NumPy Array Iterating

 Iterating Arrays in NumPy

Iteration means accessing elements of an array one by one.

With NumPy, we can easily iterate through arrays, including multi-dimensional ones, using Python’s basic for loops or NumPy’s helper functions.

Iterating 1-D Arrays

Program:

When iterating over a 1-D array, each element is accessed one by one:

import numpy as np

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

for x in arr:

print(x)

Output:

1

2

3

Iterating 2-D Arrays

Program:

In a 2-D array, iteration goes through each row:

import numpy as np

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

for x in arr:  

print(x)

Output:

[1 2 3]

[4 5 6]

Program:

To access each scalar value, you can nest loops:

arr = [[1, 2], [3, 4], [5, 6]]

for x in arr:

    for y in x:

        print(y)

Output:

1

2

3

4

5

6

Iterating 3-D Arrays

In a 3-D array, iteration goes through each 2-D array:

Program:

import numpy as np

arr = np.array([

    [[1, 2, 3], [4, 5, 6]],

    [[7, 8, 9], [10, 11, 12]]

])

for x in arr:

    print(x)

Output:

[[1 2 3]

 [4 5 6]]

[[7 8 9]

 [10 11 12]]

Program:

To access individual scalar values, use nested loops:

arr = [

    [[1, 2], [3, 4]],

    [[5, 6], [7, 8]]

]

for x in arr:

    for y in x:

        for z in y:

            print(z)

Output:

1

2

3

4

5

6

7

8

Iterating with np.nditer()

The nditer() function simplifies iterating through each scalar element, especially for high-dimensional arrays.

Program:

import numpy as np

arr = np.array([

    [[1, 2], [3, 4]],

    [[5, 6], [7, 8]]

])

for x in np.nditer(arr):

    print(x)

Output:

1

2

3

4

5

6

7

8

Iterating with Data Type Conversion

You can use the op_dtypes argument in nditer() to change data types while iterating.

To enable this, set flags=['buffered'] to allow temporary memory allocation (buffering).

Program:

(iterate as strings):

import numpy as np

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

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

    print(x)

Output:

b'1'

b'2'

b'3'

Iterating with Step Sizes

You can use slicing to skip elements during iteration.

Program:

(skip every other element):

import numpy as np

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

for x in np.nditer(arr[:, ::2]):

    print(x)

Output:

1

3

5

7

Enumerated Iteration with np.ndenumerate()

Enumeration means accessing both the index and the value while iterating.

Program:

1-D array

import numpy as np

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

for idx, x in np.ndenumerate(arr):

    print(idx, x)

Output:

(0,)1

(1,)2

(2,)3

Program: 

2-D array

import numpy as np

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

for idx, x in np.ndenumerate(arr):

    print(idx, x)

Output:

(0,0)1

(0,1)2

(0,2)3

(0,3)4

(1,0)5

(1,1)6

(1,2)7

(1,3)8