将 multiindex 添加到 pandas 数据帧，这是同一数据帧值的总和
Adding multiindex to pandas dataframe which is the sum of same dataframe's values
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'])
我正在尝试创建级别 1 索引 combined 和级别 2 索引将是当前索引级别的名称 2 category但没有 total
'Amazon',
'Apple',
'Facebook',
'Google',
'Netflix',
'Tesla',
'Uber'
这将是每个 category 的所有级别 1 索引 group 的总和，不包括级别 1 的 all group列索引 sales。基本上得到所有 groups 不包括 all, sum 每个 category.
是否也可以为 combined 索引编写 group 名称以供考虑，以便我能够对 combined categories 用于选定 groups 而不是每个 group 不包括 all?
我试过了：
c = df.reset_index()
c[(c.group.isin(['A','B']))& (c.category.isin(['Amazon','Apple','Facebook', 'Google', 'Netflix', 'Tesla', 'Uber']))].loc[:,(slice(None),'sales')].sum()
但后来我意识到这不是按 category 分组的，所以我不确定如何继续。
预期输出示例（数据不一致）：
2020-06-29 00:00:00
last_sales sales difference
group category
combined Amazon 195.000 1,268.850 0.000
Apple 61.000 18,274.385 0.000
Facebook 106.000 19,722.650 0.000
Google 61.000 55,547.255 0.000
Netflix 37.000 15,323.800 0.000
Tesla 13.000 1,688.675 0.000
Uber 4.000 1,906.000 0.000
A Amazon 50.000 3,219.650 0.000
Apple 50.000 15,852.060 0.000
Facebook 75.000 17,743.700 0.000
Google 43.000 37,795.150 0.000
Netflix 17.000 5,918.500 0.000
Tesla 14.000 1,708.750 0.000
Total 504.000 166,349.640 0.000
Uber 3.000 937.010 0.000
total 252.000 83,174.820 0.000
B Amazon 50.000 3,219.650 0.000
Apple 50.000 15,852.060 0.000
Facebook 75.000 17,743.700 0.000
Google 43.000 37,795.150 0.000
Netflix 17.000 5,918.500 0.000
Tesla 14.000 1,708.750 0.000
Total 504.000 166,349.640 0.000
Uber 3.000 937.010 0.000
total 252.000 83,174.820 0.000
all Total 2,916.000 787,625.740 0.000
total 2,916.000 787,625.740 0.000
重申我的想法，我们可以通过以下方式解决这个问题
s = df.loc[['A', 'B']].drop(['total', 'Total'], level=1).sum(level=1)
s.index = pd.MultiIndex.from_product([['combined'], s.index])
df_out = s.append(df)
结果
print(df_out)
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
category
combined Amazon 245.0 4488.500 0.0 115.0 12293.070 0.0 197.0 11217.250 0.0 126.0 13156.960 0.0
Apple 111.0 34126.445 0.0 73.0 24915.610 0.0 78.0 26475.820 0.0 83.0 26194.970 0.0
Facebook 181.0 37466.350 0.0 212.0 47132.250 0.0 226.0 56310.420 0.0 230.0 49623.510 0.0
Google 104.0 93342.405 0.0 115.0 95137.670 0.0 84.0 66796.565 0.0 101.0 78340.175 0.0
Netflix 54.0 21242.300 0.0 31.0 11242.995 0.0 47.0 16210.250 0.0 22.0 7910.120 0.0
Tesla 27.0 3397.425 0.0 30.0 4548.300 0.0 21.0 1894.610 0.0 17.0 2225.950 0.0
Uber 7.0 2843.010 0.0 8.0 3028.805 0.0 10.0 4020.450 0.0 3.0 1984.650 0.0
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
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
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
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'])
我正在尝试创建级别 1 索引 combined 和级别 2 索引将是当前索引级别的名称 2 category但没有 total
'Amazon',
'Apple',
'Facebook',
'Google',
'Netflix',
'Tesla',
'Uber'
这将是每个 category 的所有级别 1 索引 group 的总和，不包括级别 1 的 all group列索引 sales。基本上得到所有 groups 不包括 all, sum 每个 category.
是否也可以为 combined 索引编写 group 名称以供考虑，以便我能够对 combined categories 用于选定 groups 而不是每个 group 不包括 all?
我试过了：
c = df.reset_index()
c[(c.group.isin(['A','B']))& (c.category.isin(['Amazon','Apple','Facebook', 'Google', 'Netflix', 'Tesla', 'Uber']))].loc[:,(slice(None),'sales')].sum()
但后来我意识到这不是按 category 分组的，所以我不确定如何继续。
预期输出示例（数据不一致）：
2020-06-29 00:00:00
last_sales sales difference
group category
combined Amazon 195.000 1,268.850 0.000
Apple 61.000 18,274.385 0.000
Facebook 106.000 19,722.650 0.000
Google 61.000 55,547.255 0.000
Netflix 37.000 15,323.800 0.000
Tesla 13.000 1,688.675 0.000
Uber 4.000 1,906.000 0.000
A Amazon 50.000 3,219.650 0.000
Apple 50.000 15,852.060 0.000
Facebook 75.000 17,743.700 0.000
Google 43.000 37,795.150 0.000
Netflix 17.000 5,918.500 0.000
Tesla 14.000 1,708.750 0.000
Total 504.000 166,349.640 0.000
Uber 3.000 937.010 0.000
total 252.000 83,174.820 0.000
B Amazon 50.000 3,219.650 0.000
Apple 50.000 15,852.060 0.000
Facebook 75.000 17,743.700 0.000
Google 43.000 37,795.150 0.000
Netflix 17.000 5,918.500 0.000
Tesla 14.000 1,708.750 0.000
Total 504.000 166,349.640 0.000
Uber 3.000 937.010 0.000
total 252.000 83,174.820 0.000
all Total 2,916.000 787,625.740 0.000
total 2,916.000 787,625.740 0.000
重申我
s = df.loc[['A', 'B']].drop(['total', 'Total'], level=1).sum(level=1)
s.index = pd.MultiIndex.from_product([['combined'], s.index])
df_out = s.append(df)
结果
print(df_out)
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
category
combined Amazon 245.0 4488.500 0.0 115.0 12293.070 0.0 197.0 11217.250 0.0 126.0 13156.960 0.0
Apple 111.0 34126.445 0.0 73.0 24915.610 0.0 78.0 26475.820 0.0 83.0 26194.970 0.0
Facebook 181.0 37466.350 0.0 212.0 47132.250 0.0 226.0 56310.420 0.0 230.0 49623.510 0.0
Google 104.0 93342.405 0.0 115.0 95137.670 0.0 84.0 66796.565 0.0 101.0 78340.175 0.0
Netflix 54.0 21242.300 0.0 31.0 11242.995 0.0 47.0 16210.250 0.0 22.0 7910.120 0.0
Tesla 27.0 3397.425 0.0 30.0 4548.300 0.0 21.0 1894.610 0.0 17.0 2225.950 0.0
Uber 7.0 2843.010 0.0 8.0 3028.805 0.0 10.0 4020.450 0.0 3.0 1984.650 0.0
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
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
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