Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Python Dataviz
Professional data visualization using Python (matplotlib, seaborn, plotly). Create publication-quality static charts, statistical visualizations, and interac...
skill openclawclawhub Free
Professional data visualization using Python (matplotlib, seaborn, plotly). Create publication-quality static charts, statistical visualizations, and interac...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, pyproject.toml, references/colors.md, references/statistical.md, scripts/bar_chart.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- 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.
New install
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
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- matthew-a-gordon
- Source page
- View original listing
- Canonical URL
- Open canonical page
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)
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs1 Scripts1 Files
- SKILL.md
- README.md
- references/colors.md
- references/statistical.md
- scripts/bar_chart.py
- pyproject.toml