In [1]:

import os
import folium

print(folium.__version__)

0.6.0+159.g84a27d0.dirty

GeoJSON and choropleth¶

A few examples of how to do that with folium.

Using `GeoJson`¶

Let us load a GeoJSON file representing the US states.

In [2]:

import json

us_states = os.path.join('data', 'us-states.json')

geo_json_data = json.load(open(us_states))

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 :

In [3]:

m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(geo_json_data).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_0.html'))

m

Out[3]:

Note that you can avoid loading the file on yourself ; in simply providing a file path.

In [4]:

m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(us_states).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_1.html'))

m

Out[4]:

You can pass a geopandas object.

In [5]:

import geopandas

gdf = geopandas.read_file(us_states)

m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(
    gdf,
).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_3.html'))

m

Out[5]:

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 Point and MultiPoint, see http://leafletjs.com/reference.html#marker
For other features, see http://leafletjs.com/reference.html#path-options and http://leafletjs.com/reference.html#polyline-options

In [6]:

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.save(os.path.join('results', 'GeoJSON_and_choropleth_3.html'))

m

Out[6]:

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:

In [7]:

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.save(os.path.join('results', 'GeoJSON_and_choropleth_4.html'))

m

Out[7]:

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:

In [8]:

import pandas as pd

US_Unemployment_Oct2012 = os.path.join('data', 'US_Unemployment_Oct2012.csv')
unemployment = pd.read_csv(US_Unemployment_Oct2012)

unemployment.head(5)

Out[8]:

	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.

In [9]:

from branca.colormap import linear

colormap = linear.YlGn_09.scale(
    unemployment.Unemployment.min(),
    unemployment.Unemployment.max())

print(colormap(5.0))

colormap

#d8f0a3

Out[9]:

We need also to convert the table into a dictionnary, in order to map a feature to it's unemployment value.

In [10]:

unemployment_dict = unemployment.set_index('State')['Unemployment']

unemployment_dict['AL']

Out[10]:

7.0999999999999996

Now we can do the choropleth.

In [11]:

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.save(os.path.join('results', 'GeoJSON_and_choropleth_5.html'))

m

Out[11]:

Of course, if you can create and/or use a dictionnary providing directly the good color. Thus, the finishing seems faster:

In [12]:

color_dict = {key: colormap(unemployment_dict[key]) for key in unemployment_dict.keys()}

In [13]:

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)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_6.html'))

m

Out[13]:

Note that adding a color legend may be a good idea.

In [14]:

colormap.caption = 'Unemployment color scale'
colormap.add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_7.html'))

m

Out[14]:

Using `Choropleth` class¶

Now if you want to get faster, you can use the Choropleth class. Have a look at it's docstring, it has several styling options.

You can use it in providing a file name (geo_path) :

In [15]:

m = folium.Map([43, -100], zoom_start=4)

folium.Choropleth(
    geo_data=geopandas.read_file(us_states),
    fill_color='red',
    fill_opacity=0.3,
    line_weight=2,
).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_7.html'))

m

Out[15]:

Or in providing a GeoJSON string (geo_str) :

In [16]:

m = folium.Map([43, -100], zoom_start=4)

folium.Choropleth(geo_data=us_states).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_8.html'))

m

Out[16]:

Then, in playing with keyword arguments, you can get a choropleth in a few lines:

In [17]:

m = folium.Map([43, -100], zoom_start=4)

folium.Choropleth(
    geo_data=us_states,
    data=unemployment,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
).add_to(m)


m.save(os.path.join('results', 'GeoJSON_and_choropleth_9.html'))

m

Out[17]:

You can force the color scale to a given number of bins (or directly list the bins you would like), by providing the bins argument.

In [18]:

m = folium.Map([43, -100], zoom_start=4)

folium.Choropleth(
    geo_data=us_states,
    data=unemployment,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
    bins=[3, 4, 9, 11]
).add_to(m)


m.save(os.path.join('results', 'GeoJSON_and_choropleth_10.html'))

m

Out[18]:

You can also enable the highlight function, to enable highlight functionality when you hover over each area.

In [19]:

state_data = pd.read_csv(US_Unemployment_Oct2012)

m = folium.Map(location=[48, -102], zoom_start=3)
folium.Choropleth(
    geo_data=us_states,
    data=state_data,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name='Unemployment Rate (%)',
    highlight=True
).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_11.html'))

m

Out[19]:

You can customize the way missing and nan values are displayed on your map using the two parameters nan_fill_color and nan_fill_opacity.

In [20]:

m = folium.Map([43, -100], zoom_start=4)

messed_up_data = unemployment.drop(0)
messed_up_data.loc[4, 'Unemployment'] = float('nan')

folium.Choropleth(
    geo_data=us_states,
    data=messed_up_data,
    columns=['State', 'Unemployment'],
    nan_fill_color='purple',
    nan_fill_opacity=0.4,
    key_on='feature.id',
    fill_color='YlGn'
).add_to(m)


m.save(os.path.join('results', 'GeoJSON_and_choropleth_10.html'))

m

Out[20]:

Internally Choropleth uses the GeoJson or TopoJson class, depending on your settings, and the StepColormap class. Both objects are attributes of your Choropleth object called geojson and color_scale. You can make changes to them, but for regular things you won't have to. For example setting a name for in the layer controls or disabling showing the layer on opening the map is possible in Choropleth itself.

In [21]:

m = folium.Map([43, -100], zoom_start=4)

choropleth = folium.Choropleth(
    geo_data=us_states,
    data=state_data,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
    name='Unenployment',
    show=False,
).add_to(m)

# The underlying GeoJson and StepColormap objects are reachable
print(type(choropleth.geojson))
print(type(choropleth.color_scale))

folium.LayerControl(collapsed=False).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_11.html'))

m

<class 'folium.features.GeoJson'>
<class 'branca.colormap.StepColormap'>

Out[21]:

GeoJSON and choropleth¶

Using GeoJson¶

Using Choropleth class¶

Using `GeoJson`¶

Using `Choropleth` class¶