Exploring Gapminder Data: Life Expectancy Trends

Python

26Summer

data: gapminder.csv

Author

Ubaid

Published

February 5, 2026

Introduction

This short analysis looks at life expectancy trends in the classic Gapminder dataset (1952–2007) using Python and pandas.

Questions wanted to explore VS Explored

####Interesting Questions 1) How much life expectancy has increased or decreased in different continents in all the years? 2) What 5 countries has the highest life expectancy and which 5 has the loweest? 3) Which countries has increased or decreased life expectancy significantly in the past 20 years in comparison to what they had? 4) How does gpd per capita affect life expectancy sort by continents? 5) if gdp per capita affects life expectancy then which countries have shown this trend and what are th top countries. 6) Does population play any role woith gdp per capita?

What I explored

How has average life expectancy changed per continent over time?

Examine the data

Modules Loading

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Load the data

df_raw = pd.read_csv('../../../../data/gapminder.csv')
df = df_raw.copy()

Quick look at the data

print(df.head())

       country  year         pop continent  lifeExp   gdpPercap
0  Afghanistan  1952   8425333.0      Asia   28.801  779.445314
1  Afghanistan  1957   9240934.0      Asia   30.332  820.853030
2  Afghanistan  1962  10267083.0      Asia   31.997  853.100710
3  Afghanistan  1967  11537966.0      Asia   34.020  836.197138
4  Afghanistan  1972  13079460.0      Asia   36.088  739.981106

print(df['continent'].unique())

['Asia' 'Europe' 'Africa' 'Americas' 'Oceania']

Earlier Approach

Categorize data into various continents

Asia = df[df['continent']=='Asia']
Europe = df[df['continent']=='Europe']
Africa = df[df['continent']=='Africa']
Americas = df[df['continent']=='Americas']
Oceania = df[df['continent']=='Oceania']

Calculate yearly average of each continent seperatly

Asia_yearly_avg = Asia.groupby('year')["lifeExp"].mean()
Europe_yearly_avg = Europe.groupby('year')["lifeExp"].mean()
Africa_yearly_avg = Africa.groupby('year')["lifeExp"].mean()
Americas_yearly_avg = Americas.groupby('year')["lifeExp"].mean()
Oceania_yearly_avg = Oceania.groupby('year')["lifeExp"].mean()

Problem with this appraoch

I have all the yearly averages of continents but they are seperate.

Now I have to combine them again to plot them togather or I would have to call all the continents and put them togather.

This is annoying. I dont want to do that. I need a shortcut. I dont want to do AVENGERS ASSEMBLE. Once things are broken …. i am getting distracted. lets get back to work.

Better Appraoch

yearly_avg_by_continent = df.groupby(['continent', 'year'])['lifeExp'].mean().reset_index()

Lets have a quick look

print(yearly_avg_by_continent.head(15))

   continent  year    lifeExp
0     Africa  1952  39.135500
1     Africa  1957  41.266346
2     Africa  1962  43.319442
3     Africa  1967  45.334538
4     Africa  1972  47.450942
5     Africa  1977  49.580423
6     Africa  1982  51.592865
7     Africa  1987  53.344788
8     Africa  1992  53.629577
9     Africa  1997  53.598269
10    Africa  2002  53.325231
11    Africa  2007  54.806038
12  Americas  1952  53.279840
13  Americas  1957  55.960280
14  Americas  1962  58.398760

Now things are looking good. Its plotting time.

Ploting time

sns.lineplot(data=yearly_avg_by_continent, x= 'year', y= 'lifeExp', hue= 'continent', marker = 'o')
plt.title("Average life Expectancy by Continent (1952-2007")

Text(0.5, 1.0, 'Average life Expectancy by Continent (1952-2007')

Its working but the legend position is very bad. It is making the graph look bad. SO i have to adjust the legend

sns.lineplot(data=yearly_avg_by_continent, x= 'year', y= 'lifeExp', hue= 'continent', marker = 'o')
plt.title("Average life Expectancy by Continent (1952-2007")
plt.legend(
    bbox_to_anchor=(1.02, 1), 
    loc='upper left',
    borderaxespad=0.
)

plt.show()