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GeoPandas: Easy, fast and scalable geospatial analysis in Python

Authors: Van den Bossche, Joris;

GeoPandas: Easy, fast and scalable geospatial analysis in Python

Abstract

Abstract GeoPandas is an open source project to make working with geospatial vector data in Python easier. It combines the capabilities of pandas and shapely, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in Python that would otherwise require a spatial database such as PostGIS. This talk will give a brief introduction to GeoPandas, highlight some of the latest developments (faster geometry operations through new vectorized functionality in Shapely 2.0, faster IO through new GDAL bindings in pyogrio and through GeoParquet, and scaling GeoPandas with Dask using the dask-geopandas subproject), and will invite for an open discussion on how the GeoPandas and Pangeo communities can more closely collaborate.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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2
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14
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