Intelligent Visual Data Discovery with Lux — A Python library

Rethinking a Visual dataframe workflow based on the user’s intent

Parul Pandey
Towards Data Science
9 min readDec 21, 2020

Photo by Luis Tosta on Unsplash

“Exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as those that we believe to be there.” — John W Tukey

The importance and necessity of data visualization in data science cannot be emphasized enough. The fact that a picture is worth a thousand words can be aptly applied to any project's life cycle associated with data. However, a lot of times, the tools that enable these visualizations aren’t intelligent enough. This essentially means that while we have hundreds of visualization libraries, most of them require users to write a substantial amount of code for plotting even a single graph. This shifts the focus on the mechanics of the visualization rather than the critical relationships within the data.

What if there were a tool that could simplify data exploration by recommending relevant visualizations to the users? There is a new library in the town called Lux 💡 , and it has been developed to address these very questions.

This article is based upon @Doris lee’s session on Lux during the Rise Camp 2020. Special thanks to Doris for allowing me to use the resources

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Published in Towards Data Science

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Written by Parul Pandey

Principal Data Scientist @H2O.ai | Author of Machine Learning for High-Risk Applications

Responses (5)

What are your thoughts?

Thx - I didn't heard about lux before.
Installation and integration into Jupyter Notebook is very smoothly.
The default output dimension of lux graphs is width=160,height=150. Possible to enlarge that (without exporting it)?

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Good article, you can also check out OlliePy: https://github.com/ahmed-mohamed-sn/olliePy

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is there any particular installation guide to work with Kaggle? Am facing problem when doing toggle?

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