def min(self, axis=None, skipna: bool=True, *args, **kwargs):
    "\n        Return the minimum value of the Index.\n\n        Parameters\n        ----------\n        axis : {None}\n            Dummy argument for consistency with Series.\n        skipna : bool, default True\n            Exclude NA/null values when showing the result.\n        *args, **kwargs\n            Additional arguments and keywords for compatibility with NumPy.\n\n        Returns\n        -------\n        scalar\n            Minimum value.\n\n        See Also\n        --------\n        Index.max : Return the maximum value of the object.\n        Series.min : Return the minimum value in a Series.\n        DataFrame.min : Return the minimum values in a DataFrame.\n\n        Examples\n        --------\n        >>> idx = pd.Index([3, 2, 1])\n        >>> idx.min()\n        1\n\n        >>> idx = pd.Index(['c', 'b', 'a'])\n        >>> idx.min()\n        'a'\n\n        For a MultiIndex, the minimum is determined lexicographically.\n\n        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n        >>> idx.min()\n        ('a', 1)\n        "
    nv.validate_minmax_axis(axis)
    nv.validate_min(args, kwargs)
    return nanops.nanmin(self._values, skipna=skipna)
Return the minimum value of the Index.

Parameters
----------
axis : {None}
    Dummy argument for consistency with Series.
skipna : bool, default True
    Exclude NA/null values when showing the result.
*args, **kwargs
    Additional arguments and keywords for compatibility with NumPy.

Returns
-------
scalar
    Minimum value.

See Also
--------
Index.max : Return the maximum value of the object.
Series.min : Return the minimum value in a Series.
DataFrame.min : Return the minimum values in a DataFrame.

Examples
--------
>>> idx = pd.Index([3, 2, 1])
>>> idx.min()
1

>>> idx = pd.Index(['c', 'b', 'a'])
>>> idx.min()
'a'

For a MultiIndex, the minimum is determined lexicographically.

>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
>>> idx.min()
('a', 1)
def min(self, axis=None, skipna: bool=True, *args, **kwargs):
    "\n        Return the minimum value of the Index.\n\n        Parameters\n        ----------\n        axis : {None}\n            Dummy argument for consistency with Series.\n        skipna : bool, default True\n            Exclude NA/null values when showing the result.\n        *args, **kwargs\n            Additional arguments and keywords for compatibility with NumPy.\n\n        Returns\n        -------\n        scalar\n            Minimum value.\n\n        See Also\n        --------\n        Index.max : Return the maximum value of the object.\n        Series.min : Return the minimum value in a Series.\n        DataFrame.min : Return the minimum values in a DataFrame.\n\n        Examples\n        --------\n        >>> idx = pd.Index([3, 2, 1])\n        >>> idx.min()\n        1\n\n        >>> idx = pd.Index(['c', 'b', 'a'])\n        >>> idx.min()\n        'a'\n\n        For a MultiIndex, the minimum is determined lexicographically.\n\n        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n        >>> idx.min()\n        ('a', 1)\n        "
    nv.validate_minmax_axis(axis)
    nv.validate_min(args, kwargs)
    return nanops.nanmin(self._values, skipna=skipna)