Professional data visualization using Python (matplotlib, seaborn, plotly). Create publication-quality static charts, statistical visualizations, and interac...
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Create professional charts, graphs, and statistical visualizations using Python's leading libraries.
matplotlib - Static plots, publication-quality, full control
seaborn - Statistical visualizations, beautiful defaults
plotly - Interactive charts, web-friendly
cd skills/python-dataviz python3 -m venv .venv source .venv/bin/activate pip install .
import matplotlib.pyplot as plt import numpy as npData
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')
Distribution/Statistical:
plt.hist()sns.histplot()sns.boxplot()sns.violinplot()sns.kdeplot()Comparison:
plt.bar()sns.barplot()sns.barplot(hue=...)plt.barh()sns.barplot(orient='h')Relationship:
plt.scatter()sns.scatterplot()plt.plot()sns.lineplot()sns.regplot()sns.lmplot()Heatmaps:
sns.heatmap(df.corr())plt.imshow()sns.heatmap()Interactive:
plotly.expressplotly.graph_objectsplt.figure(figsize=(10, 6)) # Width x Height in inches plt.savefig('output.png', dpi=300) # Publication: 300 dpi, Web: 72-150 dpi
# Seaborn palettes (works with matplotlib too) import seaborn as sns sns.set_palette("husl") # Colorful sns.set_palette("muted") # Soft sns.set_palette("deep") # BoldCustom colors
colors = ['#667eea', '#764ba2', '#f6ad55', '#4299e1']
# Use seaborn styles even for matplotlib import seaborn as sns sns.set_theme() # Better defaults sns.set_style("whitegrid") # Options: whitegrid, darkgrid, white, dark, ticksOr matplotlib styles
plt.style.use('ggplot') # Options: ggplot, seaborn, bmh, fivethirtyeight
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
# 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')
See references/ for detailed guides:
See scripts/ for ready-to-use examples:
scripts/bar_chart.pyscripts/line_chart.pyscripts/scatter_plot.pyscripts/heatmap.pyscripts/distribution.pyscripts/interactive.pyimport 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)
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)
import numpy as npx = np.linspace(0, 10, 100) y = np.sin(x)
plt.plot(x, y) plt.savefig('output.png', dpi=300)
"No module named matplotlib"
cd skills/python-dataviz source .venv/bin/activate pip install -r requirements.txt
Blank output / "Figure is empty"
plt.savefig()plt.show()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
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)
No automatic installation available. Please visit the source repository for installation instructions.
View Installation Instructions
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