ai-analyst-lab/.claude/skills/visualization-patterns

Skill: Visualization Patterns

Purpose

Ensure every chart Claude Code produces follows high-quality design standards with named themes, consistent styling, and clear data communication.

When to Use

Apply this skill whenever generating a chart, graph, or data visualization. Always apply the active theme unless the user specifies otherwise. Default theme: minimal.

Instructions

Pre-flight: Load Learnings

Before executing, check .knowledge/learnings/index.md for relevant entries:

• Read the file. If it doesn't exist or is empty, skip silently.
• Scan for entries under "Chart Style" and "General" headings (or related categories like "Visualization Insights").
• If entries exist, incorporate them as constraints for this execution (e.g., preferred chart types, color overrides, annotation preferences).
• Never block execution if learnings are unavailable.

Core Principle: Storytelling with Data (SWD)

Every chart follows the SWD methodology by Cole Nussbaumer Knaflic:

Gray everything first. Color is reserved for the one data point that tells the story.

• Maximum 2 colors + gray per chart. Action Amber (#D97706) for the primary focus, Accent Red (#DC2626) for a secondary callout. Everything else is gray.
• Titles state the takeaway, not a description. "iOS drove the June ticket spike" not "Tickets by Platform."
• Every visual element must earn its place — if it doesn't help the reader understand the story, remove it.
• Prefer text over charts for single numbers. Prefer horizontal bars over pie charts. Prefer direct labels over legends.

Implementation: Always apply the SWD style before generating any chart:

from helpers.chart_helpers import swd_style, highlight_bar, highlight_line, action_title, save_chart

colors = swd_style()  # Loads .mplstyle + returns color palette

Use highlight_bar() for bar charts (highlights one bar, grays the rest), highlight_line() for line charts (highlights one series, grays the rest), and action_title() for all chart titles.

Declutter Checklist

Before finalizing any chart, verify each item:

• [ ] Chart border / box — removed entirely
• [ ] Top and right spines — removed (keep only bottom and left)
• [ ] Heavy gridlines — removed or very light gray (#E5E7EB), y-axis only
• [ ] Data markers — removed from line charts (the line is the data)
• [ ] Legend — replaced with direct labels on the data
• [ ] Rotated axis text — if labels need rotation, switch to horizontal bars
• [ ] Trailing zeros — use $45 not $45.00; use 12% not 12.0%
• [ ] 3D effects — never
• [ ] Background color — always warm off-white (#F7F6F2)
• [ ] Redundant axis labels — if the title says "Revenue ($M)", the y-axis doesn't need "Revenue in Millions of Dollars"
• [ ] Excessive tick marks — reduce to 4-6 ticks maximum
• [ ] Decimal precision — match the precision to the decision (12% not 12.347%)

Chart Sequencing (Multi-Chart Analyses)

When producing multiple charts for a deep dive or root cause investigation, follow Context → Tension → Resolution:

| Phase | Charts | Purpose | Example | |-------|--------|---------|---------| | Context | 1-2 | Set the baseline. What does normal look like? | "[Dataset] processes ~4,000 support tickets per month" | | Tension | 2-3 | Reveal the problem. Progressively zoom in. | "June spiked to 6,200" → "The spike was iOS payment issues" | | Resolution | 1-2 | Explain why and recommend action. | "iOS v2.3 introduced a bug → fix eliminates ~2,200 tickets/mo" |

• Each chart builds on the previous one
• Never show a chart that makes the audience ask "so what?"
• The number of charts is determined by the storyboard. Each narrative beat that requires a visualization becomes a chart.
• The final chart should make the recommended action obvious

Chart Helper Functions Reference

All chart helpers live in helpers/chart_helpers.py. The style file is helpers/analytics_chart_style.mplstyle. The full style guide with before/after examples is in helpers/chart_style_guide.md.

| Function | Purpose | Key Args | |----------|---------|----------| | swd_style() | Apply SWD matplotlib style, return color palette | — | | highlight_bar() | Bar chart with one bar highlighted, rest gray | highlight=, horizontal=True, sort=True | | highlight_line() | Line chart with one line colored, rest gray | highlight=, y_dict={} | | action_title() | Bold takeaway title + optional subtitle | title, subtitle= | | annotate_point() | Clean annotation with arrow | x, y, text, offset= | | save_chart() | Tight layout + correct DPI | fig, path, dpi=150 |

Theme Definitions

Theme: nyt (New York Times)

NYT_THEME = {
    "colors": {
        "primary": "#000000",
        "secondary": "#666666",
        "accent": "#D03A2B",
        "palette": ["#D03A2B", "#1A6B54", "#3D6CA3", "#E8912D", "#8B5E3C", "#6B4C9A"],
        "background": "#FFFFFF",
        "grid": "#E5E5E5",
    },
    "fonts": {
        "title": {"family": "Georgia", "size": 18, "weight": "bold"},
        "subtitle": {"family": "Arial", "size": 12, "weight": "normal", "color": "#666666"},
        "axis_label": {"family": "Arial", "size": 10},
        "annotation": {"family": "Arial", "size": 9, "style": "italic"},
    },
    "grid": {"show": True, "axis": "y", "style": "--", "alpha": 0.3},
    "annotations": {"style": "minimal", "callout_arrows": True},
    "title": {"position": "left-aligned", "include_subtitle": True},
}

Theme: economist (The Economist)

ECONOMIST_THEME = {
    "colors": {
        "primary": "#1F2E3C",
        "secondary": "#7C8A96",
        "accent": "#E3120B",
        "palette": ["#E3120B", "#1F6ED4", "#36B37E", "#F5A623", "#6554C0", "#00B8D9"],
        "background": "#D7E4E8",
        "grid": "#FFFFFF",
    },
    "fonts": {
        "title": {"family": "Helvetica", "size": 16, "weight": "bold"},
        "subtitle": {"family": "Helvetica", "size": 11, "weight": "normal"},
        "axis_label": {"family": "Helvetica", "size": 9},
        "annotation": {"family": "Helvetica", "size": 8},
    },
    "grid": {"show": True, "axis": "y", "style": "-", "alpha": 0.5, "color": "#FFFFFF"},
    "annotations": {"style": "inline", "red_highlight": True},
    "title": {"position": "left-aligned", "red_bar_top": True},
}

Theme: minimal

MINIMAL_THEME = {
    "colors": {
        "primary": "#333333",
        "secondary": "#999999",
        "accent": "#2563EB",
        "palette": ["#2563EB", "#DC2626", "#059669", "#D97706", "#7C3AED", "#DB2777"],
        "background": "#FFFFFF",
        "grid": "#F0F0F0",
    },
    "fonts": {
        "title": {"family": "Helvetica", "size": 14, "weight": "bold"},
        "subtitle": {"family": "Helvetica", "size": 10, "weight": "normal", "color": "#666666"},
        "axis_label": {"family": "Helvetica", "size": 9},
        "annotation": {"family": "Helvetica", "size": 8},
    },
    "grid": {"show": True, "axis": "y", "style": "-", "alpha": 0.15},
    "annotations": {"style": "minimal", "direct_labels": True},
    "title": {"position": "left-aligned", "include_subtitle": True},
}

Theme: corporate

CORPORATE_THEME = {
    "colors": {
        "primary": "#1B2A4A",
        "secondary": "#5A6B7F",
        "accent": "#0066CC",
        "palette": ["#0066CC", "#00A651", "#FF6600", "#CC0000", "#9933CC", "#00CCCC"],
        "background": "#FFFFFF",
        "grid": "#E8E8E8",
    },
    "fonts": {
        "title": {"family": "Arial", "size": 16, "weight": "bold"},
        "subtitle": {"family": "Arial", "size": 11, "weight": "normal"},
        "axis_label": {"family": "Arial", "size": 10},
        "annotation": {"family": "Arial", "size": 9},
    },
    "grid": {"show": True, "axis": "both", "style": "-", "alpha": 0.2},
    "annotations": {"style": "callout", "box_highlight": True},
    "title": {"position": "center", "include_subtitle": True},
}

Applying a Theme (matplotlib)

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

def apply_theme(fig, ax, theme):
    """Apply a named theme to a matplotlib figure."""
    fig.patch.set_facecolor(theme["colors"]["background"])
    ax.set_facecolor(theme["colors"]["background"])

    # Title styling
    ax.set_title(
        ax.get_title(),
        fontfamily=theme["fonts"]["title"]["family"],
        fontsize=theme["fonts"]["title"]["size"],
        fontweight=theme["fonts"]["title"]["weight"],
        loc="left" if theme["title"]["position"] == "left-aligned" else "center",
        pad=15,
    )

    # Grid
    if theme["grid"]["show"]:
        ax.grid(
            axis=theme["grid"]["axis"],
            linestyle=theme["grid"]["style"],
            alpha=theme["grid"]["alpha"],
            color=theme["colors"].get("grid", "#E0E0E0"),
        )
        ax.set_axisbelow(True)

    # Clean spines
    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.spines["left"].set_alpha(0.3)
    ax.spines["bottom"].set_alpha(0.3)

    # Axis labels
    ax.xaxis.label.set_fontfamily(theme["fonts"]["axis_label"]["family"])
    ax.xaxis.label.set_fontsize(theme["fonts"]["axis_label"]["size"])
    ax.yaxis.label.set_fontfamily(theme["fonts"]["axis_label"]["family"])
    ax.yaxis.label.set_fontsize(theme["fonts"]["axis_label"]["size"])

    plt.tight_layout()

Chart Type Selection

| Data Relationship | Chart Type | When to Use | |---|---|---| | Comparison (categories) | Bar chart (vertical) | Comparing ≤12 categories | | Comparison (many categories) | Bar chart (horizontal) | Comparing >7 categories or long labels | | Comparison (parts of whole) | Stacked bar | Showing composition across categories | | Change over time | Line chart | Continuous time series, trends | | Change over time (few periods) | Bar chart | Discrete periods (quarters, years) | | Correlation | Scatter plot | Relationship between two continuous variables | | Distribution | Histogram | Single variable distribution | | Distribution (compare groups) | Box plot or violin | Distribution comparison across groups | | Proportion | Donut chart | ≤5 segments, one variable | | Flow/Process | Funnel chart | Conversion or drop-off rates | | Intensity | Heatmap | Two categorical dimensions + one value | | Cumulative | Area chart | Running totals over time | | Ranking changes | Bump chart | Rank position changes over time | | Waterfall | Waterfall chart | Additive/subtractive contributions |

Annotation Standards

1. Always label key data points directly — do not rely on legends for primary story elements
2. Use direct labels on bars and line endpoints instead of requiring axis reading
3. Annotate inflection points — mark where trends change with a brief note
4. Titles are takeaways, not descriptions — "Revenue grew 23% after launch" not "Revenue by Month"
5. Subtitles provide context — "Monthly revenue, Jan–Dec 2025, in $M"
6. Source line at bottom-left in small gray text
7. Format numbers for readability — "$1.2M" not "$1,234,567"; "23%" not "0.2345"
8. Max 6 colors in any single chart — use gray for "other" or "rest"
9. Highlight the story — use accent color for the key data point, gray for context

Standard Chart Setup

def create_chart(data, chart_type, theme_name="minimal", title="", subtitle=""):
    """Standard chart creation pattern."""
    theme = {"nyt": NYT_THEME, "economist": ECONOMIST_THEME,
             "minimal": MINIMAL_THEME, "corporate": CORPORATE_THEME}[theme_name]

    fig, ax = plt.subplots(figsize=(10, 6))
    fig.patch.set_facecolor(theme["colors"]["background"])
    ax.set_facecolor(theme["colors"]["background"])

    # Plot data using theme colors
    colors = theme["colors"]["palette"]

    # Set title as takeaway
    ax.set_title(title, fontfamily=theme["fonts"]["title"]["family"],
                 fontsize=theme["fonts"]["title"]["size"],
                 fontweight=theme["fonts"]["title"]["weight"],
                 loc="left", pad=20)
    # Subtitle
    if subtitle:
        ax.text(0, 1.02, subtitle, transform=ax.transAxes,
                fontfamily=theme["fonts"]["subtitle"]["family"],
                fontsize=theme["fonts"]["subtitle"]["size"],
                color=theme["fonts"]["subtitle"].get("color", "#666666"))

    apply_theme(fig, ax, theme)
    return fig, ax

Examples

Example 1: Bar chart with NYT theme

fig, ax = plt.subplots(figsize=(10, 6))
categories = ["Mobile", "Desktop", "Tablet"]
values = [45, 35, 20]
colors = ["#D03A2B", "#666666", "#666666"]  # Accent on key finding

bars = ax.bar(categories, values, color=colors, width=0.6)
# Direct labels
for bar, val in zip(bars, values):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
            f"{val}%", ha="center", fontsize=12, fontweight="bold")

ax.set_title("Mobile drives nearly half of all sessions", loc="left",
             fontfamily="Georgia", fontsize=18, fontweight="bold")
ax.set_ylabel("")
ax.set_ylim(0, 55)
apply_theme(fig, ax, NYT_THEME)

Example 2: Line chart with annotations

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(dates, revenue, color="#2563EB", linewidth=2)
# Annotate the inflection point
ax.annotate("Feature launch\n+23% MoM", xy=(launch_date, launch_value),
            xytext=(launch_date - timedelta(days=30), launch_value + 50000),
            fontsize=9, fontstyle="italic",
            arrowprops=dict(arrowstyle="->", color="#666666"))
# Direct label on endpoint
ax.text(dates[-1], revenue[-1], f"${revenue[-1]/1e6:.1f}M",
        fontsize=11, fontweight="bold", va="bottom")
ax.set_title("Revenue grew 23% after feature launch", loc="left")
apply_theme(fig, ax, MINIMAL_THEME)

Example 3: Highlighting one segment

# Use accent for the key finding, gray for everything else
colors = ["#E0E0E0"] * len(categories)
colors[key_index] = theme["colors"]["accent"]  # Highlight the story

Anti-Patterns (Banned)

| Anti-Pattern | Why It's Bad | Use Instead | |--------------|-------------|-------------| | Pie charts | Humans can't compare angles accurately | Horizontal bar chart | | Rainbow palettes | No natural ordering, visual noise, not colorblind-safe | Gray + one highlight color (max 2 colors + gray) | | Spaghetti lines | Too many colored lines, nothing stands out | highlight_line() — gray all, highlight one | | Dual y-axes | Misleading — any two series can be made to "correlate" | Two separate charts, stacked vertically | | 3D charts | Distorts proportions, adds no information | Flat 2D versions | | Descriptive titles | Don't tell the reader what to think | Action titles via action_title() | | Legend boxes | Force the reader to look away from the data | Direct labels on the data | | Excessive gridlines | Create visual clutter | Light y-axis gridlines only, or none | | Truncated y-axes | Exaggerate small differences (for bar charts) | Start at zero for bar charts | | Cluttered annotations | Annotating every data point defeats the purpose | Annotate only the story | | Default matplotlib styling | Looks generic, unprofessional | Always apply swd_style() first | | More than 2 colors | Creates visual noise, dilutes focus | Gray + Action Amber + optional Accent Red |

Review Checklist

Before including any chart in an analysis:

• [ ] Title states the takeaway (not a description)
• [ ] Only 1-2 colors used (plus gray)
• [ ] No chart border, no top/right spines
• [ ] Direct labels instead of legend
• [ ] Gridlines removed or very light
• [ ] Axis labels are clean (no rotation, no trailing zeros)
• [ ] Annotations are minimal and support the story
• [ ] Chart type matches the data relationship
• [ ] A single number isn't charted — it's displayed as text
• [ ] The chart would be understood in 5 seconds
• [ ] YoY comparisons use lines (not two similar-colored bars)
• [ ] Labels don't collide with bars, axes, or other labels
• [ ] External context events have prominent bbox annotations
• [ ] Multi-panel charts with fig-level titles use direct savefig() (not save_chart())