Python Decorator Patterns | Implementing the Decorator Pattern in Python

Python Decorator Patterns Explained with Examples

In this tutorial, I show toy implementations of Python decorator patterns such as @measure, @repeat, @trace, @count, @singleton, and @app.route (made famous by Flask).

Introduction

In Python, functions are first class citizens: functions can be passed to other functions, can be returned from functions, and can be created on the fly. Let's see an example:

# define a function on-the-fly
pow2 = lambda x: x**2
print(pow2(2))

# take a function as a parameter
def print_twice(func: Callable, arg: Any):
    print(func(arg))
    print(func(arg))
print_twice(pow2, 3)

# take a function as a parameter and return a new function
def hello():
    print('Hello world!')
def loop(func: Callable, n: int):
    for _ in range(n):
        func()
loop(hello, 3)

Output:

4
9
9
Hello world!
Hello world!
Hello world!

Decorators in Pythons are syntactic sugar for passing functions to functions and returning a new function. Let's see how this works and how we can put it to use in practice. The code for this article is on Github.

@measure: decorator functions without arguments

Let's take a useful example of measuring how long it takes to execute a function. The best would be if we could easily annotate an existing function and get "free" measurements. Let's look at the following two functions:

from timeit import default_timer as timer
from time import sleep

def measure(func: Callable):
    def inner(*args, **kwargs):
        print(f'---> Calling {func.__name__}()')
        start = timer()
        func(*args, **kwargs)
        elapsed_sec = timer() - start
        print(f'---> Done {func.__name__}(): {elapsed_sec:.3f} secs')
    return inner

def sleeper(seconds: int = 0):
    print('Going to sleep...')
    sleep(seconds)
    print('Done!')

measure() is a function which takes a function func() as an argument, and returns a function inner() declared on the inside. inner() takes whatever arguments are passed in and passed them along to func(), but wraps this call in a few lines of to measure and print the elapsed time in seconds. sleeper() is a test function which explicitly sleeps for a while so we can measure it.

Given these, we can construct a measured sleeper() function like:

measured_sleeper = measure(sleeper)
measured_sleeper(3)

Output:

---> Calling sleeper()
Going to sleep...
Done!
---> Done sleeper(): 3.000 secs

This works, but if we're already using sleeper() in a bunch of places, we'd have to replace all those calls with measured_sleeper(). Instead, we can:

sleeper = measure(sleeper)

Here we are replacing the sleeper reference in the current scope to point to the measured version of the original sleeper() function. This is exactly the same thing as putting the @ decorator in front of the function declaration:

@measure
def sleeper(seconds: int = 0):
    print('Going to sleep...')
    sleep(seconds)
    print('Done!')

So @decorators are just syntactic sugar to passing a newly defined function to an existing decorator function, which returns a new function, and having the original function name point to this new function!

@repeat: parameterized decorator function

In the above example we took an existing function sleeper() and decorated it with a function-taking-and-returning-a-function measure(), ie. a @decorator. What if we want to pass arguments to the decorator function itself? For example, imagine we have a function, and we want to repeat it n times. To accomplish this, we just have to add one more inner function:

def repeat(n: int = 1):
    def decorator(func: Callable):
        def inner(*args, **kwargs):
            for _ in range(n):
                func(*args, **kwargs)
        return inner
    return decorator

@repeat(n=3)
def hello(name: str):
    print(f'Hello {name}!')

hello('world')

Output:

Hello world!
Hello world!
Hello world!

@trace: Decorating a class with a function

We can also decorate classes, not just functions. As an example, assume we have an existing class Foo, and we would like to trace it, ie. get a print() each time a method is called, without having to manually change each method. So we'd like to be able to put @trace before the class definition and get this functionality for free, like:

@trace
class Foo:
    i: int = 0
    def __init__(self, i: int = 0):
        self.i = i
    def increment(self):
        self.i += 1
    def __str__(self):
        return f'This is a {self.__class__.__name__} object with i = {self.i}'

What does trace() look like? It must accepts a cls argument (the newly defined class, Foo in our case), and return a new/modified class (with added tracing):

def trace(cls: type):
    def make_traced(cls: type, method_name: str, method: Callable):
        def traced_method(*args, **kwargs):
            print(f'Executing {cls.__name__}::{method_name}...')
            return method(*args, **kwargs)
        return traced_method
    for name in cls.__dict__.keys():
        if callable(getattr(cls, name)) and name != '__class__':
            setattr(cls, name, make_traced(cls, name, getattr(cls, name)))
    return cls

The implementation is quite straightforward. We go through all methods in cls.__dict__.items(), and replace the method with a wrapped method, which we manufacture with the inner make_traced() function. It works:

f1 = Foo()
f2 = Foo(4)
f1.increment()
print(f1)
print(f2)

Output:

Executing Foo::__init__...
Executing Foo::__init__...
Executing Foo::increment...
Executing Foo::__str__...
This is a Foo object with i = 1
Executing Foo::__str__...
This is a Foo object with i = 4

@singleton: The singleton pattern

A second example of decorating a class with a function is implementing the common singleton pattern:

In software engineering, the singleton pattern is a software design pattern that restricts the instantiation of a class to one "single" instance. This is useful when exactly one object is needed to coordinate actions across the system.

Our implementation as a Python decorator @singleton:

def singleton(cls: type):
    def __new__singleton(cls: type, *args, **kwargs):
        if not hasattr(cls, '__singleton'):
            cls.__singleton = object.__new__(cls) # type: ignore
        return cls.__singleton                    # type: ignore
    cls.__new__ = __new__singleton                # type: ignore
    return cls

As mentioned in the Enum articles, the __new__() class method is called to construct new objects, before __init__() is called on the newly created instance to initialize it. So, to get singleton behaviour, we just need to override __new__() to always return a single instance. Let's test it:

@singleton
class Foo:
    i: int = 0
    def __init__(self, i: int = 0):
        self.i = i
    def increment(self):
        self.i += 1
    def __str__(self):
        return f'This is a {self.__class__.__name__} object with i = {self.i}'

@singleton
class Bar:
    i: int = 0
    def __init__(self, i: int = 0):
        self.i = i
    def increment(self):
        self.i += 1
    def __str__(self):
        return f'This is a {self.__class__.__name__} object with i = {self.i}'

f1 = Foo()
f2 = Foo(4)
f1.increment()
b1 = Bar(9)
print(f1)
print(f2)
print(b1)
print(f1 is f2)
print(f1 is b1)

Output:

This is a Foo object with i = 5
This is a Foo object with i = 5
This is a Bar object with i = 9
True
False

@Count: Decorating a class with a class

The reason the above code works is that in Python, class declarations are really just syntactic sugar for a function which constructs a new type object. For example, a class Foo declared above can also be defined programatically like:

def make_class(name):
    cls = type(name, (), {})
    setattr(cls, 'i', 0)
    def __init__(self, i): self.i = i
    setattr(cls, '__init__', __init__)
    def increment(self): self.i += 1
    setattr(cls, 'increment', increment)
    def __str__(self): return f'This is a {self.__class__.__name__} object with i = {self.i}'
    setattr(cls, '__str__', __str__)
    return cls

Foo = make_class('Foo')

But, if that's the case, we can not just decorate a function with a function, a class with a function, but also a class with a class. Let's see an example of this with the @Count pattern, where we want to count the number of instances created. We have an existing class, and we'd like to be able to just put @Count before class definition, and get a "free" count of instances created, that we can then access using the decorator Count class. The solution:

class Count:
    instances: DefaultDict[str, int] = defaultdict(int) # we will use this as a class instance
    def __call__(self, cls): # here cls is either Foo or Bar
        class Counted(cls): # here cls is either Foo or Bar
            def __new__(cls: type, *args, **kwargs): # here cls is Counted
                Count.instances[cls.__bases__[0].__name__] += 1
                return super().__new__(cls) # type: ignore
        Counted.__name__ = cls.__name__
        # without this ^ , self.__class__.__name__ would
        # be 'Counted' in the __str__() functions below
        return Counted

The trick is that when a class is decorated with Count, its __call__() method is invoked by the runtime, and the class is passed in as cls. Inside, we construct a new class Counted, which has cls as its parent, but overrides __new__(), and increments a counter in the Count class variable instances (but otherwise created a new instance and returns it). The newly constructed Counted class (whose name is overridden) is then returned, and replaces the original defined class. Let's see it in action:

@Count()
class Foo:
    i: int = 0
    def __init__(self, i: int = 0):
        self.i = i
    def increment(self):
        self.i += 1
    def __str__(self):
        return f'This is a {self.__class__.__name__} object with i = {self.i}'
@Count()
class Bar:
    i: int = 0
    def __init__(self, i: int = 0):
        self.i = i
    def increment(self):
        self.i += 1
    def __str__(self):
        return f'This is a {self.__class__.__name__} object with i = {self.i}'

f1 = Foo()
f2 = Foo(6)
f2.increment()
b1 = Bar(9)
print(f1)
print(f2)
print(b1)
for class_name, num_instances in Count.instances.items():
    print(f'{class_name} -> {num_instances}')

Output:

This is a Foo object with i = 0
This is a Foo object with i = 7
This is a Bar object with i = 9
Foo -> 2
Bar -> 1

@app.route: Building a Flask-like application object by decorating functions

Finally, many of us have used Flask, and have written HTTP handler functions along the lines of:

from flask import Flask

app = Flask(__name__)

@app.route('/')
def hello():
    return 'Hello, World!'

This is yet another creative use of decorator patterns. Here we're building up an app object by adding our custom handler functions, but we don't have to worry about defining our own class derived from Flask, we just write flat functions which we decorate. This functionality is straightforward to duplicate as a toy Router class:

class Router:
    routes: dict[str, Callable] = {}

    def route(self, prefix: str):
        def decorator(func: Callable):
            self.routes[prefix] = func
        return decorator

    def default_handler(self, path):
        return f'404 (path was {path})'

    def handle_request(self, path):
        longest_match, handler_func = 0, None
        for prefix, func in self.routes.items():
            if path.startswith(prefix) and len(prefix) > longest_match:
                longest_match, handler_func = len(prefix), func
        if handler_func is None:
            handler_func = self.default_handler
        print(f'Response: {handler_func(path)}')

The only trick here is that the Router::route() can act like a decorator, and returns a function. Example usage:

app = Router()

@app.route('/')
def hello(_):
    return 'Hello to my server!'

@app.route('/version')
def version(_):
    return 'Version 0.1'

app.handle_request('/')
app.handle_request('/version')
app.handle_request('does-not-exist')

Output:

Response: Hello to my server!
Response: Version 0.1
Response: 404 (path was does-not-exist)

@decorator vs @decorator()

In the @measure example, we wrote:

@measure
def sleeper(seconds: int = 0):
    ...

Could we also write @measure() before the def? No! We would get an error:

measure() missing 1 required positional argument: 'func'

But, in the app.route() example, we do write the () parentheses. The situation is simple: roughly speaking, @decorator def func gets replaced by func = decorator(func). If we write @decorator() def func, it gets replaced by func = decorator()(func). So in the latter case, decorator() is run, and it needs to return a function which accepts a function as as an argument, and returns a function. This is how all the examples where the decorator takes an argument are structured.

Conclusion

In Python functions are first class citizens, and decorators are powerful syntactic sugar exploiting this functionality to give programmers a seemingly "magic" way to construct useful compositions of functions and classes. This is an important language feature that sets Python apart from traditional OOP languages like C++ and Java, where achieving such functionality requires more code, or more complex templated code. This dynamic nature of Python creates more runtime overhead compared to a language like C++, but it makes the code easier to wrote and comprehend. This is a win for programmers and projects; in most real-world software engineering efforts runtime performance is not a bottleneck.

Original article source at https://bytepawn.com

#python #programming

What is GEEK

Buddha Community

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type