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# pandas.Series.__array__

Series.__array__(dtype=None, copy=None)

  Return the values as a NumPy array.

  Users should not call this directly. Rather, it is invoked by `numpy.array()` and `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

      See `numpy.asarray()`.

  Returns:
    numpy.ndarray

      The values in the series converted to a `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]")
      array(['1999-12-31T23:00:00.000000000', ...],
            dtype='datetime64[ns]')