l of the minimum, while ``ndarray.argmin`` returns
        the position. To get the position, use ``series.values.argmin()``.

        Examples
        --------
        >>> s = pd.Series(data=[1, None, 4, 1],
        ...               index=['A', 'B', 'C', 'D'])
        >>> s
        A    1.0
        B    NaN
        C    4.0
        D    1.0
        dtype: float64

        >>> s.idxmin()
        'A'

        If `skipna` is False and there is an NA value in the data,
        the function returns ``nan``.

        >>> s.idxmin(skipna=False)
        nan
        rš