If you're building AI systems, data pipelines, or backend services in Python, you’ve probably run into weird bugs with mypy not picking up types or imports mysteriously failing—especially when you’re working across microservices or large codebases. Chances are… you’re using a namespace package (maybe without even knowing it). Let’s break it down.
📦 Regular Packages vs Namespace Packages
Regular Packages
- Require an init.py file
- Define a single, self-contained folder
- Easy for mypy, IDEs, linters, and tests to process
Namespace Packages
- No init.py needed
- Split across multiple folders/repos
- Used in plugin systems or modular AI/ML tooling
With namespace packages, coretools.featurestore.encoder and libs.featurestore.scaler can coexist under the same import path.
👉 Great for scalability. Nightmare for static analysis—unless configured right.
🧪 Why AI Devs & Data Teams Should Care
🔌 Modular Pipelines
🧩 Plugin Systems
🧠 Shared AI Tooling
But here's the catch…
🚨 Why mypy Can Break on Namespace Packages
✅ Your Developer Checklist to Fix mypy with Namespace Packages
- Enable namespace support
- Use your.package.name
- Set MYPYPATH + -explicit-package-bases
Still struggling? Add dummy init.pyi or init.py
🧾 Summary Table
🔍 TakeAway
If you're building modular AI pipelines, ML services, or shared tooling across teams—you need to understand how namespace packages and tools like mypy interact. It's the difference between silent bugs and confident code.
Have you hit these issues in production or CI? Let’s compare notes.
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