Geographical plotting using Python plotly

Plotly is a powerful Python library for creating interactive geographical visualizations. It provides tools to create choropleth maps, scatter plots on maps, and other geographical charts that help visualize data across different regions.

Installing Required Libraries

First, install the necessary libraries for geographical plotting ?

pip install plotly pandas

Creating a Basic Choropleth Map

A choropleth map uses different colors to represent data values across geographical regions. Here's how to create one for US states ?

import plotly.graph_objects as go
import plotly.express as px

# Sample data for US states
states = ['CA', 'TX', 'FL', 'NY', 'PA', 'IL', 'OH', 'GA', 'NC', 'MI']
values = [39.5, 29.0, 21.5, 19.8, 13.0, 12.7, 11.8, 10.7, 10.5, 10.0]

# Create choropleth map
fig = go.Figure(data=go.Choropleth(
    locations=states,
    z=values,
    locationmode='USA-states',
    colorscale='Blues',
    text=states,
    colorbar_title="Population (millions)"
))

# Update layout
fig.update_layout(
    title_text='US States Population Map',
    geo_scope='usa',
    width=800,
    height=500
)

fig.show()

Using Plotly Express for Simplified Mapping

Plotly Express provides a simpler interface for creating geographical plots ?

import plotly.express as px
import pandas as pd

# Create sample dataset
data = {
    'state': ['California', 'Texas', 'Florida', 'New York', 'Pennsylvania'],
    'abbrev': ['CA', 'TX', 'FL', 'NY', 'PA'],
    'population': [39.5, 29.0, 21.5, 19.8, 13.0],
    'gdp': [3.2, 2.4, 1.0, 1.9, 0.8]
}

df = pd.DataFrame(data)

# Create choropleth with Plotly Express
fig = px.choropleth(
    df,
    locations='abbrev',
    color='population',
    hover_name='state',
    hover_data=['gdp'],
    color_continuous_scale='Viridis',
    scope='usa',
    title='US States by Population'
)

fig.show()

World Map Visualization

You can also create world maps using country codes ?

import plotly.express as px

# Sample world data
countries = ['USA', 'CHN', 'JPN', 'DEU', 'IND', 'GBR', 'FRA', 'ITA']
gdp_values = [21.4, 14.3, 5.1, 3.8, 2.9, 2.8, 2.7, 2.1]

world_data = pd.DataFrame({
    'country': countries,
    'gdp': gdp_values
})

# Create world choropleth
fig = px.choropleth(
    world_data,
    locations='country',
    color='gdp',
    hover_name='country',
    color_continuous_scale='RdYlBu_r',
    title='World GDP by Country (Trillions USD)'
)

fig.show()

Key Parameters

Common Use Cases

Geographical plotting with Plotly is useful for ?

• Population analysis − Visualizing demographic data across regions
• Sales performance − Mapping revenue or sales by geographic area
• Election results − Showing voting patterns across states or countries
• Economic indicators − Displaying GDP, unemployment, or other economic metrics
• Disease tracking − Mapping the spread of diseases or health statistics

Conclusion

Plotly provides powerful tools for creating interactive geographical visualizations. Use plotly.express for simple maps and plotly.graph_objects for more customization. Choropleth maps are ideal for displaying data values across geographic regions with intuitive color coding.