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I have a 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'])
I am trying to create a level 2 index called combined which would be the sum of sales & last_sales of all categories except for Facebook and total / Total.
So that the df would look like this:
I tried doing it with .loc but with no success:
s = df_out.stack(0)
s['combined'] = 0
s.loc[(slice(None),[x for x in s.loc[(slice(None),:) if x != 'Facebook']].sum()
283
all in level=0, similarly drop the other unwanted level values in level=1
sum on level=0 to aggregate the frameMultindex to add the additional level combined in aggregated frames = 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
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