Comprehensions

Introduction

Comprehensions let you build lists, dicts, sets in one line — faster and more readable than equivalent loops. Beloved feature of Python.

Understanding the topic

Core concepts to understand:

• List: [x*2 for x in xs]
• Dict: {k: v for k, v in items}
• Set: {x for x in xs}
• Conditional: [x for x in xs if x > 0]

Syntax reference

Visual flow / code:

# List
squares = [x * x for x in range(10)]

# With filter
evens = [x for x in range(20) if x % 2 == 0]

# Dict comprehension
inv = {v: k for k, v in {"a": 1, "b": 2}.items()}

# Nested
matrix = [[i * j for j in range(3)] for i in range(3)]

# Set comp dedupes
unique = {word.lower() for word in text.split()}

Execution workflow

Real-world use

Comprehensions are the Pythonic way to transform collections. They're typically 30-50% faster than equivalent for-loops with append.

Best practices

• Prefer comprehensions over map/filter.
• Keep them one-liner readable.
• Switch to a loop when logic grows.

Common mistakes

• Nested comprehensions become unreadable — use loops instead.

Hands-on exercise

Interview preparation — practice these questions:

• List comp vs for-loop performance?
• When NOT to use a comprehension?
• What's a generator comprehension?

Summary

In summary: Comprehensions = idiomatic Python. Stop when readability suffers.