Lambda

Python · Reference cheat sheet

📋 Overview

A lambda creates a small anonymous function from a single expression. Use for short callbacks (sort keys, map/filter) when a full def would be noise. Prefer named def for anything non-trivial or multi-statement.

🔧 Core concepts

Lambdas cannot contain return, assignments (except walrus in 3.8+ inside expr), or multiple statements. Annotations on lambdas are awkward — prefer def.

💡 Examples

Sort and group keys:

rows = [("ada", 3), ("bob", 1), ("cy", 2)]
rows.sort(key=lambda pair: pair[1])
# [("bob", 1), ("cy", 2), ("ada", 3)]

With map / filter (often better as comprehension):

nums = [1, 2, 3, 4]
doubled = list(map(lambda n: n * 2, nums))
evens = list(filter(lambda n: n % 2 == 0, nums))
# Prefer: [n * 2 for n in nums], [n for n in nums if n % 2 == 0]

Callable default / partial-like:

ops = {
    "add": lambda a, b: a + b,
    "mul": lambda a, b: a * b,
}
print(ops["add"](2, 3))

Capture loop variable correctly:

# Wrong: all lambdas see final i
bad = [lambda: i for i in range(3)]
# Right: default-arg bind
good = [lambda i=i: i for i in range(3)]
print([f() for f in good])  # [0, 1, 2]

⚠️ Pitfalls

• Do not stuff complex logic into lambdas — readability collapses.
• Late binding in closures: loop variables need default-arg capture.
• lambda has no good docstring / name in stack traces (<lambda>).
• Prefer comprehensions over map/filter + lambda in most Python code.
• Cannot assign annotations cleanly; use def for public APIs.

🔗 Related

• Functions
• Closures
• Decorators
• Zip / enumerate / map / filter
• Comprehensions
• Walrus