3. Generators

First lets understand iterators. According to Wikipedia, an iterator is an object that enables a programmer to traverse a container, particularly lists. However, an iterator performs traversal and gives access to data elements in a container, but does not perform iteration. You might be confused so lets take it a bit slow. There are three parts namely:

  • Iterable
  • Iterator
  • Iteration

All of these parts are linked to each other. We will discuss them one by one and later talk about generators.

3.1. Iterable

An iterable is any object in Python which has an __iter__ or a __getitem__ method defined which returns an iterator or can take indexes (You can read more about them here). In short an iterable is any object which can provide us with an iterator. So what is an iterator?

3.2. Iterator

An iterator is any object in Python which has a next (Python2) or __next__ method defined. That’s it. That’s an iterator. Now let’s understand iteration.

3.3. Iteration

In simple words it is the process of taking an item from something e.g a list. When we use a loop to loop over something it is called iteration. It is the name given to the process itself. Now as we have a basic understanding of these terms let’s understand generators.

3.4. Generators

Generators are iterators, but you can only iterate over them once. It’s because they do not store all the values in memory, they generate the values on the fly. You use them by iterating over them, either with a ‘for’ loop or by passing them to any function or construct that iterates. Most of the time generators are implemented as functions. However, they do not return a value, they yield it. Here is a simple example of a generator function:

def generator_function():
    for i in range(10):
        yield i

for item in generator_function():

# Output: 0
# 1
# 2
# 3
# 4
# 5
# 6
# 7
# 8
# 9

It is not really useful in this case. Generators are best for calculating large sets of results (particularly calculations involving loops themselves) where you don’t want to allocate the memory for all results at the same time. Many Standard Library functions that return lists in Python 2 have been modified to return generators in Python 3 because generators require fewer resources.

Here is an example generator which calculates fibonacci numbers:

# generator version
def fibon(n):
    a = b = 1
    for i in range(n):
        yield a
        a, b = b, a + b

Now we can use it like this:

for x in fibon(1000000):

This way we would not have to worry about it using a lot of resources. However, if we would have implemented it like this:

def fibon(n):
    a = b = 1
    result = []
    for i in range(n):
        a, b = b, a + b
    return result

It would have used up all our resources while calculating a large input. We have discussed that we can iterate over generators only once but we haven’t tested it. Before testing it you need to know about one more built-in function of Python, next(). It allows us to access the next element of a sequence. So let’s test out our understanding:

def generator_function():
    for i in range(3):
        yield i

gen = generator_function()
# Output: 0
# Output: 1
# Output: 2
# Output: Traceback (most recent call last):
#            File "<stdin>", line 1, in <module>
#         StopIteration

As we can see that after yielding all the values next() caused a StopIteration error. Basically this error informs us that all the values have been yielded. You might be wondering why we don’t get this error when using a for loop? Well the answer is simple. The for loop automatically catches this error and stops calling next. Did you know that a few built-in data types in Python also support iteration? Let’s check it out:

my_string = "Yasoob"
# Output: Traceback (most recent call last):
#      File "<stdin>", line 1, in <module>
#    TypeError: str object is not an iterator

Well that’s not what we expected. The error says that str is not an iterator. Well it’s right! It’s an iterable but not an iterator. This means that it supports iteration but we can’t iterate over it directly. So how would we iterate over it? It’s time to learn about one more built-in function, iter. It returns an iterator object from an iterable. While an int isn’t an iterable, we can use it on string!

int_var = 1779
# Output: Traceback (most recent call last):
#   File "<stdin>", line 1, in <module>
# TypeError: 'int' object is not iterable
# This is because int is not iterable

my_string = "Yasoob"
my_iter = iter(my_string)
# Output: 'Y'

Now that is much better. I am sure that you loved learning about generators. Do bear it in mind that you can fully grasp this concept only when you use it. Make sure that you follow this pattern and use generators whenever they make sense to you. You won’t be disappointed!