numpy: indexing 1d array with multidimensional index

Question

How do I index a lower dimensional data array with a higher dimensional index array?

E.g.: Given a 1d data array and a 2d index array:

data = np.array([11,12,13])
idx = np.array([[0,1],
                [1,2])

I would like to get a 2d data array:

np.array([[11,12],
          [12,13]])

What have you tried so far?

norok2
– norok2

2018-09-20 14:45:56 +00:00
Commented Sep 20, 2018 at 14:45 — norok2
– norok2, Commented Sep 20, 2018 at 14:45

norok2 · Accepted Answer · 2018-09-20 14:48:47Z

2

This is very easy in Python / NumPy, thanks to the advanced Numpy indexing system, you just use your indexing as the slicing, e.g. data[idx].

data = np.array([11,12,13])
idx = np.array([[0,1],
                [1,2]])

# this will produce the correct result
data[idx]
# array([[11, 12],
#        [12, 13]])

answered Sep 20, 2018 at 14:48

norok2

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2 Comments

andrebeu Over a year ago

i had tried this, but now realize that what was missing was ensuring that the index array has to be int32.

Joao_PS Over a year ago

I used this to assign a 1D array with 2D array index... It's works too., but I don't know why...

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