Data Collection, Requests, and Web Scraping · Lesson 1 of 1

Data Collection, Requests, and Web Scraping

Collect data from permitted sources and parse HTTP, HTML, and structured responses responsibly.

Lesson

1. Introduction

Data collection determines what can be learned later. An official API or published dataset is usually more stable than scraping page markup. Collection must respect access permissions, terms, privacy, and rate limits.

2. Core concepts

HTTP requests have status codes, headers, and bodies. HTML describes page structure; a parser can find elements by semantic attributes, but page markup changes. Validate extracted records against an expected schema and preserve timestamps and source provenance.

3. Topics covered in this module

Collection strategy, APIs, CSV/JSON, sampling, and provenance
HTTP requests, status codes, timeouts, headers, and pagination
HTML elements, attributes, selectors, and Beautiful Soup parsing
Robots rules, terms, rate limits, privacy, and robust failure handling

4. Syntax and how to read it

import requests
from bs4 import BeautifulSoup

response = requests.get('https://example.com/catalog', timeout=10)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')

The timeout prevents an indefinitely blocked request, while raise_for_status surfaces HTTP errors. Only collect from sources where access is permitted.

5. Practical workflow

Prefer documented interfaces, request only permitted fields, set timeouts, and handle non-success responses. Parse into a stable schema, validate a sample against the source, and stop rather than bypassing access restrictions.

6. Common mistakes

Ignoring terms, robots guidance, privacy, or the server’s rate limits.
Assuming a selector always matches or treating an HTTP error page as valid data.

7. Best practices

Use documented APIs, conservative request rates, and clear provenance.
Validate field counts and stop on schema changes rather than silently producing empty data.

8. Practice task

A request times out once and then succeeds. Name two protections to keep in a production collector.

9. One possible solution

Keep an explicit timeout and bounded retry/backoff policy; also log safe source/status metadata and respect service rate limits.

10. Summary

Collect data from permitted sources and parse HTTP, HTML, and structured responses responsibly. Use documented APIs, conservative request rates, and clear provenance. Validate field counts and stop on schema changes rather than silently producing empty data.

11. What comes next

The next module, SQL for Data Science, builds on this foundation and applies it to a new part of the analytics workflow.

Visual guide

A reliable Data Collection, Requests, and Web Scraping workflow

Step 01 Frame
Define the question, input scope, and intended output.
Step 02 Work
Apply the method and validate assumptions as you go.
Step 03 Check
Compare results with known cases and communicate limitations.

Code examples

Request JSON with a timeout

PYTHON

import requests

response = requests.get('https://api.example.com/items', timeout=10)
response.raise_for_status()
data = response.json()
print(len(data))

Expected output

Prints the number of returned items after a successful response.

Select a semantic HTML element