Numpy.sort() function – How to sort arrays using numpy in python
In this program, you will learn how to write a program to sort arrays using numpy.
First we need to import the numpy library then we have to use numpy.sort function to sort the values.
Syntax: numpy.sort(a, axis=-1, kind=’quicksort’, order=None)
This function return a sorted copy of an array.
Parameters:
a : array_like
Array to be sorted.
axis : int or None, optional
Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.
kind : {‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional
Sorting algorithm. Default is ‘quicksort’.
order : str or list of str, optional
When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.
Returns:
sorted_array : ndarray
Array of the same type and shape as a.
Sorting first axis using numpy:
import numpy as np
# sort along the first axis
a = np.random.rand(3,2)
print('array values before sort',a)
arr1 = np.sort(a, axis = 0)
print ("\nAlong first axis : \n", arr1)
Output:
array values before sort [[0.698065 0.18923593] [0.24840437 0.98040561] [0.83910185 0.44233389]] Along first axis : [[0.24840437 0.18923593] [0.698065 0.44233389] [0.83910185 0.98040561]]
Sorting last axis using numpy:
in this below program we are passing axis value to -1 to sort the last axis in the sort function along with array.
import numpy as np
# sort along the last axis
a = np.random.rand(3,2)
print('\narray values (3,2) before sort',a)
arr2 = np.sort(a, axis = -1)
print ("\nsort along the last axis : \n", arr2)
Output:
array values (3,2) before sort [[0.27207839 0.31552827] [0.53569892 0.67902567] [0.84425215 0.87191413]] sort along the last axis : [[0.27207839 0.31552827] [0.53569892 0.67902567] [0.84425215 0.87191413]]
Sorting with no axis using numpy:
import numpy as np
a = np.random.rand(3,2,3)
print('\narray values (3,2,3) before sort',a)
arr1 = np.sort(a, axis = None)
print ("\nAlong none axis : \n", arr1)
Output:
array values (3,2,3) before sort [[[0.37763006 0.71538084 0.3231002 ] [0.73052758 0.31552531 0.2722308 ]] [[0.03596215 0.76927319 0.5312672 ] [0.21909521 0.0600554 0.52591726]] [[0.96099544 0.60639497 0.40885825] [0.55006936 0.28812491 0.83349558]]] Along none axis : [0.03596215 0.0600554 0.21909521 0.2722308 0.28812491 0.31552531 0.3231002 0.37763006 0.40885825 0.52591726 0.5312672 0.55006936 0.60639497 0.71538084 0.73052758 0.76927319 0.83349558 0.96099544]
Reverse sorting using numpy arrays:
Program:
import numpy as np
a = np.random.randint(1,10, 10)
print('Regular sorting',np.sort(a))
reverse = np.sort(a)[::-1]
print('Reverse sorting',reverse)
Output:
Regular sorting [1 2 3 4 5 5 6 7 9 9] Reverse sorting [9 9 7 6 5 5 4 3 2 1]
Sorting using argsort() function.
In this program, we will use argsort() function to sort values. It returns the indices that would sort an array.
Program:
import numpy as np
x = np.array([3, 1, 2])
print('argsort',np.argsort(x, axis=0))
x = np.array([[0, 3], [2, 2]])
print('\nargsort with axis=0',np.argsort(x, axis=0))
print('\nargsort with axis=0',np.argsort(x, axis=1))
Output:
argsort [1 2 0] argsort with axis=0 [[0 1] [1 0]] argsort with axis=0 [[0 1] [0 1]]
