Here’s How You Can Plot Map in Python Using Plotly

Harshit Ahluwalia 05 Mar, 2024 • 4 min read


Geographic data visualization is a field that merges data analysis with geographic mapping to unveil patterns, trends, and insights across geographical locations. In the digital age, where data is abundant, visually representing this data on maps has become crucial for decision-making across various sectors, including environmental studies, urban planning, public health, and marketing. This article aims to guide readers through plotting maps using Plotly, covering the basics of map creation, advanced mapping techniques, and interactive features to provide a comprehensive overview of geographic data visualization with Python.

Plot map in Python using plotly

Different Kinds of Maps

Below, I’ll introduce some of the primary map types available in Plotly and provide an example code for each. These examples assume you have Plotly installed (pip install plotly) and use it in a Python environment.

Scatter Plots on Maps (Scattergeo)

Scattergeo maps are useful for plotting geographical scatter plot data. They can display latitudes and longitudes on a world map.

Plot Map in Python Using Plotly

When to use: You want to display geographic locations as individual points on a world map. This is useful for visualizing the distribution of events or objects across different geographical areas, such as cities, weather stations, or any other points of interest.

Choropleth Maps

Choropleth maps, which use color shading, show area-specific data, such as population density or election results.

Plot Map in Python Using Plotly

When to use: You need to represent statistical data through various shading or coloring of predefined regions such as countries, states, or counties. Choropleth maps are ideal for comparing data across geographical regions, such as population density, election results, or economic data.

Also read: Tricks for Data Visualization using Plotly Library

Bubble Maps (Scattergeo with Size)

Similar to scattergeo, but with the addition of bubble sizes representing another dimension of data, such as population.

Plot Map in Python Using Plotly

When to use: You want to show the geographical distribution of data and emphasize the magnitude of a variable through the size of the bubbles. This is particularly useful for visualizing data like population sizes, company sizes, or quantities related to specific locations, allowing for an easy comparison of magnitude across different areas.

Density Heatmaps (Densitymapbox)

Density heatmaps are useful for displaying the density of points on a map, which can help visualize concentrations of phenomena.

Plot Map in Python Using Plotly

When to use: You need to display the density of points on a map, which helps identify concentrations or patterns of activities or phenomena across a geographic area. Density heatmaps are great for visualizing data points like crime incidents, population concentrations, or the frequency of events within a specific area.

Line Maps (Scattergeo or Mapbox for Lines)

You can use Scattergeo or Mapbox to draw lines on a map, which is useful for routes or journeys.

Plot Map in Python Using Plotly

When to use: You aim to represent paths or routes between different geographical locations. This type of map is useful for showing migration paths, shipping routes, travel itineraries, or any other type of movement between locations over the surface of the Earth.

Also read: Guide to Create Interactive Plots with Plotly Python


In conclusion, this article has explored the dynamic and powerful capabilities of Plotly for geographic data visualization in Python. By covering a range of map types, from Scattergeo plots that highlight individual locations to sophisticated Choropleth maps that illustrate data variations across regions and interactive Density Heatmaps that reveal concentration patterns, we’ve seen how Plotly can cater to diverse visualization needs. Each map type serves a unique purpose, enabling users to present data visually compellingly and uncover insights that might not be immediately apparent from raw data alone.

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