Confluence Retirement

In an effort to consolidate USGS hosted Wikis, myUSGS’ Confluence service is scheduled for retirement on January 27th, 2023. The official USGS Wiki and collaboration space is now SharePoint. Please migrate existing spaces and content to the SharePoint platform and remove it from Confluence at your earliest convenience. If you need any additional information or have any concerns about this change, please contact Thank you for your prompt attention to this matter.

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Python-Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. While it is not quite as a rich as R in terms of statistics, it's really getting there. One major benefit is that its data structures are python-native. This means that if you exploit a lot of the python specific functionality to clean up your data, you will not have to transform it for use in R. In some cases, this ca be a substantial bottleneck. There's a pretty decent tutorial video (~30 minutes) (sound cuts out 20-23mins), a class video (~3 hours), and a "tour" video (~10 minutes), among many others.


  • NumPy is a really important add-on for Python. It provide low-level functions for handling arrays. It comes with ArcGIS
  • SciPy is another really importantadd-on for Python. It's built on top of NumPy and provides higher-level (i.e., more user-friendly) functionality. It also comes with ArcGIS.
  • Python is distributed with a large standard library of modules that support various tasks, but many more are available online. An extensive collection of pre-compiled libraries are available in this collection posted by Christoph Gohlke. Key libraries of interest to scientific computing include NumPy, SciPy, matplotlib, and netCDF4.
  • Versions of the GDAL and OGR libraries are now available in Python, in a package called pypi.
  • Using Python with Fortran or C sub-page

In addition to a truly dizzying number of individual add-on libraries for Python, there are a few distributions of sets of python libraries that try to eliminate the hassle of pulling together lots of libraries. We should investigate these!