List comprehensions are one of my favorite features in Python. I love list comprehensions so much that I’ve written an article about them, done a talk about them, and held a 3 hour comprehensions tutorial at PyCon 2018.

While I love list comprehensions, I’ve found that once new Pythonistas start to really appreciate comprehensions they tend to use them everywhere. Comprehensions are lovely, but they can easily be overused!

This article is all about cases when comprehensions aren’t the best tool for the job, at least in terms of readability. We’re going to walk through a number of cases where there’s a more readable alternative to comprehensions and we’ll also see some not-so-obvious cases where comprehensions aren’t needed at all.

This article isn’t meant to scare you off from comprehensions if you’re not already a fan; it’s meant to encourage moderation for those of us (myself included) who need it.

Note: In this article, I’ll be using the term “comprehension” to refer to all forms of comprehensions (list, set, dict) as well as generator expressions. If you’re unfamiliar with comprehensions, I recommend reading this article or watching this talk (the talk dives into generator expressions a bit more deeply).

Writing comprehensions with poor spacing

Critics of list comprehensions often say they’re hard to read. And they’re right, many comprehensions are hard to read. Sometimes all a comprehension needs to be more readable is better spacing.

Take the comprehension in this function:

1 2 3 def get_factors ( dividend ): """Return a list of all factors of the given number.""" return [ n for n in range ( 1 , dividend + 1 ) if dividend % n == 0 ]

We could make that comprehension more readable by adding some well-placed line breaks:

1 2 3 4 5 6 7 def get_factors ( dividend ): """Return a list of all factors of the given number.""" return [ n for n in range ( 1 , dividend + 1 ) if dividend % n == 0 ]

Less code can mean more readable code, but not always. Whitespace is your friend, especially when you’re writing comprehensions.

In general, I prefer to write most of my comprehensions spaced out over multiple lines of code using the indentation style above. I do write one-line comprehensions sometimes, but I don’t default to them.

Writing ugly comprehensions

Some loops technically can be written as comprehensions but they have so much logic in them they probably shouldn’t be.

Take this comprehension:

1 2 3 4 5 6 7 fizzbuzz = [ f 'fizzbuzz {n}' if n % 3 == 0 and n % 5 == 0 else f 'fizz {n}' if n % 3 == 0 else f 'buzz {n}' if n % 5 == 0 else n for n in range ( 100 ) ]

This comprehension is equivalent to this for loop:

1 2 3 4 5 6 7 8 fizzbuzz = [] for n in range ( 100 ): fizzbuzz . append ( f 'fizzbuzz {n}' if n % 3 == 0 and n % 5 == 0 else f 'fizz {n}' if n % 3 == 0 else f 'buzz {n}' if n % 5 == 0 else n )

Both the comprehension and the for loop use three nested inline if statements (Python’s ternary operator).

Here’s a more readable way to write this code, using an if-elif-else construct:

1 2 3 4 5 6 7 8 9 10 fizzbuzz = [] for n in range ( 100 ): if n % 3 == 0 and n % 5 == 0 : fizzbuzz . append ( f 'fizzbuzz {n}' ) elif n % 3 == 0 : fizzbuzz . append ( f 'fizz {n}' ) elif n % 5 == 0 : fizzbuzz . append ( f 'buzz {n}' ) else : fizzbuzz . append ( n )

Just because there is a way to write your code as a comprehension, that doesn’t mean that you should write your code as a comprehension.

Be careful using any amount of complex logic in comprehensions, even a single inline if:

1 2 3 4 number_things = [ n // 2 if n % 2 == 0 else n * 3 for n in numbers ]

If you really prefer to use a comprehension in cases like this, at least give some thought to whether whitespace or parenthesis could make things more readable:

1 2 3 4 number_things = [ ( n // 2 if n % 2 == 0 else n * 3 ) for n in numbers ]

And consider whether breaking some of your logic out into a separate function might improve readability as well (it may not in this somewhat silly example).

1 2 3 4 number_things = [ even_odd_number_switch ( n ) for n in numbers ]

Whether a separate function makes things more readable will depend on how important that operation is, how large it is, and how well the function name conveys the operation.

Loops disguised as comprehensions

Sometimes you’ll encounter code that uses a comprehension syntax but breaks the spirit of what comprehensions are used for.

For example, this code looks like a comprehension:

1 [ print ( n ) for n in range ( 1 , 11 )]

But it doesn’t act like a comprehension. We’re using a comprehension for a purpose it wasn’t intended for.

If we execute this comprehension in the Python shell you’ll see what I mean:

1 2 3 4 5 6 7 8 9 10 11 12 >>> [ print ( n ) for n in range ( 1 , 11 )] 1 2 3 4 5 6 7 8 9 10 [ None , None , None , None , None , None , None , None , None , None ]

We wanted to print out all the numbers from 1 to 10 and that’s what we did. But this comprehension statement also returned a list of None values to us, which we promptly discarded.

Comprehensions build up lists: that’s what they’re for. We built up a list of the return values from the print function and the print function returns None .

But we didn’t care about the list our comprehension built up: we only cared about its side effect.

We could have instead written that code like this:

1 2 for n in range ( 1 , 11 ): print ( n )

List comprehensions are for looping over an iterable and building up new lists, while for loops are for looping over an iterable to do pretty much any operation you’d like.

When I see a list comprehension in code I immediately assume that we’re building up a new list (because that’s what they’re for). If you use a comprehension for a purpose outside of building up a new list, it’ll confuse others who read your code.

If you don’t care about building up a new list, don’t use a comprehension.

Using comprehensions when a more specific tool exists

For many problems, a more specific tool makes more sense than a general purpose for loop. But comprehensions aren’t always the best special-purpose tool for the job at hand.

I have both seen and written quite a bit of code that looks like this:

1 2 3 4 5 6 7 import csv with open ( 'populations.csv' ) as csv_file : lines = [ row for row in csv . reader ( csv_file ) ]

That comprehension is sort of an identity comprehension. Its only purpose is to loop over the given iterable ( csv.reader(csv_file) ) and create a list out of it.

But in Python, we have a more specialized tool for this task: the list constructor. Python’s list constructor can do all the looping and list creation work for us:

1 2 3 4 import csv with open ( 'populations.csv' ) as csv_file : lines = list ( csv . reader ( csv_file ))

Comprehensions are a special-purpose tool for looping over an iterable to build up a new list while modifying each element along the way and/or filtering elements down. The list constructor is a special-purpose tool for looping over an iterable to build up a new list, without changing anything at all.

If you don’t need to filter your elements down or map them into new elements while building up your new list, you don’t need a comprehension: you need the list constructor.

This comprehension converts each of the row tuples we get from looping over zip into lists:

1 2 3 4 5 6 def transpose ( matrix ): """Return a transposed version of given list of lists.""" return [ [ n for n in row ] for row in zip ( * matrix ) ]

We could use the list constructor for that too:

1 2 3 4 5 6 def transpose ( matrix ): """Return a transposed version of given list of lists.""" return [ list ( row ) for row in zip ( * matrix ) ]

Whenever you see a comprehension like this:

1 my_list = [ x for x in some_iterable ]

You could write this instead:

1 my_list = list ( some_iterable )

The same applies for dict and set comprehensions.

This is also something I’ve written quite a bit in the past:

1 2 3 4 5 6 7 8 9 10 11 12 13 states = [ ( 'AL' , 'Alabama' ), ( 'AK' , 'Alaska' ), ( 'AZ' , 'Arizona' ), ( 'AR' , 'Arkansas' ), ( 'CA' , 'California' ), # ... ] abbreviations_to_names = { abbreviation : name for abbreviation , name in states }

Here we’re looping over a list of two-item tuples and making a dictionary out of them.

This task is exactly what the dict constructor was made for:

1 abbreviations_to_names = dict ( states )

The built-in list and dict constructors aren’t the only comprehension-replacing tools. The standard library and third-party libraries also include tools that are sometimes better suited for your looping needs than a comprehension.

Here’s a generator expression that sums up an iterable-of-iterables-of-numbers:

1 2 3 4 5 6 7 def sum_all ( number_lists ): """Return the sum of all numbers in the given list-of-lists.""" return sum ( n for numbers in number_lists for n in numbers )

And here’s the same thing using itertools.chain :

1 2 3 4 5 from itertools import chain def sum_all ( number_lists ): """Return the sum of all numbers in the given list-of-lists.""" return sum ( chain . from_iterable ( number_lists ))

When you should use a comprehension and when you should use the alternative isn’t always straightforward.

I’m often torn on whether to use itertools.chain or a comprehension. I usually write my code both ways and then go with the one that seems clearer.

Readability is fairly problem-specific with many programming constructs, comprehensions included.

Needless work

Sometimes you’ll see comprehensions that shouldn’t be replaced by another construct but should instead be removed entirely, leaving only the iterable they loop over.

Here we’re opening up a file of words (with one word per line), storing file in memory, and counting the number of times each occurs:

1 2 3 4 5 6 from collections import Counter word_counts = Counter ( word for word in open ( 'word_list.txt' ) . read () . splitlines () )

We’re using a generator expression here, but we don’t need to be. This works just as well:

1 2 3 from collections import Counter word_counts = Counter ( open ( 'word_list.txt' ) . read () . splitlines ())

We were looping over a list to convert it to a generator before passing it to the Counter class. That was needless work! The Counter class accepts any iterable: it doesn’t care whether they’re lists, generators, tuples, or something else.

Here’s another needless comprehension:

1 2 3 4 5 with open ( 'word_list.txt' ) as words_file : lines = [ line for line in words_file ] for line in lines : if 'z' in line : print ( 'z word' , line , end = '' )

We’re looping over words_file , converting it to a list of lines , and then looping over lines just once. That conversion to a list was unnecessary.

We could just loop over words_file directly instead:

1 2 3 4 with open ( 'word_list.txt' ) as words_file : for line in words_file : if 'z' in line : print ( 'z word' , line , end = '' )

There’s no reason to convert an iterable to a list if all we’re going to do is loop over it once.

In Python, we often care less about whether something is a list and more about whether it’s an iterable.

Be careful not to create new iterables when you don’t need to: if you’re only going to loop over an iterable once, just use the iterable you already have.

When would I use a comprehension?

So when would you actually use a comprehension?

The simple but imprecise answer is whenever you can write your code in the below comprehension copy-pasteable format and there isn’t another tool you’d rather use for shortening your code, you should consider using a list comprehension.

1 2 3 4 new_things = [] for ITEM in old_things : if condition_based_on ( ITEM ): new_things . append ( some_operation_on ( ITEM ))

That loop can be rewritten as this comprehension:

1 2 3 4 5 new_things = [ some_operation_on ( ITEM ) for ITEM in old_things if condition_based_on ( ITEM ) ]

The complex answer is whenever comprehensions make sense, you should consider them. That’s not really an answer, but there is no one answer to the question “when should I use a comprehension”?

For example here’s a for loop which doesn’t really look like it could be rewritten using a comprehension:

1 2 3 4 5 def is_prime ( candidate ): for n in range ( 2 , candidate ): if candidate % n == 0 : return False return True

But there is in fact another way to write this loop using a generator expression, if we know how to use the built-in all function:

1 2 3 4 5 def is_prime ( candidate ): return all ( candidate % n != 0 for n in range ( 2 , candidate ) )

I wrote a whole article on the any and all functions and how they pair so nicely with generator expressions. But any and all aren’t alone in their affinity for generator expressions.

We have a similar situation with this code:

1 2 3 4 5 def sum_of_squares ( numbers ): total = 0 for n in numbers : total += n ** 2 return total

There’s no append there and no new iterable being built up. But if we create a generator of squares, we could pass them to the built-in sum function to get the same result:

1 2 def sum_of_squares ( numbers ): return sum ( n ** 2 for n in numbers )

So in addition to the “can I copy-paste my way from a loop to a comprehension” check, there’s another, fuzzier, check to consider: could your code be enhanced by a generator expression combined with an iterable-accepting function or class?

Any function or class that accepts an iterable as an argument might be a good candidate for combining with a generator expression.

Use list comprehensions thoughtfully

List comprehensions can make your code more readable (if you don’t believe me, see the examples in my Comprehensible Comprehensions talk), but they can definitely be abused.

List comprehensions are a special-purpose tool for solving a specific problem. The list and dict constructors are even more special-purpose tools for solving even more specific problems.

Loops are a more general purpose tool for times when you have a problem that doesn’t fit within the realm of comprehensions or another special-purpose looping tool.

Functions like any , all , and sum , and classes like Counter and chain are iterable-accepting tools that pair very nicely with comprehensions and sometimes replace the need for comprehensions entirely.

Remember that comprehensions are for a single purpose: creating a new iterable from an old iterable, while tweaking values slightly along the way and/or for filtering out values that don’t match a certain condition. Comprehensions are a lovely tool, but they’re not your only tool. Don’t forget the list and dict constructors and always consider for loops when your comprehensions get out of hand.

Practice Python list comprehensions right now!

The best way to learn is through regular practice. Every week I send out carefully crafted Python exercises through my Python skill-building service, Python Morsels.

If you’d like to practice your comprehensions through one Python exercise right now, you can sign up for Python Morsels using the form below. After you sign up, I’ll immediately give you one exercise to practice your comprehension copy-pasting skills.