1574756767

# How to using Python Heapq for Beginners (With Examples)

Often when working with collections of data, you may want to find the smallest or largest item. It’s easy enough to write a function that iterates through the items and returns the smallest or largest one, or use the builtin min(), max(), or sorted() functions. Another interesting way may be implementing a heap (priority) queue.

Interestingly, the heapq module uses a regular Python list to create Heap. It supports addition and removal of the smallest element in O(log n) time. Hence, it is an obvious choice for implementing priority queues.

The heapq module includes seven functions, the first four of which are used for heap operations. However, you need to provide a list as the heap object itself.

Heap data structure has a property that it always pops out the smallest of its element (Min Heap). Also, it keeps the heap structure intact despite any push or pop operation. The heap[0] would also point to the smallest value of the Heap.

In this article we will guide you using Python heapq. It is a module in Python which uses the binary heap data structure and implements Heap Queue a.k.a. Priority Queue algorithm.

## What Is A Priority Queue?

Priority Queue is an advanced data type (ADT) which is a more refined version of a Queue. It dequeues higher-priority items before the lower-priority items. Most programming languages such as Python use Binary heap to implement it.

Python heapq, as stated in the beginning, provides a min-heap implementation.

## What Is A Heap?

A heap has multiple meaning in computer science. Sometimes, it refers to a memory area in a program used for dynamic allocation. However, in this tutorial, we are talking about the Heap Data Structure, which is a complete binary tree. It helps in implementing priority queues (PQ), the heapsort, and some graph-based algorithms.

A heap has the following two variants:

• A max-heap, in which the parent is more than or equal to both of its child nodes.
• A min-heap, in which the parent is smaller or equal to the child nodes.

Below is a general representation of a binary heap.

## Heapq Module

Heapq is a Python module which provides an implementation of the Min heap. It makes use of Binary heap and exposes several functions to implement a priority queue.

You may eventually solve many programming problems using its functions. For example, find the two largest numbers from a list of integers in Python.

There happen to be many ways to address this problem. However, none is as intuitive and faster than a Heapq solution.

Out of many Python heapq functions, one is nlargest(). It returns a list type object containing the desired number of largest elements. Below is a short example before we dig into the more complicated ones.

## Python Heapq Example

``````# A brief heapq example
# Find the two largest integers from a list of numbers

import heapq as hq

list_of_integers = [21, 67, 33, 13, 40, 89, 71, 19]

# Find two largest values
largest_nums = hq.nlargest(2, list_of_integers)

print("Two largest numbers are: ", largest_nums)

``````

The output is:

``````Two largest numbers are: [89, 71]

``````

Please note that you can create a heap in either of these two ways:

• Initialize the list with [].
• Pass a pre-filled list into heapify() to convert to a heap.

Let’s now check out what functions does this module provide.

## Python Heapq Functions

The heapq module has the following methods:

### 1. Heappush()

It adds an element to the heap. Don’t apply it on any old list, instead use the one that you built using Heap functions. That is how you can ensure the elements are in the desired order.

``````# heappush() Syntax
import heapq as hq
hq.heappush(heap, element)

``````

Check out below heapq heappush() example.

``````# A brief heapq.heappush() example

import heapq as hq
import random as r

init_list = list(range(10, 99, 10))
print("Step-1: Seed data for the heap: ", init_list)

r.shuffle(init_list)
print("Step-2: Randomize the seed data: ", init_list)

# Creating heap from an empty list
heap = []
print("Step-3: Creating heap...")

# Demonstrating heapq.heappush() function
[hq.heappush(heap, x) for x in init_list]

# Printing heap content to see if the smallest item is at 0th index
print(" a. Heap contains: ", heap)

# Adding another smaller item to the heap
hq.heappush(heap, 1)
print(" b. Heap contains: ", heap)

``````

This code results in the following:

``````
Step-1: Seed data for the heap:  [10, 20, 30, 40, 50, 60, 70, 80, 90]
Step-2: Randomize the seed data:  [70, 20, 60, 80, 90, 30, 40, 10, 50]
Step-3: Creating heap...
a. Heap contains:  [10, 20, 30, 50, 90, 60, 40, 80, 70]
b. Heap contains:  [1, 10, 30, 50, 20, 60, 40, 80, 70, 90]

``````

You can observe that heap kept the smallest item at the 0th index. We added a new lower value using the heappush() function. And it pushed that at 0th position by shifting the previous value to 1st index.

### 2. Heappop()

It is used to remove the smallest item that stays at index 0. Moreover, it also ensures that the next lowest replaces this position:

``````# heappop() Syntax
import heapq as hq
hq.heappop(heap)

``````

Check out heapq heappop() example. You need to append this code to the previous heappush() example.

``````# Exercising heapq.heappop() function
print("Step-4: Removing items from heap...")
out = hq.heappop(heap)
print(" a. heappop() removed {} from heap{}".format(out, heap))
out = hq.heappop(heap)
print(" b. heappop() removed {} from heap{}".format(out, heap))
out = hq.heappop(heap)
print(" c. heappop() removed {} from heap{}".format(out, heap))

``````

It will give the following result:

``````
Step-4: Removing items from heap...
a. heappop() removed 1 from heap[10, 20, 40, 50, 30, 70, 80, 90, 60]
b. heappop() removed 10 from heap[20, 30, 40, 50, 60, 70, 80, 90]
c. heappop() removed 20 from heap[30, 50, 40, 90, 60, 70, 80]

``````

It is clear from the output that heappop() always poped off the lowest element from the heap.

### 3. Heappushpop()

This function first adds the given item in a Heap, then removes the smallest one and returns it. So, it is an increment of both heappush() and heappop(). But it tends to be a little faster than the two combined.

``````
# heappushpop() Syntax
import heapq as hq
hq.heappushpop(heap, element)

``````

Check out heapq heappushpop() example. You need to append it to the previous code sample.

``````
# Exercising heapq.heappushpop() function
print("Step-5: Adding & removing items from heap...")
new_item = 99
out = hq.heappushpop(heap, new_item)
print(" a. heappushpop() added {} and removed {} from heap{}".format(new_item, out, heap))
new_item = 999
out = hq.heappushpop(heap, new_item)
print(" b. heappushpop() added {} and removed {} from heap{}".format(new_item, out, heap))

``````

The output is:

``````
Step-5: Adding & removing items from heap...
a. heappushpop() added 99 and removed 30 from heap[40, 60, 50, 70, 90, 99, 80]
b. heappushpop() added 999 and removed 40 from heap[50, 60, 80, 70, 90, 99, 999]

``````

### 4. Heapify()

This function accepts an arbitrary list and converts it to a heap.

``````# heapify() Syntax
import heapq as hq
hq.heapify(heap)

``````

Check out heapq heapify() example.

``````
# A brief heapq.heapify() example

import heapq as hq

heap = [78, 34, 78, 11, 45, 13, 99]
print("Raw heap: ", heap)

hq.heapify(heap)
print("heapify(heap): ", heap)
``````

Here is the output:

``````Raw heap: [78, 34, 78, 11, 45, 13, 99]
heapify(heap): [11, 34, 13, 78, 45, 78, 99]

``````

You can see that the heapify() function transformed the input list and made it a heap.

### 5. Heapreplace()

It deletes the smallest element from the Heap and then inserts a new item. This function is more efficient than calling heappop() and heappush().

``````
# heapreplace() Syntax
import heapq as hq
hq.heapreplace(heap, element)

``````

Check out heapq heapreplace() example.

``````
# A brief heapq.heapreplace() example

import heapq as hq

heap = [78, 34, 78, 11, 45, 13, 99]
hq.heapify(heap)
print("heap: ", heap)

hq.heapreplace(heap, 12)
print("heapreplace(heap, 12): ", heap)

hq.heapreplace(heap, 100)
print("heapreplace(heap, 100): ", heap)

``````

The output is:

``````
heap: [11, 34, 13, 78, 45, 78, 99]
heapreplace(heap, 12): [12, 34, 13, 78, 45, 78, 99]
heapreplace(heap, 100): [13, 34, 78, 78, 45, 100, 99]

``````

### 6. Nlargest()

It finds the n largest elements from a given iterable. It also accepts a key which is a function of one argument.

The selected items have to satisfy the k function. If any of them fails, then the next higher number is considered.

``````# nlargest() Syntax
import heapq as hq
hq.nlargest(n, iterable, key=None)

``````

Check out heapq nlargest() example. It is requesting for two largest numbers.

``````
# heapq.nlargest() example without a key

import heapq as hq

heap = [78, 34, 78, 11, 45, 13, 99]
hq.heapify(heap)
print("heap: ", heap)

out = hq.nlargest(2, heap)
print("nlargest(heap, 2): ", out)

``````

The result is:

``````
heap: [11, 34, 13, 78, 45, 78, 99]
nlargest(heap, 2): [99, 78]

``````

Check out another heapq nlargest() example. It is not only requesting for two largest numbers but also has an is_even() function as the KEY.

If any of the selected numbers fail to clear the KEY function, then the next one comes in.

``````
# heapq.nlargest() example with key

import heapq as hq

def is_even(num):
if num%2 == 0: return 1
return 0

heap = [78, 34, 78, 11, 45, 13, 99]
hq.heapify(heap)
print("heap: ", heap)

out = hq.nlargest(2, heap, is_even)
print("nlargest(heap, 2): ", out)

``````

The output is:

``````heap: [11, 34, 13, 78, 45, 78, 99]
nlargest(heap, 2): [34, 78]

``````

### 7. Nsmallest()

It is also similar to the nlargest() in operation. However, it gets the n smallest elements from a given iterable. It too accepts a key which is a function of one argument.

The selected items have to satisfy the k function. If any of them fails, then the next smaller number is considered.

``````
# nsmallest() Syntax
import heapq as hq
hq.nsmallest(n, iterable, key=None)

``````

Check out heapq nsmallest() example. It is requesting for two smallest numbers.

``````
# heapq.nsmallest() example

import heapq as hq

heap = [78, 34, 78, 11, 45, 13, 99]
hq.heapify(heap)
print("heap: ", heap)

out = hq.nsmallest(2, heap)
print("nsmallest(heap, 2): ", out)

``````

Here is the result:

``````heap: [11, 34, 13, 78, 45, 78, 99]
nsmallest(heap, 2): [11, 13]

``````

You can achieve similar behavior through other ways, but the heap algorithm is more memory-efficient and even faster.

## Heapq Exercises

### First Exercise

Write a Python program to push elements and pop off the smallest one.

``````
import heapq as hq
heap = []
hq.heappush(heap, ('H', 9))
hq.heappush(heap, ('H', 7))
hq.heappush(heap, ('H', 4))
hq.heappush(heap, ('H', 1))
print("Elements in the heap:")
for ele in heap:
print(ele)
print("----------------------")
print("Calling heappushpop() to push element on the heap and return the smallest one.")
hq.heappushpop(heap, ('H', 11))
for ele in heap:
print(ele)

``````

The output:

``````Elements in the heap:
('H', 1)
('H', 4)
('H', 7)
('H', 9)
----------------------
Calling heappushpop() to push element on the heap and return the smallest one.
('H', 4)
('H', 9)
('H', 7)
('H', 11)

``````

### Second Exercise

Write a Python program to perform Heap Sort, push all items to a heap, and then take off the smallest ones one after other.

``````
import heapq as hq

def heap_sort(heap):
in_list = []
for value in heap:
hq.heappush(in_list, value)
return [hq.heappop(in_list) for i in range(len(in_list))]

out = heap_sort([9, 7, 5, 2, 1, 2, 8, 10, 6, 5, 4])
print(out)

``````

Here is the result:

``````[1, 2, 2, 4, 5, 5, 6, 7, 8, 9, 10]

``````

## Conclusion

With the heapq module, you can implement several kinds of priority queues and schedulers. It has vast usage in different areas such as Artificial Intelligence (AI), Machine Learning, Operating systems (OS), and in graphs.

Anyways, after wrapping up this tutorial, you should feel comfortable in using Python Heapq.

#python #programming

## Buddha Community

1587588495

Happy to read the article, Thanks for the post.

1619518440

## top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

### 8) Check The Memory Usage Of An Object.

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1619510796

## Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

1624291780

## Learn Python - Full Course for Beginners [Tutorial]

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:04:17) Try / Except
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

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1597751700

## How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

### 1. init

``````class AnyClass:
def __init__():
print("Init called on its own")
obj = AnyClass()
``````

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

``````Init called on its own
``````

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

``````class AnyClass:
def __init__(self, var):
self.some_var = var
return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2
``````

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

1619636760

## 42 Exciting Python Project Ideas & Topics for Beginners [2021]

### Python Project Ideas

Python is one of the most popular programming languages currently. It looks like this trend is about to continue in 2021 and beyond. So, if you are a Python beginner, the best thing you can do is work on some real-time Python project ideas.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting Python project ideas which beginners can work on to put their Python knowledge to test. In this article, you will find 42 top python project ideas for beginners to get hands-on experience on Python

Moreover, project-based learning helps improve student knowledge. That’s why all of the upGrad courses cover case studies and assignments based on real-life problems. This technique is ideally for, but not limited to, beginners in programming skills.

But first, let’s address the more pertinent question that must be lurking in your mind:

#data science #python project #python project ideas #python project ideas for beginners #python project topics #python projects #python projects for beginners