Using virtual environments in Python
In my yesterday's post, we looked at the basics of installing packages in Python using pip.
The next immediate step is to learn about virtual environments. When you install packages with pip, they are added to your 'global' library.
However, if you have multiple Python projects on your machine, each with different package requirements and dependencies, managing these packages globally can become a complex task.
A solution to this problem is to use virtual environments. A virtual environment is a lightweight, isolated Python installation specific to each project (read: folder), with its own list of installed packages.
This way, Project A can use version 1.0 of CoolPythonModule while Project B uses version 2.0, without conflicts.
There are multiple tools for managing environments: conda or poetry also do that, but one of the simplest options is venv.
To create a virtual environment, use the following command:
python3 -m venv .venv
# This creates a virtual environment in the '.venv' folder inside the current working directory
You can now activate the virtual environment:
source .venv/bin/activate
The name of the virtual env is displayed next to your user name. You can now install and do everything you need in terms of packages - they will after only this virtual env.
If you'd like to deactivate it, you do:
deactivate
Don't know which Python interpreter you're using?
which python
This will show you the path of the active interpreter.
P.S. Activate/deactivate scripts can do interesting things like assigning an environment variable upon activation.
source .venv/bin/activate
python
>>> import pandas as pd
>>> pd.DataFrame([{'a':1, 'b':2}])
>>> exit()
which python
deactivate
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