numpy apply_along_axis on a 1d array

Question

What happens when numpy.apply_along_axis takes a 1d array as input? When I use it on 1d array, I see something strange:

y=array([1,2,3,4])

First try:

apply_along_axis(lambda x: x > 2, 0, y)
apply_along_axis(lambda x: x - 2, 0, y)

returns:

array([False, False,  True,  True], dtype=bool)
array([-1,  0,  1,  2])

However when I try:

apply_along_axis(lambda x: x - 2 if x > 2 else x, 0, y)

I get an error:

The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I could of course use list comprehension then convert back to array instead, but that seems convoluted and I feel like I'm missing something about apply_along_axis when applied to a 1d array.

UPDATE: as per Jeff G's answer, my confusion stems from the fact that for 1d array with only one axis, what is being passed to the function is in fact the 1d array itself rather than the individual elements.

"numpy.where" is clearly better for my chosen example (and no need for apply_along_axis), but my question is really about the proper idiom for applying a general function (that takes one scalar and returns one scalar) to each element of an array (other than list comprehension), something akin to pandas.Series.apply (or map). I know of 'vectorize' but it seems no less unwieldy than list comprehension.

Answer 1

This answer addresses the updated addendum to your original question:

numpy.vectorize will take an elementwise function and return a new function. The new function can be applied to an entire array. It's like map, but it uses the broadcasting rules of numpy.

f = lambda x: x - 2 if x > 2 else x # your elementwise fn
fv = np.vectorize(f)
fv(np.array([1,2,3,4]))
# Out[5]: array([1, 2, 1, 2])