PrintAfter this general introduction to pandas, we come back to the geospatial domain and will talk about GDAL/OGR a bit. GDAL is a raster and vector processing library that has been developed with a strong focus on supporting a large number of file formats, being able to translate between the different formats, and fostering data exchange. As we already mentioned, GDAL and OGR were originally two separate libraries focusing on raster data (GDAL) and vector data (OGR), respectively. These have now been merged and GDAL (‘Geospatial Data Abstraction Library’) is commonly used to refer to the combined library.
GDAL had its initial release in the year 2000 and originally was mainly developed by Frank Warmerdam. But, since version 1.3.2, responsibilities have been handed over to the GDAL/OGR Project Management Committee under the umbrella of the Open Source Geospatial Foundation (OSGeo). GDAL is available under the permissiveX/MIT style free software license and has become one of the major open source GIS libraries, used in many open source GIS software, including QGIS. The GDAL Python package provides a wrapper around the GDAL C++ library that allows for using its functionality in Python. Similar support exists for other languages and it is also possible to use GDAL/OGR commands from the command line of your operating system. The classes and functions of the Python package are documented here. In the following, we show a few examples illustrating common patterns of using the GDAL library with Python.