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29
4
我有两个具有多个索引和日期作为列的数据框:df1
df1 = pd.DataFrame.from_dict({('group', ''): {0: 'A',
1: 'A',
2: 'A',
3: 'A',
4: 'A',
5: 'A',
6: 'A',
7: 'A',
8: 'B',
9: 'B',
10: 'B',
11: 'B',
12: 'B',
13: 'B',
14: 'B',
15: 'B',
16: 'C',
17: 'C',
18: 'C',
19: 'C',
20: 'C',
21: 'C',
22: 'C',
23: 'C',
24: 'D',
25: 'D',
26: 'D',
27: 'D',
28: 'D',
29: 'D',
30: 'D'},
('category', ''): {0: 'Apple',
1: 'Amazon',
2: 'Google',
3: 'Netflix',
4: 'Facebook',
5: 'Uber',
6: 'Tesla',
7: 'total',
8: 'Apple',
9: 'Amazon',
10: 'Google',
11: 'Netflix',
12: 'Facebook',
13: 'Uber',
14: 'Tesla',
15: 'total',
16: 'Apple',
17: 'Amazon',
18: 'Google',
19: 'Netflix',
20: 'Facebook',
21: 'Uber',
22: 'Tesla',
23: 'total',
24: 'Apple',
25: 'Amazon',
26: 'Google',
27: 'Netflix',
28: 'Uber',
29: 'Tesla',
30: 'total'},
(pd.Timestamp('2021-06-28 00:00:00'), 'total_orders'): {0: 88.0,
1: 66.0,
2: 191.0,
3: 558.0,
4: 12.0,
5: 4.0,
6: 56.0,
7: 975.0,
8: 90.0,
9: 26.0,
10: 49.0,
11: 250.0,
12: 7.0,
13: 2.0,
14: 44.0,
15: 468.0,
16: 36.0,
17: 52.0,
18: 94.0,
19: 750.0,
20: 10.0,
21: 0.0,
22: 52.0,
23: 994.0,
24: 16.0,
25: 22.0,
26: 5.0,
27: 57.0,
28: 3.0,
29: 33.0,
30: 136.0},
(pd.Timestamp('2021-06-28 00:00:00'), 'total_sales'): {0: 4603.209999999999,
1: 2485.059999999998,
2: 4919.39999999998,
3: 6097.77,
4: 31.22,
5: 155.71,
6: 3484.99,
7: 17237.35999999996,
8: 561.54,
9: 698.75,
10: 1290.13,
11: 4292.68000000001,
12: 947.65,
13: 329.0,
14: 2889.65,
15: 9989.4,
16: 330.8899999999994,
17: 2076.26,
18: 2982.270000000004,
19: 11978.62000000002,
20: 683.0,
21: 0.0,
22: 3812.16999999999,
23: 20963.21000000002,
24: 234.4900000000002,
25: 896.1,
26: 231.0,
27: 893.810000000001,
28: 129.0,
29: 1712.329999999998,
30: 4106.729999999996},
(pd.Timestamp('2021-07-05 00:00:00'), 'total_orders'): {0: 109.0,
1: 48.0,
2: 174.0,
3: 592.0,
4: 13.0,
5: 5.0,
6: 43.0,
7: 984.0,
8: 62.0,
9: 13.0,
10: 37.0,
11: 196.0,
12: 8.0,
13: 1.0,
14: 3.0,
15: 30.0,
16: 76.0,
17: 5.0,
18: 147.0,
19: 88.0,
20: 8.0,
21: 1.0,
22: 78.0,
23: 1248.0,
24: 1.0,
25: 18.0,
26: 23.0,
27: 83.0,
28: 0.0,
29: 29.0,
30: 154.0},
(pd.Timestamp('2021-07-05 00:00:00'), 'total_sales'): {0: 3453.02,
1: 17868.730000000003,
2: 44707.82999999999,
3: 61425.99,
4: 1261.0,
5: 1914.6000000000001,
6: 24146.09,
7: 154777.25999999998,
8: 6201.489999999999,
9: 5513.960000000001,
10: 9645.87,
11: 25086.785,
12: 663.0,
13: 448.61,
14: 26332.7,
15: 73892.415,
16: 556.749999999999,
17: 1746.859999999997,
18: 4103.219999999994,
19: 15571.52000000008,
20: 86.0,
21: 69.0,
22: 5882.759999999995,
23: 26476.11000000004,
24: 53.0,
25: 801.220000000001,
26: 684.56,
27: 1232.600000000002,
28: 0.0,
29: 15902.1,
30: 43943.48},
(pd.Timestamp('2021-07-12 00:00:00'), 'total_orders'): {0: 32.0,
1: 15.0,
2: 89.0,
3: 239.0,
4: 2.0,
5: 3.0,
6: 20.0,
7: 400.0,
8: 0.0,
9: 0.0,
10: 0.0,
11: 0.0,
12: 0.0,
13: 0.0,
14: 0.0,
15: 0.0,
16: 21.0,
17: 14.0,
18: 58.0,
19: 281.0,
20: 3.0,
21: 3.0,
22: 33.0,
23: 413.0,
24: 7.0,
25: 6.0,
26: 4.0,
27: 13.0,
28: 0.0,
29: 18.0,
30: 48.0},
(pd.Timestamp('2021-07-12 00:00:00'), 'total_sales'): {0: 2147.7000000000003,
1: 4767.3,
2: 2399.300000000003,
3: 3137.440000000002,
4: 178.0,
5: 866.61,
6: 10639.03,
7: 73235.38,
8: 0.0,
9: 0.0,
10: 0.0,
11: 0.0,
12: 0.0,
13: 0.0,
14: 0.0,
15: 0.0,
16: 220.94,
17: 727.5199999999995,
18: 2500.96999999999,
19: 4414.00999999998,
20: 15.0,
21: 196.71,
22: 2170.1,
23: 9745.24999999997,
24: 126.55,
25: 290.2,
26: 146.01,
27: 233.0,
28: 0.0,
29: 973.18,
30: 1658.940000000002}}).set_index(['group','category'])
df2
df2 = pd.DataFrame.from_dict({'group': {0: 'total_full',
1: 'total_full',
2: 'A',
3: 'A',
4: 'B',
5: 'B',
6: 'C',
7: 'C',
8: 'D',
9: 'D',
10: 'Apple_total',
11: 'Apple_total',
12: 'A',
13: 'A',
14: 'B',
15: 'B',
16: 'C',
17: 'C',
18: 'D',
19: 'D',
20: 'Amazon_total',
21: 'Amazon_total',
22: 'A',
23: 'A',
24: 'B',
25: 'B',
26: 'C',
27: 'C',
28: 'D',
29: 'D',
30: 'Google_total',
31: 'Google_total',
32: 'A',
33: 'A',
34: 'B',
35: 'B',
36: 'C',
37: 'C',
38: 'D',
39: 'D',
40: 'Facebook_total',
41: 'Facebook_total',
42: 'A',
43: 'A',
44: 'B',
45: 'B',
46: 'C',
47: 'C',
48: 'D',
49: 'D',
50: 'Netflix_total',
51: 'Netflix_total',
52: 'A',
53: 'A',
54: 'B',
55: 'B',
56: 'C',
57: 'C',
58: 'D',
59: 'D',
60: 'Tesla_total',
61: 'Tesla_total',
62: 'A',
63: 'A',
64: 'B',
65: 'B',
66: 'C',
67: 'C',
68: 'D',
69: 'D',
70: 'Uber_total',
71: 'Uber_total',
72: 'A',
73: 'A',
74: 'B',
75: 'B',
76: 'C',
77: 'C',
78: 'D',
79: 'D'},
'category': {0: 'total_full',
1: 'total_full',
2: 'group_total',
3: 'group_total',
4: 'group_total',
5: 'group_total',
6: 'group_total',
7: 'group_total',
8: 'group_total',
9: 'group_total',
10: 'Apple_total',
11: 'Apple_total',
12: 'Apple',
13: 'Apple',
14: 'Apple',
15: 'Apple',
16: 'Apple',
17: 'Apple',
18: 'Apple',
19: 'Apple',
20: 'Amazon_total',
21: 'Amazon_total',
22: 'Amazon',
23: 'Amazon',
24: 'Amazon',
25: 'Amazon',
26: 'Amazon',
27: 'Amazon',
28: 'Amazon',
29: 'Amazon',
30: 'Google_total',
31: 'Google_total',
32: 'Google',
33: 'Google',
34: 'Google',
35: 'Google',
36: 'Google',
37: 'Google',
38: 'Google',
39: 'Google',
40: 'Facebook_total',
41: 'Facebook_total',
42: 'Facebook',
43: 'Facebook',
44: 'Facebook',
45: 'Facebook',
46: 'Facebook',
47: 'Facebook',
48: 'Facebook',
49: 'Facebook',
50: 'Netflix_total',
51: 'Netflix_total',
52: 'Netflix',
53: 'Netflix',
54: 'Netflix',
55: 'Netflix',
56: 'Netflix',
57: 'Netflix',
58: 'Netflix',
59: 'Netflix',
60: 'Tesla_total',
61: 'Tesla_total',
62: 'Tesla',
63: 'Tesla',
64: 'Tesla',
65: 'Tesla',
66: 'Tesla',
67: 'Tesla',
68: 'Tesla',
69: 'Tesla',
70: 'Uber_total',
71: 'Uber_total',
72: 'Uber',
73: 'Uber',
74: 'Uber',
75: 'Uber',
76: 'Uber',
77: 'Uber',
78: 'Uber',
79: 'Uber'},
'type': {0: 'Sales_1',
1: 'Sales_2',
2: 'Sales_1',
3: 'Sales_2',
4: 'Sales_1',
5: 'Sales_2',
6: 'Sales_1',
7: 'Sales_2',
8: 'Sales_1',
9: 'Sales_2',
10: 'Sales_1',
11: 'Sales_2',
12: 'Sales_1',
13: 'Sales_2',
14: 'Sales_1',
15: 'Sales_2',
16: 'Sales_1',
17: 'Sales_2',
18: 'Sales_1',
19: 'Sales_2',
20: 'Sales_1',
21: 'Sales_2',
22: 'Sales_1',
23: 'Sales_2',
24: 'Sales_1',
25: 'Sales_2',
26: 'Sales_1',
27: 'Sales_2',
28: 'Sales_1',
29: 'Sales_2',
30: 'Sales_1',
31: 'Sales_2',
32: 'Sales_1',
33: 'Sales_2',
34: 'Sales_1',
35: 'Sales_2',
36: 'Sales_1',
37: 'Sales_2',
38: 'Sales_1',
39: 'Sales_2',
40: 'Sales_1',
41: 'Sales_2',
42: 'Sales_1',
43: 'Sales_2',
44: 'Sales_1',
45: 'Sales_2',
46: 'Sales_1',
47: 'Sales_2',
48: 'Sales_1',
49: 'Sales_2',
50: 'Sales_1',
51: 'Sales_2',
52: 'Sales_1',
53: 'Sales_2',
54: 'Sales_1',
55: 'Sales_2',
56: 'Sales_1',
57: 'Sales_2',
58: 'Sales_1',
59: 'Sales_2',
60: 'Sales_1',
61: 'Sales_2',
62: 'Sales_1',
63: 'Sales_2',
64: 'Sales_1',
65: 'Sales_2',
66: 'Sales_1',
67: 'Sales_2',
68: 'Sales_1',
69: 'Sales_2',
70: 'Sales_1',
71: 'Sales_2',
72: 'Sales_1',
73: 'Sales_2',
74: 'Sales_1',
75: 'Sales_2',
76: 'Sales_1',
77: 'Sales_2',
78: 'Sales_1',
79: 'Sales_2'},
'2021-06-28': {0: 67.5277641202152,
1: 82.7854700135998,
2: 21.50082266792856,
3: 22.03644997199996,
4: 64.460440147,
5: 10.1060499896,
6: 65.1530371974946,
7: 50.6429700519999,
8: 56.413464107792045,
9: 0,
10: 17.48074540313092,
11: 26.8376199976,
12: 52.172,
13: 61.16600000040001,
14: 20.9447844,
15: 40.69122000000001,
16: 83.55718929717925,
17: 14.98039999719995,
18: 20.806771705951697,
19: np.nan,
20: 18.3766353690825,
21: 12.82565001479992,
22: 52.425508769690694,
23: 25.661999978399994,
24: 17.88071596,
25: 24.384659998799997,
26: 91.10086982794643,
27: 12.77899003759993,
28: 16.969540811445366,
29: np.nan,
30: 18.8795397517309,
31: 26.73017999840005,
32: 53.52039700062155,
33: 58.81199999639999,
34: 12.1243325,
35: 24.0544100028,
36: 55.94068246571674,
37: 133.86376999920006,
38: 7.294127785392621,
39: np.nan,
40: 6.07807089184563,
41: 7.27483001599998,
42: 2.300470581874837,
43: 30.71300000639998,
44: 5.810764652,
45: 12.333119997600003,
46: 25.475930745418292,
47: 64.228710012,
48: 9.490904912552498,
49: np.nan,
50: 8.184780211399392,
51: 24.59321999400001,
52: 6.807138946302334,
53: 12.0879999972,
54: 0.869207661,
55: 0.324,
56: 0.5084336040970575,
57: 12.181219996800007,
58: 0,
59: np.nan,
60: 9.293956915067886,
61: 11.171379993599999,
62: 6.384936971649232,
63: 3.657999996,
64: 0.913782413,
65: 1.9992000012000002,
66: 1.5322078073061867,
67: 5.514179996399999,
68: 0.4630297231124678,
69: np.nan,
70: 36.23403557795798,
71: 53.35258999919999,
72: 21.890370397789923,
73: 9.937449997200002,
74: 5.916852561,
75: 6.319439989199998,
76: 7.03772344983066,
77: 37.095700012799995,
78: 1.3890891693374032,
79: np.nan},
'2021-07-05': {0: 65.4690491915759,
1: 98.5235100112003,
2: 21.4573181155924,
3: 241.06741999679997,
4: 67.481716829,
5: 11.60325000040002,
6: 27.5807099999998,
7: 65.8528400140003,
8: 58.949304246983736,
9: 0.0,
10: 185.65887577993723,
11: 318.9965699964001,
12: 54.517,
13: 66.55265999039996,
14: 21.92632044,
15: 43.67116000320002,
16: 87.47349898707688,
17: 208.7727500028001,
18: 21.742056352860352,
19: np.nan,
20: 16.6038963173654,
21: 25.28952001920013,
22: 54.7820864335212,
23: 36.75802000560001,
24: 18.71872129,
25: 30.1634600016,
26: 95.37075040035738,
27: 138.3680400120001,
28: 17.73233819348684,
29: np.nan,
30: 14.80302342121337,
31: 251.83851001200003,
32: 55.926190956481534,
33: 72.4443400032,
34: 12.69221484,
35: 26.032340003999998,
36: 58.56261169338368,
37: 153.36183000480003,
38: 7.622005931348156,
39: np.nan,
40: 72.24367956241771,
41: 14.83083001279999,
42: 29.5726042895728,
43: 38.723000005199985,
44: 6.083562133,
45: 12.845630001599998,
46: 26.66998281055652,
47: 63.26220000600001,
48: 9.917530329288393,
49: np.nan,
50: 8.555606693927,
51: 23.802009994800002,
52: 7.113126469779095,
53: 7.206999998399999,
54: 0.910216433,
55: 1.4089999991999997,
56: 0.5322637911479053,
57: 15.186009997200001,
58: 0.0,
59: np.nan,
60: 9.716385738295367,
61: 14.7327399948,
62: 6.671946105284065,
63: 5.691999996,
64: 0.956574175,
65: 1.0203399996,
66: 1.6040220980113027,
67: 8.020399999199999,
68: 0.4838433599999999,
69: np.nan,
70: 37.88758167841983,
71: 59.03332998119994,
72: 22.874363860953647,
73: 13.690399997999998,
74: 6.194107518,
75: 6.4613199911999954,
76: 7.367580219466185,
77: 38.881609991999944,
78: 1.4515300799999995,
79: np.nan},
'2021-07-12': {0: 607.2971827405001,
1: 88.9671100664001,
2: 21.26749278974862,
3: 17.1524199804,
4: 64.471138092,
5: 89.84481002279999,
6: 26.2044999999998,
7: 51.9698800632001,
8: 5.354051858751745,
9: 0.0,
10: 177.42361595891452,
11: 287.5395700032,
12: 52.117,
13: 47.388199995600004,
14: 20.94835038,
15: 41.4250800048,
16: 83.57340667555117,
17: 198.72629000280003,
18: 20.784858903363354,
19: np.nan,
20: 178.323907459086,
21: 185.83897002839998,
22: 52.37029646474982,
23: 27.87144997800001,
24: 17.88339044,
25: 23.645340010799984,
26: 91.11855133792106,
27: 134.3221800396,
28: 16.95166921641509,
29: np.nan,
30: 128.82813286243115,
31: 192.6867300156,
32: 53.46403160619618,
33: 41.412320006399995,
34: 12.1261155,
35: 11.840830002000002,
36: 55.95153983444301,
37: 139.43358000720002,
38: 7.286445921791947,
39: np.nan,
40: 69.04410667683521,
41: 93.877410018,
42: 28.270665735943805,
43: 27.512680004399986,
44: 5.811656147,
45: 5.2319800032,
46: 25.480875296710053,
47: 61.132750010400024,
48: 9.480909497181356,
49: np.nan,
50: 8.178601399067174,
51: 17.6743199976,
52: 6.7999699585309585,
53: 6.131999998799999,
54: 0.870099156,
55: 0.6185600004,
56: 0.5085322845362154,
57: 10.923759998400003,
58: 0.0,
59: np.nan,
60: 9.287042311133577,
61: 19.966500000000007,
62: 6.378212628950804,
63: 6.524999997600001,
64: 0.913782413,
65: 1.9303400016,
66: 1.5325051891827732,
67: 11.511160000800006,
68: 0.4625420799999998,
69: np.nan,
70: 36.21177607303267,
71: 51.3836100036,
72: 21.86731639537707,
73: 10.310769999600003,
74: 5.917744056,
75: 5.152679999999999,
76: 7.039089381655591,
77: 35.920160003999996,
78: 1.3876262399999995,
79: np.nan}}).set_index(['group','category','type'])
df2 在
df1 上合并
group, category, date (date is a column)，以便我的输出如下所示:
df2 的
sales_1 & sales_2 值，但这些行应该填充来自
group 的相应
category 和
df2 值。
2021-06-28 2021-07-05 2021-07-12
total_orders total_sales sales_1 sales_2 total_orders total_sales sales_1 sales_2 total_orders total_sales sales_1 sales_2
group category
A Apple 88.000 4,603.210
Amazon 66.000 2,485.060
Google 191.000 4,919.400
Netflix 558.000 6,097.770
Facebook 12.000 31.220
Uber 4.000 155.710
Tesla 56.000 3,484.990
total 975.000 17,237.360
B Apple 90.000 561.540
Amazon 26.000 698.750
Google 49.000 1,290.130
Netflix 250.000 4,292.680
Facebook 7.000 947.650
Uber 2.000 329.000
Tesla 44.000 2,889.650
total 468.000 9,989.400
C Apple 36.000 330.890
Amazon 52.000 2,076.260
Google 94.000 2,982.270
Netflix 750.000 11,978.620
Facebook 10.000 683.000
Uber 0.000 0.000
Tesla 52.000 3,812.170
total 994.000 20,963.210
D Apple 16.000 234.490
Amazon 22.000 896.100
Google 5.000 231.000
Netflix 57.000 893.810
Uber 3.000 129.000
Tesla 33.000 1,712.330
total 136.000 4,106.730
sales_1 & sales_2 就合并到
group & category 上并且在同一个
date 列上。
total_x 中的
df2 可以忽略，因为它可以从字段中计算出来。
total_values 不用于合并，只用于它之后的代码。
df1.reset_index().merge(df2.reset_index(), left_on=['group', 'category'], right_on=['group', 'category'])
UserWarning: merging between different levels can give an unintended result (2 levels on the left,1 on the right)
df = df1.merge(df2.unstack(), left_index=True, right_index=True) 产生:
00:00:00 ？
最佳答案
创建 DatetimeIndex在 df2 中的列先，然后 unstack并通过 MultiIndex 合并es:
f = lambda x: pd.to_datetime(x)
df = (df1.merge(df2.rename(columns=f).unstack(), left_index=True, right_index=True)
.sort_index(axis=1))
print (df.head())
2021-06-28 2021-07-05 \
Sales_1 Sales_2 total_orders total_sales Sales_1
group category
A Apple 52.172000 61.166 88.0 4603.21 54.517000
Amazon 52.425509 25.662 66.0 2485.06 54.782086
Google 53.520397 58.812 191.0 4919.40 55.926191
Netflix 6.807139 12.088 558.0 6097.77 7.113126
Facebook 2.300471 30.713 12.0 31.22 29.572604
2021-07-12 \
Sales_2 total_orders total_sales Sales_1 Sales_2
group category
A Apple 66.55266 109.0 3453.02 52.117000 47.38820
Amazon 36.75802 48.0 17868.73 52.370296 27.87145
Google 72.44434 174.0 44707.83 53.464032 41.41232
Netflix 7.20700 592.0 61425.99 6.799970 6.13200
Facebook 38.72300 13.0 1261.00 28.270666 27.51268
total_orders total_sales
group category
A Apple 32.0 2147.70
Amazon 15.0 4767.30
Google 89.0 2399.30
Netflix 239.0 3137.44
Facebook 2.0 178.00
关于python - 合并具有多个索引和列值的数据帧，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/68407112/
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