Web scraping — Python Technical Interview Questions & Answers

Web scraping

Web scraping

  • Web scraping is a specialised library. Say what job it does in one line, then one tiny call you would write. Don’t fake a full ML pipeline on a fresher page.
  • Web scraping: PyTorch = tensors/models. OpenCV = images. SimpleImputer = fill missing numbers. Folium = maps. Spark/MLlib = big data. nsetools = market quotes. Grid search = try hyper-parameters. scraping = fetch HTML then parse.
  • Viva for Web scraping: purpose + one function name + one caution (API keys, missing values, robots.txt).
  • Web scraping — output: Asha. Real scraping uses requests + BeautifulSoup. Respect robots.txt.

Technical Interview Tip: What it does + one API name + one caution.

Code Examples for Web scraping

Example

# Web scraping
html = "<h1>Asha</h1>"
start = html.find(">") + 1
end = html.find("</")
print(html[start:end])

Web scraping — output: Asha. Real scraping uses requests + BeautifulSoup. Respect robots.txt.

How to Explain Web scraping to an Interviewer

Explain Web scraping as if you are teaching a junior — definition, then one tiny script.

Web scraping — one job, one library. Web scraping — purpose + one call.

What does the example print, and why?

Web scraping — output: Asha

What mistake do freshers make with Web scraping?

Web scraping — copying a huge notebook without explaining one line.