  Measuring and quantifying dependencies between random variables (RV's) can
give critical insights into a data-set. Typical questions are: `Do underlying
relationships exist?', `Are some variables redundant?', and `Is some target
variable $Y$ highly or weakly dependent on variable $X$?' Interestingly,
despite the evident need for a general-purpose measure of dependency between
RV's, common practice of data analysis is that most data analysts use the
Pearson correlation coefficient (PCC) to quantify dependence between RV's,
while it is well-recognized that the PCC is essentially a measure for linear
dependency only. Although many attempts have been made to define more generic
dependency measures, there is yet no consensus on a standard, general-purpose
dependency function. In fact, several ideal properties of a dependency function
have been proposed, but without much argumentation. Motivated by this, in this
paper we will discuss and revise the list of desired properties and propose a
new dependency function that meets all these requirements. This general-purpose
dependency function provides data analysts a powerful means to quantify the
level of dependence between variables. To this end, we also provide Python code
to determine the dependency function for use in practice.
