ak.full_like
------------

.. py:module: ak.full_like

Defined in `awkward.operations.ak_full_like <https://github.com/scikit-hep/awkward/blob/76cb66b946cbf2ae355bddf2530b323465395bae/src/awkward/operations/ak_full_like.py>`__ on `line 20 <https://github.com/scikit-hep/awkward/blob/76cb66b946cbf2ae355bddf2530b323465395bae/src/awkward/operations/ak_full_like.py#L20>`__.

.. py:function:: ak.full_like(array, fill_value, *, dtype=None, including_unknown=False, highlevel=True, behavior=None, attrs=None)


    :param array: Array-like data (anything :py:obj:`ak.to_layout` recognizes).
    :param fill_value: Value to fill the new array with.
    :param dtype: Overrides the data type of the result.
    :type dtype: None or NumPy dtype
    :param including_unknown: If True, the ``unknown`` type is considered
                          a value type and is converted to a zero-length array of the
                          specified dtype; if False, ``unknown`` will remain ``unknown``.
    :type including_unknown: bool
    :param highlevel: If True, return an :py:obj:`ak.Array`;
                  otherwise, return a low-level :py:obj:`ak.contents.Content` subclass.
    :type highlevel: bool, default is True
    :param behavior: Custom :py:obj:`ak.behavior` for the output array, if
                 high-level.
    :type behavior: None or dict
    :param attrs: Custom attributes for the output array, if
              high-level.
    :type attrs: None or dict

This is the equivalent of NumPy's ``np.full_like`` for Awkward Arrays.

Although it's possible to produce an array of ``fill_value`` with the
structure of an ``array`` using :py:obj:`ak.broadcast_arrays`:

.. code-block:: python


    >>> array = ak.Array([[1, 2, 3], [], [4, 5]])
    >>> ak.broadcast_arrays(array, 1)
    [<Array [[1, 2, 3], [], [4, 5]] type='3 * var * int64'>,
     <Array [[1, 1, 1], [], [1, 1]] type='3 * var * int64'>]
    >>> ak.broadcast_arrays(array, 1.0)
    [<Array [[1, 2, 3], [], [4, 5]] type='3 * var * int64'>,
     <Array [[1, 1, 1], [], [1, 1]] type='3 * var * float64'>]

Such a technique takes its type from the scalar (``1`` or ``1.0``), rather than
the array. This function gets all types from the array, which might not be
the same in all parts of the structure.

Here is an extreme example:

.. code-block:: python


    >>> array = ak.Array([
    ... [{"x": 0.0, "y": []},
    ...  {"x": 1.1, "y": [1]},
    ...  {"x": 2.2, "y": [1, 2]}],
    ... [],
    ... [{"x": 3.3, "y": [1, 2, None, 3]},
    ...  False,
    ...  False,
    ...  True,
    ...  {"x": 4.4, "y": [1, 2, None, 3, 4]}]])
    >>> ak.full_like(array, 12.3).show()
    [[{x: 12.3, y: []}, {x: 12.3, y: [12]}, {x: 12.3, y: [12, 12]}],
     [],
     [{x: 12.3, y: [12, 12, None, 12]}, True, ..., True, {x: 12.3, y: [12, ...]}]]

The ``"x"`` values get filled in with ``12.3`` because they retain their type
(``float64``) and the ``"y"`` list items get filled in with ``12`` because they
retain their type (``int64``). Booleans get filled with True because ``12.3``
is not zero. Missing values remain in the same positions as in the original
``array``. (To fill them in, use :py:obj:`ak.fill_none`.)

See also :py:obj:`ak.zeros_like` and :py:obj:`ak.ones_like`.

(There is no equivalent of NumPy's ``np.empty_like`` because Awkward Arrays
are immutable.)