xpandas - python data containers for structured types and structured machine learning tasks

Data scientific tasks with structured data types, e.g., arrays, images, time series, text records, are one of the major challenge areas of contemporary machine learning and AI research beyond the ``tabular'' situation - that is, data that fits into a single classical data frame, and learning tasks on it such as the classical supervised learning task where one column is to be predicted from others.\ With xpandas, we present a python package that extends the pandas data container functionality to cope with arbitrary structured types (such as time series, images) at its column/slice elements, and which provides a transformer interface to scikit-learn's pipeline and composition workflows.\ We intend xpandas to be the first building block towards scikit-learn like toolbox interfaces for advanced learning tasks such as supervised learning with structured features, structured output prediction, image segmentation, time series forecasting and event risk modelling.