What are the best practices for using iterables and iterators in Python?

What are the best practices for using iterables and iterators in Python?

When using iterables and iterators in Python, it's important to follow a few best practices: 1. Use generators for large datasets to save memory. 2. Prefer built-in functions like map() and filter() for cleaner code. 3. Always implement the __iter__() and __next__() methods when creating custom iterators.

Can you explain why generators are more memory efficient?

Generators yield items one at a time and only when requested, which means they don't store the entire dataset in memory. This is especially useful for large datasets.

What are some common mistakes to avoid with iterators?

Common mistakes include: 1. Not resetting the iterator after it has been exhausted. 2. Modifying the iterable while iterating over it. 3. Confusing iterators with iterables.

How can I create a custom iterator?

To create a custom iterator, define a class with __iter__() and __next__() methods. The __iter__() method should return the iterator object itself, and __next__() should return the next value or raise StopIteration when done.