Instrumental variable (IV) estimation is an essential method for applied researchers across the social sciences who analyze randomized control trials marred by non-compliance or leverage partially exogenous treatment variation in observational studies. The potential outcomes framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE), and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far payed little attention to the characteristics of compliers and non-compliers. Yet profiling compliers and non-compliers is necessary to understand what subpopulation the researcher is making inferences about, and an important first step to discuss the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions needed to identify the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always- and never-takers in terms of their covariates, and easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and non-compliers as standard practice accompanying any IV analysis.