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.
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