The amin()
function computes the minimum value along a specified axis in an array.
Example
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 arrayaxis
(optional) - the axis along which the minimum value is computedkeepdims
(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,
np.amin(array)
calculates the minimum value of the flattened array. It returns the largest element in the entire array.np.amin(array, axis=0)
calculates the column-wise minimum values. It returns an array containing the minimum value for each column.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.