Numpy : multivariate indexing?

Question

I wander, is it possible to index several dimensions at once ? With some broadcasting. Example :

Suppose i have an array A, shaped (n,d). Suppose i have a indexing array, say I with integer values between 0 and d-1. Set B = A[:,I].

If shape(I) == (k,), for whaterver k, then B has shape (n,k) and B[x,y] = A[x,I[y]].

But if shape(I) == (k,p) for whatever (k,p), then i wanted B to be shaped (n,k,p) with B[x,y,z] = A[x,I[y,z]].

1° How can i get this behavior ?

2° Does it have a drawback i did not see ?

tenhjo · Accepted Answer · 2020-01-07 10:43:43Z

1

You can do it exactly as you described it:

import numpy as np
n = 100
d = 20
k = 10
p = 17

A = np.random.random((n, d))
I = np.random.randint(low=0, high=d, size=(k, p))
B = A[:, I]
print(B.shape)  # (n, k, p)

# Testing if the new array B is constructed as expected
x = 3
y = 5
z = 7
print(B[x, y, z])
print(A[x, I[y, z]])
print(B[x, y, z] == A[x, I[y, z]])

Its hard to answer if this is a good implementation or not, without context. But in general it is a good idea to use numpy and vectorization if you have speed in mind.

edited Jan 7, 2020 at 10:43

answered Jan 7, 2020 at 10:36

tenhjo

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1 Comment

lrnv Over a year ago

I dont know what got me confused. I think i have trouble undertanding what is the difference between masking and indexing, i'll read on that. Thanks for the reminder anyway ! You made my day..

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