Python

Localz's backend language (FastAPI, SQLAlchemy 2, Pydantic v2) — plus data engineering and data science tooling for broader professional breadth.

Why we need this / what value this brings

Python depth pays off directly on Localz's backend and stays transferable to data engineering / data science work outside it.

When to use this

Any FastAPI feature, backend performance question, or general Python fluency gap (concurrency, typing, packaging) worth closing.

How to use or implement this

Start with the subtopics closest to daily Localz work (WebFrameworks, DatabaseAccess, TypingAndStaticAnalysis, Concurrency) before the broader-breadth ones (DataEngineeringPipelines, DataScienceStack).

Subtopics

• LanguageInternals
• StandardLibraryIdioms
• TypingAndStaticAnalysis
• Concurrency
• PerformanceOptimization
• PackagingAndEnvironments
• TestingInPython
• WebFrameworks
• DatabaseAccess
• DeploymentAndServing
• DataEngineeringPipelines
• DataScienceStack
• CLIToolingAndScripting
• ErrorHandlingAndLogging

Research questions

• Which of these does the Localz FastAPI backend need first vs which are pure breadth-building?

Related

• Languages
• ConsistencyConcurrency (general theory backing Concurrency/)

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Empty folder — drop notes, links, and findings here as you research.