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)}

For Series this parameter is unused and defaults to 0.

Warning

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

skipna:bool, default True

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

ddof:int, default 1

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

numeric_only:bool, 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   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