How to Lazily Transform Items in Python with a Generator

Map a transform function over an iterable lazily with a generator so items are processed on demand, not up front.

Easy Python 3.9+ Aug 9, 2026 Comprehensions & generators 14 views 0 copies

generators lazy evaluation mapping iteration

Python code

24 lines

Python 3.9+

def lazy_map(items, transform):
    for item in items:
        yield transform(item)

def double(x):
    return x * 2

def upper(s):
    return s.upper()

if __name__ == "__main__":
    numbers = [1, 2, 3, 4, 5]
    doubled = lazy_map(numbers, double)
    print("Doubled numbers:", end=" ")
    for value in doubled:
        print(value, end=" ")
    print()

    words = ["apple", "banana", "cherry"]
    uppercased = lazy_map(words, upper)
    print("Uppercased words:", end=" ")
    for word in uppercased:
        print(word, end=" ")
    print()

Output

stdout

Doubled numbers: 2 4 6 8 10 
Uppercased words: APPLE BANANA CHERRY

How it works

The lazy_map function is a generator because it contains a yield statement. Each call to next() on the generator runs the loop body until the next yield, computes transform(item), and pauses. This means transform is applied one item at a time only as you iterate, so memory usage stays flat even for huge inputs. The function works with any iterable and any callable, keeping the mapping logic reusable. This mirrors the behavior of Python's built-in map, which also produces items lazily.

Common mistakes

Forgetting that a function with `yield` returns a generator object, not a list, so you can't index into it
Calling `list()` on the generator too early and losing the memory benefit
Assuming the transformation runs when `lazy_map` is called — it only runs during iteration

Variations

Use the built-in `map(transform, items)` instead of a custom generator
Write a generator expression: `(transform(item) for item in items)`

Real-world use cases

Processing rows from a large CSV file one at a time to avoid loading the whole file into memory at once.
Chaining lazy transforms on infinite or streaming data like a live sensor feed without exhausting RAM.
Applying an expensive transformation only to items actually accessed, e.g., in a paginated API response handler.

How to Lazily Transform Items in Python with a Generator

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

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Real-world use cases

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