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Modify a word count script to output results in descending frequency order.

Python/Codinghard0.5 min readPremium

**Why Descending Order Matters:** Top-N by frequency is the core of term frequency analysis, log analysis, and recommendation features (most-viewed items). **Scalability Tiers:** (1) Single file < 1GB: Counter + most_common()—in-memory, O(n log k) for top-k. (2)...

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Frequency
Low
Asked at 1 company
Category
179
questions in Python/Coding
Difficulty Split
127E|24M|28H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Impetus
Key Concepts Tested
spark

Why This Question Matters

This hard-level Python/Coding question appears frequently in data engineering interviews at companies like Impetus. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) will help you answer variations of this question confidently.

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This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
97 words

Why Descending Order Matters: Top-N by frequency is the core of term frequency analysis, log analysis, and recommendation features (most-viewed items).

Scalability Tiers: (1) Single file < 1GB: Counter + most_common()—in-memory, O(n log k) for top-k. (2) Multi-file/large: MapReduce pattern—map emits (word,1), reduce sums, final sort. (3) Spark: reduceByKey, then sortBy(col('count').desc()). (4) Streaming: maintain heap of top-K—O(n log k) space.

Cost: Full sort of 10M distinct words vs heap of top 1000—heap wins. Use most_common(1000) to avoid full sort when only top-N needed.

from collections import Counter
counts = Counter(words)
for word, count in counts.most_common():
print(f'{word}: {count}')

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