presentation on run time stack in python

• Python Basics
• Interview Questions
• Python Quiz
• Popular Packages
• Python Projects
• Practice Python
• AI With Python
• Learn Python3
• Python Automation
• Python Web Dev
• DSA with Python
• Python OOPs
• Dictionaries
• Stack Data Structure
• What is Stack? A Complete Tutorial
• Applications, Advantages and Disadvantages of Stack
• Implement a stack using singly linked list
• Introduction to Monotonic Stack - Data Structure and Algorithm Tutorials
• Difference between Stack and Queue Data Structures

Stack implementation in different language

• Stack in C++ STL
• Stack Class in Java

Stack in Python

• C# Stack with Examples
• Implementation of Stack in JavaScript
• Stack in Scala

Some questions related to Stack implementation

• Design and Implement Special Stack Data Structure | Added Space Optimized Version
• Implement two Stacks in an Array
• Implement Stack using Queues
• How to efficiently implement k stacks in a single array?
• Design a stack that supports getMin() in O(1) time and O(1) extra space
• Implement a stack using single queue
• How to implement stack using priority queue or heap?
• Create a customized data structure which evaluates functions in O(1)
• Implement Stack and Queue using Deque

Easy problems on Stack

• Convert Infix expression to Postfix expression
• Prefix to Infix Conversion
• Prefix to Postfix Conversion
• Postfix to Prefix Conversion
• Postfix to Infix
• Convert Infix To Prefix Notation
• Check for Balanced Brackets in an expression (well-formedness)
• Arithmetic Expression Evaluation
• Evaluation of Postfix Expression
• How to Reverse a Stack using Recursion
• Reverse individual words
• How to Reverse a String using Stack
• Reversing a Queue

Intermediate problems on Stack

• How to create mergeable stack?
• The Stock Span Problem
• Next Greater Element (NGE) for every element in given Array
• Next Greater Frequency Element
• Maximum product of indexes of next greater on left and right
• Iterative Tower of Hanoi
• Sort a stack using a temporary stack
• Reverse a stack without using extra space in O(n)
• Delete middle element of a stack
• Check if a queue can be sorted into another queue using a stack
• Check if an array is stack sortable
• Largest Rectangular Area in a Histogram using Stack
• Find maximum of minimum for every window size in a given array
• Find index of closing bracket for a given opening bracket in an expression
• Find maximum difference between nearest left and right smaller elements
• Delete consecutive same words in a sequence
• Check mirror in n-ary tree
• Reverse a number using stack
• Reversing the first K elements of a Queue

Hard problems on Stack

• The Celebrity Problem
• Print next greater number of Q queries
• Iterative Postorder Traversal | Set 2 (Using One Stack)
• Print ancestors of a given binary tree node without recursion
• Length of the longest valid substring
• Expression contains redundant bracket or not
• Find if an expression has duplicate parenthesis or not
• Find next Smaller of next Greater in an array
• Iterative method to find ancestors of a given binary tree
• Stack Permutations (Check if an array is stack permutation of other)
• Remove brackets from an algebraic string containing + and - operators
• Range Queries for Longest Correct Bracket Subsequence Set | 2

A stack is a linear data structure that stores items in a Last-In/First-Out (LIFO) or First-In/Last-Out (FILO) manner. In stack, a new element is added at one end and an element is removed from that end only. The insert and delete operations are often called push and pop.

The functions associated with stack are:

• empty() – Returns whether the stack is empty – Time Complexity: O(1)
• size() – Returns the size of the stack – Time Complexity: O(1)
• top() / peek() – Returns a reference to the topmost element of the stack – Time Complexity: O(1)
• push(a) – Inserts the element ‘a’ at the top of the stack – Time Complexity: O(1)
• pop() – Deletes the topmost element of the stack – Time Complexity: O(1)

Implementation:

There are various ways from which a stack can be implemented in Python. This article covers the implementation of a stack using data structures and modules from the Python library.  Stack in Python can be implemented using the following ways: 

• Collections.deque
• queue.LifoQueue

Implementation using list:

Python’s built-in data structure list can be used as a stack. Instead of push(), append() is used to add elements to the top of the stack while pop() removes the element in LIFO order.  Unfortunately, the list has a few shortcomings. The biggest issue is that it can run into speed issues as it grows. The items in the list are stored next to each other in memory, if the stack grows bigger than the block of memory that currently holds it, then Python needs to do some memory allocations. This can lead to some append() calls taking much longer than other ones.

Implementation using collections.deque:

Python stack can be implemented using the deque class from the collections module. Deque is preferred over the list in the cases where we need quicker append and pop operations from both the ends of the container, as deque provides an O(1) time complexity for append and pop operations as compared to list which provides O(n) time complexity. 

The same methods on deque as seen in the list are used, append() and pop().

Implementation using queue module

Queue module also has a LIFO Queue, which is basically a Stack. Data is inserted into Queue using the put() function and get() takes data out from the Queue. 

There are various functions available in this module: 

• maxsize – Number of items allowed in the queue.
• empty() – Return True if the queue is empty, False otherwise.
• full() – Return True if there are maxsize items in the queue. If the queue was initialized with maxsize=0 (the default), then full() never returns True.
• get() – Remove and return an item from the queue. If the queue is empty, wait until an item is available.
• get_nowait() – Return an item if one is immediately available, else raise QueueEmpty.
• put(item) – Put an item into the queue. If the queue is full, wait until a free slot is available before adding the item.
• put_nowait(item) – Put an item into the queue without blocking. If no free slot is immediately available, raise QueueFull.
• qsize() – Return the number of items in the queue.

Implementation using a singly linked list:

The linked list has two methods addHead(item) and removeHead() that run in constant time. These two methods are suitable to implement a stack. 

• getSize() – Get the number of items in the stack.
• isEmpty() – Return True if the stack is empty, False otherwise.
• peek() – Return the top item in the stack. If the stack is empty, raise an exception.
• push(value) – Push a value into the head of the stack.
• pop() – Remove and return a value in the head of the stack. If the stack is empty, raise an exception.

Below is the implementation of the above approach:

Advantages of Stack:

• Stacks are simple data structures with a well-defined set of operations, which makes them easy to understand and use.
• Stacks are efficient for adding and removing elements, as these operations have a time complexity of O(1).
• In order to reverse the order of elements we use the stack data structure.
• Stacks can be used to implement undo/redo functions in applications.

Drawbacks of Stack:

• Restriction of size in Stack is a drawback and if they are full, you cannot add any more elements to the stack.
• Stacks do not provide fast access to elements other than the top element.
• Stacks do not support efficient searching, as you have to pop elements one by one until you find the element you are looking for.

Please Login to comment...

Similar reads.

• Python-Data-Structures

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

The runtime stack

The run time stack is basically the way your programs store and handle your local non-static variables. Think of the run time stack as a stack of plates. With each function call, a new "plate" is placed onto the stacks. local variables and parameters are placed onto this plate. Variables from the calling function are no longer accessible (because they are on the plate below). When a function terminates, the local variables and parameters are removed from the stack. Control is then given back to the calling function. Understanding the workings of the run time stack is key to understanding how and why recursion works

results matching " "

No results matching " ".

A Stack Implementation in Python

About this article

In this article, I’ll explore the stack , a linear data structure we can easily implement in Python. 

This post is the first in a miniseries exploring the implementation of linear data structures. I based the series on my notes while studying for a Python technical interview.

For quick access, here is the list of posts on this series:

• A Stack Implementation in Python (this article)
• A Queue Implementation in Python
• A Priority Queue Implementation in Python
• Linked Lists Implementations In Python

Introduction to stacks

In computer science, a stack is an abstract data type serving as a collection of elements. The stack has two primary operations:

• Push , which adds an element to the collection
• Pop , which removes the most recently added element still in the collection

You add and remove elements from a stack in a last-in, first-out fashion (LIFO).

Uses for stacks

In programming, you can use stacks to implement applications and functionality that require LIFO data access. Some examples might be:

• The undo functionality in your text editor.
• The back button in your browser.
• The call stack in your favorite programming language.

Stack methods

Most stack implementations have the following methods:

• stack.push(x):  adds an element to the top of the stack.
• stack.pop():  returns and removes the element at the top of the stack.
• stack.peek():  returns the element at the top of the stack.
• stack.size():  returns the number of elements in the stack.
• stack.is_empty():  returns True is the stack is empty.

Stack implementation

For our stack implementation, we will use the double-ended queue (deque) available from the collections module in Python. The reason for using deques instead of other data types like lists are:

• In deques, appends and pops are memory efficient and thread-safe .
• In deques, appends and pops have constant time complexity O(1) .

Let’s go over our stack implementation using deques.

The Stack class

We define a Stack class with a constructor that instantiates an empty deque and a __str__ method that returns a readable representation of the stack object. At the top of the module, we must import deque from collections:

stack.is_empty():

The is_empty method returns True if the stack is empty.

This method has a constant time complexity O(1).

stack.push(item)

The push method adds an item to the stack, using the append method from deque.

stack.pop()

The pop method returns and removes the item at the top of the stack, using the pop method from deque. To avoid getting an IndexError when popping from an empty stack, we first check if it is empty and return None.

stack.peek()

The peek method returns the item at the top of the stack by slicing. As with pop, we need to return None if the stack is empty to avoid an IndexError.

stack.size()

The size method returns the length of the stack.

The full implementation

Below is the full implementation and some test cases:

In this article, we implemented a stack in Python. The stack is a linear data structure useful when you need to access data in a last-in, first-out fashion. Our implementation uses deque, allowing for thread-safe, memory efficient, and constant time pushes and pops.

In the next article , we’ll explore implementing a queue. See you there!

Wikipedia – Stack (abstract data type)

RealPython – How to implement a Python Stack

Python Documentation – deque

Share this:

You may also like:.

Javier Feliu

I am a full-stack developer from Panama. I enjoy programming in Python and JavaScript. My current interests include database design, building RESTful APIs, workflow automation, containerization, and cloud deployments.

Questions? Comments? Leave a reply to start a conversation... Cancel reply

Discover more from wander in dev.

Subscribe now to keep reading and get access to the full archive.

Type your email…

Continue reading

Understanding Stacks: Python Implementation of a Core Data Structure

By Lane Wagner on Oct 6, 2023

Curated backend podcasts, videos and articles. All free.

Want to improve your backend development skills? Subscribe to get a copy of The Boot.dev Beat in your inbox each month. It's a newsletter packed with the best content for new backend devs.

A stack is an abstract data type that serves as a collection of elements. The name “stack” originates from the analogy of items physically stacked on top of each other. Just like a stack of plates at a buffet, plates can be added, removed, and viewed from the top. However, plates further down are not immediately accessible.

A stack operates on a LIFO (last in, first out) principle. This means that the most recently added item will be the first to be removed (as opposed to a queue , which is FIFO ).

Subscribe to my YouTube channel if this video was helpful!

Basic Stack Operations: 🔗

• Push : stack.push(item) - Adds a new item on top of the stack
• Pop : stack.pop() - Removes the top item from the stack
• Peek : stack.peek() - Returns the top item without removing it
• Size : stack.size() - Returns the number of items in the stack

When might you use a stack? 🔗

Let’s take the and example from the world of game development.

When designing a weapon system in a video game, especially for weapons with magazines like a repeater crossbow, a stack can be an ideal choice for the underlying data structure. A magazine that holds arrows, for instance, would benefit from the LIFO principle: the last arrow loaded is the first one fired.

Here’s the code:

You could then use the Magazine class:

Are stacks fast? 🔗

Stacks are fast. All operations are O(1) , which means they take the same amount of time regardless of the size of the stack. Stack of 10 items? Stack of 10,000 items? It doesn’t matter, pushing and popping will be blazingly fast.

Think about it: you can only add or remove from the top of the stack. You never have to iterate through the entire stack to find an item.

Now, to be fair, if for some reason you know that an item exists in a Stack, you can pop() until you find it. This would be O(n) , but if that’s a use case for you, you should probably be using a different data structure.

The Origin of “Stack Overflow” 🔗

While many developers are familiar with the website Stack Overflow , not everyone knows the term’s origin. A programming language, such as Python, uses a stack data structure to manage function calls and their scope.

Consider this simple program:

Here, each function call is represented by a call frame in memory. When a function is invoked, a new frame is pushed onto the runtime stack. When the function completes its execution, its frame is popped off the stack.

The Dreaded Stack Overflow 🔗

A “stack overflow” refers to an error that occurs when the stack exceeds its capacity. The limit of this capacity varies based on numerous factors like the programming language and available memory. If a program surpasses this limit, the stack overflows, which often results in the program crashing. Python, however, takes measures to prevent the interpreter stack from growing excessively large, thereby averting a stack overflow. Instead, a runtime exception is raised.

Find a problem with this article?

Related articles.

Writing a Binary Search Tree in Python with Examples

Jan 12, 2021 by lane wagner.

What is a binary search tree? 🔗 A binary search tree, or BST for short, is a tree where each node is a value greater than all of its left child nodes and less than all of its right child nodes.

Building a Linked List in Python with Examples

Jan 11, 2021 by lane wagner.

A linked list is a linear data structure where elements are not stored next to each other in memory.

Can I Use Python for Web Development?

Aug 14, 2023 by natalie schooner.

I love giving a short answer to these: yes, 100%, Python is a great tool for web development.

The 28 Best Places to Learn Python Online

Apr 28, 2023 by natalie schooner.

For anyone who wants to learn Python online, it can be a bit overwhelming. When I Googled it, I came up with over 200 million hits.

The endgame Python stack debugger

PyStack reports the stack frames of a running Python process or a Python core dump

Documentation Community

Processes and core files

No matter the situation, PyStack is your reliable companion for program analysis. If a process is running more slowly than you expected, PyStack can tell you what it's doing. If it's hanging, PyStack can tell you where it got stuck. If it crashed, PyStack can even tell you what it was doing from the core dump it left behind.

Get started »

Ready for anything

PyStack thrives in extreme conditions, always ensuring accurate insights. It can handle deep call stacks like a breeze. It can get you a comprehensive report even if your Python interpreter is heavily optimized and shipped without debug information. Even if memory corruption has trashed some of the interpreter's internal data structures, PyStack can handle it!

See for yourself »

Prevent regressions

You can easily integrate PyStack into your pytest test suite. If a test gets stuck, our plugin can automatically report each thread's stack and help you figure out what went wrong.

Discover the plugin »

Uncover your program's secrets. No more deadlocks or mysterious crashes.

PyStack provides a powerful tool for inspecting the stack frames of a running Python process or a Python core dump. It allows developers to quickly and easily understand what's happening in their Python programs without delving into the intricate details of CPython internals. When you ask PyStack to analyze a running process or core file, it prints a stack trace for each thread, which shows the sequence of function calls and their corresponding line numbers in the Python code. This feature is invaluable for debugging and understanding the runtime behavior of an application, especially when encountering unexpected hangs or crashes.

No more hidden code! Peer inside native extensions.

PyStack's native mode offers an additional layer of insight by including native function calls in the stack trace. When your Python code calls into some code that isn't written in Python, PyStack shows you what native functions are executed. If one of those functions calls a function written in Python, the reported stack will switch right back to displaying Python frames.

This mode lets developers see the full stack trace, including calls to C/C++/Rust functions, while still providing context about the Python code being executed at each level. PyStack automatically demangles symbols in the native stack, and it can even include calls to inlined functions whenever there is sufficient debug information available! Native mode is particularly useful when analyzing the interactions between Python and native code in a mixed-language application — which of course is exactly where many crashes and deadlocks occur.

Deep Dive into Program State. See inside your functions!

You can ask PyStack to go beyond providing a stack trace, and to also print the values of local variables and function arguments within Python stack frames. This lets developers obtain a more detailed understanding of what the program is currently doing inside each call. This feature is especially helpful when trying to understand why a particular function was called, or to see what work was being performed when a process crashed. By examining the local variables and arguments, developers can gain crucial insights into the runtime behavior of their Python. This is one of the best ways to leverage PyStack's debugging capabilities to analyze your application and identify potential errors or unexpected behaviors.

PyStack in the media

Bloomberg publishes pystack, bloomberg- 2023-04-19, debugging python in production with pystack, talk python (podcast) - 2023-06-14.

Stacks and Queues in Python

• Introduction

Data structures organize storage in computers so that we can efficiently access and change data. Stacks and Queues are some of the earliest data structures defined in computer science.

Simple to learn and easy to implement, their uses are common and you'll most likely find yourself incorporating them in your software for various tasks.

It's common for Stacks and Queues to be implemented with an Array or Linked List . We'll be relying on the List data structure to accommodate both Stacks and Queues.

In this article, we'll go over the basics of these two data structures. We'll define what they are, and how they work, and then, we'll take a look at two of the most common ways to implement stack and queue in Python.

• What Is a Stack?

Stacks, as the name suggests, are a data structure that follows the Last-in-First-Out (LIFO) principle. As if stacking coins one on top of the other, the last coin we put on the top is the one that is the first to be removed from the stack later.

Note: Another useful example to help you wrap your head around the concept of how stacks work is to imagine people getting in and out of an elevator - the last person who enters an elevator is the first to get out!

To implement a stack, therefore, we need two simple operations:

• push - adds an element to the top of the stack:

• pop - removes the element at the top of the stack:

• What Is a Queue?

Queues, as the name suggests, follow the First-in-First-Out (FIFO) principle. As if waiting in a queue for movie tickets, the first one to stand in line is the first one to buy a ticket and enjoy the movie.

To implement a queue, therefore, we need two simple operations:

• enqueue - adds an element to the end of the queue:

• dequeue - removes the element at the beginning of the queue:

• Implementing Stacks and Queues using Lists

Python's built-in List data structure comes bundled with methods to simulate both stack and queue operations.

Let's consider a stack of letters:

We can use the same functions to implement a Queue. The pop function optionally takes the index of the item we want to retrieve as an argument.

So we can use pop with the first index of the list i.e. 0 , to get queue-like behavior.

Consider a "queue" of fruits:

Check out our hands-on, practical guide to learning Git, with best-practices, industry-accepted standards, and included cheat sheet. Stop Googling Git commands and actually learn it!

Again, here we use the append and pop operations of the list to simulate the core operations of a queue.

• Stacks and Queues with the Deque Library

Python has a deque (pronounced 'deck') library that provides a sequence with efficient methods to work as a stack or a queue.

deque is short for Double Ended Queue - a generalized queue that can get the first or last element that's stored:

Advice: If you'd like to learn more about the deque library and other types of collections Python provides, you can read our "Introduction to Python's Collections Module" article.

• Stricter Implementations in Python

If your code needs a stack and you provide a List , there's nothing stopping a programmer from calling insert() , remove() or other list methods that will affect the order of your stack! This fundamentally ruins the point of defining a stack, as it no longer functions the way it should.

There are times when we'd like to ensure that only valid operations can be performed on our data. We can create classes that only expose the necessary methods for each data structure. To do so, let's create a new file called stack_queue.py and define two classes:

The programmers using our Stack and Queue are now encouraged to use the methods provided to manipulate the data instead.

Imagine you're a developer working on a brand new word processor. You're tasked with creating an undo feature - allowing users to backtrack their actions till the beginning of the session.

A stack is an ideal fit for this scenario. We can record every action the user takes by pushing it to the stack. When the user wants to undo an action they'll pop it from the stack. We can quickly simulate the feature like this:

Queues have widespread uses in programming as well. Think of games like Street Fighter or Super Smash Brothers . Players in those games can perform special moves by pressing a combination of buttons. These button combinations can be stored in a queue.

Now imagine that you're a developer working on a new fighting game. In your game, every time a button is pressed, an input event is fired. A tester noticed that if buttons are pressed too quickly the game only processes the first one and special moves won't work!

You can fix that with a queue. We can enqueue all input events as they come in. This way it doesn't matter if input events come with little time between them, they'll all be stored and available for processing. When we're processing the moves we can dequeue them. A special move can be worked out like this:

Stacks and queues are simple data structures that allow us to store and retrieve data sequentially. In a stack, the last item we enter is the first to come out. In a queue, the first item we enter is the first come out.

We can add items to a stack using the push operation and retrieve items using the pop operation. With queues, we add items using the enqueue operation and retrieve items using the dequeue operation.

In Python, we can implement stacks and queues just by using the built-in List data structure. Python also has the deque library which can efficiently provide stack and queue operations in one object. Finally, we've made our stack and queue classes for tighter control of our data.

There are many real-world use cases for stacks and queues, understanding them allows us to solve many data storage problems in an easy and effective manner.

You might also like...

• Dictionaries vs Arrays in Python - Deep Dive
• Python Performance Optimization
• Modified Preorder Tree Traversal in Django
• Guide to Hash Tables in Python
• Python: Check if Variable is a Dictionary

Improve your dev skills!

Get tutorials, guides, and dev jobs in your inbox.

No spam ever. Unsubscribe at any time. Read our Privacy Policy.

Web Dev|Games|Music|Art|Fun|Caribbean

I love many things and coding is one of them!

In this article

Graphs in Python - Theory and Implementation

Graphs are an extremely versatile data structure. More so than most people realize! Graphs can be used to model practically anything, given their nature of...

Data Visualization in Python with Matplotlib and Pandas

Data Visualization in Python with Matplotlib and Pandas is a course designed to take absolute beginners to Pandas and Matplotlib, with basic Python knowledge, and...

© 2013- 2024 Stack Abuse. All rights reserved.

• Preferences

Runtime Stack - PowerPoint PPT Presentation

Runtime Stack

A 32-bit push operation decrements the stack pointer by 4 and copies a value ... (decrement esp by 2) push r/m32 (decrement esp by 4) push imm32 (decrement esp ... – powerpoint ppt presentation.

• Managed by the CPU, using two registers
• SS (stack segment)
• ESP (stack pointer)
• A 32-bit push operation decrements the stack pointer by 4 and copies a value into the location pointed to by the stack pointer.
• This is the same stack, after pushing two more integers
• Copies value at stackESP into a register or variable.
• PUSH syntax
• PUSH r/m16 (Decrement ESP by 2)
• PUSH r/m32 (Decrement ESP by 4)
• PUSH imm32 (Decrement ESP by 4)
• POP r/m16 (Increase ESP by 2)
• POP r/m32 (Increase ESP by 4)
• r/m meaning register/memory
• Q Why must each character be put in EAX before it is pushed?
• PUSHFD and POPFD
• push and pop the EFLAGS register
• PUSHAD pushes the 32-bit general-purpose registers on the stack
• order EAX, ECX, EDX, EBX, ESP, EBP, ESI, EDI
• POPAD pops the same registers off the stack in reverse order
• PUSHA and POPA do the same for 16-bit registers
• Large problems can be divided into smaller tasks to make them more manageable
• A procedure is the ASM equivalent of a Java or C function
• Following is an assembly language procedure named sample
• The CALL instruction calls a procedure
• pushes offset of next instruction on the stack
• copies the address of the called procedure into EIP (Note IPInstruction Pointer)
• The RET instruction returns from a procedure
• pops top of stack into EIP
• Lists the registers that will be saved
• Call a library procedure using the CALL instruction. Some procedures require input arguments.
• The INCLUDE directive copies in the procedure prototypes (declarations).
• The following example displays "1234" on the console
• Your programs link to Irvine32.lib using the linker command inside a batch file named make32.bat.
• Notice the two LIB files Irvine32.lib, and kernel32.lib
• the latter is part of the Microsoft Win32 Software Devlopment Kit

PowerShow.com is a leading presentation sharing website. It has millions of presentations already uploaded and available with 1,000s more being uploaded by its users every day. Whatever your area of interest, here you’ll be able to find and view presentations you’ll love and possibly download. And, best of all, it is completely free and easy to use.

You might even have a presentation you’d like to share with others. If so, just upload it to PowerShow.com. We’ll convert it to an HTML5 slideshow that includes all the media types you’ve already added: audio, video, music, pictures, animations and transition effects. Then you can share it with your target audience as well as PowerShow.com’s millions of monthly visitors. And, again, it’s all free.

About the Developers

PowerShow.com is brought to you by  CrystalGraphics , the award-winning developer and market-leading publisher of rich-media enhancement products for presentations. Our product offerings include millions of PowerPoint templates, diagrams, animated 3D characters and more.

Python Stack | Implementation of Stack in Python

Definition – What is Stack in Python

Python Stack is a conceptual structure consisting of a set of homogeneous elements and is based on the principle of last in first out (LIFO). It is a commonly used abstract data type with two major operations, namely, push and pop . Push and pop are carried out on the topmost element, which is the item most recently added to the stack.

The push operation in Python Stack adds an element to the stack while the pop operation removes an element from the top position of Python Stack. The stack concept is used in programming languages (ex. Python) and memory organization in computers.

Working of Python Stack

A Python stack represents a sequence of objects or elements in a linear data structure format. The stack consists of a bounded bottom and all the operations are carried out on the top position. Whenever an element is added to the stack by the push operation, the top value is incremented by one, and when an element is popped out from the stack, the top value is decremented by one. A pointer to the top position of the stack is also known as the stack pointer .

A Python stack may be fixed in size or may have a dynamic implementation where the size is allowed to change. In the case of bounded capacity stacks, trying to add an element to an already full stack causes a stack overflow exception. Similarly, a condition where a pop operation tries to remove an element from an already empty stack is known as underflow.

Operations We Can Perform On Python Stack

A stack is considered to be a restricted data structure as only a limited number of operations are allowed. Besides the push and pop operations, certain implementations may allow for advanced operations such as:

• Push:  Adds an item in the stack. Once the stack is full, it is said to be in an Overflow condition.
• Pop:  Removes an item from the stack. It follows a reversed order to pop items similar to the way when items are pushed. It is said to be an Underflow condition.
• Peek: View the topmost item in the stack.
• Duplicate: Copy the top item’s value into a variable and push it back into the stack.
• Swap: Swap the two topmost items in the stack.
• Rotate: Move the topmost elements in the stack as specified by a number or move in a rotating fashion.

Software implementations of the stack concept are done using arrays and linked lists where the top position is tracked using a variable or header pointer respectively. Many programming languages provide built-in features to support stack implementation.

Hardware stacks are implemented for the purpose of memory allocation and access using a fixed origin and size. Stack registers are used to store the value of the stack pointer.

Push Operation in Python Stack

The process of putting a new data element onto the stack is known as a Push Operation. Push operation involves a series of steps −

Steps for Push Operation in Python

• Checks if the stack is full.
• If the stack is full, produces an error and exit.
• If the stack is not full, increments  top  to point next empty space.
• Adds data element to the stack location, where the top is pointing.
• Returns success.

Pop Operation in Python Stack

Accessing the content while removing it from the stack, is known as a Pop Operation. In an array implementation of pop() operation, the data element is not actually removed, instead  top  is decremented to a lower position in the stack to point to the next value. But in linked-list implementation, pop() actually removes data element and deallocates memory space.

Steps for Pop Operation in Python

• Checks if the stack is empty.
• If the stack is empty, produces an error and exit.
• If the stack is not empty, accesses the data element at which the top  is pointing.
• Decreases the value of top by 1.

Implementing Stack in Python

Python gives you a lot of options for implementing a Python stack. You can do so by using lists, tuples or by using third-party modules or packages. However, there are some basic implementations of Python stack that will fulfil most of your needs.

Some of those implementations are as follows:

• collections.deque
• queue.LifoQueue

The functions associated with stack are:

• empty()  – Returns whether the stack is empty – Time Complexity: O(1)
• size()  – Returns the size of the stack – Time Complexity: O(1)
• top()  – Returns a reference to the topmost element of the stack – Time Complexity: O(1)
• push(g)  – Adds the element ‘g’ at the top of the stack – Time Complexity: O(1)
• pop()  – Deletes the topmost element of the stack – Time Complexity: O(1)

Implementing Stack in Python Using List

In Python, we can implement a stack by using list methods as they have the capability to insert or remove/pop elements from the end of the list.

The method that will be used:

• append(x): Appends x at the end of the list
• pop() : Removes last elements of the list

The built-in  list  is the structure that you likely use frequently in your programs can be used as a stack. Instead of  .push() , you can use  .append()  to add new elements to the top of your stack, while  .pop()  removes the elements in the LIFO order:

Program to use list stack

Implementing stack in python using collections. deque class.

The deque class implements a double-ended queue which supports addition and removal of elements from either end in  O(1)  time (non-amortized).

Because deques support adding and removing elements from both ends equally well, they can serve both as queues and as stacks.

Python’s deque objects are implemented as doubly-linked lists which gives them proper and consistent performance insertion and deletion of elements, but poor  O(n)  performance as they randomly access elements in the middle of the stack.

collections.deque is a favourable choice if you’re looking for a stack data structure in Python’s standard library with the performance characteristics of a linked-list implementation.

# Using collections.deque as a stack (LIFO):

Implementing Stack in Python Using queue.LifoQueue Class

This stack implementation in the Python standard library is synchronized and provides locking semantics to support multiple concurrent producers and consumers.

The  queue  module contains several other classes implementing multi-producer, multi-consumer queues that are useful for parallel computing.

Depending on your use case the locking semantics might be helpful, or just incur unneeded overhead. In this case, you’d be better off with using a list or a deque as a general-purpose stack.

Python Remove Duplicates From List Python Print Without Newline Learn Python the Hard Way Review PDF How to Get a Data Science Internship With No Experience Python Map

To implement the previous algorithm, we need to be able to traverse a string and break it into operands and operators.

Python provides a split method in both string objects and the re (regular expression) module. A string’s split method splits it into a list using a single character as a  delimiter . For example:

In this case, the delimiter is the space character, so the string is split at each space.

The function re.split is more powerful, allowing us to provide a regular expression instead of a delimiter. A regular expression is a way of specifying a set of strings. For example, [A-z] is the set of all letters and [0-9] is the set of all numbers. The ^ operator negates a set, so [^0-9] is the set of everything that is not a number, which is exactly the set we want to use to split up postfix expressions:

The resulting list includes the operands 123 and 456 and the operators * and /. It also includes two empty strings that are inserted after the operands.

Applications of Python Stack

Stacks are considered as the backbone of Data Structures. Most of the algorithms and applications are implemented using stacks .

Some of the key applications of Python Stacks are—

• used in “undo” mechanism in the text editor.
• space for parameters and local variables is created internally using a stack.
• compiler’s syntax check for matching braces is implemented by using stack.
• support for recursion.
• Expression evaluation like postfix or prefix in compilers.
• Backtracking (game playing, finding paths, exhaustive searching.
• Python Stack is also used in Memory management, run-time environment for nested language features. etc
• Used in Algorithms like Tower of Hanoi, tree traversals, histogram problem and also in graph algorithms like Topological Sorting.

Python Stack is simple data structures that allow us to store and retrieve data in a sequential fashion. They are very useful in real-world cases. Having a clear understanding of stacks would help us in solving data storage problems in an efficient manner.

The Python stack is an important data structure for realizing solutions to various programming problems. As such, it is even more essential to understand the running time evaluations and working mechanism of these data structures.

Happy Coding!