Python<\/strong><\/a> is a general-purpose, dynamic, high-level, and interpreted programming language. It supports the object-oriented programming approach, which makes it a popular choice for developing robust applications. Its simple and readable syntax makes it easy to learn, yet its capabilities are powerful enough for enterprise-grade solutions.<\/p>\nBeing an interpreted language<\/strong>, Python allows for rapid application development and scripting. Its dynamic typing and clean syntax make it ideal for beginners and professionals alike. However, in python intermediate interview questions<\/strong>, you\u2019ll often encounter topics that go deeper\u2014like decorators, list comprehensions, exception handling, and generators.<\/p>\nThis versatility is reflected in the variety of python intermediate questions<\/strong> asked during interviews. You may be asked to build classes, implement interfaces, or manipulate data using functional techniques like map()<\/code>, filter()<\/code>, and lambda<\/code> expressions.<\/p>\nOne of the reasons Python stands out is its dynamic nature. You don\u2019t need to specify data types explicitly. For example, simply writing a = 10<\/code> assigns an integer value to the variable without any type declaration. This flexibility speeds up development but also introduces challenges that are commonly addressed in python intermediate coding questions<\/strong>.<\/p>\nPython Intermediate Coding Questions<\/h2>\n![\"Python](\"https:\/\/www.kaashivinfotech.com\/blog\/wp-content\/uploads\/2023\/12\/Python-Intermediate-Interview-Questions.png\")
Python Intermediate Interview Questions<\/figcaption><\/figure>\n1. What are Python decorators?<\/strong><\/h3>\nAnswer:<\/strong>
Decorators are a powerful tool in Python used to modify the behavior of a function or class. They are applied using the @decorator_name<\/code> syntax above the function definition. Internally, a decorator is a higher-order function that takes a function as input and returns a new function with enhanced behavior.<\/p>\n def my_decorator(func): def wrapper(): print("Before function execution") func() print("After function execution") return wrapper@my_decoratordef greet(): print("Hello!")greet()<\/code><\/pre> <\/div> <\/div>\n
2. Explain list comprehension and its advantages.<\/strong><\/h3>\nAnswer:<\/strong>
List comprehension provides a concise way to create lists in Python. It’s faster and more readable than traditional for-loops.<\/p>\n squares = [x*x for x in range(5)]# Output: [0, 1, 4, 9, 16]<\/code><\/pre> <\/div> <\/div>\n
Advantages:<\/strong><\/p>\n\n- \n
More compact code<\/p>\n<\/li>\n
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Better performance<\/p>\n<\/li>\n
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Easier to understand when used properly<\/p>\n<\/li>\n<\/ul>\n
3. What is the difference between is<\/code> and ==<\/code> in Python?<\/strong><\/h3>\nAnswer:<\/strong><\/p>\n\n- \n
==<\/code> checks value equality<\/strong> (whether the values are the same).<\/p>\n<\/li>\n- \n
is<\/code> checks identity<\/strong> (whether the two references point to the same object in memory).<\/p>\n<\/li>\n<\/ul>\n a = [1, 2]b = [1, 2]print(a == b) # Trueprint(a is b) # False<\/code><\/pre> <\/div> <\/div>\n
4. What are Python generators?<\/strong><\/h3>\nAnswer:<\/strong>
Generators are iterators created using functions and the yield<\/code> keyword. They are memory-efficient and produce values one at a time.<\/p>\n def countdown(n): while n > 0: yield n n -= 1for i in countdown(5): print(i)<\/code><\/pre> <\/div> <\/div>\n
5. Explain Python\u2019s garbage collection.<\/strong><\/h3>\nAnswer:<\/strong>
Python has an automated garbage collector that uses reference counting and cyclic garbage collection to free up unused memory.<\/p>\n\n- \n
Reference Count: If an object\u2019s reference count drops to 0, it is garbage collected.<\/p>\n<\/li>\n
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Cyclic GC: Detects and collects cycles of unreachable objects.<\/p>\n<\/li>\n<\/ul>\n
6. What is a lambda function in Python?<\/strong><\/h3>\nAnswer:<\/strong>
Lambda functions are anonymous, single-expression functions defined using the lambda<\/code> keyword.<\/p>\n square = lambda x: x*xprint(square(5)) # Output: 25<\/code><\/pre> <\/div> <\/div>\n
7. What are *args and kwargs?<\/strong><\/h3>\nAnswer:<\/strong><\/p>\n\n- \n
*args<\/code>: Accepts variable number of positional arguments.<\/p>\n<\/li>\n- \n
**kwargs<\/code>: Accepts variable number of keyword arguments.<\/p>\n<\/li>\n<\/ul>\n def demo(*args, **kwargs): print(args) print(kwargs)demo(1, 2, 3, a=10, b=20)<\/code><\/pre> <\/div> <\/div>\n
8. What is the difference between a shallow copy and a deep copy?<\/strong><\/h3>\nAnswer:<\/strong><\/p>\n