Basic Hexbin Plot — plotnine

A hexagonal binning plot that visualizes the density of 2D point data by aggregating points into hexagonal bins. The color intensity of each hexagon represents the count of points within it, making it ideal for revealing density patterns in large datasets where traditional scatter plots would show overlapping points. Hexagonal bins provide better visual representation than square pixels due to their isotropy (equal distance to neighboring cells in all directions).

Basic Hexbin Plot rendered with plotnine

Python source (plotnine)

""" anyplot.ai
hexbin-basic: Basic Hexbin Plot
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 88/100 | Created: 2026-05-29
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    annotate,
    coord_fixed,
    element_blank,
    element_rect,
    element_text,
    geom_polygon,
    ggplot,
    guide_colorbar,
    labs,
    scale_fill_gradient,
    theme,
    theme_minimal,
)


# Theme-adaptive chrome tokens (Imprint palette)
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"

np.random.seed(42)

# Data — seismic epicenters with multi-cluster distribution (Eastern Mediterranean)
lon = np.concatenate(
    [
        np.random.normal(35.5, 0.8, 2500),  # Primary fault zone
        np.random.normal(37.5, 0.5, 1200),  # Aftershock region
        np.random.normal(33.5, 0.4, 600),  # Background cluster
        np.random.uniform(32.0, 39.0, 700),  # Diffuse regional activity
    ]
)
lat = np.concatenate(
    [
        np.random.normal(37.0, 0.7, 2500),
        np.random.normal(38.5, 0.5, 1200),
        np.random.normal(36.5, 0.4, 600),
        np.random.uniform(35.0, 40.0, 700),
    ]
)

# Vectorized hexagonal binning
gridsize = 30
hex_w = (lon.max() - lon.min() + 1.0) / gridsize
hex_h = hex_w * np.sqrt(3) / 2

row_idx = np.round(lat / hex_h).astype(int)
offset = (row_idx % 2) * (hex_w / 2)
col_idx = np.round((lon - offset) / hex_w).astype(int)

bin_df = pd.DataFrame({"cx": np.round(col_idx * hex_w + offset, 6), "cy": np.round(row_idx * hex_h, 6)})
counts = bin_df.groupby(["cx", "cy"]).size().reset_index(name="count")

# Build hex polygon vertices — oversize 1.08 minimises gaps in sparse regions
r = hex_w / np.sqrt(3) * 1.08
angles = np.linspace(0, 2 * np.pi, 7)[:-1] + np.pi / 6
n = len(counts)

hex_df = pd.DataFrame(
    {
        "x": np.repeat(counts["cx"].values, 6) + r * np.cos(np.tile(angles, n)),
        "y": np.repeat(counts["cy"].values, 6) + r * np.sin(np.tile(angles, n)),
        "hex_id": np.repeat(np.arange(n), 6),
        "count": np.repeat(counts["count"].values, 6),
    }
)

title = "Seismic Event Density · hexbin-basic · python · plotnine · anyplot.ai"
title_fontsize = max(8, round(12 * 67 / len(title)))

plot = (
    ggplot(hex_df, aes(x="x", y="y", group="hex_id", fill="count"))
    + geom_polygon(color=PAGE_BG, size=0.15)
    + scale_fill_gradient(low="#009E73", high="#4467A3", name="Event Count", guide=guide_colorbar(nbin=200))
    # Focal annotations identifying the two main seismic clusters
    + annotate("text", x=32.2, y=37.1, label="Primary\nFault Zone →", size=3, color=INK, ha="left")
    + annotate("text", x=38.8, y=38.6, label="← Aftershock\n   Region", size=3, color=INK, ha="right")
    + coord_fixed(ratio=1)
    + labs(x="Longitude (°E)", y="Latitude (°N)", title=title)
    + theme_minimal()
    + theme(
        figure_size=(8, 4.5),
        text=element_text(size=8, color=INK_SOFT),
        axis_title=element_text(size=10, color=INK),
        axis_text=element_text(size=8, color=INK_SOFT),
        plot_title=element_text(size=title_fontsize, color=INK),
        legend_title=element_text(size=8, color=INK),
        legend_text=element_text(size=8, color=INK_SOFT),
        panel_grid_major=element_blank(),
        panel_grid_minor=element_blank(),
        panel_background=element_rect(fill=PAGE_BG, color="none"),
        plot_background=element_rect(fill=PAGE_BG, color="none"),
        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
        panel_border=element_rect(color=INK_SOFT, fill=None),
    )
)

plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)

Part of Basic Hexbin Plot on anyplot.ai.

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