display_name:Python 3

language:python

name:python3

language_info:

codemirror_mode:

name:ipython

version:3

file_extension:.py

mimetype:text/x-python

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plotly:

description:How to make bubble maps in Python with Plotly.

display_as:maps

permalink:python/bubble-maps/

thumbnail:thumbnail/bubble-map.jpg

title:Python Bubble Maps | Plotly

v4upgrade:true

Plotly's Python library is free and open source by downloading the client and[reading the primer](https://plot.ly/python/getting-started/).

<br>You can set up Plotly to work in[online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or[offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in[jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).

<br>We also have a quick-reference[cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!

Plotly express functions take as argument a tidy[pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html). 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.

Plotly's python package is updated frequently. Run`pip install plotly --upgrade` to use the latest version.

import plotly.expressas px

gapminder= px.data.gapminder().query("year==2007")

fig= px.scatter_geo(gapminder,locations="iso_alpha",color="continent",

hover_name="country",size="pop",

projection="natural earth")

fig.show()

### Bubble Map with animation

import plotly

plotly.__version__

import plotly.expressas px

gapminder= px.data.gapminder()

fig= px.scatter_geo(gapminder,locations="iso_alpha",color="continent",

hover_name="country",size="pop",

animation_frame="year",

projection="natural earth")

fig.show()

Note about`sizeref`:

Seehttps://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.

import plotly.plotlyas py

import plotly.graph_objsas go

import plotly.graph_objectsas go

import pandasas pd

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"]

colors= ["royalblue","crimson","lightseagreen","orange","lightgrey"]

cities= []

scale=5000

fig= go.Figure()

for iinrange(len(limits)):

lim= limits[i]

df_sub= df[lim[0]:lim[1]]

city=go.Scattergeo(

fig.add_trace(go.Scattergeo(

locationmode='USA-states',

lon= df_sub['lon'],

lat= df_sub['lat'],

text= df_sub['text'],

marker=go.scattergeo.Marker(

marker=dict(

size= df_sub['pop']/scale,

color= colors[i],

line= go.scattergeo.marker.Line(

width=0.5,color='rgb(40,40,40)'

),

line_color='rgb(40,40,40)',

line_width=0.5,

),

name='{0} -{1}'.format(lim[0],lim[1]) )

cities.append(city)

name='{0} -{1}'.format(lim[0],lim[1])))

layout= go.Layout(

title= go.layout.Title(

),

fig.update_layout(

title_text='2014 US city populations<br>(Click legend to toggle traces)',

showlegend=True,

geo=go.layout.Geo(

geo=dict(

scope='usa',

projection= go.layout.geo.Projection(

),

showland=True,

landcolor='rgb(217, 217, 217)',

subunitwidth=1,

countrywidth=1,

subunitcolor="rgb(255, 255, 255)",

)

)

fig= go.Figure(data=cities,layout=layout)

py.iplot(fig,filename='d3-bubble-map-populations')

fig.show()

import plotly.plotlyas py

import plotly.graph_objsas go

import plotly.graph_objectsas go

import pandasas 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'}

fig= go.Figure()

for iinrange(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'],

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=go.scattergeo.Marker(

size=df[ df['Month']== i ]['Value']/50,

marker=dict(

size=df_month['Value']/50,

color= colors[i-6],

line= go.scattergeo.marker.Line(width=0)

)

)

)

)))

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

df_sept= df.query('Month == 9')

go.Choropleth(

fig.add_trace(go.Choropleth(

locationmode='country names',

locations=df[ df['Month']==9 ]['Country'],

z=df[ df['Month']==9 ]['Value'],

text=df[ df['Month']==9 ]['Country'],

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,

),

go.Scattergeo(

))

fig.add_trace(go.Scattergeo(

lon= [21.0936],

lat= [7.1881],

text= ['Africa'],

mode='text',

showlegend=False,

)

]

))

layout= go.Layout(

fig.update_layout(

title= go.layout.Title(

scope='africa',

showframe=False,

showcoastlines=True,

showland=True,

landcolor="rgb(229, 229, 229)",

countrycolor="rgb(255, 255, 255)" ,

coastlinecolor="rgb(255, 255, 255)",

projection= go.layout.geo.Projection(

),

lonaxis= go.layout.geo.Lonaxis(

range= [-15.0,-5.0 ]

),

lataxis= go.layout.geo.Lataxis(

range= [0.0,12.0 ]

),

domain= go.layout.geo.Domain(

x= [0,1 ],

y= [0,1 ]

)

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,

showland=True,

landcolor="rgb(229, 229, 229)",

showcountries=False,

domain= go.layout.geo.Domain(

x= [0,0.6 ],

y= [0,0.6 ]

),

domain=dict(x= [0,0.6 ],y= [0,0.6 ]),

bgcolor='rgba(255, 255, 255, 0.0)',

),

legend= go.layout.Legend(

)

)

fig= go.Figure(layout=layout,data=cases+inset)

py.iplot(fig,filename='West Africa Ebola cases 2014')

fig.show()

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

from IPython.displayimport display,HTML

display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))

display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))

! pip install git+https://github.com/plotly/publisher.git--upgrade

import publisher

publisher.publish(

'bubble-maps.ipynb','python/bubble-maps/','Bubble Maps',

'How to make bubble maps in Python with Plotly.',

title='Python Bubble Maps | Plotly',

has_thumbnail='true',thumbnail='thumbnail/bubble-map.jpg',

language='python',

page_type='example_index',

display_as='maps',order=3,

uses_plotly_offline=False)