What's an efficient way to plot many features for EDA in python? 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
I would suggest t-SNE (just google it). It helps you to have a general overview on what is gling on with your high-dimensional data. Please note that parameters of t-SNE are pretty sensitive so you need to put some effort to find a good embedding.