Appendix E: Python Environment Setup Guide

This guide walks you through setting up a Python environment for the optional code examples in this textbook. Python is entirely optional. Every chapter works without running any code. The code examples are there for readers who want to see concepts in action computationally — you will not miss any learning objectives by skipping them.

E.1 Why Python Is Optional

This is a textbook about AI literacy, not programming. The core skills — evaluating claims, identifying bias, understanding how AI systems work and fail, analyzing stakeholder impacts — do not require writing a single line of code.

That said, some concepts click more deeply when you can interact with data and models directly. Seeing how a training-test split works in practice, watching a classifier struggle with ambiguous inputs, or running a simple data exploration can make abstract ideas concrete.

The optional code examples in this book use Python because it is the dominant language in AI and data science, it has excellent libraries for the tasks we cover, and it has a gentle learning curve for basic tasks. If you have never programmed before, you can still follow the examples — each one is short (typically 5-15 lines), heavily commented, and designed to illustrate a concept rather than teach programming.

E.2 Three Ways to Run Python

You do not need to install anything on your computer if you do not want to. Here are three options, from simplest to most involved:

Option 1: Google Colab (Recommended for Beginners)

What it is: A free, browser-based notebook environment hosted by Google. No installation required.

Setup: 1. Go to colab.research.google.com 2. Sign in with a Google account 3. Click "New Notebook" 4. You can now write and run Python code in your browser

Pros: Zero setup, free, runs in any browser, includes most libraries pre-installed. Cons: Requires an internet connection, files are stored in Google Drive, sessions time out after inactivity.

Installing packages in Colab: If a code example requires a package not pre-installed in Colab, add a cell at the top of your notebook with:

!pip install pandas scikit-learn matplotlib

Option 2: Jupyter Notebooks (Local)

What it is: A local notebook environment that runs in your browser but uses your own computer's Python installation.

Setup: 1. Install Python (see Option 3 below for installation instructions) 2. Open a terminal or command prompt 3. Install Jupyter: pip install jupyter 4. Launch it: jupyter notebook 5. Your browser will open with a file navigator — click "New > Python 3" to create a notebook

Pros: Works offline, full control over your environment, no cloud dependency. Cons: Requires local Python installation, some troubleshooting may be needed.

Option 3: Local Python Installation

What it is: Python installed directly on your computer, running scripts from the command line or an editor.

Setup on Windows: 1. Go to python.org/downloads 2. Download the latest Python 3 release (3.10 or later recommended) 3. Important: During installation, check the box that says "Add Python to PATH" 4. Open Command Prompt and type python --version to verify

Setup on macOS: 1. Open Terminal 2. Check if Python 3 is installed: python3 --version 3. If not installed, download from python.org/downloads or install via Homebrew: brew install python

Setup on Linux: 1. Python 3 is typically pre-installed. Check with: python3 --version 2. If needed: sudo apt install python3 python3-pip (Debian/Ubuntu)

E.3 Installing Required Packages

The code examples in this book use a small set of common Python libraries. A requirements.txt file is provided in the textbook's repository.

The Packages

Package	Version	Used For
`pandas`	>= 2.0	Data loading, exploration, and manipulation
`scikit-learn`	>= 1.3	Machine learning demos (classification, clustering)
`matplotlib`	>= 3.7	Charts and visualizations
`requests`	>= 2.31	API calls (optional LLM examples)

Installation

From your terminal or command prompt:

pip install pandas scikit-learn matplotlib requests

Or, if you have the textbook's requirements.txt file:

pip install -r requirements.txt

If you see permission errors, try:

pip install --user pandas scikit-learn matplotlib requests

E.4 Testing Your Setup

Create a new Python file or notebook cell and run the following:

# Test that all required packages are installed
import pandas as pd
import sklearn
import matplotlib
import requests

print(f"pandas version: {pd.__version__}")
print(f"scikit-learn version: {sklearn.__version__}")
print(f"matplotlib version: {matplotlib.__version__}")
print(f"requests version: {requests.__version__}")
print("\nAll packages installed successfully!")

If all four version numbers print without errors, you are ready.

E.5 Troubleshooting Common Issues

"python" is not recognized / command not found

Windows: Reinstall Python and make sure to check "Add Python to PATH"
macOS/Linux: Try python3 instead of python, and pip3 instead of pip

pip install fails with permission errors

Try pip install --user <package> to install for your user only
On macOS/Linux, do not use sudo pip install — it can break system Python

Import errors after installation

Make sure you installed packages for the same Python version you are running. If you have both Python 2 and Python 3, use pip3 install explicitly.
In Jupyter, make sure the notebook kernel matches the Python environment where you installed packages.

Matplotlib charts do not display

In Jupyter notebooks, add %matplotlib inline at the top of your notebook
In Google Colab, charts should display automatically
In scripts, add plt.show() after creating a figure

Version conflicts

If you encounter version conflicts, consider creating a virtual environment: bash python -m venv ai-literacy-env # On Windows: ai-literacy-env\Scripts\activate # On macOS/Linux: source ai-literacy-env/bin/activate pip install -r requirements.txt

E.6 Chapter Code Reference

The following chapters include optional Python code examples:

Chapter	Topic	What the Code Does
Ch. 3	How Machines Learn	Simple scikit-learn classification demo on the Iris dataset
Ch. 4	Data	Pandas data exploration — loading and examining a dataset
Ch. 5	Large Language Models	Simple API call to a language model
Ch. 6	Computer Vision	Image classification with a pre-trained model
Ch. 14	Using AI Effectively	Prompt template with API call
Ch. 18	AI and the Environment	Simple carbon footprint calculator

Each code example is self-contained and can be run independently. You do not need to run earlier examples to understand later ones.

E.7 A Note on AI Coding Assistants

You may be tempted to use an AI coding assistant (like GitHub Copilot, ChatGPT, or Claude) to help you with the Python examples. That is perfectly fine for this textbook — the code is a means to an end, not the primary learning objective. However, if you do use an AI assistant, document what you asked it for and evaluate its output. That is, in itself, an exercise in AI literacy (see Chapter 14).