Introduction to Yield in Python
When we write a function, which should perform some operation and provide some result back, we generally use the return statement for returning the result.The yield statement is only used when defining a generator function and is only used in the body of the generator function. Yield are used in Python generators. A python generator is defined like a normal function, but whenever it will needto generate a value, it does so with the yield keyword rather than return. If the body of a def function contains yield, the function will becomea generator function.
A Python generator is a function return a generator iterator (just an object we can iterate over) by calling yield .Yield maybe called with a value, in which case that value is treated as the “generated” value. Yield is a keyword in Python that will used to return from a function without destroying the states of its local variable and when the function is called, the execution starts from the last yield statement. Return sends a value back to its caller, whereas yield can produce a sequence of values. We must use yield when we want to iterate over a sequence but don’t want to store the entire sequence in memory so in that case you can use yield Python generators. The function will continue the execution immediately after the last yield run. It will allow its code to produce a series of values and sending them back like a list.
Example:
In the above example, yield is the keyword and 1, 2, 3 are the values that will store in the generator and to print the value inside the generator we are using for loop. We can use yield when we want to iterate over a sequence, but don’t want to store the entire sequence in memory.
To understand what yield does, you must understand what generators are. And before generators come iterables.Aniterable is any Python object capable of returning its members one at a time, permitting it to be iterated over in a for-loop. Ex- iterables include lists, tuples, and strings – any such sequence can be iterated over in a for-loop.
In simpler words, whatever that will appear on the right-side of a for-loop: for x in iterable: is aniterable.Iterablecan be anything that you can loop over with a for loop.An object can becallediterable,if we can get an iterator from iterable. Maximum built-in containers in Python like: list, tuple, string etc. areiterables.Important property(Method) of an iterable is that it has an __iter__() method or iter() method which allows any iterable to return an iterator object.
Sequences are a very common type of iterable. Ex- for built-in sequence types are lists, strings, and tuples.
Iterable will support element access using integer indices via the __getitem()__ special method (indexing) and define a __length()__ method that returns the length of the sequence.
Also, we can use the slicing technique on them.
Many things in Python are iterables, but not all of them are sequences. Dictionaries, file objects, sets, and generators all are iterables, but none of them is a sequence.
We can loop over an iterable without using a for loop. Some of us may think that we can use a while loop and generate indices to get this output.
It seems that this approach works very well for lists and other sequence objects as well. What about the non-sequence objects? They don’t support indexing, so this approach will not work for them, we will get an error.
But how the Python’s for loop works on these iterables then? We can see that it works with sets.
When you create a list, you can read its items one by one
Iterator is an object which can be looped through and maintains its state during the iteration which means it remembers where it is during an iteration. An iterator is an object that enables a programmer to traverse a container, particularly lists. Different types of iterators are often provided via a container’s interface.
It has a __next__() method that returns the next value from the iteration and updates the state to point at the next value. Iterator is simplyan object that can be iterated upon. An object it will return data, one element at a time.Python iterator object must implement two special methods, that is __iter__() and __next__() , collectively called the iterator protocol.Thepurpose of an iterator is allow to process with every element of a container while isolating the user from the internal structure of the container. The container provides the methods for creating iterators. Iterator is an object and it will represent a stream of data. You can create an iterator object by applying the iter() function to an iterable.
Difference between iterable and iterator. Iterable is an object, which can iterate over. It will create an Iterator when passed to iter() method. Iterator is an object, which is used to iterate over an iterable object using __next__() method. For example, a list is iterable but a list is not an iterator.
When we will apply the iter() function to an iterable we will get an iterator. If we will call the iter() function on an iterator it will give us itself back in python.
You can use an iterator to manually loop over the iterable it came from. if repeated passing of iterator to the built-in function next() it will return successive items in the stream. Once, when you consumed an item from an iterator, it has gone. When data aren’t available it will give us a StopIteration and exception is raised.
Iterators also have the __iter__() method and it will return self object.
List is an iterable. When you will use a list comprehension, you create a list, and so an iterable
These iterables are handy because you can read them, but you store all the values in memory and it’s not always what you want when you have a lot of values.
The iterator objects are required to support the following two methods, which together form the iterator protocol:
We have seen some examples with iterators. Python has many built-in-classes that are iterators. Ex- an enumerate and reversed objects are iterators.
Reversed Example
The Python’s zip, map and filer objects are also iterators.
A Generator is a function use to store the value by using yield keyword. A function which returns a generator iterator. This is a normal function only except that it will contains yield expressions for producing a series of values usable in a for-loop or that can be retrieved one at a time with the next() function in python.If function will contains the yield keyword then that function will become a generator and you can print the value by using for loop.You can convert a function into generator simply you need to use yield keyword in below example you can see that we can use multiple yield statement to return the data
In above example, we defined new_generator() function in this we have assign a variable n and giving the value as 1 then print the statement and then yield the value
If we execute generator function as a normal function then it will give us the object so for that we have to store the function in the variable x then we have use next method for execution
We can use directly generator for making a list and we can store the value inside the mylist then we can print the mylist by using for loop
In the below example, we defined a function i.e. reverse_string and it is a generator because we are using yield keyword and we are iterating each word from the statement
We use a generator function or generator expression when we want to create a custom iterator. They are simpler to use and need less code to achieve the output.
Generator expressions are similar to the list comprehensions,Just like a list comprehension, the general expressions are concise. In most cases, they use to written in one line of code.
An expression that returns an iterator. It looks like a normal expression followed by a for expression defining a loop variable, range, and an optional if expression ex,
We can also add a conditional expression on the iterable. 
You can see in the above example, we did square root of the even number, we directly print the value without store the single value and we print the class i.e. generator
Yield is a simple statement. Its primary job is to control the flow of a generator function similar tothat return statements. Yield that is used to return from a function without destroying the states of its local variable and when the function is called, the execution starts from the last yield statement. Any function that will present a yield keyword is termed as generator
When you call a generator function or use a generator expression, you return a iterator called a generator. You can try to assign this generator to a variable in order to use it. When you will call to special methods on the generator, such as next(), the code within the function is executed till yield.
When the yield statement is hit, the program suspends function execution and returns the yielded value to the caller. (Return stops function execution completely.) When a function is suspended, the state of that function is saved. This includes any variable bindings local to the generator in python, the instruction pointer, the internal stack, and any exception handling.
This allows you to resume function execution whenever you will call one of the generator’s methods. Like this, all function evaluation picks back up right after yield. You can see this in action by using multiple yield statements:
In the above example, we defined a function i.e. counter consist of while loop which has one condition (x < 5) and is yielding the value of x and then it will increment by 1.
If we use yield keyword in the function then that function will become as Generator
Yield is the special keyword in python, yield will know what it execute in the last time.In the above function, when we executed first time we are getting 0 as output but in the second execution it will increment by 1 and we got 1 as output and again it will iterate the loop till the condition will not satisfy. When we use yield keyword in the function it will store the state of the local variable
Example:
In the above example, you can see that with the help of yield keyword It is easy to find the word inside the string statement and it will count the number of times that word will occur because yield keyword will know the last state
In this tutorial, you have learned about Iterable, Generator functions, Yield
The yield keyword in python is used to return a value from a function(just like return ) but this keyword also maintains the state of the local variables of the function and when the function is called again in python, the execution is started from the yield statement executed last time.We should use python yield when we want to iterate over a sequence but don’t want to store the entire sequence in memory
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