Data wrangling is typically treated as an obligatory, codified, and ideally automated step in the machine learning pipeline. In contrast, we suggest that archival data wrangling is a theory-driven process best understood as a practical craft. Drawing on empirical examples from contemporary computational social science, we identify nine core modes of data wrangling. Although these modes can be seen as a sequence, we emphasize how they are iterative and nonlinear in practice. Moreover, we discuss how data wrangling can address issues of coded bias. Although machine learning emphasizes architectural engineering, we assert that to properly engage with machine learning is to properly engage with data wrangling.
