4 Reasons Why You Should Be Using Python Generators

Generators in Python are more than just an alternative to lists

Photo by Karsten Würth on Unsplash.

A generator is a construct in Python that allows for lazy or ad hoc loading of a stream of data. They can work like a list and be looped over, but generators have the ability to maintain state.

Yes, I know this will mean nothing to you without an example, so let’s get right into it with the code snippets below:

Looking at the function above, you might be seeing an unfamiliar keyword called yield . This is similar to return. However, it is specific to generators. It allows you to return data but also stores the point at which that yield was called. This allows for the previous yield to be skipped if desired on the next call (this will make sense soon).

Let’s now run this function the way a generator should be run:

We first initialize our generator object with temp_gen1 = gen1(). Next, we use the next keyword. This allows us to jump to the next iteration of our yield statement. Given that this is the first time the next keyword is invoked, it should go to our “I am the bone of my sword” .

Let’s test it out!

Output:

I am the bone of my sword

OK, it returned the first yield statement. Not very interesting. Let’s add another next(temp_gen1) and print to the console:

print(  next(temp_gen1) )

Output:

I am the bone of my sword
Steel is my body and fire is my blood

Now see what's happening? It jumped directly to the next yield statement!

Let’s add another next(temp_gen1) and print it to the console:

print(  next(temp_gen1) )

Output:

I am the bone of my sword
Steel is my body and fire is my blood
I have…