What are Python generators? Provide an example.

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Python generators are a special type of iterator that allow you to iterate over data lazily, meaning they generate values on the fly and do not store the entire sequence in memory. They are defined using functions with the yield keyword, which pauses the function’s execution and saves its state, allowing it to resume later from where it left off.

This makes generators memory-efficient, especially for large datasets or infinite sequences, because values are produced only when needed.

Example:

• count_up_to is a generator function.

• Each call to yield produces the next value.

• The function’s state (local variables, execution point) is saved between calls, so it continues from where it left off.

Benefits of generators:

• Efficient memory usage: Only one item is in memory at a time.

• Improved performance: Especially useful when working with large datasets, like reading large files or streaming data.

• Readable code: They provide a clear, concise way to write custom iterators.

Generators are commonly used in loops, pipelines, or anywhere lazy evaluation is beneficial.

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