How to Customize Scatter Point Colors in Plotly’s Mapbox and Display Multiple Maps Side by Side

by

What will you learn?

In this tutorial, you will learn how to customize the color of scatter points in Plotly’s Mapbox and effectively display multiple maps side by side for comparative analysis.

Introduction to the Problem and Solution

When utilizing the scatter_mapbox function in Plotly, it is often necessary to differentiate specific data points by assigning them distinct colors. Furthermore, presenting multiple maps simultaneously can greatly aid in visual comparison. To address these requirements, we will adjust the marker color property of scatter traces and employ subplots to exhibit maps together seamlessly.

Code

import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Create first map with selected color for markers
fig1 = go.Figure(go.Scattermapbox(
    mode="markers",
    marker={'color': 'blue'},  # Set desired color here
))

# Create second map with selected color for markers
fig2 = go.Figure(go.Scattermapbox(
    mode="markers",
    marker={'color': 'red'},  # Set desired color here
))

# Display both maps side by side using subplots
fig = make_subplots(rows=1, cols=2)
fig.add_trace(fig1.data[0], row=1, col=1)
fig.add_trace(fig2.data[0], row=1, col=2)

# Show plots
fig.show()

# Copyright PHD

Note: Replace ‘blue’ and ‘red’ with any valid HTML color code or name based on your preference.

Explanation

Setting Marker Color:

  • Utilize marker={‘color’: ‘desired_color’} within go.Scattermapbox to assign a single color to all scatter points.
  • The chosen color should be an HTML-compatible string like a named CSS3 color or HEX value.

Creating Subplots:

  • Use make_subplots(rows=n_rows, cols=n_cols) from Plotly’s plotly.subplots module to display multiple plots simultaneously.
  • This function establishes a grid layout where each subplot can be individually addressed through row and column indices (row=i, col=j).
  • Add traces (maps) from individual figures (Figure) into specific positions within the subplot grid using .add_trace() method.

Displaying Maps:

  • The final visualization is generated through fig.show(), rendering both maps alongside each other due to the specified subplot configuration.
    How do I select a custom marker size?

    Specify the marker={‘size’: desired_size} parameter within go.Scattermapbox.

    Can I change opacity of markers?

    Yes, set marker={‘opacity’: 0.5} (example) to adjust marker transparency.

    Is it possible to add hover text on markers?

    Certainly! Include ‘hoverinfo’:’text’,’text’:[‘Hover Text’] within go.Scattermapbox.

    How can I customize marker shapes?

    Use ‘symbol’:’circle’ or other supported shape names in the marker dictionary.

    Can I overlay shapes like circles or polygons on maps?

    Yes! Integrate additional traces like Scattergeo, providing latitude/longitude coordinates of shapes.

    Conclusion

    Enhancing geospatial visualizations through customized scatter point colors in Mapbox plots not only improves data representation but also facilitates clearer dataset comparisons when displaying multiple maps concurrently. By mastering these techniques and exploring further customization options available within Plotly’s robust functionalities, users can create captivating visualizations tailored precisely to their requirements.

    Leave a Comment