The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Basic Bubble Chart in Python, R, Julia and JavaScript.
A bubble chart extending scatter plots by adding a third dimension through bubble size. Each point's position shows two variables (x, y) while the bubble size represents a third quantitative variable. This visualization is excellent for understanding relationships between three numerical variables simultaneously, revealing patterns that would be hidden in traditional 2D scatter plots.
""" anyplot.ai
bubble-basic: Basic Bubble Chart
Library: plotly 7.1.0 | Python 3.13.15
Quality: 92/100 | Updated: 2026-09-27
"""
import os
import numpy as np
import plotly.graph_objects as go
# Theme
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(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Continuous colorscale — imprint_seq (brand green → blue, single-polarity)
imprint_seq = [[0.0, "#009E73"], [1.0, "#4467A3"]]
# Data — Tech companies: R&D investment vs. product-market-fit score
np.random.seed(42)
n = 62
rd_pct = np.random.uniform(4, 42, n) # R&D spend as % of revenue
pmf = np.clip(rd_pct * 1.3 + np.random.normal(0, 17, n) + 18, 8, 98)
revenue_m = np.clip(np.abs(np.random.normal(750, 380, n)), 40, 2200)
# Anchor key data points for visual storytelling
rd_pct[0], pmf[0], revenue_m[0] = 39, 96, 2100 # R&D powerhouse
rd_pct[1], pmf[1], revenue_m[1] = 36, 91, 1800
rd_pct[2], pmf[2], revenue_m[2] = 33, 87, 1650
rd_pct[5], pmf[5], revenue_m[5] = 7, 24, 180 # underinvestors
rd_pct[6], pmf[6], revenue_m[6] = 5, 18, 120
rd_pct[10], pmf[10], revenue_m[10] = 22, 92, 920 # efficient innovator
# Bubble sizing via sizeref (Plotly's idiomatic area-based scaling).
# Max on-screen diameter capped at 34 and sizemin lowered to 5 so the dense
# low-R&D/low-PMF cluster overlaps less while the lowest-revenue bubbles
# stay clearly visible.
sizeref = 2.0 * float(revenue_m.max()) / (34.0**2)
cmin, cmax = float(revenue_m.min()), float(revenue_m.max())
# Legend swatch colors tinted along imprint_seq (green->blue) so each swatch
# matches what a real data bubble of that revenue would render.
imprint_seq_rgb = (np.array([0x00, 0x9E, 0x73]), np.array([0x44, 0x67, 0xA3]))
title = "R&D Investment vs. Product-Market Fit · bubble-basic · python · plotly · anyplot.ai"
title_size = max(11, round(16 * 67 / len(title))) if len(title) > 67 else 16
# Plot
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=rd_pct,
y=pmf,
mode="markers",
customdata=revenue_m,
marker={
"size": revenue_m,
"sizemode": "area",
"sizeref": sizeref,
"sizemin": 5,
"color": revenue_m,
"cmin": cmin,
"cmax": cmax,
"colorscale": imprint_seq,
"showscale": False,
"opacity": 0.5,
"line": {"width": 2, "color": PAGE_BG},
},
hovertemplate=(
"<b>R&D Spend:</b> %{x:.1f}%<br>"
"<b>PMF Score:</b> %{y:.0f}<br>"
"<b>Revenue:</b> $%{customdata:.0f}M<extra></extra>"
),
showlegend=False,
)
)
# Size legend — three reference bubbles (anchored near the data's min/mid/max
# revenue), tinted along imprint_seq so the swatch color matches what a real
# data bubble of that revenue would render
for size_label in [100, 600, 2000]:
t = max(0.0, min(1.0, (size_label - cmin) / (cmax - cmin)))
r, g, b = (imprint_seq_rgb[0] + (imprint_seq_rgb[1] - imprint_seq_rgb[0]) * t).round().astype(int)
fig.add_trace(
go.Scatter(
x=[None],
y=[None],
mode="markers",
marker={
"size": size_label,
"sizemode": "area",
"sizeref": sizeref,
"sizemin": 5,
"color": f"#{r:02X}{g:02X}{b:02X}",
"opacity": 0.5,
"line": {"width": 2, "color": PAGE_BG},
},
name=f"${size_label}M",
showlegend=True,
)
)
fig.update_layout(
autosize=False,
title={"text": title, "font": {"size": title_size, "color": INK}, "x": 0.5, "xanchor": "center"},
xaxis={
"title": {"text": "R&D Spend (% of Revenue)", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"gridcolor": GRID,
"gridwidth": 1,
"linecolor": INK_SOFT,
"zeroline": False,
"ticks": "",
},
yaxis={
"title": {"text": "Product-Market-Fit Score", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"gridcolor": GRID,
"gridwidth": 1,
"linecolor": INK_SOFT,
"zeroline": False,
"ticks": "",
},
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK, "family": "Lato, 'DejaVu Sans', sans-serif"},
legend={
"title": {"text": "Revenue", "font": {"size": 10, "color": INK}},
"font": {"size": 10, "color": INK_SOFT},
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 0.5,
"x": 0.02,
"y": 0.98,
"xanchor": "left",
"yanchor": "top",
},
margin={"l": 80, "r": 40, "t": 80, "b": 60},
# Subtle zone highlight around the R&D-powerhouse cluster (indices 0-2:
# high R&D spend, high PMF, high revenue) — a third layer of visual
# hierarchy beyond the two point-specific callouts below.
shapes=[
{
"type": "rect",
"x0": 29,
"x1": 41,
"y0": 83,
"y1": 99,
"line": {"color": INK_SOFT, "width": 1, "dash": "dot"},
"fillcolor": GRID,
"layer": "below",
}
],
annotations=[
{
"x": 35,
"y": 99,
"yshift": 6,
"text": "R&D powerhouses",
"font": {"size": 9, "color": INK_SOFT},
"showarrow": False,
"xanchor": "center",
"yanchor": "bottom",
},
{
"x": rd_pct[10],
"y": pmf[10],
"text": "Efficient innovator<br>(low R&D, high PMF)",
"font": {"size": 10, "color": INK},
"showarrow": True,
"arrowhead": 2,
"arrowcolor": INK_SOFT,
"ax": 40,
"ay": -40,
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 0.5,
"borderpad": 4,
},
{
"x": rd_pct[6],
"y": pmf[6],
"text": "Underinvested laggard<br>(low R&D, low PMF)",
"font": {"size": 10, "color": INK},
"showarrow": True,
"arrowhead": 2,
"arrowcolor": INK_SOFT,
"ax": -50,
"ay": 40,
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 0.5,
"borderpad": 4,
},
],
)
# Save
fig.write_image(f"plot-{THEME}.png", width=800, height=450, scale=4)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bubble-basic/plotly/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "bubble-basic",
"language": "python",
"library": "plotly",
"page": "https://anyplot.ai/bubble-basic/python/plotly",
"hub": "https://anyplot.ai/bubble-basic",
"code_json": "https://api.anyplot.ai/specs/bubble-basic/plotly/code",
"spec_json": "https://api.anyplot.ai/specs/bubble-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bubble-basic/python/plotly/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bubble-basic/python/plotly/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/bubble-basic/python/plotly/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/bubble-basic/python/plotly/plot-dark.html",
"quality_score": 92.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Bubble Chart on anyplot.ai.