{"categories":[{"label":"Testing","url":"https://skillfed.io/packages/category/software-development-testing/6"}],"enrichment":{"capability":"testbook lets you write unit tests for Jupyter Notebooks in separate Python test files, treating .ipynb files like .py modules and executing notebook code within test functions.","skillfed_tags":["jupyter-testing","notebook-validation"],"use_cases":["Test data processing pipelines defined in Jupyter notebooks without rewriting them as Python modules","Validate notebook outputs and intermediate variables in a CI/CD pipeline","Ensure notebook functions produce expected results when dependencies or data change","Build regression tests for analytical or scientific notebooks shared across a team","Verify notebook cells execute without errors before publishing or sharing them"],"what_it_does":"testbook is a unit testing framework that lets you write conventional Python unit tests for Jupyter Notebooks without embedding tests inside the notebooks themselves. Instead of mixing test code with notebook cells, you write separate test files that load and execute a notebook, then extract and assert on its outputs and variables. It depends on nbformat to parse notebooks and nbclient to execute them, bridging the gap between notebook-based development and traditional test-driven development practices.\n\nThe package treats .ipynb files as importable modules within your test suite. You decorate a test function with @testbook, pointing it to a notebook path, and the decorator handles notebook execution and provides a testbook object (tb) that lets you retrieve variables and functions defined in the notebook. This approach is useful for data science and analytics workflows where notebooks are primary development artifacts but you still want automated test coverage.","worth_installing":"Yes, with caution. testbook fills a real gap for testing notebook-based code, and the low install friction and permissive license make it accessible. However, the package is dormant\u2014no releases since 2021-06-02\u2014which raises questions about compatibility with modern Python and Jupyter versions. If your notebooks target Python 3.6\u20133.8 and stable Jupyter dependencies, it should work; for newer environments, verify compatibility before committing to production use."},"id":"testbook","links":{"html":"https://skillfed.io/packages/testbook","md":"https://skillfed.io/packages/testbook.md","pypi":"https://pypi.org/project/testbook/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2021-06-02","license_spdx":null,"license_treatment":"permissive","name":"testbook","python_support":"supports_current","summary":"A unit testing framework for Jupyter Notebooks"},"popularity":{"monthly_downloads":105132,"position":12718,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.2"}