Scatter Plot with LOWESS Regression — Seaborn

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 Seaborn

Python source (Seaborn)

""" anyplot.ai
scatter-regression-lowess: Scatter Plot with LOWESS Regression
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-14
"""

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"

# Okabe-Ito palette
BRAND = "#009E73"  # Position 1 - scatter points
REGRESSION = "#C475FD"  # Position 2 - LOWESS curve

# Configure theme
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 - create non-linear relationship with distinct local variations
np.random.seed(42)
n_points = 200
x = np.linspace(0, 10, n_points)

# Complex pattern: steep rise 0-2, plateau 2-5, sharp dip 5-6, gentle rise 6-10
y = (
    np.where(x < 2, 3 * x, 6)
    + np.where((x >= 2) & (x < 5), 0, 0)
    + np.where((x >= 5) & (x < 6), -4 * (x - 5), 0)
    + np.where(x >= 6, 0.5 * (x - 6), 0)
    + np.random.normal(0, 0.6, n_points)
)

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

# Scatter plot with LOWESS regression
sns.regplot(
    x=x,
    y=y,
    lowess=True,
    scatter_kws={"alpha": 0.6, "s": 100, "color": BRAND, "edgecolors": PAGE_BG, "linewidths": 0.5},
    line_kws={"color": REGRESSION, "linewidth": 4},
    ax=ax,
)

# Styling
ax.set_xlabel("Input Variable (x)", fontsize=20, color=INK)
ax.set_ylabel("Response Variable (y)", fontsize=20, color=INK)
ax.set_title("scatter-regression-lowess · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
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)

# Subtle grid
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)

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 — Seaborn

Python source (Seaborn)

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