将级别 2 索引添加为具有条件的其他索引的总和
[英]Adding level 2 index as a sum of other indexes with a condition
问题描述
我有一个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'])
我想创建一个2级index称为combined这将是总和sales & last_sales所有的categories ，除了Facebook和total / Total 。 所以df看起来像这样：
我尝试用.loc来做，但没有成功：
s = df_out.stack(0)
s['combined'] = 0
s.loc[(slice(None),[x for x in s.loc[(slice(None),:) if x != 'Facebook']].sum()
2 个解决方案
解决方案1
4 已采纳 2021-07-22 10:33:43
解决方案
- 在
level=0删除all，同样在level=1drop其他不需要的级别值 - 计算
level=0上的sum以聚合帧 - 创建
Multindex以添加聚合帧中combined的附加级别 - 对索引进行追加和排序以保持顺序
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()
结果
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
解决方案2
0 2021-07-22 10:46:03
您可以在0级组，并concat每组的总和，如果name不是“所有”，同时加入索引。 然后将此输出与原始数据和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)
输出：
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
如何使用熊猫等将 2 级索引转换为列索引数据框
[英]How to convert level 2 indexes to be columns index dataframe with pandas or etc
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