Iterators in Python

3 minute read

One of the things about Python that I haven’t fully appreciated are the use of iterators. I’ll go over some iterators that are a part of base Python and then go over more sophisticated applications with the itertools package in another post. Like with many new concepts, I am grateful to be able to learn from other online sources which I acknowledge.

# No packages necessary to import!

Basic iterators

(I borrow shamelessly from w3schools since their page is so helpful.)

It is easy to confuse an iterable object versus its iterator object. Once I made this distinction, it made it easier to understand how some methods worked. Examples of iterable objects are lists, dictionaries, and tuples–objects you’ve likely already used many times. You can get an iterator from these objects using the iter() method.

Here’s an example using a tuple as the iterable object.

mytuple = ("apple", "banana", "cherry")
mytupleit = iter(mytuple)    # Generate it's iterator
print(mytupleit)    # Note what the iterator object output when print() is called on it
<tuple_iterator object at 0x103531630>
# But the output of next() is a string
# ...and it advances to the next item
# ...and it advances to the next item
# ...until the iterator is exhausted

StopIteration                             Traceback (most recent call last)

<ipython-input-144-979006de96a1> in <module>()
      1 # ...until the iterator is exhausted
----> 2 print(next(mytupleit))


This is a simple example but it illustrates some interesting behavior. Note that there’s no explicit loop which you could do on the iterable object (the tuple). Instead we’re outputting each element by making a next call on the iterator object (myit). Each next call “remembers” where it is in the iterable object. (Interestingly, under the hood, the for loop is actually creating an iterator object and using next method.)

Here’s an example using a list as the iterable object.

mylist = ["Winfield", "Gwynn", "Hoffman"]
mylistit = iter(mylist)     # Generate it's iterator
# Let's be lazy and print it on one line
print(next(mylistit), next(mylistit), next(mylistit))
Winfield Gwynn Hoffman

If we want to be even more lazy, then we can also simply call list on the iterator to get back our original list (the iterable object). Remember the distinction between the iteratOR and the iterABLE objects!

# Regenerate the iterator
mylistit = iter(mylist)
# Get back the iterable object from the iterator
['Winfield', 'Gwynn', 'Hoffman']

Here’s an example using a string as the iterable object.

mystring = "SAN"
mystringit = iter(mystring)     # Generate it's iterator
print(next(mystringit), next(mystringit), next(mystringit))

Here’s an example using the range as the iterable object. Note that the range object itself is a generator-like object.

mynumbers = range(3)
mynumbersit = iter(mynumbers)
print(next(mynumbersit), next(mynumbersit), next(mynumbersit))
0 1 2

Iterator operators zip and map

Both zip and map are iterator operators that are built-in Python functions.

# Zip is commonly used to make tuples from two lists but the zip output itself is not a list
zip(mynumbers, mylist)
<zip at 0x103524208>
list(zip(mynumbers, mylist))
[(0, 'Winfield'), (1, 'Gwynn'), (2, 'Hoffman')]

From this link: “Under the hood, the zip() function works, in essence, by calling iter() on each of its arguments, then advancing each iterator returned by iter() with next() and aggregating the results into tuples. The iterator returned by zip() iterates over these tuples.”

Let’s look at an example of the map() function before discussing how it works.

# Like when invoking zip, the map object itself is not a list
map(len, mylist)
<map at 0x103548438>
list(map(len, mylist))
[8, 5, 7]

What is going on here when map() is called? Underneath, an iter() object is being first called on mylist, advancing with next() then applying the first argument (len()) to the value returned by next() at each step.


Shout outs to the following: