How to use sorting in Numpy?

NumPy provides the np.sort() function to sort arrays. It returns a sorted copy of the array without changing the original.

1. Basic Sorting

import numpy as np

arr = np.array([3, 1, 5, 2, 8])
sorted_arr = np.sort(arr)

print(sorted_arr)

Output:

[1 2 3 5 8]

By default, NumPy sorts rows individually.

import numpy as np

arr = np.array([[3, 1, 2],
                [9, 7, 4]])

print(np.sort(arr))

Output:

[[1 2 3]
 [4 7 9]]

np.sort(arr, axis=0)

np.sort(arr, axis=1)

argsort() returns indices of sorted elements.

arr = np.array([40, 10, 30, 20])
indices = np.argsort(arr)

print(indices)

Output:

[1 3 2 0]

You can use it to rearrange the array:

print(arr[indices])

data = np.array([(1, 'B'), (3, 'A'), (2, 'C')],
                dtype=[('id', int), ('name', 'U1')])

print(np.sort(data, order='id'))

You can sort an array without creating a copy:

arr.sort()
print(arr)

NumPy does not have a direct parameter like reverse=True,
but you can reverse after sorting:

arr = np.array([3, 1, 4])
desc = np.sort(arr)[::-1]

print(desc)

Function	Purpose
`np.sort()`	Returns sorted copy
`arr.sort()`	Sorts in-place
`np.argsort()`	Returns indices of sorted order
`np.sort(arr, axis=0/1)`	Sort rows or columns
`np.sort(order='field')`	Sort structured arrays