Python · Try it
Part One
One Card Becomes One Dictionary
The natural unit of scraped data is a dictionary. One article card becomes one dictionary with named fields like title, link, author, and published_at.
This uses basic Python you already know: strings, variables, dictionaries, and function calls. Scraping is not a new language. It is ordinary Python applied to HTML.
Part Two
Many Cards Become a List of Dicts
The next step is a loop: find all article cards, create one dictionary per card, and append() each dictionary to a list.
Python · Try it
This is the core data-collection loop of the whole book: find many containers, loop over them, build dictionaries, append them to a list.
Books 1, 2, and 3 all appear here: variables, list creation,
for loops, if guards, dictionaries, and append(). The scraper works because these basic tools combine well.
Part Three
List of Dicts to DataFrame
Pandas reads a list of dictionaries directly. Each dictionary becomes one row. Each key becomes one column.
Python · Try it
Why DataFrames matter: once the data is in pandas, filtering, sorting, exporting, and later analysis become much easier.
Part Four
Saving the First CSV
A DataFrame can be written to disk with to_csv(). This is the point where scraping becomes useful outside the notebook.
Python · Copy to your notebook
The CSV file will contain one row per article and two columns: title and link.
Make links absolute: many sites use relative links like
/society/.... Add the base URL before saving so the CSV contains complete links that work everywhere.
Part Five
Your Turn — Scrape One Page
The snapshot below contains four article cards. Extract the titles and links, build a DataFrame, and preview the CSV text.
Python · Your turn
What you learned in this chapter: how one article becomes one dictionary, how many dictionaries become a list, how pandas turns that list into a DataFrame, and how to save a one-page scraper to CSV. In the final chapter you will repeat the same idea across page 1, page 2, page 3, and beyond.
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