Add multiple np.newaxis as needed?

Question

I would like to pairwise compare (with <=) all elements of two NumPy ndarrays A and B, where both arrays can have arbitrary dimensions m and n, such that the result is an array of dimension m + n.

I know how to do it for given dimension of B.

1. scalar: A <= B

2. one-dimensional: A[..., np.newaxis] <= B

3. two-dimensional: A[..., np.newaxis, np.newaxis] <= B

Basically, I'm looking for a way to insert as many np.newaxis as there are dimensions in the second array.

Is there a syntax like np.newaxis * B.ndim, or another way?

Answer 1

There's builtin for that -

np.less_equal.outer(A,B)

Another way would be with reshaping to accomodate new axes -

A.reshape(list(A.shape)+[1]*B.ndim) <= B

Answer 2

The accepted answer solves OP's problem, but does not address the question in the title in the optimal way. To add multiple np.newaxis, you can do

A[(...,) + (np.newaxis,) * B.ndim]

which is maybe more readable than the reshape solution.