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Choropleth maps in Python

How to make choropleth maps in Python with Plotly.

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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.

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.

Making choropleth maps requires two main types of input:

1. 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)
2. 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.

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.

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'}

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]:

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()

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()

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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.

In [7]:

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()

px.choropleth accepts the geometry of a GeoPandas data frame as the input to geojson if the geometry contains polygons.

In [8]:

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()

Plotly comes with two built-in geometries which do not require an external GeoJSON file:

1. USA States
2. Countries

In Plotly.py 6.3 and later, the built-in countries geometry is created from the following sources:

• UN data for country borders, coastlines, land, and ocean layers.
• Natural Earth data for lakes, rivers, and subunits layers.

In earlier versions of Plotly.py, the built-in countries geometry is based on Natural Earth data only. Plotly includes data from Natural Earth "as-is". This dataset draws boundaries of countries according to de facto status. See the Natural Earth page for more details.

To use the built-in countries geometry, provide locations as three-letter ISO country codes.

In [9]:

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:

In [10]:

import plotly.express as px

fig = px.choropleth(locations=["CA", "TX", "NY"], locationmode="USA-states", color=[1,2,3], scope="usa")
fig.show()

In [11]:

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()

In [12]:

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=dict(
        title=dict(
            text="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()

In [13]:

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()

In [14]:

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()

/home/circleci/project/doc/.venv/lib/python3.9/site-packages/plotly/figure_factory/_county_choropleth.py:808: ShapelyDeprecationWarning:

The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.

/home/circleci/project/doc/.venv/lib/python3.9/site-packages/plotly/figure_factory/_county_choropleth.py:330: ShapelyDeprecationWarning:

The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.

/home/circleci/project/doc/.venv/lib/python3.9/site-packages/plotly/figure_factory/_county_choropleth.py:357: ShapelyDeprecationWarning:

The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.

/home/circleci/project/doc/.venv/lib/python3.9/site-packages/plotly/figure_factory/_county_choropleth.py:847: ShapelyDeprecationWarning:

The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.

/home/circleci/project/doc/.venv/lib/python3.9/site-packages/plotly/figure_factory/_county_choropleth.py:852: ShapelyDeprecationWarning:

The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.

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(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter

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