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apache / incubator-devlake-playground / refs/heads/dependabot/pip/idna-3.7
commite0a87dcaf5c72f2217c55382519427c626dc1dae[log] [tgz]
authordependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>Fri Apr 12 04:51:05 2024 +0000
committerGitHub <noreply@github.com>Fri Apr 12 04:51:05 2024 +0000
treecaaeab072d97a4964619cf2b1bbedfa7eac71f63
parenta9906402281fdc970f3c32734dad6014c52f1259 [diff]
build(deps): bump idna from 3.6 to 3.7

Bumps [idna](https://github.com/kjd/idna) from 3.6 to 3.7.
- [Release notes](https://github.com/kjd/idna/releases)
- [Changelog](https://github.com/kjd/idna/blob/master/HISTORY.rst)
- [Commits](https://github.com/kjd/idna/compare/v3.6...v3.7)

---
updated-dependencies:
- dependency-name: idna
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
1 file changed
tree: caaeab072d97a4964619cf2b1bbedfa7eac71f63
  1. .github/
  2. notebooks/
  3. playground/
  4. tests/
  5. .asf.yaml
  6. .gitignore
  7. .python-version
  8. DISCLAIMER
  9. LICENSE
  10. NOTICE
  11. poetry.lock
  12. poetry.toml
  13. pyproject.toml
  14. README.md
README.md

DevLake Jupyter Playground

DevLake offers an abundance of data for exploration. This playground contains a basic set-up to interact with the data using Jupyter Notebooks and Pandas.

How to play

Prerequisites

Usage

  1. Have a local clone of this repository.
  2. Run poetry install in the root directory.
  3. Either:
    • navigate to the notebooks directory and run the jupyter server poetry run jupyter notebook
    • navigate to one of the notebook files (.ipynb) in the notebooks directory from your IDE directly
  4. Make sure the notebook uses the virtual environment created by poetry.
  5. Configure your database URL in the notebook code.
  6. Run the notebook.
  7. Start exploring the data in your own notebooks!

Create your own Jupyter Notebook

A good starting point for creating a new notebook is template.ipynb. It contains the basic steps you need to go from query to output.

To define a query, use the Domain Layer Schema to get an overview of the available tables and fields.

Use Pandas api to organize, transform, and analyze the query results.

Predefined notebooks and utilities

A notebook might offer a valuable perspective on the data not available within the capabilities of a Grafana dashboard. In this case, it's worthwhile to contribute this notebook to the community as a predefined notebook, e.g., process_analysis.ipynb (it depends on graphviz for its visualization).

The same goes for utility methods with, for example, predefined Pandas data transformations offering an interesting view on the data.

Contributing

Please check the contributing guidelines.