| commit | 6319b864ea78a68f7458606d6a4dcc47d1f1906a | [log] [tgz] |
|---|---|---|
| author | Lynwee <linwei.hou@merico.dev> | Tue Mar 05 17:09:03 2024 +0800 |
| committer | GitHub <noreply@github.com> | Tue Mar 05 17:09:03 2024 +0800 |
| tree | e3e3799ad8f9898aed1b267062d5563aab36583f | |
| parent | 2454035b9dcdb4f7ea41f880550f27f4050e578c [diff] | |
| parent | 638ee2f9e75dbdd59e39a2067cd236d27890b840 [diff] |
Merge pull request #3 from xebiaquality/pylint Setup pylint, isort and black
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.
poetry install in the root directory.notebooks directory in your preferred Jupyter Notebook tool.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.
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.
Please check the contributing guidelines.