A treemap displaying hierarchical data as nested rectangles, where each rectangle's area is proportional to its value. This visualization excels at showing part-to-whole relationships in hierarchical structures, making it easy to spot large and small items at a glance. Treemaps efficiently use screen space to display large amounts of hierarchical data in a compact form.
""" anyplot.ai
treemap-basic: Basic Treemap
Library: letsplot 4.11.0 | Python 3.13.14
Quality: 88/100 | Updated: 2026-08-04
"""
import os
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_rect,
element_text,
geom_rect,
geom_text,
ggplot,
ggsize,
labs,
scale_alpha_identity,
scale_color_identity,
scale_fill_manual,
theme,
theme_void,
)
from lets_plot.export import ggsave
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"
# Okabe-Ito palette for consistent color mapping across categories
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
# Data - Budget allocation with two-level hierarchy
# Departments (main) and projects/teams (sub) for realistic budget breakdown
data = {
"category": [
"Engineering",
"Engineering",
"Engineering",
"Marketing",
"Marketing",
"Sales",
"Sales",
"Operations",
"HR",
"Finance",
],
"subcategory": [
"Backend",
"Frontend",
"DevOps",
"Digital",
"Events",
"Enterprise",
"SMB",
"Infrastructure",
"Recruiting",
"Planning",
],
"value": [15, 12, 5, 14, 8, 12, 6, 12, 7, 5],
}
df_data = pd.DataFrame(data)
df_data = df_data.sort_values("value", ascending=False).reset_index(drop=True)
def squarify(values, x, y, width, height):
"""Compute treemap rectangles using squarify algorithm.
Tracks a running remaining-total and remaining width/height (rather than
the fixed global values) so consumed area always matches the actual
remaining container, guaranteeing the tiling fully fills the bounding box.
"""
if len(values) == 0:
return []
remaining_total = sum(values)
if remaining_total == 0:
return []
rects = []
remaining_values = list(values)
remaining_x, remaining_y = x, y
remaining_w, remaining_h = width, height
while remaining_values:
if remaining_w >= remaining_h:
row_values = []
row_sum = 0
best_ratio = float("inf")
for v in remaining_values:
test_values = row_values + [v]
test_sum = row_sum + v
row_width = (test_sum / remaining_total) * remaining_w if remaining_total > 0 else 0
if row_width > 0:
worst_ratio = 0
for rv in test_values:
rect_height = (rv / test_sum) * remaining_h if test_sum > 0 else 0
ratio = (
max(row_width / rect_height, rect_height / row_width) if rect_height > 0 else float("inf")
)
worst_ratio = max(worst_ratio, ratio)
if worst_ratio <= best_ratio:
best_ratio = worst_ratio
row_values = test_values
row_sum = test_sum
else:
break
else:
row_values = test_values
row_sum = test_sum
row_width = (row_sum / remaining_total) * remaining_w if remaining_total > 0 else 0
current_y = remaining_y
for rv in row_values:
rect_height = (rv / row_sum) * remaining_h if row_sum > 0 else 0
rects.append((remaining_x, current_y, row_width, rect_height))
current_y += rect_height
remaining_x += row_width
remaining_w -= row_width
remaining_total -= row_sum
remaining_values = remaining_values[len(row_values) :]
else:
col_values = []
col_sum = 0
best_ratio = float("inf")
for v in remaining_values:
test_values = col_values + [v]
test_sum = col_sum + v
col_height = (test_sum / remaining_total) * remaining_h if remaining_total > 0 else 0
if col_height > 0:
worst_ratio = 0
for cv in test_values:
rect_width = (cv / test_sum) * remaining_w if test_sum > 0 else 0
ratio = (
max(col_height / rect_width, rect_width / col_height) if rect_width > 0 else float("inf")
)
worst_ratio = max(worst_ratio, ratio)
if worst_ratio <= best_ratio:
best_ratio = worst_ratio
col_values = test_values
col_sum = test_sum
else:
break
else:
col_values = test_values
col_sum = test_sum
col_height = (col_sum / remaining_total) * remaining_h if remaining_total > 0 else 0
current_x = remaining_x
for cv in col_values:
rect_width = (cv / col_sum) * remaining_w if col_sum > 0 else 0
rects.append((current_x, remaining_y, rect_width, col_height))
current_x += rect_width
remaining_y += col_height
remaining_h -= col_height
remaining_total -= col_sum
remaining_values = remaining_values[len(col_values) :]
return rects
# Compute treemap layout
rects = squarify(df_data["value"].tolist(), 0, 0, 100, 100)
# Build rectangle dataframe
rect_df = pd.DataFrame(
{
"xmin": [r[0] for r in rects],
"ymin": [r[1] for r in rects],
"xmax": [r[0] + r[2] for r in rects],
"ymax": [r[1] + r[3] for r in rects],
"category": df_data["category"].tolist(),
"subcategory": df_data["subcategory"].tolist(),
"value": df_data["value"].tolist(),
}
)
# Calculate label positions
rect_df["label_x"] = (rect_df["xmin"] + rect_df["xmax"]) / 2
rect_df["label_y"] = (rect_df["ymin"] + rect_df["ymax"]) / 2
rect_df["width"] = rect_df["xmax"] - rect_df["xmin"]
rect_df["height"] = rect_df["ymax"] - rect_df["ymin"]
# Shading intensity by nesting depth: within each department, the largest
# cost center is fully opaque and successive ones step down in alpha, giving
# a visual cue for the subcategory hierarchy beyond color alone.
rect_df["subcat_rank"] = rect_df.groupby("category")["value"].rank(ascending=False, method="first") - 1
rect_df["shade_alpha"] = (0.95 - 0.15 * rect_df["subcat_rank"]).clip(lower=0.55)
# Create adaptive labels for improved readability
total_value = df_data["value"].sum()
def make_label(row):
w, h = row["width"], row["height"]
pct = row["value"] / total_value * 100
# Large rectangles: show both category and subcategory with percentage
if w > 20 and h > 12:
return f"{row['category']}\n{row['subcategory']}\n{pct:.0f}%"
# Medium rectangles: subcategory and percentage
elif w > 12 and h > 8:
return f"{row['subcategory']}\n{pct:.0f}%"
# Small rectangles: just subcategory
elif w > 8 and h > 6:
return f"{row['subcategory']}"
# Very small: no label (visible in legend)
return ""
rect_df["label"] = rect_df.apply(make_label, axis=1)
# Map categories to Okabe-Ito colors
unique_categories = df_data["category"].unique().tolist()
category_colors = {cat: IMPRINT[i % len(IMPRINT)] for i, cat in enumerate(unique_categories)}
color_values = [category_colors[cat] for cat in unique_categories]
# Per-swatch label color: pick whichever of near-black/near-white ink gives
# the higher WCAG contrast against that category's fill, so labels stay
# legible on both light swatches (e.g. lavender) and dark ones (e.g. red).
DARK_INK = "#1A1A17"
LIGHT_INK = "#F0EFE8"
def relative_luminance(hex_color):
r, g, b = (int(hex_color.lstrip("#")[i : i + 2], 16) / 255 for i in (0, 2, 4))
def channel(c):
return c / 12.92 if c <= 0.03928 else ((c + 0.055) / 1.055) ** 2.4
r, g, b = channel(r), channel(g), channel(b)
return 0.2126 * r + 0.7152 * g + 0.0722 * b
def contrast_ratio(lum_a, lum_b):
lighter, darker = max(lum_a, lum_b), min(lum_a, lum_b)
return (lighter + 0.05) / (darker + 0.05)
def best_label_color(bg_hex):
bg_lum = relative_luminance(bg_hex)
dark_contrast = contrast_ratio(bg_lum, relative_luminance(DARK_INK))
light_contrast = contrast_ratio(bg_lum, relative_luminance(LIGHT_INK))
return DARK_INK if dark_contrast >= light_contrast else LIGHT_INK
label_colors = {cat: best_label_color(color) for cat, color in category_colors.items()}
rect_df["label_color"] = rect_df["category"].map(label_colors)
TEXT_SIZE = 7
# Create the plot
plot = (
ggplot(rect_df)
+ geom_rect(
aes(xmin="xmin", ymin="ymin", xmax="xmax", ymax="ymax", fill="category", alpha="shade_alpha"),
color=INK_SOFT,
size=0.7,
)
+ geom_text(aes(x="label_x", y="label_y", label="label", color="label_color"), size=TEXT_SIZE, fontface="bold")
+ scale_fill_manual(values=color_values)
+ scale_color_identity()
+ scale_alpha_identity()
+ labs(title="Budget Breakdown · treemap-basic · python · letsplot · anyplot.ai", fill="Department")
+ theme_void()
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
plot_title=element_text(size=16, color=INK, hjust=0.5),
legend_title=element_text(size=12, color=INK),
legend_text=element_text(size=10, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_position="right",
axis_title=element_blank(),
axis_text=element_blank(),
)
+ ggsize(800, 450)
)
# Save outputs with theme suffix
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Basic Treemap on anyplot.ai.