We illustrate the application of two linear compression algorithms in python: Principal component analysis (PCA) and least-squares feature selection. Both can be used to compress a passed array, and they both work by stripping out redundant columns from the array. The two differ in that PCA operates in a particular rotated frame, while the feature selection solution operates directly on the original columns. As we illustrate below, PCA always gives a stronger compression. However, the feature selection solution is often comparably strong, and its output has the benefit of being relatively easy to interpret — a virtue that is important for many applications.