pandas.DataFrame.sem

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

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).

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.

**kwargs

Additional keywords passed.

Returns:

Series or DataFrame (if level specified)

Unbiased standard error of the mean over requested axis.

See also

DataFrame.var

Return unbiased variance over requested axis.

DataFrame.std

Returns sample standard deviation over requested axis.

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