**Why Docstrings:** Documentation as code—accessible via help(), __doc__, and auto-generated APIs (Sphinx). Critical for team scale and onboarding. **Formats:** Google, NumPy, reST. Include: purpose, Args, Returns, Raises, Examples. Type hints complement docstrings....
This easy-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 (etl) will help you answer variations of this question confidently.
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.
Why Docstrings: Documentation as code—accessible via help(), __doc__, and auto-generated APIs (Sphinx). Critical for team scale and onboarding.
Formats: Google, NumPy, reST. Include: purpose, Args, Returns, Raises, Examples. Type hints complement docstrings.
Production: We enforce docstrings on public functions. CI runs pydocstyle. Sphinx generates docs for our internal ETL lib. doctest for executable examples.
def load(path: str, limit: int = 1000) -> pd.DataFrame:
"""Load and transform data.\n\n Args:\n path: Source path.\n limit: Max rows.\n Returns:\n DataFrame.\n """
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Analyze My Answer — FreeAccording 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.