Great 👍 Let’s turn the theory into lab-style exercises that students can run in either a Python module (.py) or a Jupyter notebook (.ipynb) inside Cursor.

I’ll scaffold these in increasing difficulty, with clear instructions + expected outcomes.

🔬 API Practice Labs

🧪 Lab 1 — Your First GET Request (Public API)

Goal: Call a free API and print the JSON response.

Steps

1. Install dependencies: uv add requests
2. Create a file lab1_get.py (or a notebook cell).
3. Paste: import requests url = "https://api.chucknorris.io/jokes/random" response = requests.get(url) print("Status:", response.status_code) print("JSON:", response.json()) print("Joke:", response.json()["value"])

Expected outcome:

• Status code 200
• A JSON object with fields like {"icon_url": "...", "value": "Chuck Norris joke"}
• Prints one random Chuck Norris joke.

🧪 Lab 2 — Using JSON data in Python

Goal: Work with API response data.

Steps

Modify Lab 1 code to:

joke_data = response.json()
print("Joke length:", len(joke_data["value"]))

Expected outcome:

• Prints how many characters long the joke is.

🧪 Lab 3 — Introducing Environment Variables

Goal: Store and retrieve a fake key.

Steps

1. Create a .env file: TEST_KEY=supersecret123
2. Install dotenv: uv add python-dotenv
3. Code: import os from dotenv import load_dotenv load_dotenv() key = os.getenv("TEST_KEY") print("Loaded key:", key)

Expected outcome:

• Program prints: Loaded key: supersecret123

🧪 Lab 4 — Calling OpenAI API

Goal: Make a chat completion request.

Steps

1. Sign up at platform.openai.com, get API key.
2. Add to .env: OPENAI_API_KEY=sk-xxxxx...
3. Install: uv add openai
4. Code: import os from dotenv import load_dotenv from openai import OpenAI load_dotenv() client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) messages = [ {"role": "system", "content": "You are a tutor."}, {"role": "user", "content": "Explain photosynthesis in 3 lines."} ] response = client.chat.completions.create( model="gpt-4o-mini", messages=messages ) print(response.choices[0].message.content)

Expected outcome:

• Prints a short 3-line explanation of photosynthesis.

🧪 Lab 5 — Send an Email with SendGrid

Goal: Use API + environment variable to send an email.

Steps

1. Create a SendGrid account, get API key.
2. Add to .env: SENDGRID_API_KEY=SG.xxxxx
3. Install: uv add sendgrid
4. Code: import os from dotenv import load_dotenv from sendgrid import SendGridAPIClient from sendgrid.helpers.mail import Mail load_dotenv() sg = SendGridAPIClient(os.getenv("SENDGRID_API_KEY")) message = Mail( from_email="you@example.com", to_emails="friend@example.com", subject="API Lab Test Email", html_content="<strong>This is a test email sent via SendGrid API</strong>" ) response = sg.send(message) print("Status:", response.status_code) # should be 202

Expected outcome:

• Console prints Status: 202.
• Recipient receives your test email.

🧪 Lab 6 — Handle API Errors

Goal: Learn to handle failures gracefully.

Steps

1. Intentionally break your .env key (e.g., rename SENDGRID_API_KEY → SENDGRID_API_KEY_WRONG).
2. Run the SendGrid script.

Modify code:

try:
    response = sg.send(message)
    print("Status:", response.status_code)
except Exception as e:
    print("Error:", e)

Expected outcome:

• Error is caught and printed instead of crashing.

📝 Suggested Student Assignment

Build a script that:

1. Calls the Chuck Norris API for a joke.
2. Calls the OpenAI API to summarize that joke in 5 words.
3. Sends the summary via SendGrid email to yourself.

(This combines GET request, JSON parsing, env keys, and two client libraries into one practical pipeline.)

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