pandas.Series.values#

property Series.values[source]#

Return Series as ndarray or ndarray-like depending on the dtype.

Warning

We recommend using Series.array or Series.to_numpy(), depending on whether you need a reference to the underlying data or a NumPy array.

Returns:

numpy.ndarray or ndarray-like

See also

Series.array: Reference to the underlying data.
Series.to_numpy: A NumPy array representing the underlying data.

Examples

>>> pd.Series([1, 2, 3]).values
array([1, 2, 3])

>>> pd.Series(list("aabc")).values
array(['a', 'a', 'b', 'c'], dtype=object)

>>> pd.Series(list("aabc")).astype("category").values
['a', 'a', 'b', 'c']
Categories (3, object): ['a', 'b', 'c']

Timezone aware datetime data is converted to UTC:

>>> pd.Series(pd.date_range("20130101", periods=3, tz="US/Eastern")).values
array(['2013-01-01T05:00:00.000000000',
       '2013-01-02T05:00:00.000000000',
       '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')