Stacked Area Chart — plotnine

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.

Stacked Area Chart rendered with plotnine

Python source (plotnine)

""" anyplot.ai
area-stacked: Stacked Area Chart
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-07
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    element_line,
    element_rect,
    element_text,
    geom_area,
    ggplot,
    labs,
    scale_fill_manual,
    scale_x_date,
    theme,
    theme_minimal,
)


# 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"

# Data: Website traffic sources over 24 months
np.random.seed(42)

dates = pd.date_range(start="2023-01-01", periods=24, freq="MS")

# Generate realistic traffic data with trends
base_direct = 15000 + np.cumsum(np.random.randn(24) * 500)
base_organic = 25000 + np.cumsum(np.random.randn(24) * 800) + np.arange(24) * 300
base_referral = 10000 + np.cumsum(np.random.randn(24) * 400)
base_social = 8000 + np.cumsum(np.random.randn(24) * 600) + np.arange(24) * 200

# Ensure all values are positive
direct = np.maximum(base_direct, 5000)
organic = np.maximum(base_organic, 10000)
referral = np.maximum(base_referral, 3000)
social = np.maximum(base_social, 2000)

# Create long-format DataFrame for stacking
df = pd.DataFrame(
    {
        "Date": np.tile(dates, 4),
        "Visitors": np.concatenate([direct, organic, referral, social]),
        "Source": (["Direct"] * 24 + ["Organic Search"] * 24 + ["Referral"] * 24 + ["Social Media"] * 24),
    }
)

# Order categories by average size (largest at bottom for easier reading)
source_order = ["Organic Search", "Direct", "Referral", "Social Media"]
df["Source"] = pd.Categorical(df["Source"], categories=source_order, ordered=True)

# Okabe-Ito palette
colors = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]

# Theme
anyplot_theme = theme(
    figure_size=(16, 9),
    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
    panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
    panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),
    panel_grid_minor=element_line(color=INK, size=0.2, alpha=0.05),
    plot_title=element_text(size=24, weight="bold", color=INK),
    axis_title=element_text(size=20, color=INK),
    axis_text=element_text(size=16, color=INK_SOFT),
    axis_text_x=element_text(angle=45, hjust=1),
    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 stacked area chart
plot = (
    ggplot(df, aes(x="Date", y="Visitors", fill="Source"))
    + geom_area(alpha=0.85, position="stack")
    + scale_fill_manual(values=colors)
    + scale_x_date(date_labels="%b %Y", date_breaks="3 months")
    + labs(
        title="area-stacked · plotnine · anyplot.ai", x="Month", y="Monthly Visitors (thousands)", fill="Traffic Source"
    )
    + theme_minimal()
    + anyplot_theme
)

# Save
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)

Part of Stacked Area Chart on anyplot.ai.

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