Download
Messy datasets? Missing values? missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset. Just pip install missingno to get started. This quickstart uses a sample of the NYPD Motor Vehicle Collisions Dataset dataset. The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion. At a glance, date, time, the distribution of injuries, and the contribution factor of the first vehicle appear to be completely populated, while geographic information seems mostly complete, but spottier. The sparkline at right summarizes the general shape of the data completeness and points out the rows with the maximum and minimum nullity in the dataset. This visualization will comfortably accommodate up to 50 labelled variables.
Features
- Visualization will comfortably accommodate up to 50 labelled variables
- msno.bar is a simple visualization of nullity by column
- You can switch to a logarithmic scale
- The missingno correlation heatmap measures nullity correlation
- Variables that are always full or always empty have no meaningful correlation
- Variables that are always full or always empty have no meaningful correlation,
Project Samples
