(8주차) 10월27일

• 강의영상
• data
• groupby
  • AIRLINE을 기준으로 데이터프레임을 나누고 $\to$ ARR_DELAY에 mean함수를 적용: (AIRLINE $\to$ {ARR_DELAY: mean})
  • AIRLINE,WEEKDAY $\to$ {CANCELLED: sum}
  • AIRLINE,WEEKDAY $\to$ {CANCELLED: sum, mean} , {DIVERTED: sum, mean}
  • AIRLINE,WEEKDAY $\to$ {CANCELLED: sum, mean, size} , {AIR_TIME: mean,var}
• grouping by continuous variable
  • 숙제

강의영상

- (1/5) group by (1)

- (2/5) group by (2)

- (3/5) pd.cut (1)

- (4/5) pd.cut (2)

- (5/5) 과제설명

data

import pandas as pd 
import numpy as np 

df=pd.read_csv('https://raw.githubusercontent.com/PacktPublishing/Pandas-Cookbook/master/data/flights.csv')

df

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
0	1	1	4	WN	LAX	SLC	1625	58.0	94.0	590	1905	65.0	0	0
1	1	1	4	UA	DEN	IAD	823	7.0	154.0	1452	1333	-13.0	0	0
2	1	1	4	MQ	DFW	VPS	1305	36.0	85.0	641	1453	35.0	0	0
3	1	1	4	AA	DFW	DCA	1555	7.0	126.0	1192	1935	-7.0	0	0
4	1	1	4	WN	LAX	MCI	1720	48.0	166.0	1363	2225	39.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58487	12	31	4	AA	SFO	DFW	515	5.0	166.0	1464	1045	-19.0	0	0
58488	12	31	4	F9	LAS	SFO	1910	13.0	71.0	414	2050	4.0	0	0
58489	12	31	4	OO	SFO	SBA	1846	-6.0	46.0	262	1956	-5.0	0	0
58490	12	31	4	WN	MSP	ATL	525	39.0	124.0	907	855	34.0	0	0
58491	12	31	4	OO	SFO	BOI	859	5.0	73.0	522	1146	-1.0	0	0

58492 rows × 14 columns

df.columns

Index(['MONTH', 'DAY', 'WEEKDAY', 'AIRLINE', 'ORG_AIR', 'DEST_AIR',
       'SCHED_DEP', 'DEP_DELAY', 'AIR_TIME', 'DIST', 'SCHED_ARR', 'ARR_DELAY',
       'DIVERTED', 'CANCELLED'],
      dtype='object')

• https://github.com/PacktPublishing/Pandas-Cookbook/blob/master/data/descriptions/flights_description.csv

groupby

- 데이터프레임을 여러개의 서브데이터프레임으로 나누는 기능

- 단독으로 쓸 이유는 별로 없다. $\to$ 그룹을 나누고 어떠한 "연산"을 하기 위함

df.groupby(by='AIRLINE')

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f58ee1d7b20>

• 데이터프레임을 각 항공사 별로 나눔

- 확인

grouped_df = df.groupby(by='AIRLINE')

grouped_df.groups

{'AA': [3, 6, 8, 15, 26, 32, 33, 36, 37, 41, 46, 47, 48, 55, 62, 66, 73, 75, 76, 87, 89, 97, 98, 107, 120, 122, 128, 131, 133, 146, 160, 171, 174, 178, 179, 188, 200, 201, 206, 221, 267, 270, 279, 305, 309, 311, 321, 330, 332, 342, 343, 347, 348, 380, 381, 382, 390, 393, 398, 402, 404, 405, 419, 427, 430, 431, 441, 449, 451, 455, 457, 466, 476, 491, 493, 497, 509, 514, 521, 527, 528, 537, 550, 551, 556, 560, 568, 572, 587, 589, 592, 612, 613, 618, 624, 626, 628, 634, 640, 652, ...], 'AS': [38, 198, 241, 277, 397, 450, 453, 500, 518, 591, 718, 737, 741, 808, 820, 867, 872, 1005, 1106, 1146, 1292, 1437, 1530, 1559, 1609, 1636, 1747, 1786, 1810, 1826, 1892, 1921, 2010, 2151, 2269, 2446, 2526, 2576, 2586, 2941, 3112, 3125, 3171, 3176, 3413, 3483, 3494, 3596, 3678, 3734, 3891, 3910, 3937, 3989, 4099, 4128, 4326, 4397, 4432, 4587, 4743, 4799, 4841, 4850, 4912, 4987, 5099, 5154, 5176, 5254, 5261, 5297, 5356, 5372, 5496, 5667, 5685, 5688, 5877, 5976, 6016, 6079, 6122, 6245, 6255, 6393, 6529, 6816, 6824, 6861, 6945, 6988, 7065, 7150, 7153, 7196, 7225, 7235, 7389, 7478, ...], 'B6': [123, 127, 239, 333, 548, 549, 696, 757, 945, 1175, 1361, 1453, 1547, 1561, 1564, 1650, 1670, 1691, 1800, 1939, 1998, 2251, 2253, 2256, 2330, 2385, 2434, 2607, 2650, 2692, 2721, 2781, 2800, 2884, 2936, 3060, 3306, 3648, 3793, 3983, 4347, 4552, 4578, 4639, 4779, 4820, 4885, 5036, 5125, 5131, 5205, 5211, 5228, 5260, 5351, 5483, 5577, 5585, 5777, 5956, 6126, 6151, 6418, 6901, 6952, 6977, 7792, 7798, 7853, 7877, 7904, 8030, 8082, 8091, 8118, 8145, 8177, 8364, 8507, 8541, 8543, 8558, 8732, 8828, 8912, 9093, 9173, 9275, 9371, 9389, 9447, 9482, 9574, 9686, 9896, 10066, 10254, 10416, 10600, 10727, ...], 'DL': [53, 57, 77, 79, 85, 90, 91, 101, 116, 117, 121, 129, 130, 137, 142, 152, 154, 155, 158, 159, 162, 165, 169, 172, 181, 182, 184, 185, 189, 190, 204, 207, 211, 215, 216, 222, 235, 238, 244, 245, 248, 249, 250, 253, 254, 258, 286, 289, 304, 307, 308, 310, 313, 323, 326, 328, 329, 336, 353, 354, 364, 373, 385, 395, 407, 411, 412, 413, 420, 433, 435, 439, 445, 456, 468, 481, 485, 490, 492, 508, 517, 526, 529, 531, 535, 543, 546, 559, 570, 579, 584, 588, 593, 602, 606, 607, 616, 617, 631, 642, ...], 'EV': [11, 13, 29, 40, 69, 100, 106, 118, 136, 143, 147, 164, 167, 170, 194, 196, 199, 205, 208, 219, 220, 225, 231, 233, 236, 251, 252, 261, 264, 265, 268, 282, 291, 293, 298, 312, 319, 337, 352, 362, 363, 369, 371, 383, 403, 414, 415, 424, 429, 437, 442, 443, 459, 460, 465, 470, 495, 499, 503, 505, 506, 522, 525, 530, 541, 567, 574, 580, 583, 632, 645, 654, 659, 677, 683, 689, 693, 695, 697, 714, 721, 723, 725, 730, 740, 742, 761, 764, 765, 768, 772, 776, 777, 778, 785, 787, 790, 791, 794, 799, ...], 'F9': [7, 93, 209, 232, 247, 290, 301, 386, 444, 483, 553, 563, 596, 598, 599, 605, 665, 685, 738, 844, 870, 948, 985, 1103, 1167, 1181, 1264, 1374, 1382, 1407, 1421, 1435, 1489, 1590, 1611, 1697, 1746, 1760, 1775, 1776, 1807, 1830, 1925, 1962, 2014, 2036, 2069, 2078, 2111, 2125, 2208, 2335, 2405, 2515, 2557, 2579, 2600, 2683, 2693, 2708, 2731, 2733, 2805, 2853, 2861, 3024, 3241, 3288, 3374, 3382, 3395, 3418, 3555, 3570, 3575, 3628, 3638, 3716, 3735, 3777, 3843, 3923, 3962, 3977, 4016, 4031, 4046, 4069, 4111, 4151, 4172, 4287, 4356, 4371, 4420, 4469, 4542, 4641, 4671, 4720, ...], 'HA': [582, 712, 878, 1053, 1269, 1345, 1544, 1864, 2316, 2644, 2681, 3002, 3165, 3227, 4359, 4414, 4926, 5187, 5281, 5320, 5363, 7267, 7372, 7744, 7808, 9360, 9761, 10163, 11119, 11626, 11743, 11809, 12275, 12752, 12780, 13466, 13755, 13956, 14566, 15064, 15316, 15959, 16501, 17632, 18467, 18720, 20461, 21558, 21761, 22454, 22584, 22592, 22618, 22858, 22892, 23177, 23374, 23773, 25763, 26127, 27745, 28864, 29608, 29762, 29875, 30610, 31164, 31505, 31530, 31675, 32030, 32037, 32987, 33311, 33747, 34884, 35959, 36068, 36319, 36425, 37119, 37128, 37664, 37983, 38440, 39475, 39482, 39625, 41664, 42165, 42485, 42846, 44068, 45330, 46713, 47772, 48279, 48722, 49667, 49782, ...], 'MQ': [2, 10, 18, 24, 50, 60, 71, 78, 81, 103, 105, 115, 125, 149, 180, 187, 210, 214, 228, 285, 296, 314, 320, 334, 335, 339, 341, 357, 458, 472, 484, 486, 488, 502, 516, 524, 534, 547, 581, 601, 622, 638, 639, 646, 661, 707, 711, 722, 727, 751, 769, 774, 802, 805, 830, 851, 854, 856, 865, 876, 915, 939, 955, 968, 977, 980, 1002, 1006, 1036, 1085, 1087, 1104, 1113, 1150, 1158, 1163, 1168, 1169, 1204, 1229, 1231, 1234, 1238, 1246, 1287, 1296, 1303, 1306, 1328, 1329, 1336, 1393, 1398, 1410, 1423, 1428, 1436, 1448, 1449, 1454, ...], 'NK': [17, 74, 95, 109, 166, 345, 358, 401, 434, 436, 446, 452, 482, 507, 510, 519, 533, 544, 610, 615, 620, 729, 735, 845, 858, 887, 898, 916, 970, 1017, 1095, 1097, 1153, 1188, 1208, 1257, 1342, 1392, 1536, 1579, 1580, 1657, 1732, 1761, 1863, 1943, 1948, 1990, 2026, 2068, 2071, 2118, 2124, 2149, 2177, 2196, 2214, 2244, 2299, 2334, 2378, 2424, 2425, 2447, 2523, 2537, 2592, 2613, 2639, 2643, 2740, 2749, 2752, 2764, 2765, 2770, 2786, 2875, 2886, 3018, 3061, 3065, 3072, 3151, 3153, 3174, 3248, 3315, 3381, 3435, 3566, 3598, 3674, 3696, 3701, 3708, 3840, 3877, 3894, 3934, ...], 'OO': [12, 16, 22, 25, 27, 34, 39, 42, 51, 52, 54, 58, 61, 63, 65, 82, 86, 99, 102, 113, 124, 126, 135, 138, 139, 151, 157, 161, 168, 175, 213, 223, 237, 240, 242, 255, 257, 278, 281, 284, 288, 297, 315, 317, 318, 322, 324, 327, 331, 359, 361, 366, 367, 370, 376, 378, 379, 387, 392, 418, 421, 447, 448, 540, 552, 554, 565, 569, 576, 590, 600, 603, 604, 609, 625, 629, 637, 649, 653, 658, 660, 663, 666, 671, 672, 673, 676, 679, 681, 682, 684, 691, 692, 708, 734, 754, 755, 762, 789, 793, ...], 'UA': [1, 5, 9, 14, 21, 44, 45, 67, 70, 72, 94, 108, 110, 134, 140, 141, 144, 145, 163, 177, 183, 192, 197, 246, 260, 262, 263, 266, 269, 273, 275, 283, 295, 325, 338, 340, 349, 368, 374, 375, 377, 389, 394, 396, 408, 423, 425, 438, 440, 462, 463, 469, 471, 474, 479, 501, 511, 512, 513, 520, 523, 536, 539, 542, 555, 557, 566, 573, 577, 586, 594, 595, 608, 621, 623, 630, 635, 636, 647, 650, 651, 667, 686, 687, 698, 699, 700, 701, 713, 717, 719, 745, 756, 773, 784, 801, 829, 836, 842, 843, ...], 'US': [31, 35, 49, 96, 104, 111, 112, 119, 148, 150, 153, 191, 202, 203, 224, 229, 355, 360, 372, 384, 391, 467, 473, 477, 478, 487, 494, 538, 545, 558, 561, 564, 575, 578, 619, 690, 715, 726, 728, 760, 795, 806, 881, 896, 901, 910, 954, 1028, 1033, 1068, 1134, 1135, 1143, 1155, 1164, 1201, 1210, 1245, 1274, 1288, 1290, 1291, 1335, 1355, 1377, 1399, 1406, 1446, 1459, 1462, 1470, 1471, 1476, 1481, 1493, 1499, 1503, 1552, 1554, 1565, 1589, 1593, 1603, 1661, 1680, 1716, 1731, 1733, 1735, 1743, 1749, 1754, 1755, 1758, 1767, 1773, 1790, 1821, 1904, 1905, ...], 'VX': [56, 227, 243, 417, 432, 464, 614, 641, 703, 759, 812, 825, 943, 1058, 1092, 1093, 1192, 1302, 1312, 1343, 1369, 1452, 1586, 1599, 1644, 1696, 1705, 1805, 1930, 1957, 2048, 2219, 2220, 2270, 2302, 2324, 2484, 2572, 2620, 2646, 2735, 2753, 2808, 2880, 2898, 2919, 3110, 3111, 3120, 3210, 3246, 3261, 3302, 3427, 3501, 3510, 3664, 3680, 3694, 3731, 3860, 3938, 3968, 4005, 4029, 4035, 4056, 4088, 4270, 4351, 4531, 4536, 4540, 4692, 4712, 4728, 4746, 4825, 4856, 4860, 4869, 4876, 4890, 4901, 4928, 4994, 5075, 5078, 5115, 5143, 5167, 5206, 5245, 5265, 5379, 5383, 5413, 5502, 5512, 5522, ...], 'WN': [0, 4, 19, 20, 23, 28, 30, 43, 59, 64, 68, 80, 83, 84, 88, 92, 114, 132, 156, 173, 176, 186, 193, 195, 212, 217, 218, 226, 230, 234, 256, 259, 271, 272, 274, 276, 280, 287, 292, 294, 299, 300, 302, 303, 306, 316, 344, 346, 350, 351, 356, 365, 388, 399, 400, 406, 409, 410, 416, 422, 426, 428, 454, 461, 475, 480, 489, 496, 498, 504, 515, 532, 562, 571, 585, 597, 611, 627, 633, 648, 657, 669, 670, 674, 678, 710, 720, 724, 731, 744, 747, 752, 753, 788, 797, 804, 807, 813, 815, 817, ...]}

• 너무 보기 힘듬

- 보기좋은 형태로 확인

list(grouped_df.groups)

['AA',
 'AS',
 'B6',
 'DL',
 'EV',
 'F9',
 'HA',
 'MQ',
 'NK',
 'OO',
 'UA',
 'US',
 'VX',
 'WN']

grouped_df.get_group('AA')

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
3	1	1	4	AA	DFW	DCA	1555	7.0	126.0	1192	1935	-7.0	0	0
6	1	1	4	AA	DFW	MSY	1250	84.0	64.0	447	1410	83.0	0	0
8	1	1	4	AA	ORD	STL	1845	-5.0	44.0	258	1950	-5.0	0	0
15	1	1	4	AA	DEN	DFW	1445	-6.0	93.0	641	1745	4.0	0	0
26	1	1	4	AA	LAX	AUS	1430	33.0	157.0	1242	1925	41.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58470	12	31	4	AA	DFW	FAT	1020	-3.0	196.0	1313	1156	-2.0	0	0
58475	12	31	4	AA	IAH	CLT	710	1.0	113.0	912	1037	-12.0	0	0
58476	12	31	4	AA	DFW	TPA	1020	-3.0	121.0	929	1340	-6.0	0	0
58479	12	31	4	AA	DFW	ELP	1200	3.0	94.0	551	1250	13.0	0	0
58487	12	31	4	AA	SFO	DFW	515	5.0	166.0	1464	1045	-19.0	0	0

8900 rows × 14 columns

for g in grouped_df.groups:
    print(g)
    display(grouped_df.get_group(g))

AA

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
3	1	1	4	AA	DFW	DCA	1555	7.0	126.0	1192	1935	-7.0	0	0
6	1	1	4	AA	DFW	MSY	1250	84.0	64.0	447	1410	83.0	0	0
8	1	1	4	AA	ORD	STL	1845	-5.0	44.0	258	1950	-5.0	0	0
15	1	1	4	AA	DEN	DFW	1445	-6.0	93.0	641	1745	4.0	0	0
26	1	1	4	AA	LAX	AUS	1430	33.0	157.0	1242	1925	41.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58470	12	31	4	AA	DFW	FAT	1020	-3.0	196.0	1313	1156	-2.0	0	0
58475	12	31	4	AA	IAH	CLT	710	1.0	113.0	912	1037	-12.0	0	0
58476	12	31	4	AA	DFW	TPA	1020	-3.0	121.0	929	1340	-6.0	0	0
58479	12	31	4	AA	DFW	ELP	1200	3.0	94.0	551	1250	13.0	0	0
58487	12	31	4	AA	SFO	DFW	515	5.0	166.0	1464	1045	-19.0	0	0

8900 rows × 14 columns

AS

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
38	1	1	4	AS	PHX	SEA	1505	-2.0	155.0	1107	1702	-3.0	0	0
198	1	2	5	AS	LAX	SEA	2110	5.0	145.0	954	2352	8.0	0	0
241	1	2	5	AS	LAS	PDX	650	-5.0	117.0	763	906	-3.0	0	0
277	1	2	5	AS	ORD	ANC	935	-1.0	402.0	2846	1339	-6.0	0	0
397	1	3	6	AS	LAS	SEA	1300	48.0	137.0	867	1535	47.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58305	12	30	3	AS	LAX	SEA	1325	-2.0	134.0	954	1608	-7.0	0	0
58355	12	31	4	AS	PHX	SEA	1200	-5.0	145.0	1107	1407	-24.0	0	0
58404	12	31	4	AS	SFO	SLC	2110	-2.0	80.0	599	2358	-4.0	0	0
58407	12	31	4	AS	SFO	PDX	645	-2.0	81.0	550	832	-3.0	0	0
58428	12	31	4	AS	LAX	SEA	1420	-8.0	127.0	954	1709	-25.0	0	0

768 rows × 14 columns

B6

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
123	1	1	4	B6	LAS	BOS	1230	0.0	246.0	2381	2026	-27.0	0	0
127	1	1	4	B6	LAS	BOS	2359	68.0	247.0	2381	749	46.0	0	0
239	1	2	5	B6	ORD	BOS	540	-8.0	96.0	867	856	-22.0	0	0
333	1	3	6	B6	LAX	FLL	2237	32.0	270.0	2342	619	42.0	0	0
548	1	4	7	B6	SFO	FLL	2307	-4.0	298.0	2583	724	-1.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58262	12	30	3	B6	SFO	LGB	1921	-6.0	57.0	354	2038	-14.0	0	0
58301	12	30	3	B6	LAX	JFK	630	4.0	285.0	2475	1445	-6.0	0	0
58425	12	31	4	B6	ORD	SJU	700	239.0	250.0	2072	1335	239.0	0	0
58477	12	31	4	B6	DFW	BOS	1145	12.0	161.0	1562	1608	-14.0	0	0
58483	12	31	4	B6	PHX	BOS	2236	-12.0	231.0	2300	515	-45.0	0	0

543 rows × 14 columns

DL

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
53	1	1	4	DL	LAS	MSP	713	-5.0	156.0	1299	1220	-18.0	0	0
57	1	1	4	DL	MSP	RSW	700	-1.0	169.0	1416	1130	-20.0	0	0
77	1	1	4	DL	LAX	ATL	1130	24.0	217.0	1947	1840	16.0	0	0
79	1	1	4	DL	LAX	CMH	2146	-3.0	223.0	1995	459	-13.0	0	0
85	1	1	4	DL	ATL	OKC	2059	-4.0	116.0	761	2227	-12.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58440	12	31	4	DL	ATL	CVG	1611	-4.0	61.0	373	1736	-6.0	0	0
58448	12	31	4	DL	ATL	SRQ	1610	0.0	61.0	444	1740	-13.0	0	0
58464	12	31	4	DL	LAX	SFO	700	108.0	54.0	337	825	105.0	0	0
58467	12	31	4	DL	ATL	IND	1235	-3.0	63.0	432	1407	-13.0	0	0
58485	12	31	4	DL	ATL	CMH	2206	2.0	64.0	447	2338	-8.0	0	0

10601 rows × 14 columns

EV

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
11	1	1	4	EV	ORD	JAN	1155	6.0	113.0	677	1403	5.0	0	0
13	1	1	4	EV	ORD	CMH	1010	-2.0	46.0	296	1228	-9.0	0	0
29	1	1	4	EV	ORD	IND	1025	-6.0	29.0	177	1228	-19.0	0	0
40	1	1	4	EV	IAH	CLE	1038	-3.0	126.0	1091	1425	-18.0	0	0
69	1	1	4	EV	ATL	RAP	1930	-5.0	181.0	1230	2104	-15.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58445	12	31	4	EV	DFW	TXK	850	-5.0	30.0	181	948	-17.0	0	0
58452	12	31	4	EV	DFW	SHV	1650	-4.0	32.0	190	1746	-12.0	0	0
58459	12	31	4	EV	MSP	ORD	1435	18.0	61.0	334	1609	3.0	0	0
58463	12	31	4	EV	ORD	MSN	1220	18.0	32.0	108	1319	27.0	0	0
58486	12	31	4	EV	DFW	LFT	850	21.0	52.0	351	1012	14.0	0	0

5858 rows × 14 columns

F9

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
7	1	1	4	F9	SFO	PHX	1020	-7.0	91.0	651	1315	-6.0	0	0
93	1	1	4	F9	ATL	DEN	859	16.0	181.0	1199	1026	10.0	0	0
209	1	2	5	F9	MSP	DEN	1025	-6.0	97.0	680	1134	-13.0	0	0
232	1	2	5	F9	DEN	PHX	2040	-7.0	83.0	602	2228	-18.0	0	0
247	1	2	5	F9	ORD	ATL	730	10.0	86.0	606	1020	23.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58288	12	30	3	F9	DEN	ORD	625	-4.0	136.0	888	1000	14.0	0	0
58331	12	30	3	F9	ORD	PHX	825	18.0	207.0	1440	1127	14.0	0	0
58447	12	31	4	F9	DEN	LAS	1245	13.0	94.0	628	1340	13.0	0	0
58449	12	31	4	F9	DEN	MCO	645	11.0	169.0	1546	1224	-11.0	0	0
58488	12	31	4	F9	LAS	SFO	1910	13.0	71.0	414	2050	4.0	0	0

1317 rows × 14 columns

HA

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
582	1	4	7	HA	LAX	OGG	1115	-11.0	310.0	2486	1500	-27.0	0	0
712	1	5	1	HA	LAS	HNL	900	-5.0	357.0	2762	1315	5.0	0	0
878	1	6	2	HA	PHX	HNL	800	1.0	374.0	2917	1140	3.0	0	0
1053	1	7	3	HA	LAX	HNL	1705	0.0	332.0	2556	2055	-2.0	0	0
1269	1	8	4	HA	LAX	HNL	1000	-1.0	335.0	2556	1350	0.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
55883	12	16	3	HA	LAX	HNL	835	1.0	314.0	2556	1235	-18.0	0	0
56174	12	18	5	HA	LAX	HNL	835	-5.0	342.0	2556	1235	-4.0	0	0
56350	12	19	6	HA	PHX	HNL	800	-5.0	363.0	2917	1155	-34.0	0	0
56816	12	21	1	HA	LAX	LIH	740	20.0	303.0	2615	1145	-11.0	0	0
58391	12	31	4	HA	LAX	HNL	1000	0.0	324.0	2556	1350	-9.0	0	0

112 rows × 14 columns

MQ

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
2	1	1	4	MQ	DFW	VPS	1305	36.0	85.0	641	1453	35.0	0	0
10	1	1	4	MQ	DFW	DRO	1335	28.0	104.0	674	1438	28.0	0	0
18	1	1	4	MQ	ORD	DAY	2220	19.0	37.0	240	23	20.0	0	0
24	1	1	4	MQ	DFW	BTR	730	NaN	NaN	383	853	NaN	0	1
50	1	1	4	MQ	ORD	CID	1135	-7.0	37.0	196	1238	-15.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58415	12	31	4	MQ	ORD	FWA	845	-2.0	37.0	157	1045	-4.0	0	0
58426	12	31	4	MQ	DFW	FAR	1154	4.0	124.0	968	1437	-13.0	0	0
58468	12	31	4	MQ	DFW	OKC	1720	-3.0	31.0	175	1819	-10.0	0	0
58474	12	31	4	MQ	ORD	FNT	829	4.0	40.0	223	1034	-4.0	0	0
58484	12	31	4	MQ	ORD	DSM	1333	1.0	57.0	299	1455	-7.0	0	0

3471 rows × 14 columns

NK

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
17	1	1	4	NK	DEN	DTW	1952	37.0	124.0	1123	31	54.0	0	0
74	1	1	4	NK	PHX	DFW	159	-1.0	103.0	868	502	1.0	0	0
95	1	1	4	NK	LAS	OAK	1115	22.0	62.0	407	1246	10.0	0	0
109	1	1	4	NK	MSP	ORD	616	2.0	49.0	334	745	-19.0	0	0
166	1	2	5	NK	LAS	PDX	1535	-8.0	123.0	763	1754	-4.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58160	12	29	2	NK	MSP	MCO	740	0.0	171.0	1310	1158	33.0	0	0
58197	12	30	3	NK	IAH	ORD	755	-8.0	136.0	925	1030	-2.0	0	0
58437	12	31	4	NK	ORD	DFW	1952	15.0	135.0	802	2225	23.0	0	0
58461	12	31	4	NK	ORD	LGA	1801	-5.0	84.0	733	2109	-26.0	0	0
58469	12	31	4	NK	LAS	MSY	1950	124.0	163.0	1500	112	101.0	0	0

1516 rows × 14 columns

OO

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
12	1	1	4	OO	ORD	MSP	1510	2.0	65.0	334	1646	4.0	0	0
16	1	1	4	OO	DEN	SGU	1105	21.0	66.0	517	1249	20.0	0	0
22	1	1	4	OO	LAS	LAX	1544	-4.0	39.0	236	1655	-12.0	0	0
25	1	1	4	OO	ORD	SPI	2110	-4.0	31.0	174	2205	5.0	0	0
27	1	1	4	OO	IAH	JAC	1104	-1.0	161.0	1265	1316	-1.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58451	12	31	4	OO	ATL	FWA	1905	-3.0	72.0	508	2051	-14.0	0	0
58480	12	31	4	OO	MSP	BIS	1310	-2.0	65.0	386	1449	-9.0	0	0
58482	12	31	4	OO	DEN	CPR	1850	-2.0	38.0	230	1956	1.0	0	0
58489	12	31	4	OO	SFO	SBA	1846	-6.0	46.0	262	1956	-5.0	0	0
58491	12	31	4	OO	SFO	BOI	859	5.0	73.0	522	1146	-1.0	0	0

6588 rows × 14 columns

UA

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
1	1	1	4	UA	DEN	IAD	823	7.0	154.0	1452	1333	-13.0	0	0
5	1	1	4	UA	IAH	SAN	1450	1.0	178.0	1303	1620	-14.0	0	0
9	1	1	4	UA	IAH	SJC	925	3.0	215.0	1608	1136	-14.0	0	0
14	1	1	4	UA	IAH	IND	1426	-1.0	102.0	844	1742	-20.0	0	0
21	1	1	4	UA	ORD	CLE	2102	48.0	47.0	315	2320	41.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58422	12	31	4	UA	DEN	SAN	1535	0.0	124.0	853	1704	-13.0	0	0
58432	12	31	4	UA	ORD	SAN	1915	7.0	238.0	1723	2143	-3.0	0	0
58457	12	31	4	UA	ORD	LAX	659	-1.0	241.0	1744	946	0.0	0	0
58460	12	31	4	UA	SFO	PHL	2235	-6.0	265.0	2521	700	-42.0	0	0
58481	12	31	4	UA	IAH	LAX	1433	1.0	197.0	1379	1625	-13.0	0	0

7792 rows × 14 columns

US

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
31	1	1	4	US	PHX	DEN	1810	29.0	94.0	602	1954	49.0	0	0
35	1	1	4	US	ORD	PHL	1600	-2.0	80.0	678	1857	-9.0	0	0
49	1	1	4	US	IAH	PHX	1445	-1.0	147.0	1009	1638	-7.0	0	0
96	1	1	4	US	ATL	PHL	1445	-4.0	90.0	666	1644	-11.0	0	0
104	1	1	4	US	MSP	PHX	730	-3.0	174.0	1276	1010	-20.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
31514	6	30	2	US	DEN	PHL	705	-4.0	188.0	1558	1240	1.0	0	0
31523	6	30	2	US	PHX	DEN	1451	6.0	85.0	602	1738	7.0	0	0
31535	6	30	2	US	PHX	AUS	840	-3.0	116.0	872	1304	-11.0	0	0
31561	6	30	2	US	ORD	PHX	710	-5.0	170.0	1440	901	-50.0	0	0
31582	6	30	2	US	PHX	OGG	800	-4.0	356.0	2845	1127	-13.0	0	0

1615 rows × 14 columns

VX

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
56	1	1	4	VX	LAS	SFO	900	23.0	65.0	414	1035	11.0	0	0
227	1	2	5	VX	SFO	LAS	1220	-5.0	68.0	414	1350	-5.0	0	0
243	1	2	5	VX	SFO	SEA	700	-4.0	104.0	679	905	-1.0	0	0
417	1	3	6	VX	SFO	LAS	900	-2.0	62.0	414	1030	-11.0	0	0
432	1	3	6	VX	SFO	SEA	2035	-2.0	106.0	679	2240	-2.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58332	12	30	3	VX	SFO	LAS	1950	-3.0	58.0	414	2120	-4.0	0	0
58383	12	31	4	VX	SFO	PSP	1630	-7.0	65.0	421	1755	-12.0	0	0
58400	12	31	4	VX	SFO	LAX	1125	-4.0	54.0	337	1245	-10.0	0	0
58471	12	31	4	VX	SFO	LAX	700	6.0	51.0	337	820	3.0	0	0
58478	12	31	4	VX	SFO	LAX	1530	29.0	52.0	337	1650	22.0	0	0

993 rows × 14 columns

WN

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
0	1	1	4	WN	LAX	SLC	1625	58.0	94.0	590	1905	65.0	0	0
4	1	1	4	WN	LAX	MCI	1720	48.0	166.0	1363	2225	39.0	0	0
19	1	1	4	WN	PHX	LAX	1640	51.0	58.0	370	1700	59.0	0	0
20	1	1	4	WN	ATL	BWI	1115	1.0	76.0	577	1305	-15.0	0	0
23	1	1	4	WN	ATL	HOU	1555	30.0	113.0	696	1720	18.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58455	12	31	4	WN	LAX	SMF	1420	-2.0	64.0	373	1540	-7.0	0	0
58458	12	31	4	WN	LAS	SFO	1825	25.0	67.0	414	1955	17.0	0	0
58472	12	31	4	WN	PHX	HOU	845	5.0	119.0	1020	1210	7.0	0	0
58473	12	31	4	WN	DEN	PDX	1205	4.0	130.0	991	1400	-13.0	0	0
58490	12	31	4	WN	MSP	ATL	525	39.0	124.0	907	855	34.0	0	0

8418 rows × 14 columns

AIRLINE을 기준으로 데이터프레임을 나누고 $\to$ ARR_DELAY에 mean함수를 적용: (AIRLINE $\to$ {ARR_DELAY: mean})

- 방법1

df.groupby(by='AIRLINE').agg({'ARR_DELAY':'mean'})

	ARR_DELAY
AIRLINE
AA	5.542661
AS	-0.833333
B6	8.692593
DL	0.339691
EV	7.034580
F9	13.630651
HA	4.972973
MQ	6.860591
NK	18.436070
OO	7.593463
UA	7.765755
US	1.681105
VX	5.348884
WN	6.397353

- 방법2 ($\star\star\star$)

df.groupby(by='AIRLINE').agg({'ARR_DELAY':np.mean})

	ARR_DELAY
AIRLINE
AA	5.542661
AS	-0.833333
B6	8.692593
DL	0.339691
EV	7.034580
F9	13.630651
HA	4.972973
MQ	6.860591
NK	18.436070
OO	7.593463
UA	7.765755
US	1.681105
VX	5.348884
WN	6.397353

- 방법3

df.groupby(by='AIRLINE')['ARR_DELAY'].agg('mean')

AIRLINE
AA     5.542661
AS    -0.833333
B6     8.692593
DL     0.339691
EV     7.034580
F9    13.630651
HA     4.972973
MQ     6.860591
NK    18.436070
OO     7.593463
UA     7.765755
US     1.681105
VX     5.348884
WN     6.397353
Name: ARR_DELAY, dtype: float64

- 방법4 ($\star$)

df.groupby(by='AIRLINE')['ARR_DELAY'].agg(np.mean)

AIRLINE
AA     5.542661
AS    -0.833333
B6     8.692593
DL     0.339691
EV     7.034580
F9    13.630651
HA     4.972973
MQ     6.860591
NK    18.436070
OO     7.593463
UA     7.765755
US     1.681105
VX     5.348884
WN     6.397353
Name: ARR_DELAY, dtype: float64

- 방법5

df.groupby(by='AIRLINE')['ARR_DELAY'].mean()

AIRLINE
AA     5.542661
AS    -0.833333
B6     8.692593
DL     0.339691
EV     7.034580
F9    13.630651
HA     4.972973
MQ     6.860591
NK    18.436070
OO     7.593463
UA     7.765755
US     1.681105
VX     5.348884
WN     6.397353
Name: ARR_DELAY, dtype: float64

- 방법2와 방법4는 사용자정의 함수를 쓸 수 있다는 장점이 있음

def f(x): return -np.mean(x) 

df.groupby(by='AIRLINE').agg({'ARR_DELAY':f})

	ARR_DELAY
AIRLINE
AA	-5.542661
AS	0.833333
B6	-8.692593
DL	-0.339691
EV	-7.034580
F9	-13.630651
HA	-4.972973
MQ	-6.860591
NK	-18.436070
OO	-7.593463
UA	-7.765755
US	-1.681105
VX	-5.348884
WN	-6.397353

df.groupby(by='AIRLINE').agg({'ARR_DELAY':lambda x: -np.mean(x)})

	ARR_DELAY
AIRLINE
AA	-5.542661
AS	0.833333
B6	-8.692593
DL	-0.339691
EV	-7.034580
F9	-13.630651
HA	-4.972973
MQ	-6.860591
NK	-18.436070
OO	-7.593463
UA	-7.765755
US	-1.681105
VX	-5.348884
WN	-6.397353

df.groupby(by='AIRLINE')['ARR_DELAY'].agg(lambda x: -np.mean(x))

AIRLINE
AA    -5.542661
AS     0.833333
B6    -8.692593
DL    -0.339691
EV    -7.034580
F9   -13.630651
HA    -4.972973
MQ    -6.860591
NK   -18.436070
OO    -7.593463
UA    -7.765755
US    -1.681105
VX    -5.348884
WN    -6.397353
Name: ARR_DELAY, dtype: float64

- 입력이 여러개인 사용자 정의 함수도 사용가능함

def f(x,y): return np.mean(x)**y 

df.groupby(by='AIRLINE')['ARR_DELAY'].agg(f,2)

AIRLINE
AA     30.721086
AS      0.694444
B6     75.561166
DL      0.115390
EV     49.485310
F9    185.794656
HA     24.730460
MQ     47.067715
NK    339.888677
OO     57.660681
UA     60.306954
US      2.826113
VX     28.610564
WN     40.926120
Name: ARR_DELAY, dtype: float64

df.groupby(by='AIRLINE').agg({'ARR_DELAY': lambda x: f(x,2)})

	ARR_DELAY
AIRLINE
AA	30.721086
AS	0.694444
B6	75.561166
DL	0.115390
EV	49.485310
F9	185.794656
HA	24.730460
MQ	47.067715
NK	339.888677
OO	57.660681
UA	60.306954
US	2.826113
VX	28.610564
WN	40.926120

AIRLINE,WEEKDAY $\to$ {CANCELLED: sum}

- 방법1~5

df.groupby(by=['AIRLINE','WEEKDAY']).agg({'CANCELLED':'sum'})

		CANCELLED
AIRLINE	WEEKDAY
AA	1	41
	2	9
	3	16
	4	20
	5	18
...	...	...
WN	3	18
	4	10
	5	7
	6	10
	7	7

98 rows × 1 columns

df.groupby(by=['AIRLINE','WEEKDAY']).agg({'CANCELLED':np.sum})

		CANCELLED
AIRLINE	WEEKDAY
AA	1	41
	2	9
	3	16
	4	20
	5	18
...	...	...
WN	3	18
	4	10
	5	7
	6	10
	7	7

98 rows × 1 columns

df.groupby(by=['AIRLINE','WEEKDAY'])['CANCELLED'].agg('sum')

AIRLINE  WEEKDAY
AA       1          41
         2           9
         3          16
         4          20
         5          18
                    ..
WN       3          18
         4          10
         5           7
         6          10
         7           7
Name: CANCELLED, Length: 98, dtype: int64

df.groupby(by=['AIRLINE','WEEKDAY'])['CANCELLED'].agg(np.sum)

AIRLINE  WEEKDAY
AA       1          41
         2           9
         3          16
         4          20
         5          18
                    ..
WN       3          18
         4          10
         5           7
         6          10
         7           7
Name: CANCELLED, Length: 98, dtype: int64

df.groupby(by=['AIRLINE','WEEKDAY'])['CANCELLED'].sum()

AIRLINE  WEEKDAY
AA       1          41
         2           9
         3          16
         4          20
         5          18
                    ..
WN       3          18
         4          10
         5           7
         6          10
         7           7
Name: CANCELLED, Length: 98, dtype: int64

AIRLINE,WEEKDAY $\to$ {CANCELLED: sum, mean} , {DIVERTED: sum, mean}

- 방법 1~4 (5번은 쓸 수 없다)

df.groupby(by=['AIRLINE','WEEKDAY']).agg({'CANCELLED':['sum','mean'],'DIVERTED':['sum','mean']})

		CANCELLED		DIVERTED
		sum	mean	sum	mean
AIRLINE	WEEKDAY
AA	1	41	0.032106	6	0.004699
	2	9	0.007341	2	0.001631
	3	16	0.011949	2	0.001494
	4	20	0.015004	5	0.003751
	5	18	0.014151	1	0.000786
...	...	...	...	...	...
WN	3	18	0.014118	2	0.001569
	4	10	0.007911	4	0.003165
	5	7	0.005828	0	0.000000
	6	10	0.010132	3	0.003040
	7	7	0.006066	3	0.002600

98 rows × 4 columns

df.groupby(by=['AIRLINE','WEEKDAY']).agg({'CANCELLED':[np.sum,np.mean],'DIVERTED':[np.sum,np.mean]})

		CANCELLED		DIVERTED
		sum	mean	sum	mean
AIRLINE	WEEKDAY
AA	1	41	0.032106	6	0.004699
	2	9	0.007341	2	0.001631
	3	16	0.011949	2	0.001494
	4	20	0.015004	5	0.003751
	5	18	0.014151	1	0.000786
...	...	...	...	...	...
WN	3	18	0.014118	2	0.001569
	4	10	0.007911	4	0.003165
	5	7	0.005828	0	0.000000
	6	10	0.010132	3	0.003040
	7	7	0.006066	3	0.002600

98 rows × 4 columns

df.groupby(by=['AIRLINE','WEEKDAY'])[['CANCELLED','DIVERTED']].agg(['sum','mean'])

		CANCELLED		DIVERTED
		sum	mean	sum	mean
AIRLINE	WEEKDAY
AA	1	41	0.032106	6	0.004699
	2	9	0.007341	2	0.001631
	3	16	0.011949	2	0.001494
	4	20	0.015004	5	0.003751
	5	18	0.014151	1	0.000786
...	...	...	...	...	...
WN	3	18	0.014118	2	0.001569
	4	10	0.007911	4	0.003165
	5	7	0.005828	0	0.000000
	6	10	0.010132	3	0.003040
	7	7	0.006066	3	0.002600

98 rows × 4 columns

df.groupby(by=['AIRLINE','WEEKDAY'])[['CANCELLED','DIVERTED']].agg([np.sum,np.mean])

		CANCELLED		DIVERTED
		sum	mean	sum	mean
AIRLINE	WEEKDAY
AA	1	41	0.032106	6	0.004699
	2	9	0.007341	2	0.001631
	3	16	0.011949	2	0.001494
	4	20	0.015004	5	0.003751
	5	18	0.014151	1	0.000786
...	...	...	...	...	...
WN	3	18	0.014118	2	0.001569
	4	10	0.007911	4	0.003165
	5	7	0.005828	0	0.000000
	6	10	0.010132	3	0.003040
	7	7	0.006066	3	0.002600

98 rows × 4 columns

AIRLINE,WEEKDAY $\to$ {CANCELLED: sum, mean, size} , {AIR_TIME: mean,var}

df.groupby(by=['AIRLINE','WEEKDAY']).agg({'CANCELLED':['sum','mean','size'],'AIR_TIME':['mean','var']})

		CANCELLED			AIR_TIME
		sum	mean	size	mean	var
AIRLINE	WEEKDAY
AA	1	41	0.032106	1277	147.610569	5393.806723
	2	9	0.007341	1226	143.851852	5359.890719
	3	16	0.011949	1339	144.514005	5378.854539
	4	20	0.015004	1333	141.124618	4791.524627
	5	18	0.014151	1272	145.430966	5884.592076
...	...	...	...	...	...	...
WN	3	18	0.014118	1275	104.219920	2901.873447
	4	10	0.007911	1264	107.200800	2966.568935
	5	7	0.005828	1201	107.893635	3268.717093
	6	10	0.010132	987	109.247433	3152.753719
	7	7	0.006066	1154	107.602273	3183.126889

98 rows × 5 columns

df.groupby(by=['AIRLINE','WEEKDAY'])\
.agg({'CANCELLED':[np.sum,np.mean,len],'AIR_TIME':[np.mean,lambda x: np.std(x,ddof=1)**2]})

		CANCELLED			AIR_TIME
		sum	mean	len	mean	<lambda_0>
AIRLINE	WEEKDAY
AA	1	41	0.032106	1277	147.610569	5393.806723
	2	9	0.007341	1226	143.851852	5359.890719
	3	16	0.011949	1339	144.514005	5378.854539
	4	20	0.015004	1333	141.124618	4791.524627
	5	18	0.014151	1272	145.430966	5884.592076
...	...	...	...	...	...	...
WN	3	18	0.014118	1275	104.219920	2901.873447
	4	10	0.007911	1264	107.200800	2966.568935
	5	7	0.005828	1201	107.893635	3268.717093
	6	10	0.010132	987	109.247433	3152.753719
	7	7	0.006066	1154	107.602273	3183.126889

98 rows × 5 columns

grouping by continuous variable

df

	MONTH	DAY	WEEKDAY	AIRLINE	ORG_AIR	DEST_AIR	SCHED_DEP	DEP_DELAY	AIR_TIME	DIST	SCHED_ARR	ARR_DELAY	DIVERTED	CANCELLED
0	1	1	4	WN	LAX	SLC	1625	58.0	94.0	590	1905	65.0	0	0
1	1	1	4	UA	DEN	IAD	823	7.0	154.0	1452	1333	-13.0	0	0
2	1	1	4	MQ	DFW	VPS	1305	36.0	85.0	641	1453	35.0	0	0
3	1	1	4	AA	DFW	DCA	1555	7.0	126.0	1192	1935	-7.0	0	0
4	1	1	4	WN	LAX	MCI	1720	48.0	166.0	1363	2225	39.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
58487	12	31	4	AA	SFO	DFW	515	5.0	166.0	1464	1045	-19.0	0	0
58488	12	31	4	F9	LAS	SFO	1910	13.0	71.0	414	2050	4.0	0	0
58489	12	31	4	OO	SFO	SBA	1846	-6.0	46.0	262	1956	-5.0	0	0
58490	12	31	4	WN	MSP	ATL	525	39.0	124.0	907	855	34.0	0	0
58491	12	31	4	OO	SFO	BOI	859	5.0	73.0	522	1146	-1.0	0	0

58492 rows × 14 columns

- 목표: DIST를 적당한 구간으로 나누어 카테고리화 하고 그것을 바탕으로 groupby를 수행하자.

df.DIST.hist()

<AxesSubplot:>

df.DIST.describe()

count    58492.000000
mean       872.900072
std        624.996805
min         67.000000
25%        391.000000
50%        690.000000
75%       1199.000000
max       4502.000000
Name: DIST, dtype: float64

- 구간을 아래와 같이 설정한다.

bins=[-np.inf, 400, 700, 1200, np.inf] 

- pd.cut()을 이용하여 각 구간의 obs를 카테고리화 하자.

cuts=pd.cut(df.DIST,bins=bins)
cuts

0         (400.0, 700.0]
1          (1200.0, inf]
2         (400.0, 700.0]
3        (700.0, 1200.0]
4          (1200.0, inf]
              ...       
58487      (1200.0, inf]
58488     (400.0, 700.0]
58489      (-inf, 400.0]
58490    (700.0, 1200.0]
58491     (400.0, 700.0]
Name: DIST, Length: 58492, dtype: category
Categories (4, interval[float64, right]): [(-inf, 400.0] < (400.0, 700.0] < (700.0, 1200.0] < (1200.0, inf]]

- cuts, AIRLINE $\to$ {DIVERTED: sum}

df.groupby([cuts,'AIRLINE']).agg({'DIVERTED':sum})

		DIVERTED
DIST	AIRLINE
(-inf, 400.0]	AA	0
	AS	0
	B6	0
	DL	1
	EV	3
	F9	0
	HA	0
	MQ	0
	NK	0
	OO	5
	UA	2
	US	0
	VX	0
	WN	1
(400.0, 700.0]	AA	3
	AS	0
	B6	0
	DL	12
	EV	8
	F9	1
	HA	0
	MQ	4
	NK	1
	OO	7
	UA	1
	US	0
	VX	0
	WN	2
(700.0, 1200.0]	AA	10
	AS	0
	B6	1
	DL	6
	EV	4
	F9	0
	HA	0
	MQ	1
	NK	1
	OO	5
	UA	4
	US	0
	VX	0
	WN	4
(1200.0, inf]	AA	13
	AS	0
	B6	1
	DL	5
	EV	0
	F9	1
	HA	1
	MQ	0
	NK	3
	OO	4
	UA	12
	US	1
	VX	1
	WN	8

- 아래와 비교해보자.

df.groupby(['AIRLINE']).agg({'AIRLINE':len})

	AIRLINE
AIRLINE
AA	8900
AS	768
B6	543
DL	10601
EV	5858
F9	1317
HA	112
MQ	3471
NK	1516
OO	6588
UA	7792
US	1615
VX	993
WN	8418

- cuts을 이용하여 추가그룹핑을 하면 조금 다른 특징들을 데이터에서 발견할 수 있다.

• AA항공사와 DL항공사는 모두 비슷한 우회횟수를 가지고 있음.
• AA항공사는 700회이상의 구간에서 우회를 많이하고 DL항공사는 400~700사이에서 우회를 많이 한다. (패턴이 다름)

- 구간이름에 label을 붙이는 방법

bins

[-inf, 400, 700, 1200, inf]

cuts2=pd.cut(df.DIST,bins=bins,labels=['Q1','Q2','Q3','Q4'])
cuts2

0        Q2
1        Q4
2        Q2
3        Q3
4        Q4
         ..
58487    Q4
58488    Q2
58489    Q1
58490    Q3
58491    Q2
Name: DIST, Length: 58492, dtype: category
Categories (4, object): ['Q1' < 'Q2' < 'Q3' < 'Q4']

df.groupby(by=[cuts2,'AIRLINE']).agg({'DIVERTED':sum})

		DIVERTED
DIST	AIRLINE
Q1	AA	0
	AS	0
	B6	0
	DL	1
	EV	3
	F9	0
	HA	0
	MQ	0
	NK	0
	OO	5
	UA	2
	US	0
	VX	0
	WN	1
Q2	AA	3
	AS	0
	B6	0
	DL	12
	EV	8
	F9	1
	HA	0
	MQ	4
	NK	1
	OO	7
	UA	1
	US	0
	VX	0
	WN	2
Q3	AA	10
	AS	0
	B6	1
	DL	6
	EV	4
	F9	0
	HA	0
	MQ	1
	NK	1
	OO	5
	UA	4
	US	0
	VX	0
	WN	4
Q4	AA	13
	AS	0
	B6	1
	DL	5
	EV	0
	F9	1
	HA	1
	MQ	0
	NK	3
	OO	4
	UA	12
	US	1
	VX	1
	WN	8

df.groupby(cuts2).agg({'DIVERTED':len})

	DIVERTED
DIST
Q1	15027
Q2	14697
Q3	14417
Q4	14351

숙제

구간을

bins=[-np.inf, 400, 700, 1200, np.inf]

이 아니라

bins=[-np.inf, 400, 600, 800, 1000, 1200, np.inf]

와 같이 나누고 적당한 각구간별로 해당하는 관측치의 수를 구하라.