Tutorial Playlist
200 Lessons1. Introduction to Python
2. Features of Python
3. How to install python in windows
4. How to Install Python on macOS
5. Install Python on Linux
6. Hello World Program in Python
7. Python Variables
8. Global Variable in Python
9. Python Keywords and Identifiers
10. Assert Keyword in Python
11. Comments in Python
12. Escape Sequence in Python
13. Print In Python
14. Python-if-else-statement
15. Python for Loop
16. Nested for loop in Python
17. While Loop in Python
18. Python’s do-while Loop
19. Break in Python
20. Break Pass and Continue Statement in Python
21. Python Try Except
22. Data Types in Python
23. Float in Python
24. String Methods Python
25. List in Python
26. List Methods in Python
27. Tuples in Python
28. Dictionary in Python
29. Set in Python
30. Operators in Python
31. Boolean Operators in Python
32. Arithmetic Operators in Python
33. Assignment Operator in Python
34. Bitwise operators in Python
35. Identity Operator in Python
36. Operator Precedence in Python
37. Functions in Python
38. Lambda and Anonymous Function in Python
39. Range Function in Python
40. len() Function in Python
41. How to Use Lambda Functions in Python?
42. Random Function in Python
43. Python __init__() Function
44. String Split function in Python
45. Round function in Python
46. Find Function in Python
47. How to Call a Function in Python?
48. Python Functions Scope
49. Method Overloading in Python
50. Method Overriding in Python
51. Static Method in Python
52. Python List Index Method
53. Python Modules
54. Math Module in Python
55. Module and Package in Python
56. OS module in Python
57. Python Packages
58. OOPs Concepts in Python
59. Class in Python
60. Abstract Class in Python
61. Object in Python
62. Constructor in Python
63. Inheritance in Python
64. Multiple Inheritance in Python
65. Encapsulation in Python
66. Data Abstraction in Python
67. Opening and closing files in Python
68. How to open JSON file in Python
69. Read CSV Files in Python
70. How to Read a File in Python
71. How to Open a File in Python?
72. Python Write to File
73. JSON Python
74. Python JSON – How to Convert a String to JSON
75. Python JSON Encoding and Decoding
76. Exception Handling in Python
77. Recursion in Python
78. Python Decorators
Now Reading
79. Python Threading
80. Multithreading in Python
81. Multiprocеssing in Python
82. Python Regular Expressions
83. Enumerate() in Python
84. Map in Python
85. Filter in Python
86. Eval in Python
87. Difference Between List, Tuple, Set, and Dictionary in Python
88. List to String in Python
89. Linked List in Python
90. Length of list in Python
91. Reverse a List in Python
92. Python List remove() Method
93. How to Add Elements in a List in Python
94. How to Reverse a List in Python?
95. Difference Between List and Tuple in Python
96. List Slicing in Python
97. Sort in Python
98. Merge Sort in Python
99. Selection Sort in Python
100. Sort Array in Python
101. Sort Dictionary by Value in Python
102. Datetime Python
103. Random Number in Python
104. 2D Array in Python
105. Abs in Python
106. Advantages of Python
107. Anagram Program in Python
108. Append in Python
109. Applications of Python
110. Armstrong Number in Python
111. Assert in Python
112. Binary Search in Python
113. Binary to Decimal in Python
114. Bool in Python
115. Calculator Program in Python
116. chr in Python
117. Control Flow Statements in Python
118. Convert String to Datetime Python
119. Count in python
120. Counter in Python
121. Data Visualization in Python
122. Datetime in Python
123. Extend in Python
124. F-string in Python
125. Fibonacci Series in Python
126. Format in Python
127. GCD of Two Numbers in Python
128. How to Become a Python Developer
129. How to Run Python Program
130. In Which Year Was the Python Language Developed?
131. Indentation in Python
132. Index in Python
133. Interface in Python
134. Is Python Case Sensitive?
135. Isalpha in Python
136. Isinstance() in Python
137. Iterator in Python
138. Join in Python
139. Leap Year Program in Python
140. Lexicographical Order in Python
141. Literals in Python
142. Matplotlib
143. Matrix Multiplication in Python
144. Memory Management in Python
145. Modulus in Python
146. Mutable and Immutable in Python
147. Namespace and Scope in Python
148. OpenCV Python
149. Operator Overloading in Python
150. ord in Python
151. Palindrome in Python
152. Pass in Python
153. Pattern Program in Python
154. Perfect Number in Python
155. Permutation and Combination in Python
156. Prime Number Program in Python
157. Python Arrays
158. Python Automation Projects Ideas
159. Python Frameworks
160. Python Graphical User Interface GUI
161. Python IDE
162. Python input and output
163. Python Installation on Windows
164. Python Object-Oriented Programming
165. Python PIP
166. Python Seaborn
167. Python Slicing
168. type() function in Python
169. Queue in Python
170. Replace in Python
171. Reverse a Number in Python
172. Reverse a string in Python
173. Reverse String in Python
174. Stack in Python
175. scikit-learn
176. Selenium with Python
177. Self in Python
178. Sleep in Python
179. Speech Recognition in Python
180. Split in Python
181. Square Root in Python
182. String Comparison in Python
183. String Formatting in Python
184. String Slicing in Python
185. Strip in Python
186. Subprocess in Python
187. Substring in Python
188. Sum of Digits of a Number in Python
189. Sum of n Natural Numbers in Python
190. Sum of Prime Numbers in Python
191. Switch Case in Python
192. Python Program to Transpose a Matrix
193. Type Casting in Python
194. What are Lists in Python?
195. Ways to Define a Block of Code
196. What is Pygame
197. Why Python is Interpreted Language?
198. XOR in Python
199. Yield in Python
200. Zip in Python
Python decorators are a powerful and elegant programming technique used to change or improve the behavior of functions or methods. They are executed as functions and are frequently used to encapsulate basic operations like logging, authentication, and memoization. Decorators work by enveloping the target function, allowing for the smooth insertion of pre- and post-processing logic.
A decorator is a function that takes a function as input and returns a new function that generally extends or modifies the original functionality. This is accomplished by constructing a wrapper function within the decorator that calls the original function while potentially modifying it. Decorators are called by placing the "@" symbol followed by the name of the decorator function above the function to be modified.
This strategy promotes code modularity by allowing developers to isolate concerns and reuse code across multiple functions without duplicating code. Decorators also improve readability by condensing common portions of code and supporting a clean and orderly structure. Their adaptability lends itself to a wide range of applications, from access control to execution time measurement.
In this tutorial, let's delve into detail about Python built-in decorators, decorators and generators in Python, Python multiple decorator, and other Python decorators example.
Decorators in Python are functions that alter the behavior of other functions or methods. They enclose the target function and allow you to include pre- or post-processing logic. Decorators improve code modularity by isolating concerns and encouraging reuse. They are identified by "@" followed by the name of the decorator function above the target function. This method automates functions such as logging, authentication, and caching, resulting in cleaner, more structured code.
In Python, a Decorator is a dynamic and adaptable programming construct that actively enhances the behavior of functions or methods. It works by enclosing additional functionality around the main logic, improving code modularity, and encouraging concern separation.
Decorators are skilled at dealing with cross-cutting issues including logging, caching, access control, and performance optimization. For example, by using a logging decorator, one can easily implement consistent logging capability across several methods without duplicating code.
Furthermore, decorators are critical in increasing reusability. This is accomplished by enabling developers to isolate and combine frequently used capabilities into independent decorators that are applied to various functions. This removes redundancy while also improving code maintainability and readability.
Decorators' intrinsic flexibility allows for the implementation of complicated customizations while retaining a clean and uncluttered core function. This is especially useful in situations when changing the basic logic directly might result in complex code. Developers can implement sophisticated features or behaviors without sacrificing the original function's clarity by wrapping the target function with a decorator.
Python decorators are a dynamic programming method that enables developers to actively modify and improve the behavior of functions. Decorators provide a substantial contribution to the maintenance of modular, legible, and efficient codebases by encapsulating auxiliary capabilities and fostering reusability.
Before we delve into the different types of decorators, let us first check out a simple function decorator:
Code:
def my_decorator(func):
def wrapper():
print("Something is happening before the function is called.")
func()
print("Something is happening after the function is called.")
return wrapper
@my_decorator
def say_hello():
print("Hello!")
say_hello()
In this example, my_decorator is a simple function decorator that adds behavior before and after the decorated function say_hello is called.
Code:
def decorator1(func):
def wrapper():
print("Decorator 1 before function.")
func()
print("Decorator 1 after function.")
return wrapper
def decorator2(func):
def wrapper():
print("Decorator 2 before function.")
func()
print("Decorator 2 after function.")
return wrapper
@decorator1
@decorator2
def my_function():
print("My function")
my_function()
You can chain multiple decorators by stacking them on top of a function using the @ symbol. The order in which decorators are applied matters.
Code:
def repeat(n):
def decorator(func):
def wrapper(*args, **kwargs):
for _ in range(n):
func(*args, **kwargs)
return wrapper
return decorator
@repeat(3)
def say_hello(name):
print(f"Hello, {name}!")
say_hello("Alice")
Decorators can accept arguments. In this example, the repeat decorator takes an argument n and repeats the decorated function's execution n times.
Code:
class MyDecorator:
def __init__(self, func):
self.func = func
def __call__(self):
print("Something is happening before the function is called.")
self.func()
print("Something is happening after the function is called.")
@MyDecorator
def say_hello():
print("Hello!")
say_hello()
Decorators can also be implemented as classes. A decorator class must define a __init__ method to accept the decorated function and a __call__ method to define the additional behavior.
Code:
def counter(func):
def wrapper(*args, **kwargs):
wrapper.count += 1
print(f"Function {func.__name__} has been called {wrapper.count} times.")
func(*args, **kwargs)
wrapper.count = 0
return wrapper
@counter
def say_hello():
print("Hello!")
say_hello()
say_hello()
Stateful decorators can maintain and update internal state. In this example, the counter decorator counts how many times the decorated function is called.
Code:
class MyClassDecorator:
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
print("Before the function is called.")
self.func(*args, **kwargs)
print("After the function is called.")
@MyClassDecorator
def say_hello():
print("Hello!")
say_hello()
Class decorators can also be used with class methods. The class must define a __call__ method to achieve this.
When you're working with decorators in Python, you may encounter situations where the decorated function returns something or takes arguments. Decorators can handle these scenarios, and here's how:
If the decorated function returns a value, the decorator should capture and return that value. You can do this by using the *args and **kwargs syntax in the wrapper function to accept any arguments and keyword arguments that the decorated function might return.
Code:
def my_decorator(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs) # Call the decorated function
return result # Return the result
return wrapper
@my_decorator
def add(a, b):
return a + b
result = add(3, 5)
print(result) # Output: 8
In this example, the my_decorator decorator wraps the add function. It captures the result returned by add and returns it from the wrapper function.
If the decorated function takes arguments, the decorator should accept those arguments and pass them to the decorated function. You can do this by using the *args and **kwargs syntax in the wrapper function.
Code:
def my_decorator(func):
def wrapper(*args, **kwargs):
print(f"Arguments passed: {args}, {kwargs}")
result = func(*args, **kwargs) # Call the decorated function with arguments
return result
return wrapper
@my_decorator
def multiply(a, b):
return a * b
result = multiply(3, 5)
# Output:
# Arguments passed: (3, 5), {}
In this example, the my_decorator decorator captures the arguments (3, 5) passed to the multiply function and prints them before calling multiply.
So, decorators can seamlessly handle functions that return values or accept arguments. The *args and **kwargs syntax allow decorators to work with functions of varying signatures, making them flexible and versatile for a wide range of use cases.
Decorators are a testimony to Python's versatility and elegance in the programming world. These dynamic structures have shown to be important tools for improving function behavior and structure, demonstrating their value in a plethora of circumstances.
Decorators are excellent at encouraging code modularity and separation of concerns, allowing developers to encapsulate common operations in a concise and reusable manner. Decorators decrease code duplication while simultaneously improving codebase maintainability by abstracting away cross-cutting issues such as logging, caching, and access control.
Decorators remain a robust tool for solving a combination of programming difficulties as Python continues to be a language of choice for different applications. Their ability to smoothly incorporate additional functionality, encourage code reusability, and maintain a clean and structured codebase highlights their importance. Decorators are a tribute to Python's versatility and its community's resourcefulness in building beautiful solutions in the ever-changing world of software development.
1. What is the best use of a decorator in Python?
Decorators are most commonly used in Python to address cross-cutting issues such as logging, authentication, and caching. They allow for the modular addition of functionality to functions or methods, which improves code readability and reusability while keeping the core logic focused and clean.
2. What are the most important decorators in Python?
Some of the most prominent decorators in Python are:
3. When not to use Python decorators?
Use Python decorators sparingly in cases where their complexity may obfuscate code clarity, especially for simple and easy procedures. Overuse of decorators in a codebase with few cross-cutting issues may result in excessive abstraction and impede code understanding. Furthermore, if the decorator logic has a substantial influence on performance, it is recommended to examine alternate techniques to accomplish the needed functionality while maintaining execution efficiency.
PAVAN VADAPALLI
Director of Engineering
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working …Read More
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upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...