The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, 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: plotly 6.7.0 | Python 3.13.13
Quality: 96/100 | Updated: 2026-05-10
"""
import os
import sys
import numpy as np
# Avoid module shadowing by removing current directory from path during import
cwd = sys.path[0]
if cwd in sys.path:
sys.path.remove(cwd)
import plotly.graph_objects as go
sys.path.insert(0, cwd)
# 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"
GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
# Okabe-Ito palette
BRAND = "#009E73"
ACCENT = "#C475FD"
NEUTRAL = "#1A1A1A" if THEME == "light" else "#E8E8E0"
# Data - Generate realistic regression scenario with varying residual patterns
np.random.seed(42)
n_samples = 150
# Create features with some non-linearity to show interesting residual patterns
X = np.linspace(0, 10, n_samples)
# True relationship with slight curvature (linear model will miss this)
y_true = 2 * X + 0.3 * X**1.5 + np.random.randn(n_samples) * 2
# Simple linear regression (manual fit)
X_mean = np.mean(X)
y_mean = np.mean(y_true)
slope = np.sum((X - X_mean) * (y_true - y_mean)) / np.sum((X - X_mean) ** 2)
intercept = y_mean - slope * X_mean
y_pred = slope * X + intercept
# Calculate residuals
residuals = y_true - y_pred
std_residuals = np.std(residuals)
# Identify outliers (beyond ±2 standard deviations)
outlier_mask = np.abs(residuals) > 2 * std_residuals
normal_mask = ~outlier_mask
# Create figure
fig = go.Figure()
# Add ±2 standard deviation bands (dashed lines)
fig.add_trace(
go.Scatter(
x=[y_pred.min(), y_pred.max()],
y=[2 * std_residuals, 2 * std_residuals],
mode="lines",
line=dict(color=ACCENT, width=3, dash="dash"),
name="+2 SD",
showlegend=True,
)
)
fig.add_trace(
go.Scatter(
x=[y_pred.min(), y_pred.max()],
y=[-2 * std_residuals, -2 * std_residuals],
mode="lines",
line=dict(color=ACCENT, width=3, dash="dash"),
name="-2 SD",
showlegend=True,
)
)
# Add horizontal reference line at y=0
fig.add_trace(
go.Scatter(
x=[y_pred.min(), y_pred.max()],
y=[0, 0],
mode="lines",
line=dict(color=NEUTRAL, width=3),
name="Zero Line",
showlegend=False,
)
)
# Add normal residuals (using brand green for first series)
fig.add_trace(
go.Scatter(
x=y_pred[normal_mask],
y=residuals[normal_mask],
mode="markers",
marker=dict(size=14, color=BRAND, opacity=0.7, line=dict(width=1, color=BRAND)),
name="Residuals",
hovertemplate="Fitted: %{x:.2f}<br>Residual: %{y:.2f}<extra></extra>",
)
)
# Add outlier residuals
if np.any(outlier_mask):
fig.add_trace(
go.Scatter(
x=y_pred[outlier_mask],
y=residuals[outlier_mask],
mode="markers",
marker=dict(size=16, color=ACCENT, opacity=0.9, line=dict(width=2, color=ACCENT), symbol="diamond"),
name="Outliers (>2 SD)",
hovertemplate="Fitted: %{x:.2f}<br>Residual: %{y:.2f}<extra></extra>",
)
)
# Add smoothing line to detect patterns
sorted_indices = np.argsort(y_pred)
window_size = 15
kernel = np.ones(window_size) / window_size
smoothed_residuals = np.convolve(residuals[sorted_indices], kernel, mode="same")
fig.add_trace(
go.Scatter(
x=y_pred[sorted_indices],
y=smoothed_residuals,
mode="lines",
line=dict(color=INK_SOFT, width=4),
name="Trend Line",
hovertemplate="Fitted: %{x:.2f}<br>Smoothed Residual: %{y:.2f}<extra></extra>",
)
)
# Update layout with theme-adaptive styling
fig.update_layout(
title=dict(text="residual-plot · plotly · anyplot.ai", font=dict(size=28, color=INK), x=0.5, xanchor="center"),
xaxis=dict(
title=dict(text="Fitted Values", font=dict(size=22, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
showgrid=True,
gridwidth=1,
gridcolor=GRID,
linecolor=INK_SOFT,
zeroline=False,
),
yaxis=dict(
title=dict(text="Residuals (y_true - y_pred)", font=dict(size=22, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
showgrid=True,
gridwidth=1,
gridcolor=GRID,
linecolor=INK_SOFT,
zeroline=False,
),
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font=dict(color=INK),
legend=dict(
font=dict(size=18, color=INK_SOFT),
x=0.02,
y=0.98,
xanchor="left",
yanchor="top",
bgcolor=ELEVATED_BG,
bordercolor=INK_SOFT,
borderwidth=1,
),
margin=dict(l=100, r=50, t=100, b=80),
)
# Save as PNG and HTML with theme-suffixed filenames
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/residual-plot/plotly/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": "plotly",
"page": "https://anyplot.ai/residual-plot/python/plotly",
"hub": "https://anyplot.ai/residual-plot",
"code_json": "https://api.anyplot.ai/specs/residual-plot/plotly/code",
"spec_json": "https://api.anyplot.ai/specs/residual-plot",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/plotly/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/plotly/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/plotly/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/residual-plot/python/plotly/plot-dark.html",
"quality_score": 96.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Residual Plot on anyplot.ai.