pandas.Series.array#

propertySeries.array[source]#

The ExtensionArray of the data backing this Series or Index.

Returns:

ExtensionArray

An ExtensionArray of the values stored within. For extensiontypes, this is the actual array. For NumPy native types, thisis a thin (no copy) wrapper aroundnumpy.ndarray.

.array differs from.values, which may require convertingthe data to a different form.

See also

Index.to_numpy

Similar method that always returns a NumPy array.

Series.to_numpy

Similar method that always returns a NumPy array.

Notes

This table lays out the different array types for each extensiondtype within pandas.

dtype	array type
category	Categorical
period	PeriodArray
interval	IntervalArray
IntegerNA	IntegerArray
string	StringArray
boolean	BooleanArray
datetime64[ns, tz]	DatetimeArray

For any 3rd-party extension types, the array type will be anExtensionArray.

For all remaining dtypes.array will be aarrays.NumpyExtensionArray wrapping the actual ndarraystored within. If you absolutely need a NumPy array (possibly withcopying / coercing data), then useSeries.to_numpy() instead.

Examples

For regular NumPy types like int, and float, a NumpyExtensionArrayis returned.

>>>pd.Series([1,2,3]).array<NumpyExtensionArray>[1, 2, 3]Length: 3, dtype: int64

For extension types, like Categorical, the actual ExtensionArrayis returned

>>>ser=pd.Series(pd.Categorical(['a','b','a']))>>>ser.array['a', 'b', 'a']Categories (2, object): ['a', 'b']