pandas.Index.array#
- Index.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.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 aarrays.NumpyExtensionArray
wrapping the actual ndarray stored within. If you absolutely need a NumPy array (possibly with copying / coercing data), then useSeries.to_numpy()
instead.Examples
For regular NumPy types like int, and float, a PandasArray is returned.
>>> pd.Series([1, 2, 3]).array <PandasArray> [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']