Python Interview Question Categories
Python interviews cover these core areas depending on your role:
- Core Python: data types, control flow, functions, OOP, exceptions
- Advanced Python: decorators, generators, context managers, metaclasses
- Data structures: lists, dicts, sets, tuples — when to use which
- Libraries: pandas, NumPy, requests, datetime — for data/analytics roles
- Coding challenges: algorithms and data structures using Python
- System design: how to architect a Python application
Core Python Must-Know Concepts
These basics are tested in every Python interview:
- Mutable vs. immutable types (list vs. tuple vs. frozenset)
- List comprehensions, dict comprehensions, generator expressions
- Lambda functions and when to use them vs. named functions
- *args and **kwargs — variable-length arguments
- Python's GIL (Global Interpreter Lock) — why it matters for threading
- Shallow copy vs. deep copy — the copy module
- Python memory management and garbage collection
- The difference between == and 'is'
OOP Concepts in Python
Object-Oriented Programming questions appear in almost every mid-to-senior Python interview:
- Classes, objects, __init__, and instance vs. class attributes
- Inheritance, multiple inheritance, and Method Resolution Order (MRO)
- Encapsulation: name mangling with __ prefix
- Polymorphism: method overriding and duck typing
- Abstract classes with abc module
- Dunder/magic methods: __str__, __repr__, __len__, __eq__
- Properties with @property decorator
- Dataclasses for clean data containers
Pandas & Data Manipulation (for Data Roles)
Essential for data analyst, data engineer, and data scientist roles:
- DataFrame creation, slicing (loc vs. iloc), and filtering
- groupby() + agg() for aggregation
- merge(), join(), concat() — differences and use cases
- apply() vs. map() vs. applymap()
- Handling missing data: fillna(), dropna(), interpolate()
- pivot_table() for cross-tabulation
- read_csv(), to_csv(), read_excel() — file I/O
- Memory optimisation with dtypes
Advanced Python Topics
Senior-level questions test deeper Python internals:
- Decorators: function decorators, class decorators, stacking decorators
- Generators and yield — memory-efficient iteration
- Context managers and the 'with' statement
- Asyncio: async/await for concurrent programming
- Functools: reduce(), partial(), lru_cache()
- Threading vs. multiprocessing — when to use which
Common Interview Questions & Answers
Q1. What is the difference between a list and a tuple?
Lists are mutable (can be changed after creation) and use square brackets. Tuples are immutable (cannot be changed) and use parentheses. Tuples are faster and use less memory, making them ideal for fixed collections of data.
Mention use cases: tuples for DB records or function return values.
Q2. What are decorators in Python?
Decorators are functions that wrap other functions to add functionality without modifying the original code. They use the @syntax. Common examples: @staticmethod, @classmethod, @property, @functools.lru_cache for memoisation.
Be ready to write a simple decorator from scratch.
Q3. What is the difference between deepcopy and shallow copy?
Shallow copy creates a new object but copies only references to nested objects — changes to nested objects affect both. Deep copy recursively copies all objects — completely independent. Use copy.deepcopy() for deep copy.
Demonstrate with a list of lists example.
Q4. Explain Python's GIL.
The Global Interpreter Lock is a mutex that allows only one thread to execute Python bytecode at a time. This means Python threads cannot achieve true parallelism for CPU-bound tasks. For CPU-bound tasks, use multiprocessing instead. For I/O-bound tasks, threading or asyncio work well despite the GIL.
This shows senior-level Python knowledge. Know it well.
Common Mistakes to Avoid
Confusing mutable default arguments (never use mutable objects as default function args)
Using global variables instead of proper function parameters
Not understanding the difference between is and == for object comparison
Modifying a list while iterating over it
Not knowing when to use a generator vs. a list comprehension
Expert Tips
Practice Python on HackerRank and LeetCode daily — 1 problem per day
Know your time complexities for common Python operations (list append is O(1), list insert is O(n))
Use type hints in your interview code — shows professionalism
Practice explaining code out loud as you write it
Pre-Interview Checklist
6 itemsFrequently Asked Questions
Is Python 2 still relevant for interviews?
Almost never — Python 2 reached end-of-life in 2020. Focus on Python 3.9+ features. Mentioning Python 3.10+ features like structural pattern matching (match/case) shows you are current.
What Python libraries should I know for data interviews?
Pandas, NumPy, and Matplotlib are the core three. Add SQLAlchemy for database, Scikit-learn for ML roles, and PySpark for data engineering positions.
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