Python Projects for Beginners a ten-Week Bootcamp Approach to Python Programming

Creating a Range Generator

Although the range function is not a generator, we can make our own version that’s created from a generator using the yield keyword. Let’s try it out:

1| # creating our own range generator with start, stop, and step parameters 3| def myRange(stop, start=0, step=1):
4| while start < stop:
5| print( "Generator Start Value: { }".format(start) )
6| yield start
7| start += step # increment start, otherwise infinite loop 9| for x in myRange(5):
10| print( "For Loop X Value: { }".format(x) )

Go ahead and run the cell. The two print statements are used to show you when the generator myRange is accessed, compared to when the for loop outputs the result. We’re able to call myRange like we would a normal range function because of the way that generators operate. On line 3 we declare the function like we would any other, accepting the same arguments as range would. We begin a while loop within the function on line 4 that will yield back the start value. Once the information is yielded back to the for loop, it’s able to use that value for the current iteration. Once the for loop completes its code block, it returns to the generator as the while loop condition has not been met. Normally, once a function has returned information, it is not called upon again; however, generators continue to return and store information until their condition is met. If we didn’t increment the start value with step, we would create an infinite loop. Generators, like iterators, can be useful when you need a specific sequence for iterating. Generators are useful when you need to be memory aware. Although they are not as efficient when it comes to performance, they are memory efficient when storing information. They’re useful in situations when you need to create a data pipeline, which is when you need to perform a set of executions on pieces of data.