A grid of scatter plots showing all pairwise relationships between multiple variables, with histograms or kernel density estimates on the diagonal. This comprehensive visualization enables simultaneous exploration of correlations and distributions across an entire dataset, making it invaluable for understanding multivariate data structure at a glance. Also known as a pairplot or SPLOM (Scatter Plot Matrix).
""" anyplot.ai
scatter-matrix: Scatter Plot Matrix
Library: plotly 6.7.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-09
"""
import os
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# 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
IMPRINT = [
"#009E73", # bluish green (brand, first series)
"#C475FD", # vermillion
"#4467A3", # blue
]
# Data - Weather station measurements across 4 variables
np.random.seed(42)
n = 200
# Weather data: daily measurements from 3 different geographic regions
region = np.repeat(["Coastal", "Mountain", "Desert"], n // 3)
# Temperature (°C) - region-specific distributions
temperature = np.concatenate(
[
np.random.normal(18, 2.5, n // 3), # Coastal - moderate
np.random.normal(12, 3.0, n // 3), # Mountain - cooler
np.random.normal(28, 4.0, n // 3), # Desert - hot
]
)
# Humidity (%) - inverse to temperature
humidity = np.concatenate(
[
np.random.normal(72, 8, n // 3), # Coastal - high
np.random.normal(65, 10, n // 3), # Mountain - moderate
np.random.normal(35, 12, n // 3), # Desert - low
]
)
# Pressure (hPa) - region-specific
pressure = np.concatenate(
[
np.random.normal(1013, 2, n // 3), # Coastal - sea level
np.random.normal(950, 3, n // 3), # Mountain - high altitude
np.random.normal(1010, 2, n // 3), # Desert - high
]
)
# Wind speed (m/s) - variable by region
wind_speed = np.concatenate(
[
np.random.normal(4.5, 1.5, n // 3), # Coastal - breezy
np.random.normal(6.0, 2.0, n // 3), # Mountain - stronger winds
np.random.normal(3.5, 1.2, n // 3), # Desert - lighter winds
]
)
df = pd.DataFrame(
{
"Temperature (°C)": temperature,
"Humidity (%)": humidity,
"Pressure (hPa)": pressure,
"Wind Speed (m/s)": wind_speed,
"Region": region,
}
)
# Variables for matrix
dimensions = ["Temperature (°C)", "Humidity (%)", "Pressure (hPa)", "Wind Speed (m/s)"]
region_list = ["Coastal", "Mountain", "Desert"]
region_colors = {"Coastal": IMPRINT[0], "Mountain": IMPRINT[1], "Desert": IMPRINT[2]}
n_dims = len(dimensions)
# Create subplots grid
fig = make_subplots(rows=n_dims, cols=n_dims, horizontal_spacing=0.04, vertical_spacing=0.04)
# Track legend status
legend_added = dict.fromkeys(region_list, False)
# Build scatter matrix with histograms on diagonal
for i, dim_y in enumerate(dimensions):
for j, dim_x in enumerate(dimensions):
row, col = i + 1, j + 1
if i == j:
# Diagonal: histograms
for region in region_list:
mask = df["Region"] == region
fig.add_trace(
go.Histogram(
x=df.loc[mask, dim_x],
name=region,
marker=dict(color=region_colors[region]),
opacity=0.75,
showlegend=not legend_added[region],
legendgroup=region,
nbinsx=15,
),
row=row,
col=col,
)
legend_added[region] = True
fig.update_xaxes(showticklabels=True, row=row, col=col)
fig.update_yaxes(showticklabels=False, row=row, col=col)
else:
# Off-diagonal: scatter plots
for region in region_list:
mask = df["Region"] == region
fig.add_trace(
go.Scatter(
x=df.loc[mask, dim_x],
y=df.loc[mask, dim_y],
mode="markers",
name=region,
marker=dict(
color=region_colors[region], size=8, opacity=0.7, line=dict(width=0.5, color=PAGE_BG)
),
showlegend=False,
legendgroup=region,
),
row=row,
col=col,
)
# Add axis labels on edges only
if i == n_dims - 1:
fig.update_xaxes(title_text=dim_x, row=row, col=col, title_font=dict(size=20, color=INK))
if j == 0:
fig.update_yaxes(title_text=dim_y, row=row, col=col, title_font=dict(size=20, color=INK))
# Update overall layout
fig.update_layout(
title=dict(text="scatter-matrix · plotly · anyplot.ai", font=dict(size=28, color=INK), x=0.5, xanchor="center"),
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font=dict(size=16, color=INK),
legend=dict(
bgcolor=ELEVATED_BG,
bordercolor=INK_SOFT,
borderwidth=1,
font=dict(size=18, color=INK_SOFT),
title=dict(text="Region", font=dict(size=20, color=INK)),
yanchor="top",
y=0.98,
xanchor="right",
x=0.98,
),
showlegend=True,
barmode="overlay",
margin=dict(l=100, r=100, t=120, b=100),
)
# Update all axes with theme-adaptive colors
fig.update_xaxes(tickfont=dict(size=16, color=INK_SOFT), showgrid=True, gridwidth=1, gridcolor=GRID, linecolor=INK_SOFT)
fig.update_yaxes(tickfont=dict(size=16, color=INK_SOFT), showgrid=True, gridwidth=1, gridcolor=GRID, linecolor=INK_SOFT)
# Save as PNG (square format for matrix)
fig.write_image(f"plot-{THEME}.png", width=1600, height=1600, scale=3)
# Save interactive HTML
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of Scatter Plot Matrix on anyplot.ai.