Automunge is an open source python library that has formalized and automated the data preparations for tabular learning in between the workflow boundaries of received “tidy data” (one column per feature and one row per sample) and returned dataframes suitable for the direct application of machine learning. Under automation numeric features are normalized, categoric features are binarized, and missing data is imputed. Data transformations are fit to properties of a training set for a consistent basis on any partitioned “validation data” or additional “test data”. When preparing training data, a compact python dictionary is returned recording steps and parameters of transformation, which may then serve as a key for preparing additional corresponding data on a consistent basis. In addition to data preparations under automation, Automunge may also serve as a platform for tabular engineering, as demonstrated herein.