ENH: numba apply supports positional arguments passed as **kwargs

[auderson]

• Tests added and passed if fixing a bug or adding a new feature
• All code checks passed.
• Added type annotations to new arguments/methods/functions.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.

@lithomas1

This is a followup PR that enhances prepare_function_arguments introduced in #58767. prepare_function_arguments helps to move **kwargs to *args where possible using inspect.Signature. And now it's also more reasonable to move kwargs check from get_jit_arguments to prepare_function_arguments.

ref: #58767 (comment)

We can also extend prepare_function_arguments by adding a option: num_required_args, so that all the numba functions' args & kwargs stuff can be handled by this function, instead of in get_jit_arguments . For example in groupby, we have numba_func(group, group_index, *args), in this case num_required_args = 2:

pandas/pandas/core/groupby/numba_.py

Lines 102 to 120 in 3b48b17

	@numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
	def group_agg(
	values: np.ndarray,
	index: np.ndarray,
	begin: np.ndarray,
	end: np.ndarray,
	num_columns: int,
	*args: Any,
	) -> np.ndarray:
	assert len(begin) == len(end)
	num_groups = len(begin)
	result = np.empty((num_groups, num_columns))
	for i in numba.prange(num_groups):
	group_index = index[begin[i] : end[i]]
	for j in numba.prange(num_columns):
	group = values[begin[i] : end[i], j]
	result[i, j] = numba_func(group, group_index, *args)
	return result

There are several places where the usage of get_jit_arguments is updated and prepare_function_arguments is added : groupby.agg, groupby.transform, rolling.apply and df.apply.

[github-actions]

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