exp: pygwalker data explorer in streamlit #2216
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sh-rp
reviewed
Jan 21, 2025
| def pygwalker_renderer(pipeline_name: str, table_name: str) -> StreamlitRenderer: | ||
| pipeline = dlt.attach(pipeline_name) | ||
| dataset = pipeline.dataset() | ||
| df = dataset[table_name].df() |
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this will load the full table into memory, is this desired? on a large table this will take a long time and at some point kill the host. is there some other way we could do this?
sh-rp
reviewed
Jan 21, 2025
| from dlt.helpers.streamlit_app.utils import render_with_pipeline | ||
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| def show(pipeline: dlt.Pipeline) -> None: |
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we should have at least simple tests for this page to make sure it renders.
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As discussed in the meeting today, we will keep this as a reference, alternatively if @zilto has the time, we can add a test and also derive the explorable view from an sql query which is set to a limit of 2000 items per default or something like that. |
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Description
Add a page to the streamlit dashboard available via
dlt pipeline NAME show. It uses the recentPipeline.datasetinterface to pass a dataframe topygwalker.2025-01-14.16-15-45.mp4
Goals
When first loading a pipeline, I want to know the properties of the data (null, distribution, outliers). It's reducing the friction of having to create temporary notebooks and install
ipykernel/jupyterdependenciesImplementation
pygwalker is an open-source Python library that produces an interactive widget to explore dataframes (works in Jupyter, VSCode, Streamlit, Marimo) based on Vega.
Integration surface area is small; only assumes that you can pull the data locally via the
Pipeline.datasetfeature.Pipeline.datasetto query data or pass ansqlalchemyconnection topygwalkerto handle data loading.duckdbfor efficient processing of dataTODO