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Delete tools/flights/test.ipynb

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@@ -1,1063 +0,0 @@
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- {
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 4,
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- "id": "041c9721",
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- "metadata": {},
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- "outputs": [],
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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')\n",
12
- "# df.to_csv('/home/xj/toolAugEnv/code/toolConstraint/database/flights/clean_Flights_2022.csv')"
13
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 2,
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- "id": "03d0f39e",
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "FlightDate 2022-03-15\n",
25
- "Airline Delta Air Lines Inc.\n",
26
- "Origin LAS\n",
27
- "Dest SLC\n",
28
- "Cancelled False\n",
29
- " ... \n",
30
- "ArrDel15 0.0\n",
31
- "ArrivalDelayGroups -2.0\n",
32
- "ArrTimeBlk 1600-1659\n",
33
- "DistanceGroup 2\n",
34
- "DivAirportLandings 0\n",
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- "Name: 3504987, Length: 61, dtype: object"
36
- ]
37
- },
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- "execution_count": 2,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "data.iloc[3504987]"
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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": 5,
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- "id": "036418f5",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "data_dict = data.to_dict(orient = 'split')"
55
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 13,
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- "id": "ef12c4b3",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "FlightDate 2022-01-29\n",
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- "Airline Frontier Airlines Inc.\n",
69
- "Origin COS\n",
70
- "Dest LAS\n",
71
- "Cancelled False\n",
72
- "Diverted False\n",
73
- "CRSDepTime 1558\n",
74
- "DepTime 1553.0\n",
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- "DepDelayMinutes 0.0\n",
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- "DepDelay -5.0\n",
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- "ArrTime 1646.0\n",
78
- "ArrDelayMinutes 0.0\n",
79
- "AirTime 91.0\n",
80
- "CRSElapsedTime 124.0\n",
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- "ActualElapsedTime 113.0\n",
82
- "Distance 604.0\n",
83
- "Year 2022\n",
84
- "Quarter 1\n",
85
- "Month 1\n",
86
- "DayofMonth 29\n",
87
- "DayOfWeek 6\n",
88
- "Marketing_Airline_Network F9\n",
89
- "Operated_or_Branded_Code_Share_Partners F9\n",
90
- "DOT_ID_Marketing_Airline 20436\n",
91
- "IATA_Code_Marketing_Airline F9\n",
92
- "Flight_Number_Marketing_Airline 2019\n",
93
- "Operating_Airline F9\n",
94
- "DOT_ID_Operating_Airline 20436\n",
95
- "IATA_Code_Operating_Airline F9\n",
96
- "Tail_Number N235FR\n",
97
- "Flight_Number_Operating_Airline 2019\n",
98
- "OriginAirportID 11109\n",
99
- "OriginAirportSeqID 1110902\n",
100
- "OriginCityMarketID 30189\n",
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- "OriginCityName Colorado Springs, CO\n",
102
- "OriginState CO\n",
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- "OriginStateFips 8\n",
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- "OriginStateName Colorado\n",
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- "OriginWac 82\n",
106
- "DestAirportID 12889\n",
107
- "DestAirportSeqID 1288903\n",
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- "DestCityMarketID 32211\n",
109
- "DestCityName Las Vegas, NV\n",
110
- "DestState NV\n",
111
- "DestStateFips 32\n",
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- "DestStateName Nevada\n",
113
- "DestWac 85\n",
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- "DepDel15 0.0\n",
115
- "DepartureDelayGroups -1.0\n",
116
- "DepTimeBlk 1500-1559\n",
117
- "TaxiOut 13.0\n",
118
- "WheelsOff 1606.0\n",
119
- "WheelsOn 1637.0\n",
120
- "TaxiIn 9.0\n",
121
- "CRSArrTime 1702\n",
122
- "ArrDelay -16.0\n",
123
- "ArrDel15 0.0\n",
124
- "ArrivalDelayGroups -2.0\n",
125
- "ArrTimeBlk 1700-1759\n",
126
- "DistanceGroup 3\n",
127
- "DivAirportLandings 0\n"
128
- ]
129
- }
130
- ],
131
- "source": [
132
- "for idx,unit in enumerate(data_dict['columns']):\n",
133
- " print(unit, data_dict['data'][3000020][idx])"
134
- ]
135
- },
136
- {
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- "cell_type": "code",
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- "execution_count": 12,
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- "id": "372b3fd9",
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "['2022-01-29',\n",
146
- " 'Frontier Airlines Inc.',\n",
147
- " 'COS',\n",
148
- " 'LAS',\n",
149
- " False,\n",
150
- " False,\n",
151
- " 1558,\n",
152
- " 1553.0,\n",
153
- " 0.0,\n",
154
- " -5.0,\n",
155
- " 1646.0,\n",
156
- " 0.0,\n",
157
- " 91.0,\n",
158
- " 124.0,\n",
159
- " 113.0,\n",
160
- " 604.0,\n",
161
- " 2022,\n",
162
- " 1,\n",
163
- " 1,\n",
164
- " 29,\n",
165
- " 6,\n",
166
- " 'F9',\n",
167
- " 'F9',\n",
168
- " 20436,\n",
169
- " 'F9',\n",
170
- " 2019,\n",
171
- " 'F9',\n",
172
- " 20436,\n",
173
- " 'F9',\n",
174
- " 'N235FR',\n",
175
- " 2019,\n",
176
- " 11109,\n",
177
- " 1110902,\n",
178
- " 30189,\n",
179
- " 'Colorado Springs, CO',\n",
180
- " 'CO',\n",
181
- " 8,\n",
182
- " 'Colorado',\n",
183
- " 82,\n",
184
- " 12889,\n",
185
- " 1288903,\n",
186
- " 32211,\n",
187
- " 'Las Vegas, NV',\n",
188
- " 'NV',\n",
189
- " 32,\n",
190
- " 'Nevada',\n",
191
- " 85,\n",
192
- " 0.0,\n",
193
- " -1.0,\n",
194
- " '1500-1559',\n",
195
- " 13.0,\n",
196
- " 1606.0,\n",
197
- " 1637.0,\n",
198
- " 9.0,\n",
199
- " 1702,\n",
200
- " -16.0,\n",
201
- " 0.0,\n",
202
- " -2.0,\n",
203
- " '1700-1759',\n",
204
- " 3,\n",
205
- " 0]"
206
- ]
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- },
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- "execution_count": 12,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "data_dict['data'][3000020]"
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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": 11,
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- "id": "371a85fd",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "4078318\n"
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- ]
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- }
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- ],
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- "source": [
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- "print(len(data_dict['data']))"
233
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 12,
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- "id": "64d46483",
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "FlightDate 0\n",
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- "DepTime 7\n",
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- "ArrTime 10\n",
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- "ActualElapsedTime 14\n",
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- "Distance 15\n",
250
- "OriginCityName 34\n",
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- "DestCityName 42\n"
252
- ]
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- }
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- ],
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- "source": [
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- "for idx,unit in enumerate(data_dict['columns']):\n",
257
- " if unit in ['FlightDate','DepTime','ArrTime','ActualElapsedTime','Distance','OriginCityName','DestCityName']:\n",
258
- " print(unit, str(idx))"
259
- ]
260
- },
261
- {
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- "cell_type": "code",
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- "execution_count": 6,
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- "id": "81047adf",
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- "metadata": {},
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- "outputs": [],
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- "source": [
268
- "import math\n",
269
- "def convert_to_hhmm(time_float):\n",
270
- " \"\"\"\n",
271
- " Convert a float time to hh:mm format\n",
272
- " :param time_float: Time as a float. Example: 757.0\n",
273
- " :return: Time in hh:mm format. Example: \"07:57\"\n",
274
- " \"\"\"\n",
275
- " try:\n",
276
- " hours = int(time_float // 100)\n",
277
- " minutes = int(time_float % 100)\n",
278
- " return \"{:02d}:{:02d}\".format(hours, minutes)\n",
279
- " except:\n",
280
- " return time_float\n",
281
- "\n",
282
- "def minutes_to_hours_minutes(minutes):\n",
283
- " # Check for NaN and handle it\n",
284
- " if math.isnan(minutes):\n",
285
- " return \"NaN\"\n",
286
- " \n",
287
- " # Ensure minutes is an integer or rounded to the nearest integer\n",
288
- " minutes = round(minutes)\n",
289
- " \n",
290
- " hours = minutes // 60\n",
291
- " remaining_minutes = minutes % 60\n",
292
- " return f\"{hours} hours {remaining_minutes} minutes\""
293
- ]
294
- },
295
- {
296
- "cell_type": "code",
297
- "execution_count": 7,
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- "id": "ee34cbde",
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
303
- "application/vnd.jupyter.widget-view+json": {
304
- "model_id": "b60c3d13fb6d44258103c6251365272b",
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- "version_major": 2,
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- "version_minor": 0
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- },
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- "text/plain": [
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- "0it [00:00, ?it/s]"
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- ]
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- },
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- "metadata": {},
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- "output_type": "display_data"
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- }
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- ],
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- "source": [
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- "from tqdm.autonotebook import tqdm\n",
318
- "import random\n",
319
- "new_data = []\n",
320
- "for idx, unit in tqdm(enumerate(data_dict['data'])):\n",
321
- " tmp_dict = {k:\"\" for k in ['FlightDate','DepTime','ArrTime','ActualElapsedTime','Distance','OriginCityName','DestCityName','Price']}\n",
322
- " tmp_dict['FlightDate'] = unit[0]\n",
323
- " tmp_dict['DepTime'] = convert_to_hhmm(unit[7])\n",
324
- " tmp_dict['ArrTime'] = convert_to_hhmm(unit[10])\n",
325
- " tmp_dict['ActualElapsedTime'] = minutes_to_hours_minutes(unit[14])\n",
326
- " tmp_dict['Distance'] = unit[15]\n",
327
- " tmp_dict['OriginCityName'] = unit[34].split(',')[0].split('/')[0]\n",
328
- " tmp_dict['DestCityName'] = unit[42].split(',')[0].split('/')[0]\n",
329
- " tmp_dict['Price'] = int((unit[15]) * random.uniform(0.2,0.5))\n",
330
- " new_data.append(tmp_dict)"
331
- ]
332
- },
333
- {
334
- "cell_type": "code",
335
- "execution_count": 11,
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- "id": "aee3f422",
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
341
- "text/plain": [
342
- "{'FlightDate': '2022-01-29',\n",
343
- " 'DepTime': '15:53',\n",
344
- " 'ArrTime': '16:46',\n",
345
- " 'ActualElapsedTime': '1 hours 53 minutes',\n",
346
- " 'Distance': 604.0,\n",
347
- " 'OriginCityName': 'Colorado Springs',\n",
348
- " 'DestCityName': 'Las Vegas',\n",
349
- " 'Price': 205}"
350
- ]
351
- },
352
- "execution_count": 11,
353
- "metadata": {},
354
- "output_type": "execute_result"
355
- }
356
- ],
357
- "source": [
358
- "new_data[3000020]"
359
- ]
360
- },
361
- {
362
- "cell_type": "code",
363
- "execution_count": 90,
364
- "id": "bfb243c0",
365
- "metadata": {},
366
- "outputs": [],
367
- "source": [
368
- "df = pd.DataFrame(new_data)"
369
- ]
370
- },
371
- {
372
- "cell_type": "code",
373
- "execution_count": 62,
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- "id": "f152a150",
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- "metadata": {},
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- "outputs": [
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- {
378
- "data": {
379
- "text/html": [
380
- "<div>\n",
381
- "<style scoped>\n",
382
- " .dataframe tbody tr th:only-of-type {\n",
383
- " vertical-align: middle;\n",
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- " }\n",
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- "\n",
386
- " .dataframe tbody tr th {\n",
387
- " vertical-align: top;\n",
388
- " }\n",
389
- "\n",
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- " .dataframe thead th {\n",
391
- " text-align: right;\n",
392
- " }\n",
393
- "</style>\n",
394
- "<table border=\"1\" class=\"dataframe\">\n",
395
- " <thead>\n",
396
- " <tr style=\"text-align: right;\">\n",
397
- " <th></th>\n",
398
- " <th>FlightDate</th>\n",
399
- " <th>DepTime</th>\n",
400
- " <th>ArrTime</th>\n",
401
- " <th>ActualElapsedTime</th>\n",
402
- " <th>Distance</th>\n",
403
- " <th>OriginCityName</th>\n",
404
- " <th>DestCityName</th>\n",
405
- " <th>Price</th>\n",
406
- " </tr>\n",
407
- " </thead>\n",
408
- " <tbody>\n",
409
- " <tr>\n",
410
- " <th>3488394</th>\n",
411
- " <td>2022-03-01</td>\n",
412
- " <td>07:24</td>\n",
413
- " <td>15:15</td>\n",
414
- " <td>4 hours 51 minutes</td>\n",
415
- " <td>2422.0</td>\n",
416
- " <td>Seattle</td>\n",
417
- " <td>New York</td>\n",
418
- " <td>720</td>\n",
419
- " </tr>\n",
420
- " <tr>\n",
421
- " <th>3509382</th>\n",
422
- " <td>2022-03-01</td>\n",
423
- " <td>22:29</td>\n",
424
- " <td>06:07</td>\n",
425
- " <td>4 hours 38 minutes</td>\n",
426
- " <td>2422.0</td>\n",
427
- " <td>Seattle</td>\n",
428
- " <td>New York</td>\n",
429
- " <td>484</td>\n",
430
- " </tr>\n",
431
- " <tr>\n",
432
- " <th>3736056</th>\n",
433
- " <td>2022-03-01</td>\n",
434
- " <td>23:33</td>\n",
435
- " <td>07:16</td>\n",
436
- " <td>4 hours 43 minutes</td>\n",
437
- " <td>2422.0</td>\n",
438
- " <td>Seattle</td>\n",
439
- " <td>New York</td>\n",
440
- " <td>1199</td>\n",
441
- " </tr>\n",
442
- " <tr>\n",
443
- " <th>3736260</th>\n",
444
- " <td>2022-03-01</td>\n",
445
- " <td>14:37</td>\n",
446
- " <td>22:05</td>\n",
447
- " <td>4 hours 28 minutes</td>\n",
448
- " <td>2422.0</td>\n",
449
- " <td>Seattle</td>\n",
450
- " <td>New York</td>\n",
451
- " <td>950</td>\n",
452
- " </tr>\n",
453
- " <tr>\n",
454
- " <th>3736313</th>\n",
455
- " <td>2022-03-01</td>\n",
456
- " <td>09:11</td>\n",
457
- " <td>17:17</td>\n",
458
- " <td>5 hours 6 minutes</td>\n",
459
- " <td>2422.0</td>\n",
460
- " <td>Seattle</td>\n",
461
- " <td>New York</td>\n",
462
- " <td>1050</td>\n",
463
- " </tr>\n",
464
- " <tr>\n",
465
- " <th>3776858</th>\n",
466
- " <td>2022-03-01</td>\n",
467
- " <td>21:01</td>\n",
468
- " <td>04:32</td>\n",
469
- " <td>4 hours 31 minutes</td>\n",
470
- " <td>2422.0</td>\n",
471
- " <td>Seattle</td>\n",
472
- " <td>New York</td>\n",
473
- " <td>1146</td>\n",
474
- " </tr>\n",
475
- " <tr>\n",
476
- " <th>3778565</th>\n",
477
- " <td>2022-03-01</td>\n",
478
- " <td>13:18</td>\n",
479
- " <td>21:08</td>\n",
480
- " <td>4 hours 50 minutes</td>\n",
481
- " <td>2422.0</td>\n",
482
- " <td>Seattle</td>\n",
483
- " <td>New York</td>\n",
484
- " <td>578</td>\n",
485
- " </tr>\n",
486
- " </tbody>\n",
487
- "</table>\n",
488
- "</div>"
489
- ],
490
- "text/plain": [
491
- " FlightDate DepTime ArrTime ActualElapsedTime Distance \n",
492
- "3488394 2022-03-01 07:24 15:15 4 hours 51 minutes 2422.0 \\\n",
493
- "3509382 2022-03-01 22:29 06:07 4 hours 38 minutes 2422.0 \n",
494
- "3736056 2022-03-01 23:33 07:16 4 hours 43 minutes 2422.0 \n",
495
- "3736260 2022-03-01 14:37 22:05 4 hours 28 minutes 2422.0 \n",
496
- "3736313 2022-03-01 09:11 17:17 5 hours 6 minutes 2422.0 \n",
497
- "3776858 2022-03-01 21:01 04:32 4 hours 31 minutes 2422.0 \n",
498
- "3778565 2022-03-01 13:18 21:08 4 hours 50 minutes 2422.0 \n",
499
- "\n",
500
- " OriginCityName DestCityName Price \n",
501
- "3488394 Seattle New York 720 \n",
502
- "3509382 Seattle New York 484 \n",
503
- "3736056 Seattle New York 1199 \n",
504
- "3736260 Seattle New York 950 \n",
505
- "3736313 Seattle New York 1050 \n",
506
- "3776858 Seattle New York 1146 \n",
507
- "3778565 Seattle New York 578 "
508
- ]
509
- },
510
- "execution_count": 62,
511
- "metadata": {},
512
- "output_type": "execute_result"
513
- }
514
- ],
515
- "source": [
516
- "df[(df['OriginCityName']=='Seattle') & (df['DestCityName']=='New York')& (df['FlightDate']=='2022-03-01')]"
517
- ]
518
- },
519
- {
520
- "cell_type": "code",
521
- "execution_count": 92,
522
- "id": "9f85d8e6",
523
- "metadata": {},
524
- "outputs": [],
525
- "source": [
526
- "df['Flight Number'] = df.index"
527
- ]
528
- },
529
- {
530
- "cell_type": "code",
531
- "execution_count": 93,
532
- "id": "045df94c",
533
- "metadata": {},
534
- "outputs": [],
535
- "source": [
536
- "df = df.reset_index(drop=True)"
537
- ]
538
- },
539
- {
540
- "cell_type": "code",
541
- "execution_count": null,
542
- "id": "4e1b68b7",
543
- "metadata": {},
544
- "outputs": [],
545
- "source": []
546
- },
547
- {
548
- "cell_type": "code",
549
- "execution_count": 91,
550
- "id": "5c7d3b44",
551
- "metadata": {},
552
- "outputs": [],
553
- "source": [
554
- "df.index = df.index.map(lambda x: str(x).zfill(7))"
555
- ]
556
- },
557
- {
558
- "cell_type": "code",
559
- "execution_count": 94,
560
- "id": "7a1f223c",
561
- "metadata": {},
562
- "outputs": [],
563
- "source": [
564
- "df['Flight Number'] = 'F' + df['Flight Number'].astype(str)"
565
- ]
566
- },
567
- {
568
- "cell_type": "code",
569
- "execution_count": 97,
570
- "id": "af7e3411",
571
- "metadata": {},
572
- "outputs": [],
573
- "source": [
574
- "df.to_csv('/home/xj/toolAugEnv/code/toolConstraint/database/flights/clean_Flights_2022.csv')"
575
- ]
576
- },
577
- {
578
- "cell_type": "code",
579
- "execution_count": 95,
580
- "id": "461e83ef",
581
- "metadata": {},
582
- "outputs": [],
583
- "source": [
584
- "x = df[df['OriginCityName']=='Montrose']"
585
- ]
586
- },
587
- {
588
- "cell_type": "code",
589
- "execution_count": 53,
590
- "id": "ed4e2107",
591
- "metadata": {},
592
- "outputs": [],
593
- "source": [
594
- "x = df[df['DestCityName']=='Montrose']"
595
- ]
596
- },
597
- {
598
- "cell_type": "code",
599
- "execution_count": 96,
600
- "id": "56c918e3",
601
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602
- "outputs": [
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- {
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634
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637
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638
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642
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649
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651
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652
- " <td>15:32</td>\n",
653
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654
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655
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657
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658
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659
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660
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661
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662
- " <td>2022-04-01</td>\n",
663
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664
- " <td>18:58</td>\n",
665
- " <td>1 hours 20 minutes</td>\n",
666
- " <td>196.0</td>\n",
667
- " <td>Montrose</td>\n",
668
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669
- " <td>97</td>\n",
670
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671
- " </tr>\n",
672
- " <tr>\n",
673
- " <th>7439</th>\n",
674
- " <td>2022-04-02</td>\n",
675
- " <td>13:38</td>\n",
676
- " <td>16:32</td>\n",
677
- " <td>1 hours 54 minutes</td>\n",
678
- " <td>733.0</td>\n",
679
- " <td>Montrose</td>\n",
680
- " <td>Dallas</td>\n",
681
- " <td>151</td>\n",
682
- " <td>F0007439</td>\n",
683
- " </tr>\n",
684
- " <tr>\n",
685
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686
- " <td>2022-04-02</td>\n",
687
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688
- " <td>13:29</td>\n",
689
- " <td>0 hours 52 minutes</td>\n",
690
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691
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692
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693
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694
- " <td>F0007440</td>\n",
695
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696
- " <tr>\n",
697
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698
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699
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700
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703
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704
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705
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707
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708
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709
- " <th>4045139</th>\n",
710
- " <td>2022-03-27</td>\n",
711
- " <td>11:50</td>\n",
712
- " <td>12:17</td>\n",
713
- " <td>1 hours 27 minutes</td>\n",
714
- " <td>419.0</td>\n",
715
- " <td>Montrose</td>\n",
716
- " <td>Phoenix</td>\n",
717
- " <td>133</td>\n",
718
- " <td>F4045139</td>\n",
719
- " </tr>\n",
720
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721
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722
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723
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724
- " <td>12:22</td>\n",
725
- " <td>1 hours 37 minutes</td>\n",
726
- " <td>419.0</td>\n",
727
- " <td>Montrose</td>\n",
728
- " <td>Phoenix</td>\n",
729
- " <td>188</td>\n",
730
- " <td>F4045140</td>\n",
731
- " </tr>\n",
732
- " <tr>\n",
733
- " <th>4045141</th>\n",
734
- " <td>2022-03-29</td>\n",
735
- " <td>11:35</td>\n",
736
- " <td>12:17</td>\n",
737
- " <td>1 hours 42 minutes</td>\n",
738
- " <td>419.0</td>\n",
739
- " <td>Montrose</td>\n",
740
- " <td>Phoenix</td>\n",
741
- " <td>144</td>\n",
742
- " <td>F4045141</td>\n",
743
- " </tr>\n",
744
- " <tr>\n",
745
- " <th>4045142</th>\n",
746
- " <td>2022-03-30</td>\n",
747
- " <td>11:38</td>\n",
748
- " <td>12:13</td>\n",
749
- " <td>1 hours 35 minutes</td>\n",
750
- " <td>419.0</td>\n",
751
- " <td>Montrose</td>\n",
752
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753
- " <td>125</td>\n",
754
- " <td>F4045142</td>\n",
755
- " </tr>\n",
756
- " <tr>\n",
757
- " <th>4045143</th>\n",
758
- " <td>2022-03-31</td>\n",
759
- " <td>11:40</td>\n",
760
- " <td>12:19</td>\n",
761
- " <td>1 hours 39 minutes</td>\n",
762
- " <td>419.0</td>\n",
763
- " <td>Montrose</td>\n",
764
- " <td>Phoenix</td>\n",
765
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766
- " <td>F4045143</td>\n",
767
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768
- " </tbody>\n",
769
- "</table>\n",
770
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771
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772
- ],
773
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774
- " FlightDate DepTime ArrTime ActualElapsedTime Distance \n",
775
- "4155 2022-04-01 10:42 11:34 0 hours 52 minutes 196.0 \\\n",
776
- "4156 2022-04-01 14:43 15:32 0 hours 49 minutes 196.0 \n",
777
- "4157 2022-04-01 17:38 18:58 1 hours 20 minutes 196.0 \n",
778
- "7439 2022-04-02 13:38 16:32 1 hours 54 minutes 733.0 \n",
779
- "7440 2022-04-02 12:37 13:29 0 hours 52 minutes 196.0 \n",
780
- "... ... ... ... ... ... \n",
781
- "4045139 2022-03-27 11:50 12:17 1 hours 27 minutes 419.0 \n",
782
- "4045140 2022-03-28 11:45 12:22 1 hours 37 minutes 419.0 \n",
783
- "4045141 2022-03-29 11:35 12:17 1 hours 42 minutes 419.0 \n",
784
- "4045142 2022-03-30 11:38 12:13 1 hours 35 minutes 419.0 \n",
785
- "4045143 2022-03-31 11:40 12:19 1 hours 39 minutes 419.0 \n",
786
- "\n",
787
- " OriginCityName DestCityName Price Flight Number \n",
788
- "4155 Montrose Denver 42 F0004155 \n",
789
- "4156 Montrose Denver 64 F0004156 \n",
790
- "4157 Montrose Denver 97 F0004157 \n",
791
- "7439 Montrose Dallas 151 F0007439 \n",
792
- "7440 Montrose Denver 54 F0007440 \n",
793
- "... ... ... ... ... \n",
794
- "4045139 Montrose Phoenix 133 F4045139 \n",
795
- "4045140 Montrose Phoenix 188 F4045140 \n",
796
- "4045141 Montrose Phoenix 144 F4045141 \n",
797
- "4045142 Montrose Phoenix 125 F4045142 \n",
798
- "4045143 Montrose Phoenix 129 F4045143 \n",
799
- "\n",
800
- "[2035 rows x 9 columns]"
801
- ]
802
- },
803
- "execution_count": 96,
804
- "metadata": {},
805
- "output_type": "execute_result"
806
- }
807
- ],
808
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809
- "x"
810
- ]
811
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812
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813
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814
- "execution_count": 52,
815
- "id": "74dfd3cd",
816
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817
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818
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819
- "data": {
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847
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851
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852
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853
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854
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855
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856
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860
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862
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863
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864
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865
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866
- " <td>1 hours 22 minutes</td>\n",
867
- " <td>229.0</td>\n",
868
- " <td>Washington</td>\n",
869
- " <td>New York</td>\n",
870
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871
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872
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873
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874
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875
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876
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877
- " <td>1 hours 32 minutes</td>\n",
878
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879
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880
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881
- " <td>95</td>\n",
882
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883
- " <tr>\n",
884
- " <th>1409</th>\n",
885
- " <td>2022-04-01</td>\n",
886
- " <td>19:03</td>\n",
887
- " <td>20:23</td>\n",
888
- " <td>1 hours 20 minutes</td>\n",
889
- " <td>229.0</td>\n",
890
- " <td>Washington</td>\n",
891
- " <td>New York</td>\n",
892
- " <td>71</td>\n",
893
- " </tr>\n",
894
- " <tr>\n",
895
- " <th>1436</th>\n",
896
- " <td>2022-04-01</td>\n",
897
- " <td>15:32</td>\n",
898
- " <td>17:03</td>\n",
899
- " <td>1 hours 31 minutes</td>\n",
900
- " <td>229.0</td>\n",
901
- " <td>Washington</td>\n",
902
- " <td>New York</td>\n",
903
- " <td>64</td>\n",
904
- " </tr>\n",
905
- " <tr>\n",
906
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907
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908
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909
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910
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911
- " <td>...</td>\n",
912
- " <td>...</td>\n",
913
- " <td>...</td>\n",
914
- " <td>...</td>\n",
915
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916
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917
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918
- " <td>2022-04-01</td>\n",
919
- " <td>22:55</td>\n",
920
- " <td>07:09</td>\n",
921
- " <td>5 hours 14 minutes</td>\n",
922
- " <td>2475.0</td>\n",
923
- " <td>Los Angeles</td>\n",
924
- " <td>New York</td>\n",
925
- " <td>621</td>\n",
926
- " </tr>\n",
927
- " <tr>\n",
928
- " <th>565446</th>\n",
929
- " <td>2022-04-01</td>\n",
930
- " <td>11:39</td>\n",
931
- " <td>19:49</td>\n",
932
- " <td>5 hours 10 minutes</td>\n",
933
- " <td>2475.0</td>\n",
934
- " <td>Los Angeles</td>\n",
935
- " <td>New York</td>\n",
936
- " <td>1100</td>\n",
937
- " </tr>\n",
938
- " <tr>\n",
939
- " <th>565511</th>\n",
940
- " <td>2022-04-01</td>\n",
941
- " <td>15:48</td>\n",
942
- " <td>22:16</td>\n",
943
- " <td>4 hours 28 minutes</td>\n",
944
- " <td>1620.0</td>\n",
945
- " <td>Denver</td>\n",
946
- " <td>New York</td>\n",
947
- " <td>575</td>\n",
948
- " </tr>\n",
949
- " <tr>\n",
950
- " <th>565541</th>\n",
951
- " <td>2022-04-01</td>\n",
952
- " <td>17:36</td>\n",
953
- " <td>23:05</td>\n",
954
- " <td>3 hours 29 minutes</td>\n",
955
- " <td>1620.0</td>\n",
956
- " <td>Denver</td>\n",
957
- " <td>New York</td>\n",
958
- " <td>669</td>\n",
959
- " </tr>\n",
960
- " <tr>\n",
961
- " <th>565581</th>\n",
962
- " <td>2022-04-01</td>\n",
963
- " <td>20:52</td>\n",
964
- " <td>23:48</td>\n",
965
- " <td>1 hours 56 minutes</td>\n",
966
- " <td>733.0</td>\n",
967
- " <td>Chicago</td>\n",
968
- " <td>New York</td>\n",
969
- " <td>338</td>\n",
970
- " </tr>\n",
971
- " </tbody>\n",
972
- "</table>\n",
973
- "<p>889 rows × 8 columns</p>\n",
974
- "</div>"
975
- ],
976
- "text/plain": [
977
- " FlightDate DepTime ArrTime ActualElapsedTime Distance \n",
978
- "1369 2022-04-01 09:04 10:23 1 hours 19 minutes 229.0 \\\n",
979
- "1370 2022-04-01 11:07 12:29 1 hours 22 minutes 229.0 \n",
980
- "1380 2022-04-01 13:20 14:52 1 hours 32 minutes 229.0 \n",
981
- "1409 2022-04-01 19:03 20:23 1 hours 20 minutes 229.0 \n",
982
- "1436 2022-04-01 15:32 17:03 1 hours 31 minutes 229.0 \n",
983
- "... ... ... ... ... ... \n",
984
- "565444 2022-04-01 22:55 07:09 5 hours 14 minutes 2475.0 \n",
985
- "565446 2022-04-01 11:39 19:49 5 hours 10 minutes 2475.0 \n",
986
- "565511 2022-04-01 15:48 22:16 4 hours 28 minutes 1620.0 \n",
987
- "565541 2022-04-01 17:36 23:05 3 hours 29 minutes 1620.0 \n",
988
- "565581 2022-04-01 20:52 23:48 1 hours 56 minutes 733.0 \n",
989
- "\n",
990
- " OriginCityName DestCityName Price \n",
991
- "1369 Washington New York 105 \n",
992
- "1370 Washington New York 56 \n",
993
- "1380 Washington New York 95 \n",
994
- "1409 Washington New York 71 \n",
995
- "1436 Washington New York 64 \n",
996
- "... ... ... ... \n",
997
- "565444 Los Angeles New York 621 \n",
998
- "565446 Los Angeles New York 1100 \n",
999
- "565511 Denver New York 575 \n",
1000
- "565541 Denver New York 669 \n",
1001
- "565581 Chicago New York 338 \n",
1002
- "\n",
1003
- "[889 rows x 8 columns]"
1004
- ]
1005
- },
1006
- "execution_count": 52,
1007
- "metadata": {},
1008
- "output_type": "execute_result"
1009
- }
1010
- ],
1011
- "source": [
1012
- "x[x['FlightDate']=='2022-04-01']"
1013
- ]
1014
- },
1015
- {
1016
- "cell_type": "code",
1017
- "execution_count": 58,
1018
- "id": "93c2a26f",
1019
- "metadata": {},
1020
- "outputs": [
1021
- {
1022
- "name": "stdout",
1023
- "output_type": "stream",
1024
- "text": [
1025
- "['Manhattan', 'Ft. Riley']\n"
1026
- ]
1027
- }
1028
- ],
1029
- "source": [
1030
- "print('Manhattan/Ft. Riley'.split('/'))"
1031
- ]
1032
- },
1033
- {
1034
- "cell_type": "code",
1035
- "execution_count": null,
1036
- "id": "86b394bf",
1037
- "metadata": {},
1038
- "outputs": [],
1039
- "source": []
1040
- }
1041
- ],
1042
- "metadata": {
1043
- "kernelspec": {
1044
- "display_name": "Python 3 (ipykernel)",
1045
- "language": "python",
1046
- "name": "python3"
1047
- },
1048
- "language_info": {
1049
- "codemirror_mode": {
1050
- "name": "ipython",
1051
- "version": 3
1052
- },
1053
- "file_extension": ".py",
1054
- "mimetype": "text/x-python",
1055
- "name": "python",
1056
- "nbconvert_exporter": "python",
1057
- "pygments_lexer": "ipython3",
1058
- "version": "3.9.16"
1059
- }
1060
- },
1061
- "nbformat": 4,
1062
- "nbformat_minor": 5
1063
- }