pandas.Series.values#

propertySeries.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
<ArrowStringArray>
['a', 'a', 'b', 'c']
Length: 4, dtype: str

>>> pd.Series(list("aabc")).astype("category").values
['a', 'a', 'b', 'c']
Categories (3, str): ['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年01月01日T05:00:00.000000000',
 '2013年01月02日T05:00:00.000000000',
 '2013年01月03日T05:00:00.000000000'], dtype='datetime64[ns]')
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