GDAL
GDAL: Geospatial Data Abstraction Library
Decision gist · record as of 2026-08-14
Yes, with conditions. GDAL is the de facto standard for geospatial data I/O in Python and is actively maintained with no known vulnerabilities. Install it if you need to work with raster or vector geospatial data. However, expect higher installation friction than typical Python packages: you must have system GDAL libraries and headers pre-installed, and building from source requires SWIG and compilation tools. Use conda on Windows and macOS to avoid compilation.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- libgdal system library (3.13.2 or greater) and development headers must be installed; numpy is required for array operations and must be installed before building bindings; building from source requires SWIG 4 or greater and compilation tools.
- Installation requires system-level GDAL libraries and development headers; building from source has high friction due to SWIG compilation and multiple dependencies (libgdal 3.13.2 or greater, SWIG 4 or greater, numpy, setuptools, wheel).
- Pre-built conda packages are available and recommended for Windows and macOS.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.
last release 2026-07-22 (23 days) · last repo commit 2026-08-14 · 6,020 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 384,174 downloads/mo, #7,068 on PyPI
Alternatives
Verify before relying
pip install gdal
from osgeo import gdal
from osgeo import ogr
from osgeo import osr
dataset = gdal.Open('data.tif')
band = dataset.GetRasterBand(1)
data = band.ReadAsArray()- Whether pre-built wheels cover all major platforms and Python versions or if source compilation is typically required.
- Performance characteristics for large raster datasets beyond the memory-intensive warning for ReadAsArray.
- Compatibility matrix between GDAL Python version, libgdal version, and numpy versions in practice.
What it is and what it does
GDAL is a mature geospatial data abstraction library that exposes C++ classes and methods for reading, writing, and manipulating raster (gridded) and vector (feature-based) geospatial data through Python. The package wraps GDAL/OGR functionality via SWIG-generated bindings, providing access to five major modules: gdal (raster operations), ogr (vector operations), osr (spatial reference systems), gdal_array (numpy integration), and gdalconst (constants).
Installation requires system-level GDAL libraries and headers, making it more complex than pure-Python packages. The bindings are tightly coupled to the underlying C++ library version, so your libgdal installation must match or exceed the required version. A key advanced feature is integration with numpy arrays through methods like ReadAsArray(), enabling numerical processing of raster data. The package is actively maintained, widely used in GIS workflows, and has no known security vulnerabilities.
Use it for
- Read and write raster data (GeoTIFF, HDF5, NetCDF) and convert to numpy arrays for scientific analysis.
- Process vector data (shapefiles, GeoJSON, PostGIS) to query, filter, and transform geographic features.
- Perform coordinate system transformations and spatial reference conversions between different projections.
- Build GIS utilities and command-line tools that manipulate multiple geospatial formats programmatically.
- Integrate geospatial data pipelines into larger scientific or machine-learning workflows requiring raster/vector I/O.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
GDAL is the de facto standard for geospatial data I/O in Python and is actively maintained with no known vulnerabilities. Install it if you need to work with raster or vector geospatial data. However, expect higher installation friction than typical Python packages: you must have system GDAL libraries and headers pre-installed, and building from source requires SWIG and compilation tools. Use conda on Windows and macOS to avoid compilation.
Install
gdal on PyPI
Before you install
Installation requires system-level GDAL libraries and development headers; building from source has high friction due to SWIG compilation and multiple dependencies (libgdal 3.13.2 or greater, SWIG 4 or greater, numpy, setuptools, wheel). Pre-built conda packages are available and recommended for Windows and macOS. Maintenance is active with recent releases.
libgdal system library (3.13.2 or greater) and development headers must be installed; numpy is required for array operations and must be installed before building bindings; building from source requires SWIG 4 or greater and compilation tools.
License in practice
MIT license permits commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.
Quickstart
pip install gdal
from osgeo import gdal
from osgeo import ogr
from osgeo import osr
dataset = gdal.Open('data.tif')
band = dataset.GetRasterBand(1)
data = band.ReadAsArray()Verify before relying
- Whether pre-built wheels cover all major platforms and Python versions or if source compilation is typically required.
- Performance characteristics for large raster datasets beyond the memory-intensive warning for ReadAsArray.
- Compatibility matrix between GDAL Python version, libgdal version, and numpy versions in practice.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8.0 |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Actively maintained 23 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 384,174 / month, #7,068 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: CProgramming Language :: C++Programming Language :: Python :: 3Topic :: Scientific/Engineering :: GISTopic :: Scientific/Engineering :: Information Analysis |
Evidence: gdal-3.13.2.tar.gz
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