Virtual Environments

Introduction

Virtual environments isolate per-project dependencies — non-negotiable for any real Python work.

Understanding the topic

Core concepts to understand:

• python -m venv .venv creates one.
• source .venv/bin/activate uses it.
• uv and poetry are modern wrappers.
• conda for data science with non-Python deps.

Syntax reference

Visual flow / code:

# Built-in venv
python -m venv .venv
source .venv/bin/activate
pip install requests fastapi

# uv (super-fast, modern)
uv venv
uv pip install fastapi

# Poetry
poetry init
poetry add fastapi
poetry shell

Execution workflow

Real-world use

uv (from Astral, makers of Ruff) is 10-100× faster than pip and is rapidly becoming the new standard.

Best practices

• One venv per project.
• Commit requirements.txt or poetry.lock.
• Try uv for speed.

Common mistakes

• Installing globally with sudo pip — corrupts system Python.

Hands-on exercise

Interview preparation — practice these questions:

• venv vs conda?
• What does pip install -e do?
• Why isolate dependencies?

Summary

In summary: Always activate a venv. uv is the new fast standard.