[ivansavz]

It's not surprising that R has good tools for statistical assumptions checking. It has everything with an API perfectly suited for stats professionals. In the Python world scipy and statsmodels implement most of the important tests, but probably not as many as the ones available in R.

One recent project that I looks very promising in the Python world is called Tea: a high-level language for expressing statistical analysis questions. Basically the user describes the characteristics of the data they have, their assumptions, and their hypothesis, and then the tea runtime checks all the assumptions and figures out which test can be applied to test the hypothesis. You still have to know some stats jargon, but the user-interface between human and machine is revolutionary! Here is a bunch of links I collected about tea: https://www.one-tab.com/page/aUF1eWnDT8CIyrwScWD2uA