pandas.Series.values#

property Series.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. The return type of .values depends on the dtype: it is a numpy.ndarray for some dtypes (for example int64 or float64) and an ExtensionArray for others (for example category, nullable Int64, or string dtypes), which makes it harder to write code that works across dtypes. .values also converts timezone-aware datetimes to UTC and drops the timezone. Series.array always returns the underlying array as an ExtensionArray, and Series.to_numpy() always returns a numpy.ndarray and accepts dtype, copy, and na_value arguments to control the conversion.

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

For a Series backed by a NumPy dtype such as int64, .values returns a numpy.ndarray:

>>> pd.Series([1, 2, 3]).values
array([1, 2, 3])

For extension dtypes such as the default string dtype or category, .values returns an ExtensionArray instead, while Series.to_numpy() always returns a numpy.ndarray:

>>> pd.Series(list("aabc")).values
<ArrowStringArray>
['a', 'a', 'b', 'c']
Length: 4, dtype: str
>>> pd.Series(list("aabc")).to_numpy()
array(['a', 'a', 'b', 'c'], dtype=object)

>>> 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 and the timezone is dropped, while Series.array preserves the timezone:

>>> pd.Series(pd.date_range("20130101", periods=3, tz="US/Eastern")).values
array(['2013-01-01T05:00:00.000000',
       '2013-01-02T05:00:00.000000',
       '2013-01-03T05:00:00.000000'], dtype='datetime64[us]')
>>> pd.Series(pd.date_range("20130101", periods=3, tz="US/Eastern")).array
<DatetimeArray>
['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00',
 '2013-01-03 00:00:00-05:00']
Length: 3, dtype: datetime64[us, US/Eastern]
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