Python Engineering — senior fundamentals

Python is easy to start and hard to master — most of what separates a senior engineer from a script writer is a handful of deep fundamentals: how names and objects really work, laziness, the type system that grew up, the GIL, and knowing which tool the ecosystem already built. This track covers exactly that, with an interactive lab in every module — and it stays connected to the real world: the same beta-calculation case study used in Financial Engineering Programming is verified and load-tested by Python (pandas IPV, ThreadPoolExecutor harness), and those scripts appear here as the worked examples.

1. 🏷️

   Module 1Available

   The object model & mutability

   Names bind to objects, assignment never copies — aliasing, is vs ==, and the mutable-default-argument trap every senior has been burned by.

   🎛 Names & objects lab

2. 🔁

   Module 2Available

   Iteration, generators & comprehensions

   The iterator protocol, lazy pipelines that stream a million rows in O(1) memory, and the one-shot generator trap.

   🎛 Lazy vs eager lab

3. 🧾

   Module 3Available

   Typing, dataclasses & protocols

   Type hints the tooling enforces, frozen dataclasses as Python's records, and Protocol — duck typing made checkable.

   🎛 Frozen dataclass validator

4. 🚦

   Module 4Available

   The GIL & concurrency

   Why threads don't parallelize CPU but do parallelize waiting — and how to choose between threads, processes and asyncio.

   🎛 GIL simulator

5. ⚡

   Module 5Available

   Performance: measure, then vectorize

   Profile first, vectorize with numpy second, and remember big-O beats both — plus lru_cache and its traps.

   🎛 Vectorization lab

6. 🧰

   Module 6Available

   The senior toolbelt: stdlib & libraries

   The stdlib power tools you should reach for daily, and the library map — numpy, pandas, FastAPI, pydantic, pytest — with when-to-use lines.

   🎛 Toolbelt matcher

Educational analysis, not investment advice.