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

Renders

Python source (plotnine)

""" anyplot.ai
scatter-regression-linear: Scatter Plot with Linear Regression
Library: plotnine 0.15.7 | Python 3.13.14
Quality: 88/100 | Updated: 2026-08-05
"""

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"

# Imprint palette
BRAND = "#009E73"  # Imprint position 1 — scatter points
ACCENT = "#C475FD"  # Imprint position 2 — regression line + CI band

# 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

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}"

# Title — mandated format, length within the 67-char baseline so fontsize stays at default
title = "scatter-regression-linear · python · plotnine · anyplot.ai"
title_fontsize = round(12 * (67 / len(title) if len(title) > 67 else 1.0))

# Plot
plot = (
    ggplot(df, aes(x="study_hours", y="exam_score"))
    + geom_point(size=3.0, stroke=0.6, alpha=0.70, fill=BRAND, color="white")
    + geom_smooth(method="lm", se=True, color=ACCENT, fill=ACCENT, alpha=0.25, size=1.2)
    + annotate(
        "label",
        x=2,
        y=94,
        label=annotation_text,
        ha="left",
        va="top",
        size=5,
        fontweight="bold",
        color=INK,
        fill=ELEVATED_BG,
        boxcolor=INK_SOFT,
        label_padding=0.4,
    )
    + labs(title=title, x="Study Hours", y="Exam Score (%)")
    + theme_minimal()
    + theme(
        figure_size=(8, 4.5),
        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),
        axis_line=element_line(color=INK_SOFT, size=0.5),
        axis_title=element_text(size=10, color=INK),
        axis_text=element_text(size=8, color=INK_SOFT),
        plot_title=element_text(size=title_fontsize, color=INK, weight="bold"),
        text=element_text(size=7, color=INK),
    )
)

# Save
ggsave(plot, filename=f"plot-{THEME}.png", dpi=400, width=8, height=4.5, verbose=False)

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/scatter-regression-linear/plotnine/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.

{
  "spec_id": "scatter-regression-linear",
  "language": "python",
  "library": "plotnine",
  "page": "https://anyplot.ai/scatter-regression-linear/python/plotnine",
  "hub": "https://anyplot.ai/scatter-regression-linear",
  "code_json": "https://api.anyplot.ai/specs/scatter-regression-linear/plotnine/code",
  "spec_json": "https://api.anyplot.ai/specs/scatter-regression-linear",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-regression-linear/python/plotnine/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-regression-linear/python/plotnine/plot-dark.png",
  "quality_score": 88.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

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

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