NumPy Array Iterating

Iterating means going through elements one by one. A normal Python for loop works, and NumPy adds helpers for multi-dimensional arrays.

Iterate a 1-D array

Example

import numpy as np

arr = np.array([1, 2, 3])
for x in arr:
    print(x)

Output

1
2
3

Iterate a 2-D array

A loop over a 2-D array gives you one row at a time:

Example

import numpy as np

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

Output

[1 2 3]
[4 5 6]

To reach every single value you need one loop per dimension:

Example

import numpy as np

arr = np.array([[1, 2], [3, 4]])
for row in arr:
    for x in row:
        print(x)

Output

1
2
3
4

np.nditer(): every element, any dimension

Example

import numpy as np

arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
for x in np.nditer(arr):
    print(x, end=" ")

Output

1 2 3 4 5 6 7 8 

np.ndenumerate(): elements with their index

Example

import numpy as np

arr = np.array([[10, 20], [30, 40]])
for index, value in np.ndenumerate(arr):
    print(index, value)

Output

(0, 0) 10
(0, 1) 20
(1, 0) 30
(1, 1) 40

Loops are fine for learning and for small arrays, but on real data prefer vectorized operations such as arr * 2 or arr.sum(). They do the same work in compiled code and are far faster.