  This paper presents a systematic review of Python packages with a focus on
time series analysis. The objective is to provide (1) an overview of the
different time series analysis tasks and preprocessing methods implemented, and
(2) an overview of the development characteristics of the packages (e.g.,
documentation, dependencies, and community size). This review is based on a
search of literature databases as well as GitHub repositories. Following 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 for evaluating the results produced (methods and
access to evaluation data). We also reviewed documentation aspects, the
licenses, the size of the packages' community, and the dependencies used. Among
other things, our results show that forecasting is by far the most frequently
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). We hope that this
review can help practitioners and researchers navigate the space of Python
packages dedicated to time series analysis. We will provide an updated list of
the reviewed packages online at
https://siebert-julien.github.io/time-series-analysis-python/.
