Numpy - Iterating Over Arrays

NumPy provides flexible and efficient ways to iterate over arrays of any dimensionality. For a one-dimensional array, iterating is straightforward and similar to iterating over a Python list.

Let's understand with the help of an example:

import numpy as np

# Create a 1D array
arr = np.array([1, 2, 3, 4, 5])

# Iterate over the array
for ele in arr:
    print(ele)

1
2
3
4
5

Explanation:

• Each element of the array is accessed sequentially, making iteration simple and intuitive.

Let's explore various others ways to iterate over Arrays:

Iteration Over a Two-Dimensional Array

For multidimensional arrays, iteration is performed over the first axis by default. Each iteration yields a one-dimensional sub-array.

import numpy as np 

# Create a 2D array
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Iterate over rows
for row in arr_2d:
    print(row)

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

Explanation:

• arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) creates a 2D array with three rows and three columns.
• for row in arr_2d: loops through each row of the 2D array, treating it as a separate one-dimensional array.

For Element-Wise Iteration

To iterate over individual elements of a 2D array, the array can be flattened using the .flat attribute.

import numpy as np

arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

for element in arr_2d.flat:
    print(element)

1
2
3
4
5
6
7
8
9

Iterating Over Higher-Dimensional Arrays

Iteration is similar for arrays with more than two dimensions. In such cases, iteration occurs along the first axis by default.

import numpy as np

# Create a 3D array
arr_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

# Iterate over the 3D array
for sub_array in arr_3d:
    print(sub_array)

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

Explanation:

• Each iteration returns a 2D sub-array from the 3D array.

Using nditer

The numpy.nditer object offers a various way to iterate over arrays. It allows iteration in different orders and provides better control over the iteration process.

import numpy as np

# Using nditer
arr = np.array([[1, 2], [3, 4]])

for element in np.nditer(arr):
    print(element)

1
2
3
4

We can also specify the order of iteration (row-major order 'C' or column-major order 'F'):

import numpy as np

arr = np.array([[1, 2], [3, 4]])

for element in np.nditer(arr, order='F'):
    print(element)

1
3
2
4

Using Enumerate

For multidimensional arrays, we can use enumerate to access both the index and value during iteration.

import numpy as np

# Create a 2D array
arr = np.array([[10, 20], [30, 40]])

# Enumerate through rows
for idx, row in enumerate(arr):
    print(f"Row {idx}: {row}")

Row 0: [10 20]
Row 1: [30 40]

Using Broadcasting

When working with arrays of different shapes, Broadcasting allows iteration across combined shapes seamlessly.

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([[10], [20]])

# Perform a broadcasted addition
for x, y in np.nditer([arr1, arr2]):
    print(f"{x} + {y} = {x + y}")

1 + 10 = 11
2 + 10 = 12
3 + 10 = 13
1 + 20 = 21
2 + 20 = 22
3 + 20 = 23