NumPy amax() Function

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The NumPy amax() function returns the maximum value along a specified axis of an array. If no axis is specified, it computes the maximum of all elements in the array. Additionally, conditions can be applied to obtain the maximum value based on specific criteria.

The amax() function is an alias for the max() function in NumPy, which means that both functions perform the exact same operationthey compute the maximum value of an array or along a specified axis.

Syntax

Following is the syntax of the NumPy amax() function −

numpy.amax(a, axis=None, out=None, keepdims=False, initial=None, where=True)  

Parameters

Following are the parameters of the NumPy amax() function −

• a: Input array. The array can be of any shape or data type.
• axis (optional): Axis along which to compute the maximum. If None, the maximum is computed over the flattened array.
• out (optional): Alternate output array to place the result. It must have the same shape as the expected output.
• keepdims (optional): If True, the reduced dimensions are retained as dimensions of size one in the output. Default is False.
• initial (optional): Initial value to start the comparison. If not provided, the default is the minimum possible value for the data type.
• where (optional): A Boolean array. If True, include the corresponding element in the computation; otherwise, ignore it.

Return Values

This function returns a scalar value or a NumPy array containing the maximum values along the specified axis.

Example

Following is a basic example of finding the maximum value in an array using the NumPy amax() function −

import numpy as np  
# input array  
array = np.array([3, 5, 1, 7, 9])  
# finding the maximum value  
max_value = np.amax(array)  
print("Maximum Value:", max_value)  

Output

Following is the output of the above code −

Maximum Value: 9  

Example: Maximum Along an Axis

The amax() function can find the maximum values along a specified axis of a multi-dimensional array. In the following example, we compute the maximum values along rows and columns −

import numpy as np  
# 2D input array  
array = np.array([[3, 24, 5], [8, 45, 2], [7, 9, 19]])  
# maximum along rows (axis=1)  
max_along_rows = np.amax(array, axis=1)  
print("Maximum along rows:", max_along_rows)  
# maximum along columns (axis=0)  
max_along_columns = np.amax(array, axis=0)  
print("Maximum along columns:", max_along_columns) 

Output

Following is the output of the above code −

Maximum along rows: [24 45 19]
Maximum along columns: [ 8 45 19]

Example: Maximum with 'keepdims'

The keepdims parameter retains the reduced dimension as a size-one dimension in the output. This means that when we pass a multi-dimensional array and set this parameter to True, the reduced dimension's size is kept as 1, preserving the original dimensionality of the array. In the following example, we have demonstrated its use −

import numpy as np  
# 2D input array  
array = np.array([[3, 7, 5], [8, 4, 2], [6, 10, 9]])  
# maximum along columns with keepdims=True  
max_with_keepdims = np.amax(array, axis=0, keepdims=True)  
print("Maximum with keepdims:\n", max_with_keepdims)  

Output

Following is the output of the above code −

Maximum with keepdims:
 [[ 8 10  9]]

Example: Maximum with Where Condition

The where parameter allows computation of the maximum value based on the condition. In the following example, we have computed the maximum element which is less than 8 −

import numpy as np  
# input array  
array = np.array([3, 5, 1, 7, 9]) 
max_without_condition = np.amax(array)
print("Maximum without condition:", max_without_condition)

# where condition (only include values less than 8)  
max_with_condition = np.amax(array, where=array < 8, initial=-1)  
print("Maximum with condition:", max_with_condition)   

Output

Following is the output of the above code −

Maximum without condition: 9
Maximum with condition: 7

Example: Graphical Representation of 'amax()'

In the following example, we visualize the maximum value along rows and columns of a 2D array. To achieve this, we need to import the numpy and matplotlib.pyplot modules −

import numpy as np  
import matplotlib.pyplot as plt  

# 2D input array  
array = np.array([[3, 7, 5], [8, 4, 2], [6, 1, 9]])  
# maximum along rows  
max_rows = np.amax(array, axis=1)  
# maximum along columns  
max_columns = np.amax(array, axis=0)  

plt.plot(range(len(max_rows)), max_rows, label="Maximum along rows")  
plt.plot(range(len(max_columns)), max_columns, label="Maximum along columns")  
plt.title("Visualization of amax() Results")  
plt.xlabel("Index")  
plt.ylabel("Maximum Value")  
plt.legend()  
plt.grid()  
plt.show()  

Output

The plot visualizes the maximum values along rows and columns of the array −