NumPy Array Iteration

Table of Contents

• Introduction
• Iterating Over a 1-D Array
• Iterating Over a 2-D Array
• Iterating Over a 3-D Array

Introduction

Iterating over an array means going through the array element by element. We can easily perform this using the Python for loop. However, for large multidimensional arrays, there are certain tools available in NumPy to speed up the process.

Iterating Over a 1-D Array

Once again, we begin by first importing the NumPy package and creating a random 1-D array using the numpy.random.choice function and seeding the PRNG so that results are reproducible by the reader.

Example

1import numpy as np
2R = np.random.RandomState(18) #set a random seed
3arr = R.choice(10, size=10, replace=False)
4print(arr)

[7 9 0 4 2 1 6 5 8 3]

We now iterate over the elements of arr using a for loop.

Example

1for x in arr:
2  print(x)

7
9
0
4
2
1
6
5
8
3

We can also provide an index for each element using the enumerate function.

Example

1for x in enumerate(arr):
2    print(x)

(0, 7)
(1, 9)
(2, 0)
(3, 4)
(4, 2)
(5, 1)
(6, 6)
(7, 5)
(8, 8)
(9, 3)

However, for NumPy arrays, there is actually a faster built-in function - numpy.ndenumerate() to return an iterator.

Example

1for (i,x) in np.ndenumerate(arr):
2    print(i, x)

(0,) 7
(1,) 9
(2,) 0
(3,) 4
(4,) 2
(5,) 1
(6,) 6
(7,) 5
(8,) 8
(9,) 3

The advantages of the numpy.ndenumerate() function may not seem obvious for the above 1-D array. However, for multidimensional arrays, it does away with the need for nested loops as the following sections will elaborate.

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Iterating Over a 2-D Array

Iterating over a 2-D array is similar to iterating over a 1-D array, except that 2 for loops are required.

Example

1arr2 = R.choice(12, size=(3,4), replace=False)
2print(arr2)

[[ 3  4 10  0]
 [ 8 11  6  7]
 [ 9  5  1  2]]

We now iterate over the elements of arr2 using 2 for loops.

Example

1for j in arr2: # for each row
2    for i in j:  # for each element of the row
3        print(i)

3
4
10
0
8
11
6
7
9
5
1
2

To return the indices and values at the same time, we can invoke the numpy.ndenumerate() function once again.

Example

1for (i,x) in np.ndenumerate(arr2):
2    print(i, x)

(0, 0) 3
(0, 1) 4
(0, 2) 10
(0, 3) 0
(1, 0) 8
(1, 1) 11
(1, 2) 6
(1, 3) 7
(2, 0) 9
(2, 1) 5
(2, 2) 1
(2, 3) 2

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Iterating Over a 3-D Array

Iterating over a 3-D array is similar to iterating over a 2-D array, except that 3 for loops are now required.

Example

1arr3 = R.choice(12, size=(2,3,2), replace=False)
2print(arr3)

[[[11  2]
  [ 0  6]
  [ 9  5]]

 [[ 8  3]
  [ 1 10]
  [ 4  7]]]

We may iterate over the elements of arr3 using 3 for loops.

Example

1for k in arr3: # for each frame
2    for j in k:  # for each row in frame
3        for i in j  # for each element in row
4            print(i)

11
2
0
6
9
5
8
3
1
10
4
7

To return the indices and values at the same time, we may once again invoke the numpy.ndenumerate() function.

Example

1for (i,x) in np.ndenumerate(arr3):
2    print(i, x)

(0, 0, 0) 11
(0, 0, 1) 2
(0, 1, 0) 0
(0, 1, 1) 6
(0, 2, 0) 9
(0, 2, 1) 5
(1, 0, 0) 8
(1, 0, 1) 3
(1, 1, 0) 1
(1, 1, 1) 10
(1, 2, 0) 4
(1, 2, 1) 7

In fact, the indices printed above give us an insight into the order in which the elements are iterated over.

We have now witnessed the power of the numpy.ndenumerate() function in iterating over multidimensional arrays. It is our method of choice when it comes to NumPy array iteration, especially large multidimensional arrays.