Residual Plot in Seaborn (Python)

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

Renders

Python source (Seaborn)

""" anyplot.ai
residual-plot: Residual Plot
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-10
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns


# 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
ACCENT_RED = "#C475FD"  # Okabe-Ito position 2 for trend/outliers

# Data - manufacturing quality control (wafer defect prediction)
np.random.seed(42)
n_samples = 150

# Feature: process temperature (°C)
temperature = np.random.uniform(700, 1100, n_samples)

# True relationship: exponential relationship (will show non-linearity in residuals)
y_true = 1000 + 0.5 * temperature + 0.0005 * temperature**2 + np.random.normal(0, 150, n_samples)

# Fit linear model (will miss the quadratic component)
slope, intercept = np.polyfit(temperature, y_true, 1)
y_pred = slope * temperature + intercept

# Calculate residuals
residuals = y_true - y_pred

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

# Create DataFrame for seaborn
df = pd.DataFrame(
    {"Fitted Values": y_pred, "Residuals": residuals, "Type": np.where(outlier_mask, "Outlier (|z| > 2)", "Normal")}
)

# Configure seaborn theme
sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.10,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Create plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Plot using seaborn scatterplot with hue for outliers
sns.scatterplot(
    data=df,
    x="Fitted Values",
    y="Residuals",
    hue="Type",
    palette={"Normal": BRAND, "Outlier (|z| > 2)": ACCENT_RED},
    s=180,
    alpha=0.75,
    ax=ax,
)

# Add polynomial trend line (order=2) to show non-linear pattern
sns.regplot(
    data=df,
    x="Fitted Values",
    y="Residuals",
    scatter=False,
    order=2,
    line_kws={"color": INK_SOFT, "linewidth": 2.5, "linestyle": "--", "alpha": 0.6, "label": "Trend (2nd order)"},
    ax=ax,
)

# Reference line at y=0 (perfect prediction)
ax.axhline(y=0, color=INK, linestyle="-", linewidth=2, zorder=2, alpha=0.8)

# Add ±2 standard deviation bands
ax.axhline(y=2 * residual_std, color=INK_SOFT, linestyle="--", linewidth=1.5, alpha=0.5, label="±2 SD")
ax.axhline(y=-2 * residual_std, color=INK_SOFT, linestyle="--", linewidth=1.5, alpha=0.5)

# Styling
ax.set_xlabel("Fitted Values (Predicted Defect Count)", fontsize=20, color=INK)
ax.set_ylabel("Residuals (Actual - Predicted)", fontsize=20, color=INK)
ax.set_title("residual-plot · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Remove top and right spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)

# Custom legend with better positioning
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, fontsize=14, loc="upper right", framealpha=0.95, edgecolor=INK_SOFT)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/residual-plot/seaborn/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": "seaborn",
  "page": "https://anyplot.ai/residual-plot/python/seaborn",
  "hub": "https://anyplot.ai/residual-plot",
  "code_json": "https://api.anyplot.ai/specs/residual-plot/seaborn/code",
  "spec_json": "https://api.anyplot.ai/specs/residual-plot",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/seaborn/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/seaborn/plot-dark.png",
  "quality_score": 94.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

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