You’re Decent At Python If You Can Answer These 7 Questions Correctly


You’re Decent At Python If You Can Answer These 7 Questions Correctly

Python is one of the most popular programming languages today, known for its readability and versatility. Whether you're new to Python or have been coding for a while, there are certain concepts and skills that distinguish a proficient Python programmer from a novice. Here are seven questions that test your understanding of Python. If you can answer these correctly, you’re on your way to being a competent Python developer.

1. What is the difference between deepcopy and copy in Python?

In Python, copying objects can be done using the copy module, which provides two methods: copy() and deepcopy(). Understanding the difference between these two is crucial for avoiding bugs in your code, especially when dealing with mutable objects.

Shallow Copy (copy)

A shallow copy creates a new object, but inserts references into it to the objects found in the original. In other words, it only copies the outermost container, not the elements within it. This means changes to mutable elements within the copied object will reflect in the original object.

import copy original_list = [1, 2, [3, 4]] shallow_copy = copy.copy(original_list) shallow_copy[2][0] = 'changed' print(original_list) # Output: [1, 2, ['changed', 4]] print(shallow_copy) # Output: [1, 2, ['changed', 4]]

Deep Copy (deepcopy)

A deep copy creates a new object and recursively copies all objects found in the original. This means changes to mutable elements in the copied object will not affect the original object.

import copy original_list = [1, 2, [3, 4]] deep_copy = copy.deepcopy(original_list) deep_copy[2][0] = 'changed' print(original_list) # Output: [1, 2, [3, 4]] print(deep_copy) # Output: [1, 2, ['changed', 4]]

2. How does Python's Global Interpreter Lock (GIL) affect multi-threading?

The Global Interpreter Lock (GIL) is a mutex that protects access to Python objects, preventing multiple native threads from executing Python bytecodes at once. This means that in a multi-threaded Python program, only one thread can execute Python code at a time, even if run on a multi-core processor.

Implications of GIL

  1. CPU-bound tasks: For tasks that require heavy CPU computations, using multi-threading might not lead to performance gains because threads are unable to run in parallel due to the GIL.
  2. I/O-bound tasks: For tasks that spend most of their time waiting for external events (like I/O operations), multi-threading can still provide performance benefits as the GIL is released during I/O operations.

To overcome the limitations imposed by the GIL for CPU-bound tasks, you can use multiprocessing, which involves running separate Python interpreter processes, each with its own GIL.

import threading def cpu_bound_task(): result = 0 for i in range(1000000): result += i return result # Running CPU-bound tasks using threading might not be efficient due to GIL threads = [threading.Thread(target=cpu_bound_task) for _ in range(4)] for thread in threads: thread.start() for thread in threads: thread.join()

3. What are list comprehensions and how do they differ from generator expressions?

List comprehensions and generator expressions provide concise ways to create lists and generators in Python, respectively. They share similar syntax but differ in their execution and memory usage.

List Comprehensions

List comprehensions create lists directly in a readable and concise way. They are enclosed in square brackets.

squares = [x**2 for x in range(10)] print(squares) # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Generator Expressions

Generator expressions generate values on the fly and are memory efficient. They are enclosed in parentheses.

squares_gen = (x**2 for x in range(10)) print(list(squares_gen)) # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

The primary difference is that list comprehensions generate the entire list in memory, whereas generator expressions generate items one at a time and do not store the entire list in memory.

4. Explain the use of decorators in Python.

Decorators are a powerful and expressive tool in Python that allow you to modify the behavior of a function or class method. Decorators are themselves functions (or classes) that return a callable, which is then used to wrap another function.

Creating a Decorator

A simple decorator function that prints a message before and after a function call:

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()


Something is happening before the function is called. Hello! Something is happening after the function is called.

Common Uses of Decorators

  1. Logging: Track the execution of code.
  2. Access Control: Restrict access to certain parts of the code.
  3. Memoization: Cache the results of expensive function calls.
  4. Validation: Validate inputs to functions.

5. What is a context manager and how does the with statement work in Python?

A context manager is a Python object that provides a way to allocate and release resources precisely when you want to. The most common use of context managers is to manage file streams.

The with Statement

The with statement simplifies exception handling by encapsulating common preparation and cleanup tasks. It ensures that resources are properly released even if an error occurs.

with open('example.txt', 'w') as file: file.write('Hello, world!')

In this example, the file is properly closed after the with block is executed, even if an exception is raised within the block.

Creating a Custom Context Manager

You can create a custom context manager using a class with __enter__ and __exit__ methods or by using the contextlib module.

class CustomContextManager: def __enter__(self): print("Entering the context") return self def __exit__(self, exc_type, exc_val, exc_tb): print("Exiting the context") with CustomContextManager() as manager: print("Inside the context")


Entering the context Inside the context Exiting the context

6. What are Python's built-in data structures and their uses?

Python provides several built-in data structures, each suited for different tasks:


  • Dynamic arrays: Ordered, mutable collections.
  • Use cases: Storing sequences of items, such as a collection of files or user inputs.
my_list = [1, 2, 3, 4, 5]


  • Immutable sequences: Ordered collections that cannot be modified.
  • Use cases: Storing fixed collections of items, like coordinates or multiple return values from functions.
my_tuple = (1, 2, 3)


  • Unordered collections: Unique, immutable items.
  • Use cases: Storing unique items, membership testing, and eliminating duplicates.
my_set = {1, 2, 3, 4, 5}


  • Key-value pairs: Unordered, mutable collections.
  • Use cases: Storing and retrieving items by key, like configuration settings or a phone book.
my_dict = {'name': 'Alice', 'age': 25}

7. Explain the difference between __init__ and __new__ methods in Python classes.

In Python, __init__ and __new__ are special methods used in the object creation process.

__new__ Method

__new__ is responsible for creating a new instance of a class. It is called before __init__ and is typically used when subclassing immutable types like int, str, or tuple.

class MyClass: def __new__(cls, *args, **kwargs): instance = super(MyClass, cls).__new__(cls) return instance def __init__(self, value): self.value = value

__init__ Method

__init__ initializes the instance after it has been created. It is commonly used to set initial values for instance attributes.

class MyClass: def __init__(self, value): self.value = value obj = MyClass(10) print(obj.value) # Output: 10

In summary, __new__ is for creating and returning a new instance, while __init__ is for initializing the instance after it has been created.


Mastering Python involves understanding its core features and idioms. If you can answer the above questions correctly, you have a solid grasp of important Python concepts. Keep practicing and exploring Python’s vast ecosystem, and you'll continue to improve your skills and understanding.

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