Master Python Interviews: 150+ Essential Questions for Programming, Data Science, and Automation Mastery

 

Section 1: Python Programming Interview Questions

Python Programming forms the core of any Python-related role. These questions test syntax, logic, problem-solving, and efficiency.

Beginner Level (Questions 1-20)

1. Basic Syntax Understanding: What is the output of print("Hello" + " World")? Explain why Python handles string concatenation this way in a simple script scenario.
2. Variable Assignment Scenario: In a program tracking student scores, assign variables for name (string), age (integer), and grade (float). Write a basic code example and explain data types.
3. IQ-Style Puzzle: If x = 5 and y = x + 3, what is y after x is reassigned to 10? Use this to illustrate Python's variable referencing.
4. List Basics with Example Data: Given a list [1, 2, 3, 4], how do you access the second element? Provide a real-life example like accessing items in a shopping cart list.
5. Conditional Statements: Write an if-else statement to check if a number (e.g., 7) is even or odd. Explain its use in a voting age verification system.
6. Loop Fundamentals: Use a for loop to print numbers 1 to 5. Relate it to iterating over a list of employee names in a company database.
7. Function Definition: Define a function that adds two numbers (e.g., 3 + 4). Show a code example and discuss why functions promote code reuse.
8. String Manipulation: How do you find the length of "Python"? Provide an example in a password validation script.
9. Tuple vs List: Explain the difference with example data like coordinates (1,2) as a tuple. Why use tuples for immutable data like GPS points?
10. Dictionary Basics: Create a dictionary for a person's details {name: "Alice", age: 30}. How do you access the age? Use in a user profile scenario.
11. Error Handling Intro: What happens if you divide by zero? Write a try-except block for a calculator app handling user input errors.
12. Set Operations: Add elements to a set {1,2,3} and remove duplicates. Example: Managing unique email subscribers in a newsletter system.
13. File Reading: Write code to open and read a text file "hello.txt". Discuss its role in logging application errors.
14. Module Import: Import math and use sqrt(16). Explain in a geometry program calculating distances.
15. List Comprehension: Create [x*2 for x in range(5)]. Use for doubling sales data in a report.
16. Boolean Logic: Evaluate True and False or True. Relate to user authentication checks.
17. Slicing Example: Slice "Python"[1:4] to get "yth". Apply to extracting substrings from log files.
18. Global vs Local Variables: Define a global variable and access it in a function. Scenario: Tracking total score in a game.
19. Type Conversion: Convert "123" to int. Use in parsing user input for a banking app.
20. Basic Recursion: Write a recursive function for factorial(3). Explain stack overflow risk in large computations.

Intermediate Level (Questions 21-40)

21. Decorator Usage Scenario: Write a decorator to time function execution. Apply to optimizing a web request handler.
22. Generator Expressions: Create a generator for even numbers up to 10. Use in memory-efficient data processing for large datasets.
23. Lambda Functions: Sort a list of tuples [(1,'b'), (2,'a')] by second element using lambda. Example: Sorting student grades.
24. OOP Inheritance: Define a base class Animal and subclass Dog with bark(). Illustrate in a pet simulation game.
25. Exception Raising: Raise a ValueError if input < 0 in a function. Scenario: Validating temperatures in a weather app.
26. Context Managers: Use with open() for file handling. Discuss in automated report generation.
27. Map and Filter: Use map to square [1,2,3] and filter evens. Apply to data transformation in ETL processes.
28. Multithreading Basics: Import threading and run two functions concurrently. Example: Parallel API calls in a dashboard.
29. Regular Expressions: Use re to match emails in a string. Real-life: Validating form inputs.
30. Class Methods vs Static: Define both in a class for utility functions. Use in a math helper class.
31. JSON Handling: Parse '{"name":"John"}' with json.loads(). Scenario: API response processing.
32. Args and Kwargs: Write a function accepting *args for sum. Example: Flexible logging with variable messages.
33. Metaclasses Intro: Explain metaclasses with a simple example. Relate to framework development like Django models.
34. Iterator Protocol: Implement iter and next in a custom class. Use for custom data streams.
35. Async Basics: Use async def for a coroutine. Scenario: Non-blocking I/O in web servers.
36. Deep Copy vs Shallow: Use copy.deepcopy() on nested lists. Discuss in game state cloning.
37. Property Decorators: Define a getter/setter for class attributes. Example: Encapsulating employee salary.
38. Enumeration Types: Use enum for days of week. Apply to scheduling tasks.
39. Zip Function: Zip two lists [1,2] and ['a','b']. Use in pairing data for reports.
40. Walrus Operator: Use := in a while loop. Scenario: Reading lines until condition met.

Expert Level (Questions 41-60)

41. Memory Management: Explain garbage collection and reference counting. Optimize for a long-running server.
42. Metaprogramming: Dynamically create classes. Use in plugin systems.
43. Concurrency with Asyncio: Build an async web scraper. Handle real-time data feeds.
44. Descriptor Protocol: Implement custom descriptors. Example: Lazy loading attributes.
45. GIL Impact: Discuss Global Interpreter Lock effects on multithreading. Suggest multiprocessing alternatives.
46. Bytecode Analysis: Use dis to disassemble a function. Optimize performance-critical code.
47. C Extensions: Outline integrating C code with Python. For high-speed computations.
48. Weak References: Use weakref for caching without memory leaks. Scenario: Large object graphs.
49. Coroutine Scheduling: Manage tasks with asyncio.gather(). In distributed systems.
50. Type Hinting Advanced: Use generics and protocols. For type-safe libraries.
51. JIT Compilation: Discuss PyPy vs CPython. Optimize numerical simulations.
52. Security Best Practices: Prevent SQL injection in Python scripts. Real-world: Web app vulnerabilities.
53. AST Manipulation: Parse and modify code with ast module. For code analysis tools.
54. Profiling Tools: Use cProfile for bottlenecks. Optimize data pipelines.
55. Functional Programming Paradigms: Implement monads in Python. Advanced data handling.
56. Signal Handling: Catch SIGINT with signal module. For graceful shutdowns.
57. Internals of Dict: Explain hash collisions and resizing. Performance tuning.
58. Custom Allocators: Override memory allocation. For embedded systems.
59. Concurrency Primitives: Use locks, semaphores in multiprocessing. Distributed computing scenarios.
60. Python 3.10+ Features: Pattern matching with match-case. Modernize legacy code.

Section 2: Data Science with Python Interview Questions

These questions focus on libraries like NumPy, Pandas, Matplotlib, and Scikit-learn, emphasizing data manipulation, analysis, and modeling.

Beginner Level (Questions 61-80)

61. NumPy Array Creation: Create a 1D array [1,2,3]. Explain array vs list in data storage.
62. Pandas DataFrame Basics: Load example data {'A':[1,2], 'B':[3,4]} into DataFrame. Use for CSV reading.
63. Data Types in Pandas: Convert a column to float. Scenario: Cleaning sales data.
64. Matplotlib Plotting: Plot a line graph for [1,2,3]. Basic visualization of trends.
65. Descriptive Stats: Use df.describe() on sample data. Interpret mean, std in survey results.
66. Slicing DataFrames: Select rows where column > 5. Filter customer records.
67. Handling Missing Data: Fill NaN with mean. Real-life: Imputing survey blanks.
68. GroupBy Operation: Group by category and sum. Aggregate e-commerce sales.
69. Correlation Calculation: Use df.corr() on numeric data. Analyze variable relationships.
70. Scikit-learn Intro: Fit a LinearRegression on [[1],[2]] and [2,4]. Predict simple trends.
71. Vectorization Benefits: Sum array without loops. Efficiency in large datasets.
72. Series vs DataFrame: Create a Series from list. Use for time-series data.
73. Basic SQL-like Joins: Merge two DataFrames on key. Combine datasets.
74. Histogram Plot: Plot distribution of [1,1,2,3]. Understand data skewness.
75. One-Hot Encoding: Apply to categorical data ['red','blue']. Prep for ML models.
76. Train-Test Split: Split data 80-20. Prevent overfitting in models.
77. Accuracy Metric: Calculate for binary classification. Evaluate model performance.
78. Random Seed: Set for reproducibility. Consistent experiments.
79. Pivot Tables: Create from DataFrame. Summarize sales by region.
80. Boxplot Visualization: Detect outliers in [1,2,100]. Data quality checks.

Intermediate Level (Questions 81-100)

81. NumPy Broadcasting: Add array [1,2] to [[3,4],[5,6]]. Matrix operations scenario.
82. Pandas Apply Function: Apply lambda to double a column. Custom transformations.
83. Time Series Resampling: Resample daily data to monthly. Stock price analysis.
84. Feature Scaling: Use StandardScaler. Normalize for gradient descent.
85. K-Means Clustering: Cluster [[1,1],[2,2],[10,10]]. Customer segmentation.
86. Cross-Validation: Use KFold for model eval. Robust performance metrics.
87. Dimensionality Reduction: PCA on high-dim data. Visualize reduced features.
88. Seaborn Heatmap: Plot correlation matrix. Identify multicollinearity.
89. Pipeline in Sklearn: Chain preprocessing and model. Streamline workflows.
90. Handling Imbalanced Data: Use SMOTE. Improve fraud detection models.
91. Regex in Pandas: Replace patterns in strings. Text cleaning for NLP.
92. MultiIndex DataFrames: Set and query hierarchical indexes. Complex data structs.
93. XGBoost Basics: Train a classifier. Boosted trees for competitions.
94. Confusion Matrix Analysis: Interpret TP/FP in medical diagnosis.
95. Hyperparameter Tuning: GridSearchCV for SVM. Optimize models.
96. FFT in SciPy: Apply to signal data. Frequency analysis.
97. Statsmodels Regression: Fit OLS and interpret coefficients. Econometric modeling.
98. Word Embeddings Intro: Use Gensim for simple word2vec. Text similarity.
99. Anomaly Detection: IsolationForest on outliers. Network security.
100. Bayesian Optimization: Tune params efficiently. Advanced ML tuning.

Expert Level (Questions 101-120)

101. Custom Transformers: Build Sklearn-compatible transformer. Extend pipelines.
102. AutoML Tools: Discuss TPOT or Auto-sklearn. Automate model selection.
103. Interpretability with SHAP: Explain model predictions. Black-box insights.
104. Time Series Forecasting: ARIMA on stock data. Predict future trends.
105. Graph Neural Networks: Intro with PyTorch Geometric. Social network analysis.
106. Federated Learning: Concepts in Python. Privacy-preserving ML.
107. Ensemble Methods Advanced: Stacking multiple models. Kaggle-winning strategies.
108. Sparse Matrices: Handle with SciPy for big data. Recommendation systems.
109. Causal Inference: Use DoWhy library. Infer causes from data.
110. Reinforcement Learning Basics: Q-Learning in Gym env. Game AI.
111. Big Data with Dask: Parallel Pandas on clusters. Scale computations.
112. NLP Transformers: Fine-tune BERT with Hugging Face. Sentiment analysis.
113. Bayesian Networks: Model probabilities with pgmpy. Risk assessment.
114. Adversarial Attacks: Fool models with FGSM. Security testing.
115. Multimodal Data: Fuse text and images. Advanced AI apps.
116. Optimization Solvers: Use CVXPY for convex problems. Resource allocation.
117. Survival Analysis: Kaplan-Meier in lifelines. Customer churn.
118. Geospatial Analysis: Folium maps with GeoPandas. Location-based insights.
119. Quantum Computing Intro: Qiskit for simple circuits. Future data science.
120. Ethical AI Considerations: Bias detection in models. Responsible deployment.

Section 3: Automation Preparation with Python Interview Questions

Focus on scripting, web automation (Selenium), API testing, and DevOps tools like Ansible or Docker with Python.

Beginner Level (Questions 121-135)

121. Scripting Basics: Write a script to rename files in a directory. Automate backups.
122. OS Module: Use os.listdir() to list files. File management tasks.
123. Subprocess Calls: Run 'ls' command. Integrate shell commands.
124. Selenium Setup: Import and open a browser. Web testing intro.
125. API Requests: Use requests.get('url'). Fetch weather data.
126. Cron Jobs: Schedule a Python script. Periodic tasks.
127. Logging Module: Log info to file. Debug automation scripts.
128. Argparse: Parse command-line args. CLI tools.
129. Email Automation: Send email with smtplib. Notification systems.
130. Excel Handling: Read XLS with openpyxl. Report automation.
131. PDF Extraction: Use PyPDF2 to read pages. Document processing.
132. Web Scraping Ethics: Discuss legal aspects with BeautifulSoup.
133. Virtual Environments: Create with venv. Isolate dependencies.
134. Unit Testing: Write test with unittest. Ensure script reliability.
135. Config Files: Parse INI with configparser. Manage settings.

Intermediate Level (Questions 136-150)

136. Selenium WebDriver: Find element by XPath and click. E2E testing.
137. API Testing with Pytest: Test endpoints for status 200. QA automation.
138. Docker Python Integration: Write Dockerfile for app. Containerization.
139. Ansible Playbooks: Call from Python. Infrastructure automation.
140. CI/CD Pipelines: Use GitHub Actions with Python scripts. Deployment.
141. Headless Browser: Run Selenium without UI. Server-side testing.
142. Multiprocessing for Automation: Parallel file processing. Speed up tasks.
143. OAuth Authentication: Implement for APIs. Secure access.
144. Database Automation: Connect to SQL with sqlite3. Data migration.
145. Image Processing: Use Pillow to resize. Batch editing.
146. WebSocket Clients: Connect with websockets module. Real-time apps.
147. Error Retry Mechanisms: Implement backoff in requests. Reliable fetches.
148. YAML Parsing: Load configs with PyYAML. DevOps tools.
149. Kubernetes Python Client: Basic pod management. Cloud automation.
150. Monitoring Scripts: Use psutil for system metrics. Alert systems.

Expert Level (Questions 151-165)

151. Advanced Selenium: Handle iframes and alerts. Complex web apps.
152. Mocking in Tests: Use unittest.mock for dependencies. Isolated testing.
153. Terraform with Python: Call from scripts. IaC integration.
154. Serverless Automation: AWS Lambda Python functions. Event-driven.
155. Distributed Task Queues: Celery with RabbitMQ. Scalable workers.
156. Security Automation: Scan vulnerabilities with Bandit. Code safety.
157. AI in Automation: Use OpenCV for visual testing. Smart bots.
158. Blockchain Interaction: Web3.py for Ethereum. Crypto automation.
159. Cloud Orchestration: Boto3 for AWS resources. Multi-cloud scripts.
160. Performance Profiling: Optimize automation with line_profiler.
161. Custom Frameworks: Build testing framework. Enterprise tools.
162. Zero-Downtime Deployments: Blue-green with Python scripts.
163. GraphQL Clients: Use gql library. Modern API automation.
164. IoT Automation: MQTT with paho-mqtt. Device control.
165. Compliance Auditing: Script checks for GDPR. Regulatory automation.

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