Mapbox Choropleth Maps in Python/v3
How to make a Mapbox Choropleth Map of the Florida Counties in Python with Plotly.
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Version Check¶
Run pip install plotly --upgrade to update your Plotly version
import plotly
plotly.__version__
In this tutorial, we will be creating a choropleth of Florida's counties with the Republican and Democratic counties colored red and blue respectively.
Mapbox Access Token¶
To plot on Mapbox maps with Plotly you'll need a Mapbox account and a Mapbox Access Token which you can add to your Plotly settings. If you're using a Chart Studio Enterprise server, please see additional instructions here: https://help.plot.ly/mapbox-atlas/.
Read the Data¶
Read both a GeoJSON file of the Florida counties and a webpage indicating Florida's voting registration by county
import pandas as pd
import string
import urllib
repub_democ_counties_url = 'http://dos.myflorida.com/elections/data-statistics/voter-registration-statistics/voter-registration-monthly-reports/voter-registration-current-by-county/'
florida_data_url = 'https://raw.githubusercontent.com/plotly/datasets/master/florida_county_data.geojson'
repub_democ_counties = urllib.urlopen(repub_democ_counties_url).read()
florida_data = pd.read_json(florida_data_url)
county_names = []
county_names_dict = {}
for county in florida_data['features']:
for m in range(len(county['properties']['name'])):
if county['properties']['name'][m:m+6] == 'County':
county_names.append(county['properties']['name'][0:m-1])
county_names_dict[county['properties']['name'][0:m-1]] = county['properties']['name']
print county_names
Since we want to separate the counties into Republican and Democratic for the seperate coloring, and since the county names in the GeoJSON are fuller text descriptions of each county on the website, we need to parse through and convert the names in the GeoJSON to the website format
Color the Counties¶
We now run a script to color our counties based on political party. This involves parsing through our list of counties, finding their cooresponding Republican/Democratic votes on the website, and place our data into the cooresponding list red_counties or blue_counties, based on which party has more votes
red_counties = []
blue_counties = []
for k, county in enumerate(county_names):
for j in range(len(repub_democ_counties)):
county_len = len(county)
if repub_democ_counties[j:j+county_len] == string.upper(county):
new_j = j
while True:
try:
int(repub_democ_counties[new_j])
break
except ValueError:
new_j += 1
repub_votes = ''
while repub_democ_counties[new_j] != '<':
if repub_democ_counties[new_j] != ',':
repub_votes += repub_democ_counties[new_j]
new_j += 1
# advance to next set of numbers
new_j += 11
democ_votes = ''
while repub_democ_counties[new_j] != '<':
if repub_democ_counties[new_j] != ',':
democ_votes += repub_democ_counties[new_j]
new_j += 1
try:
repub_votes = int(repub_votes)
except ValueError:
repub_votes = 0
try:
democ_votes = int(democ_votes)
except ValueError:
democ_votes = 0
if repub_votes >= democ_votes:
red_counties.append(florida_data['features'][k])
else:
blue_counties.append(florida_data['features'][k])
Create JSON Files¶
In plotly/datasets, you can find the generated JSON files below for the red counties and blue counties
import json
red_data = {"type": "FeatureCollection"}
red_data['features'] = red_counties
blue_data = {"type": "FeatureCollection"}
blue_data['features'] = blue_counties
with open('florida-red-data.json', 'w') as f:
f.write(json.dumps(red_data))
with open('florida-blue-data.json', 'w') as f:
f.write(json.dumps(blue_data))
and we can now plot our choropleth using Python. Make sure to have a Mapbox Access Tolken for the generation of the plot. For more information on plotting Mapbox maps in Python, checkout the documentation
import plotly.plotly as py
import plotly.graph_objs as graph_objs
mapbox_access_token = "ADD_YOUR_TOKEN_HERE"
data = graph_objs.Data([
graph_objs.Scattermapbox(
lat=['45.5017'],
lon=['-73.5673'],
mode='markers',
)
])
layout = graph_objs.Layout(
height=600,
autosize=True,
hovermode='closest',
mapbox=dict(
layers=[
dict(
sourcetype = 'geojson',
source = 'https://raw.githubusercontent.com/plotly/datasets/master/florida-red-data.json',
type = 'fill',
color = 'rgba(163,22,19,0.8)'
),
dict(
sourcetype = 'geojson',
source = 'https://raw.githubusercontent.com/plotly/datasets/master/florida-blue-data.json',
type = 'fill',
color = 'rgba(40,0,113,0.8)'
)
],
accesstoken=mapbox_access_token,
bearing=0,
center=dict(
lat=27.8,
lon=-83
),
pitch=0,
zoom=5.2,
style='light'
),
)
fig = dict(data=data, layout=layout)
py.iplot(fig, filename='county-level-choropleths-python')
Reference¶
See https://plotly.com/python/reference/#scattermapbox for more information about mapbox and their attribute options.
