A Choropleth Map is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build outline choropleth maps, but you can also build choropleth tile maps.

Below we show how to create Choropleth Maps using either Plotly Express' px.choropleth function or the lower-level go.Choropleth graph object.

Base Map Configuration¶

Plotly figures made with Plotly Express px.scatter_geo, px.line_geo or px.choropleth functions or containing go.Choropleth or go.Scattergeo graph objects have a go.layout.Geo object which can be used to control the appearance of the base map onto which data is plotted.

Introduction: main parameters for choropleth outline maps¶

Making choropleth maps requires two main types of input:

Geometry information:
1. This can either be a supplied GeoJSON file where each feature has either an id field or some identifying value in properties; or
2. one of the built-in geometries within plotly: US states and world countries (see below)
A list of values indexed by feature identifier.

The GeoJSON data is passed to the geojson argument, and the data is passed into the color argument of px.choropleth (z if using graph_objects), in the same order as the IDs are passed into the location argument.

Note the geojson attribute can also be the URL to a GeoJSON file, which can speed up map rendering in certain cases.

Choropleth Map with plotly.express¶

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

GeoJSON with `feature.id`¶

Here we load a GeoJSON file containing the geometry information for US counties, where feature.id is a FIPS code.

In [1]:

from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

counties["features"][0]

Out[1]:

{'type': 'Feature',
 'properties': {'GEO_ID': '0500000US01001',
  'STATE': '01',
  'COUNTY': '001',
  'NAME': 'Autauga',
  'LSAD': 'County',
  'CENSUSAREA': 594.436},
 'geometry': {'type': 'Polygon',
  'coordinates': [[[-86.496774, 32.344437],
    [-86.717897, 32.402814],
    [-86.814912, 32.340803],
    [-86.890581, 32.502974],
    [-86.917595, 32.664169],
    [-86.71339, 32.661732],
    [-86.714219, 32.705694],
    [-86.413116, 32.707386],
    [-86.411172, 32.409937],
    [-86.496774, 32.344437]]]},
 'id': '01001'}

Data indexed by `id`¶

Here we load unemployment data by county, also indexed by FIPS code.

In [2]:

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})
df.head()

Out[2]:

	fips	unemp
0	01001	5.3
1	01003	5.4
2	01005	8.6
3	01007	6.6
4	01009	5.5

Choropleth map using GeoJSON¶

Note In this example we set layout.geo.scope to usa to automatically configure the map to display USA-centric data in an appropriate projection. See the Geo map configuration documentation for more information on scopes.

In [3]:

from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})

import plotly.express as px

fig = px.choropleth(df, geojson=counties, locations='fips', color='unemp',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           scope="usa",
                           labels={'unemp':'unemployment rate'}
                          )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Indexing by GeoJSON Properties¶

If the GeoJSON you are using either does not have an id field or you wish to use one of the keys in the properties field, you may use the featureidkey parameter to specify where to match the values of locations.

In the following GeoJSON object/data-file pairing, the values of properties.district match the values of the district column:

In [4]:

import plotly.express as px

df = px.data.election()
geojson = px.data.election_geojson()

print(df["district"][2])
print(geojson["features"][0]["properties"])

11-Sault-au-Récollet
{'district': '11-Sault-au-Récollet'}

To use them together, we set locations to district and featureidkey to "properties.district". The color is set to the number of votes by the candidate named Bergeron.

Note In this example we set layout.geo.visible to False to hide the base map and frame, and we set layout.geo.fitbounds to 'locations' to automatically zoom the map to show just the area of interest. See the Geo map configuration documentation for more information on projections and bounds.

In [5]:

import plotly.express as px

df = px.data.election()
geojson = px.data.election_geojson()

fig = px.choropleth(df, geojson=geojson, color="Bergeron",
                    locations="district", featureidkey="properties.district",
                    projection="mercator"
                   )
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Choropleth maps in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & publish apps like this with Dash Enterprise or Plotly Cloud.

Out[6]:

Sign up for Dash Club → Free cheat sheets plus updates from Chris Parmer and Adam Schroeder delivered to your inbox every two months. Includes tips and tricks, community apps, and deep dives into the Dash architecture. Join now.

Discrete Colors¶

In addition to continuous colors, we can discretely-color our choropleth maps by setting color to a non-numerical column, like the name of the winner of an election.