Stacked Area Chart with Confidence Bands — lets-plot

A stacked area chart that displays multiple data series as cumulative areas, with each series surrounded by uncertainty or confidence bands. This visualization combines the composition insight of stacked areas with the statistical rigor of confidence intervals, making it ideal for showing how parts contribute to a whole while simultaneously communicating uncertainty in each component. The bands reveal where estimates are precise versus uncertain across the stacked series.

Stacked Area Chart with Confidence Bands rendered with lets-plot

Python source (lets-plot)

""" anyplot.ai
area-stacked-confidence: Stacked Area Chart with Confidence Bands
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-18
"""

import os

import numpy as np
import pandas as pd
from lets_plot import *


LetsPlot.setup_html()

# 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 (first series always #009E73)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]

# Data - Quarterly energy consumption forecast by source with uncertainty
np.random.seed(42)
quarters = pd.date_range("2023-Q1", periods=24, freq="QE")
n = len(quarters)

# Base trends for 3 energy sources (TWh)
solar_base = np.linspace(50, 120, n) + np.random.normal(0, 5, n)
wind_base = np.linspace(80, 150, n) + np.random.normal(0, 8, n)
hydro_base = np.linspace(100, 110, n) + np.random.normal(0, 3, n)

# Confidence intervals (uncertainty grows over time for forecasts)
time_factor = np.linspace(1, 2.5, n)
solar_lower = solar_base - 8 * time_factor
solar_upper = solar_base + 8 * time_factor
wind_lower = wind_base - 12 * time_factor
wind_upper = wind_base + 12 * time_factor
hydro_lower = hydro_base - 5 * time_factor
hydro_upper = hydro_base + 5 * time_factor

# Create stacked values (cumulative)
solar_stack = solar_base
wind_stack = solar_base + wind_base
hydro_stack = solar_base + wind_base + hydro_base

# Stacked confidence bounds
solar_lower_stack = solar_lower
solar_upper_stack = solar_upper
wind_lower_stack = solar_base + wind_lower
wind_upper_stack = solar_base + wind_upper
hydro_lower_stack = solar_base + wind_base + hydro_lower
hydro_upper_stack = solar_base + wind_base + hydro_upper

# Convert dates to numeric for lets-plot
x_numeric = np.arange(n)
x_labels = [f"{q.year}-Q{(q.month - 1) // 3 + 1}" for q in quarters]

# Central line data
df_lines = pd.DataFrame(
    {
        "x": np.tile(x_numeric, 3),
        "y": np.concatenate([solar_stack, wind_stack, hydro_stack]),
        "source": np.concatenate([["Solar"] * n, ["Wind"] * n, ["Hydro"] * n]),
    }
)

# Main areas data (for stacked area)
df_areas = pd.DataFrame(
    {
        "x": np.tile(x_numeric, 3),
        "y_min": np.concatenate([np.zeros(n), solar_stack, wind_stack]),
        "y_max": np.concatenate([solar_stack, wind_stack, hydro_stack]),
        "source": np.concatenate([["Solar"] * n, ["Wind"] * n, ["Hydro"] * n]),
    }
)

# Confidence band data
df_conf = pd.DataFrame(
    {
        "x": np.tile(x_numeric, 3),
        "y_lower": np.concatenate([solar_lower_stack, wind_lower_stack, hydro_lower_stack]),
        "y_upper": np.concatenate([solar_upper_stack, wind_upper_stack, hydro_upper_stack]),
        "source": np.concatenate([["Solar"] * n, ["Wind"] * n, ["Hydro"] * n]),
    }
)

# Theme-adaptive plot styling
anyplot_theme = theme(
    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
    panel_background=element_rect(fill=PAGE_BG),
    panel_grid_major_y=element_line(color=INK, size=0.3),
    panel_grid_minor=element_blank(),
    axis_title=element_text(color=INK, size=20),
    axis_text=element_text(color=INK_SOFT, size=16),
    axis_line=element_line(color=INK_SOFT, size=0.4),
    plot_title=element_text(color=INK, size=24),
    legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
    legend_text=element_text(color=INK_SOFT, size=16),
    legend_title=element_text(color=INK, size=18),
    legend_position="right",
)

# Create plot with stacked areas and confidence ribbons
plot = (
    ggplot()
    + geom_ribbon(aes(x="x", ymin="y_lower", ymax="y_upper", fill="source"), data=df_conf, alpha=0.25)
    + geom_ribbon(aes(x="x", ymin="y_min", ymax="y_max", fill="source"), data=df_areas, alpha=0.7)
    + geom_line(aes(x="x", y="y", color="source"), data=df_lines, size=1.5)
    + scale_fill_manual(values=IMPRINT, name="Energy Source")
    + scale_color_manual(values=IMPRINT, guide="none")
    + scale_x_continuous(breaks=list(range(0, n, 4)), labels=[x_labels[i] for i in range(0, n, 4)])
    + labs(
        title="area-stacked-confidence · Python · letsplot · anyplot.ai",
        x="Quarter",
        y="Energy Consumption (TWh)",
        caption="Shaded bands show 90% prediction intervals",
    )
    + theme_minimal()
    + anyplot_theme
    + ggsize(1600, 900)
)

# Save PNG (scale 3x for 4800x2700)
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)

# Save HTML for interactive viewing
ggsave(plot, f"plot-{THEME}.html", path=".")

Part of Stacked Area Chart with Confidence Bands on anyplot.ai.

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