Data Cleaning & Handling Missing Values Questions

Understand common data quality issues: missing values (NaN, null), duplicates, outliers, inconsistent formats, and incorrect data types. Know strategies for handling each: removing rows/columns with missing data, imputation (mean, median, forward fill), deduplication, type conversion, and validation checks. Understand the trade-offs of each approach.