A choropleth map visualizes data by shading geographic regions (countries, states, or counties) according to a measured variable. This technique is ideal for showing regional patterns and spatial distributions, making it easy to identify areas with high or low values at a glance. The color intensity represents the data magnitude, creating an intuitive way to understand geographic variation.
""" anyplot.ai
choropleth-basic: Choropleth Map with Regional Coloring
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 81/100 | Updated: 2026-05-15
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_fixed,
element_blank,
element_line,
element_rect,
element_text,
geom_polygon,
geom_text,
ggplot,
labs,
scale_fill_cmap,
theme,
)
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"
MISSING_DATA_COLOR = "#A9A9A9" if THEME == "light" else "#696969"
np.random.seed(42)
# Simplified European country boundaries (approximate polygon coordinates)
countries = {
"France": [(0, 0), (3, 0), (4, 2), (3, 4), (1, 4), (0, 2)],
"Germany": [(4, 2), (7, 1), (8, 3), (7, 5), (4, 5), (3, 4)],
"Spain": [(-3, -3), (1, -3), (2, -1), (0, 0), (-2, 0), (-3, -1)],
"Italy": [(5, -2), (7, -3), (9, -1), (8, 2), (6, 1), (5, 0)],
"Poland": [(8, 3), (12, 2), (13, 5), (11, 6), (8, 5), (7, 5)],
"UK": [(-2, 4), (1, 4), (2, 6), (1, 8), (-1, 8), (-2, 6)],
"Sweden": [(7, 7), (10, 6), (11, 9), (10, 12), (8, 11), (7, 9)],
"Norway": [(5, 9), (7, 7), (8, 11), (7, 14), (5, 13), (4, 11)],
"Finland": [(11, 9), (14, 8), (15, 12), (13, 14), (11, 12), (10, 12)],
"Austria": [(6, 1), (8, 0), (10, 1), (9, 3), (7, 3), (6, 2)],
"Netherlands": [(2, 5), (4, 5), (5, 6), (4, 7), (2, 7), (1, 6)],
"Belgium": [(1, 4), (3, 4), (4, 5), (2, 5), (1, 5)],
"Switzerland": [(3, 1), (5, 0), (6, 1), (5, 2), (4, 2), (3, 2)],
"Portugal": [(-4, -2), (-3, -3), (-2, -1), (-3, 0), (-4, 0)],
"Denmark": [(5, 6), (7, 5), (8, 6), (7, 7), (5, 7)],
"Czechia": [(7, 3), (9, 3), (10, 4), (9, 5), (7, 5), (6, 4)],
}
# Life expectancy at birth (years) - realistic values for 2023-2024
# Czechia has None to demonstrate missing data handling
life_expectancy = {
"France": 82.4,
"Germany": 81.8,
"Spain": 83.1,
"Italy": 83.5,
"Poland": 78.0,
"UK": 81.3,
"Sweden": 84.2,
"Norway": 84.6,
"Finland": 82.5,
"Austria": 81.9,
"Netherlands": 82.1,
"Belgium": 81.8,
"Switzerland": 84.0,
"Portugal": 82.2,
"Denmark": 81.5,
"Czechia": None,
}
# Build polygon dataframe
polygon_data = []
for country, coords in countries.items():
closed_coords = coords + [coords[0]]
for i, (x, y) in enumerate(closed_coords):
polygon_data.append(
{"country": country, "x": x, "y": y, "order": i, "life_expectancy": life_expectancy[country]}
)
df = pd.DataFrame(polygon_data)
# Calculate centroids for country labels
label_offsets = {
"Netherlands": (0.5, 1.0),
"Belgium": (-0.8, -0.5),
"Germany": (0.5, -0.5),
"Denmark": (0.5, 0.5),
"Czechia": (0, -0.5),
}
centroids = []
for country, coords in countries.items():
cx = np.mean([c[0] for c in coords])
cy = np.mean([c[1] for c in coords])
if country in label_offsets:
cx += label_offsets[country][0]
cy += label_offsets[country][1]
expectancy = life_expectancy[country]
centroids.append({"country": country, "x": cx, "y": cy, "life_expectancy": expectancy})
df_centroids = pd.DataFrame(centroids)
# Separate data with and without values for missing data handling
df_with_data = df[df["life_expectancy"].notna()].copy()
df_missing = df[df["life_expectancy"].isna()].copy()
# Create the choropleth map
plot = (
ggplot()
+ geom_polygon(
df_missing, aes(x="x", y="y", group="country"), fill=MISSING_DATA_COLOR, color=INK_SOFT, size=0.6, alpha=0.7
)
+ geom_polygon(
df_with_data, aes(x="x", y="y", group="country", fill="life_expectancy"), color=INK_SOFT, size=0.6, alpha=0.95
)
+ geom_text(df_centroids, aes(x="x", y="y", label="country"), size=7, color=INK)
+ scale_fill_cmap(cmap_name="BrBG", name="Life Expectancy\n(years)", limits=(77, 85))
+ coord_fixed(ratio=1.0)
+ labs(title="choropleth-basic · plotnine · anyplot.ai")
+ theme(
figure_size=(16, 9),
plot_title=element_text(size=24, ha="center", color=INK),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_grid=element_blank(),
panel_border=element_line(color=INK_SOFT, size=0.3),
axis_text=element_blank(),
axis_title=element_blank(),
axis_ticks=element_blank(),
legend_title=element_text(size=16, color=INK),
legend_text=element_text(size=14, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_position="right",
)
)
# Save the plot
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)
Part of Choropleth Map with Regional Coloring on anyplot.ai.