# pandas.Series.values

`Series.``values`

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

  Returns: | arr : numpy.ndarray or ndarray-like

  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(['2013-01-01T00:00:00.000000000-0500',
             '2013-01-02T00:00:00.000000000-0500',
             '2013-01-03T00:00:00.000000000-0500'], dtype='datetime64[ns]')