Top 45 TCS Python Interview Questions: The 2024 Guide

Are you gearing up for an interview with Tata Consultancy Services (TCS) for the role of a Python language expert? Congratulations on taking the first step toward an exciting career opportunity! Tata Consultancy Services (TCS) is a global IT services and consulting powerhouse. It is known for its rigorous interview processes, which ensure that they hire only the best and the brightest. In this blog, we’ll take you on a journey through TCS Python interview questions and tips you will require to impress the TCS recruiters. 

Basic TCS Python Interview Questions for Freshers

Preparing for the Python interview questions is very important as it helps you to create a positive impression and secure the job. Here are some most basic Python questions asked in TCS interview for freshers:

Q1. Can you explain the concept of negative indexing in Python lists?

Answer: In Python lists, negative indexing allows access to elements from the end of the list. Index -1 refers to the last element, -2 to the second-to-last, and so on. It provides a convenient way to retrieve elements if you don’t know the list’s length. It also simplifies codes and enhances their efficiency.

Q2. Can you differentiate between global and local variables in Python?

Answer: In Python, the global variables are declared outside functions and are accessible throughout the entire program. Local variables are declared inside functions and have scope limited to that function. Global variables can be modified globally, while local variables are confined to their respective functions, enhancing encapsulation and modularity.

Q3. What is the method for creating an array in Python?

Answer: In Python, arrays are commonly created using the array module or, more commonly, using lists. Lists are dynamic arrays that can hold elements of different data types. To create a list, use square brackets and separate elements with commas. Example: my_list = [1, 2, 3].

Q4. Can you name the two primary loop statements in Python?

Answer: The two major loop statements in Python are “for” and “while.” The “for” loop iterates over a sequence, such as a list or range, while the “while” loop continues executing as long as a specified condition remains true. These loops are fundamental for repetitive tasks and control flow in Python programs.

Q5. Can you define modules in Python?

Answer: Modules in Python are organizational units that group related code together. They consist of Python files containing functions, classes, and variables. Modules provide a way to organize and reuse code, promoting modularity. They can be imported into other Python scripts, enhancing code maintainability and readability.

Q6. Explain the types of functions in Python. 

Answer: In Python, a function is a reusable block of code that performs a specific task. Python functions enhance code modularity, readability, and reusability. There are two main types of functions in Python:

• Built-in Functions:These functions are part of the Python standard library and are readily available for use without the need for explicit definition. Examples include print(), len(), and type().
• User-Defined Functions:These functions are created by the user to encapsulate a specific set of actions. They are defined using the def keyword, followed by the function name, parameters, and a code block.

Q7. What is the difference between loc and iloc?

Answer: loc and iloc are two important methods used for accessing and manipulating data within a DataFrame.The difference between loc and iloc is mentioned below: 

Criteria	loc	iloc
Selection Method	It uses label-based indexing, meaning you refer to the actual row and column labels.	It uses integer-based indexing, where you specify integer positions of rows and columns.
Syntax	It uses row and column labels directly.	It uses integer indices for rows and columns.
Inclusive Slicing	With loc, slicing is inclusive on both ends, considering the specified start and end labels.	With iloc, slicing is inclusive for the start index and exclusive for the end index.
Subsetting	It can select rows with a particular label and condition.	It can select rows by integer locations regardless of the DataFrame index.
Example	df.loc[1, ‘column_name’]	df.iloc[1, 0]

Q8. Can you explain how memory is managed in Python?

Answer: In Python, memory management is handled by the Python Memory Manager, which utilizes a private heap space for storing objects and data structures. Python employs automatic memory management through a system called garbage collection. The key components include:

• Reference Counting: Python uses a reference counting mechanism to keep track of the number of references to each object. When an object’s reference count drops to zero, it is deallocated.
• Garbage Collection: Python’s garbage collector identifies and reclaims memory occupied by objects with zero reference counts. This helps in freeing up memory that is no longer in use.
• Memory Pool: Python uses a memory pool to efficiently allocate and manage memory for small objects. It helps avoid the overhead of frequent memory allocation and deallocation.
• Automatic Memory Management: Python’s memory management is automatic, and developers generally do not need to explicitly allocate or deallocate memory. The system handles memory tasks, making it easier for developers to focus on coding.

Q9. What is the difference between a tuple and a list?

Answer: In Python, both tuples and lists are data structures used to store collections of items. However, here is the difference between a tuple and a list in Python:

Criteria	Tuple	List
Mutability	Tuples are immutable, meaning their elements cannot be changed after creation.	Lists are mutable and allow modifications.
Syntax	Tuples are defined using parentheses ( ).	Lists use square brackets [ ].
Modification	Elements in tuples cannot be modified once assigned, ensuring data integrity.	Elements can be modified after the assignment.
Performance	Tuples are slightly faster for iteration and indexing due to their immutability.	Lists are slower, especially during dynamic resizing.
Memory Usage	Tuples consume less memory as they are fixed in size.	Lists are dynamic and consume more memory.
Use Cases	Tuples are suitable for scenarios where data should remain unchanged, providing integrity.	Lists are preferred for dynamic data that requires modifications and operations.

Q10. Can you explain the difference between modules and libraries?

Answer: Python modules help in modularizing code, promoting code reusability and maintainability. On the other hand, a library is a collection of modules that can be utilized to perform various tasks. Python libraries are essentially pre-written code that developers can use to avoid reinventing the wheel and expediting their programming tasks. 

Q11. What is a map function in Python?

Answer: In Python, the map() function is a built-in higher-order function that applies a specified function to all items in an iterable (such as a list) and returns an iterable of the results. It takes two arguments: the function to apply and the iterable. It provides a concise and efficient way to perform operations enhancing code readability and simplicity without the need for explicit loops.

Q12. How would you comment with multiple lines in Python?

Answer: In Python, you can use triple quotes (”’ or “””) to create multiline comments. This allows you to comment out multiple lines without using the ‘#’ symbol at the beginning of each line. Example:

'''
This is a
multiline comment
in Python.
'''

Q13. Do you know what is pep 8?

Answer: PEP 8, or Python Enhancement Proposal 8, is the style guide for Python code. It provides conventions for writing clean, readable, and consistent Python code. Created by Guido van Rossum, PEP 8 covers topics such as indentation, naming conventions, and other practices to enhance code quality and maintainability.

Q14. Can you explain what are decorators in Python? 

Answer: Decorators in Python are functions that modify or extend the behavior of other functions or methods. They allow you to wrap additional functionality around existing functions without modifying their code. Decorators are commonly used for tasks like logging, access control, and code instrumentation in a concise and reusable manner.

Q15. How will you shuffle the elements of a list in Python?

Answer: To shuffle elements of a list in Python, use the shuffle function from the random module. Import the module, then apply random.shuffle(your_list). This rearranges the elements randomly. Example:

import random
my_list = [1, 2, 3, 4, 5]
random.shuffle(my_list)

Check out a list of all the TCS interview questions with answers and impress your recruiters with ease.

TCS Python Interview Questions for Mid-Level Developers

If you are applying for a mid-level Python developer job at TCS, it is important to prepare for the interview by reviewing some common interview questions. Going through these questions can help ace your interview. Here are TCS Python developer interview questions for mid-level positions: 

Q16. Can you differentiate between SciPy and NumPy?

Answer: NumPy and SciPy are complementary libraries in Python. NumPy focuses on fundamental array operations, providing support for large, multi-dimensional arrays and matrices. SciPy builds on NumPy, by offering additional functionality for scientific computing, including optimization, integration, interpolation, and signal processing, making it a comprehensive toolkit for scientific and technical computing.

Q17. Does Python employ access specifiers in its object-oriented programming paradigm?

Answer: Python does not have explicit access specifiers like “private” or “public” as in some other languages. Instead, it relies on naming conventions. Attributes with a single leading underscore (e.g., _variable) are considered conventionally private, while those with a double leading underscore (e.g., __variable) undergo name mangling for stronger privacy.

Q18. Is there an equivalent to scanf() or sscanf() in Python?

Answer: In Python, the input() function can be used to read user input, somewhat analogous to scanf() in C. For more advanced formatting and parsing, the re-module can be employed. However, Python doesn’t have a direct equivalent to sscanf(); string manipulation and regular expressions are commonly used instead.

Q19. What are the methods available for incorporating view functions into Django’s urls.py file?

Answer: In Django’s urls.py, view functions can be added using:

• Function-Based Views: Directly reference functions.
• Class-Based Views: Use as view() method when associating a class-based view.
• URL Patterns: Use the path() or re_path() functions to map URLs to views.
• Include(): Organize URL patterns into separate files. 

Q20. Can you explain the location of source files, such as math.py, socket.py, and regex.py, within the Python standard library?

Answer: The source files for standard Python modules like math.py, socket.py, and regex.py are part of the Python standard library. They are typically located in the Lib directory of the Python installation. Users can access and explore these files to understand the implementation details of the corresponding modules.

Q21. Are there any interfaces for database packages in Python?

Answer: Yes, Python offers various interfaces for database packages. The standard library includes the SQLite3 module for SQLite databases. Additionally, popular third-party libraries such as SQLAlchemy, Django ORM, and psycopg2 (for PostgreSQL) provide powerful and flexible database interfaces, enabling seamless interaction with different database systems.

Q22. Can a Python class inherit from more than one parent class?

Answer: Yes, a Python class can inherit from more than one parent class, thereby supporting multiple inheritance. It can be achieved by listing multiple parent classes in the class definition. 

Q23. Can you explain the term Tkinter?

Answer: The term “Tkinter” refers to a standard GUI (Graphical User Interface) toolkit in Python. Tkinter is a built-in library that provides tools for creating desktop applications with graphical interfaces. It is based on the Tk GUI toolkit and is widely used for developing user-friendly applications in Python.

Q24. Can you explain the use of the ‘with’ statement and its syntax?

Answer: The ‘with’ statement in Python is used for context management, ensuring proper setup and cleanup of resources. Its syntax is:

with context_expression as target_variable:
    # Code block inside the 'with' statement 

Here, ‘context_expression’ is an object that supports the context management protocol, and ‘target_variable’ is the variable to which the result of the expression is assigned.

Q25. Can you describe the split(), sub(), and subn() methods found within Python’s ‘re’ module?

Answer: The re-module in Python provides several methods for working with regular expressions. Here’s a description of the split(), sub(), and subn() methods:

• split() Method: The split() method is used to split a string into a list based on a specified regular expression pattern. It takes the pattern as an argument and returns a list of substrings.
• sub() Method: The sub() method is used for replacing occurrences of a pattern in a string with a specified replacement. It takes three arguments: the pattern to search for, the replacement string, and the input string.
• subn() Method: The subn() method is similar to sub(), but it returns a tuple containing the modified string and the number of substitutions made.

Q26. What do you know about iterators in Python?

Answer: In Python, an iterator is an object that represents a stream of data and enables iteration over a sequence of elements, one at a time. It follows the iterator protocol, implementing two methods: __iter__() and __next__().

• The __iter__() method returns the iterator object itself and is called when an iterator is initialized.
• The __next__() method returns the next element in the sequence and is called for each subsequent iteration. When there are no more elements, it raises the StopIteration exception.

Q27. Do you know what *args and **kwargs mean in Python?

Answer: In Python, *args and **kwargs are used to pass a variable number of arguments to a function.

• *args allows a function to accept any number of positional arguments. It collects them into a tuple.
• **kwargs allows a function to accept any number of keyword arguments. It collects them into a dictionary.

Q28. What do you know about Dict and List Comprehension?

Answer: Python comprehensions provide a way to create modified and filtered lists, dictionaries, or sets from a given list, dictionary, or set. They are a powerful feature in Python that allows the creation of concise expressions for creating lists, dictionaries, and sets. Comprehensions eliminate the need for explicit loops, which can help reduce the size of your code and save time during development. 

Q29. What is the difference between range & xrange?

Answer: Here’s a table outlining the differences between range and xrange:

Feature	range	xrange
Memory Usage	It creates a list containing all elements in the range. It consumes more memory, especially for large ranges.	It generates elements on the fly, consuming minimal memory regardless of range size.
Type	It returns a list type.	It returns an xrange object, an iterator-like type.
Usage	It is suitable when you need an actual list of elements.	It is preferred for large ranges or situations where memory efficiency is crucial, as it generates elements as needed.
Compatibility	It is present in Python 2 and Python 3 but behaves differently in each.	It is only available in Python 2 as Python 3 no longer uses xrange(). Instead, range() is used.

Q30. What is the purpose of the “is”, “not” and “in” operators?

Answer: In Python, the “is” operator, the “not” operator, and the “in” operator serve different purposes:

• “is” Operator: The “is” operator in Python is used for identity comparison. It checks if two objects refer to the same memory location, determining if they are the same object.

• “not” Operator: The “not” operator is a logical operator in Python used for negation. It returns True if the operand is False, and vice versa. It negates the truth value of the given expression.

• “in” Operator: The “in” operator is used to test membership. It checks if a specified value exists in a sequence (like a string, list, or tuple), returning True if the value is present and False otherwise.

TCS Python Developer Interview Questions for Experienced Professionals

It is essential to delve into TCS Python developer interview questions for an experienced role. Adequate preparation ensures that you can confidently navigate complex queries and showcase your seasoned expertise. Here are the interview questions to ace TCS’s senior-level interviews: 

Q31. Can you write code to reverse a string in Python?

Answer: Certainly! Here’s a simple Python code to reverse a string:

def reverse_string(input_string):
    return input_string[::-1]

# Example
original_string = "Hello, World!"
reversed_string = reverse_string(original_string)

print("Original String:", original_string)
print("Reversed String:", reversed_string)

This code defines a function reverse_string that takes an input string and returns its reverse using slicing ([::-1]). The example then demonstrates its usage with a sample string.

Q32. Can you briefly explain how Python implements dynamic typing? 

Answer: Python implements dynamic typing by allowing variables to reference objects without explicit type declarations. The type of a variable is determined at runtime based on the assigned object. Here is an example:

x = 5      # integer
x = 'hello'  # string
x = [1, 2, 3]  # list

Here, x dynamically changes its type as it is assigned different values. This flexibility simplifies code but requires careful consideration to avoid unexpected behavior.

Q33. How can you achieve Multithreading in Python?

Answer: In Python, multithreading is achieved using the threading module. The threading module paves the way to create and manage threads. However, due to the Global Interpreter Lock (GIL) in CPython, multithreading is more suitable for I/O-bound tasks in comparison to CPU-bound tasks. For CPU-bound tasks, multiprocessing is recommended.

Q34. What is the main purpose of using cx_Freeze?

Answer: The primary purpose of cx_Freeze is to create standalone executables from Python scripts. By “freezing” a Python application, it bundles the Python interpreter and all necessary dependencies into a single executable file. This makes it easier to distribute Python applications to users who may not have Python installed on their systems.

Q35. How can you debug an extension in Python?

Answer: To debug a Python extension, I can use tools like gdb for C extensions or Python’s built-in pdb debugger. Set breakpoints, inspect variables, and step through the code to identify and fix issues. Additionally, print statements and logging can aid in debugging extension modules.

Q36. What makes Django a preferred framework for web development in Python?

Answer: Django is a high-level Python web framework popular for its simplicity, scalability, and robustness. It follows the DRY or “don’t repeat yourself” principle, promoting efficient and maintainable code. Django provides an ORM, built-in admin panel, security features, and extensive documentation, making it ideal for the rapid development of scalable and secure web applications.

Q37. How can you evaluate an arbitrary Python expression from C?

Answer: To evaluate an arbitrary Python expression from C, I  use the Python/C API. This involves using functions like PyRun_SimpleString or PyRun_String to execute Python code from within a C program. This API provides a bridge between C and Python, allowing seamless integration and code execution.

Q38. Can you demonstrate the syntax used to instantiate a class in Python?

Answer: To create an instance of a class in Python, use the class name followed by parentheses. For example, if the class is named MyClass, the syntax for instantiation is obj = MyClass(). This invokes the class constructor, creating a new object or instance of that class for further use.

Q39. Explain the mechanisms through which arguments are transferred to functions in Python.

Answer: In Python, arguments are transferred to functions using a combination of positional, keyword, and default parameters. Python doesn’t strictly follow “pass by value” or “pass by reference” like some languages; instead, it is more accurately described as “pass by object reference” due to its unique memory management.

Q40. What is the lambda function in Python?

Answer: A lambda function in Python is an anonymous, small, and inline function defined using the lambda keyword. It allows the creation of simple functions without the need for a formal function definition using def. 

Lambda functions are typically used for short-lived operations, often in situations where a full function definition would be overly verbose. They are particularly useful in functions like map(), filter(), and sorted() where a simple function is required for a short duration. 

Q41. Differentiate between append() and extend() methods?

Answer: The append() and extend() methods in Python are used to add elements to lists, but they differ in their behavior:

• append() Method:The append() method is used to add a single element to the end of a list. It takes one argument, the element to be added, and appends it to the list.

Syntax: list.append(element)

• extend() Method:The extend() method is used to add multiple elements to the end of a list. It takes an iterable (e.g., a list, tuple, or string) as an argument and adds its elements to the list.

Syntax: list.extend(iterable)

Q42. Describe how Python Flask handles database requests.

Answer: Flask provides flexibility in handling database requests, allowing developers to choose from various approaches based on their preferences and project requirements. The three common ways to request a database in Flask include:

• before_request() decorator: It is used to register a function that will be executed before each request is processed. It is commonly employed to set up resources, establish database connections, or perform any pre-processing tasks needed for the request.
• after_request() decorator: It is used to register a function that will be executed after each request is processed but before the response is sent to the client. It allows developers to modify the response or perform clean-up tasks.
• teardown_request() decorator: It is used to register a function that will be executed after each request, regardless of whether an exception occurred during processing. It is commonly used for clean-up tasks, such as closing database connections or releasing resources.

Q43. Describe how is multi-threading achieved in Python.

Answer: In Python, multi-threading is achieved using the threading module. It allows the creation of threads, each running concurrently. Python’s Global Interpreter Lock (GIL) limits true parallel execution, but threading is beneficial for I/O-bound tasks, providing concurrency by switching between threads during blocking operations, maximizing overall performance.

Q44. Can you explain monkey patching in Python?

Answer: Monkey patching in Python involves dynamically modifying or extending a module or class during runtime. It’s often used to add, modify, or override methods or attributes without changing the source code. 

Q45. Are you familiar with SVM?

Answer: Yes, I’m familiar with Support Vector Machines (SVM). It is a supervised machine-learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that best separates data points into different classes in a high-dimensional space.

Also Read: TCS Ninja Programmer Interview Questions

5 Tips to Crack TCS Python Interview Questions 

Landing a role in TCS (Tata Consultancy Services) as a Python developer requires not only a solid foundation in Python but also a strategic approach to the interview process. Here are five valuable tips to help you crack TCS Python interview questions and showcase your expertise.

1. Master Core Python Concepts

It is important to have a strong grasp of fundamental topics such as data types, loops, functions, and exception handling. Proficiency in these basics forms the backbone of your Python skills and demonstrates a solid understanding of programming principles. Therefore, it is important to prepare well in advance and brush up on these core concepts to ace your TCS interview.

2. Understand Data Structures and Algorithms

If you are preparing for a TCS interview, it is important to be able to solve problems related to lists, dictionaries, strings, and more complex algorithmic challenges. To do this effectively, it is recommended that you brush up on common algorithms, practice problem-solving, and work on optimizing your code efficiency. Demonstrating your ability to write clean, efficient, and scalable Python code will be an important way to showcase your skills during the interview process.

3. Explore Python Frameworks and Libraries

Having a good understanding of commonly used Python frameworks and libraries can be an advantage. During the evaluation process, the hiring manager may test your knowledge of web development using frameworks such as Django or Flask, or data manipulation using libraries such as NumPy and pandas. You can discuss your previous projects where you have utilized these tools, emphasizing your hands-on experience and flexibility as a Python developer.

4. Demonstrate Problem-Solving Skills

TCS considers problem-solving skills to be of great importance, and during a Python interview, you may be presented with real-world scenarios. To improve your problem-solving abilities, you can practice solving coding challenges on platforms such as HackerRank or LeetCode. Be sure to demonstrate your creativity in addressing complex issues and clearly explain your thought process during the interview.

5. Stay Updated on Industry Trends

TCS values candidates who are aware of industry developments. To showcase your adaptability and enthusiasm for staying current in the rapidly evolving tech landscape, you should familiarize yourself with emerging technologies like machine learning, artificial intelligence, and automation.

Conclusion 

If you are preparing for an upcoming TCS Python interview, it’s important to review your Python basics thoroughly and practice coding exercises. Although the interview process has various levels, each with its own set of challenges, with the right preparation, you can ace it like a pro. So, whether you’re a fresher, mid-level, or experienced candidate, you can review TCS Python interview questions before your next interview.

Did you find these questions helpful? Let us know in the comments below. You can also check out our online Python course to enhance your preparation and performance. 

Remember, the journey to your TCS Python interview is as important as the destination. So, incorporate these strategies into your preparation, and you’ll be well on your way to acing your interview! Good luck!