Is the Generators lesson free?

Yes. The first 5 lessons on PyRun are free forever — no signup, no card. Generators runs real Python in your browser via Pyodide.

How long does the Generators lesson take?

About 8 minutes end-to-end. It's a hands-on lesson — you read a paragraph, edit the code, and run it in the same tab.

Do I need to install Python to try the Generators lesson?

No install required. PyRun runs real CPython in your browser via Pyodide (Python compiled to WebAssembly). Works on Chrome, Firefox, Safari, and Edge — desktop or mobile.

What level is the Generators lesson?

Intermediate level. Best after finishing the Beginner track (syntax, control flow, functions, data types).

← All lessons

Intermediate·8 min·intermediate · iteration

Generators

You’ll be able to

Write generator functions with yield
Save memory by iterating lazily instead of building lists
Chain generators for data-pipeline patterns
Convert generators to lists when needed

Why this matters

Generators power itertools, asyncio's original coroutine model, Django QuerySet iteration, and every streaming ETL job. They're the reason you can process a 50GB log file on a laptop — and a common interview probe for memory awareness.

Common pitfalls

Iterating a generator twice — it's exhausted after the first pass; wrap in list() or rebuild if you need reuse.
Confusing yield with return — return in a generator raises StopIteration with the value, not a plain result.
Building a generator when you need len() or indexing — you'll get TypeError; use a list or itertools.tee.

A generator function uses yield. Each yield pauses the function and hands a value to the caller. The next iteration resumes right where it left off.

Why it matters

Memory: process a billion items without holding them all
Composition: pipelines of yield are tiny and readable

Generator expression

(expr for x in seq) — like a list comprehension but lazy.

Try it

Write a generator that yields the first n square numbers.
Use a generator expression with max(...) to find the longest word in a list.

Practice

3 graded exercises

Write the code, click Check. We’ll tell you exactly what to fix.

Progress0 / 3

Exercise 1
Write a generator function first_squares(n) that yields the squares of 1..n inclusive. Then print list(first_squares(5)) — expected: [1, 4, 9, 16, 25].
Exercise 2
Use a generator expression (parens, not brackets) with sum(...) to compute the sum of squares of 1..100. Print just that total. Expected: 338350.
Exercise 3
Write a generator take_while(pred, seq) that yields items from seq until pred(x) first returns False (then stops). Test with list(take_while(lambda n: n < 5, [1, 2, 4, 5, 3])) — expected: [1, 2, 4] (stops at 5, never yields the 3).

← Previous*args and **kwargs Next →Errors & try/except

Generators

You’ll be able to

Write generator functions with yield
Save memory by iterating lazily instead of building lists
Chain generators for data-pipeline patterns
Convert generators to lists when needed

Why this matters

Common pitfalls

Iterating a generator twice — it's exhausted after the first pass; wrap in list() or rebuild if you need reuse.
Confusing yield with return — return in a generator raises StopIteration with the value, not a plain result.
Building a generator when you need len() or indexing — you'll get TypeError; use a list or itertools.tee.

A generator function uses yield. Each yield pauses the function and hands a value to the caller. The next iteration resumes right where it left off.

Why it matters

Memory: process a billion items without holding them all
Composition: pipelines of yield are tiny and readable

Generator expression

(expr for x in seq) — like a list comprehension but lazy.

Try it

Write a generator that yields the first n square numbers.
Use a generator expression with max(...) to find the longest word in a list.

Practice

3 graded exercises

Write the code, click Check. We’ll tell you exactly what to fix.

Progress0 / 3

Exercise 1
Write a generator function first_squares(n) that yields the squares of 1..n inclusive. Then print list(first_squares(5)) — expected: [1, 4, 9, 16, 25].
Exercise 2
Use a generator expression (parens, not brackets) with sum(...) to compute the sum of squares of 1..100. Print just that total. Expected: 338350.
Exercise 3
Write a generator take_while(pred, seq) that yields items from seq until pred(x) first returns False (then stops). Test with list(take_while(lambda n: n < 5, [1, 2, 4, 5, 3])) — expected: [1, 2, 4] (stops at 5, never yields the 3).