Data Cleaning in Pandas (Handling Missing Data)

📰 Dev.to · saud khan

Learn to handle missing data in Pandas with NaN, dropping, filling, and interpolating methods to ensure accurate analysis and decision-making

beginner Published 24 Apr 2026

Action Steps

Import Pandas library to work with datasets
Use the isnull() function to identify missing values in a DataFrame
Drop rows or columns with missing values using dropna()
Fill missing values with mean, median, or mode using fillna()
Interpolate missing values using interpolate() to maintain data continuity

Who Needs to Know This

Data analysts and scientists benefit from this knowledge to clean and preprocess real-world datasets, ensuring reliable insights for business decisions

Key Insight

💡 Missing data can lead to incorrect analysis results, so it's crucial to handle NaN values effectively