NumPy: Create an array of (3, 4) shape and convert the array elements in smaller chunks

Last update on August 29 2025 12:45:31 (UTC/GMT +8 hours)

Chunkify (3,4) Array Elements

Write a NumPy program to create an array of (3, 4) shapes and convert the array elements into smaller chunks.

Pictorial Presentation:

Python NumPy: Create an array of (3, 4) shape and convert the array elements in smaller chunks

Sample Solution:

Python Code:

# Importing the NumPy library and aliasing it as 'np'
import numpy as np

# Creating a 1-dimensional array 'x' with values from 0 to 11 and reshaping it into a 3x4 array
x = np.arange(12).reshape(3, 4)

# Printing a message indicating the original array elements will be shown
print("Original array elements:")

# Printing the original array 'x' with its elements
print(x)

# Printing a message indicating the array will be displayed in small chunks
print("Above array in small chunks:")

# Using np.nditer to iterate through the array 'x' in Fortran order with external loop
# Iterating through 'x' in a way that external_loop flag generates chunks of elements in the Fortran order
for a in np.nditer(x, flags=['external_loop'], order='F'):
    print(a)  # Printing each chunk

Sample Output:

Original array elements:                                               
[[ 0  1  2  3]                                                         
 [ 4  5  6  7]                                                         
 [ 8  9 10 11]]                                                        
Above array in small chuncks:                                          
[0 4 8]                                                                
[1 5 9]                                                                
[ 2  6 10]                                                             
[ 3  7 11]

Explanation:

x= np.arange(12).reshape(3, 4): Create an array x with values from 0 to 11, and reshape it into a 3x4 array.

for a in np.nditer(x, flags=['external_loop'], order='F'):: Use np.nditer to create an iterator for array x. Set the flags parameter to include 'external_loop', which allows for iterating over larger chunks of the array at once, based on memory layout. Set the order parameter to 'F' to iterate over the array in Fortran/column-major order (i.e., column by column).

Finally print(a) prints the current chunk in the iteration.

For more Practice: Solve these Related Problems:

Write a NumPy program to split a (3,4) array into column-wise chunks using np.hsplit and validate each chunk.
Create a function that divides a 2D array into smaller sub-arrays (chunks) and verifies the dimensions of each.
Test chunking of a 2D array by splitting rows into multiple segments and reassembling them later.
Utilize slicing to manually divide a (3,4) array into column chunks and compare with the np.hsplit output.

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