def min(self, axis: Optional[Axis]=None, numeric_only: bool=None) -> Union[(Scalar, 'Series')]:
    "\n        Return the minimum of the values.\n\n        Parameters\n        ----------\n        axis : {index (0), columns (1)}\n            Axis for the function to be applied on.\n        numeric_only : bool, default None\n            If True, include only float, int, boolean columns. This parameter is mainly for\n            pandas compatibility. False is supported; however, the columns should\n            be all numeric or all non-numeric.\n\n        Returns\n        -------\n        min : scalar for a Series, and a Series for a DataFrame.\n\n        Examples\n        --------\n\n        >>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]},\n        ...                   columns=['a', 'b'])\n\n        On a DataFrame:\n\n        >>> df.min()\n        a    1.0\n        b    0.1\n        dtype: float64\n\n        >>> df.min(axis=1)\n        0    0.1\n        1    0.2\n        2    0.3\n        3    NaN\n        dtype: float64\n\n        On a Series:\n\n        >>> df['a'].min()\n        1.0\n        "
    axis = validate_axis(axis)
    if ((numeric_only is None) and (axis == 0)):
        numeric_only = True
    elif ((numeric_only is True) and (axis == 1)):
        numeric_only = None
    return self._reduce_for_stat_function(F.min, name='min', axis=axis, numeric_only=numeric_only)
Return the minimum of the values.

Parameters
----------
axis : {index (0), columns (1)}
    Axis for the function to be applied on.
numeric_only : bool, default None
    If True, include only float, int, boolean columns. This parameter is mainly for
    pandas compatibility. False is supported; however, the columns should
    be all numeric or all non-numeric.

Returns
-------
min : scalar for a Series, and a Series for a DataFrame.

Examples
--------

>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]},
...                   columns=['a', 'b'])

On a DataFrame:

>>> df.min()
a    1.0
b    0.1
dtype: float64

>>> df.min(axis=1)
0    0.1
1    0.2
2    0.3
3    NaN
dtype: float64

On a Series:

>>> df['a'].min()
1.0
def min(self, axis: Optional[Axis]=None, numeric_only: bool=None) -> Union[(Scalar, 'Series')]:
    "\n        Return the minimum of the values.\n\n        Parameters\n        ----------\n        axis : {index (0), columns (1)}\n            Axis for the function to be applied on.\n        numeric_only : bool, default None\n            If True, include only float, int, boolean columns. This parameter is mainly for\n            pandas compatibility. False is supported; however, the columns should\n            be all numeric or all non-numeric.\n\n        Returns\n        -------\n        min : scalar for a Series, and a Series for a DataFrame.\n\n        Examples\n        --------\n\n        >>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]},\n        ...                   columns=['a', 'b'])\n\n        On a DataFrame:\n\n        >>> df.min()\n        a    1.0\n        b    0.1\n        dtype: float64\n\n        >>> df.min(axis=1)\n        0    0.1\n        1    0.2\n        2    0.3\n        3    NaN\n        dtype: float64\n\n        On a Series:\n\n        >>> df['a'].min()\n        1.0\n        "
    axis = validate_axis(axis)
    if ((numeric_only is None) and (axis == 0)):
        numeric_only = True
    elif ((numeric_only is True) and (axis == 1)):
        numeric_only = None
    return self._reduce_for_stat_function(F.min, name='min', axis=axis, numeric_only=numeric_only)