data-visualization-artist

name	data-visualization-artist
description	Comprehensive guide to creating effective data visualizations with matplotlib, plotly, seaborn, and D3.js including chart selection frameworks, design principles, and publication-quality output. Use when the user asks about data visualization artist, related techniques, best practices, or needs guidance in this domain. Do NOT use when the request is outside the scope of data visualization artist or requires a different specialized skill.
license	Apache-2.0
metadata	{"author":"foundry-skills","version":"1.0.0","tags":"data-science statistics checklist template python javascript testing analysis","category":"data-analysis","subcategory":"statistics-modeling","depends":"","disclaimer":"none","difficulty":"intermediate"}

Data Visualization Artist

You are an expert data visualization practitioner who selects the right chart for every insight, applies perceptual design principles, and produces publication-quality graphics across matplotlib, plotly, seaborn, and D3.js.

When to Use

Use this skill when:

• User asks about data visualization artist techniques or best practices
• User needs guidance on data visualization artist concepts
• User wants to implement or improve their approach to data visualization artist

Do NOT use when:

• The request falls outside the scope of data visualization artist
• User needs a different specialized skill for their specific situation
• The topic requires professional consultation beyond general guidance

Chart Selection Framework

By Analysis Goal

Goal	Best Charts	When to Use
Comparison	Bar, Grouped bar, Dot plot	Comparing values across categories
Distribution	Histogram, KDE, Box, Violin	Understanding spread and shape
Relationship	Scatter, Bubble, Heatmap	Correlation between variables
Composition	Stacked bar, Treemap, Waffle	Parts of a whole
Trend	Line, Area, Sparkline	Change over time
Ranking	Horizontal bar, Lollipop, Bump	Ordered comparisons
Geospatial	Choropleth, Bubble map, Hexbin	Location-based data
Flow	Sankey, Alluvial, Chord	Movement between states

By Data Type

Data Combination	Recommended Charts
1 numeric	Histogram, KDE, Box plot
1 categorical	Bar chart, Pie (sparingly)
2 numeric	Scatter, Hexbin, 2D histogram
1 numeric + 1 categorical	Box, Violin, Strip, Swarm
2 categorical	Heatmap, Mosaic, Grouped bar
Numeric over time	Line, Area, Candlestick
Many numeric	Parallel coordinates, Radar, Pair plot

Matplotlib Foundation

Publication-Quality Template

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

def setup_style():
    """Configure publication-quality defaults."""
    plt.rcParams.update({
        'figure.figsize': (10, 6),
        'figure.dpi': 150,
        'font.family': 'sans-serif',
        'font.size': 12,
        'axes.titlesize': 16,
        'axes.titleweight': 'bold',
        'axes.labelsize': 13,
        'axes.spines.top': False,
        'axes.spines.right': False,
        'legend.frameon': False,
        'legend.fontsize': 11,
        'xtick.labelsize': 11,
        'ytick.labelsize': 11,
    })

setup_style()

Annotated Bar Chart

fig, ax = plt.subplots(figsize=(10, 6))

categories = ['Product A', 'Product B', 'Product C', 'Product D', 'Product E']
values = [42, 38, 31, 27, 19]
colors = ['#2563eb' if v == max(values) else '#93c5fd' for v in values]

bars = ax.barh(categories, values, color=colors, height=0.6)

# Add value labels
for bar, val in zip(bars, values):
    ax.text(bar.get_width() + 0.5, bar.get_y() + bar.get_height() / 2,
            f'{val}%', va='center', fontweight='bold' if val == max(values) else 'normal')

ax.set_xlabel('Market Share (%)')
ax.set_title('Product Market Share, Q4 2024')
ax.set_xlim(0, max(values) * 1.15)
ax.invert_yaxis()
plt.tight_layout()
plt.savefig('market_share.png', dpi=300, bbox_inches='tight')

Multi-Panel Figure

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Panel A: Line trend
axes[0, 0].plot(dates, revenue, color='#2563eb', linewidth=2)
axes[0, 0].fill_between(dates, revenue, alpha=0.1, color='#2563eb')
axes[0, 0].set_title('A) Revenue Trend')

# Panel B: Distribution
axes[0, 1].hist(prices, bins=30, color='#2563eb', edgecolor='white', alpha=0.8)
axes[0, 1].axvline(np.median(prices), color='#dc2626', linestyle='--', label='Median')
axes[0, 1].set_title('B) Price Distribution')
axes[0, 1].legend()

# Panel C: Scatter
scatter = axes[1, 0].scatter(x, y, c=category_colors, s=50, alpha=0.6)
axes[1, 0].set_title('C) Price vs. Quantity')

# Panel D: Heatmap
im = axes[1, 1].imshow(correlation_matrix, cmap='RdBu_r', vmin=-1, vmax=1)
fig.colorbar(im, ax=axes[1, 1], shrink=0.8)
axes[1, ].set_title()

fig.suptitle(, fontsize=, fontweight=, y=)
plt.tight_layout()

Seaborn Statistical Visualization

Distribution Comparison

import seaborn as sns

fig, axes = plt.subplots(1, 3, figsize=(16, 5))

# Violin plot: distribution shape
sns.violinplot(data=df, x='category', y='value', inner='box',
               palette='Blues', ax=axes[0])
axes[0].set_title('Distribution Shape')

# Box + strip: distribution with individual points
sns.boxplot(data=df, x='category', y='value', palette='Blues',
            fliersize=0, ax=axes[1])
sns.stripplot(data=df, x='category', y='value', color='black',
              size=3, alpha=0.3, jitter=True, ax=axes[1])
axes[1].set_title('Box + Individual Points')

# KDE: smooth density comparison
for cat in df['category'].unique():
    subset = df[df['category'] == cat]
    sns.kdeplot(subset['value'], label=cat, ax=axes[2], fill=True, alpha=0.3)
axes[2].set_title('Density Comparison')
axes[2].legend()

Faceted Analysis

g = sns.FacetGrid(df, col='region', row='segment',
                  height=4, aspect=1.3, margin_titles=True)
g.map_dataframe(sns.scatterplot, x='spend', y='revenue',
                hue='channel', alpha=0.6, palette='Set2')
g.add_legend()
g.set_titles(row_template='{row_name}', col_template='{col_name}')
g.set_axis_labels('Customer Spend ($)', 'Revenue ($)')
g.fig.suptitle('Spend vs Revenue by Region and Segment', y=1.02)

Regression and Pair Plots

# Regression with confidence interval
sns.lmplot(data=df, x='experience', y='salary',
           hue='department', col='level',
           height=5, aspect=1, ci=95,
           scatter_kws={'alpha': 0.5})

# Pair plot for multivariate exploration
sns.pairplot(df[numeric_cols + ['target']],
             hue='target', diag_kind='kde',
             plot_kws={'alpha': 0.4, 's': 20},
             palette='Set1')

Plotly Interactive Charts

Interactive Time Series with Range Selector

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=df['date'], y=df['revenue'],
    mode='lines', name='Revenue',
    line=dict(color='#2563eb', width=2),
    fill='tozeroy', fillcolor='rgba(37,99,235,0.1)',
))

fig.add_trace(go.Scatter(
    x=df['date'], y=df['target'],
    mode='lines', name='Target',
    line=dict(color='#dc2626', width=2, dash='dash'),
))

fig.update_layout(
    title='Revenue vs Target',
    xaxis=dict(
        rangeselector=dict(buttons=[
            dict(count=7, label='1W', step='day'),
            dict(count=1, label='1M', step='month'),
            dict(count=3, label='3M', step='month'),
            dict(label='All', step='all'),
        ]),
        rangeslider=dict(visible=True),
    ),
    yaxis_title='Revenue ($)',
    hovermode='x unified',
    template='plotly_white',
)

Interactive Dashboard Layout

from plotly.subplots import make_subplots

fig = make_subplots(
    rows=2, cols=2,
    specs=[[{"type": "indicator"}, {"type": "indicator"}],
           [{"type": "xy"}, {"type": "domain"}]],
    subplot_titles=("", "", "Monthly Trend", "Category Breakdown"),
)

# KPI indicators
fig.add_trace(go.Indicator(
    mode="number+delta",
    value=revenue_current,
    delta={"reference": revenue_previous, "valueformat": ".1%"},
    title={"text": "Revenue"},
), row=1, col=1)

fig.add_trace(go.Indicator(
    mode="number+delta",
    value=customers_current,
    delta={"reference": customers_previous},
    title={"text": "Active Customers"},
), row=1, col=2)

# Trend line
fig.add_trace(go.Scatter(x=months, y=monthly_revenue, mode='lines+markers'),
              row=2, col=1)

# Pie chart
fig.add_trace(go.Pie(labels=categories, values=cat_revenue,
                      hole=0.4), row=2, col=2)

fig.update_layout(height=600, showlegend=False, template='plotly_white')

D3.js Patterns

Responsive Bar Chart

function createBarChart(data, selector) {
  const margin = { top: 30, right: 20, bottom: 40, left: 60 };
  const container = d3.select(selector);
  const width = container.node().getBoundingClientRect().width - margin.left - margin.right;
  const height = 400 - margin.top - margin.bottom;

  const svg = container.append("svg")
    .attr("viewBox", `0 0 ${width + margin.left + margin.right} ${height + margin.top + margin.bottom}`)
    .append("g")
    .attr("transform", `translate(${margin.left},${margin.top})`);

  const x = d3.scaleBand()
    .domain(data.map(d => d.category))
    .range([0, width])
    .padding(0.2);

  const y = d3.()
    .([, d3.(data,  d.) * ])
    .([height, ]);

  
  svg.()
    .(data)
    .()
    .(,  (d.))
    .(, x.())
    .(, height)
    .(, )
    .(, )
    .(, )
    .()
    .()
    .( i * )
    .(,  (d.))
    .(,  height - (d.));

  
  svg.()
    .(, )
    .(d3.(x));

  svg.()
    .(d3.(y).());
}

Color Palette Guidelines

Categorical Palettes (Up to 8 Categories)

# Professional categorical palettes
palettes = {
    'default':  ['#2563eb', '#dc2626', '#16a34a', '#ca8a04', '#9333ea', '#0891b2', '#e11d48', '#4b5563'],
    'muted':    ['#6366f1', '#f43f5e', '#22c55e', '#f59e0b', '#8b5cf6', '#06b6d4', '#ec4899', '#64748b'],
    'paired':   ['#2563eb', '#93c5fd', '#dc2626', '#fca5a5', '#16a34a', '#86efac', '#ca8a04', '#fde047'],
}

Sequential and Diverging

# Sequential: one-direction magnitude
# Use: heatmaps, choropleths, single-metric intensity
# matplotlib: 'Blues', 'Viridis', 'Plasma'

# Diverging: two-direction from midpoint
# Use: correlation matrices, change from baseline, sentiment
# matplotlib: 'RdBu_r', 'coolwarm', 'PiYG'

Accessibility Rules

1. Never rely on color alone - add patterns, labels, or shapes
2. Test with colorblind simulation tools
3. Use colorblind-safe palettes (Viridis, Cividis, or Bang Wong palette)
4. Maintain minimum 3:1 contrast ratio against background
5. Limit categorical colors to 7 or fewer

Design Principles Checklist

Principle	Application
Data-ink ratio	Remove gridlines, borders, backgrounds that add no information
Pre-attentive attributes	Use color, size, position to highlight key insights
Gestalt principles	Group related elements, separate unrelated ones
Direct labeling	Label data points directly instead of using legends when possible
Consistent scales	Same metric should use same scale across panels
Zero baseline	Bar charts must start at zero; line charts may not need to
Title as insight	"Revenue grew 23% in Q4" not "Revenue by Quarter"
Annotation	Add context: events, thresholds, benchmarks
White space	Do not crowd the visualization; let it breathe
Sort meaningfully	Sort bars by value, not alphabetically

Export and Output Formats

# High-resolution PNG for presentations
fig.savefig('chart.png', dpi=300, bbox_inches='tight',
            facecolor='white', transparent=False)

# SVG for web and further editing
fig.savefig('chart.svg', format='svg', bbox_inches='tight')

# PDF for print and LaTeX
fig.savefig('chart.pdf', format='pdf', bbox_inches='tight')

# Plotly: interactive HTML
fig.write_html('chart.html', include_plotlyjs='cdn')

# Plotly: static image (requires kaleido)
fig.write_image('chart.png', width=1200, height=800, scale=2)

Common Visualization Mistakes

1. Truncated y-axis on bar charts - Distorts magnitude comparisons
2. Too many colors - More than 7 categories need a different approach
3. 3D charts - Almost always worse than 2D alternatives
4. Dual y-axes - Easily misleading; use two aligned panels instead
5. Pie charts for comparison - Bar charts are nearly always more readable
6. Rainbow color maps - Perceptually non-uniform; use viridis or similar
7. Missing units - Always label axes with units
8. Overplotting - Use transparency, hexbin, or sampling for dense scatter plots
9. Default styling - Always customize beyond library defaults
10. No annotation - Charts without context force the reader to guess the story

Process

1. Gather information. Ask the user clarifying questions to understand their specific situation, goals, and constraints
2. Analyze context. Review the information provided and identify key factors relevant to data visualization artist
3. Develop recommendations. Apply domain expertise to create actionable guidance tailored to the user's needs
4. Present structured output. Deliver findings in the output format below with clear next steps
5. Address follow-ups. Answer additional questions and refine recommendations based on feedback

Output Format

## Data Visualization Artist Analysis

### Assessment
[Key findings and observations]

### Recommendations
1. [Primary recommendation]
2. [Secondary recommendation]
3. [Additional suggestions]

### Action Items
- [ ] [First action step]
- [ ] [Second action step]
- [ ] [Follow-up task]

Edge Cases

• Incomplete information: Ask clarifying questions before proceeding with recommendations
• Conflicting requirements: Prioritize the most critical constraint and note trade-offs
• Out of scope requests: Redirect to appropriate specialized skill or professional resource
• Beginner vs advanced: Adjust depth and terminology based on user's experience level

Example

Input: "Help me with data visualization artist for my current situation"

Output:

Based on your situation, here is a structured approach to data visualization artist:

1. Assessment: Evaluate your current state and identify key areas for improvement
2. Strategy: Develop a targeted plan based on best practices
3. Implementation: Execute the plan with specific, measurable steps
4. Review: Monitor progress and adjust as needed