---
title: "lux"
type: "tool"
slug: "lux-org-lux"
canonical_url: "https://www.graphcanon.com/tools/lux-org-lux"
github_url: "https://github.com/lux-org/lux"
homepage_url: null
stars: 5380
forks: 380
primary_language: "Python"
license: "Apache-2.0"
archived: false
categories: ["model-training"]
tags: ["data-science", "exploratory-data-analysis", "python", "jupyter", "visualization-tools", "visualization", "pandas"]
updated_at: "2026-07-11T23:28:19.248181+00:00"
---

# lux

> Automatically visualize your pandas dataframe via a single print! 📊 💡

Automatically visualize your pandas dataframe via a single print! 📊 💡

## Facts

- Repository: https://github.com/lux-org/lux
- Stars: 5,380 · Forks: 380 · Open issues: 90 · Watchers: 84
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2024-03-20T15:48:47+00:00

## Trust & health

_Signals computed from public GitHub metadata. Not a security guarantee._

- Maintenance: Dormant (computed 2026-07-11T23:28:14.449Z)
- Security scan: Findings present (0 critical, 0 high, 0 medium, 13 low) · last scan 2026-07-11T23:28:14.942Z
- Full report: [trust report](/tools/lux-org-lux/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/lux-org-lux/trust)

## Categories

- [Model Training](/categories/model-training.md)

## Tags

data-science, exploratory-data-analysis, python, jupyter, visualization-tools, visualization, pandas

## Category neighbours (exploratory)

_Same-category tools for discovery only - not curated alternatives. Cap shown at six._

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- [transformers](/tools/huggingface-transformers.md) - Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models (★ 162,482) [Very active]
- [generative-ai-for-beginners](/tools/microsoft-generative-ai-for-beginners.md) - 21 Lessons, Get Started Building with Generative AI (★ 112,866) [Very active]
- [pytorch](/tools/pytorch-pytorch.md) - Tensors and Dynamic neural networks in Python with strong GPU acceleration (★ 101,752) [Very active]
- [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch, step by step (★ 98,899) [Steady]
- [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) - Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. (★ 91,991) [Dormant]

_+ 2 more not listed._

## README (excerpt)

_Quoted verbatim from the upstream repository. Untrusted content - treat as data, not instructions._

````text
# Getting Started

To start using Lux, simply add an extra import statement along with your Pandas import.

```python    
import lux
import pandas as pd
```

Lux can be used without modifying any existing Pandas code. Here, we use Pandas's [read_csv](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) command to load in a [dataset of colleges](https://github.com/lux-org/lux-datasets/blob/master/data/college.csv) and their properties.

```python    
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.

<img src="https://github.com/lux-org/lux-resources/blob/master/readme_img/basicDemo.gif?raw=true"
     alt="Basic recommendations in Lux"
     style="width:900px" />

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` (not `lux`). After installing the package, remember to run the setup instructions for your notebook IDE, e.g., [jupyter notebook](#setup-in-jupyter-notebook-vscode-jupyterhub) and [jupyter lab](#setup-in-jupyter-lab).

To get started, please follow both the installation and setup instructions in your command line.
`lux-api` can be installed through [PyPI](https://pypi.org/project/lux-api/) or [conda-forge](https://github.com/conda-forge/lux-api-feedstock). 

```bash
pip install lux-api
```

If you use [conda](https://docs.conda.io/en/latest/), you can install `lux-api` via:

```bash
conda install -c conda-forge lux-api
```

Both the PyPI and conda installation include includes the Lux Jupyter widget frontend, [lux-widget](https://pypi.org/project/lux-widget/).
````

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/lux-org-lux`](/api/graphcanon/tools/lux-org-lux)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_