nbcheckers

nbcheckers [options]

--ignore
    Ignores specific error codes.

--select
    Selects specific error codes to check.

--max-line-length
    Sets the maximum line length for code in the notebook.


    One or more Jupyter Notebook files to check.

The `nbcheckers` command is a suite of tools designed to analyze Jupyter Notebooks (`.ipynb` files) for various issues, including style violations, linting errors, and other common problems that can affect the quality and maintainability of the notebooks. It typically includes checkers for things like proper notebook structure, consistent code style based on standards like PEP 8, and adherence to best practices. While a specific command like `nbcheckers` isn't universally available across all Linux distributions, it usually comes as part of a larger Python package designed for notebook validation. The tool assists developers and data scientists in maintaining high-quality, well-formatted, and error-free notebooks, which are crucial for reproducible research and collaborative projects. The specific checkers available, their configuration options, and the output format might vary depending on the particular implementation of `nbcheckers` being used.
Commonly, `nbcheckers` is part of the `flake8-nb` family of tools.

The availability and specific functionality of `nbcheckers` depends on the installed Python packages and configuration. Ensure the necessary checker plugins are installed.

The command typically returns an exit code of 0 if no errors are found, and a non-zero exit code if errors are detected. This is useful for integrating `nbcheckers` into CI/CD pipelines.

`nbcheckers` tools often support configuration files (e.g., `.flake8`) to customize the checks and rules applied to notebooks. This allows teams to enforce consistent coding styles across projects.

The `nbcheckers` command likely evolved as part of the growing ecosystem of tools for Jupyter Notebook development and maintenance. As notebooks gained popularity for data science and research, the need for automated checks to ensure code quality and consistency increased, leading to the creation of tools like `nbcheckers` as extensions to existing linting and style checking tools such as `flake8`. The history and specific development details are dependent on which specific `nbcheckers` implementation is being referred to, but it mirrors the trends of increasing code quality control in data science workflows.

flake8(1), pylint(1)