Discussion 7: Iterators and Generators, Object-Oriented Programming

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Iterators

An iterable is any object that can be iterated through, or gone through one element at a time. One construct that we've used to iterate through an iterable is a for loop:

for elem in iterable:
    # do something

for loops work on any object that is iterable. We previously described it as working with any sequence -- all sequences are iterable, but there are other objects that are also iterable! We define an iterable as an object on which calling the built-in function iter function returns an iterator. An iterator is another type of object that allows us to iterate through an iterable by keeping track of which element is next in the sequence.

To illustrate this, consider the following block of code, which does the exact same thing as a the for statement above:

iterator = iter(iterable)
try:
    while True:
        elem = next(iterator)
        # do something
except StopIteration:
    pass

Here's a breakdown of what's happening:

  • First, the built-in iter function is called on the iterable to create a corresponding iterator.
  • To get the next element in the sequence, the built-in next function is called on this iterator.
  • When next is called but there are no elements left in the iterator, a StopIteration error is raised. In the for loop construct, this exception is caught and execution can continue.

Calling iter on an iterable multiple times returns a new iterator each time with distinct states (otherwise, you'd never be able to iterate through a iterable more than once). You can also call iter on the iterator itself, which will just return the same iterator without changing its state. However, note that you cannot call next directly on an iterable.

Let's see the iter and next functions in action with an iterable we're already familiar with -- a list.

>>> lst = [1, 2, 3, 4]
>>> next(lst)             # Calling next on an iterable
TypeError: 'list' object is not an iterator
>>> list_iter = iter(lst) # Creates an iterator for the list
>>> list_iter
<list_iterator object ...>
>>> next(list_iter)       # Calling next on an iterator
1
>>> next(list_iter)       # Calling next on the same iterator
2
>>> next(iter(list_iter)) # Calling iter on an iterator returns itself
3
>>> list_iter2 = iter(lst)
>>> next(list_iter2)      # Second iterator has new state
1
>>> next(list_iter)       # First iterator is unaffected by second iterator
4
>>> next(list_iter)       # No elements left!
StopIteration
>>> lst                   # Original iterable is unaffected
[1, 2, 3, 4]

Since you can call iter on iterators, this tells us that that they are also iterables! Note that while all iterators are iterables, the converse is not true - that is, not all iterables are iterators. You can use iterators wherever you can use iterables, but note that since iterators keep their state, they're only good to iterate through an iterable once:

>>> list_iter = iter([4, 3, 2, 1])
>>> for e in list_iter:
...     print(e)
4
3
2
1
>>> for e in list_iter:
...     print(e)

Analogy: An iterable is like a book (one can flip through the pages) and an iterator for a book would be a bookmark (saves the position and can locate the next page). Calling iter on a book gives you a new bookmark independent of other bookmarks, but calling iter on a bookmark gives you the bookmark itself, without changing its position at all. Calling next on the bookmark moves it to the next page, but does not change the pages in the book. Calling next on the book wouldn't make sense semantically. We can also have multiple bookmarks, all independent of each other.

Iterable Uses

We know that lists are one type of built-in iterable objects. You may have also encountered the range(start, end) function, which creates an iterable of ascending integers from start (inclusive) to end (exclusive).

>>> for x in range(2, 6):
...     print(x)
...
2
3
4
5

Ranges are useful for many things, including performing some operations for a particular number of iterations or iterating through the indices of a list.

There are also some built-in functions that take in iterables and return useful results:

  • map(f, iterable) - Creates an iterable over f(x) for x in iterable. In some cases, computing a list of the values in this iterable will give us the same result as [func(x) for x in iterable]. However, it's important to keep in mind that iterators can potentially have infinite values because they are evaluated lazily, while lists cannot have infinite elements.
  • filter(f, iterable) - Creates iterator over x for each x in iterable if f(x)
  • zip(iterables*) - Creates an iterable over co-indexed tuples with elements from each of the iterables
  • reversed(iterable) - Creates iterator over all the elements in the input iterable in reverse order
  • list(iterable) - Creates a list containing all the elements in the input iterable
  • tuple(iterable) - Creates a tuple containing all the elements in the input iterable
  • sorted(iterable) - Creates a sorted list containing all the elements in the input iterable
  • reduce(f, iterable) - Must be imported with functools. Apply function of two arguments f cumulatively to the items of iterable, from left to right, so as to reduce the sequence to a single value.

Questions

Q1: Iterators WWPD

What would Python display?

>>> s = [[1, 2]]
>>> i = iter(s)
>>> j = iter(next(i))
>>> next(j)
>>> s.append(3)
>>> next(i)
>>> next(j)
>>> next(i)

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 output.

>>> 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 (introduced in Python 3.3). 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

Questions

Q2: Filter-Iter

Implement a generator function called filter_iter(iterable, fn) that only yields elements of iterable for which fn returns True.
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Q3: Merge

Write a generator function merge that takes in two infinite generators a and b that are in increasing order without duplicates and returns a generator that has all the elements of both generators, in increasing order, without duplicates.
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OOP

In a previous lecture, you were introduced to the programming paradigm known as Object-Oriented Programming (OOP). OOP allows us to treat data as objects - like we do in real life.

For example, consider the class Student. Each of you as individuals is an instance of this class. So, a student Angela would be an instance of the class Student.

Details that all CS 61A students have, such as name, are called instance variables. Every student has these variables, but their values differ from student to student. A variable that is shared among all instances of Student is known as a class variable. An example would be the num_slip_days_allowed attribute; the number of slip days that students can use during the semester is not a property of any given student but rather of all of them.

All students are able to do homework, attend lecture, and go to office hours. When functions belong to a specific object, they are said to be methods. In this case, these actions would be bound methods of Student objects.

Here is a recap of what we discussed above:

  • class: a template for creating objects
  • instance: a single object created from a class
  • instance variable: a data attribute of an object, specific to an instance
  • class attribute: a data attribute of an object, shared by all instances of a class
  • method: an action (function) that all instances of a class may perform

Questions

Q4: OOP WWPD - Student

Below we have defined the classes Professor and Student, implementing some of what was described above. Remember that we pass the self argument implicitly to instance methods when using dot-notation. 

class Student:
    num_students = 0 # this is a class attribute
    def __init__(self, name, staff):
        self.name = name # this is an instance attribute
        self.understanding = 0
        Student.num_students += 1
        print("There are now", Student.num_students, "students")
        staff.add_student(self)

    def visit_office_hours(self, staff):
        staff.assist(self)
        print("Thanks, " + staff.name)

class Professor:
    def __init__(self, name):
        self.name = name
        self.students = {}

    def add_student(self, student):
        self.students[student.name] = student

    def assist(self, student):
        student.understanding += 1

What will the following lines output?

>>> callahan = Professor("Callahan")
>>> elle = Student("Elle", callahan)
>>> elle.visit_office_hours(callahan)
>>> elle.visit_office_hours(Professor("Paulette"))
>>> elle.understanding
>>> [name for name in callahan.students]
>>> x = Student("Vivian", Professor("Stromwell")).name
>>> x
>>> [name for name in callahan.students]

Q5: Email

We would like to write three different classes (Server, Client, and Email) to simulate a system for sending and receiving email. Fill in the definitions below to finish the implementation!

Important: We suggest that you approach this problem by first filling out the Email class, then the register_client method of Server, the Client class, and lastly the send method of the Server class.

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Inheritance

Python classes can implement a useful abstraction technique known as inheritance. To illustrate this concept, consider the following Dog and Cat classes.

class Dog():
    def __init__(self, name, owner):
        self.is_alive = True
        self.name = name
        self.owner = owner
    def eat(self, thing):
        print(self.name + " ate a " + str(thing) + "!")
    def talk(self):
        print(self.name + " says woof!")

class Cat():
    def __init__(self, name, owner, lives=9):
        self.is_alive = True
        self.name = name
        self.owner = owner
        self.lives = lives
    def eat(self, thing):
        print(self.name + " ate a " + str(thing) + "!")
    def talk(self):
        print(self.name + " says meow!")

Notice that because dogs and cats share a lot of similar qualities, there is a lot of repeated code! To avoid redefining attributes and methods for similar classes, we can write a single base class from which the similar classes inherit. For example, we can write a class called Pet and redefine Dog as a subclass of Pet:

class Pet():
    def __init__(self, name, owner):
        self.is_alive = True    # It's alive!!!
        self.name = name
        self.owner = owner
    def eat(self, thing):
        print(self.name + " ate a " + str(thing) + "!")
    def talk(self):
        print(self.name)

class Dog(Pet):
    def talk(self):
        print(self.name + ' says woof!')

Inheritance represents a hierarchical relationship between two or more classes where one class is a (no relation to the Python is operator) more specific version of the other, e.g. a dog is a pet. Because Dog inherits from Pet, we didn't have to redefine __init__ or eat. However, since we want Dog to talk in a way that is unique to dogs, we did override the talk method.

We can use the super() function to refer to a class's superclass. For example, calling super() within the class definition of Dog allows us to access the same object but as if it were an instance of its superclass, in this case Pet. This is a little bit of a simplification, and if you're interested you can read more at https://docs.python.org/3/library/functions.html#super.

Here's an example of an alternate equivalent definition of Dog that uses super() to explicitly call the __init__ method of the parent class:

class Dog(Pet):
    def __init__(self, name, owner):
        super().__init__(name, owner)
        # this is equivalent to calling Pet.__init__(self, name, owner)
    def talk(self):
        print(self.name + ' says woof!')

Keep in mind that creating the __init__ function shown above is actually not necessary, because creating a Dog instance will automatically call the __init__ method of Pet. Normally when defining an __init__ method in a subclass, we take some additional action to calling super().__init__. For example, we could add a new instance variable like the following:

def __init__(self, name, owner, has_floppy_ears):
    super().__init__(name, owner)
    self.has_floppy_ears = has_floppy_ears

Questions

Q6: Inheritance Review: That's a Constructor, __init__?

Let's say we want to create a class Monarch that inherits from another class, Butterfly. We've partially written an __init__ method for Monarch. For each of the following options, state whether it would correctly complete the method so that every instance of Monarch has all of the instance attributes of a Butterfly instance. You may assume that a monarch butterfly has the default value of 2 wings.

class Butterfly():
    def __init__(self, wings=2):
        self.wings = wings

class Monarch(Butterfly):
    def __init__(self):
        _________________________________________
        self.colors = ['orange', 'black', 'white']
super.__init__()
super().__init__()
Butterfly.__init__()
Butterfly.__init__(self)

Some butterflies like the Owl Butterfly have adaptations that allow them to mimic other animals with their wing patterns. Let's write a class for these MimicButterflies. In addition to all of the instance variables of a regular Butterfly instance, these should also have an instance variable mimic_animal describing the name of the animal they mimic. Fill in the blanks in the lines below to create this class.

class MimicButterfly(______________):
    def __init__(self, mimic_animal):
        _______________.__init__()
        ______________ = mimic_animal

What expression completes the first blank?

What expression completes the second blank?

What expression completes the third blank?

Q7: Cat

Below is a skeleton for the Cat class, which inherits from the Pet class. To complete the implementation, override the __init__ and talk methods and add a new lose_life method. We have included the Pet class as well for your convenience.

Hint: You can call the __init__ method of Pet to set a cat's name and owner.

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Q8: NoisyCat

More cats! Fill in this implementation of a class called NoisyCat, which is just like a normal Cat. However, NoisyCat talks a lot -- twice as much as a regular Cat! If you'd like to test your code, feel free to copy over your solution to the Cat class above.
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Exam Prep

Q9: Apply That Again

This problem was taken from the Spring 2015 final exam.

NOTE: We will introduce this problem in section and give you time to work on it then. If you'd like to solve it then, don't look ahead!

Difficulty: ⭐

Implement amplify, a generator function that takes a one-argument function f and a starting value x. The element at index k that it yields (starting at 0) is the result of applying f k times to x. It terminates whenever the next value it would yield is a false value, such as 0, "", [], False, etc.

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Q10: Fibonacci Generator

Difficulty: ⭐⭐

Construct the generator function fib_gen, which when called returns a generator that yields elements of the Fibonacci sequence in order. Hint: consider using the zip function.

IMPORTANT: As a warm-up, try solving this problem iteratively without using the template. Then try to find a recursive solution using the template (you may add an extra line or two, but no extra structure is necessary). We recommend running your code in a local interpreter, as sometimes the online interpreter has bugs with recursive generator functions.

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Q11: Cucumber - Fall 2015 Final Q7

Difficulty: ⭐⭐

Cucumber is a card game. Cards are positive integers (no suits). Players are numbered from 0 up to players (0, 1, 2, 3 in a 4-player game).

In each Round, the players each play one card, starting with the starter and in ascending order (player 0 follows player 3 in a 4-player game). If the card played is as high or higher than the highest card played so far, that player takes control. The winner is the last player who took control after every player has played once.

Implement Round so that Game behaves as described in the doctests below.

Hint: Here is an example of a try-catch with an AssertionError

>> try:
...   assert False, 'oh no!'
... except AssertionError as e:
...   print(e)
...
oh no!

EDIT: The first two lines in the play function should be:

assert _______________________________, f'The round is over, player {who}'
assert ______________________________, f'It is not your turn, player {who}'
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Q12: Partition Generator

Difficulty: ⭐⭐⭐

Construct the generator function partition_gen, which takes in a number n and returns an n-partition iterator. An n-partition iterator yields partitions of n, where a partition of n is a list of integers whose sum is n. The iterator should only return unique partitions; the order of numbers within a partition and the order in which partitions are returned does not matter.

Important: The skeleton code is only a suggestion; feel free to add or remove lines as you see fit.

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