20 Python Decorators Interview Questions and Answers

Python Decorators are a powerful tool that can be used to modify the behavior of a function or class. They are commonly used in web development and can be used to add functionality to a website or application. When interviewing for a position that requires knowledge of Python Decorators, it is important to be prepared to answer questions about their use and implementation. In this article, we will review some common Python Decorators interview questions and how to answer them.

Python Decorators Interview Questions and Answers

Here are 20 commonly asked Python Decorators interview questions and answers to prepare you for your interview:

1. What are decorators in Python?

Decorators are a way to dynamically modify a function or class. A decorator takes in a function or class and returns a modified version of that function or class. Decorators are often used to add functionality to a function or class, or to change the way a function or class behaves.

2. Can you explain some use cases for using decorators in Python?

Decorators can be used for a variety of purposes, but one common use case is to add additional functionality to a function without having to modify the function itself. For example, you could use a decorator to add logging to a function so that you can keep track of when the function is called and what its arguments are. Decorators can also be used to cache the results of a function so that subsequent calls to the function are faster.

3. How do you create a decorator in Python?

A decorator is a function that takes another function as an argument and extends the behavior of the passed in function, without explicitly modifying it.

4. Can you give me an example of a decorator that can be used to count the number of times a function is executed?

A decorator that can be used to count the number of times a function is executed can be written like this:

def count_executions(func):
def wrapper(*args, **kwargs):
wrapper.num_executions += 1
return func(*args, **kwargs)
wrapper.num_executions = 0
return wrapper

@count_executions
def some_function():
pass

some_function()
some_function()

print(some_function.num_executions) # 2

5. Can you explain what parameters and arguments are in the context of functions and decorators?

In Python, parameters are the variables that are passed into a function when the function is called. Arguments are the values that are actually passed into those parameters. So, if you have a function that takes two parameters, x and y, and you call the function with the arguments 3 and 4, then 3 will be assigned to x and 4 will be assigned to y.

6. How does Python’s @property decorator work?

The @property decorator is used to create properties in a class. When used, the decorator will take the following syntax: @property(getter, setter, deleter, doc). The getter is used to get the value of the property, the setter is used to set the value of the property, the deleter is used to delete the property, and the doc is used to provide documentation for the property.

7. How does Python’s @staticmethod decorator work?

The @staticmethod decorator in Python is used to create static methods within a class. Static methods are methods that are not bound to an instance of a class, and can therefore be called without an instance of the class being created. The @staticmethod decorator allows you to define a static method without having to explicitly create a staticmethod object.

8. How does Python’s @classmethod decorator work?

The @classmethod decorator is a way of indicating that a method should be treated as a class method, rather than a regular instance method. This means that the method will receive the class as its first argument, rather than an instance of the class. This can be useful for a number of different purposes, such as creating factory methods that can create instances of a class from a given set of parameters.

9. How does Python’s @synchronized decorator work?

The @synchronized decorator is used to create a lock on a method or function. This lock can be used to prevent race conditions from occurring. When the decorator is applied to a method, it will acquire a lock on the instance of the class that the method is being called on. This lock is then released when the method returns.

10. Does it make sense to have nested decorators in Python? If yes, then how many levels of nesting are possible?

Yes, it does make sense to have nested decorators in Python. There is no limit to the number of levels of nesting that are possible.

11. Is it possible to use decorators with class definitions in Python? Explain what happens when you try doing this?

Yes, it is possible to use decorators with class definitions in Python. However, when you do this, the decorator is applied to the class itself, rather than to the individual methods within the class. This can lead to some unexpected behavior, so it is generally best to avoid using decorators with class definitions unless you are confident that you understand how they will work in this context.

12. What is closure in the context of decorators?

Closure is a function that returns another function. In the context of decorators, it is used to wrap a function with another function in order to add additional functionality.

13. Can you give me an example of a recursive decorator?

A recursive decorator is a decorator that is applied to a function that calls itself. This is often used in situations where you want to keep track of how many times a function has been called, or to cache the results of a function call so that subsequent calls are faster.

14. Can you explain the difference between a factory method and a decorator?

A factory method is a method that creates objects, while a decorator is a method that modifies objects. In Python, decorators are typically used to add functionality to existing objects, such as adding a new method or attribute.

15. Can you explain what memoization is? How is it related to decorators?

Memoization is a technique for caching the results of function calls so that future calls with the same arguments can be returned more quickly. This can be especially useful for computationally expensive functions. Decorators can be used to implement memoization in Python.

16. Which data structure would you prefer for implementing memoization? Why?

I would prefer to use a dictionary to implement memoization. A dictionary is a data structure that allows you to store data in a key-value format, which makes it ideal for storing the results of function calls. Additionally, a dictionary can be easily implemented in Python, which makes it a good choice for this purpose.

17. Are there any limitations on using decorators?

Yes, there are some limitations. Decorators can only be used on functions and methods, and they need to be defined before the function or method they are decorating. Additionally, decorators can’t be used on class methods that take the self argument.

18. What are some important characteristics of decorators?

Decorators are important because they allow you to modify the behavior of a function without having to actually change the function itself. This is especially useful in situations where you need to add or remove functionality from a function on the fly. Decorators are also relatively easy to use, which makes them a good choice for many programming tasks.

19. What do you understand about metaprogramming in the context of Python?

Metaprogramming is the ability of a program to examine and modify its own structure and behavior. In Python, this is often accomplished using decorators. Decorators are a way to dynamically alter the behavior of a function or class. They can be used to add functionality, remove functionality, or change the behavior of a function or class in a wide variety of ways.

20. What are some common problems associated with using decorators?

Decorators can be difficult to debug because they can modify the behavior of a function in unexpected ways. Additionally, they can make code harder to read and understand.