pyspark.pandas.DataFrame.sem¶
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DataFrame.sem(axis: Union[int, str, None] = None, skipna: bool = True, ddof: int = 1, numeric_only: bool = None) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, Series]¶ Return unbiased standard error of the mean over requested axis.
New in version 3.3.0.
- Parameters
- axis: {index (0), columns (1)}
Axis for the function to be applied on.
- skipna: bool, default True
Exclude NA/null values when computing the result.
Changed in version 3.4.0: Supported including NA/null values.
- ddof: int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
Changed in version 3.4.0: Supported including arbitary integers.
- numeric_only: bool, default None
Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.
- Returns
- scalar(for Series) or Series(for DataFrame)
Examples
>>> psdf = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) >>> psdf a b 0 1 4 1 2 5 2 3 6
>>> psdf.sem() a 0.57735 b 0.57735 dtype: float64
>>> psdf.sem(ddof=0) a 0.471405 b 0.471405 dtype: float64
>>> psdf.sem(ddof=2) a 0.816497 b 0.816497 dtype: float64
>>> psdf.sem(axis=1) 0 1.5 1 1.5 2 1.5 dtype: float64
Support for Series
>>> psser = psdf.a >>> psser 0 1 1 2 2 3 Name: a, dtype: int64
>>> psser.sem() 0.5773502691896258
>>> psser.sem(ddof=0) 0.47140452079103173