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Your Python installation

Remember when we installed Python? That installation lives in a specific folder on your computer:
  • Windows: C:\Users\[YourName]\AppData\Local\Programs\Python\Python313\
  • macOS: /Library/Frameworks/Python.framework/Versions/3.13/
  • Linux: /usr/bin/python3
This is your “system Python” - it’s shared by everything on your computer.

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The problem with sharing

Imagine you’re working on two projects:
  • Project A needs version 1.0 of a tool
  • Project B needs version 2.0 of the same tool
If you install version 2.0 for Project B, you just broke Project A! This is where virtual environments save the day.

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What are virtual environments?

A virtual environment is like a private copy of Python for each project. Think of it as:
  • A separate folder for each project
  • Its own Python installation
  • Its own place to install packages
  • Complete isolation from other projects
Virtual environments are so important that I create a new one for EVERY project. It’s a Python best practice that will save you from many headaches.

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Understanding packages

Before we continue, let’s understand what packages are: Packages are pre-written code that others have created for us to use. Instead of writing everything from scratch, we can import these packages. For example:
  • requests - for downloading web pages
  • pandas - for working with data
  • openai - for using AI models
Each package has different versions, and different projects might need different versions.

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Create your first virtual environment

Let’s create one for our python-for-ai project. You have two methods:

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Method 1: VS Code Command Palette (easier)

  1. Open your project in VS Code
  2. Press Ctrl/Cmd + Shift + P
  3. Type “Python: Create Environment”
  4. Select “Venv”
  5. Select your Python installation
  6. VS Code creates and activates everything for you!

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Method 2: Terminal command

  1. Open the terminal in VS Code (View > Terminal)
  2. Make sure you’re in your project folder
  3. Run this command:
  • Windows
  • macOS/Linux
python -m venv .venv
What this does:
  • python -m venv - Run Python’s virtual environment module
  • .venv - The name of the folder to create (convention is to use “.venv” with a dot)

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What just happened?

Look at your project folder - you now have a new .venv folder: 
python-for-ai/
├── .venv/          # Your virtual environment (hidden folder)
├── hello.py        # Your code
└── python-for-ai.code-workspace
The dot in .venv makes it a hidden folder on Mac/Linux. This keeps your project clean since you don’t need to see this folder often.
This .venv folder contains:
  • A copy of Python
  • A place to install packages
  • Scripts to activate/deactivate

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Activate your virtual environment

VS Code makes activation super easy:
  1. Press Ctrl/Cmd + Shift + P to open Command Palette
  2. Type “Python: Select Interpreter”
  3. Choose the one that says .venv (it will show the full path like ./.venv/bin/python)
  4. That’s it! VS Code automatically activates it for you
You’ll know it’s working when you see (.venv) in your terminal: 
(.venv) PS C:\...\python-for-ai>
This is my recommended approach. VS Code remembers your choice and automatically activates the environment every time you open the project!

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Common issues

If python -m venv doesn’t work, you might need to install it:Ubuntu/Debian:
sudo apt install python3-venv
Other systems: It should come with Python by default
On macOS/Linux, you might need to make the activate script executable:
chmod +x .venv/bin/activate
  1. Reload VS Code window: Ctrl/Cmd + Shift + P > “Developer: Reload Window”
  2. Manually select interpreter again
  3. Make sure the Python extension is installed

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What about Anaconda?

You might have heard of Anaconda - it’s another tool that manages Python environments and packages. It’s popular in the data science world because it comes pre-loaded with many data science packages. However, I don’t recommend using Anaconda unless you have a specific reason to.
Stick with Python’s built-in virtual environments unless your company or a specific project requires Anaconda. You’ll have a lighter, faster, and simpler setup.

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What’s next?

Your virtual environment is ready! Now let’s learn about Python packages and how to install them.

Python packages

Understanding pip and package management