Line Plot with Confidence Interval — Matplotlib

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 Matplotlib

Python source (Matplotlib)

""" anyplot.ai
line-confidence: Line Plot with Confidence Interval
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-09
"""

import sys


sys.path.pop(0)

import os

import matplotlib.pyplot as plt
import numpy as np


# Theme-adaptive colors
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

# Data - Simulated temperature forecast with 95% confidence interval
np.random.seed(42)
days = np.arange(1, 31)  # 30 days forecast

# Central forecast (mean temperature with slight trend)
base_temp = 15 + 0.3 * days + 3 * np.sin(days / 5)
y = base_temp + np.random.randn(30) * 0.5

# Confidence interval widens over time (typical for forecasts)
uncertainty = 1.5 + 0.15 * days
y_lower = y - uncertainty
y_upper = y + uncertainty

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

# Shaded confidence band (contrasting color with semi-transparent alpha)
# Use sky blue (#2ABCCD) for the band to contrast with green line
ax.fill_between(days, y_lower, y_upper, alpha=0.25, color="#2ABCCD", label="95% Confidence Interval")

# Central trend line (prominent, brand green)
ax.plot(days, y, color="#009E73", linewidth=3, label="Forecast Mean")

# Styling
ax.set_xlabel("Days Ahead", fontsize=20, color=INK)
ax.set_ylabel("Temperature (°C)", fontsize=20, color=INK)
ax.set_title("Temperature Forecast with 95% Confidence Interval", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT, labelcolor=INK_SOFT)

# Spine styling
for spine in ["top", "right"]:
    ax.spines[spine].set_visible(False)
for spine in ["left", "bottom"]:
    ax.spines[spine].set_color(INK_SOFT)
    ax.spines[spine].set_linewidth(0.8)

# Grid (subtle, y-axis only)
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)

# Legend with theme-adaptive styling
leg = ax.legend(fontsize=16, loc="upper left", framealpha=0.95)
if leg:
    leg.get_frame().set_facecolor(ELEVATED_BG)
    leg.get_frame().set_edgecolor(INK_SOFT)
    leg.get_frame().set_linewidth(0.8)
    for text in leg.get_texts():
        text.set_color(INK_SOFT)

# Set axis limits for clean display
ax.set_xlim(1, 30)

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 — Matplotlib

Python source (Matplotlib)

Other implementations