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.
%matplotlib inline
import geopandas
import sys
sys.path.insert(0,'..')
import folium
folium.__file__
'../folium/__init__.py'
In this example, we'll use the same file as GeoPandas demo ; it's containing the boroughs of New York City.
boros = geopandas.GeoDataFrame.from_file('nybb.shp')
boros
BoroCode | BoroName | Shape_Area | Shape_Leng | geometry | |
---|---|---|---|---|---|
0 | 5 | Staten Island | 1.623847e+09 | 330454.175933 | (POLYGON ((970217.0223999023 145643.3322143555... |
1 | 3 | Brooklyn | 1.937810e+09 | 741227.337073 | (POLYGON ((1021176.479003906 151374.7969970703... |
2 | 4 | Queens | 3.045079e+09 | 896875.396449 | (POLYGON ((1029606.076599121 156073.8142089844... |
3 | 1 | Manhattan | 6.364308e+08 | 358400.912836 | (POLYGON ((981219.0557861328 188655.3157958984... |
4 | 2 | Bronx | 1.186822e+09 | 464475.145651 | (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
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_Area | Shape_Leng | geometry | style | |
---|---|---|---|---|---|---|
0 | 5 | Staten Island | 1.623847e+09 | 330454.175933 | (POLYGON ((970217.0223999023 145643.3322143555... | {'weight': 2, 'color': 'black', 'fillColor': '... |
1 | 3 | Brooklyn | 1.937810e+09 | 741227.337073 | (POLYGON ((1021176.479003906 151374.7969970703... | {'weight': 2, 'color': 'black', 'fillColor': '... |
2 | 4 | Queens | 3.045079e+09 | 896875.396449 | (POLYGON ((1029606.076599121 156073.8142089844... | {'weight': 2, 'color': 'black', 'fillColor': '... |
3 | 1 | Manhattan | 6.364308e+08 | 358400.912836 | (POLYGON ((981219.0557861328 188655.3157958984... | {'weight': 2, 'color': 'black', 'fillColor': '... |
4 | 2 | Bronx | 1.186822e+09 | 464475.145651 | (POLYGON ((1012821.805786133 229228.2645874023... | {'weight': 2, 'color': 'black', 'fillColor': '... |
m = folium.Map([40.7,-74], zoom_start=10, tiles='cartodbpositron')
folium.GeoJson(boros).add_to(m)
m
That's all folks !
Hope it'll be useful to you. Don't hesitate to provide a feedback on what can be improved, which method do you prefer, etc.