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Write maintainable, efficient Pandas or PySpark code.

Spark/Big Datamedium0.6 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Apple
Key Concepts Tested
joinpartitionpythonsparksql
Expert AnswerPremium
121 wordsInterview-ready
**Why It Matters (Architectural Logic)**: Maintainable code reduces bugs and onboarding time. Efficiency (vectorized ops, broadcast) directly impacts cost and SLA. For Pandas: use vectorized operations (`df["x"] = df["a"] + df["b"]`), avoid iterrows(), use dtype efficiently, chunk large files. For PySpark: prefer built-in functions over UDFs; use broadcast for small joins; set spark.sql.shuffle.partitions; repartition by key before wide operations; cache only when reused....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Apple. The answer also includes follow-up discussion points that interviewers commonly explore.

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