GeoPandas

GeoPandas is a free and open-source Python library for working with geographic vector data.[1] It extends pandas with geometry aware GeoSeries and GeoDa

GeoPandas
GeoPandas
Original authorKelsey Jordahl
DeveloperGeoPandas contributors
ReleaseJuly 2014[1]
Stable release
1.1.4[2] / June 26, 2026; 50 days ago (2026-06-26)[2]
Written inPython
Operating systemCross-platform
TypeSoftware library
LicenseBSD 3-Clause License
Websitegeopandas.org
Repositorygithub.com/geopandas/geopandas

GeoPandas is a free and open-source Python library for working with geographic vector data.[1] It extends pandas with geometry aware GeoSeries and GeoDataFrame data structures. The library supports geometric operations, coordinate reference system transformations, file input and output, and plotting.[3][4][5] It is used in scientific and applied geospatial work,[6][7] and several geospatial computing textbooks cover it.[3][5][8]

History

Kelsey Jordahl began GeoPandas during the 2013 SciPy Conference. Version 0.1.0 was released in July 2014, and Joris Van den Bossche became the lead maintainer in 2016.[1] Version 1.0.0 was released on 24 June 2024.[2]

Features

The GeoSeries and GeoDataFrame data structures extend the pandas Series and DataFrame data structures. A GeoSeries stores Shapely geometry objects. A GeoDataFrame combines one or more geometry columns with tabular attributes and designates one geometry column as active. The geometries can represent points, lines, and polygons, including multipart geometries.[4][3]

GeoPandas associates geometries with coordinate reference system metadata and provides transformations between coordinate systems. Its spatial operations include buffering, intersections, unions, overlays, dissolves, and spatial joins.[4][5] The library reads and writes vector data formats supported by GDAL, using pyogrio as its default input/output engine. GeoDataFrames can be plotted with Matplotlib.[9][4]

Usage

Several scientific studies have used GeoPandas for spatial analysis.[6][10][7][11] The OSMnx software library uses GeoDataFrame objects to represent OpenStreetMap features.[12] Additionally, several textbooks on geospatial analysis introduce the library. For example, McClain's Python for Geospatial Data Analysis includes a chapter on GeoPandas and spatial statistics.[3] Mastering Geospatial Analysis with Python covers GeoPandas in a chapter on vector data analysis.[13] Geocomputation with Python relies on GeoPandas as one of its principal packages,[5] and Geographic Data Science with Python introduces its use for spatial joins for feature engineering.[8][14]

See also

References

  1. ^ a b c "About GeoPandas". GeoPandas. Retrieved 2026-07-13.
  2. ^ a b c "geopandas". Python Package Index. Retrieved 2026-07-13.
  3. ^ a b c d McClain, Bonny P. (October 2022). Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence. O'Reilly Media. ISBN 978-1-0981-0478-8.
  4. ^ a b c d Tenkanen, Henrikki; Heikinheimo, Vuokko; Whipp, David. "Introduction to data structures in GeoPandas". Python for Geographic Data Analysis. Retrieved 2026-07-13.
  5. ^ a b c d Dorman, Michael; Graser, Anita; Nowosad, Jakub; Lovelace, Robin (2025). Geocomputation with Python. Chapman & Hall/CRC. ISBN 978-1-032-46065-9.
  6. ^ a b Nitze, Ingmar; Grosse, Guido; Jones, Benjamin M.; Romanovsky, Vladimir E.; Boike, Julia (2018). "Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic". Nature Communications. 9 (1): 5423. Bibcode:2018NatCo...9.5423N. doi:10.1038/s41467-018-07663-3. PMC 6303350. PMID 30575717.
  7. ^ a b Rojas, C.; Linfati, R.; Scherer, R. F.; Pradenas, L. (2023). "Using Geopandas for locating virtual stations in a free-floating bike sharing system". Heliyon. 9 (1) e12749. Bibcode:2023Heliy...912749R. doi:10.1016/j.heliyon.2022.e12749. PMC 9849969. PMID 36685435.
  8. ^ a b Rey, Sergio; Arribas-Bel, Dani; Wolf, Levi John (2023). Geographic Data Science with Python. CRC Press. ISBN 978-1-032-44595-3.
  9. ^ "geopandas/geopandas". GitHub. GeoPandas contributors. Retrieved 2026-07-13.
  10. ^ Yan, Xuejing; Liu, Shibin; Liu, Wei; Dai, Qin (2022). "An improved coverage-oriented retrieval algorithm for large-area remote sensing data". International Journal of Digital Earth. 15 (1): 606–625. Bibcode:2022IJDE...15..606Y. doi:10.1080/17538947.2022.2030816.
  11. ^ Hewitt, Christopher Macdonald (2024). "Exploring Canadian Geospatial Data with GeoPandas in Colab Notebooks". ACMLA Bulletin (173): 14–18. doi:10.15353/acmla.n173.5671.
  12. ^ Boeing, Geoff (2025). "Modeling and Analyzing Urban Networks and Amenities With OSMnx". Geographical Analysis. 57 (4): 567–577. Bibcode:2025GeoAn..57..567B. doi:10.1111/gean.70009.
  13. ^ Toms, Silas; Crickard, Paul; van Rees, Eric (April 2018). Mastering Geospatial Analysis with Python: Explore GIS Processing and Learn to Work with GeoDjango, CARTOframes and MapboxGL-Jupyter. Packt. ISBN 978-1-78829-333-4.
  14. ^ Rey, Sergio; Arribas-Bel, Dani; Wolf, Levi John (2023). "Feature engineering". Geographic Data Science with Python. Retrieved 2026-07-13.

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