When conducting exploratory data analysis, data practitioners often use both code and visualizations. However, creating visualizations to see key trends and insights can be very tedious; thereby constraining exploratory work. To automate this process, Doris Lee and other Berkeley and UIUC researchers have open sourced Lux, a Python API for intelligent visual discovery. Given a dataframe, Lux automatically generates a collection of visualizations that the user can peruse within their Jupyter notebook. Users can also specify their intent (as a set of attributes and values) to see a more focused set of visualization recommendations.