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Python Interview Questions — Day 34 of 100 Days of Data Engineering, AI and Azure Challenge
- 1. Read a CSV file and filter rows based on a specific column value. If data filtering were like social media, we’d just be blocking data younger than 25.
- 2. Merge two DataFrames using a common column. Merging DataFrames is like matchmaking — you need a common column (or a good algorithm) to bring them together!
- 3. Write a DataFrame to a CSV file without the index.
- 4. Handle missing data by replacing NaN values with the column mean. "NaN stands for Not a Number, but I like to call it ‘Not Available Now.’ Let’s fix that."
- 5. Convert a DataFrame column from string to datetime format. When strings pretend to be dates, we say, ‘Nice try, buddy,’ and use pd.to_datetime to set them straight.
- 6. Sort a list by length. Size does matter, especially when it comes to sorting lists!
- 7. Flatten a nested list. Turning layers into simplicity!
- 8. Merge dictionaries in Python. The 'b' key got an upgrade!
- 9. Access nested dictionary values with .get(). Safe navigation through deep data!
- 10. Handle outliers with IQR. Catching data that doesn’t behave!
- 11. Perform element-wise operations on NumPy arrays. Broadcasting your love of math is underrated!
- 12. Filter and mask arrays with conditional statements. Filtering the negativity out (literally)!
- 13. Use vectorized string operations in Pandas. Fast string processing, no loops needed!
- 14. Calculate eigenvalues and eigenvectors. For when linear algebra knocks on your door!
- 15. Use advanced array manipulation techniques. Repeating and tiling — because sometimes once isn’t enough!
- 16. Write data to a JSON file. JSON: Because plain text just isn’t hip enough anymore.
- 17. Append content to an existing file. Appending: The art of politely adding more stuff without overwriting.
- 18. Count word frequency in a text file. Word counting: The cousin of frequency analysis, but less mysterious.
- 19. Compress and decompress a file using gzip. Gzip: For when your file has a New Year’s resolution to lose weight.
- 20. Write a custom exception.
- 21. Explain context managers. Context managers: The cleanup crew of your Python script.
- 22. Log error messages instead of printing them. Logging: Turning your mistakes into elegant records of failure.
- 23. Use multiprocessing for parallel processing.
- 24. Use threading for concurrent execution.
- 25. Use sorting algorithms to sort lists. Sort by length because size does matter!
- 26. Perform list comprehension for compact and efficient code.
- 27. Read and write CSV files in Python.
- 28. Read and write JSON files in Python.
- 29. Utilize Pandas for data manipulation and analysis.
- 30. Utilize NumPy for numerical computing and array manipulation.
- 31. Use SQLAlchemy for database interaction.
- 32. Use psycopg2 for PostgreSQL database connection.
- 33. Use Apache Airflow for workflow management.
- 34. Use Luigi for building complex pipelines.
- 35. Use requests for making HTTP requests.
- 36. Use BeautifulSoup for web scraping.
- 37. Handle exceptions with try-except blocks.
- 38. Implement logging to record and track errors.
- 39. Utilize multiprocessing for parallel execution.
- 40. Utilize threading for concurrent execution.
- 41. Write unit tests using unittest framework.
- 42. Write tests using pytest framework.
- 43. Sort lists with custom keys for specific sorting criteria.
- 44. Reverse a linked list in Python. Reversing a linked list: Just like reversing a car, but with less honking.
- 45. Find the maximum difference between two elements in a list. Difference is key, whether in lists or life!
- 46. Check if a string is a palindrome. Palindrome: The word nerd’s version of a boomerang.
- 47. Find the intersection of two lists. Intersection: Where two lists meet and agree to disagree.
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