合并具有多个索引和列值的数据帧
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'])
我正在尝试通过 df2 合并 df1 上的 group, category, date (date is a column)，以便我的输出如下所示：
在我想要的输出示例中，我省略了 df2 的 sales_1 & sales_2 中的值，但这些行应填充 group 中相应的 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
这样 sales_1 & sales_2 就合并在 group & category 上并在同一个 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，只用于它之后的那些。
我尝试过的：
df1.reset_index().merge(df2.reset_index(), left_on=['group', 'category'], right_on=['group', 'category'])
抛出警告：
<块引用>用户警告：不同级别之间的合并可能会产生意想不到的结果（左侧 2 个级别，右侧 1 个）
这不是我期望它合并的方式。我怎样才能达到我想要的输出？
使用
df = df1.merge(df2.unstack(), left_index=True, right_index=True)
产生：
然后只是重新排序列，因为我想要一个唯一的日期和 4 列吗？或者可能是某个日期有00:00:00？
1 个答案:
答案 0 :(得分：4)
首先在DatetimeIndex的列中创建df2，然后unstack并通过MultiIndexes合并：
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
相关问题
最新问题
- 我写了这段代码，但我无法理解我的错误
- 我无法从一个代码实例的列表中删除 None 值，但我可以在另一个实例中。为什么它适用于一个细分市场而不适用于另一个细分市场？
- 是否有可能使 loadstring 不可能等于打印？卢阿
- java中的random.expovariate()
- Appscript 通过会议在 Google 日历中发送电子邮件和创建活动
- 为什么我的 Onclick 箭头功能在 React 中不起作用？
- 在此代码中是否有使用“this”的替代方法？
- 在 SQL Server 和 PostgreSQL 上查询，我如何从第一个表获得第二个表的可视化
- 每千个数字得到
- 更新了城市边界 KML 文件的来源？