How do I handle missing or "dirty" data in a large dataset?

All answers to this question.

Never start by deleting data! You first need to perform a "Null Analysis" to see if the data is Missing Completely at Random (MCAR) or if there's a pattern. For categorical data like your 'Category' column, I often use the mode or a "Unknown" label. For dates, if it's a small percentage, you might be able to infer it from other columns. In Python, I use df.fillna() or more advanced techniques like KNN Imputation for numerical values. Always document your cleaning steps so your results are reproducible and can be defended if questioned.

   Answered 2025-11-15 by Pamela Johnston

Pamela, how do you explain these "imputed" values to a business stakeholder who might be skeptical about the fact that we are technically "making up" some of the data?

   Answered 2025-11-19 by Scott Henderson

• Hi Scott! I explain it as "statistical estimation" based on existing patterns. I always provide a disclaimer showing the percentage of the data that was imputed and run a sensitivity analysis to show that the final business conclusion remains the same regardless of whether we use the imputed data or the original "clean" subset.

     Commented 2025-11-22 by Jason Fletcher

Standardizing formats is key. Use the to_datetime function in Pandas for those dates. Inconsistent strings can usually be fixed with simple .strip() and .lower() methods.

   Answered 2025-11-25 by Kelly Weaver

• Very true, Kelly. Regular Expressions (Regex) are also a lifesaver for those messy category strings. It takes a bit to learn but saves hours of manual work!

     Commented 2025-11-28 by Aaron Brooks