GraphCanon updated 3w · GitHub synced 3w
Decision brief
Automatically visualize pandas dataframes to reveal trends.
Good fit when
- When you need quick insights from pandas dataframes without manual visualization setup.
- If working in a Jupyter notebook where automatic visualization integration is beneficial.
Avoid when
- Avoid if your workflow strictly avoids third-party extensions for Pandas.
- Not suitable when detailed customization of visualizations is required, as Lux focuses on automated recommendations.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (865d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- 13 low (13 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install luxSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Lux is an automatic visualization tool for pandas dataframes that recommends interesting visual patterns and trends within datasets. It integrates seamlessly with Pandas and Jupyter notebooks.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 3, 2026)
ions for your notebook IDE, e.g., [jupyter notebook](#setup-in-jupyter-notebook-vscode-jupyterhub) and [jupyter lab](#setup-in-jupyter-lab).Source link
Tags
README
Getting Started
To start using Lux, simply add an extra import statement along with your Pandas import.
import lux
import pandas as pd
Lux can be used without modifying any existing Pandas code. Here, we use Pandas's read_csv command to load in a dataset of colleges and their properties.
df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/college.csv")
df
When the dataframe is printed out, Lux automatically recommends a set of visualizations highlighting interesting trends and patterns in the dataset.
Voila! Here's a set of visualizations that you can now use to explore your dataset further!
Installation & Setup
Note: Lux's official package name is
lux-api(notlux). After installing the package, remember to run the setup instructions for your notebook IDE, e.g., jupyter notebook and jupyter lab.
To get started, please follow both the installation and setup instructions in your command line.
lux-api can be installed through PyPI or conda-forge.
pip install lux-api
If you use conda, you can install lux-api via:
conda install -c conda-forge lux-api
Both the PyPI and conda installation include includes the Lux Jupyter widget frontend, lux-widget.
For agents
This page has a .md twin and JSON over the API.