The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, 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: plotnine 0.15.8 | Python 3.13.15
Quality: 89/100 | Updated: 2026-09-27
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_point,
geom_smooth,
ggplot,
guide_legend,
labs,
scale_color_manual,
scale_fill_manual,
scale_size_area,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
# 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"
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Data — countries across 4 regions: GDP per capita, life expectancy, population
np.random.seed(42)
regions = ["Asia-Pacific", "Europe", "Americas", "Middle East & Africa"]
region_params = {
"Asia-Pacific": {"n": 18, "gdp_mean": 25, "gdp_std": 18, "le_base": 72, "pop_mean": 3.6},
"Europe": {"n": 18, "gdp_mean": 45, "gdp_std": 15, "le_base": 78, "pop_mean": 2.6},
"Americas": {"n": 18, "gdp_mean": 30, "gdp_std": 20, "le_base": 70, "pop_mean": 3.2},
"Middle East & Africa": {"n": 18, "gdp_mean": 12, "gdp_std": 10, "le_base": 62, "pop_mean": 3.0},
}
rows = []
for region, p in region_params.items():
gdp = np.abs(np.random.normal(p["gdp_mean"], p["gdp_std"], p["n"]))
gdp = np.clip(gdp, 3, 85)
le = p["le_base"] + 0.15 * gdp + np.random.normal(0, 2.5, p["n"])
le = np.clip(le, 52, 88)
# Clip to a raised size-value floor (18-100, within the spec's 10-100 band) so
# even the smallest bubble renders with a clearly visible radius at max_size=18
pop = np.random.lognormal(mean=p["pop_mean"], sigma=0.55, size=p["n"])
pop = np.clip(pop, 18, 100)
for i in range(p["n"]):
rows.append({"gdp_per_capita": gdp[i], "life_expectancy": le[i], "population": pop[i], "region": region})
df = pd.DataFrame(rows)
df["region"] = pd.Categorical(df["region"], categories=regions, ordered=True)
# Plot
plot = (
ggplot(df, aes(x="gdp_per_capita", y="life_expectancy", size="population", fill="region"))
# Per-region fit (color/fill mapped to region) instead of one pooled trend line —
# shows each region's own GDP/life-expectancy slope and confidence band rather than
# flattening the four groups into a single line. show_legend=False keeps the legend
# limited to the bubble fill key (same scale, so colors already match).
+ geom_smooth(
aes(x="gdp_per_capita", y="life_expectancy", color="region", fill="region"),
method="lm",
se=True,
alpha=0.08,
size=0.8,
inherit_aes=False,
show_legend=False,
)
# geom_point's default shape already splits fill (region color) from edge
# (color); fixing color to the page bg gives each bubble a subtle halo that
# separates it from overlapping neighbors. alpha lowered further (0.55 -> 0.45)
# to keep the densest cluster (Middle East & Africa, ~$15-20k GDP) from merging
# into a single blob.
+ geom_point(color=PAGE_BG, alpha=0.45, stroke=0.5)
# limits=(0, 100) anchors the area scaling to a true zero baseline; without it
# plotnine anchors the sqrt-area formula to the data's own min, which maps the
# smallest (floor-clipped) bubbles to a near-zero rendered size. max_size trimmed
# from 18 to 15 so the largest bubbles overlap less severely in dense clusters.
# override_aes gives the size-legend's key glyphs a visible INK_SOFT fill/edge —
# without it they'd inherit geom_point's fixed color=PAGE_BG stroke, which blends
# into the PAGE_BG legend_key background and renders as empty boxes.
+ scale_size_area(
max_size=15,
breaks=[20, 50, 90],
limits=(0, 100),
name="Population (M)",
guide=guide_legend(override_aes={"fill": INK_SOFT, "color": INK_SOFT, "alpha": 1}),
)
+ scale_fill_manual(values=IMPRINT_PALETTE[:4], name="Region")
+ scale_color_manual(values=IMPRINT_PALETTE[:4], guide=None)
+ scale_x_continuous(labels=lambda lst: [f"${v:.0f}k" for v in lst], breaks=[10, 20, 30, 40, 50, 60, 70, 80])
+ scale_y_continuous(labels=lambda lst: [f"{v:.0f}" for v in lst])
+ labs(
x="GDP per Capita (USD thousands)",
y="Life Expectancy (years)",
title="bubble-basic · python · plotnine · anyplot.ai",
subtitle="Each region's own GDP-to-life-expectancy trend, with population size layered on top",
)
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
plot_margin_right=0.025,
text=element_text(size=7, color=INK_SOFT),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
plot_title=element_text(size=12, color=INK),
plot_subtitle=element_text(size=8, color=INK_SOFT),
legend_title=element_text(size=8, color=INK),
legend_text=element_text(size=8, color=INK_SOFT),
legend_key=element_rect(fill=PAGE_BG, color="none"),
legend_background=element_rect(fill=ELEVATED_BG, color="none"),
panel_grid_major=element_line(color=INK, size=0.3, alpha=0.15),
panel_grid_minor=element_blank(),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color="none"),
panel_border=element_blank(),
axis_line=element_line(color=INK_SOFT),
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/bubble-basic/plotnine/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": "plotnine",
"page": "https://anyplot.ai/bubble-basic/python/plotnine",
"hub": "https://anyplot.ai/bubble-basic",
"code_json": "https://api.anyplot.ai/specs/bubble-basic/plotnine/code",
"spec_json": "https://api.anyplot.ai/specs/bubble-basic",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/bubble-basic/python/plotnine/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/bubble-basic/python/plotnine/plot-dark.png",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Basic Bubble Chart on anyplot.ai.