Scatter Plot with Linear Regression — plotnine

A scatter plot that displays the relationship between two numeric variables with a fitted linear regression line and confidence interval band. This visualization extends the basic scatter plot by adding statistical modeling elements, making it ideal for understanding linear relationships, assessing model fit, and communicating the strength of correlations with visual uncertainty quantification.

Scatter Plot with Linear Regression rendered with plotnine

Python source (plotnine)

""" anyplot.ai
scatter-regression-linear: Scatter Plot with Linear Regression
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-06
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    annotate,
    element_line,
    element_rect,
    element_text,
    geom_point,
    geom_smooth,
    ggplot,
    ggsave,
    labs,
    theme,
    theme_minimal,
)
from scipy import stats


# 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

# Okabe-Ito palette
BRAND = "#009E73"  # First categorical series
ACCENT = "#C475FD"  # Second series (for regression line)

# Data - Study hours vs exam score relationship
np.random.seed(42)
n_points = 80
study_hours = np.random.uniform(1, 10, n_points)
exam_score = 45 + 5 * study_hours + np.random.normal(0, 6, n_points)
exam_score = np.clip(exam_score, 0, 100)

df = pd.DataFrame({"study_hours": study_hours, "exam_score": exam_score})

# Calculate regression statistics for annotation
slope, intercept, r_value, p_value, std_err = stats.linregress(study_hours, exam_score)
r_squared = r_value**2

# Create regression equation and R² text
equation_text = f"y = {slope:.2f}x + {intercept:.2f}"
r_squared_text = f"R² = {r_squared:.3f}"
annotation_text = f"{equation_text}\n{r_squared_text}"

# Plot
plot = (
    ggplot(df, aes(x="study_hours", y="exam_score"))
    + geom_point(size=4.5, alpha=0.70, color=BRAND)
    + geom_smooth(method="lm", se=True, color=ACCENT, fill=ACCENT, alpha=0.25, size=2)
    + annotate("text", x=2, y=92, label=annotation_text, ha="left", va="top", size=14, color=INK)
    + labs(title="scatter-regression-linear · plotnine · anyplot.ai", x="Study Hours", y="Exam Score (%)")
    + theme_minimal()
    + theme(
        figure_size=(16, 9),
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_grid_major=element_line(color=INK, size=0.3, alpha=0.08),
        panel_grid_minor=element_line(color=INK, size=0.2, alpha=0.04),
        panel_border=element_rect(color=INK_SOFT, fill=None, size=0.5),
        axis_title=element_text(size=20, color=INK),
        axis_text=element_text(size=16, color=INK_SOFT),
        axis_line=element_line(color=INK_SOFT, size=0.5),
        plot_title=element_text(size=24, color=INK),
        text=element_text(size=14, color=INK),
    )
)

# Save
ggsave(plot, filename=f"plot-{THEME}.png", dpi=300, width=16, height=9, verbose=False)

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

Scatter Plot with Linear Regression — plotnine

Python source (plotnine)

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