Choropleth Maps in Python

How to make choropleth maps in Python with Plotly.

Plotly Studio: Transform any dataset into an interactive data application in minutes with AI. Try Plotly Studio now.

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()