Bubble Maps in Python

How to make bubble maps in Python with Plotly.

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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 = [(0,3),(3,11),(11,21),(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[