@property<EOL><INDENT>def array(self) -> ExtensionArray:<DEDENT>
result = self._values<EOL>if is_datetime64_ns_dtype(result.dtype):<EOL><INDENT>from pandas.arrays import DatetimeArray<EOL>result = DatetimeArray(result)<EOL><DEDENT>elif is_timedelta64_ns_dtype(result.dtype):<EOL><INDENT>from pandas.arrays import TimedeltaArray<EOL>result = TimedeltaArray(result)<EOL><DEDENT>elif not is_extension_array_dtype(result.dtype):<EOL><INDENT>from pandas.core.arrays.numpy_ import PandasArray<EOL>result = PandasArray(result)<EOL><DEDENT>return result<EOL>
The ExtensionArray of the data backing this Series or Index.

.. versionadded:: 0.24.0

Returns
-------
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]