Key characteristics of generator functions in Python Programming #3
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Key characteristics of generator functions:
Lazy Evaluation:
Generator functions employ lazy evaluation, producing values on-demand. This means that the next value in the sequence is generated only when requested, conserving memory by not storing the entire sequence in memory at once.
State Retention:
The state of a generator function is retained between successive calls. When a generator function encounters a yield statement, it temporarily suspends its execution, preserving its state. Upon the next iteration, it resumes execution from where it left off.
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Efficient Memory Usage:
Generators are memory-efficient as they generate values one at a time, making them suitable for handling large datasets or streams of data. This is particularly advantageous when memory constraints are a concern.
Infinite Sequences:
Generator functions are well-suited for representing infinite sequences or streams of data. Since values are generated on-the-fly, a generator can theoretically produce an infinite number of values without consuming infinite memory.