Choropleth Maps using Plotly in Python (original) (raw)

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.

Key characteristics

• **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.

Why Use Plotly for Choropleth Maps

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.

Advantages of Plotly

• **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.

Comparison with alternatives:

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

Implementation: Creating a Choropleth Map with Plotly

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

Prerequisites

Before we begin, ensure the following libraries are installed:

Python `

!pip install plotly pandas

`

Step 1: Prepare the Data

• 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. Python `

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)

`

Step 2: Create the Choropleth Map

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. Python `

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' )

`

Step 3: Improve Map Layout and Display

Customize layout for better presentation.

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

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:

Chloropleth Map

Step 4: Customizing the Map

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

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:

Plotly map 2

Edge Cases and Limitations

**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.