GeoPandas dependencies

GeoPandas brings together the full capability of pandas and open-source geospatial tools Shapely, which brings manipulation and analysis of geometric objects backed by GEOS library, Fiona, 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 of PROJ.

Furthermore, GeoPandas has several optional dependencies as rtree, pygeos, mapclassify, or geopy.

Required dependencies


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.


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.


pyproj is a Python interface to PROJ (cartographic projections and coordinate transformations library). GeoPandas uses object to keep track of a projection of each GeoSeries and its Transformer object to manage re-projections.

Optional dependencies


Rtree is a ctypes Python wrapper of libspatialindex that provides a number of advanced spatial indexing features for the spatially curious Python user.


PyGEOS is a C/Python library with vectorized geometry functions. The geometry operations are done in the open-source geometry library GEOS. PyGEOS wraps these operations in NumPy ufuncs providing a performance improvement when operating on arrays of geometries.


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.

GeoPandas ecosystem

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 GeoPandas related Python ecosystem.

Spatial analysis and Machine Learning


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 model diagnostics tools.


tobler provides functionality for 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 and networkX. momepy aims to provide a wide range of tools for a systematic and exhaustive analysis of urban form. It can work with a wide range of elements, while focused on building footprints and street networks.


geosnap makes it easier to explore, model, analyze, and visualize the social and spatial dynamics of neighborhoods. geosnap provides a suite of tools for creating socio-spatial datasets, harmonizing those datasets into consistent set of time-static boundaries, modeling bespoke neighborhoods and prototypical neighborhood types, and modeling neighborhood change using classic and spatial statistical methods. It also provides a set of static and interactive visualization tools to help you display and understand the critical information at each step of the process.


mesa-geo implements a GeoSpace that can host GIS-based GeoAgents, which are like normal Agents, except they have a shape attribute that is a Shapely object. You can use Shapely directly to create arbitrary shapes, but in most cases you will want to import your shapes from a file. Mesa-geo allows you to create GeoAgents from any vector data file (e.g. shapefiles), valid GeoJSON objects or a GeoPandas GeoDataFrame.


Pyspatialml is a Python module for applying scikit-learn machine learning models to ‘stacks’ of raster datasets. Pyspatialml includes functions and classes for working with multiple raster datasets and performing a typical machine learning workflow consisting of extracting training data and applying the predict or predict_proba methods of scikit-learn estimators to a stack of raster datasets. Pyspatialml is built upon the rasterio Python module for all of the heavy lifting, and is also designed for working with vector data using the geopandas module.


PyGMI stands for Python Geoscience Modelling and Interpretation. It is a modelling and interpretation suite aimed at magnetic, gravity and other datasets.



contextily is a small Python 3 (3.6 and above) package to retrieve tile maps from the internet. It can add those tiles as basemap to matplotlib figures or write tile maps to disk into geospatial raster files. Bounding boxes can be passed in both WGS84 (EPSG:4326) and Spheric Mercator (EPSG:3857).


Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy. It features: object oriented projection definitions; point, line, polygon and image transformations between projections; integration to expose advanced mapping in Matplotlib with a simple and intuitive interface; powerful vector data handling by integrating shapefile reading with Shapely capabilities.


Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.


folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in a Leaflet map via folium. is a data-agnostic, high-performance web-based application for visual exploration of large-scale geolocation data sets. Built on top of Mapbox GL and, can render millions of points representing thousands of trips and perform spatial aggregations on the fly.


geoplot is a high-level Python geospatial plotting library. It’s an extension to cartopy and matplotlib which makes mapping easy: like seaborn for geospatial. It comes with the high-level plotting API, native projection support and compatibility with matplotlib.


GeoViews is a Python library that makes it easy to explore and visualize any data that includes geographic locations. It has particularly powerful support for multidimensional meteorological and oceanographic datasets, such as those used in weather, climate, and remote sensing research, but is useful for almost anything that you would want to plot on a map!


EarthPy is a python package that makes it easier to plot and work with spatial raster and vector data using open source tools. Earthpy depends upon geopandas which has a focus on vector data and rasterio with facilitates input and output of raster data files. It also requires matplotlib for plotting operations. EarthPy’s goal is to make working with spatial data easier for scientists.


splot provides statistical visualizations for spatial analysis. It methods for visualizing global and local spatial autocorrelation (through Moran scatterplots and cluster maps), temporal analysis of cluster dynamics (through heatmaps and rose diagrams), and multivariate choropleth mapping (through value-by-alpha maps). A high level API supports the creation of publication-ready visualizations


legendgram is a small package that provides “legendgrams” legends that visualize the distribution of observations by color in a given map. These distributional visualizations for map classification schemes assist in analytical cartography and spatial data visualization.

Geometry manipulation


Topojson is a library that is capable of creating a topojson encoded format of merely any geographical object in Python. With topojson it is possible to reduce the size of your geographical data. Mostly by orders of magnitude. It is able to do so through: eliminating redundancy through computation of a topology; fixed-precision integer encoding of coordinates and simplification and quantization of arcs.


Tool to convert geopandas vector data into rasterized xarray data.

Data retrieval


OSMnx is a Python package that lets you download spatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks. You can download and model walkable, drivable, or bikeable urban networks with a single line of Python code then easily analyze and visualize them. You can just as easily download and work with other infrastructure types, amenities/points of interest, building footprints, elevation data, street bearings/orientations, and speed/travel time.


Pyrosm is a Python library for reading OpenStreetMap data from Protocolbuffer Binary Format -files (*.osm.pbf) into Geopandas GeoDataFrames. Pyrosm makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings, Points of Interest (POI), landuse and natural elements. Also fully customized queries are supported which makes it possible to parse the data from OSM with more specific filters.


geobr is a computational package to download official spatial data sets of Brazil. The package includes a wide range of geospatial data in geopackage format (like shapefiles but better), available at various geographic scales and for various years with harmonized attributes, projection and topology.


An interface to explore and query the US Census API and return Pandas Dataframes. This package is intended for exploratory data analysis and draws inspiration from sqlalchemy-like interfaces and acs.R. With separate APIs for application developers and folks who only want to get their data quickly & painlessly, cenpy should meet the needs of most who aim to get US Census Data from Python.

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