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Bubble Maps in Python

How to make your-tutorial-chart plots in Python with Plotly.

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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

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,
            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 [2]:
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[2]:

Reference

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

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