Software:GeoPandas

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GeoPandas
Original author(s)Kelsey Jordahl
Developer(s)GeoPandas contributors
Initial releaseJuly 2014[1]
Stable release
1.1.4[2] / June 26, 2026; 4 months ago (2026-06-26)[2]
Repositorygithub.com/geopandas/geopandas
Written inPython
Operating systemCross-platform
TypeSoftware library
LicenseBSD 3-Clause License
Websitegeopandas.org

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. ↑ 1.0 1.1 1.2 "About GeoPandas". https://geopandas.org/en/stable/about.html. 
  2. ↑ 2.0 2.1 2.2 "geopandas". https://pypi.org/project/geopandas/. 
  3. ↑ 3.0 3.1 3.2 3.3 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. https://www.oreilly.com/library/view/python-for-geospatial/9781098104788/. 
  4. ↑ 4.0 4.1 4.2 4.3 Tenkanen, Henrikki; Heikinheimo, Vuokko; Whipp, David. "Introduction to data structures in GeoPandas". https://pythongis.org/part2/chapter-06/nb/01-geodataframe.html. 
  5. ↑ 5.0 5.1 5.2 5.3 Dorman, Michael; Graser, Anita; Nowosad, Jakub; Lovelace, Robin (2025). Geocomputation with Python. Chapman & Hall/CRC. ISBN 978-1-032-46065-9. https://www.routledge.com/Geocomputation-with-Python/Dorman-Graser-Nowosad-Lovelace/p/book/9781032460659. 
  6. ↑ 6.0 6.1 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. doi:10.1038/s41467-018-07663-3. PMID 30575717. Bibcode: 2018NatCo...9.5423N. 
  7. ↑ 7.0 7.1 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). doi:10.1016/j.heliyon.2022.e12749. PMID 36685435. Bibcode: 2023Heliy...912749R. 
  8. ↑ 8.0 8.1 Rey, Sergio; Arribas-Bel, Dani; Wolf, Levi John (2023). Geographic Data Science with Python. CRC Press. ISBN 978-1-032-44595-3. https://www.routledge.com/Geographic-Data-Science-with-Python/Rey-Arribas-Bel-Wolf/p/book/9781032445953. 
  9. ↑ "geopandas/geopandas". GeoPandas contributors. https://github.com/geopandas/geopandas. 
  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. doi:10.1080/17538947.2022.2030816. Bibcode: 2022IJDE...15..606Y. 
  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. doi:10.1111/gean.70009. Bibcode: 2025GeoAn..57..567B. 
  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. https://www.oreilly.com/library/view/mastering-geospatial-analysis/9781788293334/. 
  14. ↑ Rey, Sergio; Arribas-Bel, Dani; Wolf, Levi John (2023). "Feature engineering". https://geographicdata.science/book/notebooks/12_feature_engineering.html. 



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