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.