pandas.core.groupby.DataFrameGroupBy.sem#

DataFrameGroupBy.sem(ddof=1, numeric_only=False)[source]#

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.

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