Using GeoPandas.GeoDataFrame in folium#

GeoPandas is a project to add support for geographic data to pandas objects. (See 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.

[2]:

import geopandas

boros = geopandas.read_file(
    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/new_york_boroughs.zip"
)

boros

[2]:
BoroCode BoroName Shape_Leng Shape_Area geometry
0 5 Staten Island 330454.175933 1.623847e+09 MULTIPOLYGON (((970217.022 145643.332, 970227....
1 3 Brooklyn 741227.337073 1.937810e+09 MULTIPOLYGON (((1021176.479 151374.797, 102100...
2 4 Queens 896875.396449 3.045079e+09 MULTIPOLYGON (((1029606.077 156073.814, 102957...
3 1 Manhattan 358400.912836 6.364308e+08 MULTIPOLYGON (((981219.056 188655.316, 980940....
4 2 Bronx 464475.145651 1.186822e+09 MULTIPOLYGON (((1012821.806 229228.265, 101278...

To create a map with these features, simply put them in a GeoJson:

[3]:

m = folium.Map([40.7, -74], zoom_start=10, tiles="cartodbpositron")

folium.GeoJson(boros).add_to(m)

m

[3]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Quite easy.

Adding style#

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 dictionary.

[4]:

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

[4]:
BoroCode BoroName Shape_Leng Shape_Area geometry style
0 5 Staten Island 330454.175933 1.623847e+09 MULTIPOLYGON (((970217.022 145643.332, 970227.... {'fillColor': '#ff0000', 'weight': 2, 'color':...
1 3 Brooklyn 741227.337073 1.937810e+09 MULTIPOLYGON (((1021176.479 151374.797, 102100... {'fillColor': '#00ff00', 'weight': 2, 'color':...
2 4 Queens 896875.396449 3.045079e+09 MULTIPOLYGON (((1029606.077 156073.814, 102957... {'fillColor': '#0000ff', 'weight': 2, 'color':...
3 1 Manhattan 358400.912836 6.364308e+08 MULTIPOLYGON (((981219.056 188655.316, 980940.... {'fillColor': '#ffff00', 'weight': 2, 'color':...
4 2 Bronx 464475.145651 1.186822e+09 MULTIPOLYGON (((1012821.806 229228.265, 101278... {'fillColor': '#00ffff', 'weight': 2, 'color':... 
[5]:

m = folium.Map([40.7, -74], zoom_start=10, tiles="cartodbpositron")

folium.GeoJson(boros).add_to(m)

m

[5]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Use any object with __geo_interface__#

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.

[6]:

import fiona
import shapely

url = "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/route_farol.gpx"
with fiona.open(url, "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

[6]:
Make this Notebook Trusted to load map: File -> Trust Notebook