Python Yield vs Python Return: Difference and Comparison

Yield and return are terms commonly used while programming in Python. Essentially, they are keywords that are used to pass values between functions.

However, it may be challenging for those who do not deal with programming concepts regularly to understand how each is different.

Key Takeaways

  1. Yield in Python creates a generator object, allowing for the creation of iterators without consuming memory.
  2. Return in Python terminates a function and returns a value to the caller.
  3. Yield enables efficient handling of large data sets, while return suits standard function execution.

Python Yield vs Python Return

Python yield allows for the efficient processing of large data sets or infinite sequences without requiring the entire sequence to be generated and stored in memory at once. The Python return statement allows the function to return this result to the calling code that can be printed to the console.

Python Yield vs Python Return
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Python yield is essentially a keyword that is extensively used in programming. It pauses the execution of a function for a programmer.

This allows returning without affecting the state of other variables. When the yield keyword is used in a function, its execution begins only after the statement is made, which is from where it was left off.

‘Meanwhile, python return has a very similar purpose. It’‘returns’ the resulting value to the programmer.  This is where it gets its name from. ‘Meanwhile, python return has a very similar purpose. It ‘returns’ the resulting value to the programmer.  This is where it gets its name from.

However, the keyword is used to end the execution of the function altogether. Further, when used in a process, all the statements made after it are not capable of execution.

Comparison Table

Parameters of ComparisonPython YieldPython Return
MeaningIt is a keyword that returns values to the programmer without affecting other variables.It is only used in everyday functions.
ExecutionIt pauses the execution of a function for a certain period.It ends the execution of the function.
UsageIt is only used in generator functions.It is used when the data size is not as significant.
OperationThe values returned are in the form of a series.Only a single value is returned.
RunningIt can be used multiple times in a function.It can only be used once in a function.
Data sizeIt is used when the data size is large.It is used when the data size is not as large.
SpeedIt facilitates faster execution for large sets of data.It does not facilitate fast execution for large sets of data.

What is Python Yield?

Yield is a keyword that is extensively used in Python. It pauses the execution of a program and returns a series of values to the caller at that point.

Further, when the statement is dismissed, the function begins execution right back from where it left. During this, no other local variables are affected in any way.

However, this keyword is only used in generator functions. These are specific functions that are not capable of returning single values. Therefore, they are entirely based upon Python yield.  

Furthermore, the keyword can be included in such a function more than once. This is because the process does not get destroyed, and neither do the local variables.

The way yield keyword works in Python is very different from other keywords performing a similar function. When there is an iteration, the code is run in Python to find the yield statement.

On encountering it, the code stops. Further, the yielded value is sent back to the caller while the execution of the function remains paused.

Python yield is the best choice for usage if the data sets are large. The keyword facilitates efficiency and fast execution in such a case.

What is Python Return?

‘Return is another keyword in Python with a very similar function to yield. However, unlike the former, it does not”pause’ execution but completely stops it. ‘Return is another keyword in Python with a very similar function to yield. However, unlike the former, it does not ‘pause’ execution but completely stops it.

In doing so, it sends a single value back to the caller. Meanwhile, the entire function is destroyed, along with local variables. This means that it can no longer be executed.

This keyword only works in everyday functions. However, it can only be used one time. This is because the remainder of the process is destroyed when it is used.

This leaves no space for further coding. However, it must be noted that only the particular function using the return statement is affected. The rest of the code can still run without hassle.

Using the keyword in a function allows a caller to process data efficiently. This data can be sent to the main program. Further, it can be used in another part of the same code. 

However, a drawback of using the return statement is that it is not optimal for large data sets. It does not run at a fast speed and can become time-consuming. This affects the overall productivity of the function as well as the caller.

Main Differences Between Python Yield and Python Return

  1. Python yield is a keyword that returns values to the programmer without affecting other variables. In contrast, python return is a keyword that returns values to the programmer while destroying other variables.
  2. Python yield pauses the execution of a function for a certain period, whereas python return ends the execution of the function.
  3. Python yield is only used in generator functions, whereas Python return is only used in normal functions.
  4. In the case of python yield, the values returned are in the form of a series, whereas in the case of python return, only a single value is returned.
  5. Python yield can be used multiple times in a function, whereas Python return can only be used once in a function.
  6. Python yield is used when the data size is large, whereas Python return is used when the data size is not as large.
  7. Python yield facilitates faster execution for large sets of data whereas Python return does not facilitate fast execution for large sets of data.
References
  1. https://dl.acm.org/doi/abs/10.1145/2544173.2509536
  2. https://link.springer.com/chapter/10.1007/978-3-319-77935-5_14

Last Updated : 11 June, 2023

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