Python Programming Language

1. Data Structures

• Lists, Tuples, Sets, and Dictionaries: Understand the properties and use cases of each.
• Comprehensions: Efficient ways to create lists, sets, and dictionaries.
• Immutable vs. Mutable Types: Differences and their implications on program behavior.

2. Control Flow

• Conditional Statements: if, elif, and else.
• Loops: for, while, and the use of break, continue, and else with loops.
• Error Handling: try, except, finally, and custom exceptions.

3. Functions

• Defining Functions: def, parameters, default arguments, and return statements.
• Lambda Functions: Inline, anonymous functions.
• Decorators: Modifying the behavior of functions or methods.
• Closures and Higher-Order Functions: Functions that return functions and pass functions as arguments.

4. Object-Oriented Programming (OOP)

• Classes and Objects: Creating and using classes and objects.
• Inheritance and Polymorphism: Reusing code and designing flexible systems.
• Encapsulation: Managing access to data with public and private attributes.
• Dunder Methods: Special methods like __init__, __str__, and __repr__.

5. Modules and Packages

• Importing Modules: Standard libraries and custom modules.
• Creating Packages: Organizing code into modules and packages.
• Virtual Environments: Managing dependencies with venv or virtualenv.

6. File Handling

• Reading and Writing Files: open(), read(), write(), and file context management with with.
• Handling Different File Types: Text files, CSV, JSON, etc.

7. Iterators and Generators

• Iterators: Understanding iter() and next().
• Generators: Using yield to produce a sequence of results lazily.
• Generator Expressions: Similar to list comprehensions but generate values on the fly.

8. Pythonic Practices

• PEP 8: Python's style guide for writing readable code.
• Writing Idiomatic Python: Using list comprehensions, unpacking, and context managers effectively.
• Zen of Python: Understand principles like simplicity, readability, and explicitness.

9. Libraries and Frameworks

• Standard Library: Key modules like os, sys, math, itertools, datetime.
• Popular Libraries: NumPy, Pandas, Requests, Matplotlib, Scikit-learn, etc.
• Web Frameworks: Flask, Django for web development.

10. Performance and Optimization

• Time Complexity: Analyzing the performance of algorithms.
• Memory Management: Understanding how Python manages memory, reference counting, and garbage collection.
• Profiling and Debugging: Using cProfile, timeit, and debugging tools like pdb.

11. Concurrency

• Multithreading vs. Multiprocessing: Differences, GIL, and when to use each.
• Asyncio: Writing asynchronous code with async and await.

12. Testing

• Unit Testing: Using unittest, pytest for writing test cases.
• Mocking: Creating mock objects to simulate and test code behavior.

© Kevin Luzbetak