code: def idxmax(self, axis=0, skipna=True, *args, **kwargs):
          
          skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
          i = nanops.nanargmax(com.values_from_object(self), skipna=skipna)
          if i == -1:
              return np.nan
          return self.index[i]
docs: Return the row label of the maximum value.
  
          If multiple values equal the maximum, the first row label with that
          value is returned.
  
          Parameters
          ----------
          skipna : bool, default True
              Exclude NA/null values. If the entire Series is NA, the result
              will be NA.
          axis : int, default 0
              For compatibility with DataFrame.idxmax. Redundant for application
              on Series.
          *args, **kwargs
              Additional keywords have no effect but might be accepted
              for compatibility with NumPy.
  
          Returns
          -------
          Index
              Label of the maximum value.
  
          Raises
          ------
          ValueError
              If the Series is empty.
  
          See Also
          --------
          numpy.argmax : Return indices of the maximum values
              along the given axis.
          DataFrame.idxmax : Return index of first occurrence of maximum
              over requested axis.
          Series.idxmin : Return index *label* of the first occurrence
              of minimum of values.
  
          Notes
          -----
          This method is the Series version of ``ndarray.argmax``. This method
          returns the label of the maximum, while ``ndarray.argmax`` returns
          the position. To get the position, use ``series.values.argmax()``.
  
          Examples
          --------
          >>> s = pd.Series(data=[1, None, 4, 3, 4],
          ...               index=['A', 'B', 'C', 'D', 'E'])
          >>> s
          A    1.0
          B    NaN
          C    4.0
          D    3.0
          E    4.0
          dtype: float64
  
          >>> s.idxmax()
          'C'
  
          If `skipna` is False and there is an NA value in the data,
          the function returns ``nan``.
  
          >>> s.idxmax(skipna=False)
          nan