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)}, default 0: Axis for the function to be applied on. 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: 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])
>>> round(s.sem(), 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