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: matplotlib 3.10.9 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-09
"""
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec
# 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"
# Okabe-Ito palette (3 colors for 3 species)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]
# Data: Iris-like flower measurements (4 variables, 3 species)
np.random.seed(42)
species_params = {
"Setosa": {"sl": (5.0, 0.35), "sw": (3.4, 0.38), "pl": (1.5, 0.17), "pw": (0.2, 0.1)},
"Versicolor": {"sl": (5.9, 0.52), "sw": (2.8, 0.31), "pl": (4.3, 0.47), "pw": (1.3, 0.2)},
"Virginica": {"sl": (6.6, 0.64), "sw": (3.0, 0.32), "pl": (5.5, 0.55), "pw": (2.0, 0.27)},
}
n_per_species = 50
data = {var: [] for var in ["Sepal Length (cm)", "Sepal Width (cm)", "Petal Length (cm)", "Petal Width (cm)"]}
species_labels = []
var_keys = ["sl", "sw", "pl", "pw"]
var_names = ["Sepal Length (cm)", "Sepal Width (cm)", "Petal Length (cm)", "Petal Width (cm)"]
for idx, (_species, params) in enumerate(species_params.items()):
for key, name in zip(var_keys, var_names, strict=True):
mean, std = params[key]
data[name].extend(np.random.normal(mean, std, n_per_species))
species_labels.extend([idx] * n_per_species)
# Convert to arrays
data_arrays = [np.array(data[name]) for name in var_names]
species_names = list(species_params.keys())
species_indices = np.array(species_labels)
n_vars = len(var_names)
# Create figure with extra space for legend
fig = plt.figure(figsize=(16, 9), facecolor=PAGE_BG)
gs = GridSpec(n_vars, n_vars, figure=fig, left=0.08, right=0.88, wspace=0.15, hspace=0.15)
axes = [[fig.add_subplot(gs[i, j]) for j in range(n_vars)] for i in range(n_vars)]
# Plot each cell
for i in range(n_vars):
for j in range(n_vars):
ax = axes[i][j]
ax.set_facecolor(PAGE_BG)
if i == j:
# Diagonal: histograms with enhanced visual hierarchy
for species_idx, (_species, color) in enumerate(zip(species_names, IMPRINT, strict=True)):
mask = species_indices == species_idx
species_data = data_arrays[i][mask]
ax.hist(species_data, bins=12, alpha=0.85, color=color, edgecolor=INK_SOFT, linewidth=1.0)
else:
# Off-diagonal: scatter plots with enhanced marker definition
for species_idx, (_species, color) in enumerate(zip(species_names, IMPRINT, strict=True)):
mask = species_indices == species_idx
ax.scatter(
data_arrays[j][mask],
data_arrays[i][mask],
c=color,
s=140,
alpha=0.8,
edgecolors=INK_SOFT,
linewidth=0.8,
)
# Grid styling
ax.grid(True, alpha=0.12, linestyle="-", color=INK_SOFT, linewidth=0.6)
ax.tick_params(axis="both", labelsize=14, colors=INK_SOFT)
# Remove top and right spines for refined look
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["left"].set_linewidth(0.8)
ax.spines["bottom"].set_color(INK_SOFT)
ax.spines["bottom"].set_linewidth(0.8)
# Visual distinction: subtle background for diagonal (histogram) cells
if i == j:
ax.set_facecolor(ELEVATED_BG)
else:
ax.set_facecolor(PAGE_BG)
# Axis labels only on edges
if i == n_vars - 1:
ax.set_xlabel(var_names[j], fontsize=18, color=INK)
else:
ax.set_xticklabels([])
if j == 0:
ax.set_ylabel(var_names[i], fontsize=18, color=INK)
else:
ax.set_yticklabels([])
# Legend outside matrix (right side)
legend_elements = [
plt.Line2D(
[0],
[0],
marker="o",
color="w",
markerfacecolor=color,
markersize=12,
label=species,
markeredgecolor=PAGE_BG,
markeredgewidth=0.5,
)
for species, color in zip(species_names, IMPRINT, strict=True)
]
leg = fig.legend(
handles=legend_elements, loc="center right", fontsize=16, frameon=True, fancybox=False, edgecolor=INK_SOFT
)
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
for text in leg.get_texts():
text.set_color(INK)
# Title
fig.suptitle("scatter-matrix · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK, y=0.98)
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Scatter Plot Matrix on anyplot.ai.