data-visualization

Data Visualization Skill

Create publication-quality analytical charts using matplotlib and seaborn in a headless GPU sandbox. Charts are saved as PNG files to /workspace/ for retrieval.

When to Use This Skill

Use this skill when:

• Visualizing results from cuDF analysis or cuML models
• Creating charts (bar, line, scatter, heatmap, histogram, box plot)
• Building multi-panel analysis summaries
• The user asks for visual output, plots, graphs, or charts
• Presenting statistical findings with figures

Initialization (REQUIRED)

MUST call matplotlib.use('Agg') BEFORE importing pyplot. This enables headless rendering.

import matplotlib
matplotlib.use('Agg')  # Headless backend — MUST be before pyplot import
import matplotlib.pyplot as plt
import numpy as np

# Publication-quality defaults
plt.rcParams.update({
    'figure.dpi': 100,
    'savefig.dpi': 300,
    'font.size': 11,
    'axes.labelsize': 12,
    'axes.titlesize': 14,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'figure.constrained_layout.use': True,
})

# Colorblind-safe palette (Okabe-Ito)
COLORS = ['#0173B2', '#DE8F05', '#029E73', '#D55E00', '#CC78BC',
          '#CA9161', '#FBAFE4', '#949494', '#ECE133', '#56B4E9']

Saving Charts

Always save to /workspace/ with these settings:

plt.savefig('/workspace/chart_name.png', dpi=300, bbox_inches='tight',
            facecolor='white', edgecolor='none')
plt.close()
# IMPORTANT: call read_file("/workspace/<chart>.png") to display inline

• dpi=300 for print quality
• bbox_inches='tight' removes excess whitespace
• facecolor='white' ensures white background
• Always call plt.close() after saving to free memory

Displaying Charts (REQUIRED)

After saving any chart, you MUST call read_file on it to display it inline in the conversation:

read_file("/workspace/chart_name.png")

Users cannot see charts unless you do this. Every chart you save MUST be followed by a read_file call.