Python Coding Patterns

A collection of practical Python patterns that will make your code more maintainable, efficient, and professional. Each notebook focuses on real-world scenarios with working examples you can actually use.

What You'll Learn

01 Design Patterns - Essential Building Blocks

Master the fundamental patterns that every Python developer should know:

• Singleton Pattern: Database connections and configuration management
• Factory Pattern: Create different data processors based on file types
• Builder Pattern: Build complex reports with optional components
• Decorator Pattern: Add caching, logging, and validation to data sources
• Strategy Pattern: Switch between different customer analysis algorithms
• Observer Pattern: Build reactive systems for stock price monitoring

Each pattern includes performance comparisons and shows you when (and when not) to use them.

02 Pythonic Idioms - Write Code Like a Python Expert

Learn the idioms that separate good Python from great Python:

• EAFP vs LBYL: The Python philosophy that changes how you handle errors
• Context Managers: Guarantee resource cleanup with database connections and file operations
• Comprehensions: Transform data efficiently with list, dict, and set comprehensions
• Duck Typing: Build flexible data sources that work with any object that has the right methods
• Iteration Protocol: Create custom iterables for pagination, streaming, and data processing

You'll see performance benchmarks showing why these patterns matter.

03 Functional Programming - Reduce Bugs, Increase Clarity

Apply functional programming concepts to write more predictable code:

• Higher-Order Functions: Use functions as first-class objects for flexible data processing
• Immutability: Build data structures that prevent bugs through immutable design
• Pure Functions: Write functions that are easier to test and debug
• Function Composition: Chain simple operations to solve complex problems

Includes practical examples of data transformation pipelines and mathematical computations.

04 Concurrency & Parallelism - Make Your Code Actually Faster

Understand when and how to use Python's concurrency options:

• Producer-Consumer Pattern: Coordinate work between threads using thread-safe queues
• Thread Pools: Handle I/O-bound tasks efficiently with ThreadPoolExecutor
• Async/Await: Build high-concurrency applications for thousands of simultaneous operations
• Process Pools: Break free from the GIL for CPU-intensive work

Complete with performance comparisons showing the real impact of each approach.

05 Data Processing - Handle Data Like a Pro

Learn patterns for processing data efficiently at any scale:

• ETL Pipelines: Structure data workflows with proper error handling and logging
• Vectorization: Speed up numerical computations by 10-100x using NumPy and pandas
• Streaming Processing: Handle datasets larger than memory using generators and iterators
• Memory Efficiency: Process large files without running out of RAM

Includes real-world examples with sales data, log processing, and user analytics.

06 Testing Patterns - Write Tests That Actually Help

Build test suites that make development faster, not slower:

• AAA Pattern: Structure tests that are easy to read and maintain
• Fixtures: Set up test environments efficiently with proper cleanup
• Mocking: Test your code in isolation by replacing external dependencies
• Test Organization: Organize test suites that scale with your codebase

Learn to test databases, APIs, and complex business logic with practical examples.

08 Error Handling & Logging - Build Resilient Applications

Create applications that handle failures gracefully:

• Custom Exceptions: Build exception hierarchies that make debugging easier
• Retry Patterns: Handle transient failures with exponential backoff
• Graceful Degradation: Keep core functionality working when optional features fail
• Structured Logging: Create logs that help you debug production issues

Includes patterns for rate-limiting logs, security-aware logging, and error recovery.

How to Navigate This Repository

For specific problems: Jump directly to the notebook that addresses your current challenge For comprehensive learning: Work through the notebooks in order - each builds on previous concepts For hands-on practice: Every notebook includes working examples and exercises you can modify

Prerequisites

• Basic Python knowledge (functions, classes, exceptions)
• Familiarity with common data structures (lists, dicts)
• Comfortable running Python scripts and notebooks

Getting Started

git clone https://github.com/jeremiahoclark/python-coding-patterns.git
cd python-coding-patterns
pip install jupyter pandas numpy requests aiohttp pytest
jupyter notebook

What Makes This Different

• Real examples: Every pattern uses realistic data and scenarios, not abstract toy problems
• Performance focus: Benchmarks show you the actual impact of different approaches
• Production-ready: These patterns work in real applications, not just tutorials
• Modern Python: Uses current best practices and Python 3.7+ features

Start with any notebook that addresses your current needs - each one is designed to provide immediate practical value.