An icicle chart displaying hierarchical data as adjacent rectangles in a layered structure, where each rectangle's size represents its value in the hierarchy. Unlike treemaps that nest rectangles, icicle charts stack them in rows (horizontal) or columns (vertical), making parent-child relationships explicitly visible through spatial adjacency. This layout excels at showing both the hierarchy levels and the proportional values simultaneously.
""" anyplot.ai
icicle-basic: Basic Icicle Chart
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-13
"""
import os
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_rect,
element_text,
geom_rect,
geom_text,
ggplot,
ggsave,
ggsize,
labs,
scale_fill_manual,
scale_size_identity,
theme,
xlim,
ylim,
)
LetsPlot.setup_html()
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 = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
# Hierarchical data: File system example
hierarchy = [
{"name": "root", "parent": "", "value": 1000},
{"name": "Documents", "parent": "root", "value": 350},
{"name": "Media", "parent": "root", "value": 400},
{"name": "Projects", "parent": "root", "value": 250},
{"name": "Work", "parent": "Documents", "value": 200},
{"name": "Personal", "parent": "Documents", "value": 150},
{"name": "Photos", "parent": "Media", "value": 220},
{"name": "Videos", "parent": "Media", "value": 180},
{"name": "Python", "parent": "Projects", "value": 120},
{"name": "Web", "parent": "Projects", "value": 130},
{"name": "Reports", "parent": "Work", "value": 120},
{"name": "Contracts", "parent": "Work", "value": 80},
{"name": "Letters", "parent": "Personal", "value": 90},
{"name": "Receipts", "parent": "Personal", "value": 60},
{"name": "2024", "parent": "Photos", "value": 130},
{"name": "2023", "parent": "Photos", "value": 90},
{"name": "Movies", "parent": "Videos", "value": 100},
{"name": "Clips", "parent": "Videos", "value": 80},
{"name": "DataViz", "parent": "Python", "value": 70},
{"name": "ML", "parent": "Python", "value": 50},
{"name": "Frontend", "parent": "Web", "value": 75},
{"name": "Backend", "parent": "Web", "value": 55},
]
# Build tree structure
name_to_node = {row["name"]: row for row in hierarchy}
children = {}
for row in hierarchy:
parent = row["parent"]
if parent not in children:
children[parent] = []
if parent:
children[parent].append(row["name"])
# Calculate level for each node
levels = {}
for row in hierarchy:
level = 0
current = row["name"]
while name_to_node[current]["parent"]:
level += 1
current = name_to_node[current]["parent"]
levels[row["name"]] = level
max_level = max(levels.values())
# Calculate rectangle positions (horizontal icicle: root at top)
rects = []
stack = [("root", 0.0, 1.0)]
while stack:
name, x_start, x_end = stack.pop()
level = levels[name]
rects.append(
{
"name": name,
"xmin": x_start,
"xmax": x_end,
"ymin": max_level - level,
"ymax": max_level - level + 1,
"level": level,
}
)
if name in children and children[name]:
child_names = children[name]
total_value = sum(name_to_node[c]["value"] for c in child_names)
x_end_temp = x_end
for child_name in reversed(child_names):
child_value = name_to_node[child_name]["value"]
child_width = (x_end - x_start) * (child_value / total_value)
child_x_start = x_end_temp - child_width
stack.append((child_name, child_x_start, x_end_temp))
x_end_temp = child_x_start
# Create dataframe for rectangles
rect_df = pd.DataFrame(rects)
rect_df["level_str"] = rect_df["level"].astype(str)
# Calculate center positions and dimensions for labels
rect_df["x_center"] = (rect_df["xmin"] + rect_df["xmax"]) / 2
rect_df["y_center"] = (rect_df["ymin"] + rect_df["ymax"]) / 2
rect_df["width"] = rect_df["xmax"] - rect_df["xmin"]
# Show labels for rectangles with sufficient width
rect_df["label_len"] = rect_df["name"].str.len()
rect_df["show_label"] = rect_df["width"] > (rect_df["label_len"] * 0.007 + 0.01)
label_df = rect_df[rect_df["show_label"]].copy()
# Adjust font size based on level
label_df["font_size"] = label_df["level"].map({0: 14, 1: 12, 2: 8, 3: 7})
# Color palette by level using Okabe-Ito
colors = {
"0": IMPRINT[0], # Level 0: bluish green
"1": IMPRINT[1], # Level 1: vermillion
"2": IMPRINT[2], # Level 2: blue
"3": IMPRINT[3], # Level 3: reddish purple
}
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_grid=element_blank(),
axis_title=element_blank(),
axis_text=element_blank(),
axis_ticks=element_blank(),
axis_line=element_blank(),
plot_title=element_text(size=24, color=INK),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(size=16, color=INK_SOFT),
legend_title=element_text(size=18, color=INK),
)
# Create plot
plot = (
ggplot()
+ geom_rect(
aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax", fill="level_str"),
data=rect_df,
color=INK_SOFT,
size=1.5,
alpha=0.9,
)
+ geom_text(aes(x="x_center", y="y_center", label="name", size="font_size"), data=label_df, color=INK)
+ scale_fill_manual(values=colors, name="Hierarchy Level")
+ scale_size_identity()
+ xlim(-0.02, 1.02)
+ ylim(-0.1, max_level + 1.1)
+ labs(title="icicle-basic · letsplot · anyplot.ai")
+ anyplot_theme
+ ggsize(1600, 900)
)
# Save outputs
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Basic Icicle Chart on anyplot.ai.