Array Iteration
1D, 2D, and 3D NumPy Arrays

Understanding how to iterate through arrays is fundamental when working with NumPy. Whether you're dealing with a simple 1D list of numbers or a complex 3D tensor, efficient iteration allows you to manipulate, analyze, and understand your data better.

1D Array Iteration

Let’s start with the most basic structure — a 1-dimensional array.

import numpy as np

arr_1d = np.array([10, 20, 30, 40])
for element in arr_1d:
    print(element)

10
20
30
40

Explanation

This is straightforward. The for loop extracts each value in the 1D array one by one. It behaves just like a Python list during iteration.

2D Array Iteration

Now, let’s add a second dimension. A 2D array in NumPy is essentially a list of lists — or a matrix.

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
for row in arr_2d:
    print("Row:", row)

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

Explanation

Each iteration in a 2D array gives you a 1D array — essentially, each row. To access individual elements, you can add a nested loop:

for row in arr_2d:
    for item in row:
        print(item)

1
2
3
4
5
6

3D Array Iteration

3D arrays can be visualized as a stack of matrices. Each element in the first-level loop gives you a 2D array.

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

for matrix in arr_3d:
    print("Matrix:
", matrix)

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

Nested Iteration

To reach each scalar element in a 3D array, use nested loops:

for matrix in arr_3d:
    for row in matrix:
        for item in row:
            print(item)

1
2
3
4
5
6
7
8

Using NumPy's nditer() for Universal Iteration

Nested loops can get bulky. NumPy offers np.nditer() as a neat solution to iterate over any array, regardless of its dimensionality.

for item in np.nditer(arr_3d):
    print(item)

1
2
3
4
5
6
7
8

Why use nditer()?

• It's dimension neutral
• Cleaner code
• Faster performance for large arrays

Best Practices & Gotchas

• Always check array shape before looping, especially for 3D or reshaped arrays.
• Use nditer() when possible for clean, readable, and scalable code.
• Avoid writing deeply nested loops if you can vectorize or flatten your operations.
• Print intermediate values while debugging or learning — it builds intuition.

Conclusion

Iteration gives you control. Whether you're processing an image pixel-by-pixel or analyzing a dataset row-by-row, understanding how to loop through arrays is basic thing you need. With NumPy’s tools — from simple for loops to powerful utilities like nditer() — you can write clean, efficient, and intuitive code.