Heap queue (or heapq) in Python



Heap operations:

1. heapify (iterable) : — this function is used to convert an iterable to a heap data structure. those. in heap order.

2. heappush (heap, ele) : — This function is used to insert an element mentioned in its arguments into the heap.  Order is adjusted so that heap structure is preserved .

3. heappop (heap) : — this function is used to remove and return the smallest element from the heap.  The order is adjusted so that heap structure is preserved .

# Python code to demonstrate how it works
# heapify (), heappush () and heappop ()

 
# import & quot; heapq & quot; to implement a heap queue

import heapq

 
# initializing list

li = [ 5 , 7 , 9 , 1 , 3 ]

 
# using heapify to heap the list
heapq.heapify (li)

 
# print the generated heap

print ( " The created h eap is: " , end = " ")

print ( list (li))

 
# using heappush () to inserting elements into the heap
# pushes 4

heapq.heappush (li, 4 )

 
# printing the modified heap

print ( "The modified heap after push is:" , end = "")

print ( list (li))

 
# using heappop ( ) display the smallest element

print ( " The popped and smallest element is: " , end = " ")

print (heapq.heappop (li))

Output:

 The created heap is: [1, 3, 9, 7 , 5] The modified heap after push is: [1, 3, 4, 7, 5, 9] The popped and smallest element is: 1 

4. heappushpop (heap, ele) : — This function combines the functionality of the push and pop operations in a single statement, increasing efficiency. Heap order is preserved after this operation.

5. heapreplace (heap, ele) : — This function also inserts and inserts an element in one statement, but it is different from the function above. When doing this, the element is fetched first, and then the — push.ie, a value greater than push may be returned.

# Python code to demonstrate how it works
# heappushpop () and heapreplce ()

 
# import & quot; heapq & quot; to implement a heap queue

import heapq

 
# list 1 initialization

li1 = [ 5 , 7 , 9 , 4 , 3 ]

  
# initialize list 2

li2 = [ 5 , 7 , 9 , 4 , 3 ]

 
# using heapify () to heap the list
heapq .heapify (li1)
heapq.heapify (li2)

  
# using heappushpop () to bump and fetch items
# claps 2

print ( "The popped item using heappushpop () is:" , end = "")

print (heapq.heappushpop (li1, 2 ))

 
# using heapreplace () to bump and fetch elements
# claps 3

print ( " The popped item using heapreplace () is: " , end = "")

print (heapq.heapreplace (li2, 2 ))

Output:

 The popped item using heappushpop ( ) is: 2 The popped item using heapreplace () is: 3 

6. nlargest (k, iterable, key = fun) : — This function is used to return the k largest elements from the specified iterable and match the key, if mentioned.

7. nsmallest (k, iterable, key = fun) : — This function is used to return the k smallest elements from the specified iterable and match the key, if mentioned.

# Python code to demonstrate how it works
# nlargest () and nsmallest ()

 
# import & quot; heapq & quot; to implement a heap queue

import heapq

 
# initializing list

li1 = [ 6 , 7 , 9 , 4 , 3 , 5 , 8 , 10 , 1 ]

 
# using heapify () to transform the list heap
heap q.heapify (li1)

 
# using the largest to print 3 largest numbers
# prints 10, 9 and 8

print ( "The 3 largest numbers in list are:" , end = "")

print (heapq.nlargest ( 3 , li1))

 
# using nsmallest to print the 3 smallest numbers
# prints 1, 3 and 4

print ( " The 3 smallest numbers in list are: " , end = "")

print (heapq.nsmallest ( 3 , li1))

Output:

 The 3 largest numbers in list are: [10, 9, 8] The 3 smallest numbers in list are: [1, 3 , 4] 

This article courtesy of Manjeet Singh . If you are as Python.Engineering and would like to contribute, you can also write an article using contribute.python.engineering or by posting an article contribute @ python.engineering. See my article appearing on the Python.Engineering homepage and help other geeks.

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