generator

A Python generator is an iterator that produces values lazily. Since it doesn’t store all values in memory at once, you need to consume it to visualize its contents. Here are some ways to visualize a generator:

def my_generator():

    for i in range(5):

        yield i**2

gen = my_generator()

print(list(gen))  # Convert to a list for visualization

Output:

[0, 1, 4, 9, 16]

Note: Once a generator is consumed, it cannot be reused.

the range function returns a generator object.

x = range(5)

print(x)

print(type(x))

results in:

range(0, 5)

<class 'range'>

so you can see that ,while we can visualise the range function as returning something like a list, it is not a list but a generator!

Why Can't a Generator Be Reused Once Consumed?

A generator in Python is an iterator that produces values on demand and does not store them in memory. It generates each value when requested and forgets it once it moves to the next value. This makes generators efficient for working with large or infinite sequences.

However, once a generator is exhausted, it cannot be reset or reused without recreating it.

def my_generator():

    for i in range(3):

        yield i

gen = my_generator()

print(list(gen))  # Consumes the generator completely

print(list(gen))  # No output, because it's already exhausted

Output:

[0, 1, 2]

[]

Here, the second print(list(gen)) gives an empty list because the generator has already been exhausted.

What Happens Internally?

Each generator maintains an internal state, which tracks where it is in its iteration process. When all values are yielded, the generator reaches a "StopIteration" state, meaning it is done.

Once a generator is exhausted, calling it again will not restart it.

How to Reuse a Generator?

If you need to iterate multiple times, you have a few options:

1. Create a New Generator Instance

gen1 = my_generator()  # First generator

print(list(gen1))  # Consume it

gen2 = my_generator()  # Create a new one

print(list(gen2))  # Works again

2. Convert to a List (If Memory Allows)

values = list(my_generator())  # Store values in a list

print(values)  # Use multiple times

print(values)  # Still works

This is useful if the generator produces a small, finite number of values.

3. Use itertools.tee() to Create Multiple Iterators

If you need to iterate over the same generator multiple times without storing all values upfront, use itertools.tee():

from itertools import tee

gen = my_generator()

gen1, gen2 = tee(gen)  # Creates two independent iterators

print(list(gen1))  # Works

print(list(gen2))  # Also works

⚠️ Warning: tee() buffers values in memory, so it may not be ideal for very large generators.

When Does This Matter?

• If you pass a generator to a function that consumes it, you cannot use it again afterward.

• If you need multiple passes over the data, use list(), tee(), or recreate the generator.

• If you're working with large data, avoid list() and prefer re-creating the generator.