Python Generators are used to create the iterators but in a different way. Generally, generators are the functions that can return value several times. Let's imagine, if we define a function and we want to return a value in a couple of times, what we entered into that statement. We can do this statement using normal Python and check what happens, take look in below.
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def my_func(x):
return x*2
The above result shows that the function gives the value of x, and it can double only once. But, we can use generators it makes double every time when we called the next() function. This process will occur when the yield keyword comes into the picture. The yield keyword is the same as the return keyword in normal Python. But, only differences are yield can call so many times as we would like. Let's see an example, how generators will work and how it doubles the value. Have a look at below.
The above result shows whatever we gave as an input, it shows the result three times. Take a value 2 and pass it to input and finally, it ends the result as 16. Because of a three-time iteration right. Key takeaways: generators.
- We have two ways to create a generator: generator function and generator expressions.
- In the generator's function, we can use yield as a keyword. And, in the same way in regular functions, we can use return as a keyword.
- In generator expression, we need (), and list comprehension use [].
- We can only use the generator once.
Generators are not a difficult topic, but people think that it's very difficult to understand. If we spend time to learn this concept then, Python developers perform well compared to the past. Take a Big Data situation, the simple methods go fall down but generator analyses still stand top. There is a lot of data on generators but we will update them, in the upcoming blogs.
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