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PyGWalker

Project16K

Explore pandas, Polars, and PyArrow dataframes through interactive visual analysis in Python.

Added 2026-10-07
Replaces
GitHub

What you can build

  • Code a loader that combines selected CSV files, adds each filename as a source column, and passes the resulting dataframe to PyGWalker.
  • Transform timestamped events into weekly product totals, then explore the aggregated dataframe interactively.
  • Apply a reader-defined label mapping to a dataframe and produce a separate before-and-after category count file.
  • Check a dataframe against chosen missing-value, duplicate, and range rules; write flagged rows and a separate review summary.
  • Add a cohort label from reader-defined customer rules and explore the segmented dataframe using PyGWalker's documented facet views.

Try this prompt

Build an end-to-end notebook workflow that accepts a pandas dataframe and reader-supplied rules for missing values, duplicates, and numeric ranges. Preserve the original dataframe; write flagged rows and a summary to separate local files, create a review candidate without silently overwriting source data, and display that candidate with PyGWalker using documented APIs. Include a small sample input and explain how to run the workflow.

Get started

  1. Prepare Python and Jupyter

    Use the README's pip or conda-forge setup instructions and open a Jupyter notebook with your Python environment ready.

  2. Load a supported dataframe

    The README documents pandas, Polars, and PyArrow table data; its example reads a CSV into pandas before passing the dataframe to PyGWalker.

  3. Choose computation and render

    Use the documented browser or local DuckDB-backed kernel computation options. PyGWalker can render in a notebook or create HTML; review its privacy configuration for offline use.

License & notes

Apache-2.0The project is Apache-2.0 licensed, permitting commercial use subject to its license and notice obligations. The README states no package charge. You provide the Python environment and any hosting. Browser computation or local DuckDB-backed kernel computation are documented options. Cloud computation uses Kanaries services; applicable costs and service terms are separate and not specified here.

  • Exploration is for dataframes already available in Python; it is not a general-purpose data-source connector or full BI platform.
  • Notebook use requires a suitable Python environment; hosted use requires infrastructure you arrange.
  • Cloud computation uses Kanaries services and may involve separate costs and terms; no prices are stated here.
  • The documented privacy setting defaults to update-only; offline mode is available. Review configuration and data-handling requirements before using sensitive data.

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