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Isotopic composition modelling is a key aspect in many environmental studies. This work presents an open source Python library that estimates isotopic compositions through machine learning algorithms with user-defined variables. This library includes dataset preprocessing, outlier detection , statistical analysis, feature selection, model validation and calibration and postpro-cessing. This tool has the flexibility to operate with discontinuous inputs in time and space. The automatic decision-making procedures are knitted in different stages of the algorithm, although it is possible to manually complete each step. The extensive output reports, figures and maps generated by Isocompy facilitate the comprehension of stable water isotope studies. In essence, Iso-compy offers an open source foundation for isotopic studies that ensures reproducible research in environmental fields.