What are the most asked Python interview questions for freshers?

What are the most asked Python interview questions for freshers?

The Python questions freshers in India actually hear are not trick questions about the interpreter source. They cluster around data types, the difference between identity and equality, functions and mutable defaults, decorators and generators at a usable level, exceptions, modules, and one project you can narrate. If you can answer the list below out loud, with a tiny example, you are prepared for a typical fresher technical round. If you can only recognise the words, you are not.

A useful answer has three beats: the direct difference, a one-line example, and the mistake you will not make. Interviewers stop candidates who recite a definition and then freeze when asked “show me.” Practise with a blank file, not with a highlighted PDF.

These notes are a preparation guide for students and freshers, including Asmorix learners in Pondicherry. They are not a promise that a company will ask this exact list, and they are not a substitute for writing the code yourself.

What is the difference between a list and a tuple?

A list is mutable. A tuple is immutable. You can append, assign, and delete items in a list. You cannot change a tuple after you create it. Both are ordered and both can hold mixed types. Use a list when the collection will change, such as rows you are still cleaning. Use a tuple when the collection is a fixed record, such as a latitude and longitude, or when you need a dictionary key. Lists are not valid dictionary keys because they can change. Tuples can be keys if every item inside them is also hashable.

Say it this way: “If I need to add a student to a batch, I use a list. If I am storing a coordinate that should not be edited by accident, I use a tuple.” Then write points = [(1, 2), (3, 4)] and explain that the outer container is a list of tuples. The follow-up is usually “what happens if I try t[0] = 1?” The honest answer is a TypeError. Do not say “it updates.”

A second follow-up is memory and speed. You do not need a benchmark speech. It is enough to say tuples are slightly lighter and signal that the data should stay fixed. Choosing a tuple for a value that must change is the bug. Choosing a list for a dictionary key is the other bug.

What is the difference between is and == in Python?

== asks whether the values are equal. is asks whether two names point at the same object in memory. Two lists can be equal and still fail an is check, because they are two objects with the same contents. This shows up in interviews when someone writes if name is "Priya" or compares numbers carelessly. Compare values with ==. Use is for the singleton cases you actually mean: is None, and occasionally is True or is False when you really mean the boolean object.

CPython sometimes reuses small integers and some strings, so a is b can look true for a = 256 and b = 256 and false for larger integers. That is an implementation detail, not a rule to code against. If an interviewer shows it, say “identity is not equality, and I will not rely on interning.” That sentence is stronger than pretending you memorised the cache limits.

The fresher mistake is using is because a tutorial used it near None and then copying it onto every comparison. Your spoken rule can be one line: equality for values, identity for None.

What are *args and **kwargs?

*args collects extra positional arguments into a tuple. **kwargs collects extra keyword arguments into a dictionary. The names args and kwargs are convention. The stars are the syntax. A function defined as def log(level, *args, **kwargs) can be called with a level, any number of message pieces, and any number of named fields. Inside the function you loop over the tuple and you read the dictionary by key.

Interviewers ask this because wrappers and decorators use it. They also ask to see whether you know the order: normal parameters, then *args, then keyword-only parameters, then **kwargs. You do not need the full grammar on a whiteboard if you can place a decorator that passes arguments through. Say “the wrapper accepts anything and forwards it with func(*args, **kwargs).”

The follow-up is often “when would you not use this?” Use explicit parameters when the caller must know the names. A public function with only **kwargs hides the contract. Stars are for forwarding and for a genuinely variable tail, not for avoiding design.

What is a decorator?

A decorator is a function that takes a function and returns a new function, usually to add behaviour before or after the original call. The @log line above a definition is syntax for function = log(function). Freshers meet this in Flask or FastAPI routes, in timing a slow call, and in checking a login. You should be able to write a tiny decorator that prints “start” and “end” around a function and still returns the original result.

Preserve the name and docstring with functools.wraps. If you forget wraps, debugging shows the wrapper’s name and the interviewer who knows Flask will notice. Arguments travel through *args and **kwargs. If the decorator itself needs a parameter, such as @roles("admin"), you are looking at a function that returns a decorator. Explain that in two steps rather than one tangled sentence.

Do not claim you “use decorators everywhere.” Name one place in your project: a route, a timer, or a permission check. If you have never written one, say so and write one in the interview. A small honest example beats a borrowed diagram.

What is a generator, and how is it different from a list?

A generator produces values one at a time and pauses. A list holds every value in memory now. You write a generator with yield, or you write a generator expression in parentheses. Reading a large file line by line, or walking a pagination API, is the fresher example that makes sense. Building a list of a million rows “because I might need them” is the example that fails.

Say: “yield returns a value and resumes later. return ends the function.” The follow-up is often “what does next() do when the generator is finished?” It raises StopIteration, and a for loop hides that for you. You can also mention that generators are single-pass. After you consume one, you start again by calling the generator function, not by rewinding the old object.

List comprehensions and generator expressions look similar. Square brackets build the list. Parentheses build the generator. If the interviewer asks which you would use for a report of ten rows, either is fine. If they ask about a multi-gigabyte export, choose the generator and say why.

How should a fresher explain exceptions?

An exception is how Python reports that a step could not finish. You catch the specific error you can handle, you do something useful, and you do not swallow everything with a bare except:. Catching Exception around a tiny block is sometimes necessary at a program boundary, where you log and exit cleanly. Catching it around one line to hide a KeyError is how bugs survive into production.

Know the shape: try, except ValueError as exc, optional else for the success path, and finally for cleanup that must run either way. finally is the right place to close a file if you did not use a with statement. Prefer with open(...) as handle so the close is not your job. Raising your own error is allowed when the caller passed nonsense: raise ValueError("age must be positive").

The interview follow-up is “what is the difference between an error you catch and an error you let travel?” If you can recover, catch it. If the caller must decide, let it travel. Printing “something went wrong” and continuing as if the row was saved is the failure mode to describe, because you have seen it.

Why is a mutable default argument a classic bug?

Default values are created once, when the function is defined, not on every call. A default list or dictionary is therefore shared by every caller who omits that argument. The function looks pure and then “remembers” earlier appends. The fix is to default to None and create a new list inside the function when the argument was missing.

Write the broken version and the fixed version without looking. Interviewers use this question because it separates people who have been surprised by the bug from people who memorised “mutable means changeable.” Explain the surprise in one sentence: “The default object lives with the function, so the second call sees the first call’s data.”

The same idea applies to a default dictionary used as a cache by accident. If you want a cache, say so and store it on purpose. If you want a fresh container, build it inside the call.

What is the difference between a shallow copy and a deep copy?

A shallow copy makes a new outer container and still shares the inner objects. A deep copy walks the structure and copies nested objects too. Assignment copies no container at all. It only adds another name for the same object. list.copy(), a slice, and copy.copy are shallow. copy.deepcopy is deep. For a list of numbers, shallow is enough because numbers cannot be edited in place. For a list of dictionaries, a shallow copy still shares those dictionaries, so a change through one list appears in the other.

Say which one your project needed. A fresher who deep-copies a huge nested payload on every request will also be asked about cost. Deep copy is correct when you truly need isolation. It is expensive when the structure is large. The interview is happier with that trade-off than with “I always deepcopy to be safe.”

How do you explain classes, inheritance, and composition?

A class bundles data and behaviour. An instance is one object built from that class. Inheritance means a child class reuses and extends a parent. Composition means an object holds another object and calls it. Freshers overuse inheritance because textbooks lead with animals and shapes. In a small project, composition is often clearer: an Order has a PaymentClient, it does not inherit from the payment gateway.

Be ready for __init__, self, and super(). self is the instance. __init__ sets up that instance. super() calls the parent implementation so you do not duplicate its setup. You should also know that a method is just a function that receives the instance. If you cannot add one method to a class in the interview, the definition is not ready.

Name one class from your project and one reason it exists. “I made a class because OOP is important” is not a reason. “I made an Invoice so tax calculation and line items stay together” is a reason.

What should a fresher say about the GIL?

The Global Interpreter Lock is a CPython mechanism that lets only one thread execute Python bytecode at a time in a process. It makes many memory operations simpler and it limits CPU-bound threading. It does not mean “Python cannot do two things.” Threads still help when the program is waiting on disk, the network, or a database, because the lock is released during many of those waits. CPU-bound work that must run truly in parallel often uses processes, or it uses a library that releases the lock in native code.

Stop there unless the interviewer pushes. A fresher who starts a twenty-minute history of interpreter design usually cannot write a context manager. The useful sentence is: “I use threads for waiting, processes or a vectorised library for heavy CPU work, and I do not assume threads will speed up a tight Python loop.”

What is a module, a package, and a virtual environment?

A module is a Python file you can import. A package is a folder of modules, usually with an __init__.py so imports stay explicit. A virtual environment is a private set of installed libraries for one project, so project A can use one version of a library and project B can use another. Freshers who install everything into the system Python then break the next project. Say you create an environment, you install from a requirements file, and you do not commit the environment folder.

Know if __name__ == "__main__". It lets a file run as a script and still be imported without running that script. Interviewers like this because it shows you have organised code, not only pasted into one file. If your project is one file named final.py, say what you would split out next: data loading, calculation, and the command entry.

When is a comprehension the right tool?

A list comprehension builds a list from a loop in one expression. A dictionary comprehension builds a dictionary. They are readable when the transformation is one step and the filter is obvious. They are the wrong tool when the body needs three statements, an exception handler, and a log line. In that case a normal loop is kinder to the next reader, including the interviewer.

Be able to translate both ways. Write a loop that keeps positive numbers and squares them, then write the comprehension. Mention that a comprehension still builds the whole list, so a huge source might want a generator expression instead. That single link, back to the generator answer, makes you sound consistent rather than like someone who memorised separate flashcards.

How do you walk through a Python project in the interview?

Use five sentences. Who the user is. What they could not do before. What you stored, in a list, a file, a SQLite table, or an API. What broke, including one real bug such as the mutable default or a wrong join. What you would change with another week. Bring the code on a laptop or a link you can open, and be ready to change a requirement live. If the interviewer says “now ignore cancelled orders,” you should be able to point at the function that filters them.

Skip the tour of every file. Start at the entry point and follow one request. If you used pandas, say which column you grouped and which wrong assumption you fixed, such as duplicate customer ids. If you did not use pandas, do not pretend. Core Python plus a clear data shape is enough for many fresher rounds. Flask or Django is a plus only if you can explain a route, a template or a JSON response, and where the query lives.

Students preparing with Python training in Pondicherry should leave the course with this story rehearsed, not only with exercises checked off. Pair it with SQL, because Python interviews for analyst and developer freshers both drift into queries. The companion list is the SQL interview questions guide. If the role is heavier on problem solving, use the DSA interview questions guide for arrays, hashing, and complexity, and keep Python as the language you write them in.

How should a fresher practise this list?

Day one is types, is versus ==, and the mutable default, written from a blank file. Day two is functions, *args, and a decorator that only prints. Day three is generators and files. Day four is exceptions and with. Day five is one class from your project and a composition example. Day six is the five-sentence project story, recorded once. Day seven is a mixed mock: you answer, then you code the smallest version. If you cannot code it, the answer is not finished, even if the sentence sounded smooth.

Common fresher questions also include the HR frame around this technical core: why Python, why this role, and what you will do in the first ninety days. Those prompts live in the most common interview questions guide. Use that page for the conversation around the code, and use this page for the code. A company rarely hires the person who can only do one of the two.

When you are comparing roles after the interview, read offers as monthly in-hand, not as a headline. The CTC full form page is the worksheet. Python skill gets you into the conversation. The annexure tells you what the conversation pays.

Mistakes that make a correct Python answer fail

Reading the definition and never typing it. Using is for every comparison. Catching every exception and printing a smile. Building a million-row list when a generator would do. Putting a list in a default argument. Describing a class hierarchy you cannot draw. Naming five frameworks and then failing to open a file with with. And saying “Python is easy” as if that were your project story. The interviewer is not scoring affection for the language. They are scoring whether the next small change is safe in your hands.

A clean recovery, if you blank, is to restate the question and write the smallest example. Silence while you type a four-line function is better than a paragraph that dodges the question. If you do not know, say which part you know and which part you would look up. Inventing a method name is worse than a gap, because the interviewer will ask you to call it.

After the round, write down the question you missed and code it the same day. A fresher who does that three times moves faster than a fresher who collects another PDF. Keep this list as a checklist, then close the PDF and use the interpreter.

Related reading: SQL interview questions, DSA interview questions, Most common interview questions, Java interview questions.

These answers are a preparation guide for students and freshers, including learners at Asmorix in Pondicherry. A company may ask a different follow-up. This page is not a leaked paper and it is not a promise of a result. Say the short answer, show one example, and revise the same day any question you could not code or click.