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