Dict & set comprehensions with conditions

Comprehensions beyond lists

Lesson 5 of 20 · 9 min · Module 2: Work with data efficiently

Comprehensions build whole collections in one readable line. The same idea works for dictionaries and sets, not just lists.

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By the end, you’ll be able to…

Write dict and set comprehensions
Filter inside a comprehension
Know when a loop is clearer

The idea

A dict comprehension looks like {key: value for item in items}; a set comprehension like {value for item in items}. Add if at the end to filter. Keep them short: when a comprehension needs several lines, a normal loop is clearer.

scores = {"Ana": 82, "Bob": 45, "Mia": 91}
passed = {name: s for name, s in scores.items() if s >= 50}
print(passed)

01Three kinds of brackets

[x for x in xs] builds a list, {x for x in xs} a set, and {k: v for k, v in pairs} a dict. Parentheses (x for x in xs) build a lazy generator instead.

words = ["sky", "Sea", "sky"]
print({w.lower() for w in words})

Output

{'sea', 'sky'}

02Filtering

An if at the end keeps only matching items: {n: s for n, s in scores.items() if s >= 50}. Filtering and transforming in one step avoids a temporary list.

03Readability first

A comprehension that fits on one line is easy to read. If you need nested loops, conditions, and side effects, write a regular for loop with clear names.

Your turn

Write invert(mapping) that turns {"ana": "web", "bob": "data", "mia": "web"} into {"web": ["ana", "mia"], "data": ["bob"]}: each value maps to the sorted list of keys that had it. Also write domains(emails) that returns the set of lowercase domains from a list of email addresses.

What the checks look for

Inverts a mapping with sorted key lists
Inverts an empty mapping
Collects lowercase domains once
Returns a set

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Take this with you

Comprehensions are concise for simple transformations; loops are clearer for complex ones.

Go a little further

Rewrite invert with a single loop and dict.setdefault, and compare the readability.