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'),
       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]')

pandas.Series.__array__#

pandas.Series.array#