# pandas.Series.values

`Series.``values`

  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]')

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