Working with APIs and Virtual Environments

Virtual environments

python -m venv venv
source venv/bin/activate   # on Windows: venvScriptsactivate
pip install requests

A virtual environment keeps one project’s installed packages isolated from every other project on your machine. Without one, installing a specific version of a package for one project can silently break a completely different project that depends on a different version of the same package. Always create one per project rather than installing packages globally — this is standard practice on every professional Python team.

Calling a real API

import requests

response = requests.get("https://api.github.com/users/octocat")
data = response.json()
print(data["name"], data["public_repos"])

requests is the most widely used third-party library in the Python ecosystem for making HTTP calls. response.json() automatically parses a JSON response body into a Python dictionary, so you can access fields the same way you would with any dictionary you built yourself.

Checking the status code

response = requests.get("https://api.github.com/users/octocat")
print(response.status_code)   # 200 means success

if response.status_code == 200:
    data = response.json()
else:
    print(f"Request failed with status {response.status_code}")

Never assume an API call succeeded just because your code didn’t crash — a 404 or 500 response still returns normally as far as Python is concerned, so checking status_code explicitly is essential before trusting the response body.

Handling errors gracefully

try:
    response = requests.get("https://api.example.com/data", timeout=5)
    response.raise_for_status()
    data = response.json()
except requests.exceptions.RequestException as e:
    print(f"Request failed: {e}")

A timeout stops your program from hanging indefinitely if a server never responds, and raise_for_status() automatically raises an exception for any error status code, so you can catch every kind of failure — timeouts, connection errors, and bad status codes — in one place.

You’ve completed the course

From your first print() statement to calling real APIs with proper error handling and isolated environments — you now have a professional-level Python foundation. Take the certification assessment next, or apply to an internship to put it to work on a real project.