Lesson 12: Virtual Environments & Packages
Real Python projects use third-party packages (libraries). Virtual environments keep each project's packages isolated so they don't conflict. This is standard professional practice.
Key Concepts
Why Packages?
Python's standard library is powerful, but for data science you'll want pandas, for web requests requests, for machine learning scikit-learn. These are installed from PyPI (the Python Package Index) using pip.
Virtual Environments
python -m venv venv # creates a virtual env
source venv/bin/activate # Mac/Linux
venv\Scripts\activate # Windows
pip install requests pandas
Each project gets its own isolated set of packages.
requirements.txt
pip freeze > requirements.txt # saves current packages
pip install -r requirements.txt # installs from list
Commit requirements.txt to git so others can reproduce your environment exactly.
Popular Packages
requests: HTTP/API calls. pandas: data analysis. numpy: math/arrays. matplotlib: charts. flask: web apps. pytest: testing. black: code formatting. All installed with pip install
✅ Check Your Understanding
1. What command creates a virtual environment?
2. What is requirements.txt used for?
3. Where are Python packages hosted for download?