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Students in introductory computer science courses often need individualized help when they get stuck solving programming problems. But providing such help can be time-consuming and thought-intensive, and therefore difficult to scale as Computer Science classes grow larger in size. Automatically generated fixes with explanations have the potential to integrate into a variety of mechanisms for providing help to students who are stuck on a programming problem. In this paper, we present a data-driven algorithm for generating explainable fixes to student code. We evaluate a Python implementation of the algorithm by comparing its output at different stages of the algorithm to state-of-the-art systems with similar goals. Our algorithm outperforms existing systems that can analyze and fix beginner-written Python code. Further, fixes it generates conform very well to corrections written by human experts for an existing benchmark of code correction quality.