Magnetometers are a key component of heliophysics research providing valuable insight into the dynamics of electromagnetic field regimes and their coupling throughout the solar system. On satellites, magnetometers provide detailed observations of the extension of the solar magnetic field into interplanetary space and of planetary environments. At Earth, magnetometers are deployed on the ground in extensive arrays spanning the polar cap, auroral and sub-auroral zone, mid- and low-latitudes and equatorial electrojet with nearly global coverage in azimuth (longitude or magnetic local time—MLT). These multipoint observations are used to diagnose both ionospheric and magnetospheric processes as well as the coupling between the solar wind and these two regimes at a fraction of the cost of in-situ instruments. Despite their utility in research, ground-based magnetometer data can be difficult to use due to a variety of file formats, multiple points of access for the data, and limited software. In this short article we review the Open-Source Python library GMAG which provides rapid access to ground-based magnetometer data from a number of arrays in a Pandas DataFrame, a common data format used throughout scientific research.