Using colormaps#

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

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

[2]:

import pandas
import requests

geo_json_data = requests.get(
    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_states.json"
).json()
unemployment = pandas.read_csv(
    "https://raw.githubusercontent.com/python-visualization/folium-example-data/main/us_unemployment_oct_2012.csv"
)

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.

[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"

[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

[4]:

Make this Notebook Trusted to load map: File -> Trust Notebook

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.

[5]:

import branca.colormap as cm

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

step

[5]:

[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

[6]:

Make this Notebook Trusted to load map: File -> Trust Notebook

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

[7]:

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

[7]:

LinearColormap#

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

[8]:

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

linear

[8]:

[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

[9]:

Make this Notebook Trusted to load map: File -> Trust Notebook

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

[10]:

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

[10]:

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

[11]:

linear.to_step(6)

[11]:

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

[12]:

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

[12]:

And the opposite is also possible with to_linear.

[13]:

step.to_linear()

[13]:

Built-in#

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

[14]:

cm.linear.OrRd_09

[14]:

You can also use them to generate regular StepColormap.

[15]:

cm.linear.PuBuGn_09.to_step(12)

[15]:

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

[16]:

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

[16]:

[17]:

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

[17]:

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

[18]:

cm.linear

[18]:

viridis
plasma
inferno
magma
Pastel1_03
Pastel1_05
Pastel1_04
Pastel1_07
YlOrRd_04
Pastel1_09
Pastel1_08
Spectral_07
RdYlBu_05
PuBuGn_03
Set1_08
PuBuGn_05
PuBuGn_04
PuBuGn_07
PuBuGn_06
PuBuGn_09
PuBuGn_08
YlOrBr_04
YlOrBr_05
Set1_07
YlOrBr_03
Set1_05
YlOrRd_03
PuOr_06
PuOr_07
PuOr_04
PuOr_05
PuOr_03
Purples_09
Set2_06
RdYlBu_11
PuOr_08
PuOr_09
Paired_03
RdBu_03
RdYlBu_10
Paired_07
Paired_06
Paired_05
Paired_04
Paired_09
Paired_08
RdGy_03
PiYG_04
Accent_03
BuGn_08
BuGn_09
BuGn_04
BuGn_05
BuGn_06
BuGn_07
BuGn_03
YlGnBu_07
YlGnBu_06
YlGnBu_05
YlGnBu_04
YlGnBu_03
RdBu_06
RdBu_05
RdBu_04
Accent_08
RdBu_09
RdBu_08
Set2_04
YlGnBu_09
YlGnBu_08
Blues_08
Blues_09
RdPu_09
RdPu_08
Set3_07
Set3_06
RdPu_05
RdPu_04
RdPu_07
RdPu_06
Blues_06
Blues_07
RdPu_03
Blues_05
Paired_10
Paired_11
Paired_12
PuBu_06
PuBu_07
PuBu_04
PuBu_05
PuRd_05
PuBu_03
PuRd_07
PuRd_06
PuRd_09
PuRd_08
Set2_07
PuBu_08
PuBu_09
RdBu_10
RdBu_11
Accent_06
Set3_03
Set3_05
Set3_12
Set3_10
Set3_04
RdGy_11
RdGy_10
Set1_03
Set1_09
Set3_09
BuPu_08
BuPu_09
RdYlGn_11
Blues_03
Set2_05