PyGrinder is a part of PyPOTS project (a Python toolbox for data mining on Partially-Observed Time Series), was called PyCorruptor and separated from PyPOTS for decoupling missingness-creating functionalities from learning algorithms. In data analysis and modeling, sometimes we may need to corrupt the original data to achieve our goal, for instance, evaluating models' ability to reconstruct corrupted data or assessing the model's performance on only partially-observed data. PyGrinder is such a tool to help you corrupt your data, which provides several patterns to create missing values in the given data.