Data Visualization

Create clear, effective data visualizations via inference.sh CLI.

Quick Start

curl -fsSL https://cli.inference.sh | sh && infsh login

# Generate a chart with Python
infsh app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\nrevenue = [42, 48, 55, 61, 72, 89]\n\nfig, ax = plt.subplots(figsize=(10, 6))\nax.bar(months, revenue, color=\"#3b82f6\", width=0.6)\nax.set_ylabel(\"Revenue ($K)\")\nax.set_title(\"Monthly Revenue Growth\", fontweight=\"bold\")\nfor i, v in enumerate(revenue):\n    ax.text(i, v + 1, f\"${v}K\", ha=\"center\", fontweight=\"bold\")\nplt.tight_layout()\nplt.savefig(\"revenue.png\", dpi=150)\nprint(\"Saved\")"
}'

Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

Chart Selection Guide

Which Chart for Which Data?

The Pie Chart Problem

Pie charts are almost always the wrong choice:

❌ Pie chart problems:
   - Hard to compare similar-sized slices
   - Can't show more than 5-6 categories
   - 3D pie charts are always wrong
   - Impossible to read exact values

✅ Use instead:
   - Horizontal bar chart (easy comparison)
   - Stacked bar (part of whole)
   - Treemap (hierarchical parts)
   - Just a table (if precision matters)

Design Rules

Axes

Color