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 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

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)

Python Dataviz