plain": [ " FlightDate DepTime ArrTime Distance OriginCityName DestCityName \n", "165345 2022-04-01 07:57 16:28 2475.0 Los Angeles New York \\\n", "165405 2022-04-01 06:07 14:25 2475.0 Los Angeles New York \n", "165576 2022-04-01 22:58 07:15 2475.0 Los Angeles New York \n", "165636 2022-04-01 13:28 21:36 2475.0 Los Angeles New York \n", "165868 2022-04-01 06:39 14:46 2475.0 Los Angeles New York \n", "166137 2022-04-01 16:10 00:23 2475.0 Los Angeles New York \n", "166258 2022-04-01 10:57 19:13 2475.0 Los Angeles New York \n", "166433 2022-04-01 12:35 20:54 2475.0 Los Angeles New York \n", "166761 2022-04-01 07:52 16:05 2475.0 Los Angeles New York \n", "261306 2022-04-01 11:24 20:02 2475.0 Los Angeles New York \n", "261369 2022-04-01 13:57 22:19 2475.0 Los Angeles New York \n", "279321 2022-04-01 15:16 23:26 2475.0 Los Angeles New York \n", "279322 2022-04-01 00:56 09:18 2475.0 Los Angeles New York \n", "279545 2022-04-01 00:38 08:49 2475.0 Los Angeles New York \n", "280912 2022-04-01 09:01 17:35 2475.0 Los Angeles New York \n", "281749 2022-04-01 06:52 15:22 2475.0 Los Angeles New York \n", "281963 2022-04-01 17:13 01:27 2475.0 Los Angeles New York \n", "282457 2022-04-01 13:20 21:37 2475.0 Los Angeles New York \n", "284103 2022-04-01 13:18 21:56 2475.0 Los Angeles New York \n", "284133 2022-04-01 07:42 15:35 2475.0 Los Angeles New York \n", "284233 2022-04-01 21:43 05:43 2475.0 Los Angeles New York \n", "286051 2022-04-01 09:26 17:35 2475.0 Los Angeles New York \n", "287421 2022-04-01 16:56 01:00 2475.0 Los Angeles New York \n", "288677 2022-04-01 12:35 NaN 2475.0 Los Angeles New York \n", "325934 2022-04-01 23:25 07:17 2475.0 Los Angeles New York \n", "331405 2022-04-01 06:05 14:29 2475.0 Los Angeles New York \n", "565444 2022-04-01 22:55 07:09 2475.0 Los Angeles New York \n", "565446 2022-04-01 11:39 19:49 2475.0 Los Angeles New York \n", "\n", " Price \n", "165345 1121 \n", "165405 572 \n", "165576 929 \n", "165636 950 \n", "165868 965 \n", "166137 794 \n", "166258 578 \n", "166433 984 \n", "166761 1165 \n", "261306 1201 \n", "261369 501 \n", "279321 1162 \n", "279322 1169 \n", "279545 919 \n", "280912 757 \n", "281749 992 \n", "281963 981 \n", "282457 980 \n", "284103 899 \n", "284133 1232 \n", "284233 749 \n", "286051 1116 \n", "287421 1033 \n", "288677 760 \n", "325934 588 \n", "331405 746 \n", "565444 654 \n", "565446 616 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[x['FlightDate']=='2022-04-01']" ] }, { "cell_type": "code", "execution_count": null, "id": "93c2a26f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 }