A customizable grid of multiple subplots allowing different plot types in each cell, with shared or independent axes. Unlike faceted plots that repeat the same visualization for data subsets, subplot grids enable combining distinct visualizations (scatter, line, bar, histogram, etc.) into a cohesive multi-panel figure for comprehensive data presentation.
""" anyplot.ai
subplot-grid: Subplot Grid Layout
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-13
"""
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"
# Okabe-Ito palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
# Apply theme to seaborn
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,
},
)
# Data - Environmental monitoring scenario
np.random.seed(42)
# Time series: 48 hours of hourly readings
hours = np.arange(48)
dates = pd.date_range("2024-03-15", periods=48, freq="h")
# Temperature readings (Celsius) - realistic pattern with daily cycle
base_temp = 15 + 8 * np.sin(np.pi * hours / 24)
temperature = base_temp + np.random.normal(0, 0.8, 48)
# Humidity readings (%)
base_humidity = 60 + 15 * np.sin(np.pi * (hours - 6) / 24)
humidity = base_humidity + np.random.normal(0, 3, 48)
# Air quality index (AQI)
aqi = 40 + 20 * np.abs(np.sin(np.pi * hours / 48)) + np.random.normal(0, 2, 48)
# Pressure readings (hPa)
pressure = 1013 + np.cumsum(np.random.normal(0, 0.1, 48))
# Create DataFrame
df = pd.DataFrame(
{
"Hour": hours,
"DateTime": dates,
"Temperature": temperature,
"Humidity": humidity,
"AQI": aqi,
"Pressure": pressure,
}
)
# Create 2x2 subplot grid
fig, axes = plt.subplots(2, 2, figsize=(16, 9), facecolor=PAGE_BG)
# Subplot 1: Temperature Time Series (top-left)
ax1 = axes[0, 0]
sns.lineplot(data=df, x="Hour", y="Temperature", ax=ax1, color=IMPRINT[0], linewidth=3)
ax1.set_title("Temperature Over Time", fontsize=24, fontweight="medium", color=INK)
ax1.set_xlabel("Hours Since Start", fontsize=20, color=INK)
ax1.set_ylabel("Temperature (°C)", fontsize=20, color=INK)
ax1.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax1.spines["top"].set_visible(False)
ax1.spines["right"].set_visible(False)
ax1.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
# Subplot 2: Humidity Distribution (top-right)
ax2 = axes[0, 1]
sns.histplot(data=df, x="Humidity", bins=15, ax=ax2, color=IMPRINT[1], alpha=0.7, edgecolor=PAGE_BG)
ax2.set_title("Humidity Distribution", fontsize=24, fontweight="medium", color=INK)
ax2.set_xlabel("Humidity (%)", fontsize=20, color=INK)
ax2.set_ylabel("Frequency", fontsize=20, color=INK)
ax2.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax2.spines["top"].set_visible(False)
ax2.spines["right"].set_visible(False)
ax2.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
# Subplot 3: Temperature vs Humidity Scatter (bottom-left)
ax3 = axes[1, 0]
sns.scatterplot(
data=df,
x="Temperature",
y="Humidity",
hue="Hour",
palette="viridis",
s=150,
alpha=0.8,
ax=ax3,
edgecolor=PAGE_BG,
linewidth=0.5,
)
ax3.set_title("Temperature vs Humidity", fontsize=24, fontweight="medium", color=INK)
ax3.set_xlabel("Temperature (°C)", fontsize=20, color=INK)
ax3.set_ylabel("Humidity (%)", fontsize=20, color=INK)
ax3.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax3.spines["top"].set_visible(False)
ax3.spines["right"].set_visible(False)
ax3.grid(True, alpha=0.10, linewidth=0.8, color=INK)
ax3.legend(title="Hour", fontsize=12, title_fontsize=13, loc="best")
# Subplot 4: Air Quality and Pressure (bottom-right)
ax4 = axes[1, 1]
ax4_twin = ax4.twinx()
# Plot AQI as bars
bars = ax4.bar(
df["Hour"], df["AQI"], alpha=0.6, color=IMPRINT[2], label="AQI", width=0.8, edgecolor=PAGE_BG, linewidth=0.5
)
# Plot Pressure as line on twin axis
line = ax4_twin.plot(
df["Hour"], df["Pressure"], color=IMPRINT[3], linewidth=3, marker="o", markersize=6, label="Pressure"
)
ax4.set_title("Air Quality & Pressure", fontsize=24, fontweight="medium", color=INK)
ax4.set_xlabel("Hours Since Start", fontsize=20, color=INK)
ax4.set_ylabel("AQI", fontsize=20, color=INK)
ax4_twin.set_ylabel("Pressure (hPa)", fontsize=20, color=INK)
ax4.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax4_twin.tick_params(axis="y", labelsize=16, colors=INK_SOFT)
ax4.spines["top"].set_visible(False)
ax4_twin.spines["top"].set_visible(False)
ax4.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
# Combined legend
lines1, labels1 = ax4.get_legend_handles_labels()
lines2, labels2 = ax4_twin.get_legend_handles_labels()
ax4.legend(lines1 + lines2, labels1 + labels2, fontsize=14, loc="upper left")
# Main title
fig.suptitle("subplot-grid · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK, y=0.98)
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Subplot Grid Layout on anyplot.ai.