Annotated Scatter Plot with Text Labels — Plotly

A scatter plot where each data point can have a text label annotation, useful for identifying specific points of interest. This visualization enhances basic scatter plots by adding contextual information directly on the chart, making it easy to highlight outliers, named entities, or key data points that deserve special attention.
Annotated Scatter Plot with Text Labels rendered with Plotly
Python source (Plotly)

""" anyplot.ai
scatter-annotated: Annotated Scatter Plot with Text Labels
Library: plotly 6.7.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-13
"""

import os

import numpy as np
from plotly import graph_objects as go


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
BRAND = "#009E73"
BRAND_ACCENT = "#C475FD"

# Data - Top tech companies by market cap and revenue
np.random.seed(42)

companies = [
    "Apple",
    "Microsoft",
    "Alphabet",
    "Amazon",
    "Meta",
    "Tesla",
    "Nvidia",
    "Samsung",
    "TSMC",
    "Oracle",
    "Salesforce",
    "Netflix",
]

# Market cap (billions USD) - x axis
market_cap = np.array([2800, 2700, 1700, 1500, 900, 700, 1200, 350, 500, 300, 250, 250])

# Annual revenue (billions USD) - y axis
revenue = np.array([380, 210, 280, 520, 130, 95, 60, 230, 70, 50, 32, 33])

# Create figure
fig = go.Figure()

# Identify key companies (top 3 by market cap) for visual emphasis
top_companies = sorted(zip(companies, market_cap), key=lambda x: x[1], reverse=True)[:3]
top_names = {name for name, _ in top_companies}

# Color data points: key companies get accent color, others get brand
marker_colors = [BRAND_ACCENT if company in top_names else BRAND for company in companies]

# Add scatter points with differentiated colors for emphasis
fig.add_trace(
    go.Scatter(
        x=market_cap,
        y=revenue,
        mode="markers",
        marker=dict(
            size=[25 if c in top_names else 20 for c in companies],
            color=marker_colors,
            opacity=0.8,
            line=dict(width=2.5, color="white" if THEME == "light" else PAGE_BG),
        ),
        hovertemplate=(
            "<b>%{text}</b><br>"
            "Market Cap: $%{x:.0f}B<br>"
            "Annual Revenue: $%{y:.0f}B<br>"
            "Efficiency: %{customdata:.2f}x"
            "<extra></extra>"
        ),
        text=companies,
        customdata=revenue / market_cap,
    )
)


# Smart label positioning with collision detection
def calculate_label_positions(companies, market_cap, revenue, x_range=(0, 3000), y_range=(0, 600)):
    """Calculate annotation positions with directional distribution."""
    positions = {}
    directions = [(100, -30), (100, 30), (-100, -30), (-100, 30), (0, 60), (0, -60)]

    for i, company in enumerate(companies):
        positions[company] = directions[i % len(directions)]

    return positions


position_adjustments = calculate_label_positions(companies, market_cap, revenue)

# Create annotations with enhanced styling
annotations = []

for company, cap, rev in zip(companies, market_cap, revenue):
    ax, ay = position_adjustments.get(company, (0, -40))
    is_key = company in top_names

    # Enhanced annotation styling with better visual hierarchy
    annotations.append(
        dict(
            x=cap,
            y=rev,
            text=f"<b>{company}</b>",
            showarrow=True,
            arrowhead=2,
            arrowsize=1.2,
            arrowwidth=2.5,
            arrowcolor=BRAND_ACCENT if is_key else BRAND,
            ax=ax,
            ay=ay,
            font=dict(size=19 if is_key else 18, color=INK, family="Arial, sans-serif"),
            bgcolor=ELEVATED_BG,
            bordercolor=BRAND_ACCENT if is_key else INK_SOFT,
            borderwidth=2 if is_key else 1.5,
            borderpad=5,
            opacity=0.95,
        )
    )

# Update layout with enhanced visual hierarchy and polish
fig.update_layout(
    title=dict(
        text="scatter-annotated · plotly · anyplot.ai",
        font=dict(size=28, color=INK, family="Arial, sans-serif"),
        x=0.5,
        xanchor="center",
        y=0.98,
        yanchor="top",
    ),
    xaxis=dict(
        title=dict(text="Market Cap (Billion USD)", font=dict(size=22, color=INK)),
        tickfont=dict(size=18, color=INK_SOFT),
        gridcolor=GRID,
        gridwidth=1,
        showline=True,
        linewidth=2.5,
        linecolor=INK_SOFT,
        range=[-100, 3100],
        zeroline=False,
    ),
    yaxis=dict(
        title=dict(text="Annual Revenue (Billion USD)", font=dict(size=22, color=INK)),
        tickfont=dict(size=18, color=INK_SOFT),
        gridcolor=GRID,
        gridwidth=1,
        showline=True,
        linewidth=2.5,
        linecolor=INK_SOFT,
        range=[-30, 580],
        zeroline=False,
    ),
    paper_bgcolor=PAGE_BG,
    plot_bgcolor=PAGE_BG,
    annotations=annotations,
    margin=dict(l=120, r=100, t=120, b=120),
    showlegend=False,
    hovermode="closest",
    font=dict(family="Arial, sans-serif", color=INK),
)

# Save with theme-suffixed filenames
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
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Annotated Scatter Plot with Text Labels — Plotly

Python source (Plotly)

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