Python Interview Questions and Answers

Top Python Interview Questions and Answers | 2024

Interview Questions / By

Python stands out as the preferred programming language in leading companies due to its robust performance and the extensive capabilities offered by its powerful libraries. To secure a position as a Python developer in these renowned organizations, it is essential to excel in the Python Online Assessment Round and Python Interview Round.

To assist candidates in their preparation, we have compiled a comprehensive list of the top 20+ Python Interview Questions, accompanied by detailed answers, to enhance their chances of success in interviews.

Let’s dive in!

Top 20+ Python Interview Questions and Answers 

Here are some of the frequently asked Python interview questions and answers for freshers:

Is Python a compiled language or an interpreted language?

Python is an interpreted language which implies that the source code of a Python program can be translated into bytecode, which is subsequently executed by the Python virtual machine. Unlike prominent compiled languages like C and C++, Python code doesn’t need to undergo the building and linking processes required for these languages.

What are the applications of Python?

The real-time applications of Python such as:

  1. Web Applications
  2. Desktop GUI Applications
  3. Console-based Application
  4. Software Development
  5. Scientific and Numeric
  6. Business Applications
  7. Audio or Video-based Applications
  8. 3D CAD Applications
  9. Enterprise Applications
  10. Image Processing Application

What is Multithreading?

Multithreading involves the simultaneous execution of multiple threads through rapid CPU control switching, known as context switching. In Python, the Global Interpreter Lock restricts the execution of only one thread at a time, even on machines with multiple processors.

What are modules and packages in Python?

Packages and modules in Python refer to mechanisms that are carried out to achieve modular programming.  

Modules in Python are essentially files with a .py extension that contain functions, classes, or variables. You can import and initialize them using the import statement. If you only need specific functionalities, you can import individual classes or functions using the “from foo import bar.”

Packages, on the other hand, enable hierarchical organization of the module namespace through dot notation. Similar to how modules prevent conflicts with global variable names, packages help prevent clashes between module names. They provide a structured way to manage and organize code, enhancing code readability and minimizing naming conflicts.

Utilizing modularizing, you can achieve the following benefits:

  • Simplicity: Focusing on a single module allows for concentrated attention on a specific aspect of the overall problem, making development more straightforward and less prone to errors.
  • Maintainability: Modules are structured to create clear distinctions between different aspects of a problem. If they are crafted with minimal interdependency, changes within a module are less likely to impact other parts of the program, enhancing maintainability.
  • Reusability: Functions within a module can be easily employed by other sections of the application, promoting code reuse and efficiency.
  • Scoping: Modules establish distinct namespaces, preventing confusion between identifiers used in other parts of the program and contributing to a well-defined scope for variables and functions.

What is unit testing in Python?

Unit testing is crucial in software development as it allows the isolation of issues, and early detection of bugs, and facilitates efficient debugging. By testing each component independently, developers can identify and address problems early in the development process, making the code more reliable and maintainable. This approach also aids collaboration, supports refactoring, and serves as a cost-effective way to prevent and catch defects.

What are the common built-in data types in Python?

Here are some common built-in data types in Python:

Numeric Data Types

  • int: Integer type represents whole numbers.
  • float: Floating-point type, represents real numbers (numbers with a decimal point).
  • complex: Complex number type used to numbers in the form a + bj, where a and b are floats, and j is the imaginary unit.

String Data Type

    • str: String type represents sequences of characters, such as text.
  • Sequence Types:
    • list: List type, represents ordered, mutable sequences of elements.
    • tuple: Tuple type, represents ordered, immutable sequences of elements.
    • range: Range type, represents a range of numbers.
  • Binary Types:
    • bytes: Bytes type, represents sequences of bytes, immutable.
    • bytearray: Bytearray type, represents sequences of bytes, mutable.
    • memoryview: Memoryview type, provides a view of an array’s buffer.
  • Mapping Data Type:
    • dict: Dictionary type, represents a collection of key-value pairs. It is unordered and mutable.
  • Boolean Type:
    • bool: Boolean type, represents truth values True or False.
  • Set Data Types:
    • set: Set type, represents an unordered collection of unique elements.
    • frozenset: Frozenset type, represents an immutable set.

These data types provide the building blocks for creating and manipulating data in Python programs. Each type has its characteristics and use cases, offering flexibility for various programming tasks.

What is a break, continue, and pass in Python?

In Python, break, continue, and pass are control flow statements used in loops.

Break

The break statement is used to terminate a loop prematurely. When encountered, it immediately exits the loop, and the control flow continues with the statement following the loop.

Continue

The continue statement is used to skip the rest of the code in the current iteration of a loop and move on to the next iteration.

Pass

The pass keyword is a no-operation statement. It is used as a placeholder where syntactically some code is required, but no action is desired.

What is pickling and unpickling?

In Python, there’s a built-in feature for serialization, which involves transforming objects into a storable format for later retrieval. The pickle module is instrumental in this process.

Pickling

Pickling is the term used for the serialization process in Python. Essentially, it allows any Python object to be converted into a byte stream and saved as a file in memory. The pickling process is efficient, and while pickle objects can be further compressed, the module also maintains a record of the serialized objects, ensuring portability across different Python versions. The primary function for pickling is pickle.dump().

Unpickling

On the flip side, unpickling is the opposite of pickling. It involves deserializing the byte stream to reconstruct the original objects stored in a file and loading them back into memory. The key function for unpickling is pickle.load().

What is PEP?

PEP stands for Python Enhancement Proposal, and it serves as an official design document that imparts information to the Python community or outlines a new feature for Python or its processes. PEP 8 holds particular significance as it establishes the style guidelines for Python code. Adhering to these guidelines is crucial when contributing to the Python open-source community, demanding a sincere and strict commitment to maintain a consistent and readable code style.

What is Scope in Python and its types?

Scope in Python refers to the location where a variable can be found and accessed when needed.

Local Variable

Local variables are initialized within a function and are exclusive to that function. They cannot be accessed outside of the function where they are defined.

Global Variables

Global variables are defined outside any function and are not tied to a specific function. They can be accessed from any part of the program.

Module-level Scope

Module-level scope refers to global objects within the current module that are accessible throughout the program. These variables are defined at the top level of the module and can be used in various functions within that module.

Outermost Scope

The outermost scope includes built-in names that the program can call. These are the names that are part of the Python language itself and are available globally in any part of the program.

Difference between a List and Tuple?

Lists and Tuples in Python are sequence data types used to store collections of objects. Both sequences can accommodate objects of varying data types. Lists are denoted by square brackets, like [‘Laura’, 6, 0.19], while tuples are represented using parentheses, as in (‘ansh’, 5, 0.97).

Here’s the key difference lies between them:

Feature List Tuple
Mutability Mutable Immutable
Memory Consumption More Less
Operations Better for operations Appropriate for access
Iteration Time Time-consuming Faster

What is slicing in Python?

Slicing involves extracting a portion of a string, list, or tuple, allowing users to retrieve a specific range of elements by specifying their indices.  The syntax involves using square brackets with the format Object[start:stop:step]. 

“Start” designates the beginning index of the slice, while “Stop” indicates the concluding element of the slice.

What are Python namespaces?

A namespace in Python is a system where each object is assigned a unique name. Objects can include variables or methods. Python manages its namespace using a dictionary. To illustrate, consider a directory-file system structure in computers. While multiple directories may contain a file with the same name, specifying the absolute path allows one to navigate directly to the desired file.

Define Pandas DataFrame.

A Pandas DataFrame is a tabular data structure that is two-dimensional, size-mutable, and potentially heterogeneous. It features labeled axes for both rows and columns. This means that the data is organized in a tabular format with rows and columns. The three key components of a Pandas DataFrame are the data itself, the rows, and the columns.

Differentiate between NumPy and SciPy

Aspect NumPy SciPy
Primary Focus Efficient array manipulation and fundamental numerical operations Encompasses NumPy functions and extends into complex mathematical tasks
Use Cases Arrays, matrices, basic numerical operations, data manipulation, linear algebra Complex differential equations, optimization, statistical analysis, specialized mathematical functions
Module Structure Single, comprehensive library Organized into submodules catering to specific scientific disciplines
Capabilities Efficient data storage, vectorized arithmetic, broadcasting, multidimensional image processing Optimization, special functions, interpolation, eigenvalue problems, sparse matrix computations
Domain Elementary linear algebra, basic statistics, Fourier analysis, spatial data structures Extends into more advanced and specialized mathematical domains

What is Memory Management in Python?

Python uses its private heap space to manage memory. All objects and data structures are stored in this private heap space, which the programmer cannot directly access. The Python interpreter takes care of managing this space. Additionally, Python has an inbuilt garbage collector that recycles unused memory, freeing it up for the heap space.

What is Python Global Interpreter Lock (GIL)?

The Global Interpreter Lock (GIL) in Python is a process lock used when dealing with threads. Python typically uses only one thread to execute a set of statements. The presence of GIL means that even in a multi-threaded process, the performance is similar to that of a single-threaded process. Achieving true parallelism through multithreading is restricted by the global interpreter lock in Python.

What is Exception Handling in Python?

Exception handling in Python involves three main keywords: try, except, and finally. The try block monitors for errors, and the except block is executed when an error occurs. The final block is used for code that should execute after attempting the try block, regardless of whether an error occurred or not. This block is useful for necessary cleanup activities related to objects or variables.

What are Built-in Data Types in Python?

Python has several built-in data types:

  1. Numeric: Represents data with numeric values, including integers, floating-point numbers, Booleans, and complex numbers.
  2. Sequence Type: An ordered collection of similar or different data types, including strings, lists, tuples, and the range type.
  3. Mapping Types: Involves hashable data mapped to objects using a mutable dictionary.
  4. Set Types: Unordered collection of iterable, mutable, and non-duplicate elements.

Difference between range and xrange Functions.

In Python 3, there is no xrange, and the range function behaves similarly to xrange in Python 2. range() returns a list of numbers created using the range function, while xrange() returns a generator object for lazy evaluation, displaying numbers on demand and optimizing memory usage.

What is PYTHONPATH in Python?

The PYTHONPATH is an environment variable that allows users to specify additional directories where Python should look for modules and packages. By setting PYTHONPATH, you can include directories containing Python libraries that you don’t want to install in the default global location.

How to Copy an Object in Python?

In Python, the assignment statement (= operator) doesn’t create copies of objects; instead, it establishes a binding between the existing object and the target variable. To make copies, the copy module is used. Two types of copies can be created using the copy module:

  • Shallow Copy: A bit-wise copy of an object that duplicates the values in the original object. If the values are references to other objects, only the reference addresses are copied.
  • Deep Copy: Copies all values recursively from the source to the target object, including duplicating the objects referenced by the source object.

What are Decorators in Python?

Decorators in Python are functions that enhance the functionality of an existing function without altering its structure. Represented by the @decorator_name syntax, decorators are called in a bottom-up fashion.

What are Generators in Python?

Generators are functions that return iterable collections of items one at a time. They use the yield keyword to produce a generator object, offering a different approach to creating iterators.

What are Iterators in Python?

An iterator in Python is an object that remembers its state during iteration. It is initialized with the iter() method and has a next() method to retrieve the next item in the iteration. Upon reaching the end of the iterable object, next() must return a StopIteration exception. Iterators are self-iterable and used to iterate over objects like lists and strings.

What is the difference between .py and .pyc Files?

.py files contain the source code of a program, while .pyc files contain the bytecode generated after compiling the .py source file. .pyc files are not created for all executed files; they are generated only for imported files. The presence of .pyc files saves compilation time for subsequent executions.

Also Read : 5 Reasons Why You Should Choose Python for Big Data

Conclusion 

Hope this article provides a comprehensive list of the top Python interview questions and answers for newcomer as well as seasoned professionals.

This resource is designed to enhance your interview preparation and provide valuable insights for excelling in your next Python interview. To become familiar with the real-time world of Python, navigate our hands-on labs and sandboxes.

Best of luck in your preparation and upcoming interviews!

About Pavan Gumaste

Pavan Rao is a programmer / Developer by Profession and Cloud Computing Professional by choice with in-depth knowledge in AWS, Azure, Google Cloud Platform. He helps the organisation figure out what to build, ensure successful delivery, and incorporate user learning to improve the strategy and product further.

Related Posts

Leave a Comment

Your email address will not be published. Required fields are marked *


Scroll to Top