difference between z

Difference between Python Yield and Python Return

Difference between Python Yield and Python Return

In Python, the “yield” keyword is used to return a value from a function. Unlike the “return” keyword, which ends the function and returns a value, “yield” returns the value and allows the function to continue executing. This can be useful for creating generators or iterators. The “return” keyword can also be used in conjunction with “yield”, but it will end the function and return the value immediately.

What is Python Yield?

In computer programming, the yield is a keyword that is used to suspend and resume a function (particularly, a generator function) at a point where it can produce a value. When a function that contains a yield keyword is called, the code within the function body does not execute. Instead, an iterator is returned that can be used to resume execution of the function.

  • When the iterator’s next() method is called, execution of the function resumes until the next yield statement is reached. At that point, the current state of the function is saved and the value of the expression following the yield keyword is returned. Subsequent calls to next() return successive values from the function.
  • If there are no more values to return, or if a call to next() is made outside of a generator function, a Stop Iteration exception is raised. Yield is typically used in conjunction with generators. Generators are functions that return an iterable object and can be used to create data streams or sequences.
  • By using yield rather than return in a generator function, values can be generated on demand rather than all at once. This allows for efficient use of resources and can lead to better performance overall. Python’s yield keyword provides an easy way to create generators without having to write

What is Python Return?

Python return statement is used to return values from the function. It exits the function and returns a value to the caller. The return type can be anything such as int, float, str, list, tuple, dict, set etc. We can also return multiple values from a function using return statement. Python allows us to return multiple values from a function using tuples (See example below). If you don’t want to return any value, then you can use Python return statement without any value. In this case, the function will return None object.

Difference between Python Yield and Python Return

In Python, the yield keyword is used to produce a generator, while the return keyword simply returns values. When a function containing a yield keyword is called, it doesn’t actually return anything – instead, it returns a generator object. This object can be iterated over to produce the values that would have been returned by the function.

  • So, when should you use return and when should you use yield? If you want to produce a sequence of values without storing them all in memory at once, then you should use yield.
  • On the other hand, if you’re simply returning a single value, then return is the keyword you want. Keep in mind that yield can only be used inside of a function – it doesn’t make sense to use it outside of one. With that said, let’s take a closer look at how these two keywords work.

Yield works by “pausing” the execution of a function – each time it’s called, the function remembers its state so that it can pick up where it left off when it’s called again. This allows you to produce a sequence of values without having to store them all in memory at once. In contrast, return simply exits the function and returns a value – it doesn’t remember any


The Python yield and return statements are similar in that they both cause a function to stop execution and return control to the caller. However, there are some key differences. The most important distinction is that yield returns a value while return does not. Additionally, yield can be used inside of a loop while return cannot. Finally, yield allows you to pause and resume execution later, which can be useful for implementing generators. Understanding these distinctions will help you use Python more effectively in your own projects.

Share this post

Share on facebook
Share on twitter
Share on linkedin
Share on email