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What is a generator function in Python?
A generator function is a function that contains a yield expression. Unlike an ordinary function call that runs and returns a result, calling a generator function returns a generator iterator; its body starts when that iterator is advanced. The Python Language Reference describes it simply: “When a generator function is called, it returns an iterator known as a generator.” (Python Language Reference)
A generator is one kind of iterator, but not every iterator is a generator. A generator is typically used as a single-pass sequence: after it is exhausted, create a new generator by calling the function again if you need to produce the values again.
What does yield do?
yield produces a value and suspends execution of the generator. When the generator is advanced again, execution continues after the yield with its local variables and other execution state preserved. The Python Glossary says each yield “temporarily suspends processing, remembering the execution state (including local variables and pending try-statements).” (Python Glossary)
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That makes yield different from return: yield hands a value to the caller and pauses; return ends the generator. A generator can yield several values over its lifetime, then finish.
Practical example: generate values with a function
def count_up_to(limit):
number = 1
while number <= limit:
yield number
number += 1
for value in count_up_to(3):
print(value)The loop prints 1, 2, and 3. Calling count_up_to(3) creates the generator; the for loop advances it. Each time execution reaches yield number, the current number is produced and the function pauses. On the next iteration, it resumes and increments number.
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Consume a generator with for or next()
A for loop is the usual way to consume generated values. It requests values until the generator finishes and handles the end-of-iteration signal for you.
Use next() when you want to advance an iterator explicitly:
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def count_up_to(limit):
number = 1
while number <= limit:
yield number
number += 1
gen = count_up_to(2)
print(next(gen)) # 1
print(next(gen)) # 2
# Another next(gen) raises StopIterationWhen the function exits without yielding another value, advancing the generator raises StopIteration. A normal for loop treats that as completion. The generator is then exhausted; it does not restart automatically. (Python Language Reference)
Generator expression or list comprehension?
Choose based on whether you need a list immediately or can consume values as they are produced. A list comprehension builds a list; a generator expression produces an iterator that yields the corresponding values as needed.
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| Choice | Example | What it produces | Use it when |
|---|---|---|---|
| List comprehension | [number * number for number in range(10)] |
A materialized list of all results | You need the complete list, for example to index it or reuse it. |
| Generator expression | (number * number for number in range(10)) |
An iterator that yields results when consumed | Downstream code can process values sequentially rather than requiring the whole result at once. |
| Generator function | A function containing one or more yield expressions |
A generator iterator with state and step-by-step logic | Producing values requires multiple statements, branching, or retained state. |
For example, if a consumer can handle each square in turn, a generator expression can avoid materializing the full result at once. That is a memory-use option, not a promise that generators are always faster. (Python Functional Programming HOWTO)
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Delegate values with yield from
Use yield from when a generator should pass values from another iterable or subgenerator through to its caller:
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def combined(first, second):
yield from first
yield from secondWhen the caller advances combined(first, second), it receives the values from first, followed by those from second. The yield from expression delegates to the iterable; when the delegated subgenerator completes, its return value can become the value of that expression. Delegation also passes through generator control methods such as send() and throw() when the underlying iterator supports them. (Python Language Reference)
Advanced: send a value into a generator
Generators can also receive data. The first advancement starts the generator and reaches its first yield. A later send(value) resumes execution, and the suspended yield expression evaluates to that sent value.
def running_total():
total = 0
while True:
value = yield total
if value is None:
return
total += value
gen = running_total()
print(next(gen)) # 0; starts the generator
print(gen.send(5)) # 5
print(gen.send(3)) # 8
gen.send(None) # ends the generatorThis pattern is useful when a generator needs input as well as producing output, but ordinary iteration is simpler for most value-producing tasks. (Python Functional Programming HOWTO)
Keep asynchronous generators distinct
The examples above use synchronous def functions and ordinary for loops. An async def function containing yield defines an asynchronous generator, which is consumed through asynchronous iteration rather than an ordinary for loop. (Python Language Reference)
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Further reading
For a deeper treatment of iterators, generators, lazy evaluation, and yield from, see Fluent Python, 2nd Edition by Luciano Ramalho. O’Reilly classifies it as intermediate to advanced; Chapter 17 covers these topics.
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