This practical guide introduces social science researchers to creating static and interactive maps using Python, with a particular focus on the GeoPandas library. Recognising that many researchers work with geographic or regional data—but may lack experience with specialist mapping software—the guide offers a clear, accessible pathway to building high-quality maps entirely in Python. Beginning with an explanation of what geospatial data is and where it can be sourced (including publicly available datasets such as Natural Earth and the UK’s Open Geography Portal), the guide covers all the steps required to import, process, and visualise spatial data. It includes examples of working with shapefiles, GeoJSON, and CSVs, and shows how to produce multi-layered and choropleth maps suitable for research presentations, reports, and publications. The guide also covers more advanced topics such as labelling maps, highlighting specific features (e.g., cities or research sites), and exporting both images and data files in different formats. It introduces Folium for creating interactive HTML-based maps and offers troubleshooting tips and links to further resources. This resource is ideal for social science researchers with a basic understanding of Python who wish to visualise geographic data in a meaningful and engaging way, without needing deep expertise in cartography or GIS. This guide is part of a set of deliverables coming out of the Research Software Practices in the Social Sciences project, which received additional funding as part of the UK Software Sustainability Institute: Phase 4, supported through the UKRI Digital Research Infrastructure Programme (grant number AH/Z000114/1)