Chromatography has become a gold-standard method across diverse fields for precise quantitation and separation of chemical mixtures [Figure A key objective in the analysis of chromatographic data is determining the time-integrated signal of each component, a process which becomes challenging when chemically-similar components result in strongly overlapping signals [such as the blue and green symbols in Figure ]. As of this writing, many of the available tools for signal quantification, such as the open source Python 2.7 software 'HappyTools' , Microsoft Excel applications , or proprietary solutions such as Chromeleon by Thermo-Fisher and Empower by Waters, rely on manual processing of the chromatograms and curation of the resulting quantitative data. Furthermore, we are unaware of any tools that can reliably deconvolve highly overlapping signals. hplc-py provides a programmatic interface by which users can quickly and reliably quantify components of complex chromatograms in a few lines of code [Figure