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Tutorials›Python›Comprehensions
Data Structures · Chapter 14

Comprehensions

Build collections from other collections with concise, readable expressions.

Why this matters

Comprehensions can express simple transformations clearly when a full loop would add noise.

Start with the idea

A comprehension combines iteration, transformation, and optional filtering into one expression. Use it when the transformation remains easy to read.

IdeaUnderstand the purpose
→
CodeRun the smallest example
→
PracticeChange it yourself
SubjectVision learning pattern: understand the idea before memorizing syntax.

Small working example

Run this example first. Do not change several things at once; confirm the basic behavior, then experiment.

Python 
numbers = [1, 2, 3, 4]
squares = [n * n for n in numbers if n % 2 == 0]
print(squares)
Expected output
[4, 16]
What to notice

The example is intentionally small. Focus on the chapter’s main idea before adding extra syntax or framework code.

Common beginner mistake

Mistake

Packing complex business logic into a long comprehension just to make code shorter.

Better approach

Use a normal loop when several steps, branches, or side effects are involved.

Quick recap

  • Comprehensions create collections.
  • They can transform and filter.
  • Readability matters more than compactness.

Try it yourself

These are deliberately small. If you can complete them without copying the example, you are ready to continue.

  1. Create a list of uppercase names.
  2. Create a dictionary mapping numbers to squares.