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Bubble Maps in Python

How to make bubble maps in Python with Plotly.


New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Base Map Configuration

Plotly figures made with Plotly Express px.scatter_geo, px.line_geo or px.choropleth functions or containing go.Choropleth or go.Scattergeo graph objects have a go.layout.Geo object which can be used to control the appearance of the base map onto which data is plotted.

Bubble map with Plotly Express

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. With px.scatter_geo, each line of the dataframe is represented as a marker point. The column set as the size argument gives the size of markers.

In [1]:
import plotly.express as px
df = px.data.gapminder().query("year==2007")
fig = px.scatter_geo(df, locations="iso_alpha", color="continent",
                     hover_name="country", size="pop",
                     projection="natural earth")
fig.show()

Bubble Map with animation

In [2]:
import plotly.express as px
df = px.data.gapminder()
fig = px.scatter_geo(df, locations="iso_alpha", color="continent",
                     hover_name="country", size="pop",
                     animation_frame="year",
                     projection="natural earth")
fig.show()

Bubble Map with go.Scattergeo

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

In [3]:
import plotly.graph_objects as go

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 = ["royalblue","crimson","lightseagreen","orange","lightgrey"]
cities = []
scale = 5000

fig = go.Figure()

for i in range(len(limits)):
    lim = limits[i]
    df_sub = df[lim[0]:lim[1]]
    fig.add_trace(go.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_color='rgb(40,40,40)',
            line_width=0.5,
            sizemode = 'area'
        ),
        name = '{0} - {1}'.format(lim[0],lim[1])))

fig.update_layout(
        title_text = '2014 US city populations<br>(Click legend to toggle traces)',
        showlegend = True,
        geo = dict(
            scope = 'usa',
            landcolor = 'rgb(217, 217, 217)',
        )
    )

fig.show()

Ebola Cases in West Africa

In [4]:
import plotly.graph_objects as go

import pandas as pd

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

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'}

fig = go.Figure()

for i in range(6,10)[::-1]:
    df_month = df.query('Month == %d' %i)
    fig.add_trace(go.Scattergeo(
            lon = df_month['Lon'],
            lat = df_month['Lat'],
            text = df_month['Value'],
            name = months[i],
            marker = dict(
                size = df_month['Value']/50,
                color = colors[i-6],
                line_width = 0
            )))

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

# Inset
fig.add_trace(go.Choropleth(
        locationmode = 'country names',
        locations = df_sept['Country'],
        z = df_sept['Value'],
        text = df_sept['Country'],
        colorscale = [[0,'rgb(0, 0, 0)'],[1,'rgb(0, 0, 0)']],
        autocolorscale = False,
        showscale = False,
        geo = 'geo2'
    ))
fig.add_trace(go.Scattergeo(
        lon = [21.0936],
        lat = [7.1881],
        text = ['Africa'],
        mode = 'text',
        showlegend = False,
        geo = 'geo2'
    ))

fig.update_layout(
    title = go.layout.Title(
        text = '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 = go.layout.Geo(
        resolution = 50,
        scope = 'africa',
        showframe = False,
        showcoastlines = True,
        landcolor = "rgb(229, 229, 229)",
        countrycolor = "white" ,
        coastlinecolor = "white",
        projection_type = 'mercator',
        lonaxis_range= [ -15.0, -5.0 ],
        lataxis_range= [ 0.0, 12.0 ],
        domain = dict(x = [ 0, 1 ], y = [ 0, 1 ])
    ),
    geo2 = go.layout.Geo(
        scope = 'africa',
        showframe = False,
        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_traceorder = 'reversed'
)

fig.show()

Reference

See https://plotly.com/python/reference/choropleth/ and https://plotly.com/python/reference/scattergeo/ for more information and chart attribute options!

What About Dash?

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter