extension:.md

format_name:markdown

format_version:'1.1'

jupytext_version:1.1.7

jupytext_version:1.2.1

kernelspec:

display_name:Python 3

language:python

name:python

nbconvert_exporter:python

pygments_lexer:ipython3

version:3.6.5

version:3.7.3

plotly:

description:How to use hover text and formatting in Python with Plotly.

display_as:file_settings

import plotly.ioaspio

import plotly.graph_objectsasgo

import plotly.expressas px

import pandasas pd

import math

data= px.data.gapminder()

df_2007= data[data['year']==2007]

df_2007= df_2007.sort_values(['continent','country'])

bubble_size= []

df= px.data.gapminder()

A= []

B= []

for iinrange(5):

A= {'target': df['continent'][[i]].unique()}

B.append(A)

data= [{

'type':'scatter',

'mode':'markers',

'x': df['lifeExp'],

'y': df['gdpPercap'],

'text': df['continent'],

'hovertemplate':

"<extra></extra>",

'marker': {

'size': df['pop'],

'sizemode':'area',

'sizeref':200000

},

'transforms': [{

'type':'filter',

'target': df['year'],

'orientation':'=',

'value':2002

},{

'type':'groupby',

'groups': df['continent'],

'styles': B

}]

}]

layout= {'yaxis': {'type':'log'}}

pio.show({'data': data,'layout': layout},validate=False)

for index, rowin df_2007.iterrows():

bubble_size.append(math.sqrt(row['pop']))

df_2007['size']= bubble_size

continent_names= ['Africa','Americas','Asia','Europe','Oceania']

continent_data= {continent:df_2007.query("continent == '%s'"%continent)

for continentin continent_names}

fig= go.Figure()

for continent_name, continentin continent_data.items():

fig.add_trace(go.Scatter(

x=continent['gdpPercap'],

y=continent['lifeExp'],

name=continent_name,

text=df_2007['continent'],

"<extra></extra>",

marker_size=continent['size'],

))

fig.update_traces(

mode='markers',

marker={'sizemode':'area',

'sizeref':10})

fig.update_layout(

xaxis={

'title':'GDP per capita',

'type':'log'},

yaxis={'title':'Life Expectancy (years)'})

fig.show()

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

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