mapboxgl-jupyter

Create Mapbox GL JS data visualizations natively in your Jupyter Notebook workflows with Python, GeoJSON and Pandas dataframes. Mapboxgl aims to be a data visualization focused mapping library built on top of the Mapbox GL JS SDK, similar to Folium built on top of Leaflet.

Currently supported map visualization types:

1. Circles
2. Graduated Circles
3. Heatmaps

Installation

pip install mapboxgl

Development

Install the python library locally with pip:

pip install -e .

To run tests use pytest:

pytest

To run the jupiter examples,

1. cd examples
2. pip install jupyter
3. jupyter notebook

Usage

mapboxgl visualizations take GeoJSON data as input. You can convert pandas dataframes to a GeoJSON feature collection:

data = df_to_geojson(df, ['Avg Total Payments'],
                     lat='latitude', lon='longitude')

Using the CircleViz visualization to view the data with a color ramp for the total payment column. Within a Jupyter notebook:

viz = CircleViz(data,
                color_property='Avg Total Payments',
                color_stops=color_stops,
                access_token=YOUR_PUBLIC_ACCESS_TOKEN)
viz.show()

The examples/ directory contains Jupyter notebooks demonstrating more advanced usage.

Status

Under heavy development. As we move towards a 1.0 release, expect API changes. If you're interested in contributing and are curious about the direction of the project, check out ROADMAP.md.

Running the Examples

1. Install Python3.4+
2. pip install mapboxgl
3. cd to /example directory of mapboxgl-jupyter repo
4. Open the test-python.ipynb workbook
5. Put your Mapbox GL Access Token (it's free for developers!) into the notebook, cell 4.
6. Run all cells in the notebook and enjoy the interactive maps.