I'm trying to visualize data for exploratory data analysis motivated by visualizing multiple scatterplots of features simultaneously, similar to this question. But I quickly run into problems when using a large number of features (~50) and rows (~50K). While I like using seaborn pairplots the generation of a large number of plot panes can get computational intractable for a large number of features and observations. Subsetting a very large table to a smaller number of features or observations does not seem complete.
My question is: What's an efficient way to plot many features for EDA in python? If there is not an efficient way then is there a defensible way to reduce the number of features or observations