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Choropleth Maps using Plotly in Python

Last Updated : 04 Aug, 2025

Choropleth maps are an effective way to visualize geographical data by shading regions based on the value of a variable. These maps are commonly used to represent metrics such as population density, economic indicators or election results across regions. Python's Plotly library provides a straightforward way to create choropleth maps with minimal effort, making it a solid choice for data scientists and developers.

• Color gradient representation: Data values are typically represented using a light-to-dark color scale.
• Spatial pattern visualization: Choropleth maps allow easy identification of geographical patterns.
• Geographic context: They leverage spatial relationships, making them ideal for datasets with geographical attributes.

Example: A choropleth map showing unemployment rates by state in the U.S. can quickly highlight economically distressed areas.

To build such maps, we need:

• A dataset with numerical values tied to geographical identifiers (like state codes or country codes).
• A GeoJSON file (or equivalent) defining the boundaries of regions.

Plotly simplifies this by supporting built-in GeoJSON datasets for common boundaries such as U.S. states or world countries.

Plotly is an open-source Python visualization library. It supports both high-level APIs (plotly.express) for quick maps and low-level APIs (plotly.graph_objects) for detailed customization.

• Interactive by default: Users can hover, zoom and pan maps.
• Flexible rendering: Works with Jupyter notebooks and standalone HTML.
• Balance of simplicity and functionality: Easy for beginners and also customizable for advanced users.

• Matplotlib (Basemap): Good for static maps but lacks interactivity.
• GeoPandas: Great for geospatial analysis but requires more effort for interactive visualization.

We'll create a choropleth map showing population estimates for U.S. states. Darker colors will represent higher populations.

Before we begin, ensure the following libraries are installed:

!pip install plotly pandas

• Created a sample dataset containing state names, two-letter state codes and population estimates.
• The state codes will be used as location identifiers in Plotly.

import pandas as pd

# Sample dataset
data = {
    'State': ['California', 'Texas', 'Florida', 'New York', 'Illinois'],
    'State_Code': ['CA', 'TX', 'FL', 'NY', 'IL'],
    'Population': [39538223, 29145505, 21538187, 20201249, 12812508]
}
df = pd.DataFrame(data)

Used px.choropleth() from Plotly Express.

• locations: Column with state codes.
• locationmode: Set to USA-states for U.S. state boundaries.
• color: The column used to determine shading (Population).
• scope: Restrict the map to U.S. boundaries.

import plotly.express as px

# Create the choropleth map
fig = px.choropleth(
    df,
    locations='State_Code',
    locationmode='USA-states',
    color='Population',
    hover_name='State',
    color_continuous_scale='Viridis',
    scope='usa',
    title='U.S. State Population Estimates'
)

Customize layout for better presentation.

• geo: Remove the frame and show coastlines.
• projection_type: Use "Albers USA" projection for better U.S. map representation.

# Update layout and display map
fig.update_layout(
    geo=dict(showframe=False, showcoastlines=True, projection_type='albers usa'),
    margin=dict(l=0, r=0, t=50, b=0)
)

fig.show()

Output:

• Plotly supports extensive customization:
• Change color scales:
• Add extra data to the hover tooltip.

fig = px.choropleth(
    df,
    locations='State_Code',
    locationmode='USA-states',
    color='Population',
    hover_name='State',
    hover_data=['Population'],
    color_continuous_scale='Blues',
    scope='usa',
    title='U.S. State Population Estimates'
)
fig.show()

Output:

1. Data Quality: Ensure geographical codes in the dataset align with those expected by Plotly to prevent missing or incorrect location data, which can result in blank regions on the map.

2. GeoJSON Requirements: For custom regions such as cities, districts or postal codes, supply a corresponding GeoJSON file that accurately defines the region boundaries.

3. Color Scale Selection: Choose sequential color scales like Viridis for continuous data to enhance readability. Avoid diverging color scales (e.g., red-blue) for single-metric data to prevent misinterpretation.

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