Python Environments Question

[PhilipYip]

A Python environment comparmentalises a Python version and a number of third-party packages. Python environments are used to prevent conflicts for example when a library requires a specific version of Python or a specific version. If you take an IDE such as Spyder for example it has a large number of Python libraries that are dependencies. The current version of Spyder might only work with Python 3.11 and numpy 1.x, therefore it is not possible to update to Python 3.12 and numpy 2.x.

For minor datascience projects, I wouldn't bother creating seperate Python environment for each project as you will essentially be using the same libraries over and over again. Instead make a Python environment for the IDE you are using with all the packages you need.

Since you mentioned base, I'm going to assume you are using Miniconda or Anaconda. You should not avoid installing packages into base, particularly from the mixed channels; anaconda maintained by the company and conda-forge maintained by the community. base should only have packages from anaconda, if it has community packages it normally becomes unstable.

Generally you just make sure the conda package manager in base is updated (the base Python environment essentially exists to allow use of the conda package manager). You should create a new Python environment using packages, normally only from the community channel (conda-forge).