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Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on "tidy" data. 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.
import plotly.express as 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.express as 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:
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 sizeref to less than
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.
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()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()See https://plot.ly/python/reference/#choropleth and https://plot.ly/python/reference/#scattergeo for more information and chart attribute options!