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: plotnine 0.15.4 | Python 3.13.13
Quality: 80/100 | Updated: 2026-05-05
"""
import pandas as pd
from plotnine import aes, element_text, geom_rect, geom_text, ggplot, labs, scale_fill_manual, theme, theme_void
# Data - Budget allocation by department
data = {
"category": [
"Engineering",
"Engineering",
"Engineering",
"Marketing",
"Marketing",
"Sales",
"Sales",
"Sales",
"Operations",
"Operations",
"HR",
"Finance",
],
"subcategory": [
"R&D",
"Infrastructure",
"QA",
"Digital",
"Events",
"Direct",
"Channel",
"Support",
"Logistics",
"Facilities",
"Recruiting",
"Accounting",
],
"value": [450, 280, 120, 200, 80, 350, 180, 90, 150, 100, 130, 170],
}
df = pd.DataFrame(data)
# Sort by value descending for better treemap layout
df = df.sort_values("value", ascending=False).reset_index(drop=True)
# Squarified treemap layout algorithm (inline)
values = df["value"].tolist()
x, y, width, height = 0, 0, 100, 56.25 # 16:9 aspect ratio
total = sum(values)
rects = []
remaining = list(enumerate(values))
curr_x, curr_y = x, y
curr_w, curr_h = width, height
while remaining:
# Decide layout direction (horizontal or vertical)
horizontal = curr_w >= curr_h
remaining_total = sum(v for _, v in remaining)
area_scale = (curr_w * curr_h) / remaining_total if remaining_total > 0 else 0
best_row = []
best_ratio = float("inf")
for i in range(1, len(remaining) + 1):
row = remaining[:i]
row_sum = sum(v for _, v in row)
row_area = row_sum * area_scale
if horizontal:
row_width = row_area / curr_h if curr_h > 0 else 0
ratios = []
for _, v in row:
rect_h = (v * area_scale / row_width) if row_width > 0 else 0
if rect_h > 0 and row_width > 0:
ratio = max(row_width / rect_h, rect_h / row_width)
ratios.append(ratio)
else:
row_height = row_area / curr_w if curr_w > 0 else 0
ratios = []
for _, v in row:
rect_w = (v * area_scale / row_height) if row_height > 0 else 0
if rect_w > 0 and row_height > 0:
ratio = max(rect_w / row_height, row_height / rect_w)
ratios.append(ratio)
if ratios:
max_ratio = max(ratios)
if max_ratio <= best_ratio:
best_ratio = max_ratio
best_row = row
else:
break
if not best_row:
best_row = remaining[:1]
# Place the best row
row_sum = sum(v for _, v in best_row)
row_area = row_sum * area_scale
if horizontal:
row_width = row_area / curr_h if curr_h > 0 else 0
rect_y = curr_y
for idx, v in best_row:
rect_h = (v * area_scale / row_width) if row_width > 0 else 0
rects.append({"idx": idx, "x": curr_x, "y": rect_y, "dx": row_width, "dy": rect_h})
rect_y += rect_h
curr_x += row_width
curr_w -= row_width
else:
row_height = row_area / curr_w if curr_w > 0 else 0
rect_x = curr_x
for idx, v in best_row:
rect_w = (v * area_scale / row_height) if row_height > 0 else 0
rects.append({"idx": idx, "x": rect_x, "y": curr_y, "dx": rect_w, "dy": row_height})
rect_x += rect_w
curr_y += row_height
curr_h -= row_height
remaining = remaining[len(best_row) :]
# Sort by original index
rects.sort(key=lambda r: r["idx"])
rects = [{"x": r["x"], "y": r["y"], "dx": r["dx"], "dy": r["dy"]} for r in rects]
# Add rectangle coordinates to dataframe
df["x"] = [r["x"] for r in rects]
df["y"] = [r["y"] for r in rects]
df["dx"] = [r["dx"] for r in rects]
df["dy"] = [r["dy"] for r in rects]
# Calculate rectangle bounds for geom_rect
df["xmin"] = df["x"]
df["xmax"] = df["x"] + df["dx"]
df["ymin"] = df["y"]
df["ymax"] = df["y"] + df["dy"]
# Calculate center for labels
df["xcenter"] = df["x"] + df["dx"] / 2
df["ycenter"] = df["y"] + df["dy"] / 2
# Create combined label with value
df["label"] = df["subcategory"] + "\n$" + df["value"].astype(str) + "K"
# Color palette for categories (Python Blue and Yellow first, then colorblind-safe)
category_colors = {
"Engineering": "#306998", # Python Blue
"Marketing": "#FFD43B", # Python Yellow
"Sales": "#4ECDC4", # Teal
"Operations": "#FF6B6B", # Coral
"HR": "#95E1A3", # Light green
"Finance": "#DDA0DD", # Plum
}
# Create plot
plot = (
ggplot(df)
+ geom_rect(aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax", fill="category"), color="white", size=2)
+ geom_text(aes(x="xcenter", y="ycenter", label="label"), size=12, color="black", fontweight="bold")
+ scale_fill_manual(values=category_colors)
+ labs(title="Budget Allocation by Department · treemap-basic · plotnine · pyplots.ai", fill="Department")
+ theme_void()
+ theme(
figure_size=(16, 9),
plot_title=element_text(size=24, ha="center", weight="bold", margin={"b": 20}),
legend_title=element_text(size=18),
legend_text=element_text(size=16),
legend_position="right",
)
)
# Save
plot.save("plot.png", dpi=300, verbose=False)
Part of Basic Treemap on anyplot.ai.