pandas.Series.array — pandas 2.2.3 documentation (original) (raw)

Series.__array__(dtype=None, copy=None)[source]#

Return the values as a NumPy array.

Users should not call this directly. Rather, it is invoked bynumpy.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.

copybool or None, optional

Unused.

Returns:

numpy.ndarray

The values in the series converted to a numpy.ndarraywith the specified dtype.

Examples

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

For timezone-aware data, the timezones may be retained withdtype='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 withdtype='datetime64[ns]'

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