Python Dataviz

# 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

```bash cd skills/python-dataviz python3 -m venv .venv source .venv/bin/activate pip install . ```

Create a Chart

```python 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 ```python 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 ```python # 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 ```python # 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 ```python 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 ```python # 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 ```python 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 ```python 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 ```python 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" ```bash 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 ```python plt.tight_layout() # Add before plt.savefig() # Or plt.savefig('output.png', bbox_inches='tight') ```

Low resolution output ```python plt.savefig('output.png', dpi=300) # Not 72 or 100 ```

Environment

The skill includes a venv with all dependencies. Always activate before use:

```bash cd /home/matt/.openclaw/workspace/skills/python-dataviz source .venv/bin/activate ```

Dependencies: matplotlib, seaborn, plotly, pandas, numpy, kaleido (for plotly static export)