The amax()
function computes maximum value along a specified axis in an array.
Example
import numpy as np
array1 = np.array([5, 2, 8, 1, 9])
# find maximum value in array1
maxValue = np.amax(array1)
print(maxValue)
# Output: 9
amax() Syntax
The syntax of amax()
is:
numpy.amax(a, axis = None, keepdims = False)
amax() Arguments
The amax()
function takes following arguments:
a
- the input arrayaxis
(optional) - the axis along which the maximum value is computedkeepdims
(optional) - whether to preserve the input array's dimension (bool
)
amax() Return Value
The amax()
function returns the maximum element from an array or along a specified axis.
Example 1: amax() With 2-D Array
The axis
argument defines how we can find the maximum element in a 2-D array.
- If
axis
=None
, the array is flattened and the maximum value of the flattened array is returned. - If
axis
= 0, the maximum value is calculated column-wise. - If
axis
= 1, the maximum value is calculated row-wise.
import numpy as np
array1 = np.array([[10, 17, 25],
[15, 11, 22]])
# calculate the maximum value of the flattened array
result1 = np.amax(array1)
print('The maximum value of the flattened array:', result1)
# calculate the column-wise maximum values
result2 = np.amax(array1, axis=0)
print('Column-wise maximum values (axis 0):', result2)
# calculate the row-wise maximum values
result3 = np.amax(array1, axis=1)
print('Row-wise maximum values (axis 1):', result3)
Output
The maximum value of the flattened array: 25 Column-wise maximum values (axis 0): [15 17 25] Row-wise maximum values (axis 1): [25 22]
Here,
np.amax(array1)
calculates the maximum value of the flattened array. It returns the largest element in the entire array.np.amax(array1, axis=0)
calculates the column-wise maximum values. It returns an array containing the maximum value for each column.np.amax(array1, axis=1)
calculates the row-wise maximum values. It returns an array containing the maximum value for each row
Example 2: amax() With keepdims
When keepdims = True
, the dimensions of the resulting array matches the dimension of the input array.
import numpy as np
array1 = np.array([[10, 17, 25],
[15, 11, 22]])
print('Dimensions of original array:', array1.ndim)
result = np.amax(array1, axis=1)
print('\nWithout keepdims:')
print(result)
print('Dimensions of array:', result.ndim)
# set keepdims to True to retain the dimension of the input array
result = np.amax(array1, axis=1, keepdims=True)
print('\nWith keepdims:')
print(result)
print('Dimensions of array:', result.ndim)
Output
Dimensions of original array: 2 Without keepdims: [25 22] Dimensions of array: 1 With keepdims: [[25] [22]] Dimensions of array: 2
Without keepdims
, the result is simply a one-dimensional array containing the maximum values along the specified axis.
With keepdims
, the resulting array has the same number of dimensions as the input array.