Generators

We can create our own custom iterators by writing a generator function, which returns a special type of iterator called a generator. Generator functions have yield statements within the body of the function instead of return statements. Calling a generator function will return a generator object and will not execute the body of the function.

For example, let's consider the following generator function:

def countdown(n):
    print("Beginning countdown!")
    while n >= 0:
        yield n
        n -= 1
    print("Blastoff!")

Calling countdown(k) will return a generator object that counts down from k to 0. Since generators are iterators, we can call iter on the resulting object, which will simply return the same object. Note that the body is not executed at this point; nothing is printed and no numbers are outputted.

>>> c = countdown(5)
>>> c
<generator object countdown ...>
>>> c is iter(c)
True

So how is the counting done? Again, since generators are iterators, we call next on them to get the next element! The first time next is called, execution begins at the first line of the function body and continues until the yield statement is reached. The result of evaluating the expression in the yield statement is returned. The following interactive session continues from the one above.

>>> next(c)
Beginning countdown!
5

Unlike functions we've seen before in this course, generator functions can remember their state. On any consecutive calls to next, execution picks up from the line after the yield statement that was previously executed. Like the first call to next, execution will continue until the next yield statement is reached. Note that because of this, Beginning countdown! doesn't get printed again.

>>> next(c)
4
>>> next(c)
3

The next 3 calls to next will continue to yield consecutive descending integers until 0. On the following call, a StopIteration error will be raised because there are no more values to yield (i.e. the end of the function body was reached before hitting a yield statement).

>>> next(c)
2
>>> next(c)
1
>>> next(c)
0
>>> next(c)
Blastoff!
StopIteration

Separate calls to countdown will create distinct generator objects with their own state. Usually, generators shouldn't restart. If you'd like to reset the sequence, create another generator object by calling the generator function again.

>>> c1, c2 = countdown(5), countdown(5)
>>> c1 is c2
False
>>> next(c1)
5
>>> next(c2)
5

Here is a summary of the above:

• A generator function has a yield statement and returns a generator object.
• Calling the iter function on a generator object returns the same object without modifying its current state.
• The body of a generator function is not evaluated until next is called on a resulting generator object. Calling the next function on a generator object computes and returns the next object in its sequence. If the sequence is exhausted, StopIteration is raised.

• A generator "remembers" its state for the next next call. Therefore,

  • the first next call works like this:

    1. Enter the function and run until the line with yield.
    2. Return the value in the yield statement, but remember the state of the function for future next calls.

  • And subsequent next calls work like this:

    1. Re-enter the function, start at the line after the yield statement that was previously executed, and run until the next yield statement.
    2. Return the value in the yield statement, but remember the state of the function for future next calls.
• Calling a generator function returns a brand new generator object (like calling iter on an iterable object).
• A generator should not restart unless it's defined that way. To start over from the first element in a generator, just call the generator function again to create a new generator.

Another useful tool for generators is the yield from statement. yield from will yield all values from an iterator or iterable.

>>> def gen_list(lst):
...     yield from lst
...
>>> g = gen_list([1, 2, 3, 4])
>>> next(g)
1
>>> next(g)
2
>>> next(g)
3
>>> next(g)
4
>>> next(g)
StopIteration

Object-Oriented Programming

Object-oriented programming (OOP) is a style of programming that allows you to think of code in terms of "objects." Here's an example of a Car class:

class Car:
    num_wheels = 4

    def __init__(self, color):
        self.wheels = Car.num_wheels
        self.color = color

    def drive(self):
        if self.wheels <= Car.num_wheels:
            return self.color + ' car cannot drive!'
        return self.color + ' car goes vroom!'

    def pop_tire(self):
        if self.wheels > 0:
            self.wheels -= 1

Here's some terminology:

• class: a blueprint for how to build a certain type of object. The Car class (shown above) describes the behavior and data that all Car objects have.

• instance: a particular occurrence of a class. In Python, we create instances of a class like this:

  >>> my_car = Car('red')

  my_car is an instance of the Car class.

• data attributes: a variable that belongs to the instance (also called instance variables). Think of a data attribute as a quality of the object: cars have wheels and color, so we have given our Car instance self.wheels and self.color attributes. We can access attributes using dot notation:

  >>> my_car.color
  'red'
  >>> my_car.wheels
  4

• method: Methods are just like normal functions, except that they are bound to an instance. Think of a method as a "verb" of the class: cars can drive and also pop their tires, so we have given our Car instance the methods drive and pop_tire. We call methods using dot notation:

  >>> my_car = Car('red')
  >>> my_car.drive()
  'red car goes vroom!'

• constructor: Constructors build an instance of the class. The constructor for car objects is Car(color). When Python calls that constructor, it immediately calls the __init__ method. That's where we initialize the data attributes:

  def __init__(self, color):
      self.wheels = Car.num_wheels
      self.color = color

  The constructor takes in one argument, color. As you can see, this constructor also creates the self.wheels and self.color attributes.

• self: in Python, self is the first parameter for many methods (in this class, we will only use methods whose first parameter is self). When a method is called, self is bound to an instance of the class. For example:

  >>> my_car = Car('red')
  >>> car.drive()

  Notice that the drive method takes in self as an argument, but it looks like we didn't pass one in! This is because the dot notation implicitly passes in car as self for us.

Getting Started Videos

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