Starter Files

Download lab02.zip. Inside the archive, you will find starter files for the questions in this lab, along with a copy of the OK autograder.

Submission

By the end of this lab, you should have submitted the lab with python3 ok --submit. You may submit more than once before the deadline; only the final submission will be graded. Check that you have successfully submitted your code on okpy.org.

To receive full credit for this lab, all questions 1 - 5 must be attempted.

When you are ready to submit, run ok with the --submit option:

python3 ok --submit

After submitting, ok will display a submission URL, with which you can view your submission on okpy.org.

Introduction

In the last lab, you learned some basic expressions and wrote some python code. In this lab, we will introduce lists and take a look at how they can be used.

Lists

In Data 8, you have recently started working with Tables. Tables are an extremely useful and powerful data type. In CS88 we will work with other data types. Python provides several important built-in data types that we can build from. So far, you have met numberical data types (ints, floats, and booleans) and one sequence type (strings). Lists, tuples, and dictionaries are other sequence data types in Python. Here, we will take a closer look at lists. A list can contain a sequence of values of any type.

You can create a list just by placing the values, separated by commas, within square brackets. Here are some examples. As you will see in one of the examples, lists can contain other lists.

>>> [1,2,3]
[1, 2, 3]
>>> ["frog", 3, 3.1415]
['frog', 3, 3.1415]
>>> [True, [1, 2], 42]
[True, [1, 2], 42]

Open up your python interpreter and create some lists of your own.
You learned last week that what really makes a data type useful is the operations that you can perform on it. What can you do with lists?

>>> x = [1,2,3]    # assign them to variables
>>> len(x)         # get their length, i.e., the number of elements in them
3
>>> x + [4,5]      # + is concatenation
[1, 2, 3, 4, 5]
>>> [1,2] * 3        # * is replication
[1, 2, 1, 2, 1, 2]
>>> len([1,2] * 3)
6
>>> [1,2] * [3,4]    # what's this?
TypeError: can't multiply sequence by non-int of type 'list'

The in operator is very useful when working with lists. It operates on the entire list and produces a boolean that answers the question, "Is this item in the list?".

>>> 2 in [1,2,3]
True
>>> "frog" in [1,2,3]
False
>>> [1,2] in [1,2,3]
False
>>> [1,2] in [[1,2],3]
True

List Comprehensions

List comprehensions are a compact and powerful way of creating new lists out of sequences. Let's work with them directly:

>>> [i**2 for i in [1, 2, 3, 4] if i%2 == 0]
[4, 16]

is equivalent to

>>> lst = []
>>> for i in [1, 2, 3, 4]:
...     if i % 2 == 0:
...         lst += [i**2]
>>> lst
[4, 16]

The general syntax for a list comprehension is

[<expression> for <element> in <sequence> if <conditional>]

The syntax is designed to read like English: "Compute the expression for each element in the sequence if the conditional is true."

Introduction to 'Map', 'Filter', and 'Reduce'

Map

Higher order functions fit into a domain of programming known as "functional" or "functional form" programming, centered around this idea of passing and returning functions as parameters and arguments. In class, you learned the command map that is a fundamental example of higher order functions.

Let's take a closer look at how map works. At its core, map applies a function to all items in an input list. It takes in a function as the first parameter and a series of inputs as the second parameter.

map(function_to_apply, list_of_inputs)

A potentially easier way to think about map is to draw an equivalent with a list comprehension! Given the func (function to apply) and inputs (list of inputs), a map is similar to this:

[func(x) for x in inputs]

Keep in mind that the map function actually returns a map object, not a list. We need to convert this object to a list by passing it into the list() function.

Let's do a Python Tutor example to understand how map works.

Open Python Tutor in a new tab.

This code should already be there:

INCR = 2
def inc(x):
    return x+INCR

def mymap(fun, seq):
    return [fun(x) for x in seq]

result = mymap(inc, [5, 6, 7])
print(result)

So what's happening here? In the first 3 lines, we're defining a function inc which increments an input x by a certain amount, INCR.

Notice that INCR is defined once in the Global frame. This is a nice review of how Python resolves references when there are both local and global variables. When the inc method executes, python needs to find the value INCR. Since the INCR variable isn't declared locally, within the inc function, Python will look at the parent frame, the frame in which inc was declared, for the value of INCR. In this case, since the inc function was declared in the Global frame, the global INC variable value will be used.

The second function, mymap, is an example of how map works in the form of a list comprehension! Notice that mymap takes in a function as its first argument and a sequence as its second. Just like map, this list comprehension runs each element of seq through the fun method.

As you run through the program in Python Tutor, notice how the list comprehension in mymap will repeatedly call the inc function. The functional anatomy of how map works is exactly encapsulated by the mymap function.

Filter

The filter keyword is similar in nature to map with a very important distinction. In map, the function we pass in is being applied to every item in our sequence. In filter, the function we pass in filters the elements, only leaving the elements for which the function returns true. For example, if I wanted to remove all negative numbers from a list, I could use the filter function to identify values that are greater than or equal to 0, and filter out the rest.

def isPositive(number):
    return number >= 0

numbers = [-1, 1, -2, 2, -3, 3, -4, 4]
positive_nums = list(filter(isPositive, numbers))

Again, similar to map, the output of the filter function is a filter object, not a list, so you need to call list(). The equivalent for filter in the form of a list comprehension would look something along the lines of this:

positive_nums = [number for number in numbers if isPositive(number)]

Reduce

Reduce takes in three different parameters: A function, a sequence, and an identity. The function and sequence are the same parameters that we saw in map and filter. The identity can be thought of as the container where you are going to store all of your results. In the above case, the identity would be the product variable.

Reduce is very useful for performing computations on lists that involve every element in the list. Computations are performed in a rolling fashion, where the function acts upon each element one at a time.

Let's say I wanted to calculate the product of the square roots of a list of numbers. The non-reduce version of this code would look something along the lines of this:

product = 1
numbers = [4, 9, 16, 25, 36]

for num in numbers:
    product = product * num**.5

Here's the reduce version

  multiplicative_identity = 1
  nums = [4, 9, 16, 25, 36]
  def sqrtProd(x, y):
      return x * y ** .5

  reduce(sqrtProd, nums, multiplicative_identity)

Required Problems

Coding Practice

Question 1: Classify the elements

Complete the function odd_even that classifies an number as either 'odd' or 'even' and the function classify that takes in a list and applies odd_even to all elements in the list.

def odd_even(x):
    """Classify a number as odd or even.

    >>> odd_even(4)
    'even'
    >>> odd_even(3)
    'odd'
    """
    "*** YOUR CODE HERE ***"
    if (x % 2) == 0:
        return 'even'
    else:
        return 'odd'
def classify(s):
    """
    Classify all the elements of a sequence as odd or even
    >>> classify([0, 1, 2, 4])
    ['even', 'odd', 'even', 'even']
    """
    "*** YOUR CODE HERE ***"
    return [odd_even(x) for x in s]

Use OK to test your code:

python3 ok -q odd_even

Use OK to test your code:

python3 ok -q classify

Question 2: If this not that

Define if_this_not_that, which takes a list of integers i_list, and an integer this, and for each element in i_list if the element is larger than this then print the element, otherwise print that.

def if_this_not_that(i_list, this):
    """
    >>> original_list = [1, 2, 3, 4, 5]
    >>> if_this_not_that(original_list, 3)
    that
    that
    that
    4
    5
    """
    "*** YOUR CODE HERE ***"
    for elem in i_list:
        if elem <= this:
            print("that")
        else:
            print(elem)

Use OK to test your code:

python3 ok -q if_this_not_that

Question 3: Shuffle

Define a function shuffle that takes a sequence with an even number of elements (cards) and creates a new list that interleaves the elements of the first half with the elements of the second half.

Let's better understand what it means to shuffle a list in this way. Let's say there is some list [1, 2, 3, 4, 5, 6, 7, 8]. To interleave the first half [1, 2, 3, 4] with the second half [5, 6, 7, 8] means that your final list should contain the first element from the first half, then the first element from the second half, then the second element of the first half, then the second element of the second half and so on.

So the interleaved version of [1, 2, 3, 4, 5, 6, 7, 8] would be [1, 5, 2, 6, 3, 7, 4, 8].

def shuffle(cards):
    """Return a shuffled list that interleaves the two halves of cards.

    >>> lst = [1, 2, 3, 4, 5, 6, 7, 8]
    >>> shuffle(lst)
    [1, 5, 2, 6, 3, 7, 4, 8]
    >>> shuffle(range(6))
    [0, 3, 1, 4, 2, 5]
    >>> cards = ['AH', '1H', '2H', '3H', 'AD', '1D', '2D', '3D']
    >>> shuffle(cards)
    ['AH', 'AD', '1H', '1D', '2H', '2D', '3H', '3D']
    >>> cards # should not be changed
    ['AH', '1H', '2H', '3H', 'AD', '1D', '2D', '3D']
    """
    assert len(cards) % 2 == 0, 'len(cards) must be even'
    "*** YOUR CODE HERE ***"
    half = len(cards) // 2
    shuffled = []
    for i in range(half):
        shuffled.append(cards[i])
        shuffled.append(cards[half+i])
    return shuffled

Use OK to test your code:

python3 ok -q shuffle

Question 4: Perfect Pairs

Implement the function pairs, which takes in an integer n, and returns a new list of lists which contains pairs of numbers from 1 to n. Use a list comprehension.

def pairs(n):
    """Returns a new list containing two element lists from values 1 to n
    >>> pairs(1)
    [[1, 1]]
    >>> x = pairs(2)
    >>> x
    [[1, 1], [2, 2]]
    >>> pairs(5)
    [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]
    >>> pairs(-1)
    []
    """
    "*** YOUR CODE HERE ***"
    
    return [[i, i] for i in range(1, n + 1)]

Use OK to test your code:

python3 ok -q pairs

Question 5: Coordinates

Implement a function coords, which takes a function, a sequence, and an upper and lower bound on output of the function. coords then returns a list of x, y coordinate pairs (lists) such that:

Each pair contains [x, fn(x)]
The x coordinates are the elements in the sequence
Only pairs whose y coordinate is within the upper and lower bounds (inclusive)

See the doctests for examples.

One other thing: your answer can only be one line long. You should make use of list comprehensions!

def coords(fn, seq, lower, upper):
    """
    >>> seq = [-4, -2, 0, 1, 3]
    >>> def fn(x):
    ...     return x**2
    >>> coords(fn, seq, 1, 9)
    [[-2, 4], [1, 1], [3, 9]]
    """
    "*** YOUR CODE HERE ***"
    
    return [[x, fn(x)] for x in seq if lower <= fn(x) and f(x) <= upper]

Use OK to test your code:

python3 ok -q coords

Submit

Make sure to submit this assignment by running:

python3 ok --submit

Lab 2: Lists, List Comprehension and Iteration Due at 11:59:59 pm on 02/07/2023.

Starter Files

Submission

Introduction

Lists

List Comprehensions

Introduction to 'Map', 'Filter', and 'Reduce'

Map

Filter

Reduce

Required Problems

Coding Practice

Question 1: Classify the elements

Question 2: If this not that

Question 3: Shuffle

Question 4: Perfect Pairs

Question 5: Coordinates

Submit

Lab 2: Lists, List Comprehension and Iteration
Due at 11:59:59 pm on 02/07/2023.