Lux

Abstract

1. Introduction

Lux is a Python library that facilitate fast and easy data exploration by automating the visualization and data analysis process. By simply printing out a dataframe in a Jupyter notebook, Lux recommends a set of visualizations highlighting interesting trends and patterns in the dataset. Visualizations are displayed via an interactive widget that enables users to quickly browse through large collections of visualizations and make sense of their data.

Here is a 1-min video introducing Lux, and slides from a more extended talk.

Check out our notebook gallery with examples of how Lux can be used with different datasets and analyses. Or try out Lux on your own in a live Jupyter Notebook!

2. Objective

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

This project demonstrates how Python, Pandas can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/lux-org/lux.git
cd lux

import lux
import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/college.csv")
df

pip install lux-api

conda install -c conda-forge lux-api

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by lux-org and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.