Prerequisite 1: Python + Jupyter Environment

Who is this for? Students preparing for Seminar 1.

Goal: Understand what tools we use in this course, why we use them, and complete the minimum setup.

For this course, you need these prerequisites:

• Python 3

• VS Code

• Python + Jupyter extensions in VS Code

• A project virtual environment (venv)

1) Python¶

Python is the core language used throughout this course for data structures and algorithms.

As of February 2026, the newest stable release line is Python 3.14.x. For this course, any recent Python 3 version is fine (prefer 3.12+).

Why Python matters for this course:

• readable syntax → faster learning of algorithmic ideas

• huge ecosystem for scientific computing and notebooks

• standard in academia and many technical interviews

1.1 Opening a terminal¶

All commands in this guide are typed into a terminal — a text window where you give instructions to your computer by typing. Here is how to open one:

macOS: Press Cmd ⌘ + Space to open Spotlight, type Terminal, and press Enter. You can also find it under Applications → Utilities → Terminal.

Windows: Press the Windows key, type Command Prompt, and press Enter.

Once open, you will see a blinking cursor on a line called the prompt. Every time this guide shows a line starting with $ (macOS) or > (Windows), that is a command for you to type and press Enter to run.

1.2 Install Python¶

Use the official download page: https://www.python.org/downloads/

Windows installer tip: when the installer window opens, check the box “Add Python to PATH” before clicking Install. Missing this step is the most common reason Python commands are not recognised in the terminal later.

1.3 Minimal verification¶

Open a terminal and run the following commands. Pick the block that matches your operating system.

macOS / Linux:

# move to your working folder (creates it if it doesn't exist)
cd ~/Documents
mkdir -p algo
cd algo

# verify Python is installed
python3 --version

Windows (Command Prompt):

REM move to your working folder
cd "%USERPROFILE%\Documents"
mkdir algo
cd algo

REM verify Python is installed
python --version

You should see output similar to:

Python 3.12.4

If you see command not found (macOS) or 'python' is not recognized (Windows), Python was not installed correctly — re-run the installer from step 1.1 and make sure to tick Add Python to PATH.

1.4 Virtual environment¶

When working on multiple projects, you will often need different packages for each project. If you install everything globally (system-wide), all projects share the same package versions, which can easily lead to dependency conflicts when one project requires a different version than another. The Python solution to this problem is the use of virtual environments (venvs). A virtual environment is an isolated environment created specifically for one project, allowing you to use a specific Python version and install project-specific dependencies without affecting other projects. Within that environment, you can freeze the exact versions of all installed packages to ensure reproducibility and prevent conflicts.

To create and activate a virtual environment, run the commands for your OS:

macOS / Linux:

# create and activate virtual environment
python3 -m venv venv
source venv/bin/activate

Windows (Command Prompt):

python -m venv venv
venv\Scripts\activate.bat

How do you know it worked? After activation, your terminal prompt will start with (venv):

(venv) username@machine algo %           ← macOS
(venv) C:\Users\you\Documents\algo>      ← Windows

This prefix is your visual confirmation that the virtual environment is active. Any python or pip command now uses the isolated environment, not the system-wide Python.

Verify the activated environment:

python --version    # should show 3.12.x (no '3' suffix needed while venv is active)
pip --version       # should show pip XX.X from .../venv/...

When you are done working for the day, deactivate the environment:

deactivate

Next time you open a terminal, cd back into the algo folder and reactivate before doing any work.

Key concepts:

• python3 -m venv venv: tells Python to run its built-in environment tool (venv) and create a new folder named venv containing an isolated Python interpreter + pip. We create one virtual environment per project so package versions from different projects do not conflict with each other.

• after activation, use python and pip normally because venv is first on your PATH.

• >>> means you are in the interactive Python shell (REPL). You can type single Python expressions and see results immediately. Type exit() to leave it and return to the normal terminal prompt.

2) VS Code¶

VS Code is one of the most used IDEs. IDE means Integrated Development Environment. An IDE combines common developer tools in one place:

• code editor

• terminal

• debugger

• project/file explorer

• extensions for language support

Why we use VS Code in this course:

• free and cross-platform (supports most programming languages)

• excellent Python + Jupyter support

• same workflow on macOS, Windows, and Linux

Instead of maintaining custom installation steps here, use the official VS Code setup guide: https://code.visualstudio.com/docs/setup/setup-overview

After installing VS Code, do the following to open your project:

1. Launch VS Code.

2. Select File → Open Folder (on macOS this is File → Open...) and navigate to the algo folder you created in step 1.

3. Your project files will appear in the left Explorer panel (the file tree on the left side of the window).

If VS Code shows a popup “Do you trust the authors of the files in this folder?”, click Yes, I trust the authors — you created this folder yourself.

3) VS Code + Python (Extensions)¶

VS Code is powerful because most language-specific features come from extensions.

For Python work, you need at least:

• Python extension (Microsoft)

• Jupyter extension (for .ipynb notebooks)

What these enable:

• interpreter selection (venv vs system Python)

• run/debug Python files

• notebook kernel selection and execution

• richer linting/intellisense workflows

Use the official tutorial for the exact setup flow: https://code.visualstudio.com/docs/python/python-tutorial

4) Interpreter Selection (Required)¶

In VS Code, always set the project interpreter to your venv. This tells VS Code which Python installation to use when running code.

Quick steps:

1. Open the algo project folder in VS Code.

2. Open the command palette (Cmd/Ctrl + Shift + P).

3. Type and run Python: Select Interpreter.

4. Choose the interpreter path that contains venv — it will look something like ./venv/bin/python (macOS) or .\venv\Scripts\python.exe (Windows).

Where is the status bar? It is the thin horizontal bar running along the very bottom edge of the VS Code window. After selecting the interpreter, the bottom-left area will show something like:

Python 3.12.4 ('venv': venv)

If it still shows a system path (e.g. /usr/bin/python3) without venv in it, the wrong interpreter is selected — repeat the steps above.

Why does this matter? If VS Code uses the wrong Python, it will not find the packages you installed in your venv, and you will see import errors when running code.

5) Install Course Packages¶

As discussed in step 1, we now have Python virtual environments set up. For some usecases (i.e. generating figures, data wrangling etc.) we will rely on packages developed by other contributors. Python includes a built-in package management tool called pip, which allows you to easily install external packages using the command pip install followed by the package name. To do so, with your venv activated, run:

pip install --upgrade pip
pip install jupyter matplotlib numpy pandas ipywidgets

To see the installed versions use the following command:

pip freeze

These can be stored in a requirements file, typically called requirements.txt. On another machine:

pip install -r requirements.txt

6) Jupyter Notebook Check¶

Download from the course side and open 01_python_basics.ipynb in VS Code and confirm:

• notebook opens in editor

• top-right kernel points to your venv

• cells run with Shift+Enter

If kernel is wrong: click kernel name -> Select Another Kernel -> Python Environments -> choose venv.

Summary¶

You are now ready for Seminar 1.

7) Troubleshooting Common Issues¶

Still stuck? Ask during the next session or post in the course forum with the exact error message you see — copy-pasting the full error is far more helpful than a description.