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: plotly 6.7.0 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-15
"""
import os
import numpy as np
import pandas as pd
import plotly.express as px
# 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"
# Data - Life expectancy by country (years)
np.random.seed(42)
countries_data = {
"country": [
"CHN",
"IND",
"USA",
"IDN",
"BRA",
"PAK",
"NGA",
"BGD",
"RUS",
"MEX",
"JPN",
"EGY",
"PHL",
"ETH",
"VNM",
"TUR",
"IRN",
"DEU",
"THA",
"FRA",
"GBR",
"TZA",
"ITA",
"KEN",
"COL",
"ESP",
"UKR",
"DZA",
"IRQ",
"AFG",
"CAN",
"MAR",
"AUS",
"PER",
"ANG",
"GHA",
"YEM",
"NLD",
"CHE",
"POL",
"SWE",
"BEL",
"CZE",
"GRC",
"PRT",
"AUT",
"NOR",
"DNK",
"FIN",
"ISL",
],
"life_expectancy": [
78.2,
70.5,
78.9,
72.8,
76.1,
67.3,
54.7,
72.8,
72.5,
75.3,
84.5,
72.6,
71.9,
67.9,
73.8,
77.6,
77.2,
81.3,
76.9,
82.5,
81.5,
65.5,
82.8,
66.3,
76.3,
83.5,
71.4,
77.0,
70.2,
64.8,
82.3,
77.4,
83.2,
76.8,
60.8,
63.1,
66.2,
81.9,
83.6,
78.0,
84.2,
81.8,
79.1,
81.1,
81.7,
81.9,
84.1,
81.2,
82.1,
82.7,
],
}
df = pd.DataFrame(countries_data)
# Create choropleth map with world scope
fig = px.choropleth(
df,
locations="country",
color="life_expectancy",
color_continuous_scale="Viridis",
scope="world",
hover_data={"country": True, "life_expectancy": ":.1f"},
labels={"life_expectancy": "Life Expectancy (years)"},
)
# Update colorbar for theme adaptation
fig.update_layout(
coloraxis_colorbar=dict(
title=dict(text="Life Expectancy<br>(years)", font=dict(size=22, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
thickness=28,
len=0.75,
x=1.02,
tickcolor=INK_SOFT,
bordercolor=INK_SOFT,
borderwidth=1,
)
)
# Update layout for theme-adaptive rendering
fig.update_layout(
title=dict(
text="choropleth-basic · plotly · anyplot.ai",
font=dict(size=28, color=INK, family="sans-serif"),
x=0.5,
xanchor="center",
y=0.98,
yanchor="top",
),
geo=dict(
scope="world",
projection_type="natural earth",
showland=True,
landcolor="rgba(243, 243, 243, 0.5)" if THEME == "light" else "rgba(50, 50, 48, 0.5)",
showocean=True,
oceancolor="rgba(204, 229, 255, 0.3)" if THEME == "light" else "rgba(100, 140, 180, 0.2)",
showcountries=True,
countrycolor=INK_SOFT,
countrywidth=1.5,
showcoastlines=True,
coastlinecolor=INK_SOFT,
coastlinewidth=1.5,
showlakes=True,
lakecolor="rgba(200, 220, 255, 0.3)" if THEME == "light" else "rgba(100, 140, 180, 0.2)",
bgcolor=PAGE_BG,
),
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font=dict(color=INK, family="sans-serif"),
margin=dict(l=40, r=140, t=100, b=40),
height=900,
width=1600,
)
# Update traces for border visibility
fig.update_traces(marker_line_width=2.5, marker_line_color=INK_SOFT)
# Save as PNG (4800x2700 px via scale parameter)
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
# Save as interactive HTML
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of Choropleth Map with Regional Coloring on anyplot.ai.