Can You Rejuvenate a Python Generator Object for Reuse?

Rejuvenating Generator Objects in Python

Encountering a generator object that has yielded multiple values, you may seek to rejuvenate it for reuse. This pursuit stems from the desire to avoid the time-consuming preparation associated with generator creation.

Unfortunately, unlike the ouroboros, a generator cannot regenerate itself. However, several strategies offer respite:

Option 1: Reviving the Generator

Like a phoenix, you can resurrect the generator by invoking its parent function again:

<code class="python">y = FunctionWithYield()
for x in y: print(x)
y = FunctionWithYield()
for x in y: print(x)</code>

This approach ensures fresh computations but comes at the price of repeating expensive preparation steps.

Option 2: Embracing Preservation

Embracing preservation, you can store the generator's results in a data structure that allows multiple iterations:

<code class="python">y = list(FunctionWithYield())
for x in y: print(x)
# Can iterate again:
for x in y: print(x)</code>

While this method safeguards against repetitive computations, it incurs storage overhead.

Balancing Memory and Processing

The choice between these options presents the classic tradeoff between memory and processing. Option 1 sacrifices processing time while Option 2 burdens memory.

Additional Insight

Although tee, as suggested by others, provides functionality that resembles memory buffering, it still incurs the same storage overhead and performance characteristics as Option 2.

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