def sem(self, axis: Optional[Axis]=None, ddof: int=1, numeric_only: bool=None) -> Union[(Scalar, 'Series')]:
    '\n        Return unbiased standard error of the mean over requested axis.\n\n        Parameters\n        ----------\n        axis : {index (0), columns (1)}\n            Axis for the function to be applied on.\n        ddof : int, default 1\n            Delta Degrees of Freedom. The divisor used in calculations is N - ddof,\n            where N represents the number of elements.\n        numeric_only : bool, default None\n            Include only float, int, boolean columns. False is not supported. This parameter\n            is mainly for pandas compatibility.\n\n        Returns\n        -------\n        scalar(for Series) or Series(for DataFrame)\n\n        Examples\n        --------\n        >>> psdf = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})\n        >>> psdf\n           a  b\n        0  1  4\n        1  2  5\n        2  3  6\n\n        >>> psdf.sem()\n        a    0.57735\n        b    0.57735\n        dtype: float64\n\n        >>> psdf.sem(ddof=0)\n        a    0.471405\n        b    0.471405\n        dtype: float64\n\n        >>> psdf.sem(axis=1)\n        0    1.5\n        1    1.5\n        2    1.5\n        dtype: float64\n\n        Support for Series\n\n        >>> psser = psdf.a\n        >>> psser\n        0    1\n        1    2\n        2    3\n        Name: a, dtype: int64\n\n        >>> psser.sem()\n        0.5773502691896258\n\n        >>> psser.sem(ddof=0)\n        0.47140452079103173\n        '
    assert (ddof in (0, 1))
    axis = validate_axis(axis)
    if ((numeric_only is None) and (axis == 0)):
        numeric_only = True

    def std(spark_column: Column, spark_type: DataType) -> Column:
        if isinstance(spark_type, BooleanType):
            spark_column = spark_column.cast(LongType())
        elif (not isinstance(spark_type, NumericType)):
            raise TypeError('Could not convert {} ({}) to numeric'.format(spark_type_to_pandas_dtype(spark_type), spark_type.simpleString()))
        if (ddof == 0):
            return F.stddev_pop(spark_column)
        else:
            return F.stddev_samp(spark_column)

    def sem(spark_column: Column, spark_type: DataType) -> Column:
        return (std(spark_column, spark_type) / pow(Frame._count_expr(spark_column, spark_type), 0.5))
    return self._reduce_for_stat_function(sem, name='sem', numeric_only=numeric_only, axis=axis, ddof=ddof)
Return unbiased standard error of the mean over requested axis.

Parameters
----------
axis : {index (0), columns (1)}
    Axis for the function to be applied on.
ddof : int, default 1
    Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
    where N represents the number of elements.
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(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
def sem(self, axis: Optional[Axis]=None, ddof: int=1, numeric_only: bool=None) -> Union[(Scalar, 'Series')]:
    '\n        Return unbiased standard error of the mean over requested axis.\n\n        Parameters\n        ----------\n        axis : {index (0), columns (1)}\n            Axis for the function to be applied on.\n        ddof : int, default 1\n            Delta Degrees of Freedom. The divisor used in calculations is N - ddof,\n            where N represents the number of elements.\n        numeric_only : bool, default None\n            Include only float, int, boolean columns. False is not supported. This parameter\n            is mainly for pandas compatibility.\n\n        Returns\n        -------\n        scalar(for Series) or Series(for DataFrame)\n\n        Examples\n        --------\n        >>> psdf = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})\n        >>> psdf\n           a  b\n        0  1  4\n        1  2  5\n        2  3  6\n\n        >>> psdf.sem()\n        a    0.57735\n        b    0.57735\n        dtype: float64\n\n        >>> psdf.sem(ddof=0)\n        a    0.471405\n        b    0.471405\n        dtype: float64\n\n        >>> psdf.sem(axis=1)\n        0    1.5\n        1    1.5\n        2    1.5\n        dtype: float64\n\n        Support for Series\n\n        >>> psser = psdf.a\n        >>> psser\n        0    1\n        1    2\n        2    3\n        Name: a, dtype: int64\n\n        >>> psser.sem()\n        0.5773502691896258\n\n        >>> psser.sem(ddof=0)\n        0.47140452079103173\n        '
    assert (ddof in (0, 1))
    axis = validate_axis(axis)
    if ((numeric_only is None) and (axis == 0)):
        numeric_only = True

    def std(spark_column: Column, spark_type: DataType) -> Column:
        if isinstance(spark_type, BooleanType):
            spark_column = spark_column.cast(LongType())
        elif (not isinstance(spark_type, NumericType)):
            raise TypeError('Could not convert {} ({}) to numeric'.format(spark_type_to_pandas_dtype(spark_type), spark_type.simpleString()))
        if (ddof == 0):
            return F.stddev_pop(spark_column)
        else:
            return F.stddev_samp(spark_column)

    def sem(spark_column: Column, spark_type: DataType) -> Column:
        return (std(spark_column, spark_type) / pow(Frame._count_expr(spark_column, spark_type), 0.5))
    return self._reduce_for_stat_function(sem, name='sem', numeric_only=numeric_only, axis=axis, ddof=ddof)