What is the difference between yield and yield from Python?

Pearl 80 Published: 10/22/2024

What is the difference between yield and yield from Python?

I'd be happy to explain the difference between yield and yield from in Python.

In Python, generators are a type of iterable that can be used to generate a sequence of values on-the-fly. Generators are defined by a function containing one or more yield expressions. When you iterate over a generator, it executes until the next yield expression is encountered, at which point the current iteration ends and execution resumes from where the previous iteration left off.

The yield statement in Python is used to define a generator that produces values by yielding them at specific points during its execution. A yield statement can be used inside a function to specify what value should be returned to the caller when the generator is iterated over. The yield statement can also be used to create an iterable that can be iterated over in a loop.

For example, consider the following code:

def infinite_sequence():

n = 0


while True:


yield n


n += 1


gen = infinite_sequence()


for _ in range(10):


print(next(gen))

In this example, infinite_sequence is a generator that produces an infinite sequence of numbers starting from 0. The yield statement inside the function is used to specify what value should be returned when the generator is iterated over.

However, if you want to generate values from another generator or iterable, you need to use the yield from statement.

The yield from statement in Python is used to delegate iteration to another iterable. When a generator uses yield from, it will iterate over the other iterable and yield each value individually.

For example, consider the following code:

def concatenate_generators(g1, g2):

for value in g1:


yield value


for value in g2:


yield value


g1 = (x**2 for x in range(3))


g2 = ('hello' for _ in range(2))


gen = concatenate_generators(g1, g2)


for value in gen:


print(value)

In this example, concatenate_generators is a generator that takes two other generators as input and yields the values from each one individually. The yield from statement inside the function is used to delegate iteration to the other generators.

The main difference between yield and yield from is what they are used for. Yield is typically used when you need to specify what value should be returned by a generator, whereas yield from is used to iterate over another iterable or generator.

In summary, yield is used to specify what value should be returned by a generator, while yield from is used to delegate iteration to another iterable or generator.

What are the disadvantages of yield in Python?

I'd be happy to provide a response in English!

One of the most popular and widely-used constructs in Python is the yield keyword, which is used for implementing generators. While it offers many advantages, there are also some potential disadvantages to using yield.

Limited Control: When you use yield, your code execution stops at that point, and control is passed back to the caller. This can make it more challenging to implement certain types of logic or handle exceptions in a controlled manner. Inability to Return Values: In regular functions, you can return values from within the function itself. However, with yield, once your generator has yielded a value, you cannot return another value later on. This means that if you need to return multiple values or perform complex calculations involving multiple yield statements, you'll have to find alternative approaches. Difficulty Debugging: Because generators can be complex and involve recursive calls, it can be more challenging to debug them effectively. This is because the execution flow of your code may not be straightforwardly apparent when dealing with generators that use yield. Performance Overhead: Generators that use yield do introduce some performance overhead compared to regular functions. This is because Python needs to maintain a stack frame for each generator call, which can result in additional memory usage and potentially impact the overall performance of your program.

Inability to Reuse State: Once a generator has yielded its first value, it's no longer possible to reuse its internal state (e.g., local variables). This means that if you need to perform some complex calculation or store data between yield calls, you'll have to use alternative approaches like storing the state in an external data structure. Limited Support for Parallel Processing: While generators can be used to achieve parallel processing, they do introduce certain limitations when it comes to this type of work. For instance, Python's concurrent.futures module may not be able to utilize yield correctly due to the way it handles generator execution.

In summary, while yield offers many benefits for implementing generators in Python, it's essential to understand its limitations and potential drawbacks. With careful design and planning, you can still effectively use yield in your code while avoiding or mitigating these disadvantages.

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