Top 6 Python Programmer Interview Questions (2026)

Behavioral questions

Past-experience questions. Answer with the STAR method: Situation, Task, Action, Result.

1. 1

   Tell me about a Python project or script you built that automated something meaningful.

   What they're really asking: Practical application: Python's primary use case in many business environments is automation — file processing, API calls, data transformation, report generation. A specific automation project with measurable impact demonstrates applied Python more than any technical question.

Technical questions

Skill and knowledge checks. Be specific — name tools, tolerances, and methods.

1. 1

   What's the difference between a list and a tuple in Python, and when would you use each?

   What they're really asking: Python fundamentals: lists are mutable (you can add, remove, change elements); tuples are immutable (fixed after creation). Tuples are faster for iteration, can be used as dictionary keys, and signal to other developers that this data shouldn't change. Use tuples for fixed collections (coordinates, RGB values, database row returns) and lists for collections that need modification.

2. 2

   Explain Python's GIL (Global Interpreter Lock) and when it matters.

   What they're really asking: Concurrency awareness: the GIL prevents true parallel execution of Python threads, making CPU-bound multithreading ineffective. For CPU-bound work, use multiprocessing; for I/O-bound work (network calls, file I/O), threading or asyncio still provides meaningful concurrency because the GIL is released during I/O waits.

3. 3

   Write a function that reads a CSV file, filters rows where a numeric column exceeds a threshold, and returns the results as a list of dictionaries.

   What they're really asking: Practical Python competency — the kind of task Python is used for constantly. They're watching for clean code, appropriate use of the csv module or pandas, error handling, and whether the solution is readable by someone else.

   Strong answer (with explanation):

   Using pandas (preferred for data work)

   import pandas as pd def filter_csv(filepath, column, threshold): df = pd.read_csv(filepath) filtered = df[df[column] > threshold] return filtered.to_dict('records')

   Using csv module (no dependencies)

   import csv def filter_csv(filepath, column, threshold): results = [] with open(filepath, newline='') as f: reader = csv.DictReader(f) for row in reader: if float(row[column]) > threshold: results.append(row) return results

   What to explain

   I'd mention both approaches, explain that pandas is more readable and powerful for data work while the csv module has no external dependencies, and note that both should add error handling (file not found, column not in data, non-numeric value in the column) before going to production.

   Offering two approaches and explaining the tradeoff is the senior response. Knowing when to use pandas versus the standard library — and why — signals production Python experience.

   Practice answering this question out loud →
4. 4

   What are Python decorators and when would you use one?

   What they're really asking: Python-specific feature: decorators are functions that wrap other functions to add behavior without modifying the original code. Common uses: logging, timing, authentication checks, caching (functools.lru_cache), and retry logic. Knowing what they are is baseline; knowing why they're useful and when to reach for them is intermediate.

5. 5

   How do you handle errors and exceptions in Python?

   What they're really asking: Exception handling discipline: specific exceptions rather than bare except clauses, logging the exception before handling it, using finally for cleanup, and the difference between catching an exception to handle it versus catching it to log and re-raise.

How to prepare for a Python Programmer interview

• 1

  Clean, readable code is the Python value

  PEP 8 style, meaningful variable names, and functions that do one thing well are what Python interviewers look for. Code that works but is unreadable is a red flag in a language that prizes readability as a first-class feature.

• 2

  Know your domain packages

  For data: pandas, NumPy, matplotlib. For web: Django or FastAPI. For automation: requests, pathlib, subprocess. For AI: openai, anthropic, langchain. Name the packages relevant to the role you're applying for and describe what you've built with them.

• 3

  Virtual environments and dependency management matter

  pip and requirements.txt at minimum; poetry or conda for more mature workflows. Candidates who don't manage dependencies cleanly create environment problems for everyone on their team.

• 4

  Ask about their Python version and code quality standards

  Python 3.10+ versus older versions, type hints adoption, automated linting (flake8, black, ruff), and whether they use pytest. These answers tell you how much technical hygiene the team values.

Python is the most in-demand programming language globally, driven by its dominance in data science, AI/ML, and automation. Python developers with domain expertise in data pipelines, API development, or AI integration are among the most sought-after technical candidates, and the language's versatility means Python skills transfer across industries more fluidly than most other languages.