Lab 06: Mutability and Iterators

Starter Files

Download lab06.zip. Inside the archive, you will find starter files for the questions in this lab, along with a copy of the Ok autograder.

Topics

Consult this section if you need a refresher on the material for this lab. It's okay to skip directly to the questions and refer back here should you get stuck.

Iterators

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 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 iterator 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 an iterator over x for each x in iterable if f(x)
zip(iterables*) - Creates an iterator over co-indexed tuples with elements from each of the iterables
reversed(iterable) - Creates an 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.

Mutability

Mutability

We say that an object is mutable if its state can change as code is executed. The process of changing an object's state is called mutation. Examples of mutable objects include lists and dictionaries. Examples of objects that are not mutable include tuples and functions.

Required Questions

Mutability

Q1: WWPD: List-Mutation

Use Ok to test your knowledge with the following "What Would Python Display?" questions:
python3 ok -q list-mutation -u
Important: For all WWPD questions, type Function if you believe the answer is <function...>, Error if it errors, and Nothing if nothing is displayed.

>>> lst = [5, 6, 7, 8]
>>> lst.append(6)
______
>>> lst
______
>>> lst.insert(0, 9)
>>> lst
______
>>> x = lst.pop(2)
>>> lst
______
>>> lst.remove(x)
>>> lst
______
>>> a, b = lst, lst[:]
>>> a is lst
______
>>> b == lst
______
>>> b is lst
______
>>> lst = [1, 2, 3]
>>> lst.extend([4,5])
>>> lst
______
>>> lst.extend([lst.append(9), lst.append(10)])
>>> lst
______

Q2: Insert Items

Write a function which takes in a list lst, an argument entry, and another argument elem. This function will check through each item in lst to see if it is equal to entry. Upon finding an item equal to entry, the function should modify the list by placing elem into lst right after the item. At the end of the function, the modified list should be returned.

See the doctests for examples on how this function is utilized.

Important: Use list mutation to modify the original list. No new lists should be created or returned. Because of this, consider using a while loop instead of a for loop.

Note: If the values passed into entry and elem are equivalent, make sure you're not creating an infinitely long list while iterating through it. If you find that your code is taking more than a few seconds to run, the function may be in a loop of inserting new values.

def insert_items(lst, entry, elem):
    """Inserts elem into lst after each occurence of entry and then returns lst.

    >>> test_lst = [1, 5, 8, 5, 2, 3]
    >>> new_lst = insert_items(test_lst, 5, 7)
    >>> new_lst
    [1, 5, 7, 8, 5, 7, 2, 3]
    >>> double_lst = [1, 2, 1, 2, 3, 3]
    >>> double_lst = insert_items(double_lst, 3, 4)
    >>> double_lst
    [1, 2, 1, 2, 3, 4, 3, 4]
    >>> large_lst = [1, 4, 8]
    >>> large_lst2 = insert_items(large_lst, 4, 4)
    >>> large_lst2
    [1, 4, 4, 8]
    >>> large_lst3 = insert_items(large_lst2, 4, 6)
    >>> large_lst3
    [1, 4, 6, 4, 6, 8]
    >>> large_lst3 is large_lst
    True
    >>> # Ban creating new lists
    >>> from construct_check import check
    >>> check(HW_SOURCE_FILE, 'insert_items',
    ...       ['List', 'ListComp', 'Slice'])
    True
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q insert_items

Iterators

Q3: WWPD: Iterators

Use Ok to test your knowledge with the following "What Would Python Display?" questions:
python3 ok -q iterators-wwpd -u
Important: Enter StopIteration if a StopIteration exception occurs, Error if you believe a different error occurs, and Iterator if the output is an iterator object. These are tricky, so refer to the information under the tabs at the top of the page if you get stuck.

>>> s = [1, 2, 3, 4]
>>> t = iter(s)
>>> next(s)
______
>>> next(t)
______
>>> next(t)
______
>>> iter(s)
______
>>> next(iter(s))
______
>>> next(iter(t))
______
>>> next(iter(s))
______
>>> next(iter(t))
______
>>> next(t)
______

>>> r = range(6)
>>> r_iter = iter(r)
>>> next(r_iter)
______
>>> [x + 1 for x in r]
______
>>> [x + 1 for x in r_iter]
______
>>> next(r_iter)
______
>>> list(range(-2, 4))   # Converts an iterable into a list
______

>>> map_iter = map(lambda x : x + 10, range(5))
>>> next(map_iter)
______
>>> next(map_iter)
______
>>> list(map_iter)
______
>>> for e in filter(lambda x : x % 2 == 0, range(1000, 1008)):
...     print(e)
______
______
______
______
>>> [x + y for x, y in zip([1, 2, 3], [4, 5, 6])]
______
>>> for e in zip([10, 9, 8], range(3)):
...   print(tuple(map(lambda x: x + 2, e)))
______
______
______

Q4: Count Occurrences

Implement count_occurrences, which takes in an iterator t and returns the number of times the value x appears in the first n elements of t. A value appears in a sequence of elements if it is equal to an entry in the sequence.

Note: You can assume that t will have at least n elements.

def count_occurrences(t, n, x):
    """Return the number of times that x appears in the first n elements of iterator t.

    >>> s = iter([10, 9, 10, 9, 9, 10, 8, 8, 8, 7])
    >>> count_occurrences(s, 10, 9)
    3
    >>> s2 = iter([10, 9, 10, 9, 9, 10, 8, 8, 8, 7])
    >>> count_occurrences(s2, 3, 10)
    2
    >>> s = iter([3, 2, 2, 2, 1, 2, 1, 4, 4, 5, 5, 5])
    >>> count_occurrences(s, 1, 3)
    1
    >>> count_occurrences(s, 4, 2)
    3
    >>> next(s)
    2
    >>> s2 = iter([4, 1, 6, 6, 7, 7, 8, 8, 2, 2, 2, 5])
    >>> count_occurrences(s2, 6, 6)
    2
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q count_occurrences

Q5: Repeated

Implement repeated, which takes in an iterator t and returns the first value in t that appears k times in a row.

Note: You can assume that the iterator t will have a value that appears at least k times in a row. If you are receiving a StopIteration, your repeated function is likely not identifying the correct value.

Your implementation should iterate through the items in a way such that if the same iterator is passed into repeated twice, it should continue in the second call at the point it left off in the first. An example of this behavior is in the doctests.

def repeated(t, k):
    """Return the first value in iterator T that appears K times in a row.
    Iterate through the items such that if the same iterator is passed into
    the function twice, it continues in the second call at the point it left
    off in the first.

    >>> s = iter([10, 9, 10, 9, 9, 10, 8, 8, 8, 7])
    >>> repeated(s, 2)
    9
    >>> s2 = iter([10, 9, 10, 9, 9, 10, 8, 8, 8, 7])
    >>> repeated(s2, 3)
    8
    >>> s = iter([3, 2, 2, 2, 1, 2, 1, 4, 4, 5, 5, 5])
    >>> repeated(s, 3)
    2
    >>> repeated(s, 3)
    5
    >>> s2 = iter([4, 1, 6, 6, 7, 7, 8, 8, 2, 2, 2, 5])
    >>> repeated(s2, 3)
    2
    """
    assert k > 1
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q repeated