05-10, 10:30–11:00 (Europe/Paris), Louis Armand 2
There exists a tonne of Python code quality tools which can help catch bugs and fix stylistic issues. Unfortunately, the vast majority don't work out-of-the-box on Jupyter Notebooks. nbQA is a tool which addresses this issue, by allowing any standard Python code quality tool to be run on a Jupyter Notebook.
In this talk, you will learn:
- how to use nbQA;
- how nbQA works, and what its limitations are;
- how you can use nbQA to run your own custom tools.
Marco is a pandas core developer working at Quansight. He has authored several code quality tools (cython-lint, absolufy-imports, auto-walrus, and nbQA). His background is in mathematics and data science.