Published Apr 17, 2017

List Comprehensions vs. Generator Expressions

List Comprehensions vs. Generator Expressions

Do you know the difference between the following syntax?

  • [x for x in range(5)]

  • (x for x in range(5))

  • tuple(range(5))**

Let's check it

5 Facts About the Lists

Let's start off with a short review of "list," arrays in other languages.

  • List is a type of data that can be represented as a collection of elements. Simple list looks like this: [0, 1, 2, 3, 4, 5].

  • Lists take all possible types of data and combinations of data as their components:

>>> a = 12
>>> b = "this is text"
>>> my_list = [0, b, ['element', 'another element'], (1, 2, 3), a]
>>> print(my_list)
[0, 'this is text', ['element', 'another element'], (1, 2, 3), 12]
  • Lists can be indexed. You can get access to any individual element or group of elements using the following syntax:
>>> a = ['red', 'green', 'blue']
>>> print(a[0])
  • Unlike strings, lists are mutable in Python. This means you can replace, add, or remove elements.
  • You can create a list using a for loop and a range() function.
>>> my_list = []
>>> for x in range(10):
...     my_list.append(x * 2)
>>> print(my_list)
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

What is List Comprehension?

Often seen as a part of functional programming in Python, list comprehensions allow you to create lists with a for loop with less code.

Look at the implementation of the previous example using a list comprehension:

>>> comp_list = [x * 2 for x in range(10)] 
>>> print(comp_list)
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

The above example is oversimplified to help you understand the idea of syntax. The same result may be achieved simply by using list(range(0, 19, 2)) function. However, you can use a more complex modifier in the first part of comprehension, or add a condition that will filter the list. Something like this:

>>> comp_list = [x ** 2 for x in range(7) if x % 2 == 0] 
>>> print(comp_list)
[4, 16, 36]

Another available option is to use list comprehension to combine several lists and create a list of lists. At first glance, the syntax seems to be complicated. It may help to think of lists as outer and inner sequences.

It’s time to show the power of list comprehensions when you want to create a list of lists by combining two existing lists.

>>> nums = [1, 2, 3, 4, 5]
>>> letters = ['A', 'B', 'C', 'D', 'E']
>>> nums_letters = [[n, l] for n in nums for l in letters]
#the comprehensions list combines two simple lists in a complex list of lists.
>>> print(nums_letters)
>>> print(nums_letters)
[[1, 'A'], [1, 'B'], [1, 'C'], [1, 'D'], [1, 'E'], [2, 'A'], [2, 'B'], [2, 'C'], [2, 'D'], [2, 'E'], [3, 'A'], [3, 'B'], [3, 'C'], [3, 'D'], [3, 'E'], [4, 'A'], [4, 'B'], [4, 'C'], [4, 'D'], [4, 'E'], [5, 'A'], [5, 'B'], [5, 'C'], [5, 'D'], [5, 'E']]

Let’s try it with text or, or a more precise term, string object.

>>> iter_string = "some text"
>>> comp_list = [x for x in iter_string if x !=" "]
>>> print(comp_list)
['s', 'o', 'm', 'e', 't', 'e', 'x', 't']

The comprehensions are not limited to lists. You can create dicts and sets comprehensions as well.

>>> dict_comp = {x:chr(65+x) for x in range(1, 11)}
>>> type(dict_comp)
<class 'dict'>  
>>> print(dict_comp)
{1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J', 10: 'K'}

>>> set_comp = {x ** 3 for x in range(10) if x % 2 == 0}
>>> type(set_comp)
<class 'set'>  
>>> print(set_comp)
{0, 8, 64, 512, 216}

Difference Between Iterable and Iterator

It would be easier to understand the concept of generators if you get the idea of iterables and iterators.

Iterable is a "sequence" of data you can iterate over using a loop. The easiest visible example of iterable can be a list of integers — [1, 2, 3, 4, 5, 6, 7]. However, it’s possible to iterate over other types of data like strings, dicts, tuples, sets, etc.

Basically, any object that has iter() method can be used as an iterable. You can check it using hasattr() function in the interpreter.

>>> hasattr(str, '__iter__')
>>> hasattr(bool, '__iter__')

Iterator protocol is implemented whenever you iterate over a sequence of data. For example, when you use a for loop the following is happening on a background:

  • first iter() method is called on the object to converts it to an iterator object.

  • next() method is called on the iterator object to get the next element of the sequence.

  • StopIteration exception is raised when there are no elements left to call.

>>> simple_list = [1, 2, 3]
>>> my_iterator = iter(simple_list)
>>> print(my_iterator)
<list_iterator object at 0x7f66b6288630>  
>>> next(my_iterator)
>>> next(my_iterator)
>>> next(my_iterator)
>>> next(my_iterator)
Traceback (most recent call last):  
  File "<stdin>", line 1, in <module>

Generator Expressions

In Python, generators provide a convenient way to implement the iterator protocol. Generator is an iterable created using a function with a yield statement.

The main feature of generator is that it evaluates the elements on demand. When you call a normal function with a return statement the function is terminated whenever it encounters a return statement. In a function with a yield statement the state of the function is “saved” from the last call and can be picked up the next time you call a generator function.

>>> def my_gen():
...     for x in range(5):
...             yield x

Generator expression allows you to create a generator on a fly without a yield keyword. However, it doesn’t share the whole power of generator created with a yield function. The syntax and concept is similar to list comprehensions:

>>> gen_exp = (x ** 2 for x in range(10) if x % 2 == 0) 
>>> for x in gen_exp:
...     print(x)

In terms of syntax, the only difference is that you'd use parenthesis instead of square brackets. However, the type of data returned by list comprehensions and generator expressions differs.

>>> list_comp = [x ** 2 for x in range(10) if x % 2 == 0]
>>> gen_exp = (x ** 2 for x in range(10) if x % 2 == 0)
>>> print(list_comp)
[0, 4, 16, 36, 64]
>>> print(gen_exp)
<generator object <genexpr> at 0x7f600131c410>   

The main advantage of using generator over a list is that generator takes much less memory. We can check how much memory they use by using the sys.getsizeof() method.

Note: in Python 2, using range() function can't actually reflect the advantage in term of size, as it still keeps the whole list of elements in memory. In Python 3, however, this example is viable as the range() returns a range object.

>>> from sys import getsizeof
>>> my_comp = [x * 5 for x in range(1000)]
>>> my_gen = (x * 5 for x in range(1000))
>>> getsizeof(my_comp)
>>> getsizeof(my_gen)

Generator yields one item at a time, thus, it is more memory efficient compared to the list.

For example, when you want to iterate over a list, python reserves memory for the whole list. Generator won’t keep the whole sequence in memory and will only “generate” the next element of the sequence on demand.

Final Thoughts

The very first thing that might scare or discourage a newbie programmer is the scale of educational material. The trick here is to treat each concept as an option offered by language — you’re not expected to learn all the language concepts and modules all at once. There are always different ways to solve the same task. Take it as one more tool to get the job done!

This tutorial was originally posted on Django Stars Blog. This version has been edited for clarity and may appear different from the original post.