When working with arrays in NumPy, one common task is iteration—looping through array elements to process or transform data. While Python’s native for loop works, NumPy provides powerful tools to efficiently iterate through arrays, even multidimensional ones.

In this article, you’ll learn:

• ✅ How to iterate through 1D, 2D, and 3D arrays

• The role of nditer() for efficient iteration

• Modifying elements while iterating

• ⚠️ Common pitfalls and how to avoid them

• Full working example

1. Iterating Through 1D Arrays

Let’s start with a simple 1D array:

import numpy as np

arr = np.array([10, 20, 30, 40])

for x in arr:
    print(x)

Output:

10
20
30
40

This is identical to looping over a Python list.

2. Iterating Through 2D Arrays

In a 2D array, each item in the loop is a row (a 1D array):

arr = np.array([[1, 2], [3, 4], [5, 6]])

for row in arr:
    print("Row:", row)
    for item in row:
        print("Item:", item)

Output:

Row: [1 2]
Item: 1
Item: 2
...

You can nest loops to iterate each element.

3. Iterating Through 3D Arrays

In 3D arrays, the first loop gives you a 2D sub-array:

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

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

⚡ 4. Efficient Iteration with np.nditer()

NumPy provides the nditer() object for flat, efficient iteration across any dimension:

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

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

Output:

1
2
3
4

This is a more compact way to iterate element-by-element in multi-dimensional arrays.

5. Modifying Elements While Iterating

By default, nditer() does not allow modification of array elements. To enable it, use the op_flags=['readwrite'] option:

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

for x in np.nditer(arr, op_flags=['readwrite']):
    x[...] = x * 2  # Modify in place

print(arr)

Output:

[[2 4]
 [6 8]]

6. Enumerated Iteration

If you want to know the index position while iterating, use ndenumerate():

arr = np.array([[10, 20], [30, 40]])

for index, value in np.ndenumerate(arr):
    print(f"Index: {index}, Value: {value}")

Output:

Index: (0, 0), Value: 10
Index: (0, 1), Value: 20
...

7. Iterating with Different Data Types

You can specify the data type during iteration using dtype:

arr = np.array([1.5, 2.5, 3.5])

for x in np.nditer(arr, flags=['buffered'], op_dtypes=['int']):
    print(x)

This will print:

1
2
3

Full Working Example

import numpy as np

# 2D Array
arr = np.array([[1, 2, 3], [4, 5, 6]])

print("Basic Iteration:")
for row in arr:
    for item in row:
        print(item, end=' ')
print("\n")

print("Using nditer:")
for x in np.nditer(arr):
    print(x, end=' ')
print("\n")

print("Modify Elements In-place:")
for x in np.nditer(arr, op_flags=['readwrite']):
    x[...] = x * 10
print(arr)

print("Enumerated Iteration:")
for idx, val in np.ndenumerate(arr):
    print(f"Index {idx} -> {val}")

✅ Tips & Best Practices

⚠️ Common Pitfalls

Conclusion

Iteration is an essential tool when working with NumPy arrays, especially for data transformation and analysis. While Python’s native loops work, NumPy’s advanced iterators like nditer() and ndenumerate() provide more power, flexibility, and performance.

What’s Next?

Explore more about NumPy:

• Vectorized Operations in NumPy

• Reshaping Arrays in NumPy

• Stacking and Splitting Arrays

• Broadcasting in NumPy