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.
Answer from Duncan on Stack OverflowIt 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 ;-)
python lambda list filtering with multiple conditions - Stack Overflow
How do I use filter and lambda in a situation where lambda takes two arguments?
Filtering list of objects on multiple conditions
Multiple conditions in python filter function
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x = ['1', '2', '4', 'c'], so x[1]=='2', which makes the expression (x[0] != "1" and x[1] != "2" and x[2] != "3") be evaluated as False.
When conditions are joined by and, they return True only if all conditions are True, and if they are joined by or, they return True when the first among them is evaluated to be True.
['1', '2', '4', 'c']
Fails for condition
x[0] != "1"
as well as
x[1] != "2"
Instead of using or, I believe the more natural and readable way is:
lambda x: (x[0], x[1], x[2]) != ('1','2','3')
Out of curiosity, I compared three methods of, er... comparing, and the results were as expected: slicing lists was the slowest, using tuples was faster, and using boolean operators was the fastest. More precisely, the three approaches compared were
list_slice_compare = lambda x: x[:3] != [1,2,3]
tuple_compare = lambda x: (x[0],x[1],x[2]) != (1,2,3)
bool_op_compare = lambda x: x[0]!= 1 or x[1] != 2 or x[2]!= 3
And the results, respectively:
In [30]: timeit.Timer(setup="import timeit,random; rand_list = [random.randint(1,9) for _ in range(4)]; list_slice_compare = lambda x: x[:3] != [1,2,3]", stmt="list_slice_compare(rand_list)").repeat()
Out[30]: [0.3207617177499742, 0.3230015148823213, 0.31987868894918847]
In [31]: timeit.Timer(setup="import timeit,random; rand_list = [random.randint(1,9) for _ in range(4)]; tuple_compare = lambda x: (x[0],x[1],x[2]) != (1,2,3)", stmt="tuple_compare(rand_list)").repeat()
Out[31]: [0.2399928924012329, 0.23692036176475995, 0.2369164465619633]
In [32]: timeit.Timer(setup="import timeit,random; rand_list = [random.randint(1,9) for _ in range(4)]; bool_op_compare = lambda x: x[0]!= 1 or x[1] != 2 or x[2]!= 3", stmt="bool_op_compare(rand_list)").repeat()
Out[32]: [0.144389363900018, 0.1452672728203197, 0.1431527621755322]