CS5001 / CS5003:
Intensive Foundations of Computer Science

# Lecture 12: Iterators and Generators

>>> for c in "python":
...     print(c)
...
p
y
t
h
o
n
def yrange(n):
i = 0
while i < n:
yield i
i += 1

Today's topics:

1. Iterators
2. Generators
3. Lambda functions
4. The set class

# Lecture 12: Iterators and Generators

Examples for today's lecture borrowed from: https://anandology.com/python-practice-book/iterators.html

We have seen many types of iteration in this class so far:

# Lecture 12: Iterators

>>> for s in ["These", "are", "some", "words"]:
...   print(s)
...
These
are
some
words

Iterate over a list:

>>> for c in "python":
...     print(c)
...
p
y
t
h
o
n

Iterate over a string:

>>> for k in {"x": 1, "y": 2}:
...     print(k)
...
y
x

Iterate over a dict (keys only):

>>> with open("a.txt") as f:
...         print(line[:-1])
...
first line
second line

Iterate over a file:

These are all called iterable objects.

We can create an iterator from an iterable object with the built-in function, iter. Then, we can use the next function to get the values, one at a time:

# Lecture 12: Iterators

>>> x = iter([1, 2, 3])
>>> x
<listiterator object at 0x1004ca850>
>>> next(x)
1
>>> next(x)
2
>>> next(x)
3
>>> next(x)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration

We can create our own iterator using a class. The following iterator behaves like the range function:

# Lecture 12: Iterators

class myrange:
def __init__(self, n):
self.i = 0
self.n = n

def __iter__(self):
return self

def __next__(self):
if self.i < self.n:
i = self.i
self.i += 1
return i
else:
raise StopIteration()

The __iter__ method is what makes an object iterable. Behind the scenes, the iter function calls __iter__ method on the given object.

The return value of __iter__ is an iterator. It should have a __next__ method and raise StopIteration when there are no more elements.

By the way: raise means to call an exception, which can be caught in a try/except block.

Let's try it out:

# Lecture 12: Iterators

class myrange:
def __init__(self, n):
self.i = 0
self.n = n

def __iter__(self):
return self

def __next__(self):
if self.i < self.n:
i = self.i
self.i += 1
return i
else:
raise StopIteration()
>>> my_r = myrange(3)
>>> next(my_r)
0
>>> next(my_r)
1
>>> next(my_r)
2
>>> next(my_r)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 14, in __next__
StopIteration

Example: write an iterator class called reverse_iter, that takes a list and iterates it from the reverse direction. It should behave like this:

# Lecture 12: Iterators

>>> it = reverse_iter([1, 2, 3, 4])
>>> next(it)
4
>>> next(it)
3
>>> next(it)
2
>>> next(it)
1
>>> next(it)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration

Example: write an iterator class called reverse_iter, that takes a list and iterates it from the reverse direction. It should behave like this:

# Lecture 12: Iterators

class reverse_iter:
def __init__(self, lst):
self.lst = lst

def __iter__(self):
return self

def __next__(self):
if len(self.lst) > 0:
return self.lst.pop()
else:
raise StopIteration()

If we want to create an iterator, we can do it the way that we have just seen, or we can use a simpler method, called a generator. A generator simplifies the creation of iterators. It is a function that produces a sequence of results instead of a single one:

# Lecture 12: Generators

def myrange(n):
i = 0
while i < n:
yield i
i += 1

Every time the yield statement is executed, the function generates a new value:

>>> my_r = myrange(3)
>>> my_r
<generator object yrange at 0x401f30>
>>> next(my_r)
0
>>> next(my_r)
1
>>> next(my_r)
2
>>> next(my_r)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration

This is a generator function that produces a generator.

Let's see how this is working internally, with a modified generator function that has some extra print statements in it:

# Lecture 12: Generators

>>> def foo():
...     print("begin")
...     for i in range(3):
...         print("before yield", i)
...         yield i
...         print("after yield", i)
...     print("end")
...

>>> f = foo()
>>> next(f)
begin
before yield 0
0
>>> next(f)
after yield 0
before yield 1
1
>>> next(f)
after yield 1
before yield 2
2
>>> next(f)
after yield 2
end
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration
>>>

When a generator function is called, it returns a generator object without even beginning execution of the function. When the next method is called for the first time, the function starts executing until it reaches a yield statement. The yielded value is returned by the next call.

Let's see how this is working internally, with a modified generator function that has some extra print statements in it:

# Lecture 12: Generators

>>> def foo():
...     print("begin")
...     for i in range(3):
...         print("before yield", i)
...         yield i
...         print("after yield", i)
...     print("end")
...

>>> f = foo()
>>> next(f)
begin
before yield 0
0
>>> next(f)
after yield 0
before yield 1
1
>>> next(f)
after yield 1
before yield 2
2
>>> next(f)
after yield 2
end
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration
>>>

When a generator function is called, it returns a generator object without even beginning execution of the function. When the next method is called for the first time, the function starts executing until it reaches a yield statement. The yielded value is returned by the next call.

Let's see some more generator examples:

# Lecture 12: Generators

def integers():
"""Infinite sequence of integers."""
i = 1
while True:
yield i
i = i + 1
def squares():
for i in integers():
yield i * i

def take(n, seq):
"""Returns first n values from the given sequence."""
seq = iter(seq)
result = []
try:
for i in range(n):
result.append(next(seq))
except StopIteration:
pass
return result

    ints = integers()
for i in range(5):
print(next(ints))
1
2
3
4
5
1
4
9
16
25
    sqs = squares()
for i in range(5):
print(next(sqs))
take(5, squares())
1
4
9
16
25

We can create a generator using a generator expression, which is much like the list expressions we are used to.

# Lecture 12: Generators

>>> a = (x*x for x in range(10))
>>> a
<generator object <genexpr> at 0x401f08>
>>> sum(a)
285

Notice that we've replaced the list comprehension brackets with parentheses -- this creates the generator.

Remember: a generator does not produce all of its values at once, and this can lead to better performance.

Here is an interesting example. Let's generate the first n pythagorean triplets. A pythagorean triplet meets the requirement that for a triplet

# Lecture 12: Generators

pyt = ((x, y, z) for z in integers()
for y in range(1, z)
for x in range(1, y) if x*x + y*y == z*z)
\left(x, y, z\right) \\ x^2 + y^2 = z^2

Or, in Python, x*x + y*y = z*z

Here is a generator for pythagorean triplets (assuming we have the integers generator previously described):

take(5, pyt)
[(3, 4, 5), (6, 8, 10), (5, 12, 13), (9, 12, 15), (8, 15, 17)]

A lambda function is a type of function that does not have a name associated with it. Lambda functions are also known as anonymous functions for this reason.'

In Python, lambda functions are created using the lambda keyword, and they are limited to expressions (not statements). Here is an example:

# Lecture 12: Lambda Functions

>>> square = lambda x: x * x
>>> square(5)
25
>>> square(8)
64
>>>

Lambda functions do not have an explicit return statement, and they simply perform the expression on the parameters and return the value. The above example creates a square function made from a lambda function that takes x as a parameter, and returns x * x.

• You might be asking yourself, why would we use lambda functions if it is easy enough to create a regular function? This is a good question! But, sometimes it is useful to be able to craft a tiny function to take in certain parameters and call another function (for example) with other parameters.
• Here is an example from the Snake assignment:

# Lecture 12: Lambda Functions

        self.bind("<Up>", lambda e: self.keypress(e, "Up"))
self.bind("<Down>", lambda e: self.keypress(e, "Down"))
self.bind("<Left>", lambda e: self.keypress(e, "Left"))
self.bind("<Right>", lambda e: self.keypress(e, "Right"))

The bind function is part of the Tkinter graphics library, and it requires a function that takes a single argument. However, I wanted to use a function called keypress that takes two arguments, so I could pass in the direction to the function. I could have created four separate functions that each took a single argument, and then passed them on to the keypress function with an extra argument, but it was much cleaner to use a lambda function for the purpose.

• One data structure that we haven't yet covered is the set. A set is a collection that only allows unique elements. Here is an example of how it works:

# Lecture 12: The set class

>>> text = """I DO NOT LIKE THEM IN A HOUSE
... I DO NOT LIKE THEM WITH A MOUSE
... I DO NOT LIKE THEM HERE OR THERE
... I DO NOT LIKE THEM ANYWHERE
... I DO NOT LIKE GREEN EGGS AND HAM
... I DO NOT LIKE THEM, SAM-I-AM""".replace('\n',' ')
>>> text
'I DO NOT LIKE THEM IN A HOUSE I DO NOT LIKE THEM WITH A MOUSE I DO NOT LIKE THEM HERE OR
THERE I DO NOT LIKE THEM ANYWHERE I DO NOT LIKE GREEN EGGS AND HAM I DO NOT LIKE THEM,
SAM-I-AM'
>>> s = set()
>>> for word in text.split(' '):
...
>>> print(s)
{'NOT', 'OR', 'I', 'THEM,', 'ANYWHERE', 'HAM', 'DO', 'A', 'THERE', 'WITH', 'LIKE',
'SAM-I-AM', 'HERE', 'THEM', 'MOUSE', 'GREEN', 'AND', 'HOUSE', 'IN', 'EGGS'}
>>> len(text.split(' '))
44
>>> len(s)
20

Only the unique words are stored in the set.

• The set has many useful functions. Here is part of the help file for set:

# Lecture 12: The set class

 |  add(...)
|      Add an element to a set.
|
|      This has no effect if the element is already present.
|
|  clear(...)
|      Remove all elements from this set.
|
|  difference(...)
|      Return the difference of two or more sets as a new set.
|
|      (i.e. all elements that are in this set but not the others.)
|
|  intersection(...)
|      Return the intersection of two sets as a new set.
|
|      (i.e. all elements that are in both sets.)
|
|  isdisjoint(...)
|      Return True if two sets have a null intersection.
|
|  issubset(...)
|      Report whether another set contains this set.
|
|  issuperset(...)
|      Report whether this set contains another set.
|
|  pop(...)
|      Remove and return an arbitrary set element.
|      Raises KeyError if the set is empty.
|
|  union(...)
|      Return the union of sets as a new set.
|
|      (i.e. all elements that are in either set.)
|

• Here are some examples using set functions:

# Lecture 12: The set class

>>> print(s)
{'MOUSE', 'SAM-I-AM', 'HERE', 'A', 'DO', 'NOT', 'HAM', 'WITH', 'THEM', 'IN', 'OR', 'THERE', 'AND', 'HOUSE',
'GREEN', 'I', 'THEM,', 'LIKE', 'ANYWHERE', 'EGGS'}
>>> print(s2)
{'FOX', 'AND', 'BOX', 'GREEN', 'HAM', 'EGGS'}
>>> s.difference(s2)
{'THEM,', 'LIKE', 'MOUSE', 'THEM', 'IN', 'SAM-I-AM', 'HERE', 'HOUSE', 'OR', 'A', 'ANYWHERE', 'DO', 'NOT',
'I', 'THERE', 'WITH'}
>>> s2.difference(s)
{'FOX', 'BOX'}
>>> s.intersection(s2)
{'AND', 'HAM', 'GREEN', 'EGGS'}
>>> s.union(s2)
{'MOUSE', 'FOX', 'AND', 'SAM-I-AM', 'HERE', 'HOUSE', 'A', 'DO', 'NOT', 'GREEN', 'HAM', 'I', 'WITH', 'THEM,',
'LIKE', 'THEM', 'IN', 'OR', 'BOX', 'ANYWHERE', 'THERE', 'EGGS'}
>>> s - s2
{'THEM,', 'LIKE', 'MOUSE', 'THEM', 'IN', 'SAM-I-AM', 'HERE', 'HOUSE', 'OR', 'A', 'ANYWHERE', 'DO', 'NOT',
'I', 'THERE', 'WITH'}
>>> s2 - s
{'FOX', 'BOX'}
>>>
• Here are some more examples using set functions:

# Lecture 12: The set class

>>> print(s)
{'MOUSE', 'SAM-I-AM', 'HERE', 'A', 'DO', 'NOT', 'HAM', 'WITH', 'THEM', 'IN', 'OR', 'THERE', 'AND', 'HOUSE', 'GREEN', 'I', 'THEM,', 'LIKE', 'ANYWHERE', 'EGGS'}
>>> print(s3)
{'TREE', 'CAR'}
>>> print(s4)
{'SAM-I-AM', 'NOT'}
>>> s.isdisjoint(s3)
True
>>> s.isdisjoint(s4)
False
>>> s.issuperset(s3)
False
>>> s.issuperset(s4)
True
>>> s3.issubset(s)
False
>>> s4.issubset(s)
True
>>>

By Chris Gregg

• 636