Choropleth Map in Plotly

Last Updated: 08 Nov 2025

Choropleth maps use color intensity to show data by region — perfect for population density, election results, sales by state, COVID cases, or GDP per country.

Hinglish Tip: Choropleth = "jahan zyada data, wahan dark color" — jaise India ka weather map! Hover karo → exact number dikhega.

Basic Choropleth Map (World)

import plotly.express as px

# Built-in dataset
df = px.data.gapminder().query("year == 2007")

fig = px.choropleth(
    df,
    locations="iso_alpha",  # 3-letter country code
    color="gdpPercap",
    hover_name="country",
    color_continuous_scale="Viridis",
    title="World GDP per Capita (2007)"
)
fig.show()

Key Parameters in `px.choropleth()`

Parameter	Values / Examples	Purpose
locations	'iso_alpha','USA','IND'	Geo codes (ISO-3, FIPS, etc.)
locationmode	'ISO-3','USA-states', 'country names'	Type of location
color	Column name	Data to visualize
hover_name	Column	Main label on hover
hover_data	['pop']	Extra info
color_continuous_scale	'Blues','Reds','Plasma'	Color gradient
scope	'world','asia','europe'	Zoom to region
projection	'natural earth','orthographic'	Map style
title	String	Chart title

Full Example: India State-wise Literacy Rate

import plotly.express as px
import pandas as pd

# Sample India data (use real data in practice)
india_data = {
    'State': ['Maharashtra', 'Kerala', 'Tamil Nadu', 'UP', 'Bihar', 'Karnataka', 'Gujarat'],
    'Literacy (%)': [82.3, 94.0, 80.1, 67.7, 61.8, 75.4, 78.0],
    'Population (Cr)': [11.2, 3.3, 7.2, 19.9, 10.4, 6.1, 6.0],
    'State Code': ['MH', 'KL', 'TN', 'UP', 'BR', 'KA', 'GJ']
}

df = pd.DataFrame(india_data)

fig = px.choropleth(
    df,
    geojson="https://github.com/geohacker/india/blob/master/state/india_state.geojson",
    featureidkey="properties.ST_CODE",  # Match with geojson
    locations="State Code",
    color="Literacy (%)",
    hover_name="State",
    hover_data=["Population (Cr)"],
    color_continuous_scale="Oranges",
    title="India: State-wise Literacy Rate (%)",
    scope="asia",
    labels={'Literacy (%)': 'Literacy Rate'}
)

fig.update_geos(
    fitbounds="locations",
    visible=False
)

fig.update_layout(
    coloraxis_colorbar=dict(
        title="Literacy %",
        tickvals=[60, 70, 80, 90],
        ticktext=['Low', 'Medium', 'High', 'Very High']
    ),
    font=dict(size=12)
)

fig.show()

Hinglish Tip: fitbounds="locations" → sirf India dikhega, no extra world! Get GeoJSON for India: github.com/geohacker/india

USA State Map (Built-in)

df = px.data.election()

fig = px.choropleth(
    df,
    locations="district",  # FIPS codes
    locationmode="USA-states",
    color="Bergeron",
    scope="usa",
    title="Canada Election Results (Sample)"
)
fig.show()

Animated Map (Over Time)

df = px.data.gapminder()

fig = px.choropleth(
    df,
    locations="iso_alpha",
    color="lifeExp",
    hover_name="country",
    animation_frame="year",
    color_continuous_scale="Plasma",
    title="Life Expectancy Over Time (1952–2007)"
)

fig.show()

Hinglish Tip: animation_frame="year" → time travel map — dekho duniya kaise badli!

Common Mistakes to Avoid

Mistake	Fix
Wrong location codes	Use ISO-3 or FIPS. Check withpx.data
Map shows whole world	Usescope='asia' +fitbounds="locations"
GeoJSON mismatch	Matchfeatureidkey with your data column
Colors not meaningful	UseOranges (low to high),RdBu (diverging)
Can't export	Installkaleido

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