Scatter Plot with LOWESS Regression — Matplotlib

A scatter plot with a LOWESS (Locally Weighted Scatterplot Smoothing) regression curve overlaid. LOWESS is a non-parametric method that fits smooth curves by performing local weighted regressions at each point, adapting to local data patterns without assuming a specific functional form. This makes it ideal for exploring complex relationships where the underlying pattern is unknown or varies across the data range.

Scatter Plot with LOWESS Regression rendered with Matplotlib

Python source (Matplotlib)

""" anyplot.ai
scatter-regression-lowess: Scatter Plot with LOWESS Regression
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-14
"""

import os

import matplotlib.pyplot as plt
import numpy as np
from statsmodels.nonparametric.smoothers_lowess import lowess


# 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"
ACCENT = "#C475FD"

# Data - tree height vs age with realistic growth pattern (power law)
np.random.seed(42)
n_points = 150
age = np.linspace(1, 30, n_points)
# Realistic growth curve: power law with decreasing growth rate
height = 20 * (1 - np.exp(-0.15 * age)) + np.random.normal(0, 0.6, n_points)

# Compute LOWESS smoothed curve
lowess_result = lowess(height, age, frac=0.4, return_sorted=True)
age_smooth = lowess_result[:, 0]
height_smooth = lowess_result[:, 1]

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

# Scatter points
ax.scatter(age, height, s=180, alpha=0.6, color=BRAND, edgecolors=PAGE_BG, linewidth=0.8, label="Observed heights")

# LOWESS regression curve
ax.plot(age_smooth, height_smooth, color=ACCENT, linewidth=4.5, label="LOWESS smoothed trend")

# Style
ax.set_xlabel("Tree Age (years)", fontsize=20, color=INK)
ax.set_ylabel("Height (meters)", fontsize=20, color=INK)
ax.set_title("scatter-regression-lowess · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax.spines[s].set_color(INK_SOFT)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)

leg = ax.legend(fontsize=16, loc="lower right", frameon=True)
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)

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

Part of Scatter Plot with LOWESS Regression on anyplot.ai.

Scatter Plot with LOWESS Regression — Matplotlib

Python source (Matplotlib)

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