Python | Generator Expressions
In Python, to create iterators, we can use both regular functions and generators. Generators are written just like a normal function but we use
yield() instead of
return() for returning a result. It is more powerful as a tool to implement iterators. It is easy and more convenient to implement because it offers the evaluation of elements on demand. Unlike regular functions which on encountering a return statement terminates entirely, generators use yield statement in which the state of the function is saved from the last call and can be picked up or resumed the next time we call a generator function. Another great advantage of the generator over a list is that it takes much less memory.
In addition to that, two more functions
_iter_() make the generator function more compact and reliable. Example :
First result is 1 Second result is 2 Third result is 3
Difference between Generator function and Normal function –
- Once the function yields, the function is paused and the control is transferred to the caller.
- When the function terminates, StopIteration is raised automatically on further calls.
- Local variables and their states are remembered between successive calls.
- Generator function contains one or more yield statement instead of return statement.
- As the methods like
_iter_()are implemented automatically, we can iterate through the items using
There are various other expressions that can be simply coded similar to list comprehensions but instead of brackets we use parenthesis. These expressions are designed for situations where the generator is used right away by an enclosing function. Generator expression allows creating a generator without a yield keyword. However, it doesn’t share the whole power of generator created with a yield function. Example :
0 1 4 9 16 25 36 49 64 81
We can also generate a list using generator expressions :
['k', 'e', 'e', 'g']
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