从退货PANDAS创建并编制索引 [英] Create and index from returns PANDAS

问题描述

我有以下数据框，其中前两列是月报表(您必须乘以100才能得到％)

I have the following dataframe where the first two columns are monthly returns (you have to multiply by 100 to get it in %)

                in_scenario_USA  out_scenario_USA  USA
month year                                        
6     1960         0.000000          0.019524  0.0
7     1960         0.000000         -0.024772  0.0
8     1960         0.000000          0.026121  0.0
9     1960         0.000000         -0.060393  0.0
10    1960         0.000000         -0.002429  0.0
11    1960         0.000000          0.040270  0.0
12    1960         0.000000          0.046273  0.0
1     1961         0.000000          0.063156  0.0
2     1961         0.026870          0.000000  1.0
3     1961         0.025536          0.000000  1.0
                     ...               ...  ...
4     2017         0.000000          0.009091  0.0
5     2017         0.000000          0.011576  0.0
6     2017         0.000000          0.004814  0.0
7     2017         0.019349          0.000000  1.0
8     2017         0.000546          0.000000  1.0

我希望创建两个索引，对于USA列，第一个值非NaN将从1开始，然后基本上是:-第一个索引将基于第一列的返回-第二个索引将基于第二列的返回值

I wish to create two indices that would start at 1 for the first value non NaN for the USA column and then basically: - The first index will be based on the return of the first column - The second index will be based on the returns of the second column

例如第一个会是这样

                in_scenario_USA  first index 
month year                                        
6     1960         0.000000          1
7     1960         0.000000          1  
8     1960         0.000000          1
9     1960         0.000000          1
10    1960         0.000000          1
11    1960         0.000000          1
12    1960         0.000000          1
1     1961         0.000000          1
2     1961         0.026870          1.026870
3     1961         0.025536          1.053092
4     1961         0.003843          1.057139
5     1961         0.000000          1.057139
6     1961         0.000000          1.057139

我有点想有效地进行迭代.

I am kind of struggling about the way to iterate that in an efficient way.

推荐答案

我认为应该是这样的:

df['First index'] = df.in_scenario_USA.cumsum().add(1)

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