Scatter Plot with LOWESS Regression — plotnine

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 plotnine

Python source (plotnine)

""" anyplot.ai
scatter-regression-lowess: Scatter Plot with LOWESS Regression
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 97/100 | Updated: 2026-05-14
"""

import os
import sys

import numpy as np
import pandas as pd


venv_path = "/home/runner/work/anyplot/anyplot/.venv/lib/python3.13/site-packages"
if os.path.exists(venv_path):
    sys.path.insert(0, venv_path)

from plotnine import (
    aes,
    element_line,
    element_rect,
    element_text,
    geom_point,
    geom_smooth,
    ggplot,
    labs,
    theme,
    theme_minimal,
)


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 - create complex non-linear relationship (crop yield vs temperature)
np.random.seed(42)
n_points = 150

# Temperature range (x) - realistic agricultural context
x = np.linspace(5, 35, n_points)

# Yield (y) - peaks around 20-25°C, drops at extremes (realistic crop response)
# Complex non-linear pattern: quadratic-like with some local variation
y_base = -0.5 * (x - 22) ** 2 + 80  # Peak around 22°C
y_noise = np.random.normal(0, 8, n_points)  # Natural variation
y = y_base + y_noise + 3 * np.sin(x / 3)  # Add subtle local pattern

# Ensure positive yields
y = np.clip(y, 5, None)

# Create DataFrame
df = pd.DataFrame({"temperature": x, "yield": y})

# Theme configuration
anyplot_theme = theme(
    figure_size=(16, 9),
    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
    panel_background=element_rect(fill=PAGE_BG),
    panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),
    panel_grid_minor=element_line(color=INK, size=0.2, alpha=0.05),
    panel_border=element_rect(color=INK_SOFT, fill=None),
    axis_title=element_text(color=INK, size=20),
    axis_text=element_text(color=INK_SOFT, size=16),
    axis_line=element_line(color=INK_SOFT),
    plot_title=element_text(color=INK, size=24),
    text=element_text(size=14),
)

# Create plot with scatter points and LOWESS smooth
plot = (
    ggplot(df, aes(x="temperature", y="yield"))
    + geom_point(color=BRAND, alpha=0.6, size=3)
    + geom_smooth(method="lowess", span=0.4, color=ACCENT, size=2.5, se=False)
    + labs(
        x="Temperature (°C)", y="Crop Yield (tons/hectare)", title="scatter-regression-lowess · plotnine · anyplot.ai"
    )
    + theme_minimal()
    + anyplot_theme
)

# Save
plot.save(f"plot-{THEME}.png", dpi=300)

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

Scatter Plot with LOWESS Regression — plotnine

Python source (plotnine)

Other implementations