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Short Circuiting With any() and or

For more information on concepts covered in this lesson, you can check out How to Use Generators and yield in Python

00:00 In this lesson, you’ll be looking at short circuiting. What is short circuiting? Well, first off, both or and any() short circuit, so this behavior is the same in both of them.

00:11 And what it means is that if there are lots of values, they will both return on the first truthy value. So say you were using any(), and you had a list of ten values, and the second value was True.

00:23 All the rest are False. any() will look at the first value, see it as False, go to the next value, see that it’s True, and then it will return True, and it won’t check the rest of the values.

00:33 This is what short circuiting means. Now to demonstrate this behavior, you can do this with generators because generators you can customize to print something to the console every time a value is accessed or generated. And so in this way, you’ll be able to demonstrate short circuiting behavior and review generators.

00:55 Okay. So what are generator functions? Now, this lesson isn’t going to go into much detail about what generator functions are and how they work. It’s just going to go through a quick example now of a typical generator function, a simplified generator function, and it will show you how it works and then it will use these concepts to build another generator function that will illustrate the behavior of short circuiting in any(). if you want more details on generator functions, check out the article linked below, called Introduction to Python Generators.

01:28 Here’s an example of a Python generator function. As you can see, it’s called numbers_from_zero_to_nine(). What it does is it loops for number in range(10), which means that number will be 0 in the first iteration and 9 in the last iteration, going through each number in between, and here it yields a number.

01:50 It’s the yield keyword that replaces the return keyword in normal functions, and it is what makes it a generator function. What this means is that when execution reaches this line, but it says yield number, it will return that number, and it will pause itself until you ask it for another number, and then it will carry on execution, which means it will go back up to the for loop, define number as 1, and then yield 1 and return it and stop until you request another number, on and on until 9. The way you use a generator function is that you call it, and when you call this function, it will return a generator iterator.

02:33 So let’s start using this.

02:42 Okay. So now, if we look at the generator, what’s in there? It says it’s a generator object. How do you use this generator object? Well, you call the in-built next() function on the generator—

02:56 or better said, you call the next() function, and you pass the generator as an argument to it. And what this will do is it will ask the generator for its first value—the first value that it yields, which is 0.

03:11 And then if you call it again, it will return 1, and on and on and on, all the way until 9.

03:20 And then it will say—it will raise an error once it reaches the end. Functions that take in iterators, like generators, will know how to deal with this error, and it won’t raise it, like any(),

03:34 Okay, so now you’re going to build a generator function to illustrate the short circuiting behavior of or or any(). Okay, so what is this generator doing?

03:45 It takes in an iterable as an argument, and it creates a generator with that. So with this, you’ll be able to pass in a list, and it will make a generator of that list.

03:56 So it will go through each item in the list, and it will print something to the output. It will say Yielding the item that it is yielding, and then it will yield, and then it will stop until you call the next item.

04:09 So to make a generator from that, say you have this iterable of numbers, and then you call generate_items with that iterable, and it’ll create the generator that you want. It makes it easy to create generators.

04:24 But the key aspect of this generator is that every time you call an item from this generator,

04:33 it will output a message saying that it is Yielding something.

04:40 In this way, you can pass it into a function like any() that will go through each of these and yield each of these items, and you can see how far it gets into the list, and that’s where short circuiting comes in. Okay, so say you have this example here, one that you saw right at the start: is_hotspot.

05:01 So it’s just a list of True or False values. The third to last is True. So you’ll notice that any() calls all the items until True, and then it doesn’t check the last two.

05:14 So first, create the generator with the function that you defined, and pass it in

05:24 the variable that contains the list.

05:28 So now you can see that the generator is created, and if you pass that generator into any(), you’ll see that it returns True at the end, which is what you expect because there is one True value.

05:42 However, you see that it goes through each item and then once it gets the True, it stops. And that’s short circuiting, basically saving a bit of energy and not having to check anything. So if the first item was True in is_hotspot, then any() would only check the first item. This is the same with or. If you have one condition or another condition or another condition, if the first item is True, then that is the item that will be returned, and the rest of the items won’t even be checked.

06:14 And there you have it: short circuiting. In this lesson, you’ve looked at what short circuiting means, and you’ve also used generators to demonstrate how any() short circuits.

06:24 Bear in mind that or will also short circuit, except it won’t return a True or False value at the end. It will return the first truthy value itself, so if you’re comparing numbers, it will return the actual number and not True or False, like any().

06:40 Next up is a summary of this course. You’re at the end, so well done for getting this far.

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