Virtual Environments
Understand how to create and activate Python virtual environments using venv to isolate project dependencies. Learn why virtual environments prevent package version conflicts across projects, how to manage packages within these isolated setups, and how to use requirements.txt for dependency reproducibility. Gain skills essential for maintaining consistent and manageable Python development environments.
Up to this point, you may have installed packages globally using pip. While this approach is sufficient for small experiments, it quickly becomes unmanageable in real-world projects. Consider a scenario in which Project A depends on requests==1.0, while Project B requires requests==2.0. A global installation cannot satisfy both requirements simultaneously; upgrading one project’s dependency may inadvertently break the other.
To address this problem, Python provides virtual environments. A virtual environment is a self-contained directory that includes its own Python interpreter and an isolated set of installed packages. Dependencies installed within one environment do not interfere with those in another.
Using virtual environments makes each project an isolated and reproducible unit. The same dependencies and interpreter configuration can be recreated on another developer’s machine or a production server, which helps maintain consistent behavior across environments.
The problem: Global dependencies
When a package, such as requests or pandas is installed without a virtual environment, it is placed in a system-wide directory, commonly referred to as site-packages. This global location is shared by every Python program on the machine. As projects become more complex, this shared environment introduces significant risks.
The first issue is version conflicts. If two projects require different versions of the ...