A scatter plot enhanced with marginal distribution plots (histograms or kernel density estimates) on the top and right axes. This composite visualization simultaneously shows the bivariate relationship between two variables in the central scatter plot while displaying each variable's univariate distribution along its respective axis. It provides a comprehensive view of both the joint and marginal distributions in a single figure.
""" anyplot.ai
scatter-marginal: Scatter Plot with Marginal Distributions
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-09
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.layouts import column, row
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# 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" # Okabe-Ito position 1
# Data - bivariate normal with correlation
np.random.seed(42)
n_points = 200
x = np.random.randn(n_points) * 15 + 50
y = x * 0.6 + np.random.randn(n_points) * 10 + 20
source = ColumnDataSource(data={"x": x, "y": y})
# Calculate dimensions for 4800x2700 total with marginal plots
main_width = 3800
main_height = 2100
marginal_width = 3800
marginal_height = 550
side_marginal_width = 950
side_marginal_height = 2100
# Histogram bins
n_bins = 30
x_hist, x_edges = np.histogram(x, bins=n_bins)
y_hist, y_edges = np.histogram(y, bins=n_bins)
# Main scatter plot
p_scatter = figure(
width=main_width,
height=main_height,
x_axis_label="Height (cm)",
y_axis_label="Weight (kg)",
title="scatter-marginal · bokeh · anyplot.ai",
)
p_scatter.scatter(x="x", y="y", source=source, size=20, color=BRAND, alpha=0.65, line_color=None)
# Style main scatter
p_scatter.title.text_font_size = "28pt"
p_scatter.title.text_color = INK
p_scatter.xaxis.axis_label_text_font_size = "22pt"
p_scatter.yaxis.axis_label_text_font_size = "22pt"
p_scatter.xaxis.axis_label_text_color = INK
p_scatter.yaxis.axis_label_text_color = INK
p_scatter.xaxis.major_label_text_font_size = "18pt"
p_scatter.yaxis.major_label_text_font_size = "18pt"
p_scatter.xaxis.major_label_text_color = INK_SOFT
p_scatter.yaxis.major_label_text_color = INK_SOFT
p_scatter.xaxis.axis_line_color = INK_SOFT
p_scatter.yaxis.axis_line_color = INK_SOFT
p_scatter.xaxis.major_tick_line_color = INK_SOFT
p_scatter.yaxis.major_tick_line_color = INK_SOFT
p_scatter.background_fill_color = PAGE_BG
p_scatter.border_fill_color = PAGE_BG
p_scatter.outline_line_color = INK_SOFT
p_scatter.grid.grid_line_color = INK
p_scatter.grid.grid_line_alpha = 0.10
p_scatter.toolbar_location = None
# Top marginal histogram (X distribution)
p_top = figure(width=marginal_width, height=marginal_height, x_range=p_scatter.x_range, title=None)
p_top.quad(top=x_hist, bottom=0, left=x_edges[:-1], right=x_edges[1:], fill_color=BRAND, line_color=None, alpha=0.6)
p_top.xaxis.visible = False
p_top.yaxis.axis_label = "Count"
p_top.yaxis.axis_label_text_font_size = "18pt"
p_top.yaxis.axis_label_text_color = INK
p_top.yaxis.major_label_text_font_size = "14pt"
p_top.yaxis.major_label_text_color = INK_SOFT
p_top.yaxis.axis_line_color = INK_SOFT
p_top.yaxis.major_tick_line_color = INK_SOFT
p_top.background_fill_color = PAGE_BG
p_top.border_fill_color = PAGE_BG
p_top.outline_line_color = INK_SOFT
p_top.grid.grid_line_color = INK
p_top.grid.grid_line_alpha = 0.10
p_top.min_border_bottom = 0
p_top.min_border_left = p_scatter.min_border_left
p_top.toolbar_location = None
# Right marginal histogram (Y distribution)
p_right = figure(width=side_marginal_width, height=main_height, y_range=p_scatter.y_range, title=None)
p_right.quad(top=y_edges[1:], bottom=y_edges[:-1], left=0, right=y_hist, fill_color=BRAND, line_color=None, alpha=0.6)
p_right.yaxis.visible = False
p_right.xaxis.axis_label = "Count"
p_right.xaxis.axis_label_text_font_size = "18pt"
p_right.xaxis.axis_label_text_color = INK
p_right.xaxis.major_label_text_font_size = "14pt"
p_right.xaxis.major_label_text_color = INK_SOFT
p_right.xaxis.axis_line_color = INK_SOFT
p_right.xaxis.major_tick_line_color = INK_SOFT
p_right.background_fill_color = PAGE_BG
p_right.border_fill_color = PAGE_BG
p_right.outline_line_color = INK_SOFT
p_right.grid.grid_line_color = INK
p_right.grid.grid_line_alpha = 0.10
p_right.min_border_left = 0
p_right.min_border_bottom = p_scatter.min_border_bottom
p_right.toolbar_location = None
# Empty corner placeholder
p_corner = figure(width=side_marginal_width, height=marginal_height, toolbar_location=None)
p_corner.outline_line_color = None
p_corner.xaxis.visible = False
p_corner.yaxis.visible = False
p_corner.grid.visible = False
p_corner.background_fill_color = PAGE_BG
p_corner.border_fill_color = PAGE_BG
# Layout
layout = column(row(p_top, p_corner), row(p_scatter, p_right))
# Save HTML
output_file(f"plot-{THEME}.html")
save(layout)
# Screenshot with Selenium
W, H = 4800, 2700
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Scatter Plot with Marginal Distributions on anyplot.ai.