Advance Level Interview Question

Explain the Global Interpreter Lock (GIL) in Python. What are its implications on concurrency and multi-threading?
- Answer: The GIL is a mutex that protects access to Python objects, ensuring only one thread executes Python bytecode at a time. This limits multi-threaded performance for CPU-bound tasks but allows efficient I/O-bound tasks and simplifies memory management.
Discuss Python's memory management mechanism, including reference counting and garbage collection. How does it impact performance and memory usage?
- Answer: Python uses reference counting to manage object lifetimes and garbage collection (cyclic GC) to detect and clean up unused objects. While efficient for most cases, cyclic GC can introduce overhead and occasional delays due to periodic collection runs.
Explain the use of Python's asyncio module for asynchronous programming. How does it differ from threads and multiprocessing?
- Answer: asyncio enables concurrent I/O-bound operations using coroutines (async and await), managed by an event loop. Unlike threads and multiprocessing, asyncio is single-threaded but supports thousands of tasks due to cooperative multitasking, suitable for scalable network applications.

What are Python decorators, and how can they be used for metaprogramming? Provide examples of their application in modifying function behavior.

Answer: Decorators are functions that modify the behavior of other functions or methods. They are powerful for metaprogramming tasks like logging, authentication, and caching.

def my_decorator(func):
    def wrapper(*args, **kwargs):
        print('Before function execution')
        result = func(*args, **kwargs)
        print('After function execution')
        return result
    return wrapper

@my_decorator
def say_hello():
    print('Hello!')

say_hello()  # Output: Before function execution, Hello!, After function execution

Discuss the use of metaclasses in Python. Provide an example of how they can be used to customize class creation behavior.
- Answer: Metaclasses allow customization of class creation by overriding the default __new__ and __init__ methods of type. They are useful for enforcing class constraints, adding class-level methods, or modifying attribute handling during class instantiation.
```
class MyMeta(type):
    def __new__(cls, name, bases, dct):
        dct['attr'] = 100
        return super().__new__(cls, name, bases, dct)

class MyClass(metaclass=MyMeta):
    pass

print(MyClass.attr)  # Output: 100
```

Explain the concept of Python descriptors. Provide examples of how they can be used to control attribute access and modification.

Answer: Descriptors are objects that define how attribute access is handled by defining __get__, __set__, or __delete__ methods. They are used for implementing managed attributes with custom behavior.

class Temperature:
    def __init__(self, celsius=0):
        self._celsius = celsius

    def to_fahrenheit(self):
        return (self._celsius * 9/5) + 32

    def get_temperature(self):
        print("Getting value")
        return self._celsius

    def set_temperature(self, value):
        if value < -273.15:
            raise ValueError("Temperature below -273.15 is not possible")
        print("Setting value")
        self._celsius = value

    temperature = property(get_temperature, set_temperature)

# Usage
t = Temperature()
t.temperature = 30  # Setting value
print(t.temperature)  # Getting value, Output: 30

Discuss the usage and benefits of Python's collections module. Provide examples of commonly used data structures from this module.
- Answer: The collections module provides specialized data structures beyond built-in types like lists and dictionaries, optimized for specific use cases.
  - Examples include namedtuple for memory-efficient data containers, defaultdict for default values in dictionaries, Counter for counting hashable objects, and deque for double-ended queues.
Explain the purpose and usage of Python's multiprocessing module for parallel processing. How does it differ from threading?
- Answer: The multiprocessing module allows parallel execution using separate processes, leveraging multiple CPU cores. Unlike threading, each process has its own memory space, avoiding the GIL limitation and making it suitable for CPU-bound tasks.

Discuss Python's support for functional programming features like map, filter, and reduce. Provide examples of their usage.

Answer: Functional programming features in Python facilitate concise and expressive code by operating on iterables without modifying them.

# Example of map
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x**2, numbers))
print(squared)  # Output: [1, 4, 9, 16, 25]

# Example of filter
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)  # Output: [2, 4]

# Example of reduce (requires importing functools)
from functools import reduce
product = reduce(lambda x, y: x * y, numbers)
print(product)  # Output: 120

Explain the concept of Python's contextlib module. How can it be used to create context managers?

Answer: The contextlib module simplifies the creation of context managers using the contextmanager decorator, allowing resources to be managed using the with statement.

from contextlib import contextmanager

@contextmanager
def file_opener(filename, mode):
    try:
        f = open(filename, mode)
        yield f
    finally:
        f.close()

with file_opener('example.txt', 'r') as f:
    print(f.read())

Discuss Python's itertools module. Provide examples of commonly used functions and their applications.

Answer: The itertools module provides functions for creating iterators for efficient looping and data manipulation.

import itertools

# Example of itertools.cycle()
numbers = [1, 2, 3]
cycle_iter = itertools.cycle(numbers)
for _ in range(5):
    print(next(cycle_iter))  # Output: 1, 2, 3, 1, 2

# Example of itertools.chain()
list1 = [1, 2, 3]
list2 = ['a', 'b', 'c']
combined = itertools.chain(list1, list2)
print(list(combined))  # Output: [1, 2, 3, 'a', 'b', 'c']

Explain Python's functools module. Provide examples of its usage, including functools.partial and functools.lru_cache.

Answer: The functools module provides higher-order functions for functional programming tasks.

from functools import partial, lru_cache

# Example of functools.partial()
def power(base, exponent):
    return base ** exponent

square = partial(power, exponent=2)
print(square(5))  # Output: 25

# Example of functools.lru_cache()
@lru_cache(maxsize=None)
def fib(n):
    if n < 2:
        return n
    return fib(n-1) + fib(n-2)

print(fib(10))  # Output: 55

Discuss Python's logging module for structured logging. How does it help in debugging and error tracking?

Answer: The logging module provides a flexible framework for emitting log messages from Python programs. It supports multiple log levels, configurable output destinations, and formatting options, making it essential for debugging and error tracking in complex applications.

import logging

# Configure logging
logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s - %(levelname)s - %(message)s')

# Example usage
def divide(x, y):
    try:
        result = x / y
    except ZeroDivisionError:
        logging.error('Tried to divide by zero')
    else:
        logging.info(f'Division result: {result}')
    return result

divide(10, 0)

Explain the use of Python's pdb module for debugging. How can it be used to set breakpoints and step through code?
- Answer: The pdb module is Python's built-in debugger, allowing interactive debugging of Python programs.
```
import pdb

def calculate(x, y):
    result = x + y
    pdb.set_trace()  # Set breakpoint
    result *= 2
    return result

calculate(10, 5)
```
  When executed, this code will pause at the pdb.set_trace() line, allowing inspection of variables (x, y, result) and stepping through code execution.

Explain the concept of Python decorators with parameters. Provide examples of how decorators can accept arguments and modify function behavior accordingly.

Answer: Decorators with parameters are implemented using nested functions. They allow customization of decorator behavior based on arguments passed.

def repeat(num_times):
    def decorator_repeat(func):
        def wrapper(*args, **kwargs):
            for _ in range(num_times):
                result = func(*args, **kwargs)
            return result
        return wrapper
    return decorator_repeat

@repeat(num_times=3)
def greet(name):
    print(f'Hello, {name}')

greet('Alice')
# Output:
# Hello, Alice
# Hello, Alice
# Hello, Alice

Discuss Python's argparse module for command-line argument parsing. How does it handle argument parsing and validation?

Answer: The argparse module simplifies parsing command-line arguments and options in Python scripts, providing built-in support for argument types, default values, help messages, and validation.

import argparse

parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('integers', metavar='N', type=int, nargs='+',
                    help='an integer for the accumulator')
parser.add_argument('--sum', dest='accumulate', action='store_const',
                    const=sum, default=max,
                    help='sum the integers (default: find the max)')

args = parser.parse_args()
print(args.accumulate(args.integers))

Explain Python's collections.defaultdict. Provide an example of its usage and advantages over standard dictionaries.
- Answer: defaultdict is a subclass of dict from the collections module that provides a default value for missing keys.
```
from collections import defaultdict

# Example usage
d = defaultdict(int)
d['a'] = 1
print(d['a'])    # Output: 1
print(d['b'])    # Output: 0 (default value for int)
```
Discuss Python's async and await keywords for asynchronous programming. How do they facilitate concurrent execution of tasks?
- Answer: async defines an asynchronous function (coroutine), while await pauses execution until the awaited coroutine completes, allowing non-blocking concurrent execution of multiple tasks.
```
import asyncio

async def async_task():
    print('Task 1')
    await asyncio.sleep(1)
    print('Task 2')

asyncio.run(async_task())
# Output:
# Task 1
# (1 second delay)
# Task 2
```
Discuss Python's support for functional programming with lambda functions. Provide examples of their usage and limitations.
- Answer: lambda functions are anonymous functions defined using the lambda keyword, typically used for short, one-line functions.
```
# Example usage
square = lambda x: x ** 2
print(square(5))  # Output: 25

# Limitations: Limited to single expressions, cannot contain statements or multiple lines of code.
```
Explain Python's concurrent.futures module. How does it simplify concurrent programming with threads and processes?
- Answer: The concurrent.futures module provides a high-level interface for asynchronously executing callable objects (ThreadPoolExecutor for threads, ProcessPoolExecutor for processes), managing futures, and handling results asynchronously.
```
from concurrent.futures import ThreadPoolExecutor

def square(n):
    return n ** 2

with ThreadPoolExecutor() as executor:
    futures = [executor.submit(square, i) for i in range(10)]
    results = [future.result() for future in futures]
    print(results)  # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
```

Discuss Python's contextlib module. How can it be used to create context managers? Provide examples of its usage.

Answer: The contextlib module simplifies the creation of context managers using the contextmanager decorator, allowing resources to be managed using the with statement.

from contextlib import contextmanager

@contextmanager
def file_opener(filename, mode):
    try:
        f = open(filename, mode)
        yield f
    finally:
        f.close()

with file_opener('example.txt', 'r') as f:
    print(f.read())

Explain Python's threading module. How does it facilitate concurrent programming? Discuss its limitations compared to multiprocessing.
- Answer: The threading module in Python provides a way to create and manage threads for concurrent execution within a single process. It allows sharing of memory between threads but is limited by the Global Interpreter Lock (GIL), restricting CPU-bound performance compared to multiprocessing.
Discuss Python's unittest framework for unit testing. How does it facilitate test-driven development (TDD)?
- Answer: The unittest module provides a framework for writing and running tests in Python, supporting test discovery, fixtures, assertions, and test suites. It promotes TDD by encouraging developers to write tests before code implementation to ensure functionality and maintainability.

Explain Python's support for metaprogramming with __getattr__, __setattr__, and __delattr__ methods. Provide examples of their usage.

Answer: Metaprogramming in Python allows modification of class attributes and behavior dynamically.

class DynamicAttributes:
    def __init__(self):
        self._attrs = {}

    def __getattr__(self, name):
        if name in self._attrs:
            return self._attrs[name]
        else:
            raise AttributeError(f'{self.__class__.__name__} object has no attribute {name}')

    def __setattr__(self, name, value):
        self._attrs[name] = value

    def __delattr__(self, name):
        del self._attrs[name]

obj = DynamicAttributes()
obj.name = 'Alice'
print(obj.name)     # Output: Alice
del obj.name

Discuss Python's sys module. How can it be used for system-level operations and interaction with the interpreter?
- Answer: The sys module provides access to system-specific parameters and functions, such as command-line arguments (sys.argv), Python interpreter details (sys.version), and standard input/output (sys.stdin, sys.stdout, sys.stderr).

Explain Python's support for database access using modules like sqlite3 or ORM frameworks like SQLAlchemy. Provide examples of their usage.

Answer: Python supports database access through modules like sqlite3 for SQLite databases and ORM frameworks like SQLAlchemy for relational databases.

import sqlite3

# Example using sqlite3
conn = sqlite3.connect('example.db')
cursor = conn.cursor()
cursor.execute('CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, name TEXT)')
cursor.execute('INSERT INTO users (name) VALUES (?)', ('Alice',))
conn.commit()

# Example using SQLAlchemy (ORM)
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

engine = create_engine('sqlite:///example.db', echo=True)
Base = declarative_base()

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)

Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
session = Session()
user = User(name='Bob')
session.add(user)
session.commit()

Explain the purpose and usage of Python's async generators. How do they combine asynchronous programming with generator functions?
- Answer: async generators allow asynchronous iteration over a sequence of values, combining the capabilities of asynchronous programming (async and await keywords) with generator functions (yield statement), enabling efficient handling of asynchronous data streams.
```
async def async_data_stream():
    for i in range(5):
        yield i
        await asyncio.sleep(1)

async def main():
    async for value in async_data_stream():
        print(value)

asyncio.run(main())
```
Discuss Python's support for web development with frameworks like Django and Flask. How do they differ in their approach and usage?
- Answer: Django and Flask are popular Python web frameworks:
  - Django is a full-stack framework with built-in features for ORM, admin interface, authentication, and template engine, promoting rapid development of complex web applications.
  - Flask is a micro-framework providing flexibility and simplicity, allowing developers to choose components and libraries for custom applications, suitable for smaller projects and APIs.
Explain the use of Python's pickle module for object serialization. What are its advantages and potential security concerns?
- Answer: The pickle module serializes Python objects into byte streams, facilitating object persistence and data interchange between Python applications. Advantages include ease of use and support for complex data structures. Security concerns arise from potential risks of executing malicious code when loading untrusted pickle data.

Discuss Python's support for functional programming paradigms with map, filter, and reduce. How do they enhance code readability and performance?

Answer: Functional programming features (map, filter, reduce) in Python promote concise and declarative coding style:

# Example of map
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x ** 2, numbers))
print(squared)  # Output: [1, 4, 9, 16, 25]

# Example of filter
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)  # Output: [2, 4]

# Example of reduce (requires importing functools)
from functools import reduce
product = reduce(lambda x, y: x * y, numbers)
print(product)  # Output: 120

Discuss Python's collections.Counter class. How can it be used for counting hashable objects? Provide examples of its usage.
- Answer: Counter is a specialized dictionary subclass in the collections module used for counting hashable objects.
```
from collections import Counter

# Example usage
words = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
word_counts = Counter(words)
print(word_counts)  # Output: Counter({'apple': 3, 'banana': 2, 'orange': 1})
```

Explain the purpose and usage of Python's logging module for structured logging. How does it aid in application debugging and monitoring?

Answer: The logging module provides a flexible framework for emitting log messages from Python programs, supporting different log levels, output destinations, and formatting options. It aids in debugging, error tracking, and monitoring application behavior in production environments.

import logging

# Configure logging
logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s - %(levelname)s - %(message)s')

# Example usage
def divide(x, y):
    try:
        result = x / y
    except ZeroDivisionError:
        logging.error('Tried to divide by zero')
    else:
        logging.info(f'Division result: {result}')
    return result

divide(10, 0)

Discuss Python's support for functional programming with functools.partial. How can it be used to create partial functions with fixed arguments?
- Answer: functools.partial is used to create partial functions with fixed arguments from existing functions.
```
from functools import partial

# Example usage
def power(base, exponent):
    return base ** exponent

square = partial(power, exponent=2)
print(square(5))  # Output: 25
```
Explain Python's multiprocessing module. How does it support parallel processing with multiple processes?
- Answer: The multiprocessing module allows parallel execution using multiple processes, leveraging multiple CPU cores and avoiding the Global Interpreter Lock (GIL) limitation of threads. It facilitates concurrent execution of CPU-bound tasks and enhances performance in multiprocessing environments.
```
from multiprocessing import Pool

# Example usage
def square(n):
    return n ** 2

if __name__ == '__main__':
    with Pool(processes=3) as pool:
        results = pool.map(square, [1, 2, 3, 4, 5])
        print(results)  # Output: [1, 4, 9, 16, 25]
```
Discuss Python's support for metaprogramming with metaclasses. How can metaclasses be used to customize class creation behavior?
- Answer: Metaclasses allow customization of class creation behavior by overriding the __new__ and __init__ methods of the type metaclass. They can be used to enforce constraints, add class-level methods, or modify attribute handling during class instantiation.
```
class MyMeta(type):
    def __new__(cls, name, bases, dct):
        dct['attr'] = 100
        return super().__new__(cls, name, bases, dct)

class MyClass(metaclass=MyMeta):
    pass

print(MyClass.attr)  # Output: 100
```
Explain Python's support for asyncio and asynchronous programming. How does asyncio facilitate non-blocking I/O operations?
- Answer: asyncio is a Python module that provides tools for asynchronous programming using coroutines (async and await keywords) and an event loop. It facilitates non-blocking I/O operations by allowing multiple tasks to be executed concurrently within a single thread, suitable for scalable network applications.
```
import asyncio

async def async_task():
    print('Task 1')
    await asyncio.sleep(1)
    print('Task 2')

asyncio.run(async_task())
# Output:
# Task 1
# (1 second delay)
# Task 2
```
Discuss Python's support for context management with the contextlib module. How can it be used to define context managers?
- Answer: The contextlib module simplifies the creation of context managers in Python using the contextmanager decorator, allowing resources to be managed using the with statement.
```
from contextlib import contextmanager

@contextmanager
def file_opener(filename, mode):
    try:
        f = open(filename, mode)
        yield f
    finally:
        f.close()

with file_opener('example.txt', 'r') as f:
    print(f.read())
```
Explain Python's support for coroutines with async and await keywords. How do they facilitate asynchronous programming?
- Answer: Coroutines in Python are defined using the async and await keywords, allowing non-blocking concurrent execution of tasks. async defines an asynchronous function (coroutine), while await suspends execution until the awaited coroutine completes, enabling efficient handling of I/O-bound operations without blocking the event loop.
Discuss Python's support for functional programming with lambda functions. How can lambda functions be used for concise and anonymous function definitions?
- Answer: lambda functions in Python are anonymous functions defined using the lambda keyword, typically used for short, one-line function definitions.
```
# Example usage
square = lambda x: x ** 2
print(square(5))  # Output: 25
```

Explain Python's argparse module for command-line argument parsing. How does it simplify handling of command-line arguments and options?

Answer: The argparse module in Python simplifies parsing command-line arguments and options, providing support for argument types, default values, help messages, and validation, facilitating robust and user-friendly command-line interfaces for Python scripts.

import argparse

parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('integers', metavar='N', type=int, nargs='+',
                    help='an integer for the accumulator')
parser.add_argument('--sum', dest='accumulate', action='store_const',
                    const=sum, default=max,
                    help='sum the integers (default: find the max)')

args = parser.parse_args()
print(args.accumulate(args.integers))

Discuss Python's asyncio module and event loop. How does asyncio facilitate asynchronous I/O operations and concurrency?
- Answer: asyncio is a Python module that supports asynchronous I/O operations and concurrency by using coroutines (async and await keywords) and an event loop (asyncio.run()). It allows efficient scheduling of multiple I/O-bound tasks within a single-threaded environment, enhancing scalability and performance in network applications.
Explain Python's concurrent.futures module. How does it simplify concurrent programming with threads and processes?
- Answer: The concurrent.futures module provides a high-level interface for asynchronously executing callable objects (ThreadPoolExecutor for threads, ProcessPoolExecutor for processes). It simplifies concurrent programming by managing thread/process pools, futures, and results, enabling parallel execution of tasks and improving performance in CPU-bound and I/O-bound applications.
```
from concurrent.futures import ThreadPoolExecutor

def square(n):
    return n ** 2

with ThreadPoolExecutor() as executor:
    futures = [executor.submit(square, i) for i in range(10)]
    results = [future.result() for future in futures]
    print(results)  # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
```
Discuss Python's support for database access using SQLAlchemy. How does SQLAlchemy facilitate object-relational mapping (ORM) and database interactions?
- Answer: SQLAlchemy is a Python SQL toolkit and ORM framework that facilitates database access and interactions by providing a high-level, Pythonic interface for managing relational databases. It supports ORM for mapping Python objects to database tables, SQL expression language for querying databases, and database schema management, promoting code reusability, and abstraction of database operations.
```
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

engine = create_engine('sqlite:///example.db', echo=True)
Base = declarative_base()

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)

Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
session = Session()
user = User(name='Alice')
session.add(user)
session.commit()
```
Explain Python's os module. How does it facilitate interaction with the operating system, file system, and environment variables?
- Answer: The os module in Python provides a portable way to interact with the operating system, file system, and environment variables. It offers functions for manipulating files/directories (os.path), executing commands (os.system), accessing environment variables (os.environ), and managing processes (os.fork, os.kill), facilitating system-level operations and cross-platform compatibility.
Discuss Python's support for web scraping with libraries like BeautifulSoup and requests. How can these libraries be used for extracting and parsing web data?
- Answer: Python supports web scraping using libraries like BeautifulSoup for parsing HTML/XML documents and requests for making HTTP requests. Together, they enable extraction and parsing of web data by retrieving web pages (requests.get), parsing HTML content (BeautifulSoup), navigating document elements (find, find_all), and extracting structured data from web pages, facilitating data aggregation and analysis from online sources.

Explain Python's support for functional programming with map, filter, and reduce functions. How do these functions enhance code readability and performance?

Answer: Functional programming functions (map, filter, reduce) in Python promote concise and declarative coding by applying operations to iterables:

# Example of map
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x ** 2, numbers))
print(squared)  # Output: [1, 4, 9, 16, 25]

# Example of filter
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)  # Output: [2, 4]

# Example of reduce (requires importing functools)
from functools import reduce
product = reduce(lambda x, y: x * y, numbers)
print(product)  # Output: 120

Discuss Python's support for context management with with statement and contextlib module. How does it simplify resource management and exception handling?
- Answer: Python's with statement and contextlib module simplify context management and resource handling by encapsulating resource acquisition and release within a defined context (__enter__ and __exit__ methods). It ensures proper cleanup of resources (file.close(), database.commit()) and exception handling (try-except-finally) without boilerplate code, enhancing code readability and maintainability.

Explain Python's support for metaprogramming with __getattr__, __setattr__, and __delattr__ methods. How can these methods be used for attribute access and manipulation?

Answer: Metaprogramming in Python allows customization of attribute access and manipulation using special methods (__getattr__, __setattr__, __delattr__). They enable dynamic attribute retrieval (__getattr__), assignment (__setattr__), and deletion (__delattr__), facilitating object-oriented programming paradigms and metaprogramming techniques for implementing custom behavior and data encapsulation.

class DynamicAttributes:
    def __init__(self):
        self._attrs = {}

    def __getattr__(self, name):
        if name in self._attrs:
            return self._attrs[name]
        else:
            raise AttributeError(f'{self.__class__.__name__} object has no attribute {name}')

    def __setattr__(self, name, value):
        self._attrs[name] = value

    def __delattr__(self, name):
        del self._attrs[name]

obj = DynamicAttributes()
obj.name = 'Alice'
print(obj.name)     # Output: Alice
del obj.name

Discuss Python's support for metaclasses. How can metaclasses be used to customize class creation behavior and enforce constraints?
- Answer: Metaclasses in Python allow customization of class creation behavior by overriding the __new__ and __init__ methods of the type metaclass. They can enforce constraints, add class-level methods, or modify attribute handling during class instantiation, enabling advanced object-oriented programming patterns and metaprogramming techniques for implementing custom behavior and design patterns.
Explain Python's support for unit testing with the unittest framework. How does unittest facilitate test-driven development (TDD) and automated testing?
- Answer: The unittest framework in Python supports unit testing by providing a built-in testing framework for organizing and executing test cases, fixtures, and assertions. It facilitates test-driven development (TDD) by promoting writing tests before code implementation, ensuring code correctness, functionality, and maintainability through automated testing and continuous integration practices.
```
import unittest

def square(x):
    return x ** 2

class TestSquare(unittest.TestCase):
    def test_positive_numbers(self):
        self.assertEqual(square(2), 4)
        self.assertEqual(square(3), 9)

    def test_negative_numbers(self):
        self.assertEqual(square(-2), 4)
        self.assertEqual(square(-3), 9)

if __name__ == '__main__':
    unittest.main()
```

These advanced-level Python interview questions cover a broad range of topics.They are designed to assess deeper understanding and practical knowledge of Python's advanced features.