Researchpy (page 2 of 2)

Most of the methods and functions in researchpy take pandas data frames or series as arguments. The output of most researchpy methods and functions is also a pandas data frame, which makes it easy for us to use our results with any function or method that works with a traditional Pandas DataFrame object.

With researchpy, we can perform t-tests, a variety of difference tests, correlation matrices, ANOVAs, sign-rank tests, chi-square tests of cross tabulated data, and OLS regressions using the researchpy package. In this tutorial, however, I will focus on descriptive functions and methods but recognize that we can do additional data analysis tasks with this package.

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