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nbsphinx: hidden
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import folium
Using GeoJson
Loading data
Let us load a GeoJSON file representing the US states.
import requests
geo_json_data = requests.get(
"https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
).json()
It is a classical GeoJSON FeatureCollection (see https://en.wikipedia.org/wiki/GeoJSON) of the form :
{
"type": "FeatureCollection",
"features": [
{
"properties": {"name": "Alabama"},
"id": "AL",
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [[[-87.359296, 35.00118], ...]]
}
},
{
"properties": {"name": "Alaska"},
"id": "AK",
"type": "Feature",
"geometry": {
"type": "MultiPolygon",
"coordinates": [[[[-131.602021, 55.117982], ... ]]]
}
},
...
]
}
A first way of drawing it on a map, is simply to use folium.GeoJson :
m = folium.Map([43, -100], zoom_start=4)
folium.GeoJson(geo_json_data).add_to(m)
m
Note that you can avoid loading the file on yourself, by providing a (local) file path or a url.
m = folium.Map([43, -100], zoom_start=4)
url = "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
folium.GeoJson(url).add_to(m)
m
You can pass a geopandas object.
import geopandas
gdf = geopandas.read_file(url)
m = folium.Map([43, -100], zoom_start=4)
folium.GeoJson(
gdf,
).add_to(m)
m
Click on zoom
You can enable an option that if you click on a part of the geometry the map will zoom in to that.
Try it on the map below:
m = folium.Map([43, -100], zoom_start=4)
folium.GeoJson(geo_json_data, zoom_on_click=True).add_to(m)
m
Styling
Now this is cool and simple, but we may be willing to choose the style of the data.
You can provide a function of the form lambda feature: {} that sets the style of each feature.
For possible options, see:
m = folium.Map([43, -100], zoom_start=4)
folium.GeoJson(
geo_json_data,
style_function=lambda feature: {
"fillColor": "#ffff00",
"color": "black",
"weight": 2,
"dashArray": "5, 5",
},
).add_to(m)
m
What's cool in providing a function, is that you can specify a style depending on the feature. For example, if you want to visualize in green all states whose name contains the letter 'E', just do:
m = folium.Map([43, -100], zoom_start=4)
folium.GeoJson(
geo_json_data,
style_function=lambda feature: {
"fillColor": "green"
if "e" in feature["properties"]["name"].lower()
else "#ffff00",
"color": "black",
"weight": 2,
"dashArray": "5, 5",
},
).add_to(m)
m
Wow, this looks almost like a choropleth. To do one, we just need to compute a color for each state.
Let's imagine we want to draw a choropleth of unemployment in the US.
First, we may load the data:
import pandas
unemployment = pandas.read_csv(
"https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_unemployment_oct_2012.csv"
)
unemployment.head(5)
Now we need to create a function that maps one value to a RGB color (of the form #RRGGBB). For this, we'll use colormap tools from folium.colormap.
from branca.colormap import linear
colormap = linear.YlGn_09.scale(
unemployment.Unemployment.min(), unemployment.Unemployment.max()
)
print(colormap(5.0))
colormap
We need also to convert the table into a dictionary, in order to map a feature to it's unemployment value.
unemployment_dict = unemployment.set_index("State")["Unemployment"]
unemployment_dict["AL"]
Now we can do the choropleth.
m = folium.Map([43, -100], zoom_start=4)
folium.GeoJson(
geo_json_data,
name="unemployment",
style_function=lambda feature: {
"fillColor": colormap(unemployment_dict[feature["id"]]),
"color": "black",
"weight": 1,
"dashArray": "5, 5",
"fillOpacity": 0.9,
},
).add_to(m)
folium.LayerControl().add_to(m)
m
Of course, if you can create and/or use a dictionary providing directly the good color. Thus, the finishing seems faster:
color_dict = {key: colormap(unemployment_dict[key]) for key in unemployment_dict.keys()}
m = folium.Map([43, -100], zoom_start=4)
folium.GeoJson(
geo_json_data,
style_function=lambda feature: {
"fillColor": color_dict[feature["id"]],
"color": "black",
"weight": 1,
"dashArray": "5, 5",
"fillOpacity": 0.9,
},
).add_to(m)
Note that adding a color legend may be a good idea.
colormap.caption = "Unemployment color scale"
colormap.add_to(m)
m
Caveat
When using style_function in a loop you may encounter Python's 'Late Binding Closure' gotcha! See https://docs.python-guide.org/writing/gotchas/#late-binding-closures for more info. There are a few ways around it from using a GeoPandas object instead, to "hacking" your style_function to force early closure, like:
for geom, my_style in zip(geoms, my_styles):
style = my_style
style_function = lambda x, style=style: style
folium.GeoJson(
data=geom,
style_function=style_function,
).add_to(m)
Highlight function
The GeoJson class provides a highlight_function argument, which works similarly to style_function, but applies on mouse events. In the following example the fill color will change when you hover your mouse over a feature.
m = folium.Map([43, -100], zoom_start=4)
folium.GeoJson(
geo_json_data,
highlight_function=lambda feature: {
"fillColor": (
"green" if "e" in feature["properties"]["name"].lower() else "#ffff00"
),
},
).add_to(m)
m
The GeoJson class provides a popup_keep_highlighted boolean argument. Whenever a GeoJson layer is associated with a popup and a highlight function is defined, this argument allows you to decide if the highlighting should remain active while the popup is open.
m = folium.Map([43, -100], zoom_start=4)
popup = folium.GeoJsonPopup(fields=["name"])
folium.GeoJson(
geo_json_data,
highlight_function=lambda feature: {
"fillColor": (
"green" if "e" in feature["properties"]["name"].lower() else "#ffff00"
),
},
popup=popup,
popup_keep_highlighted=True,
).add_to(m)
m
