Summary

The article provides a tutorial for creating an animated bubble chart using Plotly in Python, inspired by professor Hans Rosling.

Abstract

The tutorial showcases how to create an animated bubble chart in Plotly using Python, using GDP per Capita vs Life Expectancy as an example. The process involves data preparation, merging datasets, and animation using Plotly. Express library. The final product is an interactive chart with customizable features such as titles, axis labels, and background colors.

Opinions

The author is inspired by professor Hans Rosling, who was known for his powerful and attractive animated charts in his TED talks.
The author believes that the dataset doesn't have enough information and needs to merge it with another dataset that includes continent information.
The author suggests customizing the chart further with proper titles, axis labels, and background colors for a more visually appealing chart.
The author recommends checking out their other posts for other types of beautiful charts that can be made using Plotly in Python.
The author promotes an AI service that is more cost-effective than ChatGPT Plus but provides the same performance and functions.

Make an impressive animated bubble chart with Plotly in Python — inspired by professor Hans Rosling

This tutorial is inspired by professor Hans Rosling, who was a Swedish physician, academic, and public speaker. He provided many amazing TED talks regarding population, poverty, life expectancy, etc. All the animated charts in his talks are very attractive and powerful to convey the ideas.

The animated chart I am going to generate here is GDP per Capita vs Life Expectancy. And I am going to use Ploy.Express library to create the chart and it only requires a few lines. Then you will get the animated bubble chart like this,

Part 1: Data preparation

In this part, I am going to show how I prepare data in detailed steps. If you want to skip this part, you can directly go to the Part 2. Data Animation.

#import libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.express as px

I get the data from the website called Our World in Data. You can download the data in csv format the then import the data.

#import data
df = pd.read_csv(‘life-expectancy-vs-gdp-per-capita.csv’)

The dataset has 50170 rows and 6 columns.

# rename some columns and drop any rows with missing values

df.rename(columns={‘Entity’: ‘Country’, ‘Life expectancy at birth (years)’: ‘LifeExp’,
 ‘GDP per capita ($)’:’GDPperCap’},inplace=True)

df=df.dropna(how='any')
df

The dataset looks like this,

Then I am going to convert the data type of Year from object to datetime.

#convert data type and sort the data by Year

df[‘Year’]=pd.to_datetime(df[‘Year’])
df[‘Year’]=df[‘Year’].dt.year
df=df.sort_values(by=['Year'])

Lots of data are missing before year 1950, so I will only pick data since 1950.

df=df[df[‘Year’]>1949]

The dataset doesn’t have continent information. I need this information for the chart. So I will add this information to the dataset.

#import CountryCode

CountryCode=pd.read_csv(‘https://pkgstore.datahub.io/JohnSnowLabs/country-and-continent-codes-list/country-and-continent-codes-list-csv_csv/data/b7876b7f496677669644f3d1069d3121/country-and-continent-codes-list-csv_csv.csv',sep=',')
CountryCode

I only need two columns — Continent_Name and Three_Letter_Country_Code

CountryCode=CountryCode[[‘Continent_Name’,’Three_Letter_Country_Code’]]

Merge dataset and remove the last column -Three_Letter_Country_Code.

df_final=pd.merge(df,CountryCode,left_on=’Code’,right_on=’Three_Letter_Country_Code’,how=’left’)
df_final = df_final.iloc[:, :-1] #remove last column
df_final

Now, we get the final dataset which includes the continent information.

The final step of data preparation is dropping missing values and sort the dataset with Year.

df_final=df_final.dropna(how=’any’)
df_final=df_final.sort_values(by=[‘Year’])
df_final

Now our dataset is ready to be used for creating the animated chart.

Part 2: Data Animation

fig = px.scatter(df_final,x=”GDPperCap”, y=”LifeExp”,animation_frame=”Year”, 
 animation_group=”Country”,size=”Population”, 
 color=”Continent_Name”,
 hover_name=”Country”, log_x=True, 
 size_max=45,range_x=[200,150000], range_y=[10,100]
 )
fig.layout.updatemenus[0].buttons[0].args[1][“frame”][“duration”] = 700
fig.show()

You can further beautiful the chart by adding proper titles, x and y axis labels, background colors.

fig = px.scatter(df_final,x=”GDPperCap”, y=”LifeExp”,animation_frame=”Year”, 
 animation_group=”Country”,size=”Population”, 
 color=”Continent_Name”,
 hover_name=”Country”, log_x=True, 
 size_max=45,range_x=[200,150000], range_y=[10,100]
 )
# Tune marker appearance and layout
fig.update_traces(mode=’markers’, marker=dict(sizemode=’area’,
 ))

fig.update_layout(
 title=’Life Expectancy v. Per Capita GDP, 1950~2016',
 xaxis=dict(
 title=’GDP per Capita’,
 gridcolor=’white’,
 type=’log’,
 gridwidth=2,
 ),
 yaxis=dict(
 title=’Life Expectancy (Years)’,
 gridcolor=’white’,
 gridwidth=2,
 ),
 paper_bgcolor=’rgb(243, 243, 243)’,
 plot_bgcolor=’rgb(243, 243, 243)’,
)
fig.layout.updatemenus[0].buttons[0].args[1][“frame”][“duration”] = 600
fig.show()

You can also draw the charts in different facets.

import plotly.express as px
df = px.data.gapminder()
fig = px.scatter(df_final, x=”GDPperCap”, y=”LifeExp”,animation_frame=”Year”, animation_group=”Country”,
 size=”Population”, color=”Continent_Name”, hover_name=”Country”, facet_col=”Continent_Name”,
 log_x=True, size_max=45, range_x=[100,100000], range_y=[25,90])
fig.update_layout(
 title=’Life Expectancy v. Per Capita GDP, 1950~2016',
 yaxis=dict(
 title=’Life Expectancy (Years)’,
 gridcolor=’white’,
 gridwidth=2,
 ))
fig.for_each_annotation(lambda a: a.update(text=a.text.split(“=”)[-1]))
for i in range(1,7):
 fig.update_xaxes(title_text=”GDP/Capita”, row=1, col=i)
fig.show()

If you are interested in making other types of beautiful charts, you may want to check out my other posts.