Python Interview Questions with Detailed Answers for Beginners

Introduction

Python has emerged as one of the most popular programming languages in the world, thanks to its simplicity, versatility, and wide range of applications. From web development to data science, Python is used in various domains, making it a sought-after skill in the job market. For beginners, preparing for a Python interview can be daunting, but with the right guidance and practice, it can be a rewarding experience.

This comprehensive guide is designed to help beginners navigate through common Python interview questions. Each question is accompanied by a detailed answer, ensuring that you not only know the correct response but also understand the underlying concepts. The guide is structured into clear sections, covering fundamental topics, intermediate concepts, and advanced Python features. Additionally, a FAQ section addresses common queries, and a conclusion summarizes key takeaways.

By the end of this guide, you will have a solid understanding of Python interview questions and be well-prepared to tackle them with confidence.

Section 1: Fundamental Python Concepts

1.1 What is Python, and why is it popular?

Answer:
Python is a high-level, interpreted programming language known for its simplicity and readability. It was created by Guido van Rossum and first released in 1991. Python's popularity stems from several factors:

Ease of Learning: Python's syntax is straightforward and resembles the English language, making it accessible to beginners.
Versatility: Python can be used for web development, data analysis, artificial intelligence, scientific computing, and more.
Extensive Libraries: Python has a rich ecosystem of libraries and frameworks, such as Django for web development and Pandas for data analysis, which accelerate development.
Community Support: Python has a large and active community, providing ample resources, tutorials, and forums for learning and problem-solving.

1.2 What are Python's key features?

Answer:
Python boasts several key features that contribute to its widespread adoption:

Interpreted Language: Python code is executed line by line, which makes debugging easier and allows for rapid development.
Dynamic Typing: Variables in Python do not require explicit declaration of their type, making the code more flexible and concise.
Object-Oriented: Python supports object-oriented programming (OOP), allowing developers to create reusable and modular code.
Cross-Platform: Python is compatible with various operating systems, including Windows, macOS, and Linux.
Extensible: Python can be integrated with other languages like C and C++, enabling performance optimization.

1.3 What are Python's data types?

Answer:
Python supports several built-in data types, which can be categorized as follows:

Numeric Types: int (integer), float (floating-point number), complex (complex number).
Sequence Types: str (string), list (ordered, mutable sequence), tuple (ordered, immutable sequence).
Mapping Type: dict (dictionary, an unordered collection of key-value pairs).
Set Types: set (unordered collection of unique elements), frozenset (immutable set).
Boolean Type: bool (represents True or False).
None Type: None (represents the absence of a value or a null value).

1.4 What is the difference between `list` and `tuple`?

Answer:
Both list and tuple are sequence types in Python, but they have key differences:

Mutability: Lists are mutable, meaning their elements can be modified after creation. Tuples are immutable, meaning their elements cannot be changed once defined.
Syntax: Lists are defined using square brackets [], while tuples are defined using parentheses ().
Performance: Tuples are generally faster than lists because of their immutability, which allows for optimization in memory usage.
Use Cases: Lists are used when you need a collection of items that may change over time. Tuples are used when you need a collection of items that should remain constant, such as coordinates or configuration settings.

1.5 What is a Python dictionary, and how is it used?

Answer:
A Python dictionary (dict) is an unordered collection of key-value pairs. Each key is unique and maps to a specific value. Dictionaries are highly efficient for lookups, insertions, and deletions.

Example:

# Creating a dictionary
student = {
    "name": "John",
    "age": 21,
    "courses": ["Math", "Science"]
}

# Accessing values
print(student["name"])  # Output: John

# Adding a new key-value pair
student["grade"] = "A"

# Updating a value
student["age"] = 22

# Deleting a key-value pair
del student["courses"]

Dictionaries are commonly used for storing and retrieving data in a structured manner, such as configuration settings, database records, or JSON data.

1.6 What are Python functions, and how do you define them?

Answer:
A function in Python is a block of reusable code that performs a specific task. Functions are defined using the def keyword, followed by the function name and a set of parentheses containing any parameters.

Example:

# Defining a function
def greet(name):
    return f"Hello, {name}!"

# Calling the function
message = greet("Alice")
print(message)  # Output: Hello, Alice!

Functions can take multiple parameters, return values, and even have default arguments. They help in organizing code, improving readability, and promoting code reuse.

1.7 What is the difference between `==` and `is` in Python?

Answer:
The == operator and the is keyword in Python are used for comparison, but they serve different purposes:

== Operator: Compares the values of two objects to determine if they are equal. It checks if the content of the objects is the same.
Example:
python
Copy
```
a = [1, 2, 3]
b = [1, 2, 3]
print(a == b)  # Output: True (because the contents are the same)
```
is Keyword: Compares the memory addresses of two objects to determine if they are the same object. It checks if both variables point to the same memory location.
Example:
python
Copy
```
a = [1, 2, 3]
b = a
print(a is b)  # Output: True (because both variables reference the same object)
```

In summary, == checks for value equality, while is checks for identity (i.e., whether two variables reference the same object).

1.8 What are Python modules and packages?

Answer:

Modules: A module in Python is a file containing Python code, typically with a .py extension. Modules can define functions, classes, and variables that can be reused in other Python scripts. You can import a module using the import statement.
Example:
python
Copy
```
# my_module.py
def greet(name):
    return f"Hello, {name}!"

# main.py
import my_module
print(my_module.greet("Alice"))  # Output: Hello, Alice!
```
Packages: A package is a collection of modules organized in a directory hierarchy. Packages allow for better organization of code and prevent naming conflicts. A package must contain a special file named __init__.py (which can be empty) to be recognized as a package.
Example:
Copy
```
my_package/
    __init__.py
    module1.py
    module2.py
```
You can import modules from a package using dot notation:
python
Copy
```
from my_package import module1
```

1.9 What is the purpose of `init.py` in a Python package?

Answer:
The __init__.py file is a special file in a Python package that serves two main purposes:

Package Initialization: When a package is imported, Python executes the __init__.py file. This allows you to initialize package-level variables, import submodules, or perform other setup tasks.
Package Recognition: The presence of __init__.py in a directory indicates to Python that the directory should be treated as a package. Without this file, Python will not recognize the directory as a package, and you won't be able to import modules from it.

Example:

# my_package/__init__.py
print("Initializing my_package")

# main.py
import my_package  # Output: Initializing my_package

1.10 What is the difference between `import module` and `from module import *`?

Answer:

import module: This syntax imports the entire module, and you need to use the module name as a prefix to access its contents.
Example:
python
Copy
```
import math
print(math.sqrt(16))  # Output: 4.0
```
from module import *: This syntax imports all the functions, classes, and variables from the module directly into the current namespace. This can lead to name conflicts if multiple modules have functions with the same name.
Example:
python
Copy
```
from math import *
print(sqrt(16))  # Output: 4.0
```

While from module import * can make the code shorter, it is generally recommended to use import module or from module import specific_function to avoid namespace pollution and potential conflicts.

Section 2: Intermediate Python Concepts

2.1 What are Python decorators, and how do they work?

Answer:
A decorator in Python is a function that takes another function as an argument and extends or modifies its behavior without explicitly modifying it. Decorators are often used for logging, access control, memoization, and more.

Example:

# Defining a decorator
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

# Using the decorator
@my_decorator
def say_hello():
    print("Hello!")

# Calling the decorated function
say_hello()

Output:

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

In this example, my_decorator is a decorator that adds behavior before and after the say_hello function is called.

2.2 What is the difference between `*args` and `**kwargs`?

Answer:

*args: Allows you to pass a variable number of non-keyword arguments to a function. The arguments are collected into a tuple.
Example:
python
Copy
```
def my_function(*args):
    for arg in args:
        print(arg)

my_function(1, 2, 3)  # Output: 1 2 3
```

**kwargs: Allows you to pass a variable number of keyword arguments to a function. The arguments are collected into a dictionary.

Example:

def my_function(**kwargs):
    for key, value in kwargs.items():
        print(f"{key}: {value}")

my_function(name="Alice", age=25)  # Output: name: Alice age: 25

*args and **kwargs are useful when you want to create flexible functions that can handle different numbers of inputs.

2.3 What is a lambda function in Python?

Answer:
A lambda function in Python is a small, anonymous function defined using the lambda keyword. Lambda functions can have any number of arguments but only one expression. They are often used for short, throwaway functions.

Example:

# Defining a lambda function
square = lambda x: x ** 2

# Using the lambda function
print(square(5))  # Output: 25

Lambda functions are commonly used in conjunction with higher-order functions like map(), filter(), and reduce().

2.4 What is the difference between `map()`, `filter()`, and `reduce()`?

Answer:

map(): Applies a function to all items in an input list (or other iterable) and returns an iterator that yields the results.
Example:
python
Copy
```
numbers = [1, 2, 3, 4]
squared = map(lambda x: x ** 2, numbers)
print(list(squared))  # Output: [1, 4, 9, 16]
```
filter(): Filters elements from an iterable based on a function that returns True or False. Only elements that satisfy the condition are included in the result.
Example:
python
Copy
```
numbers = [1, 2, 3, 4]
even = filter(lambda x: x % 2 == 0, numbers)
print(list(even))  # Output: [2, 4]
```
reduce(): Applies a function cumulatively to the items of an iterable, from left to right, so as to reduce the iterable to a single value. reduce() is part of the functools module.
Example:
python
Copy
```
from functools import reduce
numbers = [1, 2, 3, 4]
product = reduce(lambda x, y: x * y, numbers)
print(product)  # Output: 24
```

2.5 What is a Python generator, and how does it work?

Answer:
A generator in Python is a special type of iterator that generates values on-the-fly using the yield keyword. Unlike regular functions that return a single value and terminate, generators can yield multiple values, pausing and resuming execution between each yield.

Example:

# Defining a generator
def my_generator():
    yield 1
    yield 2
    yield 3

# Using the generator
gen = my_generator()
print(next(gen))  # Output: 1
print(next(gen))  # Output: 2
print(next(gen))  # Output: 3

Generators are memory-efficient because they generate values one at a time, rather than storing all values in memory at once. They are commonly used for large datasets or infinite sequences.

2.6 What is the difference between `yield` and `return` in Python?

Answer:

yield: Used in generators to produce a sequence of values. When a function contains yield, it becomes a generator function. Each time yield is encountered, the function's state is saved, and the yielded value is returned. The function can later resume from where it left off.
Example:
python
Copy
```
def my_generator():
    yield 1
    yield 2
    yield 3

gen = my_generator()
print(next(gen))  # Output: 1
print(next(gen))  # Output: 2
```
return: Used in regular functions to return a single value and terminate the function's execution. Once a function returns a value, it cannot be resumed.
Example:
python
Copy
```
def my_function():
    return 1
    return 2  # This line is never reached

print(my_function())  # Output: 1
```

In summary, yield is used for generating sequences of values in generators, while return is used for returning a single value in regular functions.

2.7 What is the purpose of the `with` statement in Python?

Answer:
The with statement in Python is used for resource management, particularly with file operations. It ensures that resources are properly managed and released, even if an exception occurs. The with statement is often used with context managers, which are objects that define the methods __enter__() and __exit__().

Example:

# Using the with statement to open a file
with open("example.txt", "r") as file:
    content = file.read()
    print(content)

In this example, the with statement ensures that the file is properly closed after reading, even if an error occurs during the operation.

2.8 What is the difference between `deepcopy` and `shallow copy` in Python?

Answer:

Shallow Copy: Creates a new object, but inserts references to the original objects within it. Changes to mutable elements in the original object will affect the shallow copy.
Example:
python
Copy
```
import copy

original = [[1, 2, 3], [4, 5, 6]]
shallow_copy = copy.copy(original)

original[0][0] = 99
print(shallow_copy)  # Output: [[99, 2, 3], [4, 5, 6]]
```

Deep Copy: Creates a new object and recursively copies all objects found within the original. Changes to the original object do not affect the deep copy.

Example:

import copy

original = [[1, 2, 3], [4, 5, 6]]
deep_copy = copy.deepcopy(original)

original[0][0] = 99
print(deep_copy)  # Output: [[1, 2, 3], [4, 5, 6]]

In summary, a shallow copy only copies the top-level objects, while a deep copy copies all nested objects as well.

2.9 What is the Global Interpreter Lock (GIL) in Python?

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

The GIL is a controversial feature because it can limit the performance of multi-threaded Python programs, especially those that are CPU-bound. However, the GIL simplifies memory management and makes it easier to write thread-safe code.

Workarounds:

Multiprocessing: Use the multiprocessing module to create separate processes, each with its own Python interpreter and memory space.
C Extensions: Write performance-critical code in C or Cython, which can release the GIL during execution.

2.10 What is the difference between `multithreading` and `multiprocessing` in Python?

Answer:

Multithreading: Involves running multiple threads within a single process. Threads

2.11 What is the difference between `multithreading` and `multiprocessing` in Python? (Continued)

Answer:

Multithreading: Involves running multiple threads within a single process. Threads share the same memory space, which makes communication between threads efficient. However, due to the Global Interpreter Lock (GIL), only one thread can execute Python bytecode at a time, making multithreading less effective for CPU-bound tasks. It is more suitable for I/O-bound tasks, such as file operations or network requests.
Example:
python
Copy
```
import threading

def print_numbers():
    for i in range(5):
        print(i)

# Creating threads
thread1 = threading.Thread(target=print_numbers)
thread2 = threading.Thread(target=print_numbers)

# Starting threads
thread1.start()
thread2.start()

# Waiting for threads to finish
thread1.join()
thread2.join()
```

Multiprocessing: Involves running multiple processes, each with its own memory space and Python interpreter. This allows for true parallel execution, making it suitable for CPU-bound tasks. However, inter-process communication is more complex and slower compared to multithreading.

Example:

import multiprocessing

def print_numbers():
    for i in range(5):
        print(i)

# Creating processes
process1 = multiprocessing.Process(target=print_numbers)
process2 = multiprocessing.Process(target=print_numbers)

# Starting processes
process1.start()
process2.start()

# Waiting for processes to finish
process1.join()
process2.join()

In summary, multithreading is better for I/O-bound tasks, while multiprocessing is better for CPU-bound tasks due to the GIL limitations in Python.

Section 3: Advanced Python Concepts

3.1 What are Python metaclasses, and how do they work?

Answer:
A metaclass in Python is a class of a class, meaning it defines how a class behaves. While classes are used to create objects, metaclasses are used to create classes. The most common use case for metaclasses is to control the creation and initialization of classes.

By default, Python uses the type metaclass to create classes. However, you can define your own metaclass by subclassing type and overriding its methods, such as __new__ or __init__.

Example:

# Defining a custom metaclass
class Meta(type):
    def __new__(cls, name, bases, dct):
        print(f"Creating class {name}")
        return super().__new__(cls, name, bases, dct)

# Using the metaclass
class MyClass(metaclass=Meta):
    pass

# Output: Creating class MyClass

Metaclasses are advanced features and are rarely used in everyday programming. They are typically employed in frameworks like Django or SQLAlchemy to enforce specific behaviors or constraints on classes.

3.2 What is the purpose of `slots` in Python?

Answer:
The __slots__ attribute in Python is used to explicitly declare the attributes of a class, preventing the creation of a __dict__ for instance attributes. This can lead to significant memory savings, especially when creating a large number of instances of a class.

Example:

class Person:
    __slots__ = ["name", "age"]

    def __init__(self, name, age):
        self.name = name
        self.age = age

p = Person("Alice", 25)
print(p.name)  # Output: Alice
print(p.age)   # Output: 25

# Attempting to add a new attribute will raise an AttributeError
p.address = "123 Street"  # Raises: AttributeError: 'Person' object has no attribute 'address'

Using __slots__ can improve performance and reduce memory usage, but it limits the flexibility of adding new attributes dynamically.

3.3 What is the difference between `new` and `init` in Python?

Answer:

__new__: A static method that is responsible for creating a new instance of a class. It is called before __init__ and returns the instance of the class. It is rarely overridden unless you are working with immutable types or customizing instance creation.

Example:

class MyClass:
    def __new__(cls, *args, **kwargs):
        print("Creating instance")
        instance = super().__new__(cls)
        return instance

    def __init__(self, value):
        print("Initializing instance")
        self.value = value

obj = MyClass(10)
# Output: Creating instance
#         Initializing instance

__init__: An instance method that initializes the newly created instance. It is called after __new__ and is used to set up the initial state of the object.

In summary, __new__ creates the instance, and __init__ initializes it.

3.4 What is the purpose of `call` in Python?

Answer:
The __call__ method allows an instance of a class to be called as a function. When you define __call__ in a class, you can use the instance like a function by adding parentheses after it.

Example:

class Adder:
    def __init__(self, value):
        self.value = value

    def __call__(self, x):
        return self.value + x

add_five = Adder(5)
print(add_five(10))  # Output: 15

The __call__ method is useful for creating callable objects, such as decorators or functors.

3.5 What is the difference between `str` and `repr` in Python?

Answer:

__str__: Provides a user-friendly string representation of an object. It is intended to be readable and is used by the str() function and the print() function.

Example:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def __str__(self):
        return f"{self.name} is {self.age} years old."

p = Person("Alice", 25)
print(p)  # Output: Alice is 25 years old.

__repr__: Provides an unambiguous string representation of an object, typically used for debugging and development. It is intended to be explicit and is used by the repr() function.

Example:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def __repr__(self):
        return f"Person(name='{self.name}', age={self.age})"

p = Person("Alice", 25)
print(repr(p))  # Output: Person(name='Alice', age=25)

In summary, __str__ is for end-users, while __repr__ is for developers.

3.6 What is the purpose of `getitem` and `setitem` in Python?

Answer:

__getitem__: Allows an object to support indexing, enabling you to access elements using square brackets [].

Example:

class MyList:
    def __init__(self, items):
        self.items = items

    def __getitem__(self, index):
        return self.items[index]

my_list = MyList([1, 2, 3])
print(my_list[1])  # Output: 2

__setitem__: Allows an object to support item assignment, enabling you to set elements using square brackets [].

Example:

class MyList:
    def __init__(self, items):
        self.items = items

    def __setitem__(self, index, value):
        self.items[index] = value

my_list = MyList([1, 2, 3])
my_list[1] = 99
print(my_list.items)  # Output: [1, 99, 3]

These methods are commonly used in custom container classes to emulate built-in types like lists or dictionaries.

3.7 What is the purpose of `enter` and `exit` in Python?

Answer:
The __enter__ and __exit__ methods are used to implement context managers, which are objects that manage resources using the with statement.

__enter__: Called when entering the with block. It returns the resource to be managed.
__exit__: Called when exiting the with block. It handles cleanup operations, such as closing files or releasing locks.

Example:

class FileManager:
    def __init__(self, filename, mode):
        self.filename = filename
        self.mode = mode

    def __enter__(self):
        self.file = open(self.filename, self.mode)
        return self.file

    def __exit__(self, exc_type, exc_value, traceback):
        self.file.close()

# Using the context manager
with FileManager("example.txt", "w") as file:
    file.write("Hello, World!")

In this example, the FileManager class ensures that the file is properly closed after writing, even if an exception occurs.

3.8 What is the purpose of `iter` and `next` in Python?

Answer:

__iter__: Returns an iterator object. It is called when you use the iter() function or a for loop on an object.
__next__: Returns the next value from the iterator. It is called repeatedly in a for loop until it raises a StopIteration exception.

Example:

class Counter:
    def __init__(self, start, end):
        self.current = start
        self.end = end

    def __iter__(self):
        return self

    def __next__(self):
        if self.current >= self.end:
            raise StopIteration
        else:
            self.current += 1
            return self.current - 1

# Using the iterator
for num in Counter(1, 5):
    print(num)  # Output: 1 2 3 4

These methods are used to create custom iterators in Python.

3.9 What is the purpose of `len` in Python?

Answer:
The __len__ method allows an object to define its length, which can be retrieved using the len() function. It is commonly used in custom container classes.

Example:

class MyList:
    def __init__(self, items):
        self.items = items

    def __len__(self):
        return len(self.items)

my_list = MyList([1, 2, 3])
print(len(my_list))  # Output: 3

3.10 What is the purpose of `contains` in Python?

Answer:
The __contains__ method allows an object to support the in operator, which checks if an item is present in the object.

Example:

class MyList:
    def __init__(self, items):
        self.items = items

    def __contains__(self, item):
        return item in self.items

my_list = MyList([1, 2, 3])
print(2 in my_list)  # Output: True

Section 4: Python Interview FAQs

4.1 What is the difference between Python 2 and Python 3?

Answer:

Print Function: Python 2 uses print as a statement, while Python 3 uses print() as a function.
Division: In Python 2, dividing two integers performs floor division, while in Python 3, it performs true division.
Unicode: Python 2 uses ASCII by default, while Python 3 uses Unicode.
Syntax: Python 3 has cleaner syntax and better error handling.

4.2 What is PEP 8?

Answer:
PEP 8 is the official style guide for Python code. It provides guidelines on formatting, naming conventions, and code organization to ensure consistency and readability.

4.3 What is the difference between `append()` and `extend()` in Python?

Answer:

append(): Adds a single element to the end of a list.
extend(): Adds multiple elements (from an iterable) to the end of a list.

4.4 What is the purpose of `enumerate()` in Python?

Answer:
enumerate() adds a counter to an iterable and returns it as an enumerate object. It is useful for looping with both index and value.

Example:

fruits = ["apple", "banana", "cherry"]
for index, fruit in enumerate(fruits):
    print(index, fruit)

4.5 What is the purpose of `zip()` in Python?

Answer:
zip() combines multiple iterables into a single iterable of tuples. It stops when the shortest iterable is exhausted.

Example:

names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
zipped = zip(names, ages)
print(list(zipped))  # Output: [('Alice', 25), ('Bob', 30), ('Charlie', 35)]

4.6 What is the purpose of `any()` and `all()` in Python?

Answer:

any(): Returns True if at least one element in an iterable is True.
all(): Returns True if all elements in an iterable are True.

4.7 What is the purpose of `setdefault()` in Python dictionaries?

Answer:
setdefault() returns the value of a key if it exists in the dictionary. If the key does not exist, it inserts the key with a default value and returns the default value.

Example:

student = {"name": "Alice"}
age = student.setdefault("age", 25)
print(age)  # Output: 25

4.8 What is the purpose of `collections.defaultdict` in Python?

Answer:
defaultdict is a subclass of dict that provides a default value for missing keys. It takes a factory function as an argument, which is called to provide the default value.

Example:

from collections import defaultdict

counts = defaultdict(int)
counts["apple"] += 1
print(counts["apple"])  # Output: 1

4.9 What is the purpose of `itertools` in Python?

Answer:
itertools is a module that provides functions for creating iterators for efficient looping. Common functions include itertools.chain, itertools.cycle, and itertools.permutations.

4.10 What is the purpose of `functools.lru_cache` in Python?

Answer:
lru_cache is a decorator that caches the results of a function, reducing redundant computations. It is useful for optimizing recursive functions.

Example:

from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

print(fibonacci(10))  # Output: 55

Conclusion

Preparing for a Python interview as a beginner can be challenging, but with a solid understanding of fundamental, intermediate, and advanced concepts, you can confidently tackle any question. This guide has covered a wide range of topics, from basic data types and functions to advanced features like metaclasses and decorators. Additionally, the FAQ section addresses common queries that often arise during interviews.

Remember, the key to success is practice. Write code, experiment with different concepts, and solve problems to reinforce your understanding. With dedication and persistence, you'll be well-prepared to ace your Python interview and take the next step in your programming career.

Good luck!

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Python Interview Questions with Detailed Answers for Beginners

Python Interview Questions with Detailed Answers for Beginners

Introduction

Section 1: Fundamental Python Concepts

1.1 What is Python, and why is it popular?

1.2 What are Python's key features?

1.3 What are Python's data types?

1.4 What is the difference between list and tuple?

1.5 What is a Python dictionary, and how is it used?

1.6 What are Python functions, and how do you define them?

1.7 What is the difference between == and is in Python?

1.8 What are Python modules and packages?

1.9 What is the purpose of __init__.py in a Python package?

1.10 What is the difference between import module and from module import *?

Section 2: Intermediate Python Concepts

2.1 What are Python decorators, and how do they work?

2.2 What is the difference between *args and **kwargs?

2.3 What is a lambda function in Python?

2.4 What is the difference between map(), filter(), and reduce()?

2.5 What is a Python generator, and how does it work?

2.6 What is the difference between yield and return in Python?

2.7 What is the purpose of the with statement in Python?

2.8 What is the difference between deepcopy and shallow copy in Python?

2.9 What is the Global Interpreter Lock (GIL) in Python?

2.10 What is the difference between multithreading and multiprocessing in Python?

2.11 What is the difference between multithreading and multiprocessing in Python? (Continued)

Section 3: Advanced Python Concepts

3.1 What are Python metaclasses, and how do they work?

3.2 What is the purpose of __slots__ in Python?

3.3 What is the difference between __new__ and __init__ in Python?

3.4 What is the purpose of __call__ in Python?

3.5 What is the difference between __str__ and __repr__ in Python?

3.6 What is the purpose of __getitem__ and __setitem__ in Python?

3.7 What is the purpose of __enter__ and __exit__ in Python?

3.8 What is the purpose of __iter__ and __next__ in Python?

3.9 What is the purpose of __len__ in Python?

3.10 What is the purpose of __contains__ in Python?

Section 4: Python Interview FAQs

4.1 What is the difference between Python 2 and Python 3?

4.2 What is PEP 8?

4.3 What is the difference between append() and extend() in Python?

4.4 What is the purpose of enumerate() in Python?

4.5 What is the purpose of zip() in Python?

4.6 What is the purpose of any() and all() in Python?

4.7 What is the purpose of setdefault() in Python dictionaries?

4.8 What is the purpose of collections.defaultdict in Python?

4.9 What is the purpose of itertools in Python?

4.10 What is the purpose of functools.lru_cache in Python?

Conclusion

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نموذج الاتصال

1.4 What is the difference between `list` and `tuple`?

1.7 What is the difference between `==` and `is` in Python?

1.9 What is the purpose of `init.py` in a Python package?

1.10 What is the difference between `import module` and `from module import *`?

2.2 What is the difference between `*args` and `**kwargs`?

2.4 What is the difference between `map()`, `filter()`, and `reduce()`?

2.6 What is the difference between `yield` and `return` in Python?

2.7 What is the purpose of the `with` statement in Python?

2.8 What is the difference between `deepcopy` and `shallow copy` in Python?

2.10 What is the difference between `multithreading` and `multiprocessing` in Python?

2.11 What is the difference between `multithreading` and `multiprocessing` in Python? (Continued)

3.2 What is the purpose of `slots` in Python?

3.3 What is the difference between `new` and `init` in Python?

3.4 What is the purpose of `call` in Python?

3.5 What is the difference between `str` and `repr` in Python?

3.6 What is the purpose of `getitem` and `setitem` in Python?

3.7 What is the purpose of `enter` and `exit` in Python?

3.8 What is the purpose of `iter` and `next` in Python?

3.9 What is the purpose of `len` in Python?

3.10 What is the purpose of `contains` in Python?

4.3 What is the difference between `append()` and `extend()` in Python?

4.4 What is the purpose of `enumerate()` in Python?

4.5 What is the purpose of `zip()` in Python?

4.6 What is the purpose of `any()` and `all()` in Python?

4.7 What is the purpose of `setdefault()` in Python dictionaries?

4.8 What is the purpose of `collections.defaultdict` in Python?

4.9 What is the purpose of `itertools` in Python?

4.10 What is the purpose of `functools.lru_cache` in Python?