mapboxgl-jupyter

Create Mapbox GL JS data visualizations natively in your Jupyter Notebook workflows with Python, GeoJSON and Pandas dataframes.

Installation

pip install mapboxgl

Development

Install the python locally with pip:

pip install -e .

To run tests use pytest:

pytest

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

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

Notes on Mapbox Atlas

If you have access to Mapbox Atlas Server on your enterprise network, simply pass in your map stylesheet from your local Atlas URL as opposed to a mapbox:// URL in cell 214.

# Put your Your Mapbox Access token here
# https://www.mapbox.com/help/how-access-tokens-work/
# If you use Mapbox Atlas, this isn't required.  Leave as an empty string.
mapbox_accesstoken = ''

# Map Style.  Point this to a local style, or a custom style on your Mapbox account or Atlas instance
mapStyle = "myAtlasUrl:myAtlasPort:/myStylesheetLocaiton"