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: pygal 3.1.0 | Python 3.13.13
Quality: 85/100 | Created: 2026-05-28
"""
import os
import sys
# Remove the script's own directory from sys.path so `import pygal` resolves to the
# installed package rather than this file (which shares the package name).
_here = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if os.path.abspath(p) != _here]
import numpy as np
import pygal
from pygal.style import Style
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Data — City comparison: population density vs avg commute time, bubble = green space (%)
np.random.seed(42)
n_cities = 50
# Uniform spread across density range to avoid lower-left clustering
population_density = np.concatenate(
[np.random.uniform(1500, 5000, 15), np.random.uniform(5000, 9000, 20), np.random.uniform(9000, 14000, 15)]
)
np.random.shuffle(population_density)
commute_time = 15 + population_density / 750 + np.random.normal(0, 2.8, n_cities)
green_space_pct = np.clip(52 - population_density / 380 + np.random.normal(0, 9, n_cities), 5, 55)
# Area-scaled bubble sizing via 8 tiers (pygal has no per-point size API)
gs_min, gs_max = green_space_pct.min(), green_space_pct.max()
bubble_norm = (green_space_pct - gs_min) / (gs_max - gs_min)
n_tiers = 8
tier_bins = np.clip(np.digitize(bubble_norm, np.linspace(0, 1, n_tiers + 1)[1:-1]), 0, n_tiers - 1)
# sqrt scaling for perceptual area accuracy
tier_sizes = [int(12 + 62 * ((t + 0.5) / n_tiers) ** 0.5) for t in range(n_tiers)]
# anyplot imprint_seq: #009E73 (brand green) → #4467A3 (blue), 8 equidistant stops
tier_colors = tuple(
"#{:02X}{:02X}{:02X}".format(
round(0x00 + (0x44 - 0x00) * i / (n_tiers - 1)),
round(0x9E + (0x67 - 0x9E) * i / (n_tiers - 1)),
round(0x73 + (0xA3 - 0x73) * i / (n_tiers - 1)),
)
for i in range(n_tiers)
)
# ANYPLOT_AMBER marks the focal city (9th color in the series cycle)
ANYPLOT_AMBER = "#DDCC77"
style_colors = tier_colors + (ANYPLOT_AMBER,)
# Tier labels serve as the size legend (green-space percentage ranges)
bin_edges_pct = np.linspace(gs_min, gs_max, n_tiers + 1)
tier_labels = [f"{bin_edges_pct[t]:.0f}–{bin_edges_pct[t + 1]:.0f}% green" for t in range(n_tiers)]
# Focal city: highest commute time — visual anchor for data storytelling
focal_idx = int(np.argmax(commute_time))
focal_tier = int(tier_bins[focal_idx])
# Group cities by green-space tier, excluding focal city
tier_data = {t: [] for t in range(n_tiers)}
for i in range(n_cities):
if i == focal_idx:
continue
t = int(tier_bins[i])
tier_data[t].append(
{
"value": (round(float(population_density[i]), 1), round(float(commute_time[i]), 1)),
"label": (
f"Density: {population_density[i]:,.0f}/km² | "
f"Commute: {commute_time[i]:.1f} min | "
f"Green space: {green_space_pct[i]:.0f}%"
),
}
)
focal_point_data = [
{
"value": (round(float(population_density[focal_idx]), 1), round(float(commute_time[focal_idx]), 1)),
"label": (
f"★ Peak Congestion | "
f"Density: {population_density[focal_idx]:,.0f}/km² | "
f"Commute: {commute_time[focal_idx]:.1f} min | "
f"Green space: {green_space_pct[focal_idx]:.0f}%"
),
}
]
# Style — theme-adaptive, anyplot sizing
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK,
foreground_strong=INK,
foreground_subtle=INK_MUTED,
colors=style_colors,
opacity=0.70,
opacity_hover=0.95,
title_font_size=66,
label_font_size=56,
major_label_font_size=44,
legend_font_size=44,
value_font_size=36,
tooltip_font_size=36,
title_font_family="Helvetica Neue, Helvetica, Arial, sans-serif",
label_font_family="Helvetica Neue, Helvetica, Arial, sans-serif",
major_label_font_family="Helvetica Neue, Helvetica, Arial, sans-serif",
legend_font_family="Helvetica Neue, Helvetica, Arial, sans-serif",
)
# Plot
chart = pygal.XY(
width=3200,
height=1800,
style=custom_style,
title="bubble-basic · python · pygal · anyplot.ai",
x_title="Population Density (people/km²)",
y_title="Avg Commute Time (min)",
show_legend=True,
legend_at_bottom=True,
legend_at_bottom_columns=4,
legend_box_size=36,
stroke=False,
dots_size=20,
show_x_guides=True,
show_y_guides=True,
x_value_formatter=lambda x: f"{x:,.0f}",
value_formatter=lambda x: f"{x:.1f}",
margin_top=80,
margin_bottom=200,
margin_left=100,
margin_right=80,
tooltip_border_radius=8,
tooltip_fancy_mode=True,
print_values=False,
truncate_legend=30,
spacing=30,
)
for t in range(n_tiers):
chart.add(tier_labels[t], tier_data[t] if tier_data[t] else [], dots_size=tier_sizes[t])
# Focal city rendered last in ANYPLOT_AMBER — stands out as a visual anchor
chart.add("★ Peak Congestion", focal_point_data, dots_size=tier_sizes[focal_tier] + 8)
# Save
chart.render_to_file(f"plot-{THEME}.html")
chart.render_to_png(f"plot-{THEME}.png")
Part of Basic Bubble Chart on anyplot.ai.