Integer array indexing python

Question

import numpy as np
a = np.array([[1,2], [3, 4], [5, 6]])
print(a[[0, 1, 2], [0, 1, 0]]) # Prints "[1 4 5]"
print(a[[0, 0], [1, 1]]) # Prints "[2 2]"

I don't understand why it results [1 4 5] and [2 2]

• Adi219: What is your expected output?
• BETUL: I have no expectation about that. I wonder why it results so

Answer 1

Because you're slicing the array with indexes

a[[0, 1, 2], [0, 1, 0]] is equivalent to

a[0, 0] # 1
a[1, 1] # 4
a[2, 0] # 5

whereas a[[0, 0], [1, 1]] is equivalent to twice a[0, 1]

More about Numpy indexing here

• user50619: is there a way to get a C or even fortran like array in python? The lists and othe rdata structure s seem very confusing especially combined with the strange scoping rules it seems to apply
• FabienP: Hi @user50619. Probably your question fits another thread better. Short answer is that numpy deals with C-like arrays under the hood, and you can orient the array C-like or Fortran-like. Please visit the doc for further details

Answer 2

Think of it as 2d-array access. When you initialize a you get your 2d array in the form:

[ 1 2 ]
[ 3 4 ]
[ 5 6 ]

Numpy indexing when given a 2d array works as follows: you input a list of the row indexes, then a list of the column indexes. Semantically your first index retrieval statement is saying "from row 0 retrieve element 0, from row 1 retrieve element 1, and from row 2 retrieve element 0" which corresponds to [1 4 5]. You can then figure out why you get [2 2] for the second statement.

You can read more about this advanced indexing here: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#integer-array-indexing