Write a Python lambda function which takes a list called my_list and returns a list which includes only even numbers in the same order.
My solution:
f = list(filter(lambda x: (x % 2 == 0), my_list))
print(f)
I'm having issues because I'm doing a homework problem online and it runs various test cases on it and it says my_list isn't defined. Which I understand why that is, because it isn't... but it seems like the question is looking for a broad function which I don't know how to do in this case.
2 questions:
Does my solution look correct?
If so, how do I tailor this so that the homework will accept various inputs?
It does not look correct since f is not a lambda function but a list.
OK, it uses lambda but it is not lambda.
You would probably need to write something like:
f = lambda my_list: list(filter(lambda x: (x % 2 == 0), my_list))
or similar.
For example:
f = lambda my_list: [x for x in my_list if x % 2 == 0]
should also satisfy the requirement.
Of course now print(f) will "print the function".
I'm doing a homework problem online and it runs various test cases
Your function is actually correct. But, well, online coding assessment usually use a stdin as input. Python use input() method to retrieve string from stdin. So if you want your submission accept various input, you probably need to add this.
my_list = list(map(int, input().split(' ')))
That will split string input by space character ' ' to be a list. Then, all content in list converted to integer by map function. Since the return value of map function is not a list, you need list method to convert map result into list. The new list will be saved to my_list variable.
But, again, it depends on your input specification. If your input specification are eg: "1, 2, 4, 5". use ', ' as split parameter.
I am reading Joel Grus's data science from scratch book and found something a bit mysterious. Basically, in some sample code, he wrote
a = [1, 2 ,3 ,4]
xs = [i for i,_ in enumerate(a)]
Why would he prefer to do this way? Instead of
xs = range(len(a))
Answer: personal preference of the author. I find
[i for i, _ in enumerate(xs)]
clearer and more readable than
list(range(len(xs)))
which feels clunky to me. (I don't like reading the nested functions.) Your mileage may vary (and apparently does!).
That said, I am pretty sure I didn't say not to do the second, I just happen to prefer the first.
Source: I am the author.
P.S. If you're the commenter who had no intention of reading anything I write about Python, I apologize if you read this answer by accident.
I looked at the code available on github and frankly, I do not see any other reason for this except the personal preference of the author.
However, the result needs to be a list in places like this:
indexes = [i for i, _ in enumerate(data)] # create a list of indexes
random.shuffle(indexes) # shuffle them
for i in indexes: # return the data in that order
yield data[i]
Using bare range(len(data)) in that part on Python 3 would be wrong, because random.shuffle() requires a mutable sequence as the argument, and the range objects in Python 3 are immutable sequences.
I personally would use list(range(len(data))) on Python 3 in the case that I linked to, as it is guaranteed to be more efficient and would fail if a generator/iterator was passed in by accident, instead of a sequence.
Without being the author, I would have to guess, but my guess is that it's for Python 2 and 3 compatibility.
In Python 2:
>>> a = [1,2,3,4]
>>> xs = range(len(a))
>>> xs
[0, 1, 2, 3]
>>> type(xs)
<type 'list'>
In Python 3:
>>> a = [1,2,3,4]
>>> xs = range(len(a))
>>> xs
range(0, 4)
>>> type(xs)
<class 'range'>
Now, that doesn't make a difference when you're directly iterating over the range, but if you're planning to use the index list for something else later on, the author may feel that the enumerate is simpler to understand than list(range(len(a)))
Both are ok.
When I started coding in python I was more list(range(len(a))) .
Now I am more in pythonic way .
Both are readable.
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)
It is strange how much beauty varies for different people. I find the list comprehension much clearer than filter+lambda, but use whichever you find easier.
There are two things that may slow down your use of filter.
The first is the function call overhead: as soon as you use a Python function (whether created by def or 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 map, filter and 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 ;-)
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 filter.
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)])
Although 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!).
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.
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 filter).
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).
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
generally filter is slightly faster if using a builtin function.
I would expect the list comprehension to be slightly faster in your case
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('#')[0].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('#')[0].strip() for s in lines])]
It took me some time to get familiarized with the higher order functions filter and 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 filter / map if you can achieve it with already widely spread idioms like list comprehensions. Furthermore maps and 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 (map and 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'
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.
More info at: https://blog.finxter.com/python-lists-filter-vs-list-comprehension-which-is-faster/
Curiously on Python 3, I see filter performing faster than list comprehensions.
I always thought that the list comprehensions would be more performant.
Something like:
[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
I would come to the conclusion: Use list comprehension over filter since its
more readable
more pythonic
faster (for Python 3.11, see attached benchmark, also see )
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
Summarizing other answers
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 filter + lambda expression is taking up a third of my computation time (of multiple minutes).
My case
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.
Test it for yourself
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)
Consider:
fooList = [1, 2, 3, 4] # Ints for example only, in real application using objects
for foo in fooList:
if fooChecker(foo):
remove_this_foo_from_list
How is the specific foo to be removed from the list? Note that I'm using ints for example only, in the real application there is a list of arbitrary objects.
Thanks.
Generally, you just don't want to do this. Instead, construct a new list instead. Most of the time, this is done with a list comprehension:
fooListFiltered = [foo for foo in fooList if not fooChecker(foo)]
Alternatively, a generator expression (my video linked above covers generator expressions as well as list comprehensions) or filter() (note that in 2.x, filter() is not lazy - use a generator expression or itertools.ifilter() instead) might be more appropriate (for example, a large file that is too big to be read into memory wouldn't work this way, but would with a generator expression).
If you need to actually modify the list (rare, but can be the case on occasion), then you can assign back:
fooList[:] = fooListFiltered
Iterate over a shallow copy of the list.
As you can't modify a list while iterating over so you need to iterate over a shallow copy of the list.
fooList = [1, 2, 3, 4]
for foo in fooList[:]: #equivalent to list(fooList), but much faster
if fooChecker(foo):
fooList.remove(foo)
Use filter:
newList = list(filter(fooChecker, fooList))
or
newItems = filter(fooChecker, fooList))
for item in newItems:
print item # or print(item) for python 3.x
http://docs.python.org/2/library/functions.html#filter
Is there any "pythonic way" to tell python to loop in a string (or list) starting from the last item and ending with the first one?
For example the word Hans i want python to read or sort it as snaH
Next, how can i tell pyhon the following: now from the string you resulted , search for 'a' find it ok , if you find 'n' follows 'a' , put '.' after 'n' and then print the original order of letters
The clearest and most pythonic way to do this is to used the reversed() builtin.
wrong_way = [1, 2, 3, 4]
for item in reversed(wrong_way):
print(item)
Which gives:
4
3
2
1
This is the best solution as not only will it generate a reversed iterator naturally, but it can also call the dedicated __reversed__() method if it exists, allowing for a more efficient reversal in some objects.
You can use wrong_way[::-1] to reverse a list, but this is a lot less readable in code, and potentially less efficient. It does, however, show the power of list slicing.
Note that reversed() provide iterators, so if you want to do this with a string, you will need to convert your result back to a string, which is fortunately easy, as you just do:
"".join(iterator)
e.g:
"".join(reversed(word))
The str.join() method takes an iterator and joins every element into a string, using the calling string as the separator, so here we use the empty string to place them back-to-back.
How about this?
>>> s = "Hans"
>>> for c in s[::-1]:
print c
s
n
a
H
Alternatively, if you want a new string that's the reverse of the first, try this:
>>> "".join(reversed("Hans"))
'snaH'
Sure, just use list_name[::-1]. e.g.
>>> l = ['one', 'two', 'three']
>>> for i in l[::-1]:
... print i
...
three
two
one