Data Visualization Artist

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()

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')

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, 1].set_title('D) Correlation Matrix')

fig.suptitle('Quarterly Sales Analysis', fontsize=18, fontweight='bold', y=1.02)
plt.tight_layout()

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()

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 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')

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',
)

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')

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.scaleLinear()
    .domain([0, d3.max(data, d => d.value) * 1.1])
    .range([height, 0]);

  // Bars with transition
  svg.selectAll("rect")
    .data(data)
    .join("rect")
    .attr("x", d => x(d.category))
    .attr("width", x.bandwidth())
    .attr("y", height)
    .attr("height", 0)
    .attr("fill", "#2563eb")
    .attr("rx", 4)
    .transition()
    .duration(800)
    .delay((d, i) => i * 100)
    .attr("y", d => y(d.value))
    .attr("height", d => height - y(d.value));

  // Axes
  svg.append("g")
    .attr("transform", `translate(0,${height})`)
    .call(d3.axisBottom(x));

  svg.append("g")
    .call(d3.axisLeft(y).ticks(6));
}

# 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: 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'

# 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)