[英]Adding level 2 index as a sum of other indexes with a condition
我有一個df
:
df = pd.DataFrame.from_dict({('group', ''): {0: 'A',
1: 'A',
2: 'A',
3: 'A',
4: 'A',
5: 'A',
6: 'A',
7: 'A',
8: 'A',
9: 'B',
10: 'B',
11: 'B',
12: 'B',
13: 'B',
14: 'B',
15: 'B',
16: 'B',
17: 'B',
18: 'all',
19: 'all'},
('category', ''): {0: 'Amazon',
1: 'Apple',
2: 'Facebook',
3: 'Google',
4: 'Netflix',
5: 'Tesla',
6: 'Total',
7: 'Uber',
8: 'total',
9: 'Amazon',
10: 'Apple',
11: 'Facebook',
12: 'Google',
13: 'Netflix',
14: 'Tesla',
15: 'Total',
16: 'Uber',
17: 'total',
18: 'Total',
19: 'total'},
(pd.Timestamp('2020-06-29 00:00:00'), 'last_sales'): {0: 195.0,
1: 61.0,
2: 106.0,
3: 61.0,
4: 37.0,
5: 13.0,
6: 954.0,
7: 4.0,
8: 477.0,
9: 50.0,
10: 50.0,
11: 75.0,
12: 43.0,
13: 17.0,
14: 14.0,
15: 504.0,
16: 3.0,
17: 252.0,
18: 2916.0,
19: 2916.0},
(pd.Timestamp('2020-06-29 00:00:00'), 'sales'): {0: 1268.85,
1: 18274.385000000002,
2: 19722.65,
3: 55547.255,
4: 15323.800000000001,
5: 1688.6749999999997,
6: 227463.23,
7: 1906.0,
8: 113731.615,
9: 3219.6499999999996,
10: 15852.060000000001,
11: 17743.7,
12: 37795.15,
13: 5918.5,
14: 1708.75,
15: 166349.64,
16: 937.01,
17: 83174.82,
18: 787625.7400000001,
19: 787625.7400000001},
(pd.Timestamp('2020-06-29 00:00:00'), 'difference'): {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
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: 0.0,
17: 0.0,
18: 0.0,
19: 0.0},
(pd.Timestamp('2020-07-06 00:00:00'), 'last_sales'): {0: 26.0,
1: 39.0,
2: 79.0,
3: 49.0,
4: 10.0,
5: 10.0,
6: 436.0,
7: 5.0,
8: 218.0,
9: 89.0,
10: 34.0,
11: 133.0,
12: 66.0,
13: 21.0,
14: 20.0,
15: 732.0,
16: 3.0,
17: 366.0,
18: 2336.0,
19: 2336.0},
(pd.Timestamp('2020-07-06 00:00:00'), 'sales'): {0: 3978.15,
1: 12138.96,
2: 19084.175,
3: 40033.46000000001,
4: 4280.15,
5: 1495.1,
6: 165548.29,
7: 1764.15,
8: 82774.145,
9: 8314.92,
10: 12776.649999999996,
11: 28048.075,
12: 55104.21000000002,
13: 6962.844999999999,
14: 3053.2000000000003,
15: 231049.11000000002,
16: 1264.655,
17: 115524.55500000001,
18: 793194.8000000002,
19: 793194.8000000002},
(pd.Timestamp('2020-07-06 00:00:00'), 'difference'): {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
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: 0.0,
17: 0.0,
18: 0.0,
19: 0.0},
(pd.Timestamp('2021-06-28 00:00:00'), 'last_sales'): {0: 96.0,
1: 56.0,
2: 106.0,
3: 44.0,
4: 34.0,
5: 13.0,
6: 716.0,
7: 9.0,
8: 358.0,
9: 101.0,
10: 22.0,
11: 120.0,
12: 40.0,
13: 13.0,
14: 8.0,
15: 610.0,
16: 1.0,
17: 305.0,
18: 2652.0,
19: 2652.0},
(pd.Timestamp('2021-06-28 00:00:00'), 'sales'): {0: 5194.95,
1: 19102.219999999994,
2: 22796.420000000002,
3: 30853.115,
4: 11461.25,
5: 992.6,
6: 188143.41,
7: 3671.15,
8: 94071.705,
9: 6022.299999999998,
10: 7373.6,
11: 33514.0,
12: 35943.45,
13: 4749.000000000001,
14: 902.01,
15: 177707.32,
16: 349.3,
17: 88853.66,
18: 731701.46,
19: 731701.46},
(pd.Timestamp('2021-06-28 00:00:00'), 'difference'): {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
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: 0.0,
17: 0.0,
18: 0.0,
19: 0.0},
(pd.Timestamp('2021-07-07 00:00:00'), 'last_sales'): {0: 45.0,
1: 47.0,
2: 87.0,
3: 45.0,
4: 13.0,
5: 8.0,
6: 494.0,
7: 2.0,
8: 247.0,
9: 81.0,
10: 36.0,
11: 143.0,
12: 56.0,
13: 9.0,
14: 9.0,
15: 670.0,
16: 1.0,
17: 335.0,
18: 2328.0,
19: 2328.0},
(pd.Timestamp('2021-07-07 00:00:00'), 'sales'): {0: 7556.414999999998,
1: 14985.05,
2: 16790.899999999998,
3: 36202.729999999996,
4: 4024.97,
5: 1034.45,
6: 163960.32999999996,
7: 1385.65,
8: 81980.16499999998,
9: 5600.544999999999,
10: 11209.92,
11: 32832.61,
12: 42137.44500000001,
13: 3885.1499999999996,
14: 1191.5,
15: 194912.34000000003,
16: 599.0,
17: 97456.17000000001,
18: 717745.3400000001,
19: 717745.3400000001},
(pd.Timestamp('2021-07-07 00:00:00'), 'difference'): {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
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: 0.0,
17: 0.0,
18: 0.0,
19: 0.0}}).set_index(['group','category'])
我想創建一個2級index
稱為combined
這將是總和sales & last_sales
所有的categories
,除了Facebook
和total
/ Total
。 所以df
看起來像這樣:
我嘗試用.loc
來做,但沒有成功:
s = df_out.stack(0)
s['combined'] = 0
s.loc[(slice(None),[x for x in s.loc[(slice(None),:) if x != 'Facebook']].sum()
level=0
刪除all
,同樣在level=1
drop
其他不需要的級別值level=0
上的sum
以聚合幀Multindex
以添加聚合幀中combined
的附加級別s = df.drop('all').drop(['Facebook', 'total', 'Total'], level=1).sum(level=0)
s.index = pd.MultiIndex.from_product([s.index, ['combined']])
df_out = df.append(s).sort_index()
2020-06-29 00:00:00 2020-07-06 00:00:00 2021-06-28 00:00:00 2021-07-07 00:00:00
last_sales sales difference last_sales sales difference last_sales sales difference last_sales sales difference
group category
A Amazon 195.0 1268.850 0.0 26.0 3978.150 0.0 96.0 5194.950 0.0 45.0 7556.415 0.0
Apple 61.0 18274.385 0.0 39.0 12138.960 0.0 56.0 19102.220 0.0 47.0 14985.050 0.0
Facebook 106.0 19722.650 0.0 79.0 19084.175 0.0 106.0 22796.420 0.0 87.0 16790.900 0.0
Google 61.0 55547.255 0.0 49.0 40033.460 0.0 44.0 30853.115 0.0 45.0 36202.730 0.0
Netflix 37.0 15323.800 0.0 10.0 4280.150 0.0 34.0 11461.250 0.0 13.0 4024.970 0.0
Tesla 13.0 1688.675 0.0 10.0 1495.100 0.0 13.0 992.600 0.0 8.0 1034.450 0.0
Total 954.0 227463.230 0.0 436.0 165548.290 0.0 716.0 188143.410 0.0 494.0 163960.330 0.0
Uber 4.0 1906.000 0.0 5.0 1764.150 0.0 9.0 3671.150 0.0 2.0 1385.650 0.0
combined 371.0 94008.965 0.0 139.0 63689.970 0.0 252.0 71275.285 0.0 160.0 65189.265 0.0
total 477.0 113731.615 0.0 218.0 82774.145 0.0 358.0 94071.705 0.0 247.0 81980.165 0.0
B Amazon 50.0 3219.650 0.0 89.0 8314.920 0.0 101.0 6022.300 0.0 81.0 5600.545 0.0
Apple 50.0 15852.060 0.0 34.0 12776.650 0.0 22.0 7373.600 0.0 36.0 11209.920 0.0
Facebook 75.0 17743.700 0.0 133.0 28048.075 0.0 120.0 33514.000 0.0 143.0 32832.610 0.0
Google 43.0 37795.150 0.0 66.0 55104.210 0.0 40.0 35943.450 0.0 56.0 42137.445 0.0
Netflix 17.0 5918.500 0.0 21.0 6962.845 0.0 13.0 4749.000 0.0 9.0 3885.150 0.0
Tesla 14.0 1708.750 0.0 20.0 3053.200 0.0 8.0 902.010 0.0 9.0 1191.500 0.0
Total 504.0 166349.640 0.0 732.0 231049.110 0.0 610.0 177707.320 0.0 670.0 194912.340 0.0
Uber 3.0 937.010 0.0 3.0 1264.655 0.0 1.0 349.300 0.0 1.0 599.000 0.0
combined 177.0 65431.120 0.0 233.0 87476.480 0.0 185.0 55339.660 0.0 192.0 64623.560 0.0
total 252.0 83174.820 0.0 366.0 115524.555 0.0 305.0 88853.660 0.0 335.0 97456.170 0.0
all Total 2916.0 787625.740 0.0 2336.0 793194.800 0.0 2652.0 731701.460 0.0 2328.0 717745.340 0.0
total 2916.0 787625.740 0.0 2336.0 793194.800 0.0 2652.0 731701.460 0.0 2328.0 717745.340 0.0
您可以在0級組,並concat
每組的總和,如果name
不是“所有”,同時加入索引。 然后將此輸出與原始數據和sort_index
按level=0
:
pd.concat([df,
pd.concat({(name, 'combined'): d.drop(['Facebook', 'total', 'Total'], level=1).sum()
for name,d in df.groupby(level=0) if name != 'all'}, axis=1).T
]).sort_index(level=0)
輸出:
2020-06-29 00:00:00 2020-07-06 00:00:00 2021-06-28 00:00:00 2021-07-07 00:00:00
last_sales sales difference last_sales sales difference last_sales sales difference last_sales sales difference
group category
A Amazon 195.0 1268.850 0.0 26.0 3978.150 0.0 96.0 5194.950 0.0 45.0 7556.415 0.0
Apple 61.0 18274.385 0.0 39.0 12138.960 0.0 56.0 19102.220 0.0 47.0 14985.050 0.0
Facebook 106.0 19722.650 0.0 79.0 19084.175 0.0 106.0 22796.420 0.0 87.0 16790.900 0.0
Google 61.0 55547.255 0.0 49.0 40033.460 0.0 44.0 30853.115 0.0 45.0 36202.730 0.0
Netflix 37.0 15323.800 0.0 10.0 4280.150 0.0 34.0 11461.250 0.0 13.0 4024.970 0.0
Tesla 13.0 1688.675 0.0 10.0 1495.100 0.0 13.0 992.600 0.0 8.0 1034.450 0.0
Total 954.0 227463.230 0.0 436.0 165548.290 0.0 716.0 188143.410 0.0 494.0 163960.330 0.0
Uber 4.0 1906.000 0.0 5.0 1764.150 0.0 9.0 3671.150 0.0 2.0 1385.650 0.0
combined 371.0 94008.965 0.0 139.0 63689.970 0.0 252.0 71275.285 0.0 160.0 65189.265 0.0
total 477.0 113731.615 0.0 218.0 82774.145 0.0 358.0 94071.705 0.0 247.0 81980.165 0.0
B Amazon 50.0 3219.650 0.0 89.0 8314.920 0.0 101.0 6022.300 0.0 81.0 5600.545 0.0
Apple 50.0 15852.060 0.0 34.0 12776.650 0.0 22.0 7373.600 0.0 36.0 11209.920 0.0
Facebook 75.0 17743.700 0.0 133.0 28048.075 0.0 120.0 33514.000 0.0 143.0 32832.610 0.0
Google 43.0 37795.150 0.0 66.0 55104.210 0.0 40.0 35943.450 0.0 56.0 42137.445 0.0
Netflix 17.0 5918.500 0.0 21.0 6962.845 0.0 13.0 4749.000 0.0 9.0 3885.150 0.0
Tesla 14.0 1708.750 0.0 20.0 3053.200 0.0 8.0 902.010 0.0 9.0 1191.500 0.0
Total 504.0 166349.640 0.0 732.0 231049.110 0.0 610.0 177707.320 0.0 670.0 194912.340 0.0
Uber 3.0 937.010 0.0 3.0 1264.655 0.0 1.0 349.300 0.0 1.0 599.000 0.0
combined 177.0 65431.120 0.0 233.0 87476.480 0.0 185.0 55339.660 0.0 192.0 64623.560 0.0
total 252.0 83174.820 0.0 366.0 115524.555 0.0 305.0 88853.660 0.0 335.0 97456.170 0.0
all Total 2916.0 787625.740 0.0 2336.0 793194.800 0.0 2652.0 731701.460 0.0 2328.0 717745.340 0.0
total 2916.0 787625.740 0.0 2336.0 793194.800 0.0 2652.0 731701.460 0.0 2328.0 717745.340 0.0
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