parameters: (self, *, skipna: bool = True, axis: int | None = 0)
return_statement: return self._wrap_reduction_result(axis, result)
docstring: Return the mean value of the Array.
  
          .. versionadded:: 0.25.0
  
          Parameters
          ----------
          skipna : bool, default True
              Whether to ignore any NaT elements.
          axis : int, optional, default 0
  
          Returns
          -------
          scalar
              Timestamp or Timedelta.
  
          See Also
          --------
          numpy.ndarray.mean : Returns the average of array elements along a given axis.
          Series.mean : Return the mean value in a Series.
  
          Notes
          -----
          mean is only defined for Datetime and Timedelta dtypes, not for Period.
docstring_summary: Return the mean value of the Array.
docstring_tokens: Return the mean value of the Array .
function: def mean(self, *, skipna: bool = True, axis: int | None = 0):
          """
          Return the mean value of the Array.
  
          .. versionadded:: 0.25.0
  
          Parameters
          ----------
          skipna : bool, default True
              Whether to ignore any NaT elements.
          axis : int, optional, default 0
  
          Returns
          -------
          scalar
              Timestamp or Timedelta.
  
          See Also
          --------
          numpy.ndarray.mean : Returns the average of array elements along a given axis.
          Series.mean : Return the mean value in a Series.
  
          Notes
          -----
          mean is only defined for Datetime and Timedelta dtypes, not for Period.
          """
          if is_period_dtype(self.dtype):
              # See discussion in GH#24757
              raise TypeError(
                  f"mean is not implemented for {type(self).__name__} since the "
                  "meaning is ambiguous.  An alternative is "
                  "obj.to_timestamp(how='start').mean()"
              )
  
          result = nanops.nanmean(
              self._ndarray, axis=axis, skipna=skipna, mask=self.isna()
          )
          return self._wrap_reduction_result(axis, result)
function_tokens: def mean ( self , * , skipna : bool = True , axis : int | None = 0 ) : if is_period_dtype ( self . dtype ) : # See discussion in GH#24757 raise TypeError ( f"mean is not implemented for {type(self).__name__} since the " "meaning is ambiguous.  An alternative is " "obj.to_timestamp(how='start').mean()" ) result = nanops . nanmean ( self . _ndarray , axis = axis , skipna = skipna , mask = self . isna ( ) ) return self . _wrap_reduction_result ( axis , result )