I have a list that I want to filter by an attribute of the items.
Which of the following is preferred (readability, performance, other reasons)?
xs = [x for x in xs if x.attribute == value]
xs = filter(lambda x: x.attribute == value, xs)
filter is slightly faster if using a builtin function.
I would expect the list comprehension to be slightly faster in your case
I find the second way more readable. It tells you exactly what the intention is: filter the list.
PS: do not use ‘list’ as a variable name
filter may be the “faster way”, the “Pythonic way” would be not to care about such things unless performance is absolutely critical (in which case you wouldn’t be using Python!).
It is strange how much beauty varies for different people. I find the list comprehension much clearer than
lambda, but use whichever you find easier.
There are two things that may slow down your use of
The first is the function call overhead: as soon as you use a Python function (whether created by
lambda) it is likely that filter will be slower than the list comprehension. It almost certainly is not enough to matter, and you shouldn’t think much about performance until you’ve timed your code and found it to be a bottleneck, but the difference will be there.
The other overhead that might apply is that the lambda is being forced to access a scoped variable (
value). That is slower than accessing a local variable and in Python 2.x the list comprehension only accesses local variables. If you are using Python 3.x the list comprehension runs in a separate function so it will also be accessing
value through a closure and this difference won’t apply.
The other option to consider is to use a generator instead of a list comprehension:
def filterbyvalue(seq, value): for el in seq: if el.attribute==value: yield el
Then in your main code (which is where readability really matters) you’ve replaced both list comprehension and filter with a hopefully meaningful function name.
This is a somewhat religious issue in Python. Even though Guido considered removing
reduce from Python 3, there was enough of a backlash that in the end only
reduce was moved from built-ins to functools.reduce.
Personally I find list comprehensions easier to read. It is more explicit what is happening from the expression
[i for i in list if i.attribute == value] as all the behaviour is on the surface not inside the filter function.
I would not worry too much about the performance difference between the two approaches as it is marginal. I would really only optimise this if it proved to be the bottleneck in your application which is unlikely.
Also since the BDFL wanted
filter gone from the language then surely that automatically makes list comprehensions more Pythonic 😉
An important difference is that list comprehension will return a
list while the filter returns a
filter, which you cannot manipulate like a
list (ie: call
len on it, which does not work with the return of
My own self-learning brought me to some similar issue.
That being said, if there is a way to have the resulting
list from a
filter, a bit like you would do in .NET when you do
lst.Where(i => i.something()).ToList(), I am curious to know it.
EDIT: This is the case for Python 3, not 2 (see discussion in comments).
Since any speed difference is bound to be miniscule, whether to use filters or list comprehensions comes down to a matter of taste. In general I’m inclined to use comprehensions (which seems to agree with most other answers here), but there is one case where I prefer
A very frequent use case is pulling out the values of some iterable X subject to a predicate P(x):
[x for x in X if P(x)]
but sometimes you want to apply some function to the values first:
[f(x) for x in X if P(f(x))]
As a specific example, consider
primes_cubed = [x*x*x for x in range(1000) if prime(x)]
I think this looks slightly better than using
filter. But now consider
prime_cubes = [x*x*x for x in range(1000) if prime(x*x*x)]
In this case we want to
filter against the post-computed value. Besides the issue of computing the cube twice (imagine a more expensive calculation), there is the issue of writing the expression twice, violating the DRY aesthetic. In this case I’d be apt to use
prime_cubes = filter(prime, [x*x*x for x in range(1000)])
Filter is just that. It filters out the elements of a list. You can see the definition mentions the same(in the official docs link I mentioned before). Whereas, list comprehension is something that produces a new list after acting upon something on the previous list.(Both filter and list comprehension creates new list and not perform operation in place of the older list. A new list here is something like a list with, say, an entirely new data type. Like converting integers to string ,etc)
In your example, it is better to use filter than list comprehension, as per the definition. However, if you want, say other_attribute from the list elements, in your example is to be retrieved as a new list, then you can use list comprehension.
return [item.other_attribute for item in my_list if item.attribute==value]
This is how I actually remember about filter and list comprehension. Remove a few things within a list and keep the other elements intact, use filter. Use some logic on your own at the elements and create a watered down list suitable for some purpose, use list comprehension.
Here’s a short piece I use when I need to filter on something after the list comprehension. Just a combination of filter, lambda, and lists (otherwise known as the loyalty of a cat and the cleanliness of a dog).
In this case I’m reading a file, stripping out blank lines, commented out lines, and anything after a comment on a line:
# Throw out blank lines and comments with open('file.txt', 'r') as lines: # From the inside out: # [s.partition('#').strip() for s in lines]... Throws out comments # filter(lambda x: x!= '', [s.part... Filters out blank lines # y for y in filter... Converts filter object to list file_contents = [y for y in filter(lambda x: x != '', [s.partition('#').strip() for s in lines])]
I thought I’d just add that in python 3, filter() is actually an iterator object, so you’d have to pass your filter method call to list() in order to build the filtered list. So in python 2:
lst_a = range(25) #arbitrary list lst_b = [num for num in lst_a if num % 2 == 0] lst_c = filter(lambda num: num % 2 == 0, lst_a)
lists b and c have the same values, and were completed in about the same time as filter() was equivalent [x for x in y if z]. However, in 3, this same code would leave list c containing a filter object, not a filtered list. To produce the same values in 3:
lst_a = range(25) #arbitrary list lst_b = [num for num in lst_a if num % 2 == 0] lst_c = list(filter(lambda num: num %2 == 0, lst_a))
The problem is that list() takes an iterable as it’s argument, and creates a new list from that argument. The result is that using filter in this way in python 3 takes up to twice as long as the [x for x in y if z] method because you have to iterate over the output from filter() as well as the original list.
It took me some time to get familiarized with the
higher order functions
map. So i got used to them and i actually liked
filter as it was explicit that it filters by keeping whatever is truthy and I’ve felt cool that I knew some
functional programming terms.
Then I read this passage (Fluent Python Book):
The map and filter functions are still builtins
in Python 3, but since the introduction of list comprehensions and generator ex‐
pressions, they are not as important. A listcomp or a genexp does the job of map and
filter combined, but is more readable.
And now I think, why bother with the concept of
map if you can achieve it with already widely spread idioms like list comprehensions. Furthermore
filters are kind of functions. In this case I prefer using
Anonymous functions lambdas.
Finally, just for the sake of having it tested, I’ve timed both methods (
listComp) and I didn’t see any relevant speed difference that would justify making arguments about it.
from timeit import Timer timeMap = Timer(lambda: list(map(lambda x: x*x, range(10**7)))) print(timeMap.timeit(number=100)) timeListComp = Timer(lambda:[(lambda x: x*x) for x in range(10**7)]) print(timeListComp.timeit(number=100)) #Map: 166.95695265199174 #List Comprehension 177.97208347299602
In addition to the accepted answer, there is a corner case when you should use filter instead of a list comprehension. If the list is unhashable you cannot directly process it with a list comprehension. A real world example is if you use
pyodbc to read results from a database. The
fetchAll() results from
cursor is an unhashable list. In this situation, to directly manipulating on the returned results, filter should be used:
cursor.execute("SELECT * FROM TABLE1;") data_from_db = cursor.fetchall() processed_data = filter(lambda s: 'abc' in s.field1 or s.StartTime >= start_date_time, data_from_db)
If you use list comprehension here you will get the error:
TypeError: unhashable type: ‘list’
Curiously on Python 3, I see filter performing faster than list comprehensions.
I always thought that the list comprehensions would be more performant.
[name for name in brand_names_db if name is not None]
The bytecode generated is a bit better.
>>> def f1(seq): ... return list(filter(None, seq)) >>> def f2(seq): ... return [i for i in seq if i is not None] >>> disassemble(f1.__code__) 2 0 LOAD_GLOBAL 0 (list) 2 LOAD_GLOBAL 1 (filter) 4 LOAD_CONST 0 (None) 6 LOAD_FAST 0 (seq) 8 CALL_FUNCTION 2 10 CALL_FUNCTION 1 12 RETURN_VALUE >>> disassemble(f2.__code__) 2 0 LOAD_CONST 1 (<code object <listcomp> at 0x10cfcaa50, file "<stdin>", line 2>) 2 LOAD_CONST 2 ('f2.<locals>.<listcomp>') 4 MAKE_FUNCTION 0 6 LOAD_FAST 0 (seq) 8 GET_ITER 10 CALL_FUNCTION 1 12 RETURN_VALUE
But they are actually slower:
>>> timeit(stmt="f1(range(1000))", setup="from __main__ import f1,f2") 21.177661532000116 >>> timeit(stmt="f2(range(1000))", setup="from __main__ import f1,f2") 42.233950221000214
Looking through the answers, we have seen a lot of back and forth, whether or not list comprehension or filter may be faster or if it is even important or pythonic to care about such an issue. In the end, the answer is as most times: it depends.
I just stumbled across this question while optimizing code where this exact question (albeit combined with an
in expression, not
==) is very relevant – the
lambda expression is taking up a third of my computation time (of multiple minutes).
In my case, the list comprehension is much faster (twice the speed). But I suspect that this varies strongly based on the filter expression as well as the Python interpreter used.
Here is a simple code snippet that should be easy to adapt. If you profile it (most IDEs can do that easily), you will be able to easily decide for your specific case which is the better option:
whitelist = set(range(0, 100000000, 27)) input_list = list(range(0, 100000000)) proximal_list = list(filter( lambda x: x in whitelist, input_list )) proximal_list2 = [x for x in input_list if x in whitelist] print(len(proximal_list)) print(len(proximal_list2))
If you do not have an IDE that lets you profile easily, try this instead (extracted from my codebase, so a bit more complicated). This code snippet will create a profile for you that you can easily visualize using e.g. snakeviz:
import cProfile from time import time class BlockProfile: def __init__(self, profile_path): self.profile_path = profile_path self.profiler = None self.start_time = None def __enter__(self): self.profiler = cProfile.Profile() self.start_time = time() self.profiler.enable() def __exit__(self, *args): self.profiler.disable() exec_time = int((time() - self.start_time) * 1000) self.profiler.dump_stats(self.profile_path) whitelist = set(range(0, 100000000, 27)) input_list = list(range(0, 100000000)) with BlockProfile("/path/to/create/profile/in/profile.pstat"): proximal_list = list(filter( lambda x: x in whitelist, input_list )) proximal_list2 = [x for x in input_list if x in whitelist] print(len(proximal_list)) print(len(proximal_list2))
Your question is so simple yet interesting. It just shows how flexible python is, as a programming language. One may use any logic and write the program according to their talent and understandings. It is fine as long as we get the answer.
Here in your case, it is just an simple filtering method which can be done by both but i would prefer the first one
my_list = [x for x in my_list if x.attribute == value] because it seems simple and does not need any special syntax. Anyone can understands this command and make changes if needs it.
(Although second method is also simple, but it still has more complexity than the first one for the beginner level programmers)
In terms of performance, it depends.
filter does not return a list but an iterator, if you need the list ‘immediately’ filtering and list conversion it is slower than with list comprehension by about 40% for very large lists (>1M). Up to 100K elements, there is almost no difference, from 600K onwards there starts to be differences.
If you don’t convert to a list,
filter is practically instantaneous.
I would come to the conclusion: Use list comprehension over filter since its
Keep in mind that filter returns a iterator, not a list.
python3 -m timeit '[x for x in range(10000000) if x % 2 == 0]'
1 loop, best of 5: 270 msec per loop
python3 -m timeit 'list(filter(lambda x: x % 2 == 0, range(10000000)))'
1 loop, best of 5: 432 msec per loop