Time series datasets are taken as coffee beans at PyPOTS, and POTS datasets are incomplete coffee beans with missing parts that have their own meanings.
To make various open-source time-series datasets readily available to our users, PyPOTS gets supported by its ecosystem library Time Series Data Beans (TSDB), a toolbox that makes loading time-series datasets super easy! Visit TSDB right now to learn more about this handy tool 🛠, and it now supports a total of 168 open-source datasets!

To simulate the real-world data beans with missingness, the ecosystem library PyGrinder, a toolkit helping grind your coffee beans into incomplete ones, is created. Missing patterns fall into three categories according to Robin's theory: MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random). PyGrinder supports all of them and additional functionalities related to missing values. With PyGrinder, you can introduce synthetic missing values into your datasets with a single line of code.

With ground data beans (i.e. processed datasets), PyPOTS - our coffee pot is the tool here to help brew them into a cup of delicious coffee, namely the advanced analysis results that we want. PyPOTS includes dozens of algorithms, supporting imputation, classification, clustering, and forecasting tasks on POTS data end-to-end. More state-of-the-art algorithms are on the way. Since 2022, PyPOTS has been successfully used in scientific researches and has been cited and referenced. Here is an incomplete list of them.

Now we have the beans, the grinder, and the pot, how to brew us a cup of coffee? Tutorials are necessary! Considering the future workload, PyPOTS tutorials are released in a single repo, and you can find them in BrewPOTS. Take a look at it now, and learn how to brew your POTS datasets.


☕️ Welcome to the universe of PyPOTS. Enjoy it and have fun!