Complex Data Consolidation and Transformation
Instruction: Given multiple DataFrames containing various aspects of customer data, explain how you would merge and transform these into a single, clean DataFrame ready for analysis, detailing any challenges you might face.
Context: This question evaluates the ability to manipulate, merge, and clean data from multiple sources using Pandas. It should cover aspects such as dealing with different data formats, missing values, potential data inconsistencies, and ensuring that the final DataFrame is optimized for subsequent data analysis tasks.
Updated
Official answer available
Read the opening below, then unlock the full answer and practical guidance.
Define the final grain first: one row per customer, order or event. Standardize key types without destroying meaningful leading zeros, validate provenance and timestamps, and identify authoritative fields before combining sources.
Do not fill a missing customer ID with a mean, mode or guessed identity. Quarantine or explicitly retain unmatched records...