pandas.DataFrame.sem#

DataFrame.sem(axis=0,skipna=True,ddof=1,numeric_only=False,**kwargs)[source]#

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters:

axis{index (0), columns (1)}

ForSeries this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.sem withaxis=None is deprecated,in a future version this will reduce over both axes and return a scalarTo retain the old behavior, pass axis=0 (or do not pass axis).

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.

ddofint, default 1

Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

Returns:

Series or DataFrame (if level specified)

Examples

>>>s=pd.Series([1,2,3])>>>s.sem().round(6)0.57735

With a DataFrame

>>>df=pd.DataFrame({'a':[1,2],'b':[2,3]},index=['tiger','zebra'])>>>df       a   btiger  1   2zebra  2   3>>>df.sem()a   0.5b   0.5dtype: float64

Using axis=1

>>>df.sem(axis=1)tiger   0.5zebra   0.5dtype: float64

In this case,numeric_only should be set toTrueto avoid getting an error.

>>>df=pd.DataFrame({'a':[1,2],'b':['T','Z']},...index=['tiger','zebra'])>>>df.sem(numeric_only=True)a   0.5dtype: float64