pandas.Series.array — pandas 2.2.3 documentation (original) (raw)
property Series.array[source]#
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.
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 aarrays.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 [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']