4.1 Dependencies - Python for Scientific Computing 2021
Channel: Aalto Scientific Computing
Date: 2021-10-28

Dependency management with environments and requirement files in Python.

When you have projects that depend on other libraries, you need some way of reco
rding and managing these.  Here, we learn of the basic concepts, how to record d
ependencies (requirements.txt, environment.yml), and how to use the virtual envi
ronments/conda environments.

https://aaltoscicomp.github.io/python-for-scicomp/dependencies/


02:26 Demo, two versions of numpy on one computer
10:01 Exercise 1, discussion
17:42 conda, pip, and PyPI: installing packages
38:26 Virtual environments: installing things per-project
42:19 Recording dependencies with requirements.txt and environment.yml

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Python for Scientific Computing is a bridge between basic Python courses and scientific work with Python.  This is a basic to intermediate course in Python tools such as NumPy, SciPy, Matplotlib, and Pandas.  It also covers some more advanced tools, such as Binder, releasing software, data formats, etc.  It is suitable for people who have a basic understanding of Python and want to know some internals and important libraries for science.  We don't cover anything in too much depth, but we do introduce you to all of the main tools you will need.

This course was put on as a collaboration between partners in Finland, Norway, and Sweden, coordinated by Aalto Scientific Computing.

Links:
- Playlist: https://www.youtube.com/playlist?list=PLZLVmS9rf3nOS7bHNmbcDoyTnMYaz_TJW
- Course material: https://aaltoscicomp.github.io/python-for-scicomp/
- Workshop webpage: https://scicomp.aalto.fi/training/scip/python-for-scicomp/
- Aalto Scientific Computing: https://scicomp.aalto.fi/
- CodeRefinery: https://coderefinery.org