NumPy: Create a contiguous flattened array

Last update on March 24 2025 13:39:46 (UTC/GMT +8 hours)

Flatten Array

Write a NumPy program to create a contiguous flattened array.

Pictorial Presentation:

Sample Solution:

Python Code:

# Importing the NumPy library with an alias 'np'
import numpy as np

# Creating a 2D NumPy array with two rows and three columns
x = np.array([[10, 20, 30], [20, 40, 50]])
# Displaying the original array
print("Original array:")
print(x)

# Flattening the array 'x' into a 1D array using np.ravel
y = np.ravel(x)
# Displaying the flattened array 'y'
print("New flattened array:")
print(y) 

Sample Output:

Original array:                                                        
[[10 20 30]                                                            
 [20 40 50]]                                                           
New flattened array:                                                   
[10 20 30 20 40 50] 

Explanation:

In the above exercise -

x = np.array([[10, 20, 30], [20, 40, 50]]): This line creates a two-dimensional NumPy array ‘x’ with two rows and three columns.

print(x): This line prints the ‘x’ array, which has the shape (2, 3) and contains the specified elements.

y = np.ravel(x): This line flattens the two-dimensional array ‘x’ into a one-dimensional array y using the np.ravel() function.

print(y): This line prints the flattened one-dimensional array ‘y’, which contains the elements [10, 20, 30, 20, 40, 50].

For more Practice: Solve these Related Problems:

• Flatten a multi-dimensional array using both np.flatten and np.ravel and compare their outputs.
• Create a function that collapses an array into 1D and then reconstructs the original shape using reshape.
• Verify that the order of elements in the flattened array is consistent with row-major ordering.
• Test flattening on non-contiguous arrays and analyze the differences in performance and output.

Python-Numpy Code Editor: