Programming Python

Overview

Write Python with clear data contracts, deliberate effects, and predictable failure behavior. Research baseline: Python 3.14 is stable; 3.15 is prerelease, checked 2026-09-05. Read requires-python, environment pins, dependency lockfiles, type-checker settings, and CI first. Use features supported by every required interpreter; don't raise the minimum version or replace the project's tooling during unrelated work. Recheck official releases when updating this guidance.

Mental model

Annotations describe a contract; validation enforces it. Keep boundary parsing explicit, represent trusted data with small types, and make ownership of mutable state and resources visible. A function or module is enough until a class or abstraction solves a concrete lifecycle or substitution problem.

Recent features and migration traps

Don't assume annotation values are eagerly evaluated or all strings. Use typing.get_type_hints when evaluated types are needed, or appropriate annotationlib formats for introspection; evaluating annotations can execute code. T-strings preserve literal and interpolated parts for a processor: they do not automatically escape HTML or parameterize SQL, and they are not interchangeable with str.

Types and data

• Annotate public boundaries and non-obvious internal contracts; use list[T], dict[K, V], and T | None on supported versions. Use object for unknown data that must be narrowed; isolate necessary Any at untyped integration points.
• TypedDict describes dictionary shape without runtime validation. Protocol describes structural behavior; a runtime-checkable protocol does not verify method signatures or semantic correctness.
• Use dataclasses for records; choose frozen=True when reassignment is unwanted and slots=True when its restrictions fit. Frozen fields do not freeze nested lists or dictionaries. Use default_factory for mutable defaults.
• Validate untrusted values once at entry and pass trusted values inward. Choose strict validation or explicit coercion deliberately; don't let a serializer or modeling library quietly change the input contract.
• Use an explicit is None check when zero, False, or an empty collection is valid. Truthiness-based defaults can erase meaningful input.

Errors, resources, and module design

• Catch specific failures close to the operation that can recover. A top-level handler may catch broadly to report failure, but should not pretend success. Add a domain exception when callers need that distinction; preserve causes with raise ... from ....
• Don't replace missing required configuration, failed I/O, or invalid input with defaults. Suppress only a named, expected failure whose absence is part of the contract. Keep sensitive input out of exception messages and logs.
• Use with/async with for resources and ExitStack for dynamic groups. Make cleanup observable where failure matters; finalizers and garbage collection do not guarantee timely cleanup.
• Keep imports free of heavyweight work and mutable global configuration. Public submodules are valid APIs; re-exporting everything from __init__.py is unnecessary and can introduce cycles.
• Use pyproject.toml for modern package metadata. A src/ layout helps test installed-package behavior; flat layouts and requirements files still have valid uses. Preserve the established package/environment tools unless changing them is part of the task.

Concurrency

• Use async I/O for concurrent awaitable operations and threads for blocking I/O. In a GIL-enabled interpreter, pure-Python CPU work generally needs processes, isolated interpreters, or native code that releases the GIL for parallelism.
• Free-threaded builds can execute Python threads in parallel, but extensions may re-enable the GIL. Verify runtime and dependency support; protect compound shared mutations with locks rather than relying on container internals.
• TaskGroup cancels siblings after an ordinary task failure and waits for cleanup. Propagate CancelledError after necessary cleanup; swallowing it can break task groups and timeouts. gather has different failure semantics and remains useful when those semantics are intended.
• Keep blocking work off the event loop with appropriate executors or to_thread. Bound queued work; cancelling an awaiting coroutine does not necessarily stop a running thread.
• Python 3.14 no longer defaults to fork on any platform. Make process-pool entry points importable, guard startup with if __name__ == "__main__":, and explicitly choose a multiprocessing context only when required. InterpreterPoolExecutor provides isolated interpreters, not shared mutable globals.

Example

A missing optional value may use a default; an invalid supplied value must fail:

def parse_workers(raw: str | None) -> int:
    if raw is None:
        return 4
    try:
        workers = int(raw)
    except ValueError as exc:
        raise ValueError("worker count must be an integer") from exc
    if workers < 1:
        raise ValueError("worker count must be positive")
    return workers

Checklist

• Syntax and libraries match the supported Python versions and interpreter build.
• Types are validated at boundaries; mutable defaults and truthiness don't change meaning.
• Errors, cleanup, cancellation, and worker shutdown preserve the contract.
• When authorized, use the project's formatter, linter, type checker, and focused tests. Cover invalid input and concurrent cleanup; report skipped checks without installing missing tools implicitly.

References

• Release status and Python 3.14 changes.
• Typing, dataclasses, annotation introspection, and t-strings.
• Async task contracts, free threading, multiprocessing, and executors.
• PyPA layout tradeoffs.