# Send Python Dataviz to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "python-dataviz", "name": "Python Dataviz", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/matthew-a-gordon/python-dataviz", "canonicalUrl": "https://clawhub.ai/matthew-a-gordon/python-dataviz", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/python-dataviz", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=python-dataviz", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "README.md", "SKILL.md", "pyproject.toml", "references/colors.md", "references/statistical.md", "scripts/bar_chart.py" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/python-dataviz" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/python-dataviz", "downloadUrl": "https://openagent3.xyz/downloads/python-dataviz", "agentUrl": "https://openagent3.xyz/skills/python-dataviz/agent", "manifestUrl": "https://openagent3.xyz/skills/python-dataviz/agent.json", "briefUrl": "https://openagent3.xyz/skills/python-dataviz/agent.md" } } ``` ## Documentation ### Python Data Visualization Create professional charts, graphs, and statistical visualizations using Python's leading libraries. ### Libraries & Use Cases matplotlib - Static plots, publication-quality, full control Bar, line, scatter, pie, histogram, heatmap Multi-panel figures, subplots Custom styling, annotations Export: PNG, SVG, PDF seaborn - Statistical visualizations, beautiful defaults Distribution plots (violin, box, kde, histogram) Categorical plots (bar, count, swarm, box) Relationship plots (scatter, line, regression) Matrix plots (heatmap, clustermap) Built on matplotlib, integrates seamlessly plotly - Interactive charts, web-friendly Hover tooltips, zoom, pan 3D plots, animations Dashboards via Dash framework Export: HTML, PNG (requires kaleido) ### Setup Environment cd skills/python-dataviz python3 -m venv .venv source .venv/bin/activate pip install . ### Create a Chart import matplotlib.pyplot as plt import numpy as np # Data x = np.linspace(0, 10, 100) y = np.sin(x) # Plot plt.figure(figsize=(10, 6)) plt.plot(x, y, linewidth=2, color='#667eea') plt.title('Sine Wave', fontsize=16, fontweight='bold') plt.xlabel('X Axis') plt.ylabel('Y Axis') plt.grid(alpha=0.3) plt.tight_layout() # Export plt.savefig('output.png', dpi=300, bbox_inches='tight') plt.savefig('output.svg', bbox_inches='tight') ### Chart Selection Guide Distribution/Statistical: Histogram → plt.hist() or sns.histplot() Box plot → sns.boxplot() Violin plot → sns.violinplot() KDE → sns.kdeplot() Comparison: Bar chart → plt.bar() or sns.barplot() Grouped bar → sns.barplot(hue=...) Horizontal bar → plt.barh() or sns.barplot(orient='h') Relationship: Scatter → plt.scatter() or sns.scatterplot() Line → plt.plot() or sns.lineplot() Regression → sns.regplot() or sns.lmplot() Heatmaps: Correlation matrix → sns.heatmap(df.corr()) 2D data → plt.imshow() or sns.heatmap() Interactive: Any plotly chart → plotly.express or plotly.graph_objects See references/plotly-examples.md ### 1. Figure Size & DPI plt.figure(figsize=(10, 6)) # Width x Height in inches plt.savefig('output.png', dpi=300) # Publication: 300 dpi, Web: 72-150 dpi ### 2. Color Palettes # Seaborn palettes (works with matplotlib too) import seaborn as sns sns.set_palette("husl") # Colorful sns.set_palette("muted") # Soft sns.set_palette("deep") # Bold # Custom colors colors = ['#667eea', '#764ba2', '#f6ad55', '#4299e1'] ### 3. Styling # Use seaborn styles even for matplotlib import seaborn as sns sns.set_theme() # Better defaults sns.set_style("whitegrid") # Options: whitegrid, darkgrid, white, dark, ticks # Or matplotlib styles plt.style.use('ggplot') # Options: ggplot, seaborn, bmh, fivethirtyeight ### 4. Multiple Subplots fig, axes = plt.subplots(2, 2, figsize=(12, 10)) axes[0, 0].plot(x, y1) axes[0, 1].plot(x, y2) # etc. plt.tight_layout() # Prevent label overlap ### 5. Export Formats # PNG for sharing/embedding (raster) plt.savefig('chart.png', dpi=300, bbox_inches='tight', transparent=False) # SVG for editing/scaling (vector) plt.savefig('chart.svg', bbox_inches='tight') # For plotly (interactive) import plotly.express as px fig = px.scatter(df, x='col1', y='col2') fig.write_html('chart.html') ### Advanced Topics See references/ for detailed guides: Color theory & palettes: references/colors.md Statistical plots: references/statistical.md Plotly interactive charts: references/plotly-examples.md Multi-panel layouts: references/layouts.md ### Example Scripts See scripts/ for ready-to-use examples: scripts/bar_chart.py - Bar and grouped bar charts scripts/line_chart.py - Line plots with multiple series scripts/scatter_plot.py - Scatter plots with regression scripts/heatmap.py - Correlation heatmaps scripts/distribution.py - Histograms, KDE, violin plots scripts/interactive.py - Plotly interactive charts ### Data from CSV import pandas as pd df = pd.read_csv('data.csv') # Plot with pandas (uses matplotlib) df.plot(x='date', y='value', kind='line', figsize=(10, 6)) plt.savefig('output.png', dpi=300) # Or with seaborn for better styling sns.lineplot(data=df, x='date', y='value') plt.savefig('output.png', dpi=300) ### Dictionary Data data = {'Category A': 25, 'Category B': 40, 'Category C': 15} # Matplotlib plt.bar(data.keys(), data.values()) plt.savefig('output.png', dpi=300) # Seaborn (convert to DataFrame) import pandas as pd df = pd.DataFrame(list(data.items()), columns=['Category', 'Value']) sns.barplot(data=df, x='Category', y='Value') plt.savefig('output.png', dpi=300) ### NumPy Arrays import numpy as np x = np.linspace(0, 10, 100) y = np.sin(x) plt.plot(x, y) plt.savefig('output.png', dpi=300) ### Troubleshooting "No module named matplotlib" cd skills/python-dataviz source .venv/bin/activate pip install -r requirements.txt Blank output / "Figure is empty" Check that plt.savefig() comes AFTER plotting commands Use plt.show() for interactive viewing during development Labels cut off plt.tight_layout() # Add before plt.savefig() # Or plt.savefig('output.png', bbox_inches='tight') Low resolution output plt.savefig('output.png', dpi=300) # Not 72 or 100 ### Environment The skill includes a venv with all dependencies. Always activate before use: cd /home/matt/.openclaw/workspace/skills/python-dataviz source .venv/bin/activate Dependencies: matplotlib, seaborn, plotly, pandas, numpy, kaleido (for plotly static export) ## Trust - Source: tencent - Verification: Indexed source record - Publisher: matthew-a-gordon - Version: 1.0.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-23T16:43:11.935Z - Expires at: 2026-04-30T16:43:11.935Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/python-dataviz) - [Send to Agent page](https://openagent3.xyz/skills/python-dataviz/agent) - [JSON manifest](https://openagent3.xyz/skills/python-dataviz/agent.json) - [Markdown brief](https://openagent3.xyz/skills/python-dataviz/agent.md) - [Download page](https://openagent3.xyz/downloads/python-dataviz)