How to use NumPy Broadcasting to divide 2D arrays by 1D arrays

Last update on April 29 2025 12:55:06 (UTC/GMT +8 hours)

17. Divide Each Row of 2D Array by 1D Array

Create a 2D array x of shape (5, 5) and a 1D array y of shape (5,). Write a NumPy program that divides each row of a by b using broadcasting.

Sample Solution:

Python Code:

import numpy as np

# Create a 2D array x of shape (5, 5)
x = np.array([[10, 20, 30, 40, 50],
              [5, 15, 25, 35, 45],
              [1, 2, 3, 4, 5],
              [6, 12, 18, 24, 30],
              [9, 18, 27, 36, 45]])

# Create a 1D array y of shape (5,)
y = np.array([1, 2, 3, 4, 5])

# Divide each row of x by the array y using broadcasting
result = x / y[:, np.newaxis]

print(result)

Output:

[[10.         20.         30.         40.         50.        ]
 [ 2.5         7.5        12.5        17.5        22.5       ]
 [ 0.33333333  0.66666667  1.          1.33333333  1.66666667]
 [ 1.5         3.          4.5         6.          7.5       ]
 [ 1.8         3.6         5.4         7.2         9.        ]]

Explanation:

Import NumPy: Import the NumPy library to handle array operations.
Create 2D array x: Define a 2D array x with shape (5, 5).
Create 1D array y: Define a 1D array y with shape (5,).
Broadcasting Division: Use broadcasting to divide each row of x by the corresponding element in y.
Print Result: Print the resulting array.

For more Practice: Solve these Related Problems:

Create a function that divides each row of a 2D array by a corresponding element in a 1D array and returns the result.
Implement a solution that normalizes each row of a 2D array by its maximum value using broadcasting.
Test the division on a 2D array with both positive and negative values to ensure proper broadcasting behavior.
Compare the result of row-wise division using broadcasting with that obtained by manually looping through the rows.

Go to:

Previous: Numpy Program to subtract 1D array from 3D array using Broadcasting.
Next: How to reshape arrays and perform element-wise addition using NumPy.

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