pandas.core.groupby.SeriesGroupBy.sem#

SeriesGroupBy.sem(ddof=1, numeric_only=False, skipna=True)[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.

skipnabool, default True

Exclude NA/null values. If an entire group is NA, the result will be NA.

Added in version 3.0.0.

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年01月01日",
...  "2023年01月10日",
...  "2023年01月15日",
...  "2023年02月01日",
...  "2023年02月10日",
...  "2023年02月15日",
...  ]
...  ),
... )
>>> ser.resample("MS").sem()
2023年01月01日 0.577350
2023年02月01日 1.527525
Freq: MS, dtype: float64
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