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Python · Theory

Web scraping

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Theory

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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.

Exam tip

What it does + one API name + one caution.

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.

Short notes

  • DefWeb scraping — one job, one library.
  • RuleWeb scraping — purpose + one call.
  • TrapWeb scraping — copying a huge notebook without explaining one line.

Questions

1

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

2

What does the example print, and why?

3

What mistake do freshers make with Web scraping?

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