pandas.Series.__array__

Series.__array__(dtype=None)[source]

Return the values as a NumPy array.

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

Parameters
dtypestr 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 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]")  
array(['1999-12-31T23:00:00.000000000', ...],
      dtype='datetime64[ns]')
pandas.Series.empty pandas.Series.__iter__