nborder 0.1.0

Jupyter notebook linter for hidden-state and execution-order bugs.

nborder

A fast, opinionated linter and auto-fixer for Jupyter notebook hidden-state and execution-order bugs.

What this catches

Code	Name	One-line example
NB101	Non-monotonic execution counts	Cell 1 ran with In [3]: after cell 0 ran with In [5]:.
NB102	Won't survive Restart-and-Run-All	print(df) references a name no cell in the notebook defines.
NB201	Use-before-assign across cells	Cell 0 uses df; df = ... only appears in cell 1.
NB103	Stochastic library used without seed	np.random.rand(3) runs with no seed call before it.

Each rule has a docs page under docs/rules/ explaining the bug class, a bad and good example, and the auto-fix behaviour.

Quick start

pip install nborder
nborder check notebook.ipynb
nborder check --fix notebook.ipynb
nborder check --output-format=json notebook.ipynb

The --fix flag reorders cells topologically when the dependency graph is a DAG, injects library-appropriate seed calls for stochastic libraries, and clears execution counts when they no longer reflect a reproducible run order. Every fix is a pipeline stage with a bailed outcome that does not block other fixes; running the same fix twice is a byte-stable no-op.

Pre-commit

Add this to your .pre-commit-config.yaml:

repos:
  - repo: https://github.com/moonrunnerkc/nborder
    rev: v0.1.0
    hooks:
      - id: nborder

Then pre-commit install. Full setup notes in docs/integrations/pre-commit.md.

GitHub Actions

- uses: moonrunnerkc/nborder@v0.1.0
  with:
    path: notebooks/

Diagnostics show up as inline annotations on the PR. Full options in docs/integrations/github-actions.md.

Configuration

nborder reads its configuration from [tool.nborder] in pyproject.toml:

[tool.nborder.seeds]
value = 42
libraries = ["numpy", "torch", "tensorflow", "random"]

Run nborder config to print the effective merged configuration.

FAQ

Why not use ruff? Ruff lints Python source, not notebook structure. It does not see cross-cell dataflow, so it cannot detect that df is used in cell 0 and only defined in cell 1. nborder is purpose-built for the cross-cell story; it is complementary to ruff, not competitive.

How is this different from nbqa? nbqa runs Python linters against notebook cells one at a time. nborder builds a cross-cell symbol dependency graph and reasons about the relationships between cells, which is the part nbqa explicitly does not do.

Does it work with R or Julia notebooks? Not in v0.1. Multi-language support is reserved for a future release. Python kernels cover roughly 95% of notebooks in the wild.

Will it modify my notebook outputs? No. Outputs, cell metadata, and notebook-level metadata are read-only. The only fix that touches execution_count is a clear-to-null operation that runs as part of the reorder fix.

What about magics? %line, %%cell, !shell, and shell-assignment forms (files = !ls) are stripped to typed metadata before parsing. Magic-defined bindings (e.g., %%capture out defines out) are recorded in the dataflow graph; see docs/known-limitations.md for the limits.

How do I suppress a false positive? Add # nborder: noqa (suppress all rules in the cell) or # nborder: noqa: NB201,NB102 (suppress specific rules) to any line in the cell.

What if I want to disable a rule entirely? Use the select and ignore fields in [tool.nborder] to drop a rule code from the enabled set.

Contributing

See CONTRIBUTING.md.

License

MIT. See LICENSE.