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Efficiently handling cell dependencies has become a major challenge in data analysis and computational notebook landscape. As cells are interconnected, modification in one can affect others, making it critical to have a reliable way to manage and track these relationships. This month we explored leveraging the insights from language servers to help us generate the cell dependency tree.
Prep
Install latest version of VS Code Insiders
Install latest pre-release version of Jupyter
Install latest pre-release version of Pylance and Python Extension
Enable notebook.consolidatedRunButton in VS Code
Enable "jupyter.executionAnalysis.enabled": true
Testing
We introduced four commands:
From the Run button's dropdown menu, users can now run "Execute with Precedent Cells" and "Execute with Dependent Cells".
From the Cell Toolbar's dropdown menu, users can now "Select Precedent Cells" and "Select Dependent Cells"
This is still experimental so it would be great if you can do exploratory testing. You might also run into limitations easily (like the ones listed in #14316 (comment)).
The text was updated successfully, but these errors were encountered:
Refs: #14316
Complexity: 4
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Efficiently handling cell dependencies has become a major challenge in data analysis and computational notebook landscape. As cells are interconnected, modification in one can affect others, making it critical to have a reliable way to manage and track these relationships. This month we explored leveraging the insights from language servers to help us generate the cell dependency tree.
Prep
notebook.consolidatedRunButton
in VS Code"jupyter.executionAnalysis.enabled": true
Testing
We introduced four commands:
This is still experimental so it would be great if you can do exploratory testing. You might also run into limitations easily (like the ones listed in #14316 (comment)).
The text was updated successfully, but these errors were encountered: