The interpreter will parse the code and issue the method calls as:
A[idx] = B[idx]
A.__setitem__(idx, B.__getitem__(idx))
The B method is evaluated fully before being passed to the A method. numpy doesn't alter the Python interpreter or its syntax. Rather it just adds functions, objects, and methods.
Functionally, it should be the equivalent to
temp = B[idx]
A[idx] = temp
del temp
We could do some timeit just be sure.
In [712]: A = np.zeros(10000,int)
In [713]: B = np.arange(10000)
In [714]: idx = np.arange(0,10000,100)
In [715]: timeit A[idx] = B[idx]
1.2 µs ± 3.24 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [716]: %%timeit
...: temp = B[idx]
...: A[idx] = temp
...:
1.11 µs ± 0.669 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
There are some alternative functions/methods, like add.at, copyto, place, put, that may do some copies without an intermediate, but I haven't used them much. This indexed assignment is good enough - most of the time.
Example with copyto
In [718]: wh = np.zeros(A.shape, bool)
In [719]: wh[idx] = True
In [721]: np.copyto(A, B, where=wh)
In [722]: timeit np.copyto(A, B, where=wh)
7.47 µs ± 9.92 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
So even without timing the construction of the boolean mask, copyto is slower.
put and take are no better:
In [727]: timeit np.put(A,idx, np.take(B,idx))
7.98 µs ± 8.34 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
