Vectorize a for loop based on condition using numpy

Question

I have 2 numpy arrays l1 and l2 as follows:

start, jump = 1, 2
L = 8

l1 = np.arange(start, L)
l2 = np.arange(start + jump, L+jump)

This results in:

l1 = [1 2 3 4 5 6 7]
l2 = [3 4 5 6 7 8 9]

Now, I want 2 resultant arrays r1 and r2 such that while appending elements of l1 and l2 one by one in r1 and r2 respectively, it should check if r2 does not contain $i^{th}$ element of l1.

Implementing this using for loop is easy. But I am stuck on how to implement it using only numpy (without using loops) as I am new to it.

This is what I tried and want I am expecting:

r1 = []
r2 = []

for i in range(len(l1)):
    if (l1[i] not in r2):
        r1.append(l1[i])
        r2.append(l2[i])

This gives:

r1 = [1, 2, 5, 6]
r2 = [3, 4, 7, 8]

Thanks in advance :)

This is also probably the most direct option, you could speed it up using a set, but I don't know if numpy vectorize operations are a good fit for it — Dani Mesejo
– Dani Mesejo, Commented Dec 30, 2022 at 16:41
Your problem is tough to vectorize because the required behavior for element i is dependent on the results of the computations done for the previous iterations. The for loop is probably the best you can do. — Chrysophylaxs
– Chrysophylaxs, Commented Dec 30, 2022 at 16:42
In that case, I suspect that r1 and r2 will always follow some sort of pattern dependent on start, jump and L. It might be most efficient to construct them directly using those values — Chrysophylaxs
– Chrysophylaxs, Commented Dec 30, 2022 at 17:09

EducateMe · Accepted Answer · 2022-12-30 18:27:20Z

1

As suggested by @Chrysoplylaxs in comments, I made a boolean mask and it worked like a charm!

mask = np.tile([True]*jump + [False]*jump, len(l1)//jump).astype(bool)
r1 = l1[mask[:len(l1)]]
r2 = l2[mask[:len(l2)]]

answered Dec 30, 2022 at 18:27

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