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Optimization Tips for Python Code
  • Difficulty Level : Medium
  • Last Updated : 19 Nov, 2020

In this article, some interesting optimization tips for Faster Python Code are discussed. These techniques help to produce result faster in a python code.

  1. Use builtin functions and libraries: Builtin functions like map() are implemented in C code. So the interpreter doesn’t have to execute the loop, this gives a considerable speedup.
    The map() function applies a function to every member of iterable and returns the result. If there are multiple arguments, map() returns a list consisting of tuples containing the corresponding items from all iterables.

    # Python program to illustrate library functions
    # save time while coding with the example of map()
    import time
    # slower (Without map())
    start = time.clock() 
    s = 'geeks'
    U = []
    for c in s:
    print (U)
    elapsed = time.clock()
    e1 = elapsed - start
    print ("Time spent in function is: ", e1)
    # Faster (Uses builtin function map())
    s = 'geeks'
    start = time.clock() 
    U = map(str.upper, s) 
    print (U)
    elapsed = time.clock()
    e2 = elapsed - start
    print ("Time spent in builtin function is: ", e2)


    ['G', 'E', 'E', 'K', 'S']
    Time spent in function is:  0.0394747945637
    ['G', 'E', 'E', 'K', 'S']
    Time spent in builtin function is:  0.0212335531192

    The packages are platform-specific, which means that we need the appropriate package for the platform we’re using. If we are doing string operation, consider using an existing module ‘collections’ like deque which is highly optimized for our purposes.

    # Python program to illustrate
    # importing list-like container with 
    # fast appends and pops on either end
    from collections import deque
    s = 'geek'
    # make a new deque
    d = deque(s)
    # add a new entry to the right side
    # add a new entry to the left side
    print (d)
    d.pop() # return and remove the rightmost item
    d.popleft() # return and remove the lefttmost item
    # print list deque in reverse
    print (list(reversed(d))) 


    deque(['h', 'g', 'e', 'e', 'k', 'y'])
    ['k', 'e', 'e', 'g']

    # importing iteration tools
    import itertools
    iter = itertools.permutations([1,2,3])
    print (list(iter))


    [(1, 2, 3), (1, 3, 2), (2, 1, 3), (2, 3, 1), (3, 1, 2), (3, 2, 1)]
  2. Use keys for sorts: In Python, we should use the key argument to the built-in sort instead, which is a faster way to sort.

    # Python program to illustrate
    # using keys for sorting
    somelist = [1, -3, 6, 11, 5]
    print (somelist)
    s = 'geeks'
    # use sorted() if you don't want to sort in-place:
    s = sorted(s)
    print (s)


    [-3, 1, 5, 6, 11]
    ['e', 'e', 'g', 'k', 's']

    In each case the list is sorted according to the index you select as part of the key argument. This approach works just as well with strings as it does with numbers.

  3. Optimizing loops: Write idiomatic code: This may sound counter-intuitive but writing idiomatic code will make your code faster in most cases. This is because Python was designed to have only one obvious/correct way to do a task.
    For example (String Concatenation):

    # Python program to illustrate using
    # optimized loops for faster coding
    # slow O(n^2) - ( Note: In latest implementations it is O(n) )
    s = 'hellogeeks'
    slist = ''
    for i in s:
        slist = slist + i
    print (slist)
    # string concatenation (idiomatic and fast O(n))
    st = 'hellogeeks'
    slist = ''.join([i for i in s])
    print (slist)
    # Better way to iterate a range
    evens = [ i for i in range(10) if i%2 == 0]
    print (evens)
    # Less faster
    i = 0
    evens = []
    while i < 10:
        if i %2 == 0
            i += 1
            print (evens)
    # slow
    v = 'for'
    s = 'geeks ' + v + ' geeks'
    print (s)
    # fast
    s = 'geeks %s geeks' % v
    print (s)


    [0, 2, 4, 6, 8]
    geeks for geeks

    Every time running a loop to s(i), Python evaluates the method. However, if you place the evaluation in a variable, the value is already known and Python can perform tasks faster.

  4. Try multiple coding approaches: Using precisely the same coding approach every time we create an application will almost certainly result in some situations where the application runs slower than it might.
    For example (Initializing Dictionary Elements):

    # Python program to illustrate trying
    # multiple coding approaches 
    # for getting faster result
    # slower
    mydict = {'g':1,'e':1,'e':1,'k':1}
    word = 'geeksforgeeks'
    for w in word:
        if w not in mydict:
            mydict[w] = 0
        mydict[w] += 1
    print (mydict)
    # faster
    mydict = {'g':1,'e':1,'e':1,'k':1}
    word = 'geeksforgeeks'
    for w in word:
            mydict[w] += 1
        except KeyError:
            mydict[w] = 1
    print (mydict)


    {'e': 5, 'g': 3, 'f': 1, 'k': 3, 'o': 1, 's': 2, 'r': 1}

    The output is the same in both cases. The only difference is how the output is obtained.

  5. Use xrange instead of range:range() – This returns a list of numbers created using range() function.
    xrange() – This function returns the generator object that can be used to display numbers only by looping. Only particular range is displayed on demand and hence called “lazy evaluation”.

    # slower
    x = [i for i in range(0,10,2)]
    print (x)
    # faster
    x = [i for i in range(0,10,2)]
    print (x)


    [1, 3, 5, 7, 9]

    This could save you system memory because xrange() will only yield one integer element in a sequence at a time. Whereas range(), it gives you an entire list, which is unnecessary overhead for looping.

  6. Use Python multiple assignment to swap variables: This is elegant and faster in Python.

    # Python program to illustrate swapping
    # of a variable in one line 
    # slower
    x = 2
    y = 5
    temp = x
    x = y
    y = temp
    print (x,y)
    x,y = 3,5
    # faster
    x, y = y, x
    print (x,y)


    5 2
    5 3
  7. Use local variable if possible: Python is faster retrieving a local variable than retrieving a global variable. That is, avoid the “global” keyword. So if you are going to access a method often (inside a loop) consider writing it to a variable.

    # Python program to illustrate trying
    # to use local variables to make code
    # run faster
    class Test:
        def func(self,x):
            print (x+x)
    # Declaring variable that assigns class method object
    Obj = Test()
    mytest = Obj.func # Declaring local variable
    n = 2
    for i in range(n):
        mytest(i) # faster than Obj.func(i)




This article is contributed by Afzal Ansari. If you like GeeksforGeeks and would like to contribute, you can also write an article using or mail your article to See your article appearing on the GeeksforGeeks main page and help other Geeks.

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