The same plot in 14 other libraries — Python: Altair, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Residual Plot in Python, R, Julia and JavaScript.
A residual plot displays the difference between observed and predicted values (residuals) against fitted values in regression analysis. This diagnostic visualization helps identify violations of regression assumptions including non-linearity, heteroscedasticity (non-constant variance), and outliers. A well-fitting model shows residuals randomly scattered around zero with no discernible pattern.
""" anyplot.ai
residual-plot: Residual Plot
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 98/100 | Updated: 2026-05-10
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import Band, ColumnDataSource, Label, Span
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 (residuals)
OUTLIER_COLOR = "#C475FD" # Okabe-Ito position 2 (outliers)
# Data - Linear regression example with realistic housing price prediction
np.random.seed(42)
n_points = 150
# Generate fitted values (predicted house prices in $1000s)
y_pred = np.linspace(150, 450, n_points) + np.random.randn(n_points) * 20
# Generate residuals with some heteroscedasticity pattern for demonstration
base_residuals = np.random.randn(n_points) * 25
# Add a few outliers beyond 2 standard deviations
outlier_indices = [10, 45, 89, 120, 140]
base_residuals[outlier_indices] = np.array([62, -68, 58, -65, 70])
residuals = base_residuals
y_true = y_pred + residuals
# Calculate statistics for reference bands
residual_std = np.std(residuals)
upper_band = 2 * residual_std
lower_band = -2 * residual_std
# Identify outliers (beyond ±2 standard deviations)
is_outlier = np.abs(residuals) > 2 * residual_std
# Prepare data sources
source_normal = ColumnDataSource(data={"x": y_pred[~is_outlier], "y": residuals[~is_outlier]})
source_outliers = ColumnDataSource(data={"x": y_pred[is_outlier], "y": residuals[is_outlier]})
# Band data for ±2 SD region
band_source = ColumnDataSource(
data={
"x": np.array([min(y_pred) - 10, max(y_pred) + 10]),
"lower": np.array([lower_band, lower_band]),
"upper": np.array([upper_band, upper_band]),
}
)
# Create figure (4800x2700 for 16:9)
p = figure(
width=4800,
height=2700,
title="residual-plot · bokeh · anyplot.ai",
x_axis_label="Fitted Values (Predicted Price in $1000s)",
y_axis_label="Residuals (Observed - Predicted)",
tools="pan,wheel_zoom,box_zoom,reset,save",
x_range=(min(y_pred) - 20, max(y_pred) + 20),
y_range=(min(residuals) - 20, max(residuals) + 20),
)
# Add ±2 SD band (light fill with theme-adaptive color)
band = Band(
base="x", lower="lower", upper="upper", source=band_source, fill_alpha=0.15, fill_color=INK_SOFT, line_width=0
)
p.add_layout(band)
# Add horizontal reference line at y=0
zero_line = Span(location=0, dimension="width", line_color=BRAND, line_width=4, line_dash="solid")
p.add_layout(zero_line)
# Add dashed lines at ±2 SD boundaries
upper_line = Span(location=upper_band, dimension="width", line_color=INK_SOFT, line_width=2, line_dash="dashed")
lower_line = Span(location=lower_band, dimension="width", line_color=INK_SOFT, line_width=2, line_dash="dashed")
p.add_layout(upper_line)
p.add_layout(lower_line)
# Plot normal points (Brand color)
p.scatter("x", "y", source=source_normal, size=18, color=BRAND, alpha=0.7, legend_label="Residuals")
# Plot outliers (Okabe-Ito color 2)
p.scatter(
"x",
"y",
source=source_outliers,
size=22,
color=OUTLIER_COLOR,
alpha=0.9,
line_color=BRAND,
line_width=2,
legend_label="Outliers (>2 SD)",
)
# Add labels for ±2 SD bands (larger font for better visibility)
label_upper = Label(
x=min(y_pred) + 10,
y=upper_band + 3,
text="+2 SD",
text_font_size="24pt",
text_color=INK_SOFT,
text_font_style="italic",
)
label_lower = Label(
x=min(y_pred) + 10,
y=lower_band + 3,
text="-2 SD",
text_font_size="24pt",
text_color=INK_SOFT,
text_font_style="italic",
)
p.add_layout(label_upper)
p.add_layout(label_lower)
# Styling - Text sizes for 4800x2700 canvas
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
# Grid styling
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.10
p.ygrid.grid_line_alpha = 0.10
# Legend styling
p.legend.location = "top_left"
p.legend.label_text_font_size = "20pt"
p.legend.label_text_color = INK_SOFT
p.legend.background_fill_color = ELEVATED_BG
p.legend.border_line_color = INK_SOFT
p.legend.padding = 15
p.legend.spacing = 10
# Axis styling
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
# Background
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
# Save HTML output
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome (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()
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/residual-plot/bokeh/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": "residual-plot",
"language": "python",
"library": "bokeh",
"page": "https://anyplot.ai/residual-plot/python/bokeh",
"hub": "https://anyplot.ai/residual-plot",
"code_json": "https://api.anyplot.ai/specs/residual-plot/bokeh/code",
"spec_json": "https://api.anyplot.ai/specs/residual-plot",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/bokeh/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/bokeh/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/bokeh/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/bokeh/plot-dark.html",
"quality_score": 98.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Residual Plot on anyplot.ai.