Element-wise multiplication with reshaped arrays using NumPy

Last update on March 26 2025 13:11:47 (UTC/GMT +8 hours)

5. Reshape Arrays for Element-wise Multiplication

Given two arrays, x = np.array([1, 2, 3]) and y = np.array([4, 5, 6, 7, 8, 9]). Write a Numpy program that reshapes x and y to make them broadcast-compatible and perform element-wise multiplication.

Sample Solution:

Python Code:

# Import the NumPy library
import numpy as np

# Create a 1D array x
x = np.array([1, 2, 3])

# Create a 1D array y
y = np.array([4, 5, 6, 7, 8, 9])

# Reshape x to be a column vector (3, 1)
x_reshaped = x.reshape(3, 1)

# Reshape y to be a row vector (1, 6)
y_reshaped = y.reshape(1, 6)

# Perform element-wise multiplication using broadcasting
result = x_reshaped * y_reshaped

# Print the reshaped arrays and the result
print("Reshaped Array x:\n", x_reshaped)
print("Reshaped Array y:\n", y_reshaped)
print("Result of element-wise multiplication:\n", result)

Output:

Reshaped Array x:
 [[1]
 [2]
 [3]]
Reshaped Array y:
 [[4 5 6 7 8 9]]
Result of element-wise multiplication:
 [[ 4  5  6  7  8  9]
 [ 8 10 12 14 16 18]
 [12 15 18 21 24 27]]

Explanation:

• Import the NumPy library: This step imports the NumPy library, which is essential for numerical operations.
• Create a 1D array x: We create a 1D array x with elements [1, 2, 3].
• Create a 1D array y: We create a 1D array y with elements [4, 5, 6, 7, 8, 9].
• Reshape x to be a column vector (3, 1): We use x.reshape(3, 1) to change x into a column vector with shape (3, 1).
• Reshape y to be a row vector (1, 6): We use y.reshape(1, 6) to change y into a row vector with shape (1, 6).
• Perform element-wise multiplication using broadcasting: NumPy automatically broadcasts the reshaped arrays to a compatible shape and performs element-wise multiplication.
• Print the reshaped arrays and the result: This step prints the reshaped arrays x_reshaped and y_reshaped.

For more Practice: Solve these Related Problems:

• Reshape two 1D arrays into compatible dimensions for broadcasting, perform element-wise multiplication, and verify the resulting shape.
• Create a function that takes two 1D arrays, reshapes one to (n, 1) and the other to (1, n), then multiplies them to form an outer product.
• Implement a solution that uses np.reshape to align dimensions and then computes the element-wise product, followed by a sum of each row.
• Test the function on arrays with different lengths and verify that the multiplication produces the correct 2D array.

Python-Numpy Code Editor:

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