Python Libraries for Geospatial Data

The Python ecosystem

Anyone familiar with the Python ecosystem knows there are thousands of packages available that cater to virtually any task imaginable. As of March 2023, precisely 436,422 packages were accessible through the PyPI repository. These packages can be installed in the Python environment using popular package managers like PIP or Conda.

The Python ecosystem offers a wealth of tools and libraries for various applications, including data manipulation, visualization, and machine learning, and GIS is no exception. A brief description of the most relevant packages is given below.

Most relevant geospatial packages

For geospatial analysis, here are the most relevant packages that a beginner must be aware of.

GDAL

The Geospatial Data Abstraction Library (GDAL) is a powerful, open-source geospatial data processing library that supports numerous raster and vector file formats and provides a wide range of tools for manipulating geospatial data.

GDAL is written originally in C++ and is known for its performance, making it a popular choice among GIS professionals for large-scale data processing tasks. It also provides several command-line utilities for data translation and processing.

Although it's been written in C++, GDAL can also be used directly from Python using the GDAL/OGR Python bindings. GDAL is also the foundation for many other Python geospatial libraries, as it offers robust support for data format conversion, reprojection, and data manipulation tasks. Instead of using GDAL directly by command line or through Python bindings, other libraries exist, such as Rasterio and FIONA, that use GDAL in the backend and are more user friendly to learn.