Quando usar a interoperabilidade com Python

Python interop is powerful, but it is a tool, not a default. Knowing when to use it keeps your programs both easy and fast.

Reach for interop when a battle-tested Python library already solves your problem, like plotting, data loading, or web requests.

Interop shines as glue code: stitch existing Python pieces together quickly while you explore an idea or prototype.

Each crossing of the bridge has a small cost. Calling Python inside a hot inner loop can quietly erase your speed gains.

For the performance-critical core, write pure Mojo. That is where native types and the compiler give you the biggest wins.

When you do need Python, call it outside tight loops: load data once, then crunch it with fast Mojo code.

Prefer one big Python call over many tiny ones. Batching reduces how often you pay the bridge crossing cost.

Tasks like file reading, network calls, and formatting are fine in Python: they are not the bottleneck, and the bridge cost is tiny there.

Interop lets you port gradually: start in Python, then rewrite the slow hot path in Mojo, keeping the rest as is.

Let measurements guide you. Profile first, then keep Python where it is cheap and move to Mojo where time is spent.

Used wisely, interop gives you Python's reach and Mojo's speed at once, instead of forcing a painful tradeoff. 🎚️

Recall the smart place to put Python calls.

You learned to use interop for libraries and glue, keep hot paths in native Mojo, and let profiling guide the line. 🎯