How do generators work, and when are they useful in Python?

Explains suspended generator execution, lazy production, deferred failures, cleanup, and one-pass consumption.

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A function containing yield returns a generator iterator when called. The body does not run to completion immediately. Each request for the next value resumes execution until the next yield, preserving local state between resumptions. When the function returns, iteration ends. Generators are useful for pipelines, large inputs, and values produced over time because callers can process items without first building the entire result in memory.

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A practical model of generator functions and judgment about streaming, resource lifetime, and eager versus lazy work.

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Generators are useful because they express stateful lazy iteration without manually implementing the iterator protocol.
A function containing yield returns a generator iterator when called. The body does not run to completion immediately. Each request for the next value resumes execution until the next yield, preserving local state between resumptions. When the function returns, iteration ends. Generators are useful for pipelines, large inputs, and values produced over time because callers can process items without first building the entire result in memory.
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Laziness changes timing: validation, I/O, and exceptions occur when the generator is consumed, not necessarily when it is created. A generator is normally single-use, so a second loop after exhaustion is empty. can delegate to another iterable. Cleanup in a block can run when the generator completes or is closed, but relying on eventual garbage collection for scarce resources is weaker than giving the consumer a clear context-managed lifetime.

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Saying a generator computes all values when called.
Assuming laziness always makes code faster.
Reusing an exhausted generator.
Returning a resource-backed generator after closing its owner.

How do generators work, and when are they useful in Python?

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