Line Plot with Confidence Interval — lets-plot

A line plot with a confidence interval displays a central trend line (typically mean or median) surrounded by a shaded band representing uncertainty or variability. The combination of a clear central line and semi-transparent confidence region effectively communicates both the estimated value and its associated uncertainty, making it essential for visualizing statistical estimates, model predictions, and forecast ranges.

Line Plot with Confidence Interval rendered with lets-plot

Python source (lets-plot)

""" anyplot.ai
line-confidence: Line Plot with Confidence Interval
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 87/100 | Updated: 2026-05-09
"""

import os
import shutil

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"
RULE = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"

BRAND = "#009E73"  # Okabe-Ito position 1

# Data - Monthly sales forecast with 95% confidence interval
np.random.seed(42)
months = np.arange(1, 25)
trend = 50 + months * 2.5 + np.sin(months * np.pi / 6) * 10
noise = np.random.normal(0, 3, len(months))
y = trend + noise

# Calculate confidence interval (simulating forecast uncertainty that grows over time)
std_error = 3 + months * 0.3
y_lower = y - 1.96 * std_error
y_upper = y + 1.96 * std_error

df = pd.DataFrame({"Month": months, "Sales": y, "Lower": y_lower, "Upper": y_upper})

# Create plot with legend
plot = (
    ggplot(df)
    + geom_ribbon(aes(x="Month", ymin="Lower", ymax="Upper", fill="95% Confidence Interval"), alpha=0.25)
    + geom_line(aes(x="Month", y="Sales", color="Sales Forecast"), size=2)
    + geom_point(aes(x="Month", y="Sales"), color=BRAND, size=4)
    + scale_color_manual(values=[BRAND])
    + scale_fill_manual(values=[BRAND])
    + labs(x="Month", y="Sales (thousands)", title="line-confidence · letsplot · anyplot.ai")
    + scale_x_continuous(breaks=list(range(0, 25, 3)))
    + theme(
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG),
        panel_grid_major=element_line(color=RULE, size=0.3),
        panel_grid_minor=element_blank(),
        axis_title=element_text(size=20, color=INK),
        axis_text=element_text(size=16, color=INK_SOFT),
        axis_line=element_line(color=INK_SOFT, size=0.5),
        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=16, color=INK),
        legend_position="top",
    )
    + ggsize(1600, 900)
)

# Save as PNG with explicit pixel dimensions (4800 × 2700 px)
png_path = ggsave(plot, f"plot-{THEME}.png", w=4800, h=2700, unit="px", dpi=100)
shutil.move(png_path, f"plot-{THEME}.png")

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

Part of Line Plot with Confidence Interval on anyplot.ai.

Line Plot with Confidence Interval — lets-plot

Python source (lets-plot)

Other implementations