plotnine static visualization (ggplot2 syntax for Python). Geoms, aesthetics, scales, coordinates, facets, themes. Use for static publication-quality figures with grammar-of-graphics syntax. For interactive charts use plotly; for maps use geopandas. R equivalent: ggplot2 (use when execution language is R).
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plotnine static visualization library for Python, implementing the grammar of graphics (ggplot2 syntax). Covers geoms (point, line, bar, histogram, boxplot, smooth), aesthetics, scales, coordinates, facets, and themes. Use when creating static publication-quality figures with ggplot2-style syntax, producing charts for print or reports, or working with an R ggplot2 background. Prefer over plotly when static output is needed.
Quick reference for creating data visualizations with plotnine, a Python implementation of the grammar of graphics (ggplot2).
plotnine is a data visualization library based on the grammar of graphics:
+| File | Purpose | When to Read |
|---|---|---|
quickstart.md | Installation, imports, basic syntax | Starting out |
geoms.md | Geometric objects (points, lines, bars) | Choosing chart types |
aesthetics.md | Mapping data to visual properties | Customizing appearance |
scales-coords.md | Scales, coordinates, positions | Axis/color control |
facets-themes.md | Multi-panel plots and styling | Layout and themes |
gotchas.md | Common errors and best practices | Debugging |
The reference-file routing in this skill applies to advisory and brainstorming turns as much as implementation. Recommending an approach, reviewing a plan, or answering a question that touches a routed topic calls for reading the routed reference file just as much as writing code does — the reference files carry curated caveats and environment-specific constraints that this overview and general knowledge lack.
What kind of plot?
├─ Scatter plot (geom_point) → ./references/geoms.md
├─ Line plot (geom_line) → ./references/geoms.md
├─ Bar chart (geom_bar, geom_col) → ./references/geoms.md
├─ Histogram (geom_histogram) → ./references/geoms.md
├─ Box plot (geom_boxplot) → ./references/geoms.md
└─ Other geoms → ./references/geoms.md
What to customize?
├─ Colors, sizes, shapes → ./references/aesthetics.md
├─ Axis limits/labels → ./references/scales-coords.md
├─ Color palettes → ./references/scales-coords.md
├─ Overall theme → ./references/facets-themes.md
├─ Title/labels → ./references/facets-themes.md
└─ Multiple panels (faceting) → ./references/facets-themes.md
Common issues?
├─ Plot not showing → ./references/quickstart.md
├─ Column not found → ./references/gotchas.md
├─ Color not applying → ./references/aesthetics.md
├─ Unexpected grouping → ./references/gotchas.md
└─ Syntax errors → ./references/gotchas.md
Important: In data research pipelines (see CLAUDE.md), all visualizations are generated through script files in scripts/stage8_analysis/, not interactively. This ensures auditability and reproducibility.
The pattern:
scripts/stage8_analysis/{step}_{plot-name}.pyClosely read agent_reference/SCRIPT_EXECUTION_REFERENCE.md for the mandatory file-first execution protocol covering complete code file writing, output capture, and file versioning rules.
See:
agent_reference/WORKFLOW_PHASE4_ANALYSIS.md — Stage 8 (Analysis & Visualization)The examples below show plotnine syntax. In research workflows, wrap them in scripts following the file-first pattern.
from plotnine import ggplot, aes, geom_point
(
ggplot(df, aes(x="col_x", y="col_y"))
+ geom_point()
)
from plotnine import * # All components
from plotnine.data import mtcars # Built-in datasets
| Geom | Use Case |
|---|---|
geom_point() | Scatter plots |
geom_line() | Line plots |
geom_bar() | Count bars |
geom_col() | Value bars |
geom_histogram() | Distributions |
geom_boxplot() | Box plots |
geom_smooth() | Trend lines |
| Aesthetic | Controls |
|---|---|
x, y | Position |
color | Point/line color |
fill | Area fill color |
size | Point/line size |
shape | Point shape |
alpha | Transparency |
p = ggplot(df, aes("x", "y")) + geom_point()
p.save("plot.png", width=10, height=8, dpi=300)
| Topic | Reference File |
|---|---|
| Installation | ./references/quickstart.md |
| Basic syntax | ./references/quickstart.md |
| Chart types | ./references/geoms.md |
| Data mapping | ./references/aesthetics.md |
| Color/shape values | ./references/aesthetics.md |
| Axis scales | ./references/scales-coords.md |
| Color scales | ./references/scales-coords.md |
| Coordinates | ./references/scales-coords.md |
| Faceting | ./references/facets-themes.md |
| Themes | ./references/facets-themes.md |
| Labels/titles | ./references/facets-themes.md |
| Common errors | ./references/gotchas.md |
| Best practices | ./references/gotchas.md |
When this library is used as a primary analytical tool, include in the report's Software & Tools references:
Kibirige, H. et al. plotnine: Grammar of graphics for Python [Computer software]. https://plotnine.org/
Cite when: plotnine is the primary visualization library producing figures included in the report. Do not cite when: Only used for quick exploratory plots not included in deliverables.
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Guide for creating DAAF agent definition files. Covers 12-section template, hook registration, skills-in-frontmatter, integration checklist. Use when adding or revising agents. For SKILL.md files, use skill-authoring instead.
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Researcher-facing analytic triangulation ("creative verification") for stress-testing provisional findings and designing pre-specified challenges before an analysis runs. Turns a researcher's concern or focal claim into competing explanations, bounded discriminating tests, audited diagnostic artifacts, and evidence-calibrated interpretations in a hypothesis/evidence ledger — behavioral, output-level verification replacing line-by-line code review. Covers 12 challenge families organized by Shadish-Cook-Campbell validity type, named sensitivity statistics, pre-committed interpretation rules, anti-fishing guardrails, and interactive diagnostic-artifact QA. Use in Ad Hoc Collaboration to verify, challenge, or stress-test a result, or to design discriminating tests before an analysis runs. Not code-change verification (verify skill / code review) and not final adjudication or re-execution (data-verifier, Reproducibility Verification) — prospective, hypothesis-driven challenge design of analytic claims.
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In-situ smoke testing that verifies a LIVE DAAF deployment functions end-to-end in whatever provider route it is configured for. Auto-detects the active install route (anthropic-subscription, openrouter, chatgpt-subscription shim, openai-api shim) and runs tiered probes: Tier 0 free preflight (route/env coherence, hooks, statuslines, shim health); Tier 1 one live round-trip (~cents); Tier 2 a six-probe functional battery (dispatch, coding, web, skill loading, isolation-strip, nested-dispatch deny); Tier D a zero-cost deterministic battery (bats, Pester, lint, safety-hook tests). Use after install, after a rebuild or update, after any provider/config change (new API key, model remap, shim change), for pre-release validation, or when asked "is my install working" or to "smoke test the deployment/config." Requires the DAAF_DEV=1 dev image; a contributor tool, not end-user-facing. NOT DAAFBench (model behavioral-adherence evaluation under benchmarks/) and NOT scripts/smoke_tests/ (R/Python library-package smoke tests).
日本語の概要は準備中です。原文の説明を表示しています。
Operational framework for the DAAF orchestrator. Defines engagement modes, confirmation protocol, subagent dispatch, context budget, and reference-loading. Loaded exclusively by the orchestrator — not for subagents or user questions.
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Data science methodology and method-selection routing for quantitative research. Covers EDA, data validation, descriptive analysis, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial analysis, network analysis, and visualization design. Contains the canonical method-to-library routing tree, routed by execution language — Python: statsmodels (OLS/GLM/time series), pyfixest (FE/DiD), linearmodels (RE/GMM/SUR), svy (complex surveys), scikit-learn (clustering/prediction ML), geopandas (spatial), igraph (network analysis); R: r-stats (OLS/GLM/time series), fixest (FE/DiD), plm (panel/RE/IV), survey-r (complex surveys), tidymodels (ML), sf-terra (spatial), igraph-r (network analysis). Load the routed tool-specific skill before giving tool-specific advice or writing code — library skills encode environment constraints and curated caveats absent from general knowledge.
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Interpretation guidance for Urban Institute Education Data Portal datasets. The Portal is a curation layer over federal data: lowercase variable names, integer-encoded categoricals, standardized missing codes (-1 missing, -2 not applicable, -3 suppressed). Covers year definitions, grade encoding (grade=-1 is Pre-K, not missing), suppression rates, ODC-By licensing, and cross-source join identifiers. Load before analyzing any Portal data. Routes to source-specific deep-dive skills for individual datasets.
日本語の概要は準備中です。原文の説明を表示しています。