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Comprehensions and Generators

Comprehensions are easy ways to build new lists, sets, and dicts. Generators are an easy way to make lazy iterators. I’ll be discussing both because the syntax is so similar (at first). The examples below all follow the same basic formula of expression for item in iterator [if test] wrapped in [], {}, and finally (). In the end, we’ll discuss yield, but we’re getting ahead of ourselves already.

This is not a discussion on what an iterator, comprehension, or generator is - it’s a quickstart to recognizing and using them.

List Comprehension with “[]”

Create simple lists is with list comprehensions:

>>> [char for char in 'hello']
['h', 'e', 'l', 'l', 'o']

>>> [char for char in 'hello' if char not in 'oh']
['e', 'l', 'l']

Set Comprehension with “{}”

Create simple sets is with set comprehensions:

>>> {char for char in 'hello'}
set(['h', 'e', 'l', 'o'])

>>> {char for char in 'hello' if char not in 'oh'}
set(['e', 'l'])

Dictionary Comprehension with “{}”

Create simple dictionaries is with dictionary comprehensions:

>>> {i: char for i, char in enumerate('hello')}
{0: 'h', 1: 'e', 2: 'l', 3: 'l', 4: 'o'}

>>> {i: char for i, char in enumerate('hello') if char not in 'oh'}
{1: 'e', 2: 'l', 3: 'l'}

Dictionary comprehensions diverge from the others because of the need to specify a key: value pair. The iterator shoud provide items that can unpack into two variables (the key and value), like:

>>> for pair in [('key','value'), ('key2','value2')]:
...     key, value = pair  # <- unpacking going on here!
...     print('%s: %s' % (key, value))
key: value
key2: value2

So, what was enumerate('hello') all about?

>>> enumerate('hello')
<enumerate at 0x...>

Run help(enumerate) and read the description. Here’s the important part: “The enumerate object yields pairs …” When we’re talking about yielding values, we’re talking about generators … (Well, I mean, here we’re actually talking about iterators, but we’re getting to generators - I promise!)

>>> import inspect
>>> inspect.isgenerator(enumerate(''))

Run help(inspect.isgenerator) for hints on what makes a generator more than just an iterator. And make sure to read up on send(), which we won’t cover in this post, if you want to be a total generator hipster.

Generator Expressions with “()”

Here we go. Generators are all about being lazy.

>>> chars = (char for char in 'hello')
>>> chars
<generator object <genexpr> at 0x...>

>>> import inspect
>>> inspect.isgenerator(chars)

Instead of immediately operating over each item in 'hello', we got back a generator object. Generator objects are iterators, and each iteration yields the next value until there are no more values to yield!

>>> next(chars)
>>> ''.join(chars)

# did you know next could take a default value?
>>> next(chars, 'default')

OK. Let’s go all the way back to our first example [char for char in 'hello'] and pretend we actually want to end up with upper case letters …

List Comprehension:

>>> [char.upper() for char in 'hello']
['H', 'E', 'L', 'L', 'O']


(char.upper() for char in 'hello')
<generator object <genexpr> at 0x...>

The benefit of the generator is that there’s no need to operate over every value and build a list. Instead, we just get the values we want from the generator without the overhead. This is generally preferable, especially when dealing with lots of items.

Yielding values

Everything we’ve looked at so far fits nicely into a single line with a single expression and maybe a condition. But, life isn’t always that simple. Fortunately, building complex generators is simple - as simple as defining a function using yield instead of return.

Let’s start slow, because most resources online dive in without any good intuition up-front:

>>> def fib():
...     yield 1
...     yield 1
...     yield 2
...     yield 3
...     yield 5

>>> fib_values = fib()
>>> fib_values
<generator object fib at 0x...>

>>> next(fib_values), next(fib_values), next(fib_values)
(1, 1, 2)

>>> for value in fib_values: print(value)

>>> import inspect
>>> inspect.isgeneratorfunction(fib)

Hopefully this gives you an intuition for two things:

Knowing that, let’s work on our fib() generator a bit …

>>> def fib(n):
...     """ Generate the first `n` fibonacci numbers """
...     a, b = 0, 1
...     for n in xrange(n):
...         a, b = b, a + b
...         yield a

>>> tuple(fib(20))
(1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765)

Check it out:

Tuple Comprehension?

Not really, but passing a generator expression to tuple() is elegant way to create a tuple (even though it appears that evaluating a list comprehension to pass to tuple() may perform faster in some cases - like this one).

>>> tuple(char for char in 'hello')
('h', 'e', 'l', 'l', 'o')

In this case, tuple([char for char in 'hello']) runs almost 30% faster. Oh well. As you see above, you don’t even need the enclosing () around the generator expression if the generator is the only argument to the function. It’s pretty.

Here’s some stuff to keep in mind


Comprehensions run in the local scope. This means that the name(s) you create in the comprehension must not collide with a name in your local scope or your the value bound to your local name will be overwritten.

>>> char = 'x'
>>> chars = [char for char in 'hello']
>>> char

This issue has been addressed in Python 3. Link below.

Since comprehensions run in the local scope, you may be tempted to use them as a shorthand for a loop. Don’t do this. It’s confusing and annoying. Use list comprehensions to make lists.

>>> def foo(bar):
...     # do stuff
...     print(bar)

>>> [foo(bar) for bar in range(3)]  # <- throwing this away. just wanted to run foo().
[None, None, None]


Comprehensions are just expressions, and they can be nested like anything else. But don’t do it. If you’re not doing something simple, go ahead and write the equivalent loop:

>>> [sum(t) for t in [[(x+y)**2 for y in range(3)] for x in range(3)]]
[5, 14, 29]

# ... vs ...

>>> tripples = []
>>> for x in range(3):
...     tripples.append([(x+y)**2 for y in range(3)])
>>> map(sum, tripples)
[5, 14, 29]

Yes, it’s four lines of code vs one, but it’s a world of difference to follow along.

Python 3

Iterators are favored in Python 3, so it’s good to get used to them now. Functions like range, map, and zip will return iterators instead of requiring the use of xrange and itertools .imap and .izip.

That’s it

Hopefully that was a valuable, quick intro to Comprehensions and Generators. If you’re interested in a deeper knowledge about the inner workings of iterators and generators, I’d recommend starting here: