Snake in the Stacks: Python Hunting for Anagrams in the Library

Imagine you're helping refine the digital catalog system at your local library. One morning, our favorite librarian, Mrs. Robin, sighs and says:

"We keep getting duplicate book entries because titles are slightly different — like ‘Friend’ vs ‘friend’ or ‘Finder’ vs ‘finder.’ Is there a better way to match titles that look like they contain the same letters? Does this all have to be done manually? I’m gonna need thicker glasses and probably grow a few more white hairs."

The Mission: Detect Anagram-Like Duplicates

Your goal? Write a tool that loops through book titles, compares them in pairs, and flags titles that are made of the same letters — even if they’re typed differently.

What You Build

You write this Python function:

This simple function powers a script that scans the catalog, normalizes casing when needed, and catches subtle duplicates.

• Comparing them in pairs

• Flagging titles like "Finder" and "friend" as potential duplicates since they consist the same letters.

Mrs. Robin is thrilled:

“Now we can clean up this catalog without losing our sanity. I’ll save my glasses budget for something more exciting, perhaps a new hardcover book!”

What Concepts You Used (and Why These Matter):

Takeaway:

Sometimes, learning to write one thoughtful Python function is the first step to making digital experiences just a little less cluttered and more delightful for others around us.

3 common uses of this same logic in our everyday interactions:

• E-commerce: matching products with typo-filled titles

• Search engines: enabling fuzzy matching and tolerant queries

• AI/ML preprocessing: normalizing text input before analysis