What Is A Stack in Python and How Is It Implemented

In this Python article, let's learn about What is a Stack in Python? How To Implement Python Stack? Stack is a linear type of data structure that enables efficient data storage and access. As the literal meaning of stack indicates, this data structure is based on the logic of storing elements one on top of another. There are plenty of real-world examples of the stack from our daily lives, such as a Stack of plates, a stack of notes, a stack of clothes, etc. Like any other efficient programming language, Python also allows a smooth stack implementation and various other data structures. Today, in this article, we will learn about the Python stack and how to implement it. 

What is Stack in Python? 

Stack is a linear data structure that works on the principle of ‘Last In First Out (LIFO). This means that the element that goes in the stack first comes out last. The term that we use for sending the elements to a stack is known as ‘Push’, whereas the term for deleting the elements from a stack is known as ‘Pop’. Hence, we can say that since a stack has only one open end, pushing and popping can’t take place simultaneously. A pictorial representation of the PUSH and POP operation in the stack has been shown below:

Pictorial representation of stack, push, and pop

The inbuilt datatype of Python that we use to implement Python is the Python list. Further, for exercising PUSH and POP operations on a stack, we use the append() and pop() function of the Python list.

Get your hands on the Python Stack course and learn more about it.

Methods of Stack

The most basic methods associated with a Stack in python are as follows:

• push(n)– This is a user-defined stack method used for inserting an element into the stack. The element to be pushed is passed in its argument.
• pop()– We need this method to remove the topmost element from the stack. 
• isempty()– We need this method to check whether the stack is empty or not. 
• size()– We need this method to get the size of the stack. 
• top()– This stacking method will be used for returning the reference to the topmost element or, lastly pushed element in a stack.

Functions associated with Python Stack

There are a bunch of useful functions in Python that help us deal with a stack efficiently. Let’s take a brief look at these functions –  

• len()– This stack method is used for returning the size of the stack. This function can also be used in the definition of isempty() method in a Python stack.
• append(n)– This Python function is used for inserting an element into the stack. The element to be pushed is passed in its argument.
• pop()– This method, associated with the Python lists, is used for deleting the topmost element from the stack. 

Implementation of Stack

There are four ways in which we can carry out the implementation of a stack in Python-

• list
• collections.deque
• queue.LifoQueue
• Singly-linked list  

Out of these three, the easiest and the most popular way for implementing a stack in Python is list. Let’s see the implementation of a stack in Python using lists.

Implementation Using List

# Stack Creation
def create_stack():
    stack = list()            #declaring an empty list
    return stack


# Checking for empty stack
def Isempty(stack):
    return len(stack) == 0


# Inserting items into the stack
def push(stack, n):
    stack.append(n)
    print("pushed item: " + n)


# Removal of an element from the stack
def pop(stack):
    if (Isempty(stack)):
        return "stack is empty"
    else:
        return stack.pop()

# Displaying the stack elements
def show(stack):
    print("The stack elements are:")
    for i in stack:
        print(i)
        
stack = create_stack()
push(stack, str(10))
push(stack, str(20))
push(stack, str(30))
push(stack, str(40))
print("popped item: " + pop(stack))
show(stack)

 

However, the speed issue becomes a major limitation here when dealing with a growing stack. The items in a list are stored one after the other inside the memory. Hence, if the stack grows bigger than the block of memory allocated to the list, Python needs to do some new memory allocations, resulting in some append() taking much longer than the rest while calling.

Implementation using collections.deque

We can also use the deque class of the Python collections module to implement a stack. Since a deque or double ended queue allow us to insert and delete element from both front and rear sides, it might be more suitable at times when we require faster append() and pop() operations. 

from collections import deque  

def create_stack():  
    stack = deque()    #Creating empty deque
    return stack 
  
# PUSH operation using append()
def push(stack, item):
    stack.append(item)

  
#POP operation
def pop(stack):
    if(stack):
        print('Element popped from stack:')
        print(stack.pop())
    else:
        print('Stack is empty')
    

#Displaying Stack
def show(stack):
    print('Stack elements are:')
    print(stack)
    
new_stack=create_stack()
push(new_stack,25)
push(new_stack,56)
push(new_stack,32)
show(new_stack)

pop(new_stack)
show(new_stack)

Implementation using queue.LifoQueue

The queue module of Python consists of a LIFO queue. A LIFO queue is nothing but a stack. Hence, we can easily and effectively implement a stack in Python using the queue module. For a LifoQueue, we have certain functions that are useful in stack implementation, such as qsize(), full(), empty(), put(n), get() as seen in the following piece of code. The max size parameter of LifoQueue defines the limit of items that the stack can hold.

from queue import LifoQueue
  
# Initializing a stack
def new():
    stack = LifoQueue(maxsize=3)   #Fixing the stack size
    return stack

#PUSH using put(n) 
def push(stack, item):
    if(stack.full()):                      #Checking if the stack is full
        print("The stack is already full")
    else:
        stack.put(item)
        print("Size: ", stack.qsize())     #Determining the stack size

#POP using get()
def pop(stack):
    if(stack.empty()):              #Checking if the stack is empty
        print("Stack is empty")
    else:
        print('Element popped from the stack is ', stack.get())         #Removing the last element from stack
        print("Size: ", stack.qsize())

stack=new()
pop(stack)
push(stack,32)
push(stack,56)
push(stack,27)
pop(stack)

 

Implementation using a singly linked list

Singly-linked lists are the most efficient and effective way of implementing dynamic stacks. We use the class and object approach of Python OOP to create linked lists in Python. We have certain functions at our disposal in Python that are useful in stack implementation, such as getSize(), isEmpty(), push(n), and pop(). Let’s take a look at how each of these functions helps in implementing a stack.

#Node creation
class Node:
	def __init__(self, value):
		self.value = value
		self.next = None

#Stack creation
class Stack:
    #Stack with dummy node
	def __init__(self):
		self.head = Node("head")
		self.size = 0

	#  For string representation of the stack
	def __str__(self):
		val = self.head.next
		show = ""
		while val:
			show += str(val.value) + " , "
			val = val.next
		return show[:-3]

	# Retrieve the size of the stack
	def getSize(self):
		return self.size

	# Check if the stack is empty
	def isEmpty(self):
		return self.size == 0

	# Retrieve the top item of the stack
	def peek(self):
		# Check for empty stack.
		if self.isEmpty():
			raise Exception("This is an empty stack")
		return self.head.next.value

	# Push operation
	def push(self, value):
		node = Node(value)
		node.next = self.head.next
		self.head.next = node
		self.size += 1

	# Pop Operation
	def pop(self):
		if self.isEmpty():
			raise Exception("Stack is empty")
		remove = self.head.next
		self.head.next = self.head.next.next
		self.size -= 1
		return remove.value


#Driver Code
if __name__ == "__main__":
	stack = Stack()
	n=20
	for i in range(1, 11):
		stack.push(n)
		n+=5
	print(f"Stack:{stack}")

	for i  in range(1, 6):
		remove = stack.pop()
		print(f"Pop: {remove}")
	print(f"Stack: {stack}")

Deque Vs. List

Deque	List
You need to import the collections module for using deque in Python	You need not import any external module for using a list in Python. It’s an inbuilt-data structure
Time complexity of deque for append() and pop() functions is O(1)	Time complexity of lists for append() and pop() functions is O(n)
They are double-ended, i.e. elements can be inserted into and removed from either of the ends	It is a single-ended structure that allows append() to insert the element at the end of the list and pop() to remove the last element from the list
Stack with bigger sizes can be easily and efficiently implemented via deques	The list is suitable for fixed-length operations and stack implementation via lists becomes difficult when its size starts growing bigger.

Python Stacks and Threading

Python is a multi-threaded language, i.e. it allows programming that involves running multiple parts of a process in parallel. We use threading in Python for running multiple threads like function calls, and tasks simultaneously. Python lists and deques both work differently for a program with threads. You would not want to use lists for data structures that ought to be accessed by multiple threads since they are not thread-safe. 

Your thread program is safe with deques as long as you are strictly using append() and pop() only. Besides, even if you succeed at creating a thread-safe deque program, it might expose your program to chances of being misused and give rise to race conditions at some later point in time. So, neither list nor a deque is very good to call when dealing with a threaded program. The best way to make a stack in a thread-safe environment is queue.LifoQueue. We are free to use its methods in a threaded environment. Nevertheless, your stack operations in queue.LifoQueue may take a little longer owing to making thread-safe calls. 

Note: Threading in Python does not mean that different threads are executed on different processors. If 100% of the CPU time is already being consumed, Python threads will no longer be helpful in making your program faster. You can switch to parallel programming in such cases.

Which Implementation of Stack should one consider?

When dealing with a non-threading program, you should go for a deque. When your program requires a thread-safe environment, you better opt for LifoQueue unless your program performance and maintenance are highly affected by the speed of the stack operations. 

Now, the list is a bit risky since it might raise memory reallocation issues. Besides, Python lists are not safe for multithreading environments. The list and deque interfaces are the same, except for such issues as in the list. Hence, a Python deque can be seen as the best alternative for stack implementation.

Conclusion 

Now that, you have come to the end of this article, you must have got a hang of stack in Python. The foremost essential part is to recognize the situations where you need to implement a stack. You have learned about various ways of implementing stack in Python, so you know it is significant to know the requirements of your program to be able to choose the best stack implementation option. 

You should be clear if you are writing a multi-threaded program or not. Python lists are not thread-safe, and thus you would prefer going for deques in case of a multi-threading environment. The drawback of slow stack operations can be overlooked as long as your program performance does not decline because of these factors. 

Frequently Asked Questions

What is a Python stack?

A stack is a form of linear data structure in Python that allows the storage and retrieval of elements in the LIFO (Last In First Out) manner.

Can you create a stack in Python?

Yes, we can easily create a stack in Python using lists, LifoQueues, or deques. For a dynamic stack, you can create single linked lists as well in Python for that matter.

When would you use a stack in Python?

Stack of books, a stack of documents, a stack of plates, etc., all real-world use cases of the stack. You would use a stack in Python whenever seeking a way to store and access elements in a LIFO manner. Suppose a developer, working on a new Word editor, has to build an undo feature where backtracking up to the very first action is required. For such a scenario, using a Python stack would be ideal for storing the actions of the users working on the Word editor.

What is a stack in Python example?

Example: A record of students entering a hall for a seminar where they must leave the hall in a LIFO manner.

Is Python full-stack?

Yes, Python can be very well used for full-stack development. Though, full-stack development and stack are two completely things altogether. To know more about the stack in Python, go back to the article given above. 

How do I know if a Python stack is full?

When implementing a stack in the form of lists or linked lists, you can use the size() function to check if the stack has reached its maximum limit. You have the full() method in LifoQueue to check whether the stack is full or not.

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Original article source at: https://www.mygreatlearning.com

#python 

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

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

2. add

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
    def __add__(self, other_obj):
        print("Calling the add method")
        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

Authentication In MEAN Stack - A Quick Guide

I consider myself an active StackOverflow user, despite my activity tends to vary depending on my daily workload. I enjoy answering questions with angular tag and I always try to create some working example to prove correctness of my answers.

To create angular demo I usually use either plunker or stackblitz or even jsfiddle. I like all of them but when I run into some errors I want to have a little bit more usable tool to undestand what’s going on.

Many people who ask questions on stackoverflow don’t want to isolate the problem and prepare minimal reproduction so they usually post all code to their questions on SO. They also tend to be not accurate and make a lot of mistakes in template syntax. To not waste a lot of time investigating where the error comes from I tried to create a tool that will help me to quickly find what causes the problem.

Angular demo runner
Online angular editor for building demo.
ng-run.com
<>

Let me show what I mean…

Template parser errors#

There are template parser errors that can be easy catched by stackblitz

It gives me some information but I want the error to be highlighted

#mean stack #angular 6 passport authentication #authentication in mean stack #full stack authentication #mean stack example application #mean stack login and registration angular 8 #mean stack login and registration angular 9 #mean stack tutorial #mean stack tutorial 2019 #passport.js