Exporting a Color‑Mapped Scatter Plot to a Self‑Contained HTML File with Plotly Express

Desired Outcome

Generate an interactive scatter plot that uses a categorical column to color the points, then export that figure as a self‑contained HTML file that can be opened in any browser without external dependencies.

Prerequisites

• Python 3.9+ installed.
• Installed packages: pandas, plotly==5.24.0 (or later).
• A CSV or DataFrame containing at least three columns: x, y, and a categorical column (e.g., category).
• Write permission to the directory where the HTML will be saved.

Step‑by‑Step Procedure

1. Import Libraries

   import pandas as pd
   import plotly.express as px
   

2. Load Your Data

   # Replace 'data.csv' with your file path
   # Ensure the categorical column is of type 'category' for optimal legend handling
   
   df = pd.read_csv('data.csv')
   # Example columns: 'x', 'y', 'group'
   
   df['group'] = df['group'].astype('category')
   

3. Create the Scatter Plot

   fig = px.scatter(
       df,
       x='x',
       y='y',
       color='group',          # categorical mapping
       title='Interactive Scatter Plot',
       labels={'x': 'X‑Axis', 'y': 'Y‑Axis', 'group': 'Group'},
       color_discrete_sequence=px.colors.qualitative.G10  # optional palette
   )
   # Add a subtle layout tweak (optional)
   fig.update_layout(legend_title_text='Group')
   

4. Export to Self‑Contained HTML

   output_path = 'scatter.html'
   fig.write_html(output_path, include_plotlyjs='cdn', full_html=True)
   print(f"HTML file written to {output_path}")
   

   Parameters explained:

   • include_plotlyjs='cdn' – pulls Plotly.js from a CDN. Use 'cdn' for smaller files; 'cdn' is the default. If you need offline operation, use 'cdn' too but ensure the environment can reach the CDN.
   • full_html=True – creates a complete HTML document with <html> and <body> tags.
5. Verify the Output

   import os
   assert os.path.exists(output_path), 'File was not created'
   

Expected Checks

• Open scatter.html in a modern browser (Chrome, Firefox, Edge). The plot should display, with axes labeled, a legend, and hover tooltips showing x, y, and group values.
• Inspect the browser console for errors. A missing plotly.min.js or a 404 from the CDN indicates a network restriction.
• Run fig.to_dict() in Python to confirm that the data array contains trace objects with the correct x, y, and marker.color fields.

Recovery Options

• Missing CDN Script – If the browser blocks external scripts, switch to include_plotlyjs='cdn' to 'cdn' or 'cdn' to 'cdn' and host Plotly.js locally. For a quick fix, download plotly.min.js and set include_plotlyjs='cdn' to a local path.
• Large Datasets – The exported HTML grows with data size. For millions of points, consider using Plotly’s scattergl or a server‑side rendering approach. Alternatively, downsample the data before plotting.
• NaN Values – Plotly silently drops points with NaN. Clean your DataFrame with df.dropna(subset=['x', 'y', 'group']) before plotting.
• Path Issues on Windows – Use raw strings or pathlib.Path to avoid escape character problems: output_path = r'scatter.html' or output_path = Path('scatter.html').

Limitations and Alternatives

• Plotly Express is a thin wrapper; for advanced callbacks or dynamic updates you’ll need Dash or custom JavaScript.
• The exported HTML file is static. If you need real‑time updates, host the figure in a Dash app or use Plotly’s streaming API.
• Color mapping relies on Plotly’s discrete palette. Custom color scales can be applied with color_discrete_map for fine‑grained control.
• For very large HTML files, consider server‑side rendering with fig.write_image to PNG or SVG and embed that image instead.

Practical Checklist

Step	Verification
Figure created	fig is not None; fig.data length > 0
File written	os.path.exists('scatter.html')
Browser render	Plot appears; hover shows data; legend matches categories
Console	No 404 or JS errors
Data integrity	fig.to_dict() contains correct columns

Follow this guide to quickly produce interactive, shareable scatter plots that map categorical variables to color and bundle everything into one portable HTML file.