+++ slug = "comprehension-patterns" title = "Comprehension Patterns" section = "Collections" summary = "Comprehensions can use multiple for clauses and filters when the shape stays clear." doc_path = "/tutorial/datastructures.html#list-comprehensions" see_also = [ "comprehensions", "generator-expressions", "for-loops", ] +++

Comprehensions can contain more than one for clause and more than one if filter. The clauses are read in the same order as nested loops.

Use these forms only while the shape remains easy to scan. If a comprehension starts needing several names, comments, or branches, an explicit loop is usually better.

Nested comprehensions build concrete collections immediately, just like simpler list, dict, and set comprehensions.

:::program

colors = ["red", "blue"]
sizes = ["S", "M"]
variants = [(color, size) for color in colors for size in sizes]
print(variants)

numbers = range(10)
filtered = [n for n in numbers if n % 2 == 0 if n > 2]
print(filtered)

:::

:::cell Multiple for clauses behave like nested loops. The leftmost for is the outer loop, and the next for runs inside it.

colors = ["red", "blue"]
sizes = ["S", "M"]
variants = [(color, size) for color in colors for size in sizes]
print(variants)

[('red', 'S'), ('red', 'M'), ('blue', 'S'), ('blue', 'M')]

:::

:::cell Multiple if clauses filter values. They are useful for simple conditions, but an explicit loop is clearer when the rules need names or explanation.

numbers = range(10)
filtered = [n for n in numbers if n % 2 == 0 if n > 2]
print(filtered)

[4, 6, 8]

:::

:::note

• Read comprehension clauses from left to right.
• Multiple for clauses act like nested loops.
• Prefer an explicit loop when the comprehension stops being obvious. :::