Virtual Environments

Virtual environments are isolated Python environments for each project, preventing dependency conflicts between projects that require different versions of the same packages. They are considered essential practice in Python development.

Why Virtual Environments?

Without virtual environments, all projects share the same global Python packages. This causes problems when:

• Project A needs requests==2.28 but Project B needs requests==2.31
• A global pip upgrade breaks a project that depended on an older version
• You can't reproduce someone else's environment from their requirements.txt

Built-in venv Module

Python 3.3+ includes the venv module. It creates a lightweight virtual environment with its own Python binary and site-packages directory.

Alternative Tools

• virtualenv: Third-party, faster than venv, supports older Python versions. pip install virtualenv.
• conda: Manages non-Python dependencies too (C libraries, R). Popular in data science.
• Poetry: Modern dependency management with lock files, version resolution, and publishing. Uses pyproject.toml.
• Pipenv: Combines pip and virtualenv with Pipfile and Pipfile.lock.
• uv: Extremely fast Python package installer and resolver written in Rust. Drop-in replacement for pip and venv.

Dependency Management

• pip freeze > requirements.txt — export all installed packages with exact versions
• pip install -r requirements.txt — install all dependencies from file
• Split into requirements-dev.txt for testing tools and requirements-prod.txt for production
• Consider pip-compile from pip-tools for deterministic dependency resolution