  This paper presents a systematic review of Python packages focused on time
series analysis. The objective is first to provide an overview of the different
time series analysis tasks and preprocessing methods implemented, but also to
give an overview of the development characteristics of the packages (e.g.,
dependencies, community size, etc.). This review is based on a search of
literature databases as well as GitHub repositories. After the filtering
process, 40 packages were analyzed. We classified the packages according to the
analysis tasks implemented, the methods related to data preparation, and the
means to evaluate the results produced (methods and access to evaluation data).
We also reviewed the licenses, the packages community size, and the
dependencies used. Among other things, our results show that forecasting is by
far the most implemented task, that half of the packages provide access to real
datasets or allow generating synthetic data, and that many packages depend on a
few libraries (the most used ones being numpy, scipy and pandas). One of the
lessons learned from this review is that the process of finding a given
implementation is not inherently simple, and we hope that this review can help
practitioners and researchers navigate the space of Python packages dedicated
to time series analysis.
