Chapter 3: Generators and Iterators
The chapter explores the concepts of iterators and generators in Python, emphasizing their efficiency in handling data streams. It covers the iterator protocol, the definition and benefits of generators, the use of 'yield' and 'yield from', and practical applications like lazy evaluation and data pipelines. Through clear examples, it demonstrates how these constructs can optimize memory usage and streamline data processing in Python applications.
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Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Iterators use iter() and next() to manage data sequences.
- Generators simplify iterator creation with the yield keyword.
- yield enables function pausing, while yield from facilitates nested generator operations.
Key Concepts
- -- Iterator
- An object that allows iteration over elements in a sequence, implementing iter() and next() methods.
- -- Generator
- A special type of iterator that can yield values and maintain state between yields, defined by functions using the yield keyword.
- -- Coroutine
- A function that can pause execution to allow data exchange, enabling two-way communication within generators.
- -- Lazy Evaluation
- The concept of generating values only as needed, conserving memory and CPU resources.
- -- Generator Expression
- A concise way to create generators using a syntax similar to list comprehensions, producing values on demand.
Additional Learning Materials
Supplementary resources to enhance your learning experience.