As you point out there are still a few failing tests. The The number of failing tests on both is very low but not zero. The PyPy team has had good collaboration with Pandas and NumPy, but there are some deeper issues with these packages depending on refcount semantics in some edge cases, IMO rarely seen in real world scenarios. For numpy this means using an out keyword argument can be tricky, and for Pandas it means some galse positives in determining when a dataframe is being held by another reference, making it read-only

Of course if your workflow depends on those features, they are critical. We are working on full compatibility and also on increasing speed.