pygwalker
PyGWalker: Turn your dataframe into an interactive UI for visual analysis
PyGWalker turns a pandas dataframe into a drag-and-drop Tableau-style chart explorer inside your Jupyter Notebook, build charts by dragging columns, no extra code needed per visualization.
PyGWalker is a Python library that turns a pandas dataframe — the standard table-shaped object data scientists work with in Python — into an interactive visual interface, right inside a Jupyter Notebook. Instead of writing more code each time you want to look at your data a different way, you drag and drop columns into rows, columns, and color slots to build charts, the way you would in Tableau.
The name is a play on "Python binding of Graphic Walker"; under the hood it integrates Jupyter with Graphic Walker, which the README calls an open-source alternative to Tableau. You install it with pip install pygwalker or with conda from conda-forge. The basic usage is two lines after loading your dataframe: import pygwalker as pyg, then walker = pyg.walk(df). The interactive UI then appears in the notebook and you can build visualisations, zoom, pan, filter, change the chart type, create concat views by adding multiple measures, or split into a facet view by dropping a dimension into rows or columns.
The README also lists features for cleaning and transforming the data visually, spotting outliers, and creating new variables from existing ones, as well as natural-language queries. There is a separate R wrapper called GWalkR, and a no-code Desktop App for working offline without writing code. You would use it when you have a dataframe and want to explore it visually without writing chart code each time, or when you want a Tableau-style point-and-click experience embedded in your notebook. The full README is longer than what was provided.
Where it fits
- Explore a CSV dataset visually by dragging columns into chart axes in a Jupyter Notebook without writing plot code each time.
- Build faceted or concatenated views to compare multiple metrics across categories in an interactive notebook UI.
- Clean and transform a dataframe visually and spot outliers before running statistical analysis.
- Use natural-language queries to filter and visualize data without writing pandas code.