Numpy 简明教程

NumPy - Sort, Search & Counting Functions

NumPy 中提供了各种与排序相关的函数。这些排序函数实现了不同的排序算法，它们各自以执行速度、最坏情况性能、所需的作业空间和算法的稳定性为特征。下表显示了三种排序算法的比较。

A variety of sorting related functions are available in NumPy. These sorting functions implement different sorting algorithms, each of them characterized by the speed of execution, worst case performance, the workspace required and the stability of algorithms. Following table shows the comparison of three sorting algorithms.

kind

speed

worst case

work space

stable

‘quicksort’

O(n^2)

‘mergesort’

O(n*log(n))

~n/2

yes

‘heapsort’

O(n*log(n))

numpy.sort()

sort() 函数返回输入数组的已排序副本。它有以下参数 −

The sort() function returns a sorted copy of the input array. It has the following parameters −

numpy.sort(a, axis, kind, order)

其中，

Where,

Sr.No.	Parameter & Description
1	a Array to be sorted
2	axis The axis along which the array is to be sorted. If none, the array is flattened, sorting on the last axis
3	kind Default is quicksort
4	order If the array contains fields, the order of fields to be sorted

Example

import numpy as np
a = np.array([[3,7],[9,1]])

print 'Our array is:'
print a
print '\n'

print 'Applying sort() function:'
print np.sort(a)
print '\n'

print 'Sort along axis 0:'
print np.sort(a, axis = 0)
print '\n'

# Order parameter in sort function
dt = np.dtype([('name', 'S10'),('age', int)])
a = np.array([("raju",21),("anil",25),("ravi", 17), ("amar",27)], dtype = dt)

print 'Our array is:'
print a
print '\n'

print 'Order by name:'
print np.sort(a, order = 'name')

它将生成如下输出：

It will produce the following output −

Our array is:
[[3 7]
 [9 1]]

Applying sort() function:
[[3 7]
 [1 9]]

Sort along axis 0:
[[3 1]
 [9 7]]

Our array is:
[('raju', 21) ('anil', 25) ('ravi', 17) ('amar', 27)]

Order by name:
[('amar', 27) ('anil', 25) ('raju', 21) ('ravi', 17)]

numpy.argsort()

numpy.argsort() 函数沿给定轴对输入数组执行间接排序，并使用指定类型的排序来返回数据索引数组。此索引数组用于构造已排序的数组。

The numpy.argsort() function performs an indirect sort on input array, along the given axis and using a specified kind of sort to return the array of indices of data. This indices array is used to construct the sorted array.

Example

import numpy as np
x = np.array([3, 1, 2])

print 'Our array is:'
print x
print '\n'

print 'Applying argsort() to x:'
y = np.argsort(x)
print y
print '\n'

print 'Reconstruct original array in sorted order:'
print x[y]
print '\n'

print 'Reconstruct the original array using loop:'
for i in y:
   print x[i],

它将生成如下输出：

It will produce the following output −

Our array is:
[3 1 2]

Applying argsort() to x:
[1 2 0]

Reconstruct original array in sorted order:
[1 2 3]

Reconstruct the original array using loop:
1 2 3

numpy.lexsort()

函数使用一系列键执行间接排序。键可以看作是电子表格中的一列。该函数返回一个索引数组，可以使用该索引获取已排序的数据。请注意，最后一个键恰好是排序的主键。

function performs an indirect sort using a sequence of keys. The keys can be seen as a column in a spreadsheet. The function returns an array of indices, using which the sorted data can be obtained. Note, that the last key happens to be the primary key of sort.

Example

import numpy as np

nm = ('raju','anil','ravi','amar')
dv = ('f.y.', 's.y.', 's.y.', 'f.y.')
ind = np.lexsort((dv,nm))

print 'Applying lexsort() function:'
print ind
print '\n'

print 'Use this index to get sorted data:'
print [nm[i] + ", " + dv[i] for i in ind]

它将生成如下输出：

It will produce the following output −

Applying lexsort() function:
[3 1 0 2]

Use this index to get sorted data:
['amar, f.y.', 'anil, s.y.', 'raju, f.y.', 'ravi, s.y.']

NumPy 模块具有用于在数组内搜索的大量函数。提供了用于查找最大值、最小值以及满足给定条件的元素的函数。

NumPy module has a number of functions for searching inside an array. Functions for finding the maximum, the minimum as well as the elements satisfying a given condition are available.

numpy.argmax() and numpy.argmin()

这两个函数分别返回沿给定轴的最大元素和最小元素的索引。

These two functions return the indices of maximum and minimum elements respectively along the given axis.

Example

import numpy as np
a = np.array([[30,40,70],[80,20,10],[50,90,60]])

print 'Our array is:'
print a
print '\n'

print 'Applying argmax() function:'
print np.argmax(a)
print '\n'

print 'Index of maximum number in flattened array'
print a.flatten()
print '\n'

print 'Array containing indices of maximum along axis 0:'
maxindex = np.argmax(a, axis = 0)
print maxindex
print '\n'

print 'Array containing indices of maximum along axis 1:'
maxindex = np.argmax(a, axis = 1)
print maxindex
print '\n'

print 'Applying argmin() function:'
minindex = np.argmin(a)
print minindex
print '\n'

print 'Flattened array:'
print a.flatten()[minindex]
print '\n'

print 'Flattened array along axis 0:'
minindex = np.argmin(a, axis = 0)
print minindex
print '\n'

print 'Flattened array along axis 1:'
minindex = np.argmin(a, axis = 1)
print minindex

它将生成如下输出：

It will produce the following output −

Our array is:
[[30 40 70]
 [80 20 10]
 [50 90 60]]

Applying argmax() function:
7

Index of maximum number in flattened array
[30 40 70 80 20 10 50 90 60]

Array containing indices of maximum along axis 0:
[1 2 0]

Array containing indices of maximum along axis 1:
[2 0 1]

Applying argmin() function:
5

Flattened array:
10

Flattened array along axis 0:
[0 1 1]

Flattened array along axis 1:
[0 2 0]

numpy.nonzero()

` numpy.nonzero() ` 函数返回输入数组中非零元素的索引。

The numpy.nonzero() function returns the indices of non-zero elements in the input array.

Example

import numpy as np
a = np.array([[30,40,0],[0,20,10],[50,0,60]])

print 'Our array is:'
print a
print '\n'

print 'Applying nonzero() function:'
print np.nonzero (a)

它将生成如下输出：

It will produce the following output −

Our array is:
[[30 40 0]
 [ 0 20 10]
 [50 0 60]]

Applying nonzero() function:
(array([0, 0, 1, 1, 2, 2]), array([0, 1, 1, 2, 0, 2]))

numpy.where()

where() 函数返回输入数组中满足给定条件的元素的索引。

The where() function returns the indices of elements in an input array where the given condition is satisfied.

Example

import numpy as np
x = np.arange(9.).reshape(3, 3)

print 'Our array is:'
print x

print 'Indices of elements > 3'
y = np.where(x > 3)
print y

print 'Use these indices to get elements satisfying the condition'
print x[y]

它将生成如下输出：

It will produce the following output −

Our array is:
[[ 0. 1. 2.]
 [ 3. 4. 5.]
 [ 6. 7. 8.]]

Indices of elements > 3
(array([1, 1, 2, 2, 2]), array([1, 2, 0, 1, 2]))

Use these indices to get elements satisfying the condition
[ 4. 5. 6. 7. 8.]

numpy.extract()

` extract() ` 函数返回满足任何条件的元素。

The extract() function returns the elements satisfying any condition.

import numpy as np
x = np.arange(9.).reshape(3, 3)

print 'Our array is:'
print x

# define a condition
condition = np.mod(x,2) == 0

print 'Element-wise value of condition'
print condition

print 'Extract elements using condition'
print np.extract(condition, x)

它将生成如下输出：

It will produce the following output −

Our array is:
[[ 0. 1. 2.]
 [ 3. 4. 5.]
 [ 6. 7. 8.]]

Element-wise value of condition
[[ True False True]
 [False True False]
 [ True False True]]

Extract elements using condition
[ 0. 2. 4. 6. 8.]