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Home/Questions/Python/Coding/Read data from three files into a Pandas DataFrame, perform transformations, remove columns, filter rows, search for strings

Read data from three files into a Pandas DataFrame, perform transformations, remove columns, filter rows, search for strings

Python/Codingeasy0.5 min readPremium

**Why This Pattern:** Multi-file concat + transform + filter is the core ETL pattern. At JP Morgan, regulatory data often comes as daily files—concat, validate, filter, load. **Scalability:** pd.concat([df1,df2,df3]) loads all into memory. For 3 large files: read each with...

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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
JP Morgan
Key Concepts Tested
etl

Why This Question Matters

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

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
98 words

Why This Pattern: Multi-file concat + transform + filter is the core ETL pattern. At JP Morgan, regulatory data often comes as daily files—concat, validate, filter, load.

Scalability: pd.concat([df1,df2,df3]) loads all into memory. For 3 large files: read each with chunksize, concat chunks, process incrementally. Or use Dask for out-of-core. For string search: df['col'].str.contains('pat', na=False)—vectorized; avoid apply with regex for big data.

Best Practice: Specify dtypes, parse_dates. Log row counts per step. Use query() for complex filters. Write Parquet for columnar storage.

dfs = [pd.read_csv(f) for f in files]
df = pd.concat(dfs).drop(columns=['drop'])
df = df[df['amount']>0 & df['name'].str.contains('X', na=False)]

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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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