pandas.core.groupby.SeriesGroupBy.sem

SeriesGroupBy.sem(ddof=1, numeric_only=False)

Compute standard error of the mean of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex.

Parameters:

ddofint, default 1

Degrees of freedom.

numeric_onlybool, default False

Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: numeric_only now defaults to False.

Returns:

Series or DataFrame

Standard error of the mean of values within each group.

See also

DataFrame.sem

Return unbiased standard error of the mean over requested axis.

Series.sem

Return unbiased standard error of the mean over requested axis.

Examples

For SeriesGroupBy:

>>> lst = ["a", "a", "b", "b"]
>>> ser = pd.Series([5, 10, 8, 14], index=lst)
>>> ser
a     5
a    10
b     8
b    14
dtype: int64
>>> ser.groupby(level=0).sem()
a    2.5
b    3.0
dtype: float64

For DataFrameGroupBy:

>>> data = [[1, 12, 11], [1, 15, 2], [2, 5, 8], [2, 6, 12]]
>>> df = pd.DataFrame(
...     data,
...     columns=["a", "b", "c"],
...     index=["tuna", "salmon", "catfish", "goldfish"],
... )
>>> df
           a   b   c
    tuna   1  12  11
  salmon   1  15   2
 catfish   2   5   8
goldfish   2   6  12
>>> df.groupby("a").sem()
      b  c
a
1    1.5  4.5
2    0.5  2.0

For Resampler:

>>> ser = pd.Series(
...     [1, 3, 2, 4, 3, 8],
...     index=pd.DatetimeIndex(
...         [
...             "2023-01-01",
...             "2023-01-10",
...             "2023-01-15",
...             "2023-02-01",
...             "2023-02-10",
...             "2023-02-15",
...         ]
...     ),
... )
>>> ser.resample("MS").sem()
2023-01-01    0.577350
2023-02-01    1.527525
Freq: MS, dtype: float64