Index.sem(axis=None, skipna=True, ddof=1, split_every=False, numeric_only=None)

Return unbiased standard error of the mean over requested axis.

This docstring was copied from pandas.core.frame.DataFrame.sem.

Some inconsistencies with the Dask version may exist.

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

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

For Series this parameter is unused and defaults to 0.

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the result will 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.

Series or DataFrame (if level specified)


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

With a DataFrame

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

Using axis=1

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

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

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