Use np.maximum.reduce to find maximum element along an Axis in NumPy

Last update on April 30 2025 12:56:16 (UTC/GMT +8 hours)

20. Ufunc: Maximum Reduction Along Axis

Using ufuncs in Aggregations:

Write a NumPy program that uses np.maximum.reduce to find the maximum element along a specified axis of a 2D array.

Sample Solution:

Python Code:

import numpy as np

# Create a 2D NumPy array
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Find the maximum element along the specified axis (e.g., axis=0)
max_elements_axis0 = np.maximum.reduce(array_2d, axis=0)

# Find the maximum element along the specified axis (e.g., axis=1)
max_elements_axis1 = np.maximum.reduce(array_2d, axis=1)

# Print the original 2D array and the results
print("Original 2D Array:")
print(array_2d)

print("\nMaximum elements along axis 0:")
print(max_elements_axis0)

print("\nMaximum elements along axis 1:")
print(max_elements_axis1)

Output:

Original 2D Array:
[[1 2 3]
 [4 5 6]
 [7 8 9]]

Maximum elements along axis 0:
[7 8 9]

Maximum elements along axis 1:
[3 6 9]

Explanation:

Import NumPy Library:

Import the NumPy library to handle array operations.

Create 2D Array:

Define a 2D NumPy array array_2d with some example data.

Maximum Along Axis 0:

Use "np.maximum.reduce()" to find the maximum elements along axis 0 (columns) of the 2D array, storing the result in max_elements_axis0.

Maximum Along Axis 1:

Use "np.maximum.reduce()" to find the maximum elements along axis 1 (rows) of the 2D array, storing the result in 'max_elements_axis1'.

Finally print the original 2D array and the results of the maximum element computations along the specified axes to verify the operation.

For more Practice: Solve these Related Problems:

Write a Numpy program to use np.maximum.reduce to find the maximum value along each column of a 2D array.
Write a Numpy program to compute the maximum element along a specified axis using np.maximum.reduce and then compare it with np.max.
Write a Numpy program to apply np.maximum.reduce on a 2D array with mixed positive and negative values and then identify the row with the highest maximum.
Write a Numpy program to combine np.maximum.reduce with a custom function that returns both the maximum value and its index along an axis.

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