Generate Data with Python Comprehensions and Generators

Shows list, dict compregensions and generator expressions plus a Fibonacci generator to produce data lazily.

Easy Python 3.9+ Aug 9, 2026 Comprehensions & generators 15 views 0 copies

comprehensions generators lazy-evaluation yield itertools

Python code

33 lines

Python 3.9+

# Data generation helpers using comprehensions and generators
from itertools import islice


def fibonacci(limit):
    """Generate Fibonacci numbers up to a limit."""
    a, b = 0, 1
    while a <= limit:
        yield a
        a, b = b, a + b


def main():
    # List comprehension: squares of even numbers
    squares = [x ** 2 for x in range(1, 11) if x % 2 == 0]
    print(f"Even squares: {squares}")

    # Dictionary comprehension: number -> its cube
    cubes = {x: x ** 3 for x in range(1, 6)}
    print(f"Cubes dict: {cubes}")

    # Generator expression: filter odd numbers, take first 5
    odds = (x for x in range(1, 100) if x % 2 != 0)
    first_five_odds = list(islice(odds, 5))
    print(f"First 5 odds: {first_five_odds}")

    # Use the fibonacci generator
    fib_numbers = list(fibonacci(50))
    print(f"Fibonacci up to 50: {fib_numbers}")


if __name__ == "__main__":
    main()

Output

stdout

Even squares: [4, 16, 36, 64, 100]
Cubes dict: {1: 1, 2: 8, 3: 27, 4: 64, 5: 125}
First 5 odds: [1, 3, 5, 7, 9]
Fibonacci up to 50: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

How it works

List comprehensions build a full list in memory, while generator expressions produce items lazily, saving memory for large data. The dictionary comprehension constructs a key-value mapping directly. The fibonacci function uses yield to create a generator that produces values on demand, ideal for infinite sequences. Using itertools.islice limits the generator to the first five odd numbers without consuming the whole range.

Common mistakes

Forgetting parentheses around generator expressions when outputting to list()
Using a list comprehension when a generator would save memory for large data
Not resetting a generator before iterating it again—it gets exhausted
Missing the `yield` keyword in a generator function, turning it into a regular return

Variations

Use a set comprehension `{x for x in range(10)}` to get unique values
Use `range(1, 100, 2)` to generate odds directly without filtering

Real-world use cases

Creating lookup tables by mapping IDs to computed values for quick access in production code.
Generating test data streams lazily for performance testing without loading everything into memory.
Feeding an infinite Fibonacci sequence to algorithms that need a tail of the series.

Generate Data with Python Comprehensions and Generators

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from Comprehensions & generators

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