data-visualization

data-visualization

Data Visualization

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

Quick Start

Requires inference.sh CLI (infsh). Install instructions

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\")"
}'

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)

infsh 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)

infsh 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

infsh 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

infsh 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

infsh 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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