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: plotnine 0.15.4 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-13
"""
import os
import pandas as pd
from plotnine import (
aes,
element_rect,
element_text,
geom_rect,
geom_text,
ggplot,
labs,
scale_fill_manual,
theme,
theme_void,
)
# 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 hierarchy levels
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
# Data: File system hierarchy with sizes (MB)
data = [
{"name": "root", "parent": "", "value": 0},
{"name": "Documents", "parent": "root", "value": 0},
{"name": "Photos", "parent": "root", "value": 0},
{"name": "Projects", "parent": "root", "value": 0},
{"name": "Archive", "parent": "root", "value": 0},
{"name": "Reports", "parent": "Documents", "value": 450},
{"name": "Invoices", "parent": "Documents", "value": 280},
{"name": "Notes", "parent": "Documents", "value": 180},
{"name": "Vacation", "parent": "Photos", "value": 680},
{"name": "Family", "parent": "Photos", "value": 520},
{"name": "Events", "parent": "Photos", "value": 340},
{"name": "WebApp", "parent": "Projects", "value": 0},
{"name": "DataSci", "parent": "Projects", "value": 0},
{"name": "Mobile", "parent": "Projects", "value": 380},
{"name": "Frontend", "parent": "WebApp", "value": 320},
{"name": "Backend", "parent": "WebApp", "value": 420},
{"name": "Config", "parent": "WebApp", "value": 200},
{"name": "Models", "parent": "DataSci", "value": 520},
{"name": "Scripts", "parent": "DataSci", "value": 300},
{"name": "Old2023", "parent": "Archive", "value": 1200},
{"name": "Old2022", "parent": "Archive", "value": 950},
]
df = pd.DataFrame(data)
# Build lookup tables
name_to_idx = {row["name"]: idx for idx, row in df.iterrows()}
children_map = {name: df[df["parent"] == name]["name"].tolist() for name in df["name"]}
# Calculate values for non-leaf nodes (bottom-up aggregation)
processed = set()
while len(processed) < len(df):
for _, row in df.iterrows():
name = row["name"]
if name in processed:
continue
kids = children_map[name]
if len(kids) == 0:
processed.add(name)
elif all(k in processed for k in kids):
total = sum(df.loc[name_to_idx[k], "value"] for k in kids)
df.loc[name_to_idx[name], "value"] = total
processed.add(name)
# Calculate depths (distance from root)
depths = {"root": 0}
queue = ["root"]
while queue:
current = queue.pop(0)
for child in children_map[current]:
depths[child] = depths[current] + 1
queue.append(child)
max_depth = max(depths.values())
# Build icicle rectangles using iterative BFS
rects = []
layout_queue = [("root", 0.0, 1.0)]
while layout_queue:
name, x_start, x_end = layout_queue.pop(0)
depth = depths[name]
y_top = max_depth - depth + 1
y_bottom = max_depth - depth
value = df.loc[name_to_idx[name], "value"]
rects.append(
{"name": name, "xmin": x_start, "xmax": x_end, "ymin": y_bottom, "ymax": y_top, "depth": depth, "value": value}
)
# Queue children proportionally
kids = children_map[name]
if kids:
kid_values = [(k, df.loc[name_to_idx[k], "value"]) for k in kids]
kid_values.sort(key=lambda x: -x[1])
total_value = sum(v for _, v in kid_values)
if total_value > 0:
curr_x = x_start
for kid, val in kid_values:
width = (val / total_value) * (x_end - x_start)
layout_queue.append((kid, curr_x, curr_x + width))
curr_x += width
rect_df = pd.DataFrame(rects)
# Color palette by depth using Okabe-Ito
colors = {0: IMPRINT[0], 1: IMPRINT[1], 2: IMPRINT[2], 3: IMPRINT[3], 4: IMPRINT[4], 5: IMPRINT[5]}
rect_df["fill_color"] = rect_df["depth"].map(colors)
# Calculate label positions and widths
rect_df["width"] = rect_df["xmax"] - rect_df["xmin"]
rect_df["x_center"] = (rect_df["xmin"] + rect_df["xmax"]) / 2
rect_df["y_center"] = (rect_df["ymin"] + rect_df["ymax"]) / 2
# Labels: show name + value for wide rectangles, name only for medium, hide for very narrow
rect_df["label"] = rect_df.apply(
lambda r: f"{r['name']}\n({int(r['value'])} MB)" if r["width"] > 0.05 else (r["name"] if r["width"] > 0.02 else ""),
axis=1,
)
# Convert depth to categorical with descriptive labels
level_labels = {
0: "Level 0 (Root)",
1: "Level 1 (Folders)",
2: "Level 2 (Subfolders)",
3: "Level 3 (Groups)",
4: "Level 4 (Items)",
5: "Level 5 (Leaf)",
}
rect_df["depth_label"] = pd.Categorical(
rect_df["depth"].map(level_labels), categories=list(level_labels.values()), ordered=True
)
# Separate light and dark backgrounds for text color contrast
dark_bg = rect_df[rect_df["depth"].isin([0, 1, 3])]
light_bg = rect_df[rect_df["depth"].isin([2, 4, 5])]
# Create plot
plot = (
ggplot(rect_df)
+ geom_rect(aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax", fill="depth_label"), color="white", size=1.5)
+ geom_text(aes(x="x_center", y="y_center", label="label"), data=dark_bg, size=11, color=INK)
+ geom_text(
aes(x="x_center", y="y_center", label="label"),
data=light_bg,
size=11,
color=INK if THEME == "light" else INK_SOFT,
)
+ scale_fill_manual(
values={
"Level 0 (Root)": IMPRINT[0],
"Level 1 (Folders)": IMPRINT[1],
"Level 2 (Subfolders)": IMPRINT[2],
"Level 3 (Groups)": IMPRINT[3],
"Level 4 (Items)": IMPRINT[4],
"Level 5 (Leaf)": IMPRINT[5],
},
name="Hierarchy Level",
)
+ labs(title="icicle-basic · plotnine · anyplot.ai")
+ theme_void()
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
figure_size=(16, 9),
plot_title=element_text(size=28, ha="center", weight="bold", color=INK),
legend_position="right",
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_title=element_text(size=18, color=INK),
legend_text=element_text(size=14, color=INK_SOFT),
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)
Part of Basic Icicle Chart on anyplot.ai.