I agree that they should have included a link to the main repository in the introduction, but the 4th paragraph from TFA describes Ray:

Pandas on Ray is an early stage DataFrame library that wraps Pandas and transparently distributes the data and computation. The user does not need to know how many cores their system or cluster has, nor do they need to specify how to distribute the data. In fact, users can continue using their previous Pandas notebooks while experiencing a considerable speedup from Pandas on Ray, even on a single machine. Only a modification of the import statement is needed, as we demonstrate below. Once you’ve changed your import statement, you’re ready to use Pandas on Ray just like you would Pandas.