Welcome to the Python data structures tutorial! This guide will help you understand the fundamental and advanced data structures in Python, along with their use cases and implementation examples.

Common Data Structures

Here are the most commonly used data structures in Python:

1. Lists 📋

  • Mutable and ordered collection of elements
  • Supports dynamic resizing and various operations like append(), insert(), pop()
  • Example: 
    fruits = ["apple", "banana", "cherry"]
python_list

2. Tuples 📦

  • Immutable and ordered collection
  • Used for fixed data like coordinates or database records
  • Example: 
    coordinates = (10, 20)

3. Dictionaries 📚

  • Key-value pairs for fast data retrieval
  • Unordered and mutable
  • Example: 
    student = {"name": "Alice", "age": 25}
python_dictionary

4. Sets 🔁

  • Unordered collection of unique elements
  • Efficient for membership testing and eliminating duplicates
  • Example: 
    unique_numbers = {1, 2, 3, 3, 4}  # Automatically removes duplicates

Advanced Data Structures

Explore more complex structures like:

  • Stack (LIFO principle)
  • Queue (FIFO principle)
  • Tree (hierarchical data organization)
  • Graph (network of nodes and edges)

For a deeper dive into Python programming concepts, check out our Python Programming Basics tutorial.

Practice Tips

  • Use list() for dynamic collections
  • Prefer tuple() for immutable data
  • Leverage dict() for fast lookups
  • Opt for set() when uniqueness matters

Let me know if you'd like to explore a specific data structure further! 🚀