Generator functions are used to generate values on-demand, one at a time, which makes them memory-efficient and ideal for processing extensive or infinite datasets.

While generators excel at sequential data processing, they maintain an inherent state. Once a generator object is exhausted, iterating over it again produces no further values.

However, there are scenarios where reiterating over the same sequence becomes necessary, whether due to changing requirements or the need for multiple passes over the data. In such cases, resetting the generator object becomes crucial. In this article, we will see the different ways to reset the generator object in Python.

Recreating the generator object

The simplest way to reset a generator object is by recreating it. By calling the generator function or expression again, a fresh generator object is created, ready for a new iteration. Following is the example:

def countdown(n):
    while n > 0:
        yield n
        n -= 1

# Create the generator object
countdown_gen = countdown(5)

# Perform the initial iteration
print("Initial iteration:")
for num in countdown_gen:
    print(num, end=" ")
# Output: 5 4 3 2 1

# Recreate the generator object
countdown_gen = countdown(5)

# Reset the iteration
print("\n\nReset iteration:")
for num in countdown_gen:
    print(num, end=" ")
# Output: 5 4 3 2 1

Reset the generator using itertools.tee()

As an alternative way, we can also use the itertools.tee() function to create multiple independent iterators from a single iterable. It is primarily used for creating independent streams but it can also be used to create a fresh iterator for a generator object.

Following is the example:

import itertools

# Create the generator object
countdown_gen = countdown(5)

# Create a new iterator from the generator object
iterator, _ = itertools.tee(countdown_gen)

# Reset the iteration
print("Reset iteration:")
for num in iterator:
    print(num, end=" ")
# Output: 5 4 3 2 1

In the above example, we used the itertools.tee() to create a new iterator from the generator object and achieved a reset of the iteration without recreating the generator itself.

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