Ecosystem — GeoPandas 1.2.1.dev0+g199fc53c8.d20260928 documentation

GeoPandas brings together the full capability of pandas and the open-source geospatial tools Shapely, which brings manipulation and analysis of geometric objects backed by GEOS library, pyogrio, allowing us to read and write geographic data files using GDAL, and pyproj, a library for cartographic projections and coordinate transformations, which is a Python interface to PROJ.
Furthermore, GeoPandas has several optional dependencies as mapclassify, or geopy.
pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
Shapely is a BSD-licensed Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployed GEOS (the engine of PostGIS) and JTS (from which GEOS is ported) libraries. Shapely is not concerned with data formats or coordinate systems, but can be readily integrated with packages that are.
Pyogrio provides a GeoPandas-oriented API to OGR vector data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources have geometries, such as points, lines, or polygons, and associated records with potentially many columns worth of data.
pyproj is a Python interface to PROJ (cartographic projections and coordinate transformations library). GeoPandas uses a pyproj.crs.CRS object to keep track of the projection of each GeoSeries and its Transformer object to manage re-projections.
mapclassify provides functionality for Choropleth map classification. Currently, fifteen different classification schemes are available, including a highly-optimized implementation of Fisher-Jenks optimal classification. Each scheme inherits a common structure that ensures computations are scalable and supports applications in streaming contexts.
geopy is a Python client for several popular geocoding web services. geopy makes it easy for Python developers to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources.
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits.
Fiona is GDAL’s neat and nimble vector API for Python programmers. Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as pyproj, Rtree, and Shapely.
Various packages are built on top of GeoPandas addressing specific geospatial data processing needs, analysis, and visualization. Below is an incomplete list (in no particular order) of tools which form the GeoPandas-related Python ecosystem.
PySAL, the Python spatial analysis library, is an open source cross-platform library for geospatial data science with an emphasis on geospatial vector data written in Python. PySAL is a family of packages, some of which are listed below.
libpysal provides foundational algorithms and data structures that support the rest of the library. This currently includes the following modules: input/output (io), which provides readers and writers for common geospatial file formats; weights (weights), which provides the main class to store spatial weights matrices, as well as several utilities to manipulate and operate on them; computational geometry (cg), with several algorithms, such as Voronoi tessellations or alpha shapes that efficiently process geometric shapes; and an additional module with example data sets (examples).
esda implements methods for the analysis of both global (map-wide) and local (focal) spatial autocorrelation, for both continuous and binary data. In addition, the package increasingly offers cutting-edge statistics about boundary strength and measures of aggregation error in statistical analyses.
segregation package calculates over 40 different segregation indices and provides a suite of additional features for measurement, visualization, and hypothesis testing that together represent the state of the art in quantitative segregation analysis.
mgwr provides scalable algorithms for estimation, inference, and prediction using single- and multi-scale geographically weighted regression models in a variety of generalized linear model frameworks, as well as model diagnostics tools.
tobler provides functionality for areal interpolation and dasymetric mapping. tobler includes functionality for interpolating data using area-weighted approaches, regression model-based approaches that leverage remotely-sensed raster data as auxiliary information, and hybrid approaches.
MovingPandas is a package for dealing with movement data. MovingPandas implements a Trajectory class and corresponding methods based on GeoPandas. A trajectory has a time-ordered series of point geometries. These points and associated attributes are stored in a GeoDataFrame. MovingPandas implements spatial and temporal data access and analysis functions as well as plotting functions.
momepy is a library for quantitative analysis of urban form - urban morphometrics. It is built on top of GeoPandas, PySAL a