Effective Python 86 - 90 ~ Math Crumbs

Click here for the first post, which contains the context of this series.

Item #86: Consider module-scoped code to configure deployment environments.

Programs often need to run in multiple deployment environments that each have unique assumptions and configurations.
You can tailor a module's contents to different deployment environments by using normal Python statements in module scope.
Module contents can be the product of any external condition, including host introspection through the sys and os modules.

Defining root exceptions for modules allows API consumers to insulate themselves from an API.
Catching root exceptions can help you find bugs in code that consumes an API.
Catching the Python Exception base class can help you find bugs in API implementations.
Intermediate root exceptions let you add more specific types of exceptions in the future without breaking your API consumers.

Circular dependencies happen when two modules must call into each other at import time. They can cause your program to crash at startup.
The best way to break a circular dependency is by refactoring mutual dependencies into a separate module at the bottom of the dependency tree.
Dynamic imports are the simplest solution for breaking a circular dependency between modules while minimizing refactoring and complexity.

The warnings module can be used to notify callers of your API about deprecated usage. Warning messages encourage such callers to fix their code before later changes break their programs.
Raise warnings as errors by using the -W error command-line argument to the Python interpreter. This is especially useful in automated tests to catch potential regressions of dependencies.
In production, you can replicate warnings into the logging module to ensure that your existing error reporting systems will capture warnings at runtime.
It's useful to write tests for the warnings that your code generates to make sure that they'll be triggered at the right time in any of your downstream dependencies.

Python has special syntax and the typing built-in module for annotating variables, fields, functions, and methods with type information.
Static type checkers can leverage type information to help you avoid many common bugs that would otherwise happen at runtime.
There are a variety of best practices for adopting types in your programs, using them in APIs, and making sure they don't get in the way of your productivity.