def to_numpy(self, dtype=None, copy=False):
if is_datetime64tz_dtype(self.dtype) and dtype is None:<EOL><INDENT>dtype = "<STR_LIT:object>"<EOL><DEDENT>result = np.asarray(self._values, dtype=dtype)<EOL>if copy:<EOL><INDENT>result = result.copy()<EOL><DEDENT>return result<EOL>
A NumPy ndarray representing the values in this Series or Index.

.. versionadded:: 0.24.0

Parameters
----------
dtype : str or numpy.dtype, optional
    The dtype to pass to :meth:`numpy.asarray`
copy : bool, default False
    Whether to ensure that the returned value is a not a view on
    another array. Note that ``copy=False`` does not *ensure* that
    ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
    a copy is made, even if not strictly necessary.

Returns
-------
numpy.ndarray

See Also
--------
Series.array : Get the actual data stored within.
Index.array : Get the actual data stored within.
DataFrame.to_numpy : Similar method for DataFrame.

Notes
-----
The returned array will be the same up to equality (values equal
in `self` will be equal in the returned array; likewise for values
that are not equal). When `self` contains an ExtensionArray, the
dtype may be different. For example, for a category-dtype Series,
``to_numpy()`` will return a NumPy array and the categorical dtype
will be lost.

For NumPy dtypes, this will be a reference to the actual data stored
in this Series or Index (assuming ``copy=False``). Modifying the result
in place will modify the data stored in the Series or Index (not that
we recommend doing that).

For extension types, ``to_numpy()`` *may* require copying data and
coercing the result to a NumPy type (possibly object), which may be
expensive. When you need a no-copy reference to the underlying data,
:attr:`Series.array` should be used instead.

This table lays out the different dtypes and default return types of
``to_numpy()`` for various dtypes within pandas.

================== ================================
dtype              array type
================== ================================
category[T]        ndarray[T] (same dtype as input)
period             ndarray[object] (Periods)
interval           ndarray[object] (Intervals)
IntegerNA          ndarray[object]
datetime64[ns]     datetime64[ns]
datetime64[ns, tz] ndarray[object] (Timestamps)
================== ================================

Examples
--------
>>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.to_numpy()
array(['a', 'b', 'a'], dtype=object)

Specify the `dtype` to control how datetime-aware data is represented.
Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`
objects, each with the correct ``tz``.

>>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
>>> ser.to_numpy(dtype=object)
array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'),
       Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
      dtype=object)

Or ``dtype='datetime64[ns]'`` to return an ndarray of native
datetime64 values. The values are converted to UTC and the timezone
info is dropped.

>>> ser.to_numpy(dtype="datetime64[ns]")
... # doctest: +ELLIPSIS
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
      dtype='datetime64[ns]')