Optimization of Python codes deals with selecting the best option among a number of possible options that are feasible to use for developers. Python is the most popular, dynamic, versatile, and one of the most sought after languages for web and AI development. Right from the programming projects like machine learning and data mining, Python is still the best and most relevant language for application developers. In this post, I will discuss some of the optimization procedures and patterns in Python coding in an effort to improve the overall coding knowledge of developers. Optimizing Slow Code First When you run your Python program, the is compiled using saved in a folder and at the end interpreted by . Since Python uses an interpreter to execute the bytecode at runtime which makes the process a bit slower. source code .py CPython into bytecode (.pyc file) (_pycache_) Python virtual M2M code (machine to machine) To make your execution faster on that include two different interpreters and the feature of its predecessor and . Compared to it is faster which is critical for coding. CPython, get PyPy v7.1 PyPy3.6-beta Python 2.7 CPython 7.6X Programs in takes a lot of space, but takes up less space in programs. Have issues in Python coding? CPython PyPy Spotting Performing Issues in Python? Choosing data structures in Python can affect the performance of codes. Profiling of codes can be of great help in analyzing how codes perform in different situations and enable to identify the time that the program takes in its operations. Tools such as can be useful in faster and more efficient profiling of Python codes: PyCallGraph, cProfile, and gProf2dot cProfile is to identify how long the various parts of Python codes are executed. PyCallGraph creates visual representations of graphs to represent the relationship between Python codes. This example demonstrates a simple use of pycallgraph. From pycallgraph import PyCallGraph From pycallgraph.output import GraphvizOutput = []

    def add_juice(self, juice):
        self.juices.append(juice)

    def drink_juices(self):
        [juice.drink() juice self.juices]
        self.no_juices()


def main():
    graphviz = GraphvizOutput()
    graphviz.output_file = PyCallGraph(output=graphviz):
        person = Person() a xrange( ):
            person.add_juice(Banana())
        person.drink_juices() __name__ == :
    main() : ( ): : ( ): . () ( ): . class Juice def drink self pass class Person def __init__ self self no_juices def no_juices self self juices for in 'basic.png' with for in 10 if '__main__' Concatenation of Strings Python strings are generally immutable and subsequently perform quite slow. There are several ways to optimize Python strings in different situations. For example, the + (plus) operator is the ideal option for the few Python string objects. If you use (+) operator to link multiple strings in Python codes, each time it will create a new object and creation of so many string objects will hamper the utilization of memory. Iterator such as a List has multiple Strings, the best way to concatenate them is by utilizing the . .join() method Let’s see an example of how the .join() and the += operator works: timeit

# create a list words
list_of_words = [ ] * def using_join(list_of_words): .join(list_of_words)

def using_concat_operator(list_of_words):  
    final_string = i list_of_words:
        final_string += i final_string

print( .format(timeit.timeit( , )))

print( .format(timeit.timeit( , ))) import of 1000 "foo " 1000 return "" "" for in return "Using join() takes {} s" 'using_join(list_of_words)' 'from __main__ import using_join, list_of_words' "Using += takes {} s" 'using_concat_operator(list_of_words)' 'from __main__ import using_concat_operator, list_of_words' Output $ python join-vs-concat.py 
Using join() takes s  
Using += takes s  
$ 
$ python join-vs-concat.py 
Using join() takes s  
Using += takes s 14.0949640274 79.5631570816 13.3542580605 76.3233859539 List Comprehension vs Loops in Python Coding Loops are common in Python coding that provides a concise way to create new lists that also support various conditions. For example, if you want to get the list of the cube root of all the odd numbers in a defined range using the for loop ; new_list = [] n range( , ): n % == :
        new_list.append(n** ) for in 1 9 if 3 0 3 The list comprehension version of the loop would be like: new_list = [ n** n range( , ) n% == ] 3 for in 1 9 if 3 0 So, it’s clear that list comprehension is more precise, shorter, and notably faster in execution time than . for loops Optimizing Memory Footprint in Python Dealing with loops in Python coding, you will need to create a complete list of integers for help in executing the for-loops. Here, in this case, functions such as and will be useful in your coding. The functionality of both is the same, but they are different in that the range returns a list object but the returns an object. range xrange xrange xrange If you need to generate a large number of integers, then function is an ideal choice for this purpose. Why? It is because works as a generator in that doesn’t provide the final result, but it gives you the ability to generate the values in the final list. xrange xrange - vs : Let’s see the difference between these two functions in terms of memory consumption xrange range $ python
Python ( , Oct , : : )  
[GCC Compatible Apple LLVM (clang )] on darwin
Type , , or more information.  
>>> sys
>>> 
>>> r = range( )
>>> x = xrange( )
>>> 
>>> print(sys.getsizeof(r)) >>> 
>>> print(sys.getsizeof(x)) >>> 
>>> print(type(r))
<type >  
>>> print(type(x))
<type > 2.7 .10 default 23 2015 19 19 21 4.2 .1 7.0 .0 -700.0 .59 .5 "help" "copyright" "credits" "license" for import 1000000 1000000 8000072 40 'list' 'xrange' Hence, it’s clear that function is a much better option in saving memory than the range function. The range function consumed 8000072 bytes, on the other hand, function consumed only 40 bytes of memory. xrange xrange Summary Hope these optimization tips enables robust and reliable execution of Python codes at multiple levels of granularity from profiling to data structures to string concatenation and memory optimization with . All of these points aimed at supporting in their day to day programming work and to help them write quality code. xrange function Python programmers

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