NumPy amin()

import numpy as np

array1 = np.array([5, 2, 8, 1, 9])

# find minimum value from array1
max_value = np.amin(array1)

print(max_value)  

# Output: 1

amin() Syntax

The syntax of amin() is:

numpy.amin(a, axis = None, keepdims = False)

amin() Arguments

The amin() function takes following arguments:

• a - the input array
• axis (optional) - the axis along which the minimum value is computed
• keepdims (optional) - whether to preserve the input array's dimension (bool)

amin() Return Value

The amin() function returns the minimum element from an array or along a specified axis.

Example 1: amin() With 2-D Array

The axis argument defines how we can find the minimum element in a 2-D array.

• If axis = None, the array is flattened and the minimum value of the flattened array is returned.
• If axis = 0, the minimum value is calculated column-wise.
• If axis = 1, the minimum value is calculated row-wise.

import numpy as np

array = np.array([[10, 17, 25], 
                              [15, 11, 22]])
                  
# calculate the minimum value of the flattened array
result1 = np.amin(array)

print('The minimum value of the flattened array:', result1)

# calculate the column-wise minimum values
result2 = np.amin(array, axis=0)

print('Column-wise minimum values (axis 0):', result2)

# calculate the row-wise minimum values
result3 = np.amin(array, axis=1)

print('Row-wise minimum values (axis 1):', result3)

Output

The minimum value of the flattened array: 10
Column-wise minimum values (axis 0): [10 11 22]
Row-wise minimum values (axis 1): [10 11]

Here,

1. np.amin(array) calculates the minimum value of the flattened array. It returns the largest element in the entire array.
2. np.amin(array, axis=0) calculates the column-wise minimum values. It returns an array containing the minimum value for each column.
3. np.amin(array, axis=1) calculates the row-wise minimum values. It returns an array containing the minimum value for each row

Example 2: amin() With keepdims

When keepdims = True, the dimensions of the resulting array matches the dimension of an input array.

import numpy as np

array1 = np.array([[10, 17, 25], 
                                [15, 11, 22]])

print('Dimensions of original array:', array1.ndim)

result = np.amin(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.amin(array1, axis=1, keepdims=True)

print('\nWith keepdims:')
print(result)
print('Dimensions of array:', result.ndim)

Output

Dimensions of original array: 2

Without keepdims:
[10 11]
Dimensions of array: 1

With keepdims:
[[10]
 [11]]
Dimensions of array: 2

Without keepdims, the result is simply a one-dimensional array containing the minimum values along the specified axis.

With keepdims, the resulting array has the same number of dimensions as the input array.