Hello all,

I have been working on a Python installation for our new Data Analysis and Visualization Lab. The lab will contain some higher-powered computers for analysis and viz, somewhere between a personal PC and an HPC. While we will provide access to SPSS, Tableau, etc., many of our researchers are using Python and R.

When I learned Python, we used Anaconda. We can't use Anaconda in this environment because of the licensing issues, so this is my first time setting up a Python computing environment "from scratch." Our IT strongly prefers that we don't allow patrons to install Python packages themselves. Setup basics:

  *
Install Python, Visual Studio Code, and Jupyter Lab
  *
Use requirements.txt file to preload expected packages (I'm still figuring this out)
     *
Pandas, plotly, etc.
  *
All machines will be "deep frozen," meaning they will revert back to their previous state upon restart when the patron is finished working.

Questions:

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Should we encourage or require patrons to use a virtual environment for their projects? If so, how?
     *
My current understanding is that this isn't necessary or helpful since the computers will be frozen.
  *
Do we need to worry about security if patrons can install their own packages? I understood Python packages to be vetted by the community, and not really the same as installing other software. Is it possible for a package installed through pip to be malware?

I greatly appreciate any guidance or ideas you all have!

Stay well,

----

Kayla Abner

(she/her)

Data Visualization and Analysis Librarian

Research Data and Design Commons

Library, Museums and Press

University of Delaware

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