pandas.Index.array#

The ExtensionArray of the data backing this Series or Index.

Returns:

ExtensionArray

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

.array differs from .values, which may require converting the 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 extension dtype 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 an ExtensionArray.

For all remaining dtypes .array will be a arrays.NumpyExtensionArray wrapping the actual ndarray stored within. If you absolutely need a NumPy array (possibly with copying / coercing data), then use Series.to_numpy() instead.

Examples

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

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

For extension types, like Categorical, the actual ExtensionArray is returned

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