@@ -1440,12 +1440,61 @@ def var(
14401440 return self ._downsample ("var" , ddof = ddof , numeric_only = numeric_only )
14411441
14421442 @final
1443- @doc (GroupBy .sem )
14441443 def sem (
14451444 self ,
14461445 ddof : int = 1 ,
14471446 numeric_only : bool = False ,
14481447 ):
1448+ """
1449+ Compute standard error of the mean of groups, excluding missing values.
1450+
1451+ For multiple groupings, the result index will be a MultiIndex.
1452+
1453+ Parameters
1454+ ----------
1455+ ddof : int, default 1
1456+ Degrees of freedom.
1457+
1458+ numeric_only : bool, default False
1459+ Include only `float`, `int` or `boolean` data.
1460+
1461+ .. versionadded:: 1.5.0
1462+
1463+ .. versionchanged:: 2.0.0
1464+
1465+ numeric_only now defaults to ``False``.
1466+
1467+ Returns
1468+ -------
1469+ Series or DataFrame
1470+ Standard error of the mean of values within each group.
1471+
1472+ See Also
1473+ --------
1474+ DataFrame.sem : Return unbiased standard error of the mean over requested axis.
1475+ Series.sem : Return unbiased standard error of the mean over requested axis.
1476+
1477+ Examples
1478+ --------
1479+
1480+ >>> ser = pd.Series(
1481+ ... [1, 3, 2, 4, 3, 8],
1482+ ... index=pd.DatetimeIndex(
1483+ ... [
1484+ ... "2023-01-01",
1485+ ... "2023-01-10",
1486+ ... "2023-01-15",
1487+ ... "2023-02-01",
1488+ ... "2023-02-10",
1489+ ... "2023-02-15",
1490+ ... ]
1491+ ... ),
1492+ ... )
1493+ >>> ser.resample("MS").sem()
1494+ 2023-01-01 0.577350
1495+ 2023-02-01 1.527525
1496+ Freq: MS, dtype: float64
1497+ """
14491498 return self ._downsample ("sem" , ddof = ddof , numeric_only = numeric_only )
14501499
14511500 @final
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