为什么np.std()和ivot_table(aggfunc = np.std)返回不同的结果 [英] why np.std() and pivot_table(aggfunc=np.std) return the different result

问题描述

我有一些代码，不明白为什么会出现这种差异:

I have some code and do not understand why the difference occurs:

np.std()单独使用时默认ddof = 0.

np.std() which default ddof=0,when it's used alone.

但是为什么当它用作数据透视表(aggfunc = np.std)中的参数时，它会自动更改为ddof = 1.

but why when it's used as an argument in pivot_table(aggfunc=np.std),it changes into ddof=1 automatically.

import numpys as np
import pandas as pd
dft = pd.DataFrame({'A': ['one', 'one'],
               'B': ['A', 'A'],
               'C': ['bar', 'bar'],
               'D': [-0.866740402,1.490732028]})



np.std(dft['D'])
#equivalent:np.std([-0.866740402,1.490732028]) (which:defaualt ddof=0) 
#the result: 1.178736215

dft.pivot_table(index=['A', 'B'],columns='C',aggfunc=np.std)
#equivalent:np.std([-0.866740402,1.490732028],ddof=1) 
#the result:1.666985

推荐答案

pivot使用DataFrame.groupby.agg，当您提供聚合函数时，它将尝试找出确切的方法来

pivot uses DataFrame.groupby.agg and when you supply an aggregation function it's going to try to figure out exactly how to _aggregate.

arg=np.std将在此处处理，相关代码为

arg=np.std will get handled here, the relevant code being

f = self._get_cython_func(arg)
if f and not args and not kwargs:
    return getattr(self, f)(), None

此表隐藏在DataFrame类中:

Hidden in the DataFrame class is this table:

pd.DataFrame()._cython_table
#OrderedDict([(<function sum>, 'sum'),
#             (<function max>, 'max'),
#             ...
#             (<function numpy.std>, 'std'),
#             (<function numpy.nancumsum>, 'cumsum')])

pd.DataFrame()._cython_table.get(np.std)
#'std'

因此，np.std仅用于选择要调用的属性，默认值ddof被完全忽略，而使用pandas默认值ddof=1.

And so np.std is only used to select the attribute to call, the default ddof are completely ignored, and instead the pandas default of ddof=1 is used.

getattr(dft['D'], 'std')()
#1.6669847417133286

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