pandas 中多索引数据框的累积百分比 [英] Cumulative percentage for multi index dataframe in pandas

问题描述

我想为熊猫中的多索引数据帧计算累积百分比，但无法使其正常工作.

I want to compute a cumulative percentage for a multi index dataframe in pandas and just can not get it to work.

import pandas as pd

to_df = {'domain': {(12, 12): 2, (14, 14): 1, (15, 15): 2, (15, 17): 2, (17, 17): 1},
 'time': {(12, 12): 1, (14, 14): 1, (15, 15): 2, (15, 17): 1, (17, 17): 1},
 'weight': {(12, 12): 3,
  (14, 14): 4,
  (15, 15): 1,
  (15, 17): 2,
  (17, 17): 5}}

df = pd.DataFrame.from_dict(to_df)

       domain  time  weight
12 12       2     1       3
14 14       1     1       4
15 15       2     2       1
   17       2     1       2
17 17       1     1       5


df = df.groupby(['time', 'domain']).apply(
 pd.DataFrame.sort_values, 'weight', ascending=True)

cumsum()可以正常工作

cumsum() works as intended

df["cum_sum_time_domain"] = df.groupby(['time', 'domain'])['weight'].cumsum()



               domain  time  weight  cum_sum_time_domain
time domain                                                 
1    1      14 14       1     1       4                    4
            17 17       1     1       5                    9
     2      15 17       2     1       2                    2
            12 12       2     1       3                    5
2    2      15 15       2     2       1                    1

运行命令本身确实有效

df.groupby(['time', 'domain']).weight.sum()
df.groupby(['time', 'domain'])['weight'].sum()

但是，两个作业突然都产生了"NaN"

however both assignments suddenly yield 'NaNs'

df["sum_time_domain"] = df.groupby(['time', 'domain']).weight.sum()
df
df["sum_time_domain"] = df.groupby(['time', 'domain'])['weight'].sum()
df

将两者合并会产生错误:未实现在多索引上合并一个以上级别的重叠"

combining the two gives error: 'merging with more than one level overlap on a multi-index is not implemented'

df["cum_perc_time_domain"] = 100 * df.groupby(['time', 'domain'])['weight'].cumsum() / df.groupby(
 ['time', 'domain'])['weight'].sum()

推荐答案

我认为您需要 sort_values :

I think you need transform with sum. Also for sorting groupby is not necessary, use only sort_values:

df = df.sort_values(['time','domain','weight'])

print (df.groupby(['time', 'domain']).weight.transform('sum'))
14  14    9
17  17    9
15  17    5
12  12    5
15  15    1
Name: weight, dtype: int64

df["cum_perc_time_domain"] = 100 * df.groupby(['time', 'domain'])['weight'].cumsum() / 
                                   df.groupby(['time', 'domain']).weight.transform('sum')
print (df)
       domain  time  weight  cum_perc_time_domain
14 14       1     1       4             44.444444
17 17       1     1       5            100.000000
15 17       2     1       2             40.000000
12 12       2     1       3            100.000000
15 15       2     2       1            100.000000

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