E.B Advanced Python Roadmap

Use this elective when your prototype starts repeating logic, waiting on slow calls, streaming data, or registering tools dynamically.

See the Engineering Path First

Advanced Python is useful when it makes code more observable, reusable, and easier to control.

Run the Smallest Async Trace

import asyncio

async def fetch(name, delay):
    await asyncio.sleep(delay)
    return f"{name}:done"

async def main():
    results = await asyncio.gather(
        fetch("retrieval", 0.1),
        fetch("rerank", 0.05),
    )
    print(results)

asyncio.run(main())

Expected output:

['retrieval:done', 'rerank:done']

This is the smallest async habit: launch independent work, wait for all results, then keep a trace.

Learn in This Order

Step	Lesson	Practice Output
1	E.B.1 Decorators	Add timing or logging without changing business code
2	E.B.2 Iterators and Generators	Stream rows without loading everything at once
3	E.B.3 Concurrency	Run async tasks with timeout and cancellation thinking
4	E.B.4 Metaprogramming	Register tools or handlers explicitly

Pass Check

You pass this module when you can build one traceable pipeline that uses a decorator, generator, async call, or registry, and can explain why the code became easier to debug.