missingno
Missing data visualization module for Python.
Decision gist · record as of 2026-08-14
Yes, if you work regularly with data exploration and need quick visual assessment of missing data. Low install friction, permissive MIT license, no known vulnerabilities, and a stable API make it a safe choice. Dormant maintenance is not a blocker for a mature visualization tool, but don't expect new features or rapid responses to edge cases. Worth keeping in your toolkit for exploratory data analysis.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction install with four common data-science dependencies (numpy, matplotlib, scipy, seaborn).
- Dormant maintenance status since February 2023, but no active issues reported and the library is explicitly in maintenance mode accepting bugfixes.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions, making it safe to adopt in most projects without licensing concerns.
last release 2023-02-26 (1265 days) · last repo commit 2024-05-14 · 4,213 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 536,975 downloads/mo, #6,122 on PyPI
Alternatives
Verify before relying
pip install missingno
import missingno as msno
msno.matrix(df)
msno.bar(df)
msno.heatmap(df)
msno.dendrogram(df)- Whether the library actively handles modern API changes in its dependencies beyond bugfixes
- Performance characteristics on very large datasets with millions of rows
- Current state of maintenance beyond the stated bugfix policy
What it is and what it does
missingno is a lightweight visualization toolkit for understanding missing data patterns. It provides four complementary views: a matrix showing row-by-row nullity patterns with sparklines, a bar chart of nullity by column, a correlation heatmap measuring how the presence of one variable affects another, and a dendrogram using hierarchical clustering to reveal deeper completion relationships. The library is built on numpy, matplotlib, scipy, and seaborn, integrating naturally into standard data-science workflows.
You use it to quickly spot data quality issues—which columns are sparse, whether missingness is random or patterned, and which variables tend to be missing together. It's most useful in exploratory data analysis when you need a visual summary before deciding on imputation or filtering strategies. The library is in maintenance mode, meaning it receives bugfixes but no major new features.
Use it for
- Inspect a newly loaded dataset to identify which columns have missing values and spot patterns before preprocessing
- Diagnose whether missingness is random or correlated across variables
- Communicate data quality to stakeholders using visual summaries instead of raw null counts
- Detect data entry errors by finding unexpected correlations between variables
- Profile time-series data by specifying periodicity to see how completeness varies over time
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work regularly with data exploration and need quick visual assessment of missing data.
Low install friction, permissive MIT license, no known vulnerabilities, and a stable API make it a safe choice. Dormant maintenance is not a blocker for a mature visualization tool, but don't expect new features or rapid responses to edge cases. Worth keeping in your toolkit for exploratory data analysis.
Install
missingno on PyPI
Before you install
Low friction install with four common data-science dependencies (numpy, matplotlib, scipy, seaborn). Dormant maintenance status since February 2023, but no active issues reported and the library is explicitly in maintenance mode accepting bugfixes.
License in practice
MIT License permits commercial and private use with minimal restrictions, making it safe to adopt in most projects without licensing concerns.
Quickstart
pip install missingno
import missingno as msno
msno.matrix(df)
msno.bar(df)
msno.heatmap(df)
msno.dendrogram(df)Verify before relying
- Whether the library actively handles modern API changes in its dependencies beyond bugfixes
- Performance characteristics on very large datasets with millions of rows
- Current state of maintenance beyond the stated bugfix policy
Package facts
| License | MIT License permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpymatplotlibscipyseaborn |
| Maintenance | Dormant 1,265 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 536,975 / month, #6,122 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: missingno-0.5.2-py3-none-any.whl
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