Delegating Generators with yield from
When working with generators in Python, you may encounter situations where you want to delegate part of a generator's operations to another generator. The yield from statement provides an elegant solution for this, allowing one generator to yield all values from another iterable or generator as if they were part of the original generator. This not only simplifies code but also improves readability, especially when combining, flattening, or chaining sequences.
The syntax for yield from is straightforward: you use yield from <iterable>, where <iterable> can be any generator or iterable. When the statement is executed, the current generator yields each value from the delegated iterable, one at a time, until it is exhausted. Afterward, the generator resumes execution after the yield from statement. This pattern is especially useful for breaking complex iteration logic into smaller, reusable pieces or for flattening nested generators.
Common use cases for yield from include:
- Combining multiple sequences into a single stream;
- Delegating part of a generator's logic to a helper generator;
- Flattening nested generators or lists of iterables;
- Building pipelines of generators that process data in stages.
12345678910111213def numbers(): yield from range(3) yield from [10, 20] def letters(): yield from "ab" def combined(): yield from numbers() yield from letters() result = list(combined()) print(result) # Output: [0, 1, 2, 10, 20, 'a', 'b']
In this example, the combined generator uses yield from to delegate iteration to both the numbers and letters generators. When you iterate over combined, it seamlessly yields all values from both sources in order, returning a single, flattened stream of values.
1. Which of the following best describes the effect of using yield from in a generator?
2. In which scenarios is using yield from especially beneficial?
Danke für Ihr Feedback!
Fragen Sie AI
Fragen Sie AI
Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen
Awesome!
Completion rate improved to 6.67Delegating Generators with yield from
Swipe um das Menü anzuzeigen
When working with generators in Python, you may encounter situations where you want to delegate part of a generator's operations to another generator. The yield from statement provides an elegant solution for this, allowing one generator to yield all values from another iterable or generator as if they were part of the original generator. This not only simplifies code but also improves readability, especially when combining, flattening, or chaining sequences.
The syntax for yield from is straightforward: you use yield from <iterable>, where <iterable> can be any generator or iterable. When the statement is executed, the current generator yields each value from the delegated iterable, one at a time, until it is exhausted. Afterward, the generator resumes execution after the yield from statement. This pattern is especially useful for breaking complex iteration logic into smaller, reusable pieces or for flattening nested generators.
Common use cases for yield from include:
- Combining multiple sequences into a single stream;
- Delegating part of a generator's logic to a helper generator;
- Flattening nested generators or lists of iterables;
- Building pipelines of generators that process data in stages.
12345678910111213def numbers(): yield from range(3) yield from [10, 20] def letters(): yield from "ab" def combined(): yield from numbers() yield from letters() result = list(combined()) print(result) # Output: [0, 1, 2, 10, 20, 'a', 'b']
In this example, the combined generator uses yield from to delegate iteration to both the numbers and letters generators. When you iterate over combined, it seamlessly yields all values from both sources in order, returning a single, flattened stream of values.
1. Which of the following best describes the effect of using yield from in a generator?
2. In which scenarios is using yield from especially beneficial?
Danke für Ihr Feedback!
