Understanding API Documentation
Before writing code that calls an API, you need to understand what that API offers and requires. API documentation is your guide — it tells you how to authenticate, what endpoints exist, what data you'll receive, and what can go wrong. Learning to read documentation efficiently is a skill that transfers to every API you'll ever use.
What Documentation Contains
Good API documentation covers several essential areas:
Authentication: How you prove your identity. Most APIs require an API key — a secret string that identifies your application.
Endpoints: The URLs you can call and what each one does. Documentation describes the path, required parameters, and what you'll get back.
Rate Limits: How often you can make requests. Exceeding limits typically results in temporary blocks or error responses.
Errors: What can go wrong and what error codes mean. This helps you handle failures gracefully.
Reading an Endpoint
Here's what typical endpoint documentation looks like:
GET /api/v1/stock/{symbol}/quote
Path Parameters:
symbol (required): Stock ticker symbol (e.g., "AAPL")
Headers:
Authorization: Bearer {api_key}
Response 200:
{
"symbol": "AAPL",
"price": 178.52,
"timestamp": "2024-01-15T16:00:00Z"
}
Response 404:
{"error": "Symbol not found"}
This tells you everything: the URL pattern, what to send, and what you'll receive.
Authentication Patterns
APIs authenticate requests differently. The most common patterns:
API key in header:
headers = {"Authorization": "Bearer sk-abc123..."}
API key in query string:
url = "https://api.example.com/data?apikey=abc123..."
Keeping Keys Safe
Never hardcode API keys in your source code. Anyone who sees your code — including when you push to GitHub — can steal your key.
# Wrong - key exposed
api_key = "sk-abc123..."
# Right - key from environment
import os
api_key = os.environ.get("STOCK_API_KEY")
Store keys in environment variables or .env files that aren't committed to version control.
Respecting Rate Limits
APIs limit requests to prevent abuse. Before building, check the limits — they affect your design. If you're fetching multiple stocks, you might need delays between requests:
import time
for symbol in symbols:
data = fetch_stock(symbol)
time.sleep(0.5) # Respect rate limits
Using AI to Understand Docs
When documentation confuses you, AI can help interpret it. Share the relevant section and ask specific questions about authentication, endpoints, or response formats.