Top Python Interview Questions and Answers for 2023

Preparing for any interview can be challenging and time-consuming, so here we have curated a list of top Python interview questions and answers for 2023. If you are looking to ace a Python interview, then this collection of Python interview questions is your go to guide. This blog is like a comprehensive list for anyone who is looking to kick-start their careers in Python programming.

Before going more in-depth into the Python interview questions and answers, you can start off by learning what is a variable in Python. 

So, let’s get on with the list of some of the most frequently asked Python interview questions and answers. 

List of Common Questions and Answers for Python Interview 

1. What is Python and what are its advantages? 

Python is a high-level, interpreted programming language known for its versatility and ease of use. It has gained widespread popularity across various domains, including web development, data analysis, artificial intelligence, and scientific computing.

It’s advantages are given here:

• Readable and Expressive: Python's clean and readable syntax promotes efficient coding and reduces the learning curve, making it accessible to beginners and experienced developers alike.
• Rich Standard Library: Python offers an extensive collection of pre-built modules and functions, simplifying complex tasks and accelerating development.
• Cross-Platform Compatibility: Applications written in Python can seamlessly run on different operating systems, ensuring flexibility and broad accessibility.
• Active Community Support: A vibrant and active community provides a wealth of resources, tutorials, and solutions, facilitating problem-solving and continuous learning.
• Interoperability: Python can be easily integrated with other languages like C, C++, and Java, enabling developers to combine the strengths of various languages in their projects.

Python's versatility, coupled with its clear advantages, positions it as a versatile and powerful choice for a wide range of programming needs.

2. What are the key features of Python? 

Here's a succinct list of the key features of Python:

• Interpreted: Python code is executed line by line by an interpreter, which provides immediate feedback and speeds up development.
• Dynamically Typed: Variable types are determined at runtime, allowing for flexible and adaptable code without explicit type declarations.
• Object-Oriented: Python supports object-oriented programming (OOP) principles, facilitating code organization and modularity.
• Concise and Simple: Python's clean and readable syntax promotes efficient and expressive coding, enhancing productivity.
• Free and Open Source: Python is freely available and can be used, modified, and distributed without any licensing fees.
• Large Community: Python boasts a vast and active community of developers, providing ample resources, libraries, and support.

Python's blend of features makes it an excellent introductory language to programming and a versatile choice for various applications.

3. What is an interpreted language?

An Interpreted language processes its instructions sequentially, executing them line by line. Examples of Interpreted languages include Python, Javascript, R, PHP, and Ruby. In these languages, programs run directly from the source code without the need for an intermediate compilation step.

4. What is the use of “self” in Python? 

In Python, the term "self" refers to the instance of the class. It provides access to the class's attributes and methods, essentially connecting the attributes with specific arguments. Unlike in C++, "self" is not a keyword in Python, even though it's commonly used and associated with object-oriented programming.

5. What is object-oriented programming (OOP) in Python?

Object-oriented programming (OOP) is a coding approach that models real-world entities and concepts using objects.

In Python, this is achieved through classes, which serve as blueprints for creating objects. Objects, in turn, are instances of these classes. OOP brings the benefits of encapsulation, inheritance, and polymorphism to organize and manage code effectively.

6. How to handle Exceptions in Python?

Exceptions are occurrences of errors during program execution. Python provides a try-except mechanism to manage exceptions. The try block holds the potentially problematic code, while the except block holds the code to manage the exception.

Multiple except blocks can be employed to address distinct exception types. Additionally, a final block can be utilized to ensure specific code runs, regardless of whether an exception is encountered.

For interview responses, it's crucial to present your answers in a well-organized and succinct manner.

7. What is a Tuple in Python?

In Python, a tuple is a sequential and unchangeable grouping of elements. Comparable to a list, a tuple's contents remain fixed once established. Tuples are commonly employed to hold correlated data, like a person's name and age.

8. Describe a Python Function? 

A function is a segment of code crafted for a single purpose, executed whenever required by the program. It's an encapsulated set of statements with a recognizable name, defined parameters, and a body. This modular approach enhances programming efficiency by accomplishing specific tasks. Python offers both built-in functions like duplicate(), len(), and count(), as well as the ability for users to create their own functions.

Functions can be categorized as:

• Built-In Functions: Examples include duplicate(), len(), and count(), which come prepackaged.
• User-Defined Functions: Created by users to fulfill specific needs.
• Anonymous Functions (Lambda): Also known as lambda functions, they deviate from the standard def keyword for declaration.

9. What is the difference between the List and Tuple in Python?

In Python, a list is mutable, allowing modifications after creation. In contrast, a tuple is immutable, preventing alterations after creation. Tuples tend to offer faster performance but less flexibility compared to lists.

10. What is pep 8?

PEP 8, short for Python Enhancement Proposal 8, outlines guidelines for formatting Python code to enhance its readability and maintainability.

11. What are Python namespaces and why are they used? 

Python namespaces guarantee unique and conflict-free object naming within a program. These namespaces are implemented as dictionaries, where names correspond to objects. This setup accommodates distinct objects sharing the same name across multiple namespaces. Noteworthy namespace categories include:

• Local Namespace: Contains function-specific names. Created during function calls, it vanishes when the function ends.
• Global Namespace: Comprises names from imported packages/modules. Established upon import and persists until script execution concludes.
• Built-in Namespace: Encompasses core Python functions and exception types.

Namespace lifecycles align with the object's scope. When an object's scope ends, its associated namespace concludes. Consequently, accessing inner namespace objects from an outer namespace isn't feasible.

12. What Does the // Operator Do?

In Python, the / operator conducts division and delivers the result as a floating-point quotient.

For instance: 5 / 2 yields 2.5

In contrast, the // operator yields an integer quotient.

For instance: 5 // 2 yields 2

13. What are Decorators in Python and how are they used?

Decorators in Python are a way to modify or enhance the behavior of a function without altering its core structure. They are essentially functions that wrap around other functions, allowing you to add functionality before or after the wrapped function's execution.

To use a decorator:

• Define the decorator function.
• Place the decorator above the target function, indicating that it should be applied to that function.

This ensures that the decorator's effects are applied to the specified function when it is called. Decorators are particularly useful for tasks like logging, authorization, and code reuse.

14. How to use a Slicing operator in Python? 

Slicing is a method used to extract specific portions of sequences like lists, tuples, and strings. The syntax for slicing is [start:end:step], where 'step' can be omitted. When 'start' and 'end' are provided, [start:end] retrieves all sequence elements from the start (inclusive) to the end-1 element.

In cases where negative indexing is used, the ith element from the end corresponds to -i. The 'step' denotes the interval or number of elements to skip during slicing.

15. Explain PYTHONPATH. 

PYTHONPATH is an environment variable that is used when you import a module. When a module is imported, PYTHONPATH is checked to see if the imported modules are present in various folders. It is used by the interpreter to determine which module to load.

16. Is indentation necessary in Python?

Yes, indentation is essential in Python. It serves as a visual marker for code blocks, encapsulating statements within loops, classes, functions, and other structures. Indentation is crucial for maintaining code structure and readability. Conventionally, four spaces are used for indentation. Failing to indent properly will lead to incorrect execution of code and result in errors.

17. What is a Lambda function and why is it used in Python?

A Lambda function in Python is an anonymous function, commonly employed for short-term or single-use scenarios. Unlike traditional functions, a Lambda function can take multiple parameters, but it's limited to a single statement. Lambda functions find utility in situations demanding a concise, temporary function. They can be used in two ways:

• Assigning a Lambda function to a variable.
• Embedding a Lambda function within another function for specific functionality.

18. Compare range and xrange in Python.

Both range and xrange serve the same purpose of generating a list of integers for various uses. Functionally, they are identical. The key distinction lies in their memory usage:

• range produces a Python list, consuming considerable memory, which can be problematic for memory-intensive applications like phones.
• xrange, on the other hand, returns an xrange object, conserving memory by generating values on-the-fly as needed. This makes it memory-efficient, particularly in scenarios where memory is limited or a concern.

To sum up, while range and xrange are functionally equivalent, xrange is more memory-efficient and suitable for situations where memory optimization is critical.

19. What are negative indexes in Python?

Negative indexes refer to positions counted from the end of a list, tuple, or string.

For instance, Arr[-1] points to the final element of the array.

20. What are Docstrings?

Docstrings, short for documentation strings, are explanatory text enclosed in triple quotation marks. Unlike regular comments, docstrings are not assigned to variables and are often used to provide documentation and information about functions, classes, or modules.

21. What do the 'not', 'is', and 'in' operators do in Python?

• The 'not' operator inverses a boolean value, returning its opposite.
• The 'is' operator evaluates to true when both operands are identical.
• The 'in' operator checks whether a particular element exists within a sequence.

22. What is a dictionary in Python?

A dictionary is a built-in datatype in Python and creates a direct mapping between keys and values. Dictionaries store key-value pairs, where keys are utilized for indexing.

23. What are Python Libraries?

Python libraries are collections of Python packages that provide specialized functionalities. Commonly used Python libraries include Numpy, Pandas, Matplotlib, Scikit-learn, among others.

24. What are iterators in Python? 

Iterating through a collection of elements, akin to a list, is facilitated by iterators in Python. These iterators encompass lists, tuples, or dictionaries, which hold the items to iterate over. Python employs the iter and next() methods to traverse the stored elements using an iterator. In Python, loops are primarily utilized to emphasize working with collections (like lists and tuples).

In essence, iterators are objects designed for traversal and iteration purposes.

25. Is NumPY arrays better than a list? 

NumPy arrays offer three advantages over lists:

• Enhanced Speed
• Reduced Memory Usage
• Improved Convenience for Operations

26. What is a generator in Python? 

In Python, a generator serves as an approach to crafting iterators. It resembles a regular function, differing mainly in its utilization of the yield expression. This simplifies the process by eliminating the need for explicit iter and next() methods, thus reducing extraneous complexities.

If a function incorporates a yield statement, it transforms into a generator. The yield keyword temporarily suspends the ongoing execution, preserving its state for future resumption. This mechanism efficiently manages iterative processes by preserving and restoring states as needed.

27. What is a Python module?

In Python, a module is a script encompassing import statements, functions, classes, variables, and executable Python code. Modules can also encompass ZIP files and DLL files, extending their functionality. Each module carries its name as a string stored in a global variable.

28. What is pickling and unpickling in Python?

The Python "pickle" module is designed to convert Python objects into a string representation. By employing the "dump" function, it duplicates the Python object into a file, a process termed as Pickling.

On the flip side, "Unpickling" refers to the act of retrieving the original Python objects from the stored string representation.

29. How do you compare NumPY and SciPy? 

NumPy:

• Stands for Numerical Python.
• Used for efficient and general numeric computations on arrays, including sorting, indexing, reshaping, etc.
• Offers basic linear algebraic functions.
• Primarily focused on fundamental numerical operations.

SciPy:

• Stands for Scientific Python.
• Provides a collection of tools for scientific and technical computing.
• Includes advanced operations like integration, differentiation, and more.
• Offers comprehensive and full-fledged algebraic functions.
• Geared towards advanced scientific computations.

In summary, NumPy is focused on core numerical tasks with basic linear algebra, while SciPy extends its capabilities to encompass a broader range of scientific and technical computations.

30. Write a code to get the indices of N maximum values from a NumPy array?

To obtain the indices of the top N maximum values from a NumPy array, you can use the following code snippet:

import numpy as np

array = np.array([1, 3, 2, 4, 5, 6])

indices_of_max_values = array.argsort()[-N:][::-1]

print(indices_of_max_values)

Replace N with the desired number of maximum values you want to retrieve. This code will display the indices of the N highest values in the array.

31. What is Polymorphism in Python? 

Polymorphism embodies the capacity of code to exhibit various forms. For instance, when a parent class features a method labeled XYZ, a child class can also define its own version of the method, also named XYZ, complete with distinct variables and parameters.

32. What is the easiest way to calculate percentiles when using Python?

To efficiently compute percentiles in Python, utilizing NumPy arrays and their functions is a straightforward approach.

Here's an illustration:

import numpy as np

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data = np.array([1, 2, 3, 4, 5, 6, 7])

percentile_value = np.percentile(data, 50)  # Calculates the 50th percentile (median)

print(percentile_value)

This code exemplifies using NumPy's percentile function to compute and print the 50th percentile (also known as the median) of the given dataset.

33. How to create an empty class in Python?

An empty class is one that lacks any code within its body. The "pass" keyword can be employed to create such a class. While objects of this class can be instantiated externally, the "pass" command holds no functional impact when utilized in Python.

34. What is the usage of help() and dir() functions in Python?

Two functionalities, "help()" and "dir()", are accessible within the Python interpreter and serve to inspect a comprehensive listing of built-in functionalities.

The "help()" Function:

The "help()" function provides an interface for accessing help on modules, keywords, attributes, and also displays documentation strings. It assists in obtaining detailed information about various Python elements.

The "dir()" Method:

The "dir()" function offers insight into the defined symbols within the current scope. It presents a collection of available attributes and methods for an object, aiding in exploration and utilization.

These features are valuable tools for exploring and understanding Python's built-in capabilities.

35. Is Django better as compared to Flask? 

Django and Flask both link URLs or addresses entered in web browsers to Python functions.

Flask offers simplicity over Django, but it necessitates more detailed specifications. Django, on the other hand, provides extensive automation, reducing user effort. While Django presents prewritten code that users need to understand, Flask empowers users to write custom code, enhancing its learnability. Each possesses distinct strengths and weaknesses, making both frameworks technically proficient in their own right.

Conclusion 

Anyone who wishes to succeed in their Python programming career will have to prepare well for the interview. You can browse through this list of top Python questions and answers as these are something that are commonly asked during interviews.Meanwhile, to get skilled in Python programming, you can also check out the Lifetime FREE access to the keySkillset Python course at our website. 

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