Python Stack Data Structure: When and Why to Use it

What is a stack in Python data structure

A stack in Python is a type of collection used to organize and manage data in a specific order. Elements in a stack are added and removed from only one end, called the top.

To visualize it, imagine stacking books or bowls. You place items one on top of another, and when you need one, you remove it from the top and not the bottom.

Here are some common stack analogies you’ve likely encountered:

Let’s also look at an example from everyday computing – the undo button. When you use a text editor or drawing app, every action you take is recorded in order. Pressing undo removes the most recent action first, just like taking the top item off a stack.

The Python stack follows a specific rule called Last-In, First-Out (LIFO). Let’s understand this concept.

What is the LIFO principle in a stack?

LIFO stands for Last-In, First-Out. It’s the rule that defines how a stack works: the last element you add is the first one to be removed. Here’s how we can picture it:

Stacks can store different data types depending on the need. In the image, numerical values are added and removed from the stack to demonstrate how the LIFO principle works.

The two key operations that make a stack work are:

• Push: This operation adds a new item to the top of the stack.
• Pop: This operation removes the item from the top of the stack.

Check out the Create a Stack in Python article to learn how to implement these operations in Python.

Stacks behave this way by design, and the LIFO pattern becomes especially useful in situations involving reversal of order or tracking recent actions.

For instance:

• Predictable behavior: The following item to be removed is always the most recently added. This consistent behavior makes stacks well-suited for managing temporary data.

• Efficient memory use: Because all access and removal occur at the top, stacks avoid unnecessary scanning through other elements. This results in faster operations, especially in time-sensitive contexts.

• Logical fit for many problems: Stacks naturally support scenarios like undo mechanisms, backtracking in mazes, and evaluating expressions where actions are reversed or revisited in order.

Now, let us check how stacks are built into many everyday tools and programming tasks.

Common use cases of Python stack

A stack is more than just a theoretical concept. It plays a key role in several everyday applications and programming tasks, especially where reversing, nesting, or backtracking logic is needed. Here are some of the most common use cases:

• Undo/Redo Systems: Many text editors and design tools use stacks to record user actions. Each action is pushed onto a stack. When “Undo” is triggered, the most recent action is popped and reversed. For “Redo,” the action may be pushed back onto another stack.

• Browser History Navigation: Every time a webpage is visited, its URL is pushed onto a history stack. Clicking the “Back” button pops the current page and shows the previous one. This is a perfect example of LIFO in user interfaces.

• Function Calls and Recursion (Call Stack): During program execution, each function call is placed on a stack. When a function finishes, it’s removed (popped) from the stack. This is especially important in recursion, where each call depends on returning in reverse order.

• Validating Brackets or Expressions: Compilers and interpreters use stacks to validate whether expressions like ([{}]) are correctly nested. An opening symbol is pushed onto a stack, and the closing symbols must match the ones at the top, ensuring proper order.

• Depth-First Search in Graphs: DFS algorithms use stacks to remember which node to visit next. Each time a new node is reached, it’s pushed to the stack. Backtracking happens when popping from the stack and trying a new path.

Let’s explore when to use stacks in Python programming.

When to use a stack in Python?

Stacks are commonly used in programming for tasks like backtracking, memory storage, parsing expressions, and scenarios where computations need to be reversed, such as undo functionality or depth-first search.

Choosing a stack depends on how your data should be accessed and managed.

Ask these questions before choosing a stack:

• Do you need to reverse a sequence of operations?

Stacks help when the most recent task must be completed or undone first—like in undo actions or parsing expressions.

• Are you working with nested structures?

Stacks are perfect for tracking opening and closing elements, such as parentheses or HTML tags.

• Is your logic recursive?

If a function calls itself, the call stack stores each call’s context, ensuring the most recent call is handled first.

• Do you need consistent LIFO behavior with fast operations?

Stacks shine in performance when push and pop operations dominate the workload.

A stack is almost always the right fit when the data flow naturally follows a last-in, first-out pattern.

Comparing stack with other data structures

It is helpful to understand how stacks differ from similar data structures like lists and queues. Let’s break down how each one behaves and when to use them.

Stack vs list

Stacks provide a more controlled environment, ensuring that the most recently added item is the first one removed. Lists, on the other hand, allow random access and can be misused when intended to function as a stack.

Stack vs queue

Stacks excel when there is a need to reverse the order of operations, while queues are suited for managing tasks that follow a natural order of execution (like waiting in line).

Now that we’ve compared stacks with lists and queues, we can look at how Python allows us to implement a stack effectively.

Different ways to implement a stack in Python

In Python, a stack can be implemented in several different ways. The three main methods are using the built-in list data structure, using the collections.deque, or using linked lists. Let’s explore these methods.

Using list to implement a stack

A list is the most common and easiest way to implement a stack in Python. The .append() and .pop() methods make it easy to push and remove elements from the top. This approach is straightforward and beginner-friendly.

Example:

stack = [] 
stack.append(1)               # Push 1 to the stack 
stack.append("hello world")   # Push "hello world" to the stack 
stack.append(True)            # Push 3 to the stack 
stack.pop()                   # Removes True from the stack 
print(stack)   

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Advantages:

• Very easy to use and understand.
• Built-in functionality, so no extra imports are needed.
• Generally sufficient for most small to medium-sized applications.

Disadvantages: Performance can degrade with large stacks. The time complexity of popping from the list is O(1), but resizing the list can cause O(n) time complexity for push operations when the list grows.

Using deque to implement stack

The deque (double-ended queue) from the collections module is optimized for stack and queue behavior. Just like with a list, .append() adds elements to the stack, but the .pop() operation is more efficient with deques, particularly with large collections.

Example:

from collections import deque 
stack = deque() 
stack.append(1)              # Push 1 to the stack 
stack.append("hello world")  # Push hello world to the stack 
stack.append(True)           # Push True to the stack 
stack.pop()                  # Removes True from the stack 
print(stack) 

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Advantages:

• Operations like append and pop are O(1), making them faster for larger collections compared to lists.
• Ideal for applications where performance with large stacks is critical.

Disadvantages:

• Requires importing collections, which adds a slight overhead compared to using a list.
• While more efficient than lists, it may still not be as fast in all scenarios as other specialized data structures.

Using linked list to implement stack

A linked list implementation of a stack involves creating nodes that each hold data and a reference (or pointer) to the next node. The stack’s “top” is typically the head of the list, and the push and pop operations modify this head.

Example:

class Node:  
  def init(self, value):  
    self.value = value  
    self.next = None 

class Stack:  
  def init(self): 
    self.top = None 

  def push(self, value):  
    new_node = Node(value)  
    new_node.next = self.top  
    self.top = new_node 
  
  def pop(self):  
    if self.top is None:  
      return None  
    value = self.top.value  
    self.top = self.top.next  
    return value 
 
# Usage:  
stack = Stack()  
stack.push(1)             # Push 1 to the stack  
stack.push("hello world") # Push hello world to the stack  
stack.push(True)          # Push True to the stack  
stack.pop()               # Removes True from the stack  
print(stack) 

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Advantages:

• Both push and pop operations are O(1) in time complexity, making it efficient even for very large stacks.
• No resizing of a data structure like in lists, leading to constant-time operations even with large data.

Disadvantages:

• It is more complex to implement and manage compared to lists or deques.
• Requires handling of pointers or references, which adds overhead in code complexity and maintenance.

With the implementation methods explored, let’s conclude with what makes stacks effective and when to use them wisely.

Conclusion

In this article, we explored what a stack is in Python, how it follows the Last-In-First-Out (LIFO) principle, and where it proves most useful. From undo operations and recursion to expression evaluation and DFS traversal, stacks offer a clean, efficient way to manage temporary or nested data. We also discussed when to choose stacks over other structures like lists and queues, and compared the different ways to implement them using lists, deques, and even linked lists.

To explore stacks in depth and practice hands-on, check out Codecademy’s Learn Data Structures and Algorithms with Python course.

Frequently asked questions

1. Can I always use a list as a stack in Python?

Yes, Python lists can be used as stacks using the .append() and pop() methods. However, for large-scale or performance-critical applications, collections.deque or a custom linked list-based stack may offer better efficiency and structure.

2. What are the different types of stacks?

Stacks can be implemented in several ways:

• Array-based stacks – fixed or dynamic size using lists or arrays.
• Linked list stacks – dynamic memory usage, ideal when size is unpredictable.
• Double-ended stacks – allow operations from both ends (rare, but used in specific contexts).

3. When is a stack better than a queue?

Use a stack when the most recent item needs to be accessed first (LIFO). Scenarios include:

• Undo/redo functionality
• Backtracking algorithms
• Syntax parsing

A queue (FIFO) is better for scheduling, buffering, or handling tasks in arrival order.

4. What are some signs that I should switch from recursion to a stack?

Switch to a stack when:

• Recursion depth is too large, risking a stack overflow.
• You need to manage the order of operations or execution explicitly.
• You want more control over memory and function calls (e.g., in iterative DFS).