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Classe « Series »

Méthode pandas.Series.__array__

Signature de la méthode __array__

def __array__(self, dtype: 'npt.DTypeLike | None' = None, copy: 'bool | None' = None) -> 'np.ndarray' 

Description

help(Series.__array__)

Return the values as a NumPy array.

Users should not call this directly. Rather, it is invoked by
:func:`numpy.array` and :func:`numpy.asarray`.

Parameters
----------
dtype : str or numpy.dtype, optional
    The dtype to use for the resulting NumPy array. By default,
    the dtype is inferred from the data.

copy : bool or None, optional
    Unused.

Returns
-------
numpy.ndarray
    The values in the series converted to a :class:`numpy.ndarray`
    with the specified `dtype`.

See Also
--------
array : Create a new array from data.
Series.array : Zero-copy view to the array backing the Series.
Series.to_numpy : Series method for similar behavior.

Examples
--------
>>> ser = pd.Series([1, 2, 3])
>>> np.asarray(ser)
array([1, 2, 3])

For timezone-aware data, the timezones may be retained with
``dtype='object'``

>>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
>>> np.asarray(tzser, 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 the values may be localized to UTC and the tzinfo discarded with
``dtype='datetime64[ns]'``

>>> np.asarray(tzser, dtype="datetime64[ns]")  # doctest: +ELLIPSIS
array(['1999-12-31T23:00:00.000000000', ...],
      dtype='datetime64[ns]')

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