June 5, 2025
Whether you’re preparing for your very first tech interview or aiming to land a junior Python developer role, you’re in the right place.
Python remains one of the most popular programming languages in 2025, not just for its simplicity, but because it powers everything from automation scripts and web apps to machine learning models and cloud systems. That makes it a go-to skill for a wide range of roles, especially for freshers breaking into software development, data, or DevOps.
But here’s the thing: interviewers don’t just test your syntax. They want to know how well you understand core concepts, how you think through problems, and whether you can communicate your logic clearly.
This blog compiles some of the most frequently asked Python interview questions for freshers, carefully organized from beginner to advanced. Whether you’re from a CS background, a coding bootcamp, or self-taught, these questions will help you:
Instead of surface-level Q&A, you’ll find detailed explanations behind each answer, so you can walk into interviews not just knowing what to say, but why it matters.
Whether you’ve just started learning or already finished a few projects, this guide will help you feel more interview-ready.
Answer:
Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms (procedural, object-oriented, functional) and has a massive ecosystem of libraries across domains like web development, data science, automation, and machine learning.
Why it's widely adopted:
It's a language that grows with you, from writing your first script to building scalable systems.
Answer:
Python’s design is guided by the Zen of Python, a set of principles that promote code clarity and simplicity. Key ideas include:
These principles encourage developers to:
This mindset makes Python code easy to read, debug, and scale—even in large teams.
Answer:
In Python, variable types are determined at runtime, not declared in advance. This is called dynamic typing.
Implications:
In real-world development, dynamic typing encourages flexibility, but also demands careful coding and testing. Tools like type hints (def add(a: int, b: int) -> int) and static analyzers (e.g., mypy) help mitigate risks in large codebases.
Answer:
Python code is executed line-by-line by the interpreter, rather than being compiled into machine code before execution.
Pros:
Cons:
This trade-off makes Python ideal for development speed, automation, and data workflows, even if it's not always the fastest language.
Answer:
Python offers a wide range of built-in types, broadly categorized into:
Understanding the characteristics of each type (e.g., mutability, hashability, ordering) is essential when choosing the right data structure for a task.
Answer:
The key difference is mutability:
Use cases:
Tuples are also slightly faster and use less memory, making them suitable when immutability is a design requirement.
Answer:
A set is an unordered collection of unique elements, whereas a list maintains order and allows duplicates.
Key differences:
Use sets when:
Answer:
Slicing allows you to extract parts of sequences using the [start:stop:step] syntax.
Defaults:
Common use cases:
Slicing helps avoid verbose loops and keeps your code clean.
Answer:
Understanding this distinction is important when comparing objects, especially mutable ones like lists or custom classes.
Answer:
This condition allows a Python file to be both:
It’s commonly used to:
This helps in keeping your code modular and reusable.
Answer:
To run a Python script like a shell command on Unix:
#!/usr/bin/env python3
chmod +x script.py
./script.py
This is especially useful in DevOps, automation, or command-line tool development.
Answer:
PEP 8 is the official style guide for writing readable, consistent Python code.
It covers:
Following PEP 8 ensures:
Popular tools like Black, Flake8, or Pylint help enforce these standards automatically.
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Answer:
Python uses the LEGB rule to resolve variable names:
This rule defines how Python looks up variables from inner to outer scopes. Understanding it helps prevent shadowing and unintended overrides.
Answer:
If a local variable has the same name as a global one, the local version shadows the global one within that scope.
To modify the global variable inside a function, you must explicitly declare it with the global keyword. However, doing so is generally discouraged unless necessary.
Use function arguments or return values to avoid side effects and improve readability.
Answer:
Use nonlocal when writing closures or decorators where you want to persist changes across multiple calls without using global state.
Answer:
Each one helps control flow in loops and conditionals, but using them excessively can reduce code clarity, so they should be applied purposefully.
Answer:
Functions are defined using the def keyword. Default arguments let you provide fallback values if no input is given for certain parameters.
Example:
The default is evaluated once at definition time, so avoid using mutable objects (like lists) as defaults to prevent shared state issues.
Answer:
They’re useful when:
Answer:
As of Python 3.8+, function arguments can be explicitly declared as:
This gives finer control over how functions are called.
Example:
Here:
This prevents ambiguity and improves readability in APIs.
Answer:
Python does not support traditional function overloading based on differing argument types or counts.
Instead, you handle it by:
For more complex cases, consider single dispatch with functools.singledispatch to simulate function overloading by type.
Answer:
Use:
Answer:
__init__() is the constructor method. It initializes an object’s attributes at creation.
It’s automatically called when an object is instantiated and typically sets up the object’s internal state using arguments passed to the class.
Though not strictly required, using __init__() helps ensure every instance starts in a valid, usable state.
Answer:
self refers to the current instance of the class. It must be the first parameter of instance methods and is used to access or modify instance variables and other methods.
While not a keyword, self is a naming convention that clarifies you’re working with instance-specific data.
It’s essential to ensure each object maintains its own independent state.
Answer:
In Python:
Each object holds its own values for the attributes defined in the class, allowing for scalable and reusable design.
Answer:
You can define a class with attributes like name and price, and a method that calculates the total with tax.
The key is encapsulating logic within the object, so every product "knows" how to compute its total cost.
This is a common exercise to assess object-oriented thinking, even without requiring complex syntax.
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Answer:
This affects:
Tip: Avoid using mutable objects like lists or dicts as default function arguments.
Answer:
Understanding this helps prevent subtle bugs, especially when comparing objects like lists, strings, or custom class instances.
Answer:
Use modules for organizing features logically, and packages to structure larger applications or reusable libraries.
Answer:
A dictionary is an unordered collection of key-value pairs. Keys must be unique and hashable; values can be any type.
Use cases:
Dictionaries are one of Python’s most versatile and optimized data types.
Answer:
Use a set when:
Use a list when:
Answer:
Choosing the right type depends on:
Answer:
Python includes many high-level tools that simplify iteration:
Using these avoids manual loops and keeps code clean and idiomatic.
Answer:
Use the copy module:
Understanding this distinction is critical when working with nested lists or complex data structures.
Answer:
Python uses automatic memory management:
Memory profiling tools (like sys.getsizeof()) and generator-based techniques help optimize memory usage in large applications.
Answer:
These are special methods used to define how an object is represented:
Best practice: Make __repr__() accurate and detailed. __str__() is optional but improves readability.
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Answer:
Duck typing means Python focuses on behavior rather than type.
“If it walks like a duck and quacks like a duck, it’s a duck.”
Instead of checking types, you assume objects support expected methods:
# Instead of checking isinstance(x, list)
# Just do: for item in x:
This encourages more flexible, reusable, and loosely coupled code.
Answer:
A lambda is an anonymous, one-line function used where defining a full function would be overkill.
Use cases:
Avoid complex logic in lambdas—they’re best for short, simple expressions.
Answer:
A descriptor is an object that defines behavior for attribute access via:
Descriptors power many core Python features, such as:
They're an advanced but powerful way to control how attributes are managed in classes.
Answer:
Python supports functional programming, which treats functions as first-class citizens and emphasizes immutability and declarative style.
These are useful for data transformations, but can become hard to read. Prefer list comprehensions or generator expressions when clarity matters.
Answer:
Decorators are functions that modify other functions or methods. They’re often used to add reusable behavior like:
They use the @decorator_name syntax and are widely used in frameworks like Flask (@app.route) or Django (@login_required).
Decorators can improve separation of concerns by keeping cross-cutting logic out of core functions.
Answer:
A context manager manages resources like files or connections, ensuring proper setup and cleanup. It's used with the with statement.
When the block starts, __enter__() runs. When it ends (even due to an exception), __exit__() runs.
This ensures resource safety without manually closing or cleaning up. You can write custom context managers using:
Answer:
unittest is Python’s built-in framework for unit testing. It provides tools to:
Testing helps:
Many developers also use pytest for its concise syntax and plugin ecosystem.
Answer:
All three serve different phases of development — from validation to diagnosis.
Answer:
Common debugging techniques include:
Good debugging also involves reading error messages carefully and isolating problems through elimination.
Answer:
Asynchronous programming lets you handle I/O-bound operations without blocking the main thread. This is useful when working with:
It’s not a performance boost for CPU-bound tasks — for that, use multiprocessing.
Answer:
The GIL is a mutex that allows only one thread to execute Python bytecode at a time (in CPython). This limits parallel execution in CPU-bound multithreaded programs.
Impact:
Understanding the GIL helps you choose the right concurrency model for your workload.
Answer:
Monkey patching means modifying or extending code at runtime, often used to override behavior without modifying the original source.
It can be useful for:
However, it can also introduce:
Use monkey patching cautiously and document it well.
Answer:
A metaclass is a class that creates classes. In Python, everything is an object — including classes, which are instances of metaclasses (by default, type).
Use cases:
Metaclasses are powerful but complex — use only when simpler alternatives (like decorators or class factories) don't suffice.
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Answer:
Python encourages modular design through:
Best practices include:
This approach promotes maintainability, testability, and team collaboration.
Answer:
Some well-known idiomatic constructs in Python include:
These idioms reduce boilerplate and reflect "Pythonic" style, making your code cleaner and easier to read.
Answer:
Virtual environments isolate project dependencies, ensuring each project has its own version of packages. This prevents version conflicts and promotes reproducibility.
Commands:
Use virtual environments for every project, especially when using third-party libraries.
Answer:
Use:
Tools like pipenv or poetry provide additional control and lock files.
Answer:
Using __slots__ limits what attributes can be added to an object and avoids the overhead of a dynamic __dict__. It reduces memory usage, especially in large collections of objects.
Use it in performance-sensitive, memory-heavy applications — but be aware of its limitations (e.g., no adding new attributes dynamically).
Answer:
Being aware of these helps you avoid subtle bugs and write more robust code.
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Preparing for Python interviews as a fresher can feel overwhelming at first — but it doesn't have to be. The key isn't memorizing 100 questions. It's about understanding core principles, thinking like a developer, and being able to communicate your logic clearly.
If you've gone through this guide, you now have a solid foundation in everything from Python basics to object-oriented programming, data structures, and advanced concepts like decorators, async, and memory management. These aren't just interview questions — they’re building blocks of real-world development.
But to truly stand out in interviews, you need more than knowledge. You need practice, feedback, and confidence — and that’s where mentorship makes all the difference.
Whether you're prepping for placements, switching careers, or aiming for your first developer role, Topmate connects you with industry professionals who’ve been on both sides of the hiring table.
And if you need personalized guidance or mentorship, we at Topmate connect you with experienced professionals who can offer one-on-one mock interviews, code reviews, and real-world advice tailored to your career goals.
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