\n
import time\r\n\r\ndef timing_decorator(func):\r\n\r\ndef wrapper(*args, **kwargs):\r\n\r\nstart_time = time.time()\r\n\r\nresult = func(*args, **kwargs)\r\n\r\nend_time = time.time()\r\n\r\nprint(f\"Elapsed time: {end_time - start_time}\")\r\n\r\nreturn result\r\n\r\nreturn wrapper\r\n\r\n@timing_decorator\r\n\r\ndef my_function():\r\n\r\ntime.sleep(2)\r\n\r\nmy_function()<\/pre>\n<\/div>\nIn this example, the `@timing_decorator` decorator is defined and applied to the `my_function` function. This decorator records the start time of the function’s execution and, after the process completes, records the end time and calculates the elapsed time between them. Then, it prints this time as output.<\/p>\n
In this example, by using the `@timing_decorator` decorator, there is no need to change the code of the `my_function` function, and by applying this decorator, you can easily calculate its execution time.<\/p>\n
Are there other decorators in Python?<\/h2>\n
Yes, there are various valuable decorators in\u00a0Python. <\/strong>In the following, I mention some of these decorators:<\/p>\n\n- `@staticmethod`:<\/strong> This decorator defines static functions that can be called independently of an instance of the class.<\/li>\n
- \u00a0`@classmethod`:<\/strong> This decorator defines class functions that are useful for performing operations related to the class.<\/li>\n
- `@property`:<\/strong> This decorator defines automatic properties (computed properties) that, when called, calculate a value and return it as output.<\/li>\n
- `@abstractmethod`:<\/strong> This decorator defines abstract methods in classes that must be implemented in subclasses.<\/li>\n
- `@staticmethod`:<\/strong> This decorator defines static functions that can be called independently of an instance of the course.<\/li>\n
- `@typing`:<\/strong>\u00a0This decorator is used to specify the type of inputs and outputs of functions.<\/li>\n
- `@wraps`:<\/strong> This decorator keeps information such as the function name and documentation inside another part.<\/li>\n
- `@lru_cache`:<\/strong> This decorator is used to save the arithmetic results of the process and prevent them from being recalculated in subsequent calls.<\/li>\n
- @asyncio:<\/strong> This decorator defines asynchronous operations widely used in small and large programs.<\/li>\n
- `@retry` decorator:<\/strong> This decorator is used to repeat the execution of a process in case of an error. Using this decorator, the execution of the process can be repeated a specified number of times to retry in case of a mistake.<\/li>\n
- `@cache` decorator:<\/strong>\u00a0This decorator is used to save the arithmetic results of the function and prevent them from being recalculated in subsequent calls.\u00a0By using this decorator, you can improve the performance of the program and reduce its execution time.<\/li>\n
- `@login_required` decorator:<\/strong> This decorator protects content that requires a user login. Using this decorator, it is possible to allow access to specific pages only to users who are logged in.<\/li>\n
- `@validate_args` decorator:<\/strong> This decorator is used to validate function inputs.\u00a0Using this decorator, you can verify that the function inputs are correct and complete and return an error if there is a problem.<\/li>\n
- `@synchronized` decorator:<\/strong> This decorator locks access to a code section. Using this decorator, avoiding concurrency in the code and ensuring that only one thread has access to one part of the code is possible.<\/li>\n
- `@log` decorator:<\/strong> This decorator registers code execution logs. Using this decorator, the activities performed in the program can be recorded and checked if needed.<\/li>\n
- `@ @suppress` decorator:<\/strong> This decorator is used to silence errors. Using this decorator, you can ignore these errors and continue running the program from the error to the information level.<\/li>\n<\/ul>\n
These are just some examples of decorators in\u00a0Python<\/strong>.\u00a0Using decorators, code can be made simpler and more readable, improve program performance, and reduce errors.<\/p>\nCan decorators be combined?<\/h2>\n
Yes,\u00a0decorators can be combined in\u00a0Python. <\/strong>This helps to make decorators more useful and expand their capabilities.<\/p>\nTo combine two decorators, they can be placed back-to-back. For example, if we want to define a function as a class with the `@staticmethod` decorator and also use the `@cache` decorator to store arithmetic results, we can do as follows:<\/p>\n
\n
Class MyClass:\r\n\r\n@staticmethod\r\n\r\n@cache\r\n\r\ndef my_static_method(x):<\/pre>\n<\/div>\n# This function is defined as a static function, and when called, its calculation result is stored using the cache decorator.<\/p>\n
\n
return x ** 2<\/pre>\n<\/div>\nWith this, the “my_static_method” function is defined as a static function, and when called, its arithmetic result, if any, is stored in memory and is not recalculated in subsequent calls.<\/p>\n
Also, it is possible to specify more than two decorators for a function and combine them appropriately.\u00a0This can be done directly by using decorators.<\/p>\n
How to combine decorators with functions?<\/h2>\n
In\u00a0Python<\/strong>, decorators can be combined with other functions. Decorators are considered first-type functions in\u00a0Python so that<\/strong> they can be used as arguments for different functions.<\/p>\nFor example, suppose we have a function that receives a number as input, and its output is the sum of odd numbers more minor than that. Now we want to define this function as a function with two decorators, “@memoize” and “@timed”, so that in case of subsequent calls, its arithmetic result will be stored in memory, and its execution time will be accurately recorded.<\/p>\n
We can do as follows:<\/h4>\n\n
import time\r\n\r\ndef memoize(func):\r\n\r\ncache = {}\r\n\r\ndef wrapper(*args):\r\n\r\nif args in cache:\r\n\r\nreturn cache[args]\r\n\r\nOtherwise:\r\n\r\nresult = func(*args)\r\n\r\ncache[args] = result\r\n\r\nreturn result\r\n\r\nreturn wrapper\r\n\r\ndef timed(func):\r\n\r\ndef wrapper(*args):\r\n\r\nstart = time.time()\r\n\r\nresult = func(*args)\r\n\r\nend = time.time()\r\n\r\nprint(f\"Elapsed time: {end-start:.6f} seconds\")\r\n\r\nreturn result\r\n\r\nreturn wrapper\r\n\r\n@memoize\r\n\r\n@timed\r\n\r\ndef sum_of_odd_numbers(n):\r\n\r\nreturn sum(filter(lambda x: x % 2 == 1, range(n)))\r\n\r\nprint(sum_of_odd_numbers(1000000))\r\n\r\nprint(sum_of_odd_numbers(1000000))<\/pre>\n<\/div>\nIn this code, the “memoize” function is used to save the process’s arithmetic results, and the “timed” function is used to record the execution time of the process in each call. Then, using `memoize` and `timed` decorators, the `sum_of_odd_numbers` function is defined. When calling this function, its arithmetic result, if any, is stored in memory and is not recalculated in subsequent calls.<\/p>\n
Also, the execution time of the function is recorded and printed in each call using the `timed’ decorator. This way, you can add more functionality to your functions by combining decorators with other parts.<\/p>\n
How to combine decorators with classes?<\/h2>\n
In\u00a0Python<\/strong>, decorators can also be combined with classes. Decorators defined for functions are the most supported form of decorators, but decorators can also be defined for types.<\/p>\nFor example, suppose we have a style that stores a person’s characteristics, and we want to use the decorators `@property` and `@classmethod` to define the course’s properties.<\/p>\n
We can do as follows:<\/h3>\n\n
Class Person:\r\n\r\ndef __init__(self, name, age):\r\n\r\nself._name = name\r\n\r\nself._age = age\r\n\r\n@property\r\n\r\ndef name(self):\r\n\r\nreturn self._name\r\n\r\n@property\r\n\r\ndef age(self):\r\n\r\nreturn self._age\r\n\r\n@classmethod\r\n\r\ndef from_birth_year(cls, name, birth_year):\r\n\r\nage = cls.calculate_age(birth_year)\r\n\r\nreturn cls(name, age)\r\n\r\n@staticmethod\r\n\r\ndef calculate_age(birth_year):\r\n\r\nreturn 2023 - birth_year<\/pre>\n<\/div>\nIn this code, `@property` defines the `name` and `age` properties. Also, `@classmethod` represents the `from_birth_year` function, which takes a person’s birth year and returns their name and age as an object from the class. Also, the “calculate_age” function is defined as a static function used to calculate age. In this way, you can add more functionality by combining decorators with styles.<\/p>\n
Can other functions be used as decorators for classes?<\/h2>\n
Yes,<\/strong> other functions can be used in Python as decorators for classes. Any function that takes a class as input and returns a new class can be used as a decorator for classes. This helps to make classes more practical and expand their capabilities.<\/p>\nFor example, suppose we have a class that receives a number as input, and its output is the sum of odd numbers more minor than that number.<\/p>\n
Now we want to define this class as a class with two decorators, “@memoize” and “@timed”, so that in case of subsequent calls, its arithmetic result will be stored in memory, and its execution time will be accurately recorded.<\/p>\n
We can do as follows:<\/h4>\n\n
import time\r\n\r\ndef memoize(cls):\r\n\r\ncache = {}\r\n\r\ninit = cls.__init__\r\n\r\ndef wrapper(self, n):\r\n\r\nif n in cache:\r\n\r\nreturn cache[n]\r\n\r\nOtherwise:\r\n\r\ninstance = cls.__new__(cls)\r\n\r\ninit(instance, n)\r\n\r\nresult = instance.sum_of_odd_numbers()\r\n\r\ncache[n] = result\r\n\r\nreturn result\r\n\r\ncls.__init__ = wrapper\r\n\r\nreturn cls\r\n\r\ndef timed(cls):\r\n\r\nold_sum_of_odd_numbers = cls.sum_of_odd_numbers\r\n\r\ndef new_sum_of_odd_numbers(self):\r\n\r\nstart = time.time()\r\n\r\nresult = old_sum_of_odd_numbers(self)\r\n\r\nend = time.time()\r\n\r\nprint(f\"Elapsed time: {end-start:.6f} seconds\")\r\n\r\nreturn result\r\n\r\nCLS.sum_of_odd_numbers = new_sum_of_odd_numbers\r\n\r\nreturn cls\r\n\r\n@memoize\r\n\r\n@timed\r\n\r\nClass SumOfOddNumbers:\r\n\r\ndef __init__(self, n):\r\n\r\nself.n = n\r\n\r\ndef sum_of_odd_numbers(self):\r\n\r\nreturn sum(filter(lambda x: x % 2 == 1, range(self.n)))\r\n\r\ns = SumOfOddNumbers(1000000)\r\n\r\nprint(s.sum_of_odd_numbers())\r\n\r\nprint(s.sum_of_odd_numbers())<\/pre>\n<\/div>\nIn this code, the “memoize” function is used to cache the process’s arithmetic results, and the “timed” function is used to record the process’s execution time for each call. Then, using `memoize` and `timed` decorators, the `SumOfOddNumbers` class is defined.<\/p>\n
When calling the `sum_of_odd_numbers’ function in this class, its arithmetic result, if any, is stored in memory and is not recalculated in subsequent calls.<\/p>\n
Additionally, the function’s execution time is recorded and printed with each call using the `timed’ decorator.<\/p>\n
In this way, combining other functions with classes as decorators allows you to add more features to types and design them more effectively.<\/p>\n
Are there other decorators available for defining automatic properties?<\/h2>\n
Yes,\u00a0there are other decorators in\u00a0Python to define automatic properties.\u00a0<\/strong>For example, the `@property’ decorator describes readable properties, `@classmethod’ describes a class method, and `@staticmethod’ defines a static function. Also, `@setter` and `@deleter` decorators portray mutable and deleted properties.<\/p>\nFor example, suppose we want to define a class to display a person’s date of birth. First, we can add a readable property to set the date of birth and then define two properties that are changeable and delete to change and delete the date of delivery.<\/p>\n
We can do as follows:<\/h4>\n\n
Birthday class:\r\n\r\ndef __init__(self, year, month, day):\r\n\r\nself._year = year\r\n\r\nself._month = month\r\n\r\nself._day = day\r\n\r\n@property\r\n\r\ndef year(self):\r\n\r\nreturn self._year\r\n\r\n@property\r\n\r\ndef month(self):\r\n\r\nreturn self._month\r\n\r\n@property\r\n\r\ndef day(self):\r\n\r\nreturn self._day\r\n\r\n@year.setter\r\n\r\ndef year(self, value):\r\n\r\nself._year = value\r\n\r\n@month.setter\r\n\r\ndef month(self, value):\r\n\r\nself._month = value\r\n\r\n@day.setter\r\n\r\ndef day(self, value):\r\n\r\nself._day = value\r\n\r\n@year.deleter\r\n\r\ndef year(self):\r\n\r\ndel self._year\r\n\r\n@month.deleter\r\n\r\ndef month(self):\r\n\r\ndel self._month\r\n\r\n@day.deleter\r\n\r\ndef day(self):\r\n\r\ndel self._day\r\n\r\ndef __str__(self):\r\n\r\nreturn f\"{self._year}\/{self._month}\/{self._day}\"<\/pre>\n<\/div>\nIn this code, the decorator `@property` defines the readable properties `year,` `month,` and `day.` Also, the `@setter` decorator is used to determine the mutable attributes `year,` `month,` and `day,` and the `@deleter` decorator defines the delectable attributes `year,` `month,` and `day.` Finally, by overriding the `__str__` method, the date of birth is printed as a string in the format year\/month\/day.<\/p>\n
In this way, by using the `@property’, `@setter`, and `@deleter` decorators, we can add automatic properties to classes and design them more effectively.<\/p>\n
FAQ<\/h2>\n\n
\n
\n
What is a Python decorator?<\/h3>\n\n\n
A decorator is a callable that takes another function or class, adds functionality, and returns a modified version.<\/p>\n\n<\/div>\n<\/div>\n
\n
How do you implement a decorator?<\/h3>\n\n\n
Define a wrapper function inside a decorator that accepts *args and **kwargs, and use the @decorator_name syntax above the target function.<\/p>\n\n<\/div>\n<\/div>\n
\n
Where are decorators commonly used?<\/h3>\n\n\n
Decorators are used for logging, authentication, caching, validation, and method modifiers like @staticmethod or @classmethod.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"
Decorators In Python Are A Type Of Functions That Can Be Used To Change The Behavior Of A Function Or Class. These changes can include adding functionality to a function, modifying a process’s inputs or outputs, or altering a class’s behavior. Decorators are applied to a part or type using the @ character. A decorated […]<\/p>\n","protected":false},"author":6,"featured_media":178811,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[316],"tags":[320],"class_list":["post-178810","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-python"],"acf":[],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/posts\/178810","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/comments?post=178810"}],"version-history":[{"count":5,"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/posts\/178810\/revisions"}],"predecessor-version":[{"id":266786,"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/posts\/178810\/revisions\/266786"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/media\/178811"}],"wp:attachment":[{"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/media?parent=178810"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/categories?post=178810"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ded9.com\/tr\/wp-json\/wp\/v2\/tags?post=178810"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}