inference-sh/data-visualization

Data Visualization

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

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

# Generate a chart with Python
belt 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\")"
}'

Chart Selection Guide

Which Chart for Which Data?

| Data Relationship | Best Chart | Never Use | |------------------|-----------|-----------| | Change over time | Line chart | Pie chart | | Comparing categories | Bar chart (horizontal for many categories) | Line chart | | Part of a whole | Stacked bar, treemap | Pie chart (controversial but: bar is always clearer) | | Distribution | Histogram, box plot | Bar chart | | Correlation | Scatter plot | Bar chart | | Ranking | Horizontal bar chart | Vertical bar, pie | | Geographic | Choropleth map | Bar chart | | Composition over time | Stacked area chart | Multiple pie charts | | Single metric | Big number (KPI card) | Any chart (overkill) | | Flow / process | Sankey diagram | Bar chart |

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

| Rule | Why | |------|-----| | Always start Y-axis at 0 (bar charts) | Prevents misleading visual | | Line charts CAN start above 0 | When showing change, not absolute values | | Label both axes | Reader shouldn't have to guess units | | Remove unnecessary gridlines | Reduce visual noise | | Use horizontal labels | Vertical text is hard to read | | Sort bar charts by value | Don't use alphabetical order unless there's a reason |

Color

| Principle | Application | |-----------|------------| | Max 5-7 colors per chart | More becomes unreadable | | Highlight one thing | Grey everything else, color the focus | | Sequential for magnitude | Light → dark for low → high | | Diverging for positive/negative | Red ← neutral → blue | | Categorical for groups | Distinct hues, similar brightness | | Colorblind-safe | Avoid red/green only — add shapes or labels | | Consistent meaning | If blue = revenue, keep it blue everywhere |

Good Color Palettes

# Sequential (low to high)
sequential = ["#eff6ff", "#bfdbfe", "#60a5fa", "#2563eb", "#1d4ed8"]

# Diverging (negative to positive)
diverging = ["#ef4444", "#f87171", "#d1d5db", "#34d399", "#10b981"]

# Categorical (distinct groups)
categorical = ["#3b82f6", "#f59e0b", "#10b981", "#8b5cf6", "#ef4444"]

# Colorblind-safe
cb_safe = ["#0077BB", "#33BBEE", "#009988", "#EE7733", "#CC3311"]

Text and Labels

| Element | Rule | |---------|------| | Title | States the insight, not the data type. "Revenue doubled in Q2" not "Q2 Revenue Chart" | | Annotations | Call out key data points directly on the chart | | Legend | Avoid if possible — label directly on chart lines/bars | | Font size | Minimum 12px, 14px+ for presentations | | Number format | Use K, M, B for large numbers (42K not 42,000) | | Data labels | Add to bars/points when exact values matter |

Chart Recipes

Line Chart (Time Series)

belt app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(12, 6))\nfig.patch.set_facecolor(\"white\")\n\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\nthis_year = [120, 135, 148, 162, 178, 195, 210, 228, 245, 268, 290, 320]\nlast_year = [95, 102, 108, 115, 122, 130, 138, 145, 155, 165, 178, 190]\n\nax.plot(months, this_year, color=\"#3b82f6\", linewidth=2.5, marker=\"o\", markersize=6, label=\"2024\")\nax.plot(months, last_year, color=\"#94a3b8\", linewidth=2, linestyle=\"--\", label=\"2023\")\nax.fill_between(range(len(months)), last_year, this_year, alpha=0.1, color=\"#3b82f6\")\n\nax.annotate(\"$320K\", xy=(11, 320), fontsize=14, fontweight=\"bold\", color=\"#3b82f6\")\nax.annotate(\"$190K\", xy=(11, 190), fontsize=12, color=\"#94a3b8\")\n\nax.set_ylabel(\"Revenue ($K)\", fontsize=12)\nax.set_title(\"Revenue grew 68% year-over-year\", fontsize=16, fontweight=\"bold\")\nax.legend(fontsize=12)\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nax.grid(axis=\"y\", alpha=0.3)\nplt.tight_layout()\nplt.savefig(\"line-chart.png\", dpi=150)\nprint(\"Saved\")"
}'

Horizontal Bar Chart (Comparison)

belt app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(10, 6))\n\ncategories = [\"Email\", \"Social\", \"SEO\", \"Paid Ads\", \"Referral\", \"Direct\"]\nvalues = [12, 18, 35, 22, 8, 5]\ncolors = [\"#94a3b8\"] * len(values)\ncolors[2] = \"#3b82f6\"  # Highlight the winner\n\n# Sort by value\nsorted_pairs = sorted(zip(values, categories, colors))\nvalues, categories, colors = zip(*sorted_pairs)\n\nax.barh(categories, values, color=colors, height=0.6)\nfor i, v in enumerate(values):\n    ax.text(v + 0.5, i, f\"{v}%\", va=\"center\", fontsize=12, fontweight=\"bold\")\n\nax.set_xlabel(\"% of Total Traffic\", fontsize=12)\nax.set_title(\"SEO drives the most traffic\", fontsize=16, fontweight=\"bold\")\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nplt.tight_layout()\nplt.savefig(\"bar-chart.png\", dpi=150)\nprint(\"Saved\")"
}'

KPI / Big Number Card

belt app run infsh/html-to-image --input '{
  "html": "<div style=\"display:flex;gap:20px;padding:20px;background:white;font-family:system-ui\"><div style=\"background:#f8fafc;border:1px solid #e2e8f0;border-radius:12px;padding:24px;width:200px;text-align:center\"><p style=\"color:#64748b;font-size:14px;margin:0\">Monthly Revenue</p><p style=\"font-size:48px;font-weight:900;margin:8px 0;color:#1e293b\">$89K</p><p style=\"color:#22c55e;font-size:14px;margin:0\">↑ 23% vs last month</p></div><div style=\"background:#f8fafc;border:1px solid #e2e8f0;border-radius:12px;padding:24px;width:200px;text-align:center\"><p style=\"color:#64748b;font-size:14px;margin:0\">Active Users</p><p style=\"font-size:48px;font-weight:900;margin:8px 0;color:#1e293b\">12.4K</p><p style=\"color:#22c55e;font-size:14px;margin:0\">↑ 8% vs last month</p></div><div style=\"background:#f8fafc;border:1px solid #e2e8f0;border-radius:12px;padding:24px;width:200px;text-align:center\"><p style=\"color:#64748b;font-size:14px;margin:0\">Churn Rate</p><p style=\"font-size:48px;font-weight:900;margin:8px 0;color:#1e293b\">2.1%</p><p style=\"color:#ef4444;font-size:14px;margin:0\">↑ 0.3% vs last month</p></div></div>"
}'

Heatmap

belt app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(10, 6))\n\ndays = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\nhours = [\"9AM\", \"10AM\", \"11AM\", \"12PM\", \"1PM\", \"2PM\", \"3PM\", \"4PM\", \"5PM\"]\ndata = np.random.randint(10, 100, size=(len(hours), len(days)))\ndata[2][1] = 95  # Tuesday 11AM peak\ndata[2][3] = 88  # Thursday 11AM\n\nim = ax.imshow(data, cmap=\"Blues\", aspect=\"auto\")\nax.set_xticks(range(len(days)))\nax.set_yticks(range(len(hours)))\nax.set_xticklabels(days, fontsize=12)\nax.set_yticklabels(hours, fontsize=12)\n\nfor i in range(len(hours)):\n    for j in range(len(days)):\n        color = \"white\" if data[i][j] > 60 else \"black\"\n        ax.text(j, i, data[i][j], ha=\"center\", va=\"center\", fontsize=10, color=color)\n\nax.set_title(\"Website Traffic by Day & Hour\", fontsize=16, fontweight=\"bold\")\nplt.colorbar(im, label=\"Visitors\")\nplt.tight_layout()\nplt.savefig(\"heatmap.png\", dpi=150)\nprint(\"Saved\")"
}'

Storytelling with Data

The Narrative Arc

| Step | What to Do | Example | |------|-----------|---------| | 1. Context | Set up what the reader needs to know | "We track customer acquisition cost monthly" | | 2. Tension | Show the problem or change | "CAC increased 40% in Q3" | | 3. Resolution | Show the insight or solution | "But LTV increased 80%, so unit economics improved" |

Title as Insight

❌ Descriptive titles (what the chart shows):
   "Q3 Revenue by Product Line"
   "Monthly Active Users 2024"
   "Customer Satisfaction Survey Results"

✅ Insight titles (what the chart means):
   "Enterprise product drives 70% of revenue growth"
   "User growth accelerated after the free tier launch"
   "Support response time is the #1 satisfaction driver"

Annotation Techniques

| Technique | When to Use | |-----------|------------| | Call-out label | Highlight a specific data point ("Peak: 320K") | | Reference line | Show target/benchmark ("Goal: 100K") | | Shaded region | Mark a time period ("Product launch window") | | Arrow + text | Draw attention to trend change | | Before/after line | Show impact of an event |

Dark Mode Charts

belt app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\n# Dark theme\nplt.rcParams.update({\n    \"figure.facecolor\": \"#0f172a\",\n    \"axes.facecolor\": \"#0f172a\",\n    \"axes.edgecolor\": \"#334155\",\n    \"axes.labelcolor\": \"white\",\n    \"text.color\": \"white\",\n    \"xtick.color\": \"white\",\n    \"ytick.color\": \"white\",\n    \"grid.color\": \"#1e293b\"\n})\n\nfig, ax = plt.subplots(figsize=(12, 6))\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\nvalues = [45, 52, 58, 72, 85, 98]\n\nax.plot(months, values, color=\"#818cf8\", linewidth=3, marker=\"o\", markersize=8)\nax.fill_between(range(len(months)), values, alpha=0.15, color=\"#818cf8\")\nax.set_title(\"MRR Growth: On track for $100K\", fontsize=18, fontweight=\"bold\")\nax.set_ylabel(\"MRR ($K)\", fontsize=13)\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nax.grid(axis=\"y\", alpha=0.2)\n\nfor i, v in enumerate(values):\n    ax.annotate(f\"${v}K\", (i, v), textcoords=\"offset points\", xytext=(0, 12), ha=\"center\", fontsize=11, fontweight=\"bold\")\n\nplt.tight_layout()\nplt.savefig(\"dark-chart.png\", dpi=150, facecolor=\"#0f172a\")\nprint(\"Saved\")"
}'

Common Mistakes

| Mistake | Problem | Fix | |---------|---------|-----| | Pie charts | Hard to compare, always misleading | Use bar charts or treemaps | | Y-axis not starting at 0 (bar charts) | Exaggerates differences | Start at 0 for bars, OK to truncate for lines | | Too many colors | Visual noise, confusing | Max 5-7 colors, highlight only what matters | | No title or generic title | Reader doesn't know the insight | Title = the takeaway, not the data type | | 3D charts | Distorts data, looks unprofessional | Always use 2D | | Dual Y-axes | Misleading, hard to read | Use two separate charts | | Alphabetical sort on bar charts | Hides the story | Sort by value (largest first) | | No labels on axes | Reader can't interpret | Always label with units | | Chartjunk (decorative elements) | Distracts from data | Remove everything that doesn't convey information | | Red/green only for color coding | Colorblind users can't read | Use shapes, patterns, or colorblind-safe palettes |

Related Skills

npx skills add inference-sh/skills@pitch-deck-visuals
npx skills add inference-sh/skills@technical-blog-writing
npx skills add inference-sh/skills@competitor-teardown

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