GeoPandas: Fundamental data structures for vector spatial data in Python

Spatial analysis depends upon accessible, flexible tools. As a leading language for data science, Python requires reliable packages to handle complex geospatial tasks effectively. This article presents GeoPandas, a package providing a high-level, user-friendly API that extends standard pandas data structures to support vector geometries, simplifying spatial data handling for a broad scientific audience. The package is built around three core data structures (GeometryArray, GeoSeries, and GeoDataFrame) that unify geometric operations and tabular data management. It achieves performance and scalability by wrapping fast, compiled libraries—handling geometry operations, coordinate transformations, and file reading and writing—behind a Pythonic interface. Over its ten-year history, GeoPandas has evolved from a single package to a cornerstone of geospatial analysis in Python, anchoring a broader ecosystem of tools. Thousands of downstream projects rely upon GeoPandas, spanning scientific fields from Earth observation and climate modelling to urban analytics and epidemiology. This article provides an overview of the package's history, design, social and technical infrastructure, and future plans. By making spatial analysis more accessible with a standard open-source toolset, GeoPandas democratises access to spatial analysis, facilitates reproducibility, and accelerates scientific discovery.