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Data analysts use computational notebooks to write code for analyzing and visualizing data. Notebooks can help analysts iteratively develop code by letting them interleave code and output, and selectively execute cells. But as analysis progresses, notebooks become cluttered and inconsistent as analysts leave behind old code and outputs, write cells out of order, and overwrite code. Messy notebooks make it difficult to reuse and extend analysis results. Say this is my notebook, and I want to share this result and the code that produced it. This code is scattered across a large notebook, and some of it might even be missing. Recovering this code will be tedious or even impossible in current tools. We introduce code gathering tools as an extension to computational notebooks. These tools help analysts find, clean, and compare versions of code in messy notebooks. They enable what we call post-hoc mess management, helping analysts clean and navigate their code, whether or not they followed