Discussion 5: Sequences, Mutability, Object-Oriented Programming
Walkthrough Videos
Feel free to try these problems on the worksheet in discussion or on your own, and then come back to reference these walkthrough videos as you study.
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Sequences
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
andfilter
built-ins have slightly different behavior than themy_map
andmy_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
.
def my_map(fn, seq):
"""Applies fn onto each element in seq and returns a list.
>>> my_map(lambda x: x*x, [1, 2, 3])
[1, 4, 9]
"""
result = []
for elem in seq:
result += [fn(elem)]
return result
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.
def my_filter(pred, seq):
"""Keeps elements in seq only if they satisfy pred.
>>> my_filter(lambda x: x % 2 == 0, [1, 2, 3, 4]) # new list has only even-valued elements
[2, 4]
"""
result = []
for elem in seq:
if pred(elem):
result += [elem]
return result
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
.
def my_reduce(combiner, seq):
"""Combines elements in seq using combiner.
seq will have at least one element.
>>> my_reduce(lambda x, y: x + y, [1, 2, 3, 4]) # 1 + 2 + 3 + 4
10
>>> my_reduce(lambda x, y: x * y, [1, 2, 3, 4]) # 1 * 2 * 3 * 4
24
>>> my_reduce(lambda x, y: x * y, [4])
4
>>> my_reduce(lambda x, y: x + 2 * y, [1, 2, 3]) # (1 + 2 * 2) + 2 * 3
11
"""
total = seq[0]
for elem in seq[1:]:
total = combiner(total, elem)
return total
Mutability
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)
: Addel
to the end of the list. ReturnNone
.extend(lst)
: Extend the list by concatenating it withlst
. ReturnNone
.insert(i, el)
: Insertel
at indexi
. This does not replace any existing elements, but only adds the new elementel
. ReturnNone
.remove(el)
: Remove the first occurrence ofel
in list. Errors ifel
is not in the list. ReturnNone
otherwise.pop(i)
: Remove and return the element at indexi
.
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]
OOP
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
staff.add_student(self)
print("Added", self.name)
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
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 Button
s and stores these Button
s 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
.
class Button:
def __init__(self, pos, key):
self.pos = pos
self.key = key
self.times_pressed = 0
class Keyboard:
"""A Keyboard takes in an arbitrary amount of buttons, and has a
dictionary of positions as keys, and values as Buttons.
>>> b1 = Button(0, "H")
>>> b2 = Button(1, "I")
>>> k = Keyboard(b1, b2)
>>> k.buttons[0].key
'H'
>>> k.press(1)
'I'
>>> k.press(2) # No button at this position
''
>>> k.typing([0, 1])
'HI'
>>> k.typing([1, 0])
'IH'
>>> b1.times_pressed
2
>>> b2.times_pressed
3
"""
def __init__(self, *args):
self.buttons = {}
for button in args:
self.buttons[button.pos] = button
def press(self, info):
"""Takes in a position of the button pressed, and
returns that button's output."""
if info in self.buttons.keys():
b = self.buttons[info]
b.times_pressed += 1
return b.key
return ''
def typing(self, typing_input):
"""Takes in a list of positions of buttons pressed, and
returns the total output."""
accumulate = ''
for pos in typing_input:
accumulate+=self.press(pos)
return accumulate