pandas.Series.values#

propertySeries.values[source]#

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

Warning

We recommend usingSeries.array orSeries.to_numpy(), depending on whether you needa 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]).valuesarray([1, 2, 3])

>>>pd.Series(list('aabc')).valuesarray(['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')).valuesarray(['2013-01-01T05:00:00.000000000',       '2013-01-02T05:00:00.000000000',       '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')