Residual Plot in Pygal (Python)

The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, 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.

Residual Plot rendered with Pygal

Renders

Python source (Pygal)

""" anyplot.ai
residual-plot: Residual Plot
Library: pygal 3.1.0 | Python 3.13.13
Quality: 95/100 | Updated: 2026-05-10
"""

import os

import numpy as np
import pygal
from pygal.style import Style


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

# Okabe-Ito palette
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")

# Data - Linear regression example with heteroscedasticity
np.random.seed(42)
n_points = 100

# Generate fitted values (x-axis) - house price predictions in $1000s
fitted_values = np.linspace(150, 500, n_points)

# Generate residuals with heteroscedasticity and outliers
base_residuals = np.random.normal(0, 20, n_points)
heteroscedasticity = (fitted_values / 500) * np.random.normal(0, 15, n_points)
residuals = base_residuals + heteroscedasticity

# Add outliers
outlier_indices = [15, 45, 78]
residuals[outlier_indices] = [85, -75, 90]

# Calculate standard deviation for reference bands
std_residuals = np.std(residuals)
upper_band = 2 * std_residuals
lower_band = -2 * std_residuals

# Identify outliers (beyond 2 standard deviations)
outlier_mask = np.abs(residuals) > 2 * std_residuals

# Custom style for 4800x2700 canvas with theme-adaptive tokens
custom_style = Style(
    background=PAGE_BG,
    plot_background=PAGE_BG,
    foreground=INK,
    foreground_strong=INK,
    foreground_subtle=INK_MUTED,
    colors=IMPRINT,
    title_font_size=28,
    label_font_size=22,
    major_label_font_size=20,
    legend_font_size=18,
    value_font_size=14,
    stroke_width=3,
    guide_stroke_color=INK_MUTED,
    guide_stroke_dasharray="3, 3",
)

# Create XY scatter chart for residual plot
chart = pygal.XY(
    width=4800,
    height=2700,
    style=custom_style,
    title="residual-plot · pygal · anyplot.ai",
    x_title="Fitted Values - Predicted Price ($1000s)",
    y_title="Residuals - Actual minus Predicted ($1000s)",
    show_legend=True,
    legend_at_bottom=True,
    legend_at_bottom_columns=5,
    show_x_guides=True,
    show_y_guides=True,
    stroke=False,
    dots_size=12,
    truncate_legend=-1,
    x_label_rotation=0,
    xrange=(140, 510),
    range=(-100, 110),
)

# Set explicit x-axis labels
chart.x_labels = [150, 200, 250, 300, 350, 400, 450, 500]

# Prepare data points - separate normal and outlier points
normal_points = [(float(fitted_values[i]), float(residuals[i])) for i in range(n_points) if not outlier_mask[i]]
outlier_points = [(float(fitted_values[i]), float(residuals[i])) for i in range(n_points) if outlier_mask[i]]

# Add data series
chart.add("Residuals", normal_points)
chart.add("Outliers (>2σ)", outlier_points)

# Add zero reference line
zero_line_points = [(float(x), 0.0) for x in np.linspace(150, 500, 50)]
chart.add("Zero Reference (Perfect Fit)", zero_line_points, stroke=True, show_dots=False, stroke_style={"width": 5})

# Add +2σ reference band line
upper_band_points = [(float(x), float(upper_band)) for x in np.linspace(150, 500, 50)]
chart.add(
    "+2σ Threshold", upper_band_points, stroke=True, show_dots=False, stroke_style={"width": 3, "dasharray": "10, 8"}
)

# Add -2σ reference band line
lower_band_points = [(float(x), float(lower_band)) for x in np.linspace(150, 500, 50)]
chart.add(
    "-2σ Threshold", lower_band_points, stroke=True, show_dots=False, stroke_style={"width": 3, "dasharray": "10, 8"}
)

# Save as PNG and HTML
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
    f.write(chart.render())

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/residual-plot/pygal/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": "pygal",
  "page": "https://anyplot.ai/residual-plot/python/pygal",
  "hub": "https://anyplot.ai/residual-plot",
  "code_json": "https://api.anyplot.ai/specs/residual-plot/pygal/code",
  "spec_json": "https://api.anyplot.ai/specs/residual-plot",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/pygal/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/pygal/plot-dark.png",
  "interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/pygal/plot-light.html",
  "interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/pygal/plot-dark.html",
  "quality_score": 95.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

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