NumPy: Collapse a 3-D array into one dimension array

Last update on March 24 2025 13:37:07 (UTC/GMT +8 hours)

Collapse 3D Array to 1D

Write a NumPy program to collapse a 3-D array into a one-dimensional array.

Pictorial Presentation:

Sample Solution:

Python Code:

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

# Creating a 3x3 identity matrix using np.eye
x = np.eye(3)

# Printing the original 3x3 identity matrix
print("3-D array:")
print(x)

# Raveling the 3x3 matrix in Fortran (column-major) order to create a one-dimensional array
f = np.ravel(x, order='F')

# Printing the resulting one-dimensional array after raveling
print("One dimension array:")
print(f) 

Sample Output:

3-D array:                                                             
[[ 1.  0.  0.]                                                         
 [ 0.  1.  0.]                                                         
 [ 0.  0.  1.]]                                                        
One dimension array:                                                   
[ 1.  0.  0.  0.  1.  0.  0.  0.  1.]

Explanation:

In the above code -

x = np.eye(3): This part creates a 3x3 identity matrix using the np.eye() function. In an identity matrix, the diagonal elements are 1, and the off-diagonal elements are 0.
f = np.ravel(x, order='F'): The np.ravel() function is used to flatten the input matrix x into a one-dimensional array. The order parameter is set to 'F' (Fortran order), which specifies that the elements should be flattened column-wise (i.e., going down columns first, then moving to the next column).

print(f) prints the resulting one-dimensional NumPy array f, which contains the flattened elements of the identity matrix x in Fortran order.

For more Practice: Solve these Related Problems:

• Flatten a 3D array into a 1D array using np.reshape with -1 and verify the total number of elements.
• Write a function that collapses any multi-dimensional array into a one-dimensional array while preserving order.
• Compare the outputs of np.flatten and np.ravel for a non-contiguous 3D array to understand their differences.
• Test the flattening process on a large 3D array and measure the time efficiency of the operation.

Python-Numpy Code Editor: