juniorPandas
How to handle missing values in Pandas?
Updated May 17, 2026
Short answer
Use isna(), fillna(), or dropna() to manage missing data.
Deep explanation
Pandas represents missing values as NaN. You can detect them using isna(), remove them using dropna(), or impute values using fillna() based on strategies like mean, median, or forward fill.
Real-world example
Filling missing sensor readings in IoT datasets.
Common mistakes
- Dropping too many rows and losing valuable data.
Follow-up questions
- What is forward fill?
- When should you impute vs drop data?