Re-use the GapMinder dataset to plot, in Jupyter using Matplotlib, a scatter plot of world life expectancy against GDP per capita for 1952, 1977 and 2007. Add a title, axis labels and legend to your figure.
Find the country with the highest GDP per capita for 1952, 1977 and 2007.
Re-write the function gdp_stats_by_continent_and_year()
using Pandas.
The output for the countries with highest GDP per capita should look like this, however will not be evaluated below:
country continent year lifeExp pop gdpPercap
840 Kuwait Asia 1952 55.565 160000 108382.3529
country continent year lifeExp pop gdpPercap
845 Kuwait Asia 1977 69.343 1140357 59265.47714
country continent year lifeExp pop gdpPercap
1139 Norway Europe 2007 80.196 4627926 49357.19017
Rewriting the function gdp_stats_by_continent_and_year()
should give the following output:
Americas GDP per Capita in 1952
{'mean': 4079.0625522000005, 'median': 3048.3029, 'stdev': 3001.7275216326566}
For the sake of evaluation perform the following code:
print(gdp_stats_by_continent_and_year('Americas'))
This last command will evaluate this exercise.