• Corpus ID: 245385933

Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows

@inproceedings{Lee2021LuxAV,
  title={Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows},
  author={Doris Jung Lin Lee and Dixin Tang and Kunal Agarwal and Thyne Boonmark and Caitlyn Chen and Jake Tan Jun Kang and Ujjaini Mukhopadhyay and Jerry Song and Micah Yong and Marti A. Hearst and Aditya G. Parameswaran},
  year={2021}
}
Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, visually exploring data in dataframes remains tedious, requiring substantial programming effort for visualization and mental effort to determine what analysis to perform next. We propose Lux, an always-on framework for accelerating visual insight discovery in dataframe workflows. When users print a dataframe in their… 

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