Show Sidebar Hide Sidebar

Bubble Maps in Python

How to make bubble 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 Bubble Map¶

Note about sizeref:

To scale the bubble size, use the attribute sizeref. We recommend using the following formula to calculate a sizeref value:

sizeref = 2. * max(array of size values) / (desired maximum marker size ** 2)

Note that setting sizeref to a value greater than $1$, decreases the rendered marker sizes, while setting sizeref to less than $1$, increases the rendered marker sizes.

See https://plot.ly/python/reference/#scatter-marker-sizeref for more information. Additionally, we recommend setting the sizemode attribute: https://plot.ly/python/reference/#scatter-marker-sizemode to area.

In [1]:
import plotly.plotly as py

import pandas as pd

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

df['text'] = df['name'] + '<br>Population ' + (df['pop']/1e6).astype(str)+' million'
limits = [(0,2),(3,10),(11,20),(21,50),(50,3000)]
colors = ["rgb(0,116,217)","rgb(255,65,54)","rgb(133,20,75)","rgb(255,133,27)","lightgrey"]
cities = []
scale = 5000

for i in range(len(limits)):
    lim = limits[i]
    df_sub = df[lim[0]:lim[1]]
    city = dict(
        type = 'scattergeo',
        locationmode = 'USA-states',
        lon = df_sub['lon'],
        lat = df_sub['lat'],
        text = df_sub['text'],
        marker = dict(
            size = df_sub['pop']/scale,
            # sizeref = 2. * max(df_sub['pop']/scale) / (25 ** 2),
            color = colors[i],
            line = dict(width=0.5, color='rgb(40,40,40)'),
            sizemode = 'area'
        ),
        name = '{0} - {1}'.format(lim[0],lim[1]) )
    cities.append(city)

layout = dict(
        title = '2014 US city populations<br>(Click legend to toggle traces)',
        showlegend = True,
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showland = True,
            landcolor = 'rgb(217, 217, 217)',
            subunitwidth=1,
            countrywidth=1,
            subunitcolor="rgb(255, 255, 255)",
            countrycolor="rgb(255, 255, 255)"
        ),
    )

fig = dict(data=cities, layout=layout)
py.iplot(fig, validate=False, filename='d3-bubble-map-populations')
Out[1]:

Ebola Cases in West Africa¶

In [1]:
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[1]:

Reference¶

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

Still need help?
Contact Us

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