In this chapter, we delve into the world of iterators and generators, essential tools for efficient data processing in Python. 📚
Key Concepts
- Iterator Protocol: Objects that implement
__iter__()
and__next__()
methods. - Generator Functions: Use
yield
to produce a sequence of values lazily. - Generator Expressions: Compact way to create generators using parentheses.
Example: Generator Function
def count_up_to(n):
count = 1
while count <= n:
yield count
count += 1
Practical Applications
- Memory-efficient handling of large datasets.
- Infinite sequences (e.g.,
yield from
for recursive generation). - Pipelines for data transformation.
For deeper insights, explore our Python Essentials Guide. 🌐
📌 Summary
Generators and iterators are powerful for managing data flow. Use them to optimize performance and simplify complex operations!