The point biserial correlation coefficient (rpb) is a correlation coefficient used when one variable (e.g. Y) is dichotomous; Y can either be "naturally" dichotomous, like whether a coin lands heads or tails, or an artificially dichotomized variable. In most situations it is not advisable to dichotomize variables artificially. When a new variable is artificially dichotomized the new dichotomous variable may be conceptualized as having an underlying continuity. If this is the case, a biserial correlation would be the more appropriate calculation.
The point-biserial correlation is mathematically equivalent to the Pearson (product moment) correlation coefficient; that is, if we have one continuously measured variable X and a dichotomous variable Y, rXY = rpb. This can be shown by assigning two distinct numerical values to the dichotomous variable.
The computational steps in this small program are based on Bruning and Kintz's, Computational Handbook of Statistics. A version of this book can be found here: https://archive.org/details/computationalhan0000brun
Simply run the Python code and load the data from the csv file matrix.csv
Obviously, modern statistical packages can compute rpb using built-in libraries, the present code is for those that wish to verify Bruning & Kintz's distinct computational steps.
Official page on github: https://github.com/himonides/pointbiserial
