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: letsplot 4.10.1 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-28
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_point,
geom_smooth,
ggplot,
ggsave,
ggsize,
guide_legend,
guides,
labs,
layer_tooltips,
scale_color_manual,
scale_size_area,
scale_x_continuous,
theme,
theme_minimal,
)
LetsPlot.setup_html()
# 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"
RULE = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
ANYPLOT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030"]
# Data - market analysis: companies by revenue, growth rate, and market share
np.random.seed(42)
sectors = ["Technology", "Healthcare", "Finance", "Energy", "Consumer Goods"]
rev_ranges = [(15, 120), (20, 140), (50, 200), (60, 195), (10, 130)]
growth_params = [(28, -0.10), (18, -0.04), (10, -0.02), (7, -0.01), (15, -0.05)]
share_means = [10, 14, 20, 22, 12]
counts = [10, 10, 9, 8, 8]
rows = []
for sector, (rev_lo, rev_hi), (g_base, g_slope), s_mean, n in zip(
sectors, rev_ranges, growth_params, share_means, counts, strict=True
):
rev = np.random.uniform(rev_lo, rev_hi, n)
growth = g_base + g_slope * rev + np.random.randn(n) * 2.5
share = np.clip(np.random.randn(n) * 5 + s_mean, 2, 30)
for r, g, s in zip(rev, growth, share, strict=True):
rows.append({"revenue": r, "growth_rate": g, "market_share": s, "sector": sector})
df = pd.DataFrame(rows)
# Plot
plot = (
ggplot(df, aes(x="revenue", y="growth_rate", size="market_share", color="sector"))
+ geom_point(
alpha=0.7,
tooltips=layer_tooltips()
.format("revenue", "${.1f}M")
.format("growth_rate", "{.1f}%")
.format("market_share", "{.1f}%")
.line("@sector")
.line("Revenue|@revenue")
.line("Growth|@growth_rate")
.line("Market Share|@market_share"),
)
+ geom_smooth(
aes(x="revenue", y="growth_rate"),
method="loess",
color=INK_SOFT,
size=1.5,
alpha=0.12,
inherit_aes=False,
show_legend=False,
)
+ scale_size_area(max_size=22, name="Market Share (%)", breaks=[5, 10, 15, 20, 25])
+ scale_color_manual(values=ANYPLOT_PALETTE, name="Sector")
+ scale_x_continuous(expand=[0.02, 10])
+ guides(
color=guide_legend(nrow=1, override_aes={"size": 7}),
size=guide_legend(nrow=1, override_aes={"color": ANYPLOT_PALETTE[0], "alpha": 0.7}),
)
+ labs(x="Revenue (Million USD)", y="Growth Rate (%)", title="bubble-basic · python · letsplot · anyplot.ai")
+ theme_minimal()
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
axis_title=element_text(size=12, color=INK),
axis_text=element_text(size=10, color=INK_SOFT),
axis_line=element_line(color=INK_SOFT),
plot_title=element_text(size=16, color=INK),
plot_margin=[30, 20, 20, 20],
legend_title=element_text(size=10, color=INK),
legend_text=element_text(size=10, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT, size=0.5),
panel_grid_major=element_line(size=0.3, color=RULE),
panel_grid_minor=element_blank(),
legend_position="bottom",
)
+ ggsize(800, 450)
)
# Save
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Basic Bubble Chart on anyplot.ai.