Python generators offer an innovative approach to iterating over large datasets efficiently, using the yield statement to produce values one at a time rather than returning all values at once. This on-demand generation minimizes memory usage and enhances data processing capabilities. Unlike regular functions, generators maintain their state, allowing execution to pause and resume seamlessly. Their independent stack frames ensure that the execution context remains stable, providing a powerful tool for managing large sequences without overwhelming memory resources.
Python generators provide an elegant mechanism for handling iteration, particularly for large datasets where traditional approaches may be memory-intensive.
The defining characteristic is the yield statement, which fundamentally alters the function's execution model.
What makes generators special is their ability to pause execution and preserve state. When a generator reaches a yield statement, execution pauses.
Generators operate with independent stack frames, meaning their execution context remains intact between successive calls.
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