Discussion 5: Sequences, Mutability, Object-Oriented Programming

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Sequences are ordered collections of values that support element-selection and have length. We've worked with lists, but other Python types are also sequences, including strings.

Q1: Map, Filter, Reduce

Many languages provide map, filter, reduce functions for sequences. Python also provides these functions (and we'll formally introduce them later on in the course), but to help you better understand how they work, you'll be implementing these functions in the following problems.

In Python, the map and filter built-ins have slightly different behavior than the my_map and my_filter functions we are defining here.

my_map takes in a one argument function fn and a sequence seq and returns a list containing fn applied to each element in seq.

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my_filter takes in a predicate function pred and a sequence seq and returns a list containing all elements in seq for which pred returns True.

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my_reduce takes in a two argument function combiner and a non-empty sequence seq and combines the elements in seq into one value using combiner.

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Some objects in Python, such as lists and dictionaries, are mutable, meaning that their contents or state can be changed. Other objects, such as numeric types, tuples, and strings, are immutable, meaning they cannot be changed once they are created.

Let's imagine you order a mushroom and cheese pizza from La Val's, and they represent your order as a list:

>>> pizza = ['cheese', 'mushrooms']

With list mutation, they can update your order by mutate pizza directly rather than having to create a new list:

>>> pizza.append('onions')
>>> pizza
['cheese', 'mushrooms', 'onions']

Aside from append, there are various other list mutation methods:

  • append(el): Add el to the end of the list. Return None.
  • extend(lst): Extend the list by concatenating it with lst. Return None.
  • insert(i, el): Insert el at index i. This does not replace any existing elements, but only adds the new element el. Return None.
  • remove(el): Remove the first occurrence of el in list. Errors if el is not in the list. Return None otherwise.
  • pop(i): Remove and return the element at index i.

We can also use list indexing with an assignment statement to change an existing element in a list. For example:

>>> pizza[1] = 'tomatoes'
>>> pizza
['cheese', 'tomatoes', 'onions']

Q2: WWPD: Mutability

What would Python display? In addition to giving the output, draw the box and pointer diagrams for each list to the right.

>>> s1 = [1, 2, 3]
>>> s2 = s1
>>> s1 is s2
>>> s2.extend([5, 6])
>>> s1[4]
>>> s1.append([-1, 0, 1])
>>> s2[5]
>>> s3 = s2[:]
>>> s3.insert(3, s2.pop(3))
>>> len(s1)
>>> s1[4] is s3[6]
>>> s3[s2[4][1]]
>>> s1[:3] is s2[:3]
>>> s1[:3] == s2[:3]
>>> s1[4].append(2)
>>> s3[6][3]


Object-oriented programming (OOP) is a programming paradigm that 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.

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. For example, the max_slip_days attribute is a class variable as it is a property of all students.

All students are able to do homework, attend lecture, and go to office hours. When functions belong to a specific object, they are called methods. In this case, these actions would be 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 variable: a data attribute of an object, shared by all instances of a class
  • method: a bound function that may be called on all instances of a class

Instance variables, class variables, and methods are all considered attributes of an object.

Q3: WWPD: Student OOP

Below we have defined the classes Professor and Student, implementing some of what was described above. Remember that Python passes the self argument implicitly to methods when calling the method directly on an object.

class Student:

    max_slip_days = 3 # this is a class variable

    def __init__(self, name, staff):
        self.name = name # this is an instance variable
        self.understanding = 0
        print("Added", self.name)

    def visit_office_hours(self, staff):
        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

    def grant_more_slip_days(self, student, days):
        student.max_slip_days = days

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]
>>> elle.max_slip_days
>>> callahan.grant_more_slip_days(elle, 7)
>>> elle.max_slip_days
>>> Student.max_slip_days

Q4: Keyboard

We'd like to create a Keyboard class that takes in an arbitrary number of Buttons and stores these Buttons in a dictionary. The keys in the dictionary will be ints that represent the postition on the Keyboard, and the values will be the respective Button. Fill out the methods in the Keyboard class according to each description, using the doctests as a reference for the behavior of a Keyboard.

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