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name: visualizations description: "Guides missingno matrix, bar, heatmap, and dendrogram missing-data visualization workflows, configuration, interpretation, and plotting troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

missingno Visualizations

Use this sub-skill when a task asks how to make, configure, interpret, save, or troubleshoot missingno plots for pandas DataFrames.

Choose the plot

Need	Use	Why
See row-by-row and column-by-column missingness patterns	msno.matrix(df, ...)	Dense nullity matrix with optional sparkline for row completeness.
Compare per-column completeness counts/ratios	msno.bar(df, ...)	Simpler bar view for variable completeness, with optional log scale.
Find pairwise nullity correlation between variables	msno.heatmap(df, ...)	Correlates missing/present masks and annotates relationships.
Group variables by similar missingness patterns	msno.dendrogram(df, ...)	Uses SciPy hierarchical clustering of nullity masks.

For column filtering or completeness sorting before plotting, read ../nullity-utilities/SKILL.md.

Fast workflow

import matplotlib.pyplot as plt
import missingno as msno

# df is a pandas.DataFrame.
ax = msno.matrix(df, sparkline=False)
ax.figure.savefig("missingness-matrix.png", bbox_inches="tight")
plt.close(ax.figure)

In headless environments, set MPLBACKEND=Agg before running the script. For a safe end-to-end package check, run the root helper: ../../scripts/missingno_smoke_check.py.

References

• Read references/workflows.md for plot-selection recipes, interpretation notes, saving figures, large-column handling, and time-index examples.
• Read references/api-reference.md for verified function signatures, parameter behavior, return values, and source-backed implementation notes.
• Read references/troubleshooting.md for headless matplotlib, label overlap, freq errors, dropped heatmap columns, stale inline/geoplot mentions, and dependency issues.

Decision points

1. If a user asks for a single visual overview, start with matrix for small to medium column counts and bar when only completeness percentages are needed.
2. If a user asks whether missingness in two columns is related, use heatmap; explain that columns that are always full or always empty are removed before correlation.
3. If a user asks about broader missingness groups or many variables, use dendrogram; consider filtering to relevant columns first.
4. If a plot is unreadable because there are many variables, reduce labels, change orientation where supported, or route to ../nullity-utilities/SKILL.md for filter='bottom', n=..., and p=... preparation.
5. If the task uses a time index with matrix(freq=...), confirm the DataFrame index is a PeriodIndex or DatetimeIndex and that the requested frequency produces ticks found in the index range.

Boundaries

• This sub-skill does not cover generic seaborn/matplotlib styling except where needed to save/customize returned axes.
• This sub-skill does not claim geoplot or inline support for this snapshot.
• This sub-skill does not require network sample data; use small synthetic frames for examples and checks.