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.
| kanaries/pygwalker | jaakkopasanen/autoeq | pyecharts/pyecharts | |
|---|---|---|---|
| Stars | 15,774 | 15,776 | 15,760 |
| Language | Python | Python | Python |
| Setup difficulty | easy | easy | easy |
| Complexity | 2/5 | 3/5 | 2/5 |
| Audience | data | general | data |
Figures from each repo's GitHub metadata at analysis time.
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.
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.
Mainly Python. The stack also includes Python, pandas, Jupyter.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly data.
This repo across BitVibe Labs
Verify against the repo before relying on details.