Mapbox county choropleth

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 tile-map choropleth maps, but you can also build outline choropleth maps using our non-Mapbox trace types.

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

Mapbox Access Tokens and Base Map Configuration¶

To plot on Mapbox maps with Plotly you may need a Mapbox account and a public Mapbox Access Token. See our Mapbox Map Layers documentation for more information.

Introduction: main parameters for choropleth tile maps¶

Making choropleth Mapbox maps requires two main types of input:

GeoJSON-formatted geometry information where each feature has either an id field or some identifying value in properties.
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_mapbox (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.

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]

Data indexed by `id`¶

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

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 plotly.express and carto base map (no token needed)¶

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.

With px.choropleth_mapbox, each row of the DataFrame is represented as a region of the choropleth.

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_mapbox(df, geojson=counties, locations='fips', color='unemp',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           mapbox_style="carto-positron",
                           zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
                           opacity=0.5,
                           labels={'unemp':'unemployment rate'}
                          )
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.

```python hide_code=true from IPython.display import IFrame snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/' IFrame(snippet_url + 'mapbox-county-choropleth', width='100%', height=630)

### Indexing by GeoJSON Properties

If the GeoJSON you are using either does not have an `id` field or you wish you 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:

```python
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.

import plotly.express as px

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

fig = px.choropleth_mapbox(df, geojson=geojson, color="Bergeron",
                           locations="district", featureidkey="properties.district",
                           center={"lat": 45.5517, "lon": -73.7073},
                           mapbox_style="carto-positron", zoom=9)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

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.

import plotly.express as px

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

fig = px.choropleth_mapbox(df, geojson=geojson, color="winner",
                           locations="district", featureidkey="properties.district",
                           center={"lat": 45.5517, "lon": -73.7073},
                           mapbox_style="carto-positron", zoom=9)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Using GeoPandas Data Frames¶

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

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_mapbox(geo_df,
                           geojson=geo_df.geometry,
                           locations=geo_df.index,
                           color="Joly",
                           center={"lat": 45.5517, "lon": -73.7073},
                           mapbox_style="open-street-map",
                           zoom=8.5)
fig.show()

Choropleth map using plotly.graph_objects and carto base map (no token needed)¶

If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Choroplethmapbox class from plotly.graph_objects.

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.graph_objects as go

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.fips, z=df.unemp,
                                    colorscale="Viridis", zmin=0, zmax=12,
                                    marker_opacity=0.5, marker_line_width=0))
fig.update_layout(mapbox_style="carto-positron",
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Mapbox Light base map: free token needed¶

token = open(".mapbox_token").read() # you will need your own token


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.graph_objects as go

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.fips, z=df.unemp,
                                    colorscale="Viridis", zmin=0, zmax=12, marker_line_width=0))
fig.update_layout(mapbox_style="light", mapbox_accesstoken=token,
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Reference¶

See function reference for px.(choropleth_mapbox) or https://plotly.com/python/reference/choroplethmapbox/ for more information about mapbox and their attribute options.