What are the performance differences between iterators and lists?

What are the performance differences between iterators and lists?

Iterators generally provide better performance for large datasets as they allow for lazy evaluation, meaning they generate items on-the-fly without loading the entire dataset into memory. Lists, on the other hand, require all elements to be stored in memory, which can lead to higher memory usage and slower performance for large collections.

Can you give an example of when to use an iterator instead of a list?

Certainly! If you're processing a large file line by line, using an iterator would be more efficient. It allows you to read one line at a time without loading the entire file into memory, which is especially useful for large files.

Are there any downsides to using iterators?

Yes, one downside is that iterators can only be traversed once. If you need to access the data multiple times, you would have to recreate the iterator or convert it to a list, which could negate the performance benefits.

How do I convert an iterator to a list in Python?

You can convert an iterator to a list in Python using the list() function. For example, my_list = list(my_iterator) will create a list from the elements produced by my_iterator.