

In [1]:

    
import os
import folium

print(folium.__version__)









    



0.8.3+52.g2758dc7.dirty

Using `folium.colormap`

A few examples of how to use folium.colormap in choropleths.

Let's load a GeoJSON file, and try to choropleth it.



In [2]:

    
import json
import pandas as pd
import requests


url = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data'
us_states = f'{url}/us-states.json'
US_Unemployment_Oct2012 = f'{url}/US_Unemployment_Oct2012.csv'

geo_json_data = json.loads(requests.get(us_states).text)
unemployment = pd.read_csv(US_Unemployment_Oct2012)

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

Self-defined

You can build a choropleth in using a self-defined function. It has to output an hexadecimal color string of the form #RRGGBB or #RRGGBBAA.



In [3]:

    
def my_color_function(feature):
    """Maps low values to green and high values to red."""
    if unemployment_dict[feature['id']] > 6.5:
        return '#ff0000'
    else:
        return '#008000'



In [4]:

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

folium.GeoJson(
    geo_json_data,
    style_function=lambda feature: {
        'fillColor': my_color_function(feature),
        'color': 'black',
        'weight': 2,
        'dashArray': '5, 5'
    }
).add_to(m)

m.save(os.path.join('results', 'Colormaps_0.html'))

m









    Out[4]:

StepColormap

But to help you define your colormap, we've embedded StepColormap in folium.colormap.

You can simply define the colors you want, and the index (thresholds) that correspond.



In [5]:

    
import branca.colormap as cm


step = cm.StepColormap(
    ['green', 'yellow', 'red'],
    vmin=3, vmax=10,
    index=[3, 4, 8, 10],
    caption='step'
)

step









    Out[5]:



In [6]:

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

folium.GeoJson(
    geo_json_data,
    style_function=lambda feature: {
        'fillColor': step(unemployment_dict[feature['id']]),
        'color': 'black',
        'weight': 2,
        'dashArray': '5, 5'
    }
).add_to(m)

m.save(os.path.join('results', 'Colormaps_1.html'))

m









    Out[6]:

If you specify no index, colors will be set uniformely.



In [7]:

    
cm.StepColormap(['r', 'y', 'g', 'c', 'b', 'm'])









    Out[7]:

LinearColormap

But sometimes, you would prefer to have a continuous set of colors. This can be done by LinearColormap.



In [8]:

    
linear = cm.LinearColormap(
    ['green', 'yellow', 'red'],
    vmin=3, vmax=10
)

linear









    Out[8]:



In [9]:

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

folium.GeoJson(
    geo_json_data,
    style_function=lambda feature: {
        'fillColor': linear(unemployment_dict[feature['id']]),
        'color': 'black',
        'weight': 2,
        'dashArray': '5, 5'
    }
).add_to(m)

m.save(os.path.join('results', 'Colormaps_2.html'))

m









    Out[9]:

Again, you can set the index if you want something irregular.



In [10]:

    
cm.LinearColormap(
    ['red', 'orange', 'yellow', 'green'],
    index=[0, 0.1, 0.9, 1.0]
)









    Out[10]:

If you want to transform a linear map into a step one, you can use the method to_step.



In [11]:

    
linear.to_step(6)









    Out[11]:

You can also use more sophisticated rules to create the thresholds.



In [12]:

    
linear.to_step(
    n=6,
    data=[30.6, 50, 51, 52, 53, 54, 55, 60, 70, 100],
    method='quantiles',
    round_method='int'
)









    Out[12]:

And the opposite is also possible with to_linear.



In [13]:

    
step.to_linear()









    Out[13]:

Build-in

For convenience, we provide a (small) set of built-in linear colormaps, in folium.colormap.linear.



In [14]:

    
cm.linear.OrRd_09









    Out[14]:

You can also use them to generate regular StepColormap.



In [15]:

    
cm.linear.PuBuGn_09.to_step(12)









    Out[15]:

Of course, you may need to scale the colormaps to your bounds. This is doable with .scale.



In [16]:

    
cm.linear.YlGnBu_09.scale(3, 12)









    Out[16]:



In [17]:

    
cm.linear.RdGy_11.to_step(10).scale(5, 100)









    Out[17]:

At last, if you want to check them all, simply ask for linear in the notebook.



In [18]:

    
cm.linear









    Out[18]:





        
        
        viridis 
        
        Pastel1_03 
        
        Pastel1_05 
        
        Pastel1_04

Using folium.colormap

Self-defined

StepColormap

LinearColormap

Build-in

Using `folium.colormap`