Setup

The concepts we cover are general and apply to many languages and tools. In this series we demonstrate how to code with Python using the Cursor Integrated Development Environment (IDE), which provides a simple way to incorporate AI into the coding process. What you learn throughout the workshop can apply to many environments and tools, and this is simply one way of doing things.

Complete this page before the hands-on workshops (especially workshops 2 and 3). We will use Cursor for AI-assisted coding and Python with Jupyter notebooks (.ipynb) to run examples, together with pandas and matplotlib.

We use Python in these workshops because it is widely used for data analysis and it integrates well with Cursor. You can write and run Python code alongside AI chat in the same workspace, which is practical for real projects.

What you need

Python, packages, and Jupyter

In this workshop we use Python 3.10 or newer, plus these libraries and packages:

Package / tool Role
pandas Load and work with tables
matplotlib Create plots
notebook Jupyter Notebook server so you can open and run .ipynb files (used in the workshops)

If you already have Python installed, skip to the Cursor section.

If you need to install Python and don’t have a preferred distribution, Miniconda is a lightweight option:

  1. Go to https://www.anaconda.com/download/success and download the Miniconda installer for your operating system.
  2. Run the installer and follow the prompts.

Cursor

Cursor logo

  1. Download Cursor from https://cursor.com/download and install it for your operating system.
  2. Open Cursor. You will be prompted to sign in or create a free account.
  3. Follow the setup prompts (plugins are not required).

Policy Notes for Using Cursor :

  • Privacy Policy — how Cursor collects and uses data when you use the app.
  • Pricing — free vs paid features; check what your use case needs (this workshop uses the free version).

Only use Cursor with files that can be made public. All files in a Cursor workspace may be indexed and shared with AI tools, even if you don’t enter them into the chat. Never use Cursor with personal or confidential data.

More detail: UBC AI guidance.

Palmer Penguins dataset

Download the CSV dataset we’ll use in the workshop:

  1. Create a project folder (you will open this folder later with Cursor)
  2. In your project folder, create a folder named data
  3. Download penguins.csv and save it in your data folder (right-click the link and select Save Link As… or Download Linked File As…)

Preview of the data we’ll work with:

species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
Adelie Torgersen 39.1 18.7 181 3750 male 2007
Adelie Torgersen 39.5 17.4 186 3800 female 2007
Adelie Torgersen 40.3 18.0 195 3250 female 2007
Chinstrap Dream 46.5 17.9 192 3500 female 2007
Gentoo Biscoe 46.1 13.2 211 4500 female 2007

344 rows × 8 columns

Palmer Penguins illustrations

Source: Palmer Penguins
Artwork: Illustrations by @allison_horst

A Python environment for this project

A Python environment is a separate workspace that contains its own copy of Python and any libraries you install, so your projects do not interfere with each other.

Option 1

If you know how to create a Python environment, use your preferred method and install pandas, matplotlib, and notebook.

Option 2

If you’re new to Python environments, use Cursor to setup an environment suitable fo this workshop.

We only recommend this as a fast way to get workshop notebooks running in Cursor. For long-term projects, use a more managed Python workflow (for example: pinned dependencies and reproducible environment files).

  1. In Cursor, open the project folder you created above:
    • Menu: File -> Open Folder…
    • Select your workshop project folder.
  2. Create a notebook file (.ipynb):
    • Menu: File -> New File…
    • Save it as setup_check.ipynb inside your project folder.

    If you don’t see a File -> New File menu option, Cursor might have opened in “Agents” view. Go to File -> Open Editor Window, then start over at step 1

  3. Open that notebook, then use the kernel selector in the top-right and choose Select Another Kernel….

    Step 1: Select another kernel

  4. Choose Python Environments….

    Step 2: Select Python environment

  5. Choose Create Python Environment and select Venv.

    Step 3: Choose venv type

  6. Pick the Python interpreter Cursor should use to create the environment (3.10 or higher).

    Step 4: Select Python interpreter

  7. Install workshop packages in the new environment:
    • Menu: Terminal -> New Terminal
    • Run the command below in the terminal pane.
    pip install pandas matplotlib notebook
  8. Quick check in a notebook cell:
    • Near the top of the setup_check.ipynb file, click the + Code button to add a new code block.
    • Enter the code below, then click the triangle icon to run it.
    import pandas as pd
import matplotlib.pyplot as plt
print("Setup worked!")

If the kernel does not switch automatically, reopen the kernel picker and select the newly created .venv interpreter.

Quick start workshops

  1. Workshop 1: Fundamentals
  2. Workshop 2: Data analysis & visualization
  3. Workshop 3: Building with AI

Workshops build on each other, but you can go at your own pace if you prefer.

View in GitHub

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