mapbox-county-choropleth.md

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jupytext kernelspec language_info plotly notebook_metadata_filter text_representation all extension format_name format_version jupytext_version .md markdown 1.2 1.3.0 display_name language name Python 3 python python3 codemirror_mode file_extension mimetype name nbconvert_exporter pygments_lexer version name version ipython 3 .py text/x-python python python ipython3 3.7.3 description display_as language layout name order page_type permalink thumbnail How to make a Mapbox Choropleth Map of US Counties in Python with Plotly. maps python base Mapbox Choropleth Maps 1 example_index python/mapbox-county-choropleth/ thumbnail/mapbox-choropleth.png

Mapbox Access Token

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 mapbox charts

Making choropleth maps requires two main types of input: GeoJSON-formatted geometry information where each feature has an id and a list of values indexed by feature id. The GeoJSON data is passed to the geojson attribute, and the data is passed into the z (color for px.choropleth_mapbox) attribute, in the same order as the IDs are passed into the location attribute.

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 "tidy" data.

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 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 function 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 https://plot.ly/python/reference/#choroplethmapbox for more information about mapbox and their attribute options.