parameters: (self)
return_statement: return result
docstring: The ExtensionArray of the data backing this Series or Index.
  
          .. versionadded:: 0.24.0
  
          Returns
          -------
          array : ExtensionArray
              An ExtensionArray of the values stored within. For extension
              types, this is the actual array. For NumPy native types, this
              is a thin (no copy) wrapper around :class:`numpy.ndarray`.
  
              ``.array`` differs ``.values`` which may require converting the
              data to a different form.
  
          See Also
          --------
          Index.to_numpy : Similar method that always returns a NumPy array.
          Series.to_numpy : Similar method that always returns a NumPy array.
  
          Notes
          -----
          This table lays out the different array types for each extension
          dtype within pandas.
  
          ================== =============================
          dtype              array type
          ================== =============================
          category           Categorical
          period             PeriodArray
          interval           IntervalArray
          IntegerNA          IntegerArray
          datetime64[ns, tz] DatetimeArray
          ================== =============================
  
          For any 3rd-party extension types, the array type will be an
          ExtensionArray.
  
          For all remaining dtypes ``.array`` will be a
          :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
          stored within. If you absolutely need a NumPy array (possibly with
          copying / coercing data), then use :meth:`Series.to_numpy` instead.
  
          Examples
          --------
  
          For regular NumPy types like int, and float, a PandasArray
          is returned.
  
          >>> pd.Series([1, 2, 3]).array
          <PandasArray>
          [1, 2, 3]
          Length: 3, dtype: int64
  
          For extension types, like Categorical, the actual ExtensionArray
          is returned
  
          >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
          >>> ser.array
          [a, b, a]
          Categories (2, object): [a, b]
docstring_summary: The ExtensionArray of the data backing this Series or Index.
docstring_tokens: The ExtensionArray of the data backing this Series or Index .
function: def array(self):
          # type: () -> ExtensionArray
          """
          The ExtensionArray of the data backing this Series or Index.
  
          .. versionadded:: 0.24.0
  
          Returns
          -------
          array : ExtensionArray
              An ExtensionArray of the values stored within. For extension
              types, this is the actual array. For NumPy native types, this
              is a thin (no copy) wrapper around :class:`numpy.ndarray`.
  
              ``.array`` differs ``.values`` which may require converting the
              data to a different form.
  
          See Also
          --------
          Index.to_numpy : Similar method that always returns a NumPy array.
          Series.to_numpy : Similar method that always returns a NumPy array.
  
          Notes
          -----
          This table lays out the different array types for each extension
          dtype within pandas.
  
          ================== =============================
          dtype              array type
          ================== =============================
          category           Categorical
          period             PeriodArray
          interval           IntervalArray
          IntegerNA          IntegerArray
          datetime64[ns, tz] DatetimeArray
          ================== =============================
  
          For any 3rd-party extension types, the array type will be an
          ExtensionArray.
  
          For all remaining dtypes ``.array`` will be a
          :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray
          stored within. If you absolutely need a NumPy array (possibly with
          copying / coercing data), then use :meth:`Series.to_numpy` instead.
  
          Examples
          --------
  
          For regular NumPy types like int, and float, a PandasArray
          is returned.
  
          >>> pd.Series([1, 2, 3]).array
          <PandasArray>
          [1, 2, 3]
          Length: 3, dtype: int64
  
          For extension types, like Categorical, the actual ExtensionArray
          is returned
  
          >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
          >>> ser.array
          [a, b, a]
          Categories (2, object): [a, b]
          """
          result = self._values
  
          if is_datetime64_ns_dtype(result.dtype):
              from pandas.arrays import DatetimeArray
              result = DatetimeArray(result)
          elif is_timedelta64_ns_dtype(result.dtype):
              from pandas.arrays import TimedeltaArray
              result = TimedeltaArray(result)
  
          elif not is_extension_array_dtype(result.dtype):
              from pandas.core.arrays.numpy_ import PandasArray
              result = PandasArray(result)
  
          return result
function_tokens: def array ( self ) : # type: () -> ExtensionArray result = self . _values if is_datetime64_ns_dtype ( result . dtype ) : from pandas . arrays import DatetimeArray result = DatetimeArray ( result ) elif is_timedelta64_ns_dtype ( result . dtype ) : from pandas . arrays import TimedeltaArray result = TimedeltaArray ( result ) elif not is_extension_array_dtype ( result . dtype ) : from pandas . core . arrays . numpy_ import PandasArray result = PandasArray ( result ) return result