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An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks

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ipyslickgrid

This is a fork of qgrid and it has been created for the sole purpose of supporting Jupyterlab 3. However, over time, it may be used for more than that.

DISCLAIMER

qgrid is licensed under the Apache License, Version 2.0. We did our best to track all modifiaction we did to the original source code to comply with the Apache License standard. If you find we could improve our modification tracking, please do let us know how. We're happy to get your feedback.

Running from source & testing your changes

  1. Clone the repository from GitHub and cd into the top-level directory:
git clone https://github.com/8080labs/ipyslickgrids.git
cd ipyslickgrids
  1. Install the current project in editable mode:
pip install -e .
  1. Install the node packages that ipyslickgrids depends on and build ipyslickgrids's javascript using webpack:
cd js && npm install .
  1. Install and enable ipyslickgrids's javascript in your local jupyter notebook environment:
jupyter nbextension install --py --symlink --sys-prefix ipyslickgrids && jupyter nbextension enable --py --sys-prefix ipyslickgrids
  1. If desired, install the labextension:
jupyter labextension link js/
  1. Run the notebook as you normally would with the following command:
jupyter notebook

Manually testing server-side changes

If the code you need to change is in ipyslickgrids's python code, then restart the kernel of the notebook you're in and rerun any ipyslickgrids cells to see your changes take effect.

Manually testing client-side changes

If the code you need to change is in ipyslickgrids's javascript or css code, repeat step 3 to rebuild ipyslickgrids's npm package, then refresh the browser tab where you're viewing your notebook to see your changes take effect.

Running automated tests

There is a small python test suite which can be run locally by running the command pytest in the root folder of the repository.

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An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks

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  • Python 64.1%
  • JavaScript 29.1%
  • CSS 6.8%