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# pandas.Index.array

Index.array

  The ExtensionArray of the data backing this Index.

  This property provides direct access to the underlying array data of an Index without requiring conversion to a NumPy array. It returns an ExtensionArray, which is the native storage format for pandas extension dtypes.

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

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

      >>> idx = pd.Index(pd.Categorical(["a", "b", "a"]))
      >>> idx.array
      ['a', 'b', 'a']
      Categories (2, str): ['a', 'b']