Python for DS

Python Interview Q&A for Data Science

Practical Python concepts frequently asked in DS interviews.

1Why is Python popular in Data Science?easy
Answer: It has rich libraries, readable syntax, fast prototyping, and strong community support.
2NumPy vs Python lists?easy
Answer: NumPy arrays are faster, memory-efficient, and support vectorized operations.
3What is vectorization?medium
Answer: Performing operations on whole arrays instead of Python loops for speed and clarity.
4What is pandas DataFrame?easy
Answer: A 2D labeled tabular structure for data manipulation and analysis.
5How do you handle missing values in pandas?medium
Answer: Use isna(), then drop rows/columns or impute via mean/median/mode/model-based approaches.
6What is groupby used for?medium
Answer: Split-apply-combine pattern to aggregate metrics across categories.
7Difference between loc and iloc?easy
Answer: loc uses labels; iloc uses integer positions.
8Matplotlib vs Seaborn?easy
Answer: Matplotlib is low-level and flexible; Seaborn is higher-level with better statistical defaults.
9What is a lambda function in Python?easy
Answer: An anonymous inline function useful for small transformation tasks.
10Why use virtual environments?easy
Answer: They isolate project dependencies and prevent package/version conflicts.
11How to speed up slow pandas code?medium
Answer: Avoid loops, use vectorized ops, optimize dtypes, and leverage chunking or parallel tools when needed.
12What are Python generators?medium
Answer: Lazy iterators that yield items one at a time, saving memory for large datasets.
13Pickle vs joblib for model persistence?medium
Answer: Both serialize objects; joblib is often preferred for large NumPy arrays.
14Common Python interview pitfall in DS projects?hard
Answer: Writing loop-heavy, non-reproducible notebooks instead of modular, tested, and vectorized pipelines.
15One-line Python-for-DS summary?easy
Answer: Python is the productivity layer that connects data prep, modeling, and deployment in one ecosystem.
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