Use np.maximum.reduce to find maximum element along an Axis in NumPy
NumPy: Universal Functions Exercise-20 with Solution
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.
Python-Numpy Code Editor:
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