Best Practices for Data Cleaning and Preparation with Pandas
Instruction: Discuss the best practices for cleaning and preparing data using Pandas before analysis.
Context: This question evaluates the candidate's familiarity with best practices in data cleaning and preparation, ensuring data quality for analysis.
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Start with the analysis question, row grain, key definitions, units and data provenance. Profile types, ranges, missingness and key frequencies; a plausible summary does not establish that the data is correct.
Preserve the raw input and make transformations reproducible. Parse numbers and dates with explicit formats/timezones where needed. Report invalid parses instead of silently treating every failure as an ordinary missing value. Define duplicate semantics...