Essential Python Geospatial Libraries

Fri 12 July 2013

Just so I don’t forget, here is a list of really awesome Python libraries that I’m using these days to do lots of fun things with spatial data [UPDATE: I’ve added a few more]:

  • pandas - For data handling and munging
  • shapely - For geometry handling
  • cartopy - For plotting spatial data
  • rtree - For efficiently querying spatial data
  • nodebox-opengl - For playing around with animations
  • statsmodels - For models and stats in Python (otherwise I’d use R)
  • numpy - For pretty much anything that involves arrays
  • geopy - For geolocating and things like that
  • ipython - For a wondering interactive environment in which to play
  • freetype-py - For converting font glyphs to polygons (odd I know…)
  • ogr/gdal - For reading, writing, and transforming geospatial data formats
  • pyqgis - For anything and everything GIS
  • fiona - For making it easy to read/write geospatial data formats
  • matplotlib - For all my plotting needs
  • networkx - For working with networks (duh!)
  • pelican - For blogging about all this stuff…
  • pysal - For all your spatial econometrics needs (and more)
  • descartes - For plotting geometries in matplotlib

Based on Twitter and some of the comments below, I should also add:

  • geographiclib - For solving geodesic problems
  • pyshp - For reading and writing shapefiles (in pure Python)
  • pyproj - For conversions between projections

Any others I’ve missed?

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