Line Plot with Confidence Interval — Seaborn

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 Seaborn

Python source (Seaborn)

""" anyplot.ai
line-confidence: Line Plot with Confidence Interval
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-09
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns


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

# Set theme for seaborn
sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.10,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Data - Daily temperature forecast with growing confidence interval
np.random.seed(42)

# Generate daily forecast for 30 days
days = np.arange(1, 31)

# Create a realistic temperature trend with daily variation
base_temp = 18 + days * 0.3  # Warming trend
daily_cycle = 3 * np.sin(2 * np.pi * days / 7)  # Weekly pattern
noise = np.random.randn(len(days)) * 1.5

# Central forecast
y_forecast = base_temp + daily_cycle + noise

# Confidence interval - widens as forecast horizon extends
uncertainty = 1.5 + days * 0.15  # Growing uncertainty
y_lower = y_forecast - 1.96 * uncertainty
y_upper = y_forecast + 1.96 * uncertainty

# Create plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)

# Plot confidence band using fill_between
ax.fill_between(days, y_lower, y_upper, alpha=0.25, color=BRAND, label="95% Confidence Interval")

# Plot central line
ax.plot(days, y_forecast, color=BRAND, linewidth=3, label="Forecast", marker="o", markersize=5)

# Style
ax.set_xlabel("Day", fontsize=20, color=INK)
ax.set_ylabel("Temperature (°C)", fontsize=20, color=INK)
ax.set_title(
    "Temperature Forecast · line-confidence · seaborn · anyplot.ai", fontsize=24, color=INK, fontweight="medium"
)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Remove top and right spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)

# Grid
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, linestyle="-")

# Legend - position to avoid overlap with data
ax.legend(fontsize=16, loc="lower right", framealpha=0.95)

# Set axis limits with padding
ax.set_xlim(0, 31)
ax.set_ylim(min(y_lower) - 2, max(y_upper) + 2)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

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

Line Plot with Confidence Interval — Seaborn

Python source (Seaborn)

Other implementations