Creating and slicing a 1D NumPy array

Last update on April 29 2025 12:56:00 (UTC/GMT +8 hours)

19. Sub-matrix Strides in Reshaped Array

Write a NumPy program that creates a 1D array of 20 elements and use reshape() to create a (4, 5) matrix. Slice a (2, 3) sub-matrix and print its strides.

Sample Solution:

Python Code:

import numpy as np

# Step 1: Create a 1D array of 20 elements
original_1d_array = np.arange(20)
print("Original 1D array:\n", original_1d_array)

# Step 2: Reshape the 1D array into a (4, 5) matrix
reshaped_matrix = original_1d_array.reshape(4, 5)
print("\nReshaped (4, 5) matrix:\n", reshaped_matrix)

# Step 3: Slice a (2, 3) sub-matrix from the reshaped matrix
sub_matrix = reshaped_matrix[:2, :3]
print("\nSliced (2, 3) sub-matrix:\n", sub_matrix)

# Step 4: Print the strides of the sub-matrix
print("\nStrides of the sub-matrix:\n", sub_matrix.strides)

Output:

Original 1D array:
 [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]

Reshaped (4, 5) matrix:
 [[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]]

Sliced (2, 3) sub-matrix:
 [[0 1 2]
 [5 6 7]]

Strides of the sub-matrix:
 (20, 4)

Explanation:

• Create a 1D array: A 1D array of 20 elements is created using np.arange(20).
• Reshape to (4, 5) matrix: The 1D array is reshaped into a (4, 5) matrix.
• Slice a sub-matrix: A (2, 3) sub-matrix is sliced from the reshaped matrix.
• Print strides: The strides of the sub-matrix are printed.

For more Practice: Solve these Related Problems:

• Write a NumPy program to extract various sub-matrices from a reshaped 1D array and analyze their strides.
• Write a NumPy program to compare the strides of a sub-matrix obtained via slicing versus one obtained using np.take.
• Write a NumPy program to create a 1D array, reshape it to a 2D matrix, then extract a non-contiguous sub-matrix and print its strides.
• Write a NumPy program to slice a 2D array into multiple overlapping sub-matrices and display their strides for comparison.

Go to:

• NumPy Memory Layout Exercises Home ↩
• NumPy Exercises Home ↩

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