def to_numpy(self, dtype: (npt.DTypeLike | None)=None, copy: bool=False, na_value=lib.no_default, **kwargs) -> np.ndarray:
    '\n        A NumPy ndarray representing the values in this Series or Index.\n\n        Parameters\n        ----------\n        dtype : str or numpy.dtype, optional\n            The dtype to pass to :meth:`numpy.asarray`.\n        copy : bool, default False\n            Whether to ensure that the returned value is not a view on\n            another array. Note that ``copy=False`` does not *ensure* that\n            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that\n            a copy is made, even if not strictly necessary.\n        na_value : Any, optional\n            The value to use for missing values. The default value depends\n            on `dtype` and the type of the array.\n\n            .. versionadded:: 1.0.0\n\n        **kwargs\n            Additional keywords passed through to the ``to_numpy`` method\n            of the underlying array (for extension arrays).\n\n            .. versionadded:: 1.0.0\n\n        Returns\n        -------\n        numpy.ndarray\n\n        See Also\n        --------\n        Series.array : Get the actual data stored within.\n        Index.array : Get the actual data stored within.\n        DataFrame.to_numpy : Similar method for DataFrame.\n\n        Notes\n        -----\n        The returned array will be the same up to equality (values equal\n        in `self` will be equal in the returned array; likewise for values\n        that are not equal). When `self` contains an ExtensionArray, the\n        dtype may be different. For example, for a category-dtype Series,\n        ``to_numpy()`` will return a NumPy array and the categorical dtype\n        will be lost.\n\n        For NumPy dtypes, this will be a reference to the actual data stored\n        in this Series or Index (assuming ``copy=False``). Modifying the result\n        in place will modify the data stored in the Series or Index (not that\n        we recommend doing that).\n\n        For extension types, ``to_numpy()`` *may* require copying data and\n        coercing the result to a NumPy type (possibly object), which may be\n        expensive. When you need a no-copy reference to the underlying data,\n        :attr:`Series.array` should be used instead.\n\n        This table lays out the different dtypes and default return types of\n        ``to_numpy()`` for various dtypes within pandas.\n\n        ================== ================================\n        dtype              array type\n        ================== ================================\n        category[T]        ndarray[T] (same dtype as input)\n        period             ndarray[object] (Periods)\n        interval           ndarray[object] (Intervals)\n        IntegerNA          ndarray[object]\n        datetime64[ns]     datetime64[ns]\n        datetime64[ns, tz] ndarray[object] (Timestamps)\n        ================== ================================\n\n        Examples\n        --------\n        >>> ser = pd.Series(pd.Categorical([\'a\', \'b\', \'a\']))\n        >>> ser.to_numpy()\n        array([\'a\', \'b\', \'a\'], dtype=object)\n\n        Specify the `dtype` to control how datetime-aware data is represented.\n        Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`\n        objects, each with the correct ``tz``.\n\n        >>> ser = pd.Series(pd.date_range(\'2000\', periods=2, tz="CET"))\n        >>> ser.to_numpy(dtype=object)\n        array([Timestamp(\'2000-01-01 00:00:00+0100\', tz=\'CET\'),\n               Timestamp(\'2000-01-02 00:00:00+0100\', tz=\'CET\')],\n              dtype=object)\n\n        Or ``dtype=\'datetime64[ns]\'`` to return an ndarray of native\n        datetime64 values. The values are converted to UTC and the timezone\n        info is dropped.\n\n        >>> ser.to_numpy(dtype="datetime64[ns]")\n        ... # doctest: +ELLIPSIS\n        array([\'1999-12-31T23:00:00.000000000\', \'2000-01-01T23:00:00...\'],\n              dtype=\'datetime64[ns]\')\n        '
    if is_extension_array_dtype(self.dtype):
        return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
    elif kwargs:
        bad_keys = list(kwargs.keys())[0]
        raise TypeError(f"to_numpy() got an unexpected keyword argument '{bad_keys}'")
    result = np.asarray(self._values, dtype=dtype)
    if (copy or (na_value is not lib.no_default)):
        result = result.copy()
        if (na_value is not lib.no_default):
            result[self.isna()] = na_value
    return result
A NumPy ndarray representing the values in this Series or Index.

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 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.
na_value : Any, optional
    The value to use for missing values. The default value depends
    on `dtype` and the type of the array.

    .. versionadded:: 1.0.0

**kwargs
    Additional keywords passed through to the ``to_numpy`` method
    of the underlying array (for extension arrays).

    .. versionadded:: 1.0.0

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'),
       Timestamp('2000-01-02 00:00:00+0100', tz='CET')],
      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]')
def to_numpy(self, dtype: (npt.DTypeLike | None)=None, copy: bool=False, na_value=lib.no_default, **kwargs) -> np.ndarray:
    '\n        A NumPy ndarray representing the values in this Series or Index.\n\n        Parameters\n        ----------\n        dtype : str or numpy.dtype, optional\n            The dtype to pass to :meth:`numpy.asarray`.\n        copy : bool, default False\n            Whether to ensure that the returned value is not a view on\n            another array. Note that ``copy=False`` does not *ensure* that\n            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that\n            a copy is made, even if not strictly necessary.\n        na_value : Any, optional\n            The value to use for missing values. The default value depends\n            on `dtype` and the type of the array.\n\n            .. versionadded:: 1.0.0\n\n        **kwargs\n            Additional keywords passed through to the ``to_numpy`` method\n            of the underlying array (for extension arrays).\n\n            .. versionadded:: 1.0.0\n\n        Returns\n        -------\n        numpy.ndarray\n\n        See Also\n        --------\n        Series.array : Get the actual data stored within.\n        Index.array : Get the actual data stored within.\n        DataFrame.to_numpy : Similar method for DataFrame.\n\n        Notes\n        -----\n        The returned array will be the same up to equality (values equal\n        in `self` will be equal in the returned array; likewise for values\n        that are not equal). When `self` contains an ExtensionArray, the\n        dtype may be different. For example, for a category-dtype Series,\n        ``to_numpy()`` will return a NumPy array and the categorical dtype\n        will be lost.\n\n        For NumPy dtypes, this will be a reference to the actual data stored\n        in this Series or Index (assuming ``copy=False``). Modifying the result\n        in place will modify the data stored in the Series or Index (not that\n        we recommend doing that).\n\n        For extension types, ``to_numpy()`` *may* require copying data and\n        coercing the result to a NumPy type (possibly object), which may be\n        expensive. When you need a no-copy reference to the underlying data,\n        :attr:`Series.array` should be used instead.\n\n        This table lays out the different dtypes and default return types of\n        ``to_numpy()`` for various dtypes within pandas.\n\n        ================== ================================\n        dtype              array type\n        ================== ================================\n        category[T]        ndarray[T] (same dtype as input)\n        period             ndarray[object] (Periods)\n        interval           ndarray[object] (Intervals)\n        IntegerNA          ndarray[object]\n        datetime64[ns]     datetime64[ns]\n        datetime64[ns, tz] ndarray[object] (Timestamps)\n        ================== ================================\n\n        Examples\n        --------\n        >>> ser = pd.Series(pd.Categorical([\'a\', \'b\', \'a\']))\n        >>> ser.to_numpy()\n        array([\'a\', \'b\', \'a\'], dtype=object)\n\n        Specify the `dtype` to control how datetime-aware data is represented.\n        Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`\n        objects, each with the correct ``tz``.\n\n        >>> ser = pd.Series(pd.date_range(\'2000\', periods=2, tz="CET"))\n        >>> ser.to_numpy(dtype=object)\n        array([Timestamp(\'2000-01-01 00:00:00+0100\', tz=\'CET\'),\n               Timestamp(\'2000-01-02 00:00:00+0100\', tz=\'CET\')],\n              dtype=object)\n\n        Or ``dtype=\'datetime64[ns]\'`` to return an ndarray of native\n        datetime64 values. The values are converted to UTC and the timezone\n        info is dropped.\n\n        >>> ser.to_numpy(dtype="datetime64[ns]")\n        ... # doctest: +ELLIPSIS\n        array([\'1999-12-31T23:00:00.000000000\', \'2000-01-01T23:00:00...\'],\n              dtype=\'datetime64[ns]\')\n        '
    if is_extension_array_dtype(self.dtype):
        return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
    elif kwargs:
        bad_keys = list(kwargs.keys())[0]
        raise TypeError(f"to_numpy() got an unexpected keyword argument '{bad_keys}'")
    result = np.asarray(self._values, dtype=dtype)
    if (copy or (na_value is not lib.no_default)):
        result = result.copy()
        if (na_value is not lib.no_default):
            result[self.isna()] = na_value
    return result