This python library implements the discrimination discovery and prevention method proposed in the paper: “A methodology for direct and indirect discrimination prevention in data mining”, Hajian and Domingo-Ferrer, 2012. The method evaluates and treats data sets of individuals removing direct and/or indirect discrimination biases preserving their data quality. The developed python library includes data transformation methods, based on measures for both direct and indirect discrimination and can deal with several discriminatory attributes. For this purpose, the library also include metrics that specify which records should be changed and how those records should be changed during the data transformation for discrimination prevention. In addition, we also implement the necessary metrics to measure the information loss and discrimination risk resulting of the data transformation process. On a more technical side, the antidiscrimination tool is offered as an open source python library supporting standard CSV datasets with any type of attributes. It includes a “readme” file describing its use and a testing file showing how to protect an example dataset and how to calculate the metrics measuring the information loss and discrimination risk of the protected data. The testing file can be easily executed by non-developers from the command line, as described in the readme file. Programmatically, the implemented library can be used through calls to the developed API, as it is also described in the readme file.