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Solve a regex problem

Python/Codingeasy0.3 min readPremium

**Why Regex in Data Eng:** Log parsing, field extraction, validation (email, phone), and data quality checks. Compile once, reuse many times. **Functions:** re.match (anchored start), re.search (anywhere), re.findall (all matches), re.sub (replace). Groups capture subpatterns....

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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
McKinsey

Why This Question Matters

This easy-level Python/Coding question appears frequently in data engineering interviews at companies like McKinsey. While less common, it tests deeper understanding that distinguishes strong candidates.

How to Approach This

Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.

Expert Answer
64 words

Why Regex in Data Eng: Log parsing, field extraction, validation (email, phone), and data quality checks. Compile once, reuse many times.

Functions: re.match (anchored start), re.search (anywhere), re.findall (all matches), re.sub (replace). Groups capture subpatterns.

Performance: re.compile(r'pat') for reuse—avoids re-parsing. For 1M rows: df['col'].str.extract(pat) is vectorized. Catastrophic backtracking: (a+)+ on 'aaaX' can hang—use atomic groups or limit alternation.

pat = re.compile(r'^[\w.-]+@[\w.-]+\.\w+$')
matches = df['email'].str.match(pat)

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According to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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