ignature.
    Each group's index will be passed to the user defined function
    and optionally available for use.

    If a string is chosen, then it needs to be the name
    of the groupby method you want to use.
*args
    Positional arguments to pass to func.
engine : str, default None
    * ``'cython'`` : Runs the function through C-extensions from cython.
    * ``'numba'`` : Runs the function through JIT compiled code from numba.
    * ``None`` : Defaults to ``'cython'`` or the global setting ``compute.use_numba``

engine_kwargs : dict, default None
    * For ``'cython'`` engine, there are no accepted ``engine_kwargs``
    * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
      and ``parallel`` dictionary keys. The values must either be ``True`` or
      ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
      ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be
      applied to the function

**kwargs
    Keyword arguments to be passed into func.

Returns
-------
%(klass)s

See Also
--------
%(klass)s.groupby.apply : Apply function ``func`` group-wise and combine
    the results together.
%(klass)s.groupby.aggregate : Aggregate using one or more
    operations over the specified axis.
%(klass)s.transform : Call ``func`` on self producing a %(klass)s with the
    same axis shape as self.

Notes
-----
Each group is endowed the attribute 'name' in case you need to know
which group you are working on.

The current implementation imposes three requirements on f:

* f must return a value that either has the same shape as the input
  subframe or can be broadcast to the shape of the input subframe.
  For example, if `f` returns a scalar it will be broadcast to have the
  same shape as the input subframe.
* if this is a DataFrame, f must support application column-by-column
  in the subframe. If f also supports application to the entire subframe,
  then a fast path is used starting from the second chunk.
* f must not mutate groups. Mutation is not supported and may
  produce unexpected results. See :ref:`gotchas.udf-mutation` for more details.

When using ``engine='numba'``, there will be no "fall back" behavior internally.
The group data and group index will be passed as numpy arrays to the JITed
user defined function, and no alternative execution attempts will be tried.

.. versionchanged:: 1.3.0

    The resulting dtype will reflect the return value of the passed ``func``,
    see the examples below.

.. versionchanged:: 2.0.0

    When using ``.transform`` on a grouped DataFrame and the transformation function
    returns a DataFrame, pandas now aligns the result's index
    with the input's index. You can call ``.to_numpy()`` on the
    result of the transformation function to avoid alignment.

Examples
--------
%(example)saP