# Python bindings

This Python package and extensions are a number of tools for programming and manipulating the GDAL Geospatial Data Abstraction Library.

The GDAL project maintains SWIG generated Python bindings for GDAL/OGR. Generally speaking the classes and methods mostly match those of the GDAL and OGR C++ classes. There is no Python specific reference documentation, but the tutorials includes Python examples.

## Dependencies

> - libgdal and header files (gdal-devel)

> - numpy (1.0.0 or greater) and header files (numpy-devel) (not explicitly required, but many examples and utilities will not work without it)

## Installation

### Conda

GDAL can be quite complex to build and install, particularly on Windows and MacOS. Pre built binaries are provided for the conda system:

https://docs.conda.io/en/latest/

By the conda-forge project:

https://conda-forge.org/

Once you have Anaconda or Miniconda installed, you should be able to install GDAL with:

`conda install -c conda-forge gdal`

### Unix

The GDAL Python bindings requires setuptools.

### pip

GDAL can be installed from the Python Package Index:

    pip install GDAL

It will be necessary to have libgdal and its development headers installed if pip is expected to do a source build because no wheel is available for your specified platform and Python version.

To install the version of the Python bindings matching your native GDAL library:

    pip install GDAL=="$(gdal-config --version).*"

## Building as part of the GDAL library source tree

Python bindings are generated by default when building GDAL from source. For more detail, see Python bindings options.

The GDAL Python package is built using SWIG. The currently supported version is SWIG >= 4

## Usage

### Imports

There are five major modules that are included with the GDAL Python bindings.:

    >>> from osgeo import gdal
    >>> from osgeo import ogr
    >>> from osgeo import osr
    >>> from osgeo import gdal_array
    >>> from osgeo import gdalconst

### API

API documentation is available at osgeo package

## Numpy

One advanced feature of the GDAL Python bindings not found in the other language bindings is integration with the Python numerical array facilities. The gdal.Dataset.ReadAsArray() method can be used to read raster data as numerical arrays, ready to use with the Python numerical array capabilities.

## Tutorials

Chris Garrard has given courses at Utah State University on "Geoprocessing with Python using Open Source GIS" (http://www.gis.usu.edu/~chrisg/python). There a re many slides, examples, test data... and homework ;-) that can -be greatly helpful for beginners with GDAL/OGR in Python.

A cookbook full of recipes for using the Python GDAL/OGR bindings : http://pcjericks.github.io/py-gdalogr-cookbook/index.html

## Gotchas

Although GDAL's and OGR's Python bindings provide a fairly "Pythonic" wrapper around the underlying C++ code, there are several ways in which the Python bindings differ from typical Python libraries. These differences can catch Python programmers by surprise and lead to unexpected results. These differences result from the complexity of developing a large, long-lived library while continuing to maintain backward compatibility. They are being addressed over time, but until they are all gone, please review this list of Python Gotchas in the GDAL and OGR Python Bindings.

## Examples

- An assortment of other samples are available in the Python github samples directory with some description in the Python Sample scripts.

- Several GDAL utilities are implemented in Python and can be useful examples.

- The majority of GDAL regression tests are written in Python. They are available at https://github.com/OSGeo/gdal/tree/master/autotest

- Some examples of GDAL/numpy integration can be found is found in the following scripts:

  - gdal_calc.py

  - val_repl.py

  - gdal_merge.py

  - gdal2tiles.py

  - gdal2xyz.py

  - pct2rgb.py

  - gdallocationinfo.py

One example of GDAL/numpy integration is found in the val_repl.py script.

Note

Performance Notes

ReadAsArray expects to make an entire copy of a raster band or dataset unless the data are explicitly subsetted as part of the function call. For large data, this approach is expected to be prohibitively memory intensive.