---

name: missingno description: "Guides missingno missing-data visualization, nullity filtering and sorting, package smoke checks, and troubleshooting for pandas DataFrames." disable-model-invocation: true metadata: disco-role: operating license: MIT

missingno Repo Skill

Use this skill when a task involves the Python package missingno, missing-data visualization, nullity plots, or DataFrame completeness filtering/sorting. It is an operating guide for future Researcher sessions; it is not a maintainer checkout dependency.

First checks

For ordinary use, install the public package and verify imports:

python -m pip install missingno
python - <<'PY'
import missingno as msno
print(msno.__version__)
print([name for name in ["matrix", "bar", "heatmap", "dendrogram", "nullity_filter", "nullity_sort"] if hasattr(msno, name)])
PY

Expected public API in this snapshot:

• msno.matrix(df, ...)
• msno.bar(df, ...)
• msno.heatmap(df, ...)
• msno.dendrogram(df, ...)
• msno.nullity_filter(df, ...)
• msno.nullity_sort(df, ...)

There is no verified package CLI entry point and no verified geoplot export in this repository snapshot.

Route map

User task	Read next
Choose or configure a nullity matrix, bar chart, heatmap, or dendrogram	sub-skills/visualizations/SKILL.md
Interpret nullity correlations, dendrogram clusters, time-index frequency ticks, labels, sparklines, or headless plotting	sub-skills/visualizations/SKILL.md
Filter to the most/least complete columns or sort rows/columns by completeness	sub-skills/nullity-utilities/SKILL.md
Understand filter, n, p, sort, or axis behavior used by plot APIs	sub-skills/nullity-utilities/SKILL.md
Diagnose installation, import, dependency, headless plotting, no-CLI, stale-docs, or smoke-check failures	references/troubleshooting.md
Edit or test the upstream repository rather than just use the package	references/maintainer-notes.md
Decide whether this generated skill is stale for a checkout	references/repo-provenance.md

Shared smoke helper

Run the bundled smoke helper when an environment should be checked without network downloads or a notebook display:

python scripts/missingno_smoke_check.py --skip-plots
MPLBACKEND=Agg python scripts/missingno_smoke_check.py --plot all --output-dir /tmp/missingno-smoke

The helper uses a synthetic pandas DataFrame, validates nullity_filter and nullity_sort, then optionally renders matrix/bar/heatmap/dendrogram plots via a headless matplotlib backend. Read the helper before adapting it: scripts/missingno_smoke_check.py.

Operating boundaries

• Prefer small synthetic DataFrames for examples and diagnostics. The public README's collision dataset example is useful conceptually but uses network data and is not required for package operation.
• Keep SKILL.md files as routers. Use the linked references for API tables, examples, and troubleshooting details.
• Do not claim inline= or geoplot support for this snapshot: installed signatures and exports did not verify those capabilities even though older documentation text mentions them.
• Plotting APIs return matplotlib Axes objects. In scripts or CI, set MPLBACKEND=Agg, save figures explicitly, and close figures after checks.

Public provenance and router metadata

• references/repo-provenance.md records the source commit/tag, package version, dirty-state summary, and evidence paths.
• references/repo-routing-metadata.json contains structured metadata for a future managed repo-skill import. This run intentionally does not import the skill.