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

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

For Series this parameter is unused and defaults to 0.


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).

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.


Additional keywords passed.

Series or DataFrame (if level specified)

Unbiased standard error of the mean over requested axis.

See also


Return unbiased variance over requested axis.


Returns sample standard deviation over requested axis.


>>> 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