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HomeBlogProgrammingYield in Python - Take Your Functions To The Next Level
In this comprehensive article, we will learn about Python's yield keyword. If you’re a Python developer, you must be knowing the Generators in Python. The key to defining a Python generator is to use the “yield” keyword. The Python yield generator is largely used in scenarios when we need large collections, improve the readability of our code, and in other certain scenarios such as multi-threading. You may or may not know how to use the “yield” keyword correctly yet. Let’s understand what exactly the yield in Python is, yield in python examples and how it makes the lives of developers exponentially easier.
Yield is a statement that is used in functions and that function can provide a result back to the caller (like a return in a normal function), but the function will then continue to execute with all of the local variables staying intact. Any function in Python that contains a yield keyword is termed a generator function. In essence, the yield statement is used to create a generator function in Python, instead of returning everything at once using the return keyword.
Yield is a very useful Python feature when we are dealing with huge datasets.
Basically, the python yield statement converts a function into a generator function, i.e., a function which can return an intermediate value to the caller (the "next" value) but maintains the function's local state so that the function can resume again at the place it left off.
The generator can then be used in any place a normal iterative statement can be used, for e.g., inside a for-loop.
A generator function in Python is defined as a normal function, but whenever it needs to generate a new value, it does so use the yield keyword rather than the return keyword. If the body of the function contains yield, the function can automatically be termed a generator function.
Any generator function returns a generator object. Generator objects are used either by calling the next method on the generator object or using the generator object in any loop.
So, a generator function returns a generator object that can be used as an Iterator.
Below is a Python program to demonstrate the object with the next method for clear understanding.
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CODE:-
def simpleGeneratorFun() yield 1 yield 2 yield 3 x=simpleGeneratorFun() print(x.next()) print(x.next()) print(x.next())
To overcome generator exhaustion, we can follow three approaches:
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In Python, we can generate sequences of data, but can only make them concrete when we consume the generator or evaluate the mapping or range.
We can materialize the Generators in Python by calling list(). Calling list() on the generator transforms it into a normal list.
Now we will understand how yield works behind the scenes.
When the yield return statement is reached in the iterator method, an expression is returned, and the current location of the code is retained. The execution is restarted from that location the next time the iterator function is called.
The yield shall be used, when we want to iterate over a sequence, but don’t want to store the entire sequence in memory.
Below are some examples for a better understanding of the working of yield in Python.
CODE:-
def SquareOfNumbers(): i=1 while True: yield i*i i+=1 for num in SquareOfNumbers(): if num>100 break print(num)
CODE: -
def print_even(test_string): for i in test_string: if i==”demo”: yield i demo_string=”This is demo string,\ This is demo string, This is demo string” count = 0 print(“The number of demo in string is : ”, end=””) demo_string=demo_string.split() for j in print_even(demo_string): count=count+1 print(count)
The yield statement suspends the function’s execution and sends the value back to the caller but retains enough state to enable the function to resume/start where it left off. When the function resumes, it continues execution immediately after the last yield run. This allows its code to produce a series of values over time, rather than computing them at once and sending them back like a list.
Let’s understand this with an example:
CODE:-
def YieldFunction(): yield 1 yield 2 yield 3 for value in YieldFunction(): print(value)
Now we will understand why we should use the yield keyword rather than the return keyword in Python.
Whenever the control reaches the return statement in our program, the execution of the program is terminated, and the remaining statements will not be executed.
However, in the case of yield, whenever the control reaches the yield statement in our program, the execution of our program is paused, and later we can continue other statements in the function.
It is recommended to use yield when we want to iterate over a sequence, however, because of resource constraints or simply when we don’t want to store the entire sequence in memory. For other cases, we can think of using a return statement instead of a yield.
If we want faster execution or computation over large datasets, yield is a better option. Also, when the size of returned data is quite large, instead of storing them in a list, we can use yield to reduce memory consumption.
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Some of the advantages of using yield are mentioned below.
Although using the yield keyword in Python has many advantages, it comes with a few disadvantages too, some of which are mentioned below.
Summing up, in this article we learned how we can explore and use the yield keyword in Python to optimize programs in terms of both speed and memory. How we can use the yield statements in Python to return multiple values from generator functions. Yield is highly memory-efficient and increases the overall performance of the code by saving time. It saves memory because it stores the values to be returned as local variables state, and also each time when the function is executed, it need not start from the beginning as the previous states are retained. This is what makes yield keywords highly popular among python developers and makes their lives easier. It is a great alternative to return statements in Python.
Yield keyword is generally used to convert a regular Python function into a generator, whereas return keyword is generally used for the end of the execution and it “returns” the result to the caller statement.
Yield keyword is used extensively as an alternative to returning a statement in a function. This function then returns a generator that can be iterated upon instead of output. We can iterate through the generator to extract items.
When yield and return are used in the same function in Python, the function still remains a generator. A generator function can also contain return statements.
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