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)
Quick Start
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
Best Practices
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 chartsscripts/line_chart.py
- Line plots with multiple seriesscripts/scatter_plot.py
- Scatter plots with regressionscripts/heatmap.py
- Correlation heatmapsscripts/distribution.py
- Histograms, KDE, violin plotsscripts/interactive.py
- Plotly interactive charts
Common Patterns
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)
