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Entry Python Interview Questions

Curated Entry-level Python interview questions for developers targeting entry positions. 20 questions available.

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Python Interview Questions & Answers

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Welcome to our comprehensive collection of Python interview questions and answers. This page contains expertly curated interview questions covering all aspects of Python, from fundamental concepts to advanced topics. Whether you're preparing for an entry-level position or a senior role, you'll find questions tailored to your experience level.

Our Python interview questions are designed to help you:

  • Understand core concepts and best practices in Python
  • Prepare for technical interviews at all experience levels
  • Master both theoretical knowledge and practical application
  • Build confidence for your next Python interview

Each question includes detailed answers and explanations to help you understand not just what the answer is, but why it's correct. We cover topics ranging from basic Python concepts to advanced scenarios that you might encounter in senior-level interviews.

Use the filters below to find questions by difficulty level (Entry, Junior, Mid, Senior, Expert) or focus specifically on code challenges. Each question is carefully crafted to reflect real-world interview scenarios you'll encounter at top tech companies, startups, and MNCs.

Questions

20 questions
Q1:

What are Python's key features?

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Answer

Python features:
• Interpreted, dynamically typed, high-level
• Object-oriented and functional support
• Large standard library and community
• Automatic memory management
• Portable and readable syntax
Quick Summary: Python key features: simple and readable syntax, dynamically typed, interpreted, garbage-collected memory management, extensive standard library, multi-paradigm (OOP, functional, procedural), large ecosystem (pip packages), cross-platform, and strong community. Popular for web (Django, Flask), data science (pandas, numpy), ML/AI, scripting, and automation.
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Q2:

What is the difference between Python 2 and Python 3?

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Answer

Key differences:
• print is a function in Python 3
• / returns float in Python 3
• Strings are Unicode by default
• Most libraries support Python 3 only
Quick Summary: Python 2 (EOL 2020): print is a statement, integer division by default, str is bytes, unicode is separate type, range() returns a list. Python 3: print() is a function, true division by default, str is unicode, bytes is separate, range() returns a lazy iterator. Python 3 is the only supported version now - all new code should use Python 3.
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Q3:

What are Python data types?

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Answer

Python data types:
• Numeric: int, float, complex
• Sequence: list, tuple, range
• Text: str
• Mapping: dict
• Set: set, frozenset
• Boolean: bool
• NoneType: None
Quick Summary: Python built-in data types: int, float, complex (numbers), str (text), bool (True/False), list (mutable ordered sequence), tuple (immutable ordered sequence), dict (key-value mapping), set (unique unordered elements), frozenset (immutable set), bytes, bytearray, NoneType. Everything in Python is an object including these primitives.
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Q4:

Explain Python variable scoping rules.

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Answer

Scopes:
• Local
• Enclosing
• Global
• Built-in
Use global and nonlocal to modify outer variables.
Quick Summary: Python scoping follows LEGB rule: Local (inside function), Enclosing (in outer function for nested functions), Global (module level), Built-in (Python built-ins). Variables are looked up in this order. global keyword declares a variable is from global scope. nonlocal keyword (Python 3) modifies variable from enclosing function scope.
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Q5:

How does Python manage memory?

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Answer

Python uses automatic memory management:
• Reference counting
• Garbage collector for circular references
• No manual allocation/deallocation needed.
Quick Summary: Python uses reference counting as primary memory management - each object tracks how many references point to it. When count drops to 0, memory is freed immediately. A cyclic garbage collector handles reference cycles (A references B, B references A). CPython manages memory in pools and arenas via pymalloc. You can force GC with gc.collect() but rarely need to.
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Q6:

What are lists, tuples, sets, and dictionaries?

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Answer

List: ordered, mutable
Tuple: ordered, immutable
Set: unique elements, unordered
Dictionary: key-value pairs, mutable
Quick Summary: List: mutable, ordered, allows duplicates [1, 2, 3]. Tuple: immutable, ordered, allows duplicates (1, 2, 3) - faster than list, used for fixed data. Set: mutable, unordered, unique elements {1, 2, 3} - O(1) membership test. Dict: mutable, key-value pairs, keys must be hashable {key: value}. Choose based on: mutability, order needs, uniqueness, and lookup pattern.
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Q7:

Explain Python functions.

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Answer

Functions use def keyword.
Support default, keyword, and variable-length args.
Support lambdas and decorators.
Functions are first-class objects.
Quick Summary: Python functions are first-class objects - assignable to variables, passable as arguments, returnable from other functions. def defines a function, lambda creates anonymous single-expression functions. Default parameter values, *args (variable positional args), **kwargs (variable keyword args). Closures capture variables from enclosing scope. Nested functions, decorators, and higher-order functions are all supported.
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Q8:

What are Python modules and packages?

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Answer

Module: single .py file.
Package: directory with __init__.py.
Provide modularity and namespace management.
Quick Summary: Module: a single .py file. Package: a directory with __init__.py containing multiple modules. Import with import module or from module import name. Python searches sys.path for modules. pip installs third-party packages from PyPI into site-packages. Virtual environments (venv) isolate packages per project. __init__.py can define what the package exposes.
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Q9:

Explain Python exception handling.

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Answer

Use try/except/finally/else.
Create custom exceptions by subclassing Exception.
Ensures robust error handling.
Quick Summary: Python exception handling: try block runs code that might fail. except catches specific exceptions (except ValueError) or all exceptions (except Exception). else runs if no exception occurred. finally always runs (cleanup). raise raises an exception manually. Use specific exception types - never bare except: which hides bugs. Create custom exceptions by subclassing Exception.
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Q10:

What are Python decorators?

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Answer

Decorators modify functions dynamically.
Use @decorator syntax.
Common for logging, authorization, caching.
Quick Summary: Decorators are functions that wrap another function to add behavior without modifying its code. Applied with @decorator syntax above function definition. Common uses: logging, timing, authentication, caching. A decorator takes a function, returns a new function that calls the original. Use functools.wraps(func) to preserve the wrapped function's name and docstring.
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Q11:

How does Python handle file operations?

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Answer

Use open() with modes: r, w, a, rb, wb.
Use with statement for auto cleanup.
Supports read(), write(), seek().
Quick Summary: Python file I/O: open(filename, mode) returns a file object. Modes: "r" (read), "w" (write), "a" (append), "b" (binary), "x" (exclusive create). Use with open(...) as f: (context manager) to automatically close the file even if an exception occurs. Read: f.read(), f.readline(), f.readlines(). Write: f.write(). Use pathlib.Path for modern path handling.
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Q12:

Explain Python iterators and generators.

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Answer

Iterator: implements __iter__() and __next__().
Generator: uses yield for lazy evaluation.
Efficient for large datasets.
Quick Summary: Iterator: object with __iter__() and __next__() methods that produces values one at a time. Generator: function using yield that automatically becomes an iterator - pauses at each yield and resumes from there. Generators are lazy (compute on demand, memory efficient). Use generators for large sequences where computing all values upfront would be expensive. Generator expression: (x**2 for x in range(10)).
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Q13:

How do Python comprehensions work?

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Answer

List: [x*x for x in range(5)]
Set: {x for x in range(5)}
Dict: {x: x*x}
Fast, concise, readable.
Quick Summary: Comprehensions create collections concisely. List: [expr for item in iterable if condition]. Dict: {k: v for k, v in items}. Set: {expr for item in iterable}. Generator: (expr for item in iterable) - lazy, memory efficient. More readable and often faster than equivalent for loops. Avoid deeply nested comprehensions - they become hard to read.
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Q14:

Explain *args and **kwargs.

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Answer

*args: variable positional args.
**kwargs: variable keyword args.
Useful for flexible function definitions.
Quick Summary: *args captures any number of positional arguments as a tuple. def func(*args): receives func(1, 2, 3) as args = (1, 2, 3). **kwargs captures keyword arguments as a dict. def func(**kwargs): receives func(a=1, b=2) as kwargs = {"a": 1, "b": 2}. Order in function signature: positional, *args, keyword-only, **kwargs. Unpacking: func(*mylist) and func(**mydict).
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Q15:

What are Python's built-in structures for stacks and queues?

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Answer

Stack: list with append/pop.
Queue: collections.deque.
queue.Queue for thread-safe queues.
Quick Summary: Stack: use a list with .append() (push) and .pop() (pop from end) - O(1). Or collections.deque for thread-safe stack. Queue (FIFO): collections.deque with .append() and .popleft() - O(1) both ends. queue.Queue for thread-safe producer-consumer. PriorityQueue: queue.PriorityQueue or heapq module. Avoid using list.pop(0) for queue - it's O(n).
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Q16:

How does Python support OOP?

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Answer

Supports classes, inheritance, polymorphism.
Uses __init__, __str__, __repr__, and super().
Supports multiple inheritance.
Quick Summary: Python supports OOP with classes using class keyword. Features: encapsulation (instance variables and methods), inheritance (class Child(Parent)), polymorphism (method overriding), multiple inheritance (class C(A, B)). Special methods (__init__, __str__, __repr__, __len__) customize behavior. Python is duck-typed - objects are used based on behavior, not type.
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Q17:

How does Python handle memory for objects?

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Answer

Uses reference counting.
Circular refs handled by GC.
Immutable objects are interned for performance.
Quick Summary: Python objects live on the heap. Each object has a reference count tracked by CPython. When you assign a = [1,2,3], the list object is created on the heap and "a" is a name binding to it. Multiple names can reference the same object (a = b = [] means a and b point to the same list). Objects are garbage collected when no references remain. Large objects may need explicit gc.collect().
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Q18:

How do you manage packages in Python?

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Answer

Use pip to install packages.
Use virtual environments for isolation.
requirements.txt for dependency tracking.
Quick Summary: Package management: pip install packagename installs from PyPI. pip freeze > requirements.txt exports dependencies. pip install -r requirements.txt installs from file. Virtual environments: python -m venv venv creates isolated environment. activate it before installing packages. Poetry and Pipenv are modern alternatives that handle virtual envs and dependencies together. conda for data science environments with binary packages.
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Q19:

Explain Python's Global Interpreter Lock (GIL).

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Answer

GIL allows only one thread to execute Python bytecode at a time.
Limits CPU-bound threading.
I/O-bound threads work well.
Multiprocessing bypasses GIL.
Quick Summary: The GIL (Global Interpreter Lock) is a mutex in CPython that allows only one thread to execute Python bytecode at a time. This means CPU-bound multi-threaded code doesn't actually run in parallel. I/O-bound threads still benefit (GIL is released during I/O). For CPU-bound parallelism, use multiprocessing (separate processes, no GIL) or C extensions that release the GIL.
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Q20:

What are __init__.py and __main__ used for?

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Answer

__init__.py marks a folder as a package.
__main__ allows a script to run directly using main block.
Quick Summary: __init__.py makes a directory a Python package (can be empty). Controls what gets imported with from package import *. __main__.py lets a package be run directly: python -m mypackage. if __name__ == "__main__": guard runs code only when script is executed directly (not when imported as a module). Essential for making scripts work both as executables and importable modules.
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Curated Sets for Python

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People Also Ask - Related Python Questions

What are Python's key features?
What is the difference between Python 2 and Python 3?
What are Python data types?
Explain Python variable scoping rules.
How does Python manage memory?
What are lists, tuples, sets, and dictionaries?
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