Fundamentals
Python fundamentals notes covering key definitions, core concepts, worked examples, and practice problems.
Python - Wyatt's Notes
Python programming language notes covering fundamentals, advanced concepts, and practical examples.
sources:
- text: Standard textbook reference
Python is a programming language with a rich type system and ecosystem. These notes cover the language from fundamentals to advanced topics, with worked examples, practice problems, and flashcards.
Topics
Data structures
Python data structures notes covering key definitions, core concepts, worked examples, and practice problems.
Object oriented
Python object oriented notes covering key definitions, core concepts, worked examples, and practice problems.
Standard library
Python standard library notes covering key definitions, core concepts, worked examples, and practice problems.
Async
Python async notes covering key definitions, core concepts, worked examples, and practice problems.
Best practices
Python best practices notes covering key definitions, core concepts, worked examples, and practice problems.
Advanced topics
Python advanced topics notes covering key definitions, core concepts, worked examples, and practice problems.
Flashcards python basics
Python flashcards python basics notes covering key definitions, core concepts, worked examples, and practice problems.
Practice interactive
Python practice interactive notes covering key definitions, core concepts, worked examples, and practice problems.
Practice python basics
Python practice python basics notes covering key definitions, core concepts, worked examples, and practice problems.
Intuition
Python’s philosophy emphasises readability and simplicity: The Zen of Python (“Readability counts,” “Simple is better than complex”) guides language design. This philosophy makes Python code maintainable and collaborative.
Why it matters: Python’s versatility spans data science, web development, automation, and education. It is the most taught programming language in universities.
The key insight: Python’s dynamic typing and interpreted nature make it excellent for rapid prototyping but require discipline (type hints, testing) for large projects.
What You Will Find
- Fundamentals: Types, functions, control flow, and error handling
- Data Structures: Lists, dictionaries, sets, tuples, and comprehensions
- Object-Oriented: Classes, decorators, descriptors, and the data model
- Standard Library: Essential modules (os, sys, collections, itertools, pathlib)
- Async Programming: asyncio, async/await, and concurrent execution
- Advanced Topics: Type hints, metaclasses, descriptors, and the GIL
Why This Matters
Python is the most popular language for data science, machine learning, web development, and automation. Its readability and extensive standard library make it ideal for rapid development, while its type system and async capabilities support large-scale production applications. Understanding Python’s object model, memory management, and concurrency limitations is essential for writing efficient, maintainable code.
Common Mistakes
Assuming Python is slow because it is interpreted: CPython is slower than C for raw loops, but NumPy, Pandas, and other libraries use C extensions for hot paths. Profile before optimising — the bottleneck is in most cases not where you think.
Overusing global variables: Global variables create implicit coupling between functions. They make testing harder and introduce race conditions in concurrent code. Prefer function parameters and return values for data flow.
Ignoring virtual environments: System-wide package installation causes version conflicts between projects. Always use venv or conda to isolate project dependencies. This prevents “it works on my machine” problems.