Iterators and Generators in Python

1. Introduction

In Python, iterators and generators are essential tools for managing data streams and sequences. This lesson will cover:

• What iterators are and how to use them.
• What generators are and how they differ from iterators.
• Best practices for using these concepts effectively.

2. Iterators

An iterator is an object that implements the __iter__() and __next__() methods, allowing sequential access to its elements.

Creating an Iterator

To create a custom iterator, define a class with __iter__() and __next__() methods:

class MyIterator:
    def __init__(self, limit):
        self.limit = limit
        self.current = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.current < self.limit:
            value = self.current
            self.current += 1
            return value
        else:
            raise StopIteration

iterator = MyIterator(5)
for num in iterator:
    print(num)  # Outputs: 0, 1, 2, 3, 4
        

3. Generators

Generators are a simpler way to create iterators using the yield statement. Instead of returning a value and terminating, yield saves the state of the function and allows it to resume from where it left off.

Creating a Generator

Here's how to create a generator function:

def my_generator(limit):
    current = 0
    while current < limit:
        yield current
        current += 1

for num in my_generator(5):
    print(num)  # Outputs: 0, 1, 2, 3, 4
        

4. Comparison

While both iterators and generators can be used to iterate over data, they have key differences:

5. Best Practices

Here are some best practices when using iterators and generators:

• Use generators for large datasets to save memory.
• Keep iterator classes simple and focused on a single responsibility.
• Always handle StopIteration exceptions properly when using iterators.

6. FAQ