Missingno
What it is: Python visualization library for missing data. Creates intuitive charts showing where your data has null values and how missingness correlates across columns.
What It Does Best
Instant missing data overview. Matrix plot shows all missing values at a glance. See which rows and columns have problems immediately.
Correlation detection. Bar charts and heatmaps reveal if missing values in one column correlate with missing values in another. Helps identify systematic data collection issues.
Simple API. One line: msno.matrix(df). Built on matplotlib. Integrates seamlessly with pandas workflow.
Pricing
Free. Open source, MIT license.
When to Use It
✅ Starting data analysis on new dataset
✅ Deciding imputation strategy
✅ Reporting data quality to stakeholders
✅ Debugging data collection pipelines
When NOT to Use It
❌ No missing data in your dataset
❌ Need interactive visualizations (static plots only)
❌ Working with very wide datasets (plots get cluttered)
Bottom line: Does one thing perfectly: visualize missing data. Before you impute or drop null values, use missingno to understand the patterns. Two minutes to install, saves hours of confusion.