Python Interview Questions

• Question: What is Python?
• Answer: Python stands as a high-level, interpreted programming language that serves as a versatile tool for creating a wide spectrum of applications. As a general-purpose language, it boasts the capability to construct virtually any form of software when supplemented with appropriate tools and libraries. Furthermore, Python encompasses support for objects, modules, threads, exception handling, and automated memory management. This robust feature set facilitates the modeling of real-world challenges and the subsequent development of applications aimed at their resolution.

• Question: What are the benefits of using Python?
• Answer: The benefits are Python are mentioned below:
  • Python is an elevated, interpreted programming language that adheres to an object-oriented paradigm and exhibits dynamic semantics.
  • Python seamlessly operates across various platforms such as Windows, Mac, Linux, Raspberry Pi, and more.
  • In comparison to alternative programming languages, Python boasts a simplistic syntax.
  • Relative to its counterparts, Python empowers developers to craft programs with fewer lines of code.
  • Since Python functions on an interpreter model, the code can be executed promptly after composition, enabling rapid prototyping.
  • Python’s versatility encompasses procedural, object-oriented, and functional programming approaches.
  • The Python interpreter, accompanied by an expansive standard library, is available for free distribution in binary or source form on all major platforms.

• Question: What type of language is Python, is it Programming or scripting?
• Answer: Python can do scripting, but overall, people usually think of it as a language that can be used for many different kinds of programming tasks.

• Question: What do you understand by Interpreted language?
• Answer: An interpreted language follows instructions one by one, line after line. Languages like Python, Java Script, R, PHP, and Ruby are good examples of interpreted languages. When you write programs in an interpreted language, they run directly from the original code without needing a separate compilation step in between.

• Question: What do you understand by “Typed Language”?
• Answer: Typing in programming is like checking the kinds of things we use. In a strong-typed language like Python, if we try to write “5”+7, we’ll get an error because these languages don’t mix different types easily. But in a weak-typed language like JavaScript, it might just add them together and give the result as “57”. There are two ways we check types:
  • Static: We check types before we start doing anything with them.
  • Dynamic: We check types while we’re actually doing things with them.

• Question: Why Python is a dynamic-typed language?
• Answer: Python operates as an interpreted language, sequentially executing each line of code and conducting on-the-fly type checking during runtime. This characteristic classifies Python as a dynamically typed language.

• Question: What is PEP8 and why is it important?
• Answer: PEP means Python Enhancement Proposal. It’s like an official paper that tells the Python community about new ideas or features for Python. PEP 8 is extra special because it tells everyone how Python code should look. If you want to help with Python and share your code, you need to stick to these style rules really carefully.

• Question: What are the namespaces in Python?
• Answer: In Python, think of a namespace as a name given to each object you create, such as variables and functions. When you create any object, its name and the place it’s stored are also set up. Imagine this like a list where names (called namespaces) are the keys, and the storage places are the values. There are four kinds of namespaces in Python:
  • Built-in namespace: This one keeps all the ready-to-use things in Python that you can use anytime.
  • Global namespace: It’s where you create namespaces in your main program.
  • Enclosing namespaces: These are like higher-level namespaces where you put objects in outer functions.
  • Local namespaces: These are spaces inside specific functions where things live temporarily.

• Question: What are decorators in Python?
• Answer: Decorators work as a way to give a function a special style or behavior, without messing up how the function is built. Normally, you make a decorator before the function it’s going to change. To use it, you make the decorator first. Then, you make the actual function, and you put the decorator’s name above it. You just use the “@” sign right before the decorator’s name.

• Question: What are lists and tuples? How are they different from each other?
• Answer: Lists and tuples are both ways to put many objects together in Python. They can hold different kinds of objects, like names, numbers, or decimals. When we write lists, we put objects inside square brackets like [‘name’, 67, 1.01]. Tuples use round brackets like (‘name1’, 19, 1.01). But the main difference is that lists can change, while tuples cannot. This means we can add, change, or cut parts of lists whenever we want. However, tuples stay the same after they’re made, and we can’t change them at all.

• Question: What does “pass” mean in Python?
• Answer: The word “pass” in Python stands for doing nothing. It’s like a placeholder that you put in empty spaces of your code. This helps when you have parts that should work later, but you’re not ready to write them yet. If we don’t use “pass” in certain places, our code might not work properly when we run it.

• Question: What are the basic built-in data types in Python?
• Answer: Python has various built-in data types. Although you don’t need to say the type when making a variable, not paying attention to types might lead to errors. Python provides functions like type() and isinstance() to check types. These types can be grouped into categories:
  • None Type: The “None” keyword is used for null values in Python. We can do boolean equality checks using NoneType objects.
  • Numeric Types: There are three numeric types: integers (whole numbers), floating-point numbers (with decimals), and complex numbers. Booleans are like a type of integer.
  • Sequence Types: There are lists, tuples, and ranges. Lists can change, tuples can’t, and ranges are for numbers. Strings, used for text, are also a type of sequence.
  • Mapping Types: Dictionaries can map one thing to another in Python. They are changeable (mutable).
  • Set Types: Sets have unique things and can be modified (mutable), while frozensets can’t be changed once made (immutable).
  • Modules: Modules help organize code. You can’t change them directly.
  • Callable Types: These are things you can use like a function, like functions you make, methods, and certain built-in things.

• Question: What are Python Modules and Packages?
• Answer:  In Python, modules and packages are two different techniques for achieving modular programming, offering a range of advantages for modules:
  • Simplicity: Modules allow developers to focus on specific portions of a problem by isolating functionality. This reduces complexity, making code development more manageable and less prone to errors.
  • Maintainability: Modules enforce logical boundaries, minimizing interdependencies. This ensures that changes to one module are less likely to affect other parts of the program, enhancing code maintainability.
  • Reusability: Functions, classes, and variables defined in modules can be easily reused across different parts of an application, promoting code reusability.
  • Scoping: Modules establish separate namespaces, preventing identifier conflicts with other parts of the program.
• Modules, in essence, are Python files with a .py extension, containing functions, classes, or variables. They can be imported using the ‘import’ statement. For partial functionality, specific classes or functions can be imported using the ‘from foo import bar.’
• Packages, on the other hand, enable hierarchical organization of the module namespace using dot notation. They help prevent naming conflicts between modules. Creating a package is straightforward—simply place modules into a folder, and the folder’s name becomes the package name. To import a module or its contents from a package, use the package name as a prefix followed by a dot.”

• Question: What do you understand by Keywords in Python
• Answer: In Python, keywords are reserved words with special significance. They are primarily employed to designate variable types and have restrictions—they cannot be utilized as variable or function names. Python features a set of 33 keywords, including:
  1. and
  2. or
  3. not
  4. if
  5. elif
  6. else
  7. for
  8. while
  9. break
  10. as
  11. def
  12. lambda
  13. pass
  14. return
  15. True
  16. False
  17. try
  18. with
  19. assert
  20. class
  21. continue
  22. del
  23. except
  24. finally
  25. from
  26. global
  27. import
  28. in
  29. is
  30. None
  31. nonlocal
  32. raise
  33. yield
• These keywords play essential roles in defining the structure and logic of Python programs.”

Question: What are Dict and List comprehensions?
Answer: In Python, dictionary (dict) and list comprehensions are concise and expressive ways to create dictionaries and lists using a more compact syntax. They are powerful tools for simplifying code and making it more readable.
List Comprehensions: List comprehensions provide a concise way to create lists in Python. They follow this general structure:
new_list = [expression for item in iterable if condition]
Here’s what each part does:
•expression: The expression that is evaluated for each item in the iterable.
•item: A variable that takes on the value of each item in the iterable.
•iterable: An iterable (e.g., a list, tuple, or string) that you want to loop through.
•condition (optional): An optional filter that allows you to include only items that meet a certain condition.
For example, you can create a list of squares of even numbers from 1 to 10 using a list comprehension:
squares_of_evens = [x**2 for x in range(1, 11) if x % 2 == 0]

Dictionary Comprehensions: Dictionary comprehensions are similar to list comprehensions but are used to create dictionaries. They have a similar structure:
new_dict = {key_expression: value_expression for item in iterable if condition}
Here’s how each part works:
•key_expression: The expression for generating keys in the new dictionary.
•value_expression: The expression for generating values in the new dictionary.
•item: A variable that takes on the value of each item in the iterable.
•iterable: An iterable that you want to loop through.
•condition (optional): An optional filter that allows you to include items that meet a certain condition.
Here’s an example of creating a dictionary that maps numbers to their squares using a dictionary
squares_dict = {x: x**2 for x in range(1, 6)}

Both list and dictionary comprehensions are more concise than traditional loops and can make your code more readable and efficient when used appropriately.