Classe « Series »

Méthode pandas.Series.__array__

Signature de la méthode __array__

def __array__(self, dtype=None) -> numpy.ndarray 

Description

__array__.__doc__

        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.

        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', freq='D'),
               Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
              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]')