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TCS · Python Developer

TCS Python Developer Interview Questions (2026): process, questions and answers

Updated 23 October 2026 · prep guide

The TCS Python Developer interview usually focuses on Python fundamentals, OOP, web frameworks, SQL, testing, and practical problem solving. Candidates commonly report a mix of technical and behavioural questions, so use this page to revise core concepts and practice short, clear answers out loud.

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How the TCS Python Developer interview usually works

  1. Resume screening: TCS usually starts by matching your resume to Python development, web, database, and testing experience. Be ready to explain each project clearly, especially your role, the tools you used, and the business problem you solved.
  2. Technical discussion: Interviewers often ask fundamentals first, then move to frameworks, APIs, SQL, and debugging. Expect follow-up questions based on your answers, so do not give memorised definitions without understanding.
  3. Hands-on problem solving: Candidates commonly report coding or scenario-based questions on logic, data handling, and API design. Show how you think step by step, mention edge cases, and write clean code if asked.
  4. Behavioural and project review: You may be asked about teamwork, handling pressure, working with clients, and resolving production issues. Keep examples practical and tie them to delivery, communication, and ownership.
  5. Final fit discussion: The last discussion often checks how well you fit a consulting environment with multiple stakeholders. Be prepared to discuss adaptability, learning speed, and how you handle changing requirements.

Technical questions and answers

What is the difference between a list and a tuple in Python?

A list is mutable, so you can add, remove, or change items after creation. A tuple is immutable, so its values cannot be changed. Lists are usually used for dynamic collections, while tuples are useful when the data should stay fixed, like coordinates or configuration values. Tuples can also be slightly faster and can be used as dictionary keys if all elements inside are hashable.

Explain the difference between == and is in Python.

== compares values, while is checks whether two variables point to the same object in memory. For example, two separate lists with the same contents can be == but not is. Use == for value comparison and is for checking identity, such as comparing with None using is None. This is a common Python interview point.

What are decorators in Python?

A decorator is a function that wraps another function or method to add behaviour without changing the original code directly. It is commonly used for logging, access control, timing, or validation. In Python, the @decorator_name syntax is used above the function definition. In interviews, explain that decorators are higher-order functions and can return a new function with added logic.

How does inheritance work in OOP, and what is method overriding?

Inheritance lets one class reuse and extend another class. The child class gets the parent class's attributes and methods, and it can add new ones or replace existing behaviour. Method overriding means the child class provides its own version of a method with the same name. This supports code reuse and polymorphism, which are common OOP interview topics.

What is the difference between Django and Flask?

Django is a full-stack framework with built-in features like ORM, admin, authentication, and project structure. Flask is a lightweight micro-framework that gives you more control and requires you to choose extensions for features such as forms, auth, or ORM. For enterprise projects, Django is often preferred for faster standardised development, while Flask is useful for smaller or highly customised services.

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How do REST APIs work, and what are common HTTP methods?

A REST API exposes resources over HTTP using standard methods. GET reads data, POST creates, PUT or PATCH updates, and DELETE removes data. Good REST design uses clear URLs, proper status codes, and JSON responses. In interviews, mention statelessness, authentication, validation, and idempotency where relevant.

How would you use pandas to clean a dataset?

In pandas, I would load the data with read_csv or read_excel, inspect it with head(), info(), and isnull().sum(), then handle missing values, duplicates, and wrong data types. I would use fillna(), dropna(), astype(), and drop_duplicates() as needed. For analysis, I would group data with groupby() and filter rows with boolean conditions. Always explain why each cleanup step is needed.

What is SQL JOIN, and when would you use INNER JOIN versus LEFT JOIN?

A JOIN combines rows from two tables based on a related column. INNER JOIN returns only matching rows from both tables. LEFT JOIN returns all rows from the left table and matching rows from the right table, with NULL where there is no match. Use INNER JOIN when you only need valid matches, and LEFT JOIN when you want to keep all records from the main table.

How do you test Python code, and what is the difference between unit and integration testing?

Unit testing checks a small piece of code in isolation, such as a function or method. Integration testing checks whether multiple parts work together, such as a service layer calling a database or API. In Python, interviewers often expect you to mention pytest or unittest, assertions, fixtures, and mocking. Good testing also covers edge cases, error handling, and predictable inputs and outputs.

What is async programming in Python, and when would you use it?

Async programming helps handle many I/O-bound tasks efficiently, such as API calls, file operations, or database requests, without blocking the whole program. In Python, async and await are used with an event loop to switch between tasks while waiting. It is useful for services that manage concurrent network requests. It is not mainly for CPU-heavy work, where multiprocessing is usually better.

Scenario questions

You need to build a Python service that reads customer data from an API, cleans it, stores it in a database, and exposes a new endpoint for reporting. How would you approach it?

Start with the data flow: fetch, validate, transform, store, and expose. Mention using a framework like Django or Flask for the endpoint, pandas for cleanup if the dataset is tabular, SQL for storage, and structured logging for monitoring. Talk about validation, retries for API failures, and tests for each layer. Also mention authentication, error handling, and response codes because TCS projects often involve enterprise integration.

A production API is slow during peak hours. What would you check first?

Begin with logs, response time metrics, database query time, and external dependency latency. Check whether slow SQL queries, missing indexes, repeated API calls, or synchronous code are causing the delay. If the issue is I/O-bound, consider caching, pagination, query optimisation, background jobs, or async processing where appropriate. Show that you would diagnose before changing code, and that you understand both application and database bottlenecks.

Your team asks you to convert a CSV-based report into a REST API with the same business logic. How would you handle it?

Explain that you would first understand the existing report logic, inputs, outputs, and edge cases. Then separate business logic from presentation so the same core code can be reused in the API. Use pandas or SQL depending on the source data, create clear API contracts, and add tests to compare old and new outputs. Mention versioning and stakeholder review to avoid breaking existing consumers.

HR and behavioural questions

Tell me about yourself.

Structure it as present, past, future: who you are now, what relevant experience you have, and what role you want next. Keep it focused on Python, APIs, databases, and teamwork. Sample answer: "I am a Python developer with experience in building backend features, writing SQL queries, and supporting testing. I have worked on projects where reliability and clear communication mattered. I am now looking for a role where I can build scalable services and contribute to client-facing delivery."

Describe a time you solved a difficult technical problem.

Use the STAR method: situation, task, action, result. Choose a real issue such as a failing API, a bad SQL query, or a data mismatch. Sample answer: "We had a report that was returning incorrect totals. I traced the issue to a join condition, fixed the query, and added test cases to prevent recurrence. The report became accurate and the team could release on time."

How do you handle tight deadlines and changing requirements?

Show prioritisation, communication, and calm execution. Explain how you break work into must-have and nice-to-have items, confirm changes early, and keep stakeholders updated. Sample answer: "When requirements change, I first confirm the new priority, estimate impact, and adjust the plan. I keep the team informed and focus on the highest-value work first. This helps me deliver on time without losing quality."

How do you work with a difficult teammate or stakeholder?

Focus on listening, clarity, and facts. Avoid blaming anyone, and describe how you align on the problem rather than the person. Sample answer: "I try to understand the other person's goal, share facts from logs, code, or requirements, and propose a practical next step. In one project, this helped us resolve a disagreement on API behaviour quickly and keep the delivery moving."

Your last 48 hours before the interview

  1. Revise Python basics, especially lists, tuples, dictionaries, functions, exceptions, and file handling.
  2. Practice explaining OOP concepts, inheritance, polymorphism, abstraction, and encapsulation in simple words.
  3. Review one Django or Flask project, including routing, serializers or forms, authentication, and error handling.
  4. Solve a few SQL questions on JOIN, GROUP BY, filtering, and subqueries, then explain them out loud.
  5. Prepare two project stories, one technical challenge and one teamwork example, using short STAR structure.
  6. Do a timed mock interview for APIs, pandas, testing, and async, then fix weak spots in your answers.

Common mistakes to avoid

Frequently asked questions

What Python questions are asked in a TCS Python Developer interview?

Candidates commonly report questions on data types, loops, functions, exception handling, OOP, decorators, and common built-in methods. You may also get coding questions on strings, lists, dictionaries, and basic algorithms. Be ready to explain your reasoning clearly, not just give definitions.

Is Django or Flask more important for TCS Python Developer interviews?

Django is often more relevant if the role is backend-heavy, but Flask can also appear depending on the project. Understand routing, request handling, templates or serializers, authentication, and how APIs are built. The key is to show that you can build and support a web service, not just name the framework.

Do I need to know SQL for a Python Developer role at TCS?

Yes, SQL is commonly asked because many Python backend projects work with relational databases. Be comfortable with SELECT, JOIN, GROUP BY, filtering, sorting, and basic query optimisation ideas. You should also be able to explain how Python code interacts with the database layer.

How should I answer behavioural questions in a TCS interview?

Use a clear structure like situation, action, and result. Keep the example relevant to teamwork, delivery, client communication, or problem solving. A short, specific answer is better than a long story with no outcome.

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This is an independent preparation guide based on commonly reported interview patterns. The process varies by team, location and year. Interview AI Helper is not affiliated with TCS.

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