python - numpy shorthand for taking jagged slice

Question

I have an operation that I'm doing commonly which I'm calling a "jagged-slice" because I don't know the real name for it. It's best explained by example:

a = np.random.randn(50, 10)
entries_of_interest = np.random.randint(10, size = 50)  # Vector of 50 indices between 0 and 9
# Now I want the values contained in each row of a at the corresponding index in "entries of interest"
jagged_slice_of_a = a[np.arange(a.shape[0]), entries_of_interest]
# jagged_slice_of_a is now a vector with 50 elements.  Good.

Only problem is it's a bit cumbersome to do this a[np.arange(a.shape[0]), entries_of_interest] indexing (it seems silly to have to construct the "np.arange(a.shape[0])" just for the sake of this). I'd like something like the : operator for this, but the : does something else. Is there any more succinct way to do this operation?

Best answer:

No, there is no better way with native numpy. You can create a helper function for this if you want.

Answer 1

I think that your current method is probably the best way.

You can also use choose for this kind of selection. This is syntactically clearer, but is trickier to get right and potentially more limited. The equivalent with this method would be:

entries_of_interest.choose(a.T)