A stacked area chart displays multiple data series as areas stacked on top of each other, with each series starting where the previous one ends. This visualization emphasizes both individual contributions and cumulative totals over a continuous axis (typically time). It is ideal for showing how parts contribute to a whole while tracking changes over time, making patterns of composition and overall trends immediately visible.
""" anyplot.ai
area-stacked: Stacked Area Chart
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-07
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_area,
ggplot,
ggsize,
labs,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
)
from lets_plot.export import ggsave
LetsPlot.setup_html()
# Theme tokens (see prompts/default-style-guide.md)
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 (first series always #009E73)
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]
# Data: Monthly revenue by product category over 2 years
np.random.seed(42)
months = pd.date_range("2023-01", periods=24, freq="ME")
# Generate revenue data for each product category (in thousands)
# Largest categories at the bottom for easier reading
base_electronics = 80 + np.cumsum(np.random.randn(24) * 3)
base_clothing = 50 + np.cumsum(np.random.randn(24) * 2)
base_home = 35 + np.cumsum(np.random.randn(24) * 2)
base_sports = 25 + np.cumsum(np.random.randn(24) * 1.5)
# Add seasonality
seasonality = 10 * np.sin(np.linspace(0, 4 * np.pi, 24))
electronics = np.maximum(base_electronics + seasonality, 20)
clothing = np.maximum(base_clothing + seasonality * 0.7, 15)
home = np.maximum(base_home + seasonality * 0.5, 10)
sports = np.maximum(base_sports + seasonality * 0.3, 8)
# Create long-format dataframe for lets-plot
df = pd.DataFrame(
{
"Month": list(months) * 4,
"Revenue": np.concatenate([electronics, clothing, home, sports]),
"Category": ["Electronics"] * 24 + ["Clothing"] * 24 + ["Home & Garden"] * 24 + ["Sports"] * 24,
}
)
# Convert to numeric for x-axis (months since start)
df["MonthNum"] = df.groupby("Category").cumcount()
# Reorder categories for stacking (largest at bottom)
category_order = ["Electronics", "Clothing", "Home & Garden", "Sports"]
df["Category"] = pd.Categorical(df["Category"], categories=category_order, ordered=True)
# Create stacked area chart
plot = (
ggplot(df, aes(x="MonthNum", y="Revenue", fill="Category"))
+ geom_area(alpha=0.85, position="stack", size=0.5, color=PAGE_BG)
+ scale_fill_manual(values=IMPRINT)
+ scale_x_continuous(
name="Month", breaks=[0, 6, 12, 18, 23], labels=["Jan 2023", "Jul 2023", "Jan 2024", "Jul 2024", "Dec 2024"]
)
+ scale_y_continuous(name="Revenue (Thousands USD)")
+ labs(title="area-stacked · letsplot · anyplot.ai", fill="Product Category")
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major=element_line(color=INK_SOFT, size=0.5),
panel_grid_minor=element_blank(),
plot_title=element_text(size=24, face="bold", color=INK),
axis_title=element_text(size=20, color=INK),
axis_text=element_text(size=16, color=INK_SOFT),
axis_line=element_line(color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_title=element_text(size=18, color=INK),
legend_text=element_text(size=16, color=INK_SOFT),
legend_position="right",
)
+ ggsize(1600, 900)
)
# Save as PNG (scale 3x for 4800x2700 px)
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
# Save interactive HTML version
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Stacked Area Chart on anyplot.ai.