How to loop through 2D numpy array using x and y coordinates without getting out of bounds error?

Question

I have tried the following:

import numpy as np a = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]]) print a rows = a.shape[0] cols = a.shape[1] print rows print cols  for x in range(0, cols - 1):     for y in range(0, rows -1):         print a[x,y] 

This will only print numbers 1 - 6.

I have also tried only subtracting 1 from either rows or cols in the range, but that either leads to out of bounds error or not all numbers printed.

Answer 1

You get prettier code with:

for iy, ix in np.ndindex(a.shape):     print(a[iy, ix]) 

resulting in:

1 2 3 4 5 6 7 8 9 10 11 12 

Answer 2

a.shape[0] is the number of rows and the size of the first dimension, while a.shape[1] is the size of the second dimension. You need to write:

for x in range(0, rows):     for y in range(0, cols):         print a[x,y] 

Note how rows and cols have been swapped in the range() function.

Edit: It has to be that way because an array can be rectangular (i.e. rows != cols). a.shape is the size of each dimension in the order they are indexed. Therefore if shape is (10, 5) when you write:

a[x, y] 

the maximum of x is 9 and the maximum for y is 4. x and y are actually poor names for array indices, because they do not represent a mathematical cartesisan coordinate system, but a location in memory. You can use i and j instead:

for i in range(0, rows):     for j in range(0, cols):         print a[i,j] 

The documentation is a bit long but has a good in-depth description of indices and shapes.