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Sort, Search & Counting Functions
  • 时间：2024-10-18

NumPy - Sort, Search & Counting Functions

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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 stabipty of algorithms. Following table shows the comparison of three sorting algorithms.

kind speed worst case work space stable
‘quicksort’ 1 O(n^2) 0 no
‘mergesort’ 2 O(n*log(n)) ~n/2 yes
‘heapsort’ 3 O(n*log(n)) 0 no

numpy.sort()

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

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

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  
 

print  Applying sort() function:  
print np.sort(a) 
print  
  
  
print  Sort along axis 0:  
print np.sort(a, axis = 0) 
print  
   

# 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  
   

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

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()

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  
   

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

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

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

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()

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  
   

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

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 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()

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  
  

print  Applying argmax() function:  
print np.argmax(a) 
print  
   

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

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

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

print  Applying argmin() function:  
minindex = np.argmin(a) 
print minindex 
print  
   
   
print  Flattened array:  
print a.flatten()[minindex] 
print  
   

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

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

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()

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  
   

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

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()

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]

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()

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)

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.]
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