如何在Pandas中的一组空列上做groupby？ [英] How to do a groupby on an empty set of columns in Pandas?

问题描述

我正在打熊猫的角落案件。我正在尝试使用agg fn，但没有进行groupby。假设我想要在整个 dataframe 上进行汇总，即来自熊猫的

I am hitting on a corner case in pandas. I am trying to use the agg fn but without doing a groupby. Say I want an aggregation on the entire dataframe, i.e.

from pandas import *
DF = DataFrame( randn(5,3), index = list( "ABCDE"), columns = list("abc") )
DF.groupby([]).agg({'a' : np.sum, 'b' : np.mean } ) # <--- does not work

并且 DF.agg（{'a'...}）也不起作用。

我的解决方法是做 DF ['Total'] ='Total'然后做一个 DF.groupby（['Total']）但这似乎有点虚假。

My workaround is to do DF['Total'] = 'Total' then do a DF.groupby(['Total']) but this seems a bit artificial.

有没有人有更清晰的解决方案？

Has anyone got a cleaner solution?

推荐答案

它也不是很好，但是对于这种情况，如果传递一个返回True的函数，至少不需要更改 df ：

It's not so great either, but for this case, if you pass a function returning True at least it wouldn't require changing df:

>>> from pandas import *
>>> df = DataFrame( np.random.randn(5,3), index = list( "ABCDE"), columns = list("abc") )
>>> df.groupby(lambda x: True).agg({'a' : np.sum, 'b' : np.mean } )
             a         b
True  1.836649 -0.692655
>>> 
>>> df['total'] = 'total'
>>> df.groupby(['total']).agg({'a' : np.sum, 'b' : np.mean } ) 
              a         b
total                    
total  1.836649 -0.692655

您可以使用各种内置函数而不是 lambda x：True 但他们不那么明确，只是偶然地工作。

You could use various builtins instead of lambda x: True but they're less explicit and only work accidentally.

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