pandas 多索引如何通过第二层掩盖数据 [英] pandas multi-index how to mask the data by the second level

问题描述

我有一个具有多重索引的数据框，如下所示:

I have a data-frame with multi-index like this:

Date      Period     Value \n
20130101    0          12 \n
20130101    1          13
20130102    0          13
20130102    1          14

第一层是日期，第二层是期间.我想将周期不为零的值设置为零，输出将是这样的:

The first level is Date and the second level is period. I would like to set the values where the period is not zero to zero, the output would be something like this:

Date      Period     Value
20130101    0          12
20130101    1          0
20130102    0          13
20130102    1          0

如果第二级是与索引相对的一列，则解决方案将很容易df.Value.loc[df.Period == 0] =0.

If the second level was a column as opposed to index, the solution would be easy df.Value.loc[df.Period == 0] =0.

有没有一种方法可以通过仅使用索引来实现?

Is there a way to achieve this, by just using index?

推荐答案

尝试一下:

df.loc[df.index.get_level_values('Period') != 0, 'Value'] = 0

说明:

In [5]: df
Out[5]:
                 Value
Date     Period
20130101 0          12
         1          13
20130102 0          13
         1          14

In [6]: df.index.get_level_values('Period')
Out[6]: Int64Index([0, 1, 0, 1], dtype='int64', name='Period')

In [7]: df.index.get_level_values('Period') != 0
Out[7]: array([False,  True, False,  True], dtype=bool)

In [8]: df[df.index.get_level_values('Period') != 0]
Out[8]:
                 Value
Date     Period
20130101 1          13
20130102 1          14

In [9]: df.loc[df.index.get_level_values('Period') != 0, 'Value'] = 0

In [10]: df
Out[10]:
                 Value
Date     Period
20130101 0          12
         1           0
20130102 0          13
         1           0

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