4. Advanced Python
This volume dives into Python internals, concurrency, and performance optimization.
Each chapter is focused on one advanced concept, so you can study, recall, or master topics in small steps.
Part I: Deep Dive into Python Internals
- 1. Memory Model
- 2. Object Lifecycle
- 3. Reference Counting
- 4. Garbage Collection
- 5. Global Interpreter Lock
- 6. Introspection Basics
- 7. Reflection With Inspect
- 8. Dunder New Method
- 9. Dunder Call Method
- 10. Dunder Getattr Method
Part II: Advanced OOP & Metaprogramming
- 11. Class Decorators
- 12. Descriptors Get Set Delete
- 13. Slots For Memory
- 14. Abstract Base Classes
- 15. Metaclasses Basics
- 16. Creating Classes Dynamically
- 17. Dynamic Code Exec
- 18. Ast Manipulation
Part III: Concurrency & Parallelism
- 19. Threading Basics
- 20. Threading Use Cases
- 21. Multiprocessing Basics
- 22. Multiprocessing Vs Threading
- 23. Asyncio Intro
- 24. Asyncio Event Loop
- 25. Asyncio Tasks
- 26. Concurrency Patterns
- 27. When To Use Threads Vs Processes Vs Async
Part IV: Performance & Optimization
- 28. Profiling With Cprofile
- 29. Timeit And Benchmarks
- 30. Line Profiler Usage
- 31. Memory Profiling
- 32. Memory Optimization
- 33. Speeding With Cython
- 34. Numpy For Performance
- 35. Jit With Pypy
- 36. Jit With Numba
Part V: Design Patterns in Python
- 37. Singleton Pattern
- 38. Factory Pattern
- 39. Strategy Pattern
- 40. Observer Pattern
- 41. Pythonic Patterns
- 42. When To Apply Patterns
- 43. When To Avoid Patterns
Part VI: Interfacing & Extending Python
- 44. C Extensions Basics
- 45. Ctypes Module
- 46. Cffi Usage
- 47. Embedding Python
- 48. Foreign Function Interfaces
Part VII: Advanced Capstone Project
- 49. Project Overview
- 50. Async Web Requests
- 51. Handling Timeouts And Retries
- 52. Structured Data Output
- 53. Using Multiprocessing For Parsing
- 54. Cli With Options
- 55. Performance Benchmarking
- 56. Logging In Project
By completing this volume, you will gain mastery of Python’s internals, concurrency models, metaprogramming, and performance optimization techniques, making you an expert-level Python developer.