如何对 Numpy 数组进行排序 | Python

在本文中，我们将学习如何对 Numpy 数组进行排序。 Numpy 中有多种方法可以根据需要对数组进行排序。让我们尝试借助示例来理解它们。

示例#1：简单地使用 sort() 方法基于轴对给定的数组进行排序。

# importing libraries
import numpy as np
   
# sort along the first axis
a = np.array([[12, 15], [10, 1]])
arr1 = np.sort(a, axis = 0)        
print ("Along first axis : \n", arr1)        
   
   
# sort along the last axis
a = 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)

输出：

Along first axis : 
 [[10  1]
 [12 15]]

Along first axis : 
 [[10 15]
 [ 1 12]]

Along none axis : 
 [ 1 10 12 15]

示例 #2：使用 argsort() 方法获取可以返回排序数组的索引

# Python code to demonstrate 
# working of  numpy.argsort
import numpy as np
   
# Numpy array created
a = np.array([9, 3, 1, 7, 4, 3, 6])
   
# unsorted array print
print('Original array:\n', a)
   
# Sort array indices
b = 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 b
c = np.zeros(len(b), dtype = int)
for i in range(0, len(b)):
    c[i]= a[b[i]]
print('Sorted array->', c)

输出：

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]

示例 #3：使用一系列键获得稳定的排序。

import numpy as np
   
# Numpy array created
# First column
a = np.array([9, 3, 1, 3, 4, 3, 6])
   
# Second column 
b = 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 b
ind = np.lexsort((b, a)) 
print('Sorted indices->', ind)

输出：

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]