Generators are powerful tools in Python for handling large data streams efficiently. Here's a breakdown of advanced concepts:

1. Generator Expressions

A concise way to create generators using a syntax similar to list comprehensions.

numbers = (x**2 for x in range(10))
for num in numbers:
    print(num)
generator_expression

2. Yield vs Return

  • yield pauses the function and saves its state, resuming from where it left off
  • return exits the function entirely
    Example:
def count_up_to(n):
    count = 0
    while count < n:
        yield count
        count += 1

3. Iterators Protocol

Generators automatically implement the iterator protocol via __iter__() and __next__() methods.

iteration_protocol

4. Advanced Use Cases

  • Chaining generators:
    def chain(*iterables):
        for i in iterables:
            yield from i
    
  • Infinite sequences:
    def infinite_sequence():
        num = 0
        while True:
            yield num
            num += 1
    

5. Best Practices

  • Avoid using generators for small datasets
  • Use yield from to delegate to sub-generators
  • Combine with itertools for enhanced functionality

For more examples, check our Python Generator Basics Tutorial. 📚