Hi everyone!
I investigated the data on GDP and life expectancy from the World Health Organization and the World Bank.
I used pandas, numpy, seaborn and matplotlib.pyplot libriaries.

Key Observations:

Based on the plots we see one outlier for life expectancy (Zimbabwe), and the other outlier for GDP (USA). Median life expectancy is around 77 years. Median GDP is around 200 billion US dollars.

GDP increased over time in all countries.

Life expectancy increased over time in all countries.

GDP and life expectancy are positively correlated.

Hello!
If i am not mistaken you did the project with someone as a group - that is great!
I have just completed this project too and i want to give you some feedback.

Firtly it is really easy to see what code is about ( thanks to comments ) and what is outputs. That is a huge advantage of Jupyter Notebook. I wish i did my project in Jupyter too.
However the last piece of codedoes not seem to be that effective. Instead of writing the same code 6 times you should try implement this with for loop:

for i, country in enumerate( countries ):
cols = i % 3
rows = i // 3
ax = axes[rows, cols]
......
......

if you have questions how to use for loop in plot creating you can visit my code implementation.

I would like to add that you showed the scatter graphs for gdp and life expectancy correlation fore every country separately. However, if you plot all the points for all countries on the same graph, you would find that there is a poor correlation between the two variables, thus you would not be able to predict a countries life expectancy given a specific GDP. Therefore I think your results show more precisely that gdp growth is highly correlated to life expectancy growth.