nbdev
Framework for building tested, documented Python packages from Jupyter notebooks.
Our View
Assess whether this fits the group’s research and teaching workflows. nbdev can help notebook-heavy projects become importable, documented, and tested packages while preserving an exploratory authoring workflow.
Provisional positions are editorial proposals awaiting group review.
Teaching — Recorded in topic offerings; consult the dated event links for availability.
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Related themes: Scientific Computing
Related topics: Automating Research WorkflowsRobust and Reusable Software
Common use cases
- Develop Python packages from notebooks
- Keep narrative documentation close to executable examples
- Generate tests and API documentation from notebook sources
Getting started
nbdev turns notebooks into package source files, documentation, and tests. It is useful for teams that want to keep the narrative strength of notebooks while producing reusable Python software.
Why It Matters in Research Workflows
Many research projects begin in notebooks because notebooks are excellent for explanation and exploration. nbdev offers a path from that exploratory work toward maintainable packages without forcing the team to abandon notebook-based development immediately.
In Our Teaching
We discuss nbdev as an advanced option in reusable scientific software courses, especially when learners are deciding how to package notebook-driven analysis methods for collaborators.