import os
import folium
print(folium.__version__)
0.8.3+52.g2758dc7.dirty
GeoPandas is a project to add support for geographic data to pandas objects. (See https://github.com/geopandas/geopandas)
It provides (among other cool things) a GeoDataFrame object that represents a Feature collection.
When you have one, you may be willing to use it on a folium map. Here's the simplest way to do so.
In this example, we'll use the same file as GeoPandas demo ; it's containing the boroughs of New York City.
import geopandas
url = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data'
nybb = f'{url}/nybb.zip'
boros = geopandas.read_file(nybb)
boros
| BoroCode | BoroName | Shape_Leng | Shape_Area | geometry | |
|---|---|---|---|---|---|
| 0 | 5 | Staten Island | 330454.175933 | 1.623847e+09 | (POLYGON ((970217.0223999023 145643.3322143555... |
| 1 | 3 | Brooklyn | 741227.337073 | 1.937810e+09 | (POLYGON ((1021176.479003906 151374.7969970703... |
| 2 | 4 | Queens | 896875.396449 | 3.045079e+09 | (POLYGON ((1029606.076599121 156073.8142089844... |
| 3 | 1 | Manhattan | 358400.912836 | 6.364308e+08 | (POLYGON ((981219.0557861328 188655.3157958984... |
| 4 | 2 | Bronx | 464475.145651 | 1.186822e+09 | (POLYGON ((1012821.805786133 229228.2645874023... |
To create a map with these features, simply put them in a GeoJson:
m = folium.Map([40.7, -74], zoom_start=10, tiles='cartodbpositron')
folium.GeoJson(boros).add_to(m)
m.save(os.path.join('results', 'geopandas_0.html'))
m
Quite easy.
Well, you can also take advantage of your GeoDataFrame structure to set the style of the data. For this, just create a column style containing each feature's style in a dictionnary.
boros['style'] = [
{'fillColor': '#ff0000', 'weight': 2, 'color': 'black'},
{'fillColor': '#00ff00', 'weight': 2, 'color': 'black'},
{'fillColor': '#0000ff', 'weight': 2, 'color': 'black'},
{'fillColor': '#ffff00', 'weight': 2, 'color': 'black'},
{'fillColor': '#00ffff', 'weight': 2, 'color': 'black'},
]
boros
| BoroCode | BoroName | Shape_Leng | Shape_Area | geometry | style | |
|---|---|---|---|---|---|---|
| 0 | 5 | Staten Island | 330454.175933 | 1.623847e+09 | (POLYGON ((970217.0223999023 145643.3322143555... | {'fillColor': '#ff0000', 'weight': 2, 'color':... |
| 1 | 3 | Brooklyn | 741227.337073 | 1.937810e+09 | (POLYGON ((1021176.479003906 151374.7969970703... | {'fillColor': '#00ff00', 'weight': 2, 'color':... |
| 2 | 4 | Queens | 896875.396449 | 3.045079e+09 | (POLYGON ((1029606.076599121 156073.8142089844... | {'fillColor': '#0000ff', 'weight': 2, 'color':... |
| 3 | 1 | Manhattan | 358400.912836 | 6.364308e+08 | (POLYGON ((981219.0557861328 188655.3157958984... | {'fillColor': '#ffff00', 'weight': 2, 'color':... |
| 4 | 2 | Bronx | 464475.145651 | 1.186822e+09 | (POLYGON ((1012821.805786133 229228.2645874023... | {'fillColor': '#00ffff', 'weight': 2, 'color':... |
m = folium.Map([40.7, -74], zoom_start=10, tiles='cartodbpositron')
folium.GeoJson(boros).add_to(m)
m.save(os.path.join('results', 'geopandas_1.html'))
m
Folium should work with any object that implements the __geo_interface__ but be aware that sometimes you may need to convert your data to epsg='4326' before sending it to folium.
import fiona
import shapely
fname = f'{url}/2014_08_05_farol.gpx'
with fiona.open(fname, 'r', layer='tracks') as records:
tracks = [
shapely.geometry.shape(record['geometry']) for record in records
]
track = tracks[0]
m = folium.Map(tiles='cartodbpositron')
folium.GeoJson(track).add_to(m)
m.fit_bounds(m.get_bounds())
m.save(os.path.join('results', 'geopandas_2.html'))
m