Show Sidebar Hide Sidebar

Choropleth Maps in Python

How to make choropleth maps in Python with Plotly.

New to Plotly?¶

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!

United States Choropleth Map¶

In [1]:
import plotly.plotly as py
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)

scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
            [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]

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']

data = [ dict(
        type='choropleth',
        colorscale = scl,
        autocolorscale = False,
        locations = df['code'],
        z = df['total exports'].astype(float),
        locationmode = 'USA-states',
        text = df['text'],
        marker = dict(
            line = dict (
                color = 'rgb(255,255,255)',
                width = 2
            ) ),
        colorbar = dict(
            title = "Millions USD")
        ) ]

layout = dict(
        title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)',
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showlakes = True,
            lakecolor = 'rgb(255, 255, 255)'),
             )
    
fig = dict( data=data, layout=layout )
py.iplot( fig, filename='d3-cloropleth-map' )
Out[1]:

World Choropleth Map¶

In [2]:
import plotly.plotly as py
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

data = [ dict(
        type = 'choropleth',
        locations = df['CODE'],
        z = df['GDP (BILLIONS)'],
        text = df['COUNTRY'],
        colorscale = [[0,"rgb(5, 10, 172)"],[0.35,"rgb(40, 60, 190)"],[0.5,"rgb(70, 100, 245)"],\
            [0.6,"rgb(90, 120, 245)"],[0.7,"rgb(106, 137, 247)"],[1,"rgb(220, 220, 220)"]],
        autocolorscale = False,
        reversescale = True,
        marker = dict(
            line = dict (
                color = 'rgb(180,180,180)',
                width = 0.5
            ) ),
        colorbar = dict(
            autotick = False,
            tickprefix = '$',
            title = 'GDP<br>Billions US$'),
      ) ]

layout = dict(
    title = '2014 Global GDP<br>Source:\
            <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
            CIA World Factbook</a>',
    geo = dict(
        showframe = False,
        showcoastlines = False,
        projection = dict(
            type = 'Mercator'
        )
    )
)

fig = dict( data=data, layout=layout )
py.iplot( fig, validate=False, filename='d3-world-map' )
Out[2]:

Choropleth Inset Map¶

In [3]:
import plotly.plotly as py
import plotly.graph_objs as go

import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv')
df.head()

cases = []
colors = ['rgb(239,243,255)','rgb(189,215,231)','rgb(107,174,214)','rgb(33,113,181)']
months = {6:'June',7:'July',8:'Aug',9:'Sept'}

for i in range(6,10)[::-1]:
    cases.append(go.Scattergeo(
        lon = df[ df['Month'] == i ]['Lon'], #-(max(range(6,10))-i),
        lat = df[ df['Month'] == i ]['Lat'],
        text = df[ df['Month'] == i ]['Value'],
        name = months[i],
        marker = dict(
            size = df[ df['Month'] == i ]['Value']/50,
            color = colors[i-6],
            line = dict(width = 0)
        ),
    ) )

cases[0]['text'] = df[ df['Month'] == 9 ]['Value'].map('{:.0f}'.format).astype(str)+' '+\
    df[ df['Month'] == 9 ]['Country']
cases[0]['mode'] = 'markers+text'
cases[0]['textposition'] = 'bottom center'

inset = [
    go.Choropleth(
        locationmode = 'country names',
        locations = df[ df['Month'] == 9 ]['Country'],
        z = df[ df['Month'] == 9 ]['Value'],
        text = df[ df['Month'] == 9 ]['Country'],
        colorscale = [[0,'rgb(0, 0, 0)'],[1,'rgb(0, 0, 0)']],
        autocolorscale = False,
        showscale = False,
        geo = 'geo2'
    ),
    go.Scattergeo(
        lon = [21.0936],
        lat = [7.1881],
        text = ['Africa'],
        mode = 'text',
        showlegend = False,
        geo = 'geo2'
    )
]

layout = go.Layout(
    title = 'Ebola cases reported by month in West Africa 2014<br> \
Source: <a href="https://data.hdx.rwlabs.org/dataset/rowca-ebola-cases">\
HDX</a>',
    geo = dict(
        resolution = 50,
        scope = 'africa',
        showframe = False,
        showcoastlines = True,
        showland = True,
        landcolor = "rgb(229, 229, 229)",
        countrycolor = "rgb(255, 255, 255)" ,
        coastlinecolor = "rgb(255, 255, 255)",
        projection = dict(
            type = 'Mercator'
        ),
        lonaxis = dict( range= [ -15.0, -5.0 ] ),
        lataxis = dict( range= [ 0.0, 12.0 ] ),
        domain = dict(
            x = [ 0, 1 ],
            y = [ 0, 1 ]
        )
    ),
    geo2 = dict(
        scope = 'africa',
        showframe = False,
        showland = True,
        landcolor = "rgb(229, 229, 229)",
        showcountries = False,
        domain = dict(
            x = [ 0, 0.6 ],
            y = [ 0, 0.6 ]
        ),
        bgcolor = 'rgba(255, 255, 255, 0.0)',
    ),
    legend = dict(
           traceorder = 'reversed'
    )
)

fig = go.Figure(layout=layout, data=cases+inset)
py.iplot(fig, validate=False, filename='West Africa Ebola cases 2014')
Out[3]:

Full County Choropleths¶

For the full county choropleth doc page checkout https://plot.ly/python/county-choropleth/

In [2]:
import plotly.plotly as py
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, centroid_marker={'opacity': 0},
    asp=2.9, title='USA by Unemployment %',
    legend_title='% unemployed'
)
py.iplot(fig, filename='choropleth_full_usa')
The draw time for this plot will be slow for clients without much RAM.
Out[2]:

Reference¶

See https://plot.ly/python/reference/#choropleth for more information and chart attribute options!

Still need help?
Contact Us

For guaranteed 24 hour response turnarounds, upgrade to a Developer Support Plan.