Maps in Plotly

Plotly is a popular library for creating interactive graphs and visualizations. One of the features that Plotly offers is the ability to create maps that can be customized to display data in different ways. In this article, we will explore how to create a map using Plotly and how to customize it with various styling options.

Creating a Map in Plotly

To create a map in Plotly, you first need to import the necessary libraries and load the required data. Here's an example code snippet that creates a basic map of the United States:

import plotly.express as px
import pandas as pd

# Load data for US states
df = pd.read_csv('https://gist.githubusercontent.com/mbostock/3014076/raw/6b8d9e523a6f4da3b0b0cfc6f9a53551/us_states.csv')

# Create map of the US states using Plotly Express
fig = px.choropleth(df, geojson='https://gist.githubusercontent.com/mbostock/4090827/raw/f6b135cbf0a1dce5e100f37a1d1a07dd/us_states.json',
                     locations='NAME',
                     color='population',
                     range_color=[10, 1000000],
                     title="Population Density of US States")
fig.show()

In this code snippet, we use the px.choropleth() function from Plotly Express to create a choropleth map of the United States. The geojson parameter points to the geographical data for the states, while the locations parameter specifies which column contains state names. We also set the color parameter to specify which column contains the data we want to display on the map (in this case, population). Finally, we use the range_color parameter to specify the minimum and maximum values for the color scale used in the map.

Customizing a Map with Plotly

Once you have created a basic map using Plotly, there are many styling options available to customize it further. Here's an example code snippet that demonstrates some of these options:

import plotly.express as px
import pandas as pd

# Load data for US states
df = pd.read_csv('https://gist.githubusercontent.com/mbostock/3014076/raw/6b8d9e523a6f4da3b0b0cfc6f9a53551/us_states.csv')

# Create map of the US states using Plotly Express with customized style
fig = px.choropleth(df, geojson='https://gist.githubusercontent.com/mbostock/4090827/raw/f6b135cbf0a1dce5e100f37a1d1a07dd/us_states.json',
                     locations='NAME',
                     color='population',
                     range_color=[10, 1000000],
                     title="Population Density of US States",
                     hover_name=None,
                     animation_frame='population',
                     animation_group='NAME',
                     animation_mode='update')
fig.add_trace(px.choropleth(df, geojson='https://gist.githubusercontent.com/mbostock/4090827/raw/f6b135cbf0a1dce5e100f37a1d1a07dd/us_states.json',
                              locations='NAME',
                              color_continuous=px.Color(df, 'population'),
                              range_color=[10, 1000000],
                              title="Population Density of US States",
                              hover_name=None,
                              animation_frame='population',
                              animation_group='NAME',
                              animation_mode='update'))
fig.show()

In this code snippet, we use the same basic map as before, but have customized it with several styling options. Here's a breakdown of what each option does:

• hover_name=None hides the state names that appear when hovering over the map.
• animation_frame='population' and animation_group='NAME' enable animation to show how population density varies across states.
• animation_mode='update' enables smooth transitions between each frame of the animation.
• We also use a separate trace to create a color-continuous map with a different range of colors ([10, 1000000]). This allows us to display both absolute and relative population values on the same map.

Conclusion

Plotly is an excellent library for creating interactive visualizations, including maps. By using Plotly's built-in functions and styling options, you can create customized maps that showcase your data in a clear and engaging way. Whether you are working with geographic data or simply want to display information on a map, Plotly provides an easy and flexible solution for creating beautiful visualizations.