Choropleth Maps in Python
How to make choropleth maps in Python with Plotly.
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Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.
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:
- This can either be a supplied GeoJSON file where each feature has either an
idfield or some identifying value inproperties; or - one of the built-in geometries within
plotly: US states and world countries (see below)
- This can either be a supplied GeoJSON file where each feature has either an
- 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.
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]
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
dtype={"fips": str})
df.head()
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.
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:
import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()
print(df["district"][2])
print(geojson["features"][0]["properties"])
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.
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 & deploy apps like this with Dash Enterprise.
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.
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.
import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()
fig = px.choropleth(df, geojson=geojson, color="winner",
locations="district", featureidkey="properties.district",
projection="mercator", hover_data=["Bergeron", "Coderre", "Joly"]
)
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
import plotly.express as px
import geopandas as gpd
df = px.data.election()
geo_df = gpd.GeoDataFrame.from_features(
px.data.election_geojson()["features"]
).merge(df, on="district").set_index("district")
fig = px.choropleth(geo_df,
geojson=geo_df.geometry,
locations=geo_df.index,
color="Joly",
projection="mercator")
fig.update_geos(fitbounds="locations", visible=False)
fig.show()
Using Built-in Country and State Geometries¶
Plotly comes with two built-in geometries which do not require an external GeoJSON file:
- USA States
- Countries as defined in the Natural Earth dataset.
Note and disclaimer: cultural (as opposed to physical) features are by definition subject to change, debate and dispute. Plotly includes data from Natural Earth "as-is" and defers to the Natural Earth policy regarding disputed borders which read:
Natural Earth Vector draws boundaries of countries according to defacto status. We show who actually controls the situation on the ground.
To use the built-in countries geometry, provide locations as three-letter ISO country codes.
import plotly.express as px
df = px.data.gapminder().query("year==2007")
fig = px.choropleth(df, locations="iso_alpha",
color="lifeExp", # lifeExp is a column of gapminder
hover_name="country", # column to add to hover information
color_continuous_scale=px.colors.sequential.Plasma)
fig.show()
To use the USA States geometry, set locationmode='USA-states' and provide locations as two-letter state abbreviations:
import plotly.express as px
fig = px.choropleth(locations=["CA", "TX", "NY"], locationmode="USA-states", color=[1,2,3], scope="usa")
fig.show()
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')
fig = go.Figure(data=go.Choropleth(
locations=df['code'], # Spatial coordinates
z = df['total exports'].astype(float), # Data to be color-coded
locationmode = 'USA-states', # set of locations match entries in `locations`
colorscale = 'Reds',
colorbar_title = "Millions USD",
))
fig.update_layout(
title_text = '2011 US Agriculture Exports by State',
geo_scope='usa', # limite map scope to USA
)
fig.show()
Customize choropleth chart¶
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')
for col in df.columns:
df[col] = df[col].astype(str)
df['text'] = df['state'] + '<br>' + \
'Beef ' + df['beef'] + ' Dairy ' + df['dairy'] + '<br>' + \
'Fruits ' + df['total fruits'] + ' Veggies ' + df['total veggies'] + '<br>' + \
'Wheat ' + df['wheat'] + ' Corn ' + df['corn']
fig = go.Figure(data=go.Choropleth(
locations=df['code'],
z=df['total exports'].astype(float),
locationmode='USA-states',
colorscale='Reds',
autocolorscale=False,
text=df['text'], # hover text
marker_line_color='white', # line markers between states
colorbar_title="Millions USD"
))
fig.update_layout(
title_text='2011 US Agriculture Exports by State<br>(Hover for breakdown)',
geo = dict(
scope='usa',
projection=go.layout.geo.Projection(type = 'albers usa'),
showlakes=True, # lakes
lakecolor='rgb(255, 255, 255)'),
)
fig.show()
World Choropleth Map¶
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
fig = go.Figure(data=go.Choropleth(
locations = df['CODE'],
z = df['GDP (BILLIONS)'],
text = df['COUNTRY'],
colorscale = 'Blues',
autocolorscale=False,
reversescale=True,
marker_line_color='darkgray',
marker_line_width=0.5,
colorbar_tickprefix = '$',
colorbar_title = 'GDP<br>Billions US$',
))
fig.update_layout(
title_text='2014 Global GDP',
geo=dict(
showframe=False,
showcoastlines=False,
projection_type='equirectangular'
),
annotations = [dict(
x=0.55,
y=0.1,
xref='paper',
yref='paper',
text='Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
CIA World Factbook</a>',
showarrow = False
)]
)
fig.show()
County Choropleth Figure Factory¶
Plotly also includes a legacy "figure factory" for creating US county-level choropleth maps.
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
df_sample = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/laucnty16.csv')
df_sample['State FIPS Code'] = df_sample['State FIPS Code'].apply(lambda x: str(x).zfill(2))
df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(lambda x: str(x).zfill(3))
df_sample['FIPS'] = df_sample['State FIPS Code'] + df_sample['County FIPS Code']
colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef", "#b3d2e9", "#9ecae1",
"#85bcdb", "#6baed6", "#57a0ce", "#4292c6", "#3082be", "#2171b5", "#1361a9",
"#08519c", "#0b4083", "#08306b"
]
endpts = list(np.linspace(1, 12, len(colorscale) - 1))
fips = df_sample['FIPS'].tolist()
values = df_sample['Unemployment Rate (%)'].tolist()
fig = ff.create_choropleth(
fips=fips, values=values, scope=['usa'],
binning_endpoints=endpts, colorscale=colorscale,
show_state_data=False,
show_hover=True,
asp = 2.9,
title_text = 'USA by Unemployment %',
legend_title = '% unemployed'
)
fig.layout.template = None
fig.show()
Reference¶
See function reference for px.(choropleth) or https://plotly.com/python/reference/choropleth/ for more information and chart attribute options!
What About Dash?¶
Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.
Learn about how to install Dash at https://dash.plot.ly/installation.
Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:
import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )
from dash import Dash, dcc, html
app = Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
])
app.run_server(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter
