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map creates a new list by applying a function to every element of the source: xs = [1, 2, 3] # all of those are equivalent — the output is [2, 4, 6] # 1. map ys = map (lambda x: x * 2, xs) # 2. list comprehension ys = [x * 2 for x in xs] # 3. explicit loop ys = [] for x in xs: ys.append (x * 2) n-ary map is equivalent to zipping input ...
You can define lmap function (on the analogy of python2's imap) that returns list: Then calling lmap instead of map will do the job: lmap(str, x) is shorter by 5 characters (30% in this case) than list(map(str, x)) and is certainly shorter than [str(v) for v in x]. You may create similar functions for filter too.
866. map may be microscopically faster in some cases (when you're not making a lambda for the purpose, but using the same function in map and a list comprehension). List comprehensions may be faster in other cases and most (not all) Pythonistas consider them more direct and clearer. An example of the tiny speed advantage of map when using ...
A list of lists named xss can be flattened using a nested list comprehension: flat_list = [ x for xs in xss for x in xs ] The above is equivalent to: flat_list = [] for xs in xss: for x in xs: flat_list.append(x) Here is the corresponding function: def flatten(xss): return [x for xs in xss for x in xs]
arr = list(map(int, input().rstrip().split())) There's a variable arr which is being assigned the value to the statement on the right. On the right, the data is being fetched using the input method, followed by stripping off the trailing spaces and splitting the input to a list with whitespace as the separator.
If doSomething is very "tiny", map can be a lot faster than for loop or a list-comprehension: # Python 3.1.1 (r311:74483, Aug 17 2009, 17:02:12) [MSC v.1500 32 bit (Intel)] on win32 from timeit import timeit do = lambda i: i+1 def _for(): for i in range(1000): do(i) def _map(): map(do, range(1000)) def _list(): [do(i) for i in range(1000 ...
Note that as you don't need the result of filtered list and you just want to pass it to map it's more efficient that use ifilter because it returns a generator and you can save much memory for long lists ;) (its all in python 2 and in python 3 filter returns generator)
If you specify a list of args (tuples), i.e. args = [(page, additionalArgument) for page in pages] map will not unpack the args tuple and will pass only one argument (tuple) to myFunc: pool.map(myFunc, args) # map does not unpack the tuple. You will need to use multiprocessing.starmap instead.
There are several methods to convert string numbers in a list to integers. In Python 2.x you can use the map function: >>> results = ['1', '2', '3'] >>> results = map(int, results) >>> results. [1, 2, 3] Here, It returns the list of elements after applying the function. In Python 3.x you can use the same map.
Converting map() from 2 to 3 might not just be a simple case of sticking a list( ) around it. Guido also says: If the input sequences are not of equal length, map() will stop at the termination of the shortest of the sequences. For full compatibility with map() from Python 2.x, also wrap the sequences in itertools.zip_longest(), e.g.