In this article, we will learn how to sort a Numpy array. There are multiple ways in Numpy to sort an array, based on the requirement using Python. Let’s try to understand them with the help of different methods. In this article, we will see How to sort a Numpy Array in Python.
Sort a Numpy Array using the sort()
Here we sort the given array based on the axis using the sort() method i.e. create a sorted copy of the given numpy array.
Python3
# importing librariesimport numpy as np# sort along the first axisa = np.array([[12, 15], [10, 1]])arr1 = np.sort(a, axis = 0) print ("Along first axis : \n", arr1) # sort along the last axisa = np.array([[10, 15], [12, 1]])arr2 = np.sort(a, axis = -1) print ("\nAlong first axis : \n", arr2)a = np.array([[12, 15], [10, 1]])arr1 = np.sort(a, axis = None) print ("\nAlong none axis : \n", arr1) |
Output:
Along first axis : [[10 1] [12 15]] Along first axis : [[10 15] [ 1 12]] Along none axis : [ 1 10 12 15]
Sort a Numpy Array using sort() function
Here we sort the given array using the sort() method i.e. we sort the given numpy array inplace.
Python3
# importing librariesimport numpy as npa = np.array([12, 15, 10, 1])print("Array before sorting",a)a.sort()print("Array after sorting",a) |
Output:
Array before sorting [12 15 10 1] Array after sorting [ 1 10 12 15]
Sort a Numpy Array using argsort()
Get the indices which can return sorted array using argsort() method.
Python3
import numpy as np# Numpy array createda = np.array([9, 3, 1, 7, 4, 3, 6])# unsorted array printprint('Original array:\n', a)# Sort array indicesb = np.argsort(a)print('Sorted indices of original array->', b)# To get sorted array using sorted indices# c is temp array created of same len as of bc = np.zeros(len(b), dtype = int)for i in range(0, len(b)): c[i]= a[b[i]]print('Sorted array->', c) |
Output:
Original array: [9 3 1 7 4 3 6] Sorted indices of original array-> [2 1 5 4 6 3 0] Sorted array-> [1 3 3 4 6 7 9]
Sort a Numpy Array using a sequence of keys
Get stable sort using a sequence of keys.
Python3
import numpy as np# Numpy array created# First columna = np.array([9, 3, 1, 3, 4, 3, 6])# Second columnb = np.array([4, 6, 9, 2, 1, 8, 7])print('column a, column b')for (i, j) in zip(a, b): print(i, ' ', j)# Sort by a then by bind = np.lexsort((b, a))print('Sorted indices->', ind) |
Output:
column a, column b 9 4 3 6 1 9 3 2 4 1 3 8 6 7 Sorted indices-> [2 3 1 5 4 6 0]
