# PyGWalker

PyGWalker: Turn your dataframe into an interactive UI for visual analysis

Repository: https://github.com/Kanaries/pygwalker
Canonical: https://ross.abutalabs.com/products/pygwalker
Homepage: https://kanaries.net/pygwalker
Language: Python
License: Apache-2.0
License Family: permissive
Topics: data-analysis, pandas, tableau, tableau-alternative, visualization, data-exploration, dataframe, matplotlib, plotly
Last push: 2026-08-10T21:29:13+00:00
Link (homepage): https://kanaries.net/pygwalker
Link (site_page): https://docs.kanaries.net/
Link (site_page): https://kanaries.net/subscribe
Link (site_page): https://kanaries.net/features
Link (site_page): https://docs.kanaries.net/pygwalker

## Health v2 (maintenance only)
Score: 81/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 53, longevity 92
- inputs: {"age_days": 1294, "days_push": 23, "days_rel": 152, "gap_med": 166, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 15944, forks 886 (observed 2026-08-28T04:11:14.525155+00:00)

## What it is
PyGWalker is a Python library that turns pandas (or Polars/Arrow) dataframes into an interactive drag-and-drop visual analysis UI inside Jupyter Notebooks, powered by Graphic Walker, an open-source Tableau alternative. It supports visualization, data cleaning, annotation, and natural-language-driven exploration with one line of code.

## Use cases
- explore a pandas dataframe interactively in jupyter
- open-source tableau alternative for notebooks
- make charts by dragging and dropping fields instead of writing matplotlib code
- clean outliers from a dataframe with an eraser tool
- visualize geographic data on interactive maps
- embed interactive data visualization in streamlit apps
- query my data with natural language to get charts

## When to choose
- you work in Jupyter and want fast no-code exploratory data analysis
- you want Tableau-style drag-and-drop visualization without leaving Python
- you need to clean or annotate data visually during analysis
- you want to embed interactive visual exploration in Streamlit or share analysis apps

## When to avoid
- you need publication-quality programmatically reproducible plots like matplotlib or plotly
- you need fully automated dashboard pipelines rather than interactive exploration
- you work outside notebook or Python environments (consider GWalkR for R or PyGWalker Desktop)

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, nlp
- domain: data-visualization, data-science, developer-tools
- platform: python, cross-platform
- tags: jupyter, pandas, tableau-alternative, exploratory-data-analysis, dataframe, drag-and-drop, streamlit

## Member repositories
- Kanaries/pygwalker (main) score 81
- Kanaries/graphic-walker (frontend) score 79

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:14.525155+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:05:19.196329+00:00, confidence not recorded.
  - readme: https://github.com/Kanaries/pygwalker (fetched 2026-08-28T04:11:14.525155+00:00, sha 159c9cbb22b2)
  - homepage: https://kanaries.net/pygwalker (fetched 2026-08-29T08:03:41.426326+00:00, sha 00ba49e1c71d)
  - site_page: https://docs.kanaries.net/ (fetched 2026-08-29T08:03:41.430238+00:00, sha 9796863256e6)
  - site_page: https://kanaries.net/features (fetched 2026-08-29T08:03:41.435827+00:00, sha 11f0347ac9f8)
  - site_page: https://docs.kanaries.net/pygwalker (fetched 2026-08-29T08:03:41.437725+00:00, sha a44cba1a8a10)
  - registry_pypi: https://pypi.org/pypi/pygwalker/json (fetched 2026-08-29T08:03:41.441486+00:00, sha 13f89397358a)
  - site_page: https://kanaries.net/subscribe (fetched 2026-08-29T08:03:41.433811+00:00, sha 83b8f046e5be)
  - site_page: https://kanaries.net/integrations (fetched 2026-08-29T08:03:41.439786+00:00, sha 3e76743d11d4)
- Data as of 2026-08-30T08:39:29.467469+00:00.