numpy: summing along all but last axis

Question

If I have an ndarray of arbitrary shape and I would like to compute the sum along all but the last axis I can, for instance, achieve it by doing

all_but_last = tuple(range(arr.ndim - 1))
sum = arr.sum(axis=all_but_last)

Now, tuple(range(arr.ndim - 1)) is not exactly intuitive I feel. Is there a more elegant/numpy-esque way to do this?

Moreover, if I want to do this for multiple arrays of varying shape, I'll have to calculate a separate dimension tuple for each of them. Is there a more canonical way to say "regardless of what the dimensions are, just give me all but one axis"?

Answer 1

You can use np.apply_over_axes to sum over multiple axes.

np.apply_over_axes(np.sum, arr, [0,2]) #sum over axes 0 and 2

np.apply_over_axes(np.sum, arr, range(arr.ndim - 1)) #sum over all but last axis

Answer 2

You could reshape the array so that all axes except the last are flattened (e.g. shape (k, l, m, n) becomes (k*l*m, n)), and then sum over the first axis.

For example, here's your calculation:

In [170]: arr.shape
Out[170]: (2, 3, 4)

In [171]: arr.sum(axis=tuple(range(arr.ndim - 1)))
Out[171]: array([2.85994792, 2.8922732 , 2.29051163, 2.77275709])

Here's the alternative:

In [172]: arr.reshape(-1, arr.shape[-1]).sum(axis=0)
Out[172]: array([2.85994792, 2.8922732 , 2.29051163, 2.77275709])