In total, hplc-py provides a programmatic interface that allows experimentalists to rapidly quantify chemical signals from chromatograms, even when there is exceedingly high overlap between analytes. While we have tailored the default parameters of the hplc-py methods to be amenable to typical HPLC chromatographic outputs, we have also made it simple to manually adjust different aspects of the peak quantification algorithm, including parameters controlling the degree of background subtraction, the constraint of fitting parameters, and even enforcing the estimation of peaks with low topographic prominence. Additionally, we have developed heuristics the user can employ to assess quality of the reconstruction, though we emphasize that this is not to be used as a measure of uncertainty. We hope that hplc-py can act as a tool that can make scientific interpretation of results, rather than their generation, the next bottleneck in HPLC-based experiments.