Python generators are a convenient way to create iterators. Unlike regular functions, generators are functions that return an object on which you can call next, and the generator function will resume where it left off. This allows you to create lazy iterators, which only generate the values you need, when you need them.
As an example, consider a situation where you want to create a list of the first billion numbers. Using a regular loop, this would require a lot of memory, as the list would have to be created in memory all at once. However, using a generator, you can create an iterator that will yield each number as it is needed, without ever creating the entire list in memory.
Here is an example of how you could create a list of the first billion numbers using a regular loop:
numbers = [] for i in range(1000000000): numbers.append(i)
As you can see, this code creates an empty list, and then uses a loop to append the first billion numbers to the list. This would require a lot of memory, as the entire list of a billion numbers would need to be stored in memory at once.
On the other hand, here is how you could use a generator to create the same list of numbers:
def generator():
for i in range(1000000000):
yield i
numbers = generator()
In this example, the generator function is a generator that yields each number in the sequence as it is called. The numbers variable is assigned to the result of calling the generator function, which returns an iterator on which you can call next to retrieve the next value in the sequence.
This allows you to create the list of numbers without ever having to store the entire list in memory. You can retrieve the values one at a time, as they are needed, which can be much more efficient than creating the entire list in memory all at once.
In conclusion, python generators are a convenient way to create iterators that allow you to generate values on demand, without having to store the entire sequence in memory at once. This can be a useful tool when working with large datasets, as it can help you avoid running out of memory.
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