Using Kepler.gl in Jupyter Notebook for Geospatial Data Visualization

Kepler.gl Overview

Kepler.gl is an open‑source geospatial data visualization tool from Uber that can be used inside Jupyter notebooks to create interactive maps.

Installation

Install via pip:

<code>pip install keplergl</code>

If you are on macOS with notebook version 5.3 or newer you can skip the nbextension steps; otherwise run:

<code>jupyter nbextension install --py --sys-prefix keplergl  # can be skipped for notebook 5.3 and above
jupyter nbextension enable --py --sys-prefix keplergl  # can be skipped for notebook 5.3 and above</code>

Simple Example

Creating an empty map verifies the installation:

<code>from keplergl import KeplerGl

# create a KeplerGl object
map_1 = KeplerGl(height=500)

# display the map in the notebook
map_1</code>

Adding Data

Kepler.gl accepts CSV, GEOJSON, and pandas DataFrame objects. The example below loads a CSV file with pandas and adds it to a map:

<code>import pandas as pd

df = pd.read_csv('rocket_launch_site_elevation_2019-10-27.csv')
df.head()</code>

<code># create a KeplerGl object
map_2 = KeplerGl(height=600)

# display the map
map_2

# add the DataFrame as a layer named 'rocket'
map_2.add_data(name='rocket', data=df)

# display the updated map
map_2</code>

Customizing the Chart

Unlike libraries such as pyecharts or matplotlib, Kepler.gl lets you configure colors, chart types, and other visual aspects through the graphical interface rather than extensive code.

Retrieving Configuration

You can obtain the current map configuration (including UI changes) with the .config attribute:

<code>map_2.config</code>

Exporting the Map

Use .save_to_html() to export the map to an HTML file. Parameters include data , config , file_name (default keplergl_map.html ), and read_only to lock the map.

<code>map_3.save_to_html(file_name='kepler_example.html')</code>

Opening the generated HTML file in a browser launches an interactive visualization.

Conclusion

Kepler.gl is easy to get started with, requiring minimal code for basic visualizations and offering a powerful GUI for creating attractive geospatial maps, making it a solid choice for projects that need geographic data visualization.