Loading data#
Let us load a GeoJSON file representing the US states.
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import requestsgeo_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 :
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m = folium.Map([43, -100], zoom_start=4)folium.GeoJson(geo_json_data).add_to(m)m
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Note that you can avoid loading the file on yourself, by providing a (local) file path or a url.
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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
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You can pass a geopandas object.
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import geopandasgdf = geopandas.read_file(url)m = folium.Map([43, -100], zoom_start=4)folium.GeoJson( gdf,).add_to(m)m
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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:
[6]:
m = folium.Map([43, -100], zoom_start=4)folium.GeoJson(geo_json_data, zoom_on_click=True).add_to(m)m
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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:
For
PointandMultiPoint, see https://leafletjs.com/reference.html#markerFor other features, see https://leafletjs.com/reference.html#path and https://leafletjs.com/reference.html#polyline
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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
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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:
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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
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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:
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import pandasunemployment = pandas.read_csv( "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_unemployment_oct_2012.csv")unemployment.head(5)
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| State | Unemployment | |
|---|---|---|
| 0 | AL | 7.1 |
| 1 | AK | 6.8 |
| 2 | AZ | 8.1 |
| 3 | AR | 7.2 |
| 4 | CA | 10.1 |
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.
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from branca.colormap import linearcolormap = linear.YlGn_09.scale( unemployment.Unemployment.min(), unemployment.Unemployment.max())print(colormap(5.0))colormap
#d8f0a3ff
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We need also to convert the table into a dictionary, in order to map a feature to it’s unemployment value.
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unemployment_dict = unemployment.set_index("State")["Unemployment"]unemployment_dict["AL"]
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np.float64(7.1)
Now we can do the choropleth.
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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
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Of course, if you can create and/or use a dictionary providing directly the good color. Thus, the finishing seems faster:
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color_dict = {key: colormap(unemployment_dict[key]) for key in unemployment_dict.keys()}
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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)
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<folium.features.GeoJson at 0x7f0b2053fb00>
Note that adding a color legend may be a good idea.
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colormap.caption = "Unemployment color scale"colormap.add_to(m)m
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Caveat
When using
style_functionin 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” yourstyle_functionto 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.
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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
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Keep highlighted while popup is open#
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.
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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
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