The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Plotly, 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: matplotlib 3.11.2 | Python 3.13.15
Quality: 86/100 | Updated: 2026-09-30
"""
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FuncFormatter, NullLocator
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
# 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint palette — 8 hues, canonical order
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Data — retail product portfolio: price vs quality rating, sales volume as bubble size
np.random.seed(42)
category_specs = [
{
"name": "Electronics",
"price_mean": 320,
"price_std": 170,
"quality_mean": 7.2,
"quality_std": 1.0,
"sales_mean": 45,
"sales_std": 22,
"n": 24,
},
{
"name": "Furniture",
"price_mean": 680,
"price_std": 240,
"quality_mean": 6.7,
"quality_std": 1.2,
"sales_mean": 18,
"sales_std": 9,
"n": 20,
},
{
"name": "Kitchenware",
"price_mean": 55,
"price_std": 30,
"quality_mean": 6.1,
"quality_std": 1.4,
"sales_mean": 68,
"sales_std": 32,
"n": 26,
},
{
"name": "Outdoor Gear",
"price_mean": 190,
"price_std": 85,
"quality_mean": 7.6,
"quality_std": 0.9,
"sales_mean": 30,
"sales_std": 14,
"n": 20,
},
]
price_parts, quality_parts, sales_parts, category_parts = [], [], [], []
for spec in category_specs:
n = spec["n"]
prices = np.random.normal(spec["price_mean"], spec["price_std"], n)
qualities = np.random.normal(spec["quality_mean"], spec["quality_std"], n)
sales = np.random.normal(spec["sales_mean"], spec["sales_std"], n)
# Explicit outlier pair per category — a bargain-bin low and a flagship
# high, derived from that category's own mean/std — so every category
# visibly spans its full realistic range instead of relying on luck.
bargain_price = max(spec["price_mean"] - 2.2 * spec["price_std"], 5)
flagship_price = spec["price_mean"] + 2.6 * spec["price_std"]
prices = np.append(prices, [bargain_price, flagship_price])
qualities = np.append(
qualities, [spec["quality_mean"] - 1.6 * spec["quality_std"], spec["quality_mean"] + 1.7 * spec["quality_std"]]
)
sales = np.append(
sales, [spec["sales_mean"] + 1.4 * spec["sales_std"], spec["sales_mean"] - 0.9 * spec["sales_std"]]
)
price_parts.append(prices)
quality_parts.append(qualities)
sales_parts.append(sales)
category_parts.extend([spec["name"]] * (n + 2))
price = np.clip(np.concatenate(price_parts), 8, 1200)
quality_rating = np.clip(np.concatenate(quality_parts), 1, 10)
sales_volume = np.clip(np.concatenate(sales_parts), 3, 130)
category = np.array(category_parts)
category_names = [spec["name"] for spec in category_specs]
category_colors = IMPRINT_PALETTE[:4]
# Scale bubble sizes by area for accurate visual perception (tuned for 3200×1800 canvas).
size_scaled = (sales_volume / sales_volume.max()) * 380 + 25
# Plot
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
for name, color in zip(category_names, category_colors, strict=True):
mask = category == name
ax.scatter(
price[mask],
quality_rating[mask],
s=size_scaled[mask],
alpha=0.5,
color=color,
edgecolors=PAGE_BG,
linewidths=0.8,
label=name,
zorder=3,
)
# Size legend — a real inset panel (matplotlib-native technique), placed in the
# one clear gap in the data (high price, low quality) so it never covers bubbles
legend_caps = [15, 60, 120]
legend_ax = inset_axes(
ax,
width="20%",
height="30%",
loc="lower left",
bbox_to_anchor=(0.62, 0.04, 1, 1),
bbox_transform=ax.transAxes,
borderpad=0,
)
legend_ax.set_facecolor(ELEVATED_BG)
legend_ax.set_xlim(0, 1)
legend_ax.set_ylim(0, 1)
legend_ax.set_xticks([])
legend_ax.set_yticks([])
for spine in legend_ax.spines.values():
spine.set_color(INK_SOFT)
spine.set_linewidth(0.8)
legend_ax.set_title("Sales Volume", fontsize=10, fontweight="bold", color=INK, loc="left", pad=4)
for cap, y in zip(legend_caps, (0.18, 0.5, 0.84), strict=True):
legend_ax.scatter(
0.28,
y,
s=(cap / sales_volume.max()) * 380 + 25,
color=INK_MUTED,
alpha=0.5,
edgecolors=PAGE_BG,
linewidths=0.8,
transform=legend_ax.transAxes,
clip_on=False,
)
legend_ax.text(0.55, y, f"{cap}K units", fontsize=9, color=INK_SOFT, va="center", transform=legend_ax.transAxes)
# Category color legend — upper right, bold title leads the eye ahead of the body text
category_legend = ax.legend(
fontsize=9,
loc="upper right",
framealpha=1.0,
facecolor=ELEVATED_BG,
edgecolor=INK_SOFT,
title="Product Category",
title_fontsize=10,
markerscale=0.7,
handletextpad=0.5,
borderpad=0.7,
)
plt.setp(category_legend.get_title(), color=INK, fontweight="bold")
plt.setp(category_legend.get_texts(), color=INK_SOFT)
# Style — descriptive prefix clarifies the retail domain; title fontsize scales
# down since the prefix pushes past the 67-char baseline the style-guide default
# is tuned for.
title = "Retail Product Portfolio · bubble-basic · python · matplotlib · anyplot.ai"
title_fontsize = max(8, round(12 * 67 / len(title))) if len(title) > 67 else 12
ax.set_xlabel("Retail Price ($, log scale)", fontsize=10, color=INK, labelpad=8)
ax.set_ylabel("Customer Quality Rating (1–10)", fontsize=10, color=INK, labelpad=8)
ax.set_title(title, fontsize=title_fontsize, fontweight="medium", color=INK, pad=12)
ax.tick_params(axis="both", labelsize=8, labelcolor=INK_SOFT, length=0)
for spine in ax.spines.values():
spine.set_visible(False)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK, zorder=0)
ax.xaxis.grid(False)
# Log x-axis: retail price is heavily right-skewed (Kitchenware ~$55 mean vs
# Furniture ~$680 mean), so a linear axis crushes the low-price cluster into an
# unreadable clump. Log spacing gives that region room to breathe while keeping
# the high-price tail proportionate.
ax.set_xscale("log")
ax.set_xlim(7, 1400)
ax.set_xticks([10, 30, 100, 300, 1000])
ax.xaxis.set_minor_locator(NullLocator())
ax.xaxis.set_major_formatter(FuncFormatter(lambda v, _: f"${v:,.0f}"))
ax.set_ylim(1, 10.8)
fig.subplots_adjust(left=0.09, right=0.97, top=0.92, bottom=0.12)
# Save
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bubble-basic/matplotlib/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": "matplotlib",
"page": "https://anyplot.ai/bubble-basic/python/matplotlib",
"hub": "https://anyplot.ai/bubble-basic",
"code_json": "https://api.anyplot.ai/specs/bubble-basic/matplotlib/code",
"spec_json": "https://api.anyplot.ai/specs/bubble-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bubble-basic/python/matplotlib/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bubble-basic/python/matplotlib/plot-dark.png",
"quality_score": 86.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Bubble Chart on anyplot.ai.