In research on photovoltaic (PV) device degradation, current–voltage ( $I$ – $V$ ) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of $I$ – $V$ studies to millions of $I$ – $V$ curves, we have developed a data-driven method to extract $I$ – $V$ curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to $I$ – $V$ curves individually, which requires solving a transcendental equation, this data-driven method can be applied to large volumes of $I$ – $V$ data in a short time. Our data-driven feature extraction technique is tested on $I$ – $V$ curves generated with the single-diode model and applied to $I$ – $V$ curves with different data point densities collected from three different sources. This method has a high repeatability for extracting $I$ – $V$ features, without requiring knowledge of the device or expected parameters to be input by the researcher. We also demonstrate how this method can be applied to large datasets and accommodates nonstandard $I$ – $V$ curves including those showing artifacts of connection problems or shading where bypass diode activation produces multiple “steps.” These features together make the data-driven $I$ – $V$ feature extraction method ideal for evaluating time-series $I$ – $V$ data and analyzing power degradation mechanisms in PV modules through cross comparisons of the extracted parameters.