Dataset
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
%matplotlib inline
plt.style.use('ggplot')
countries= pd.read_csv('../data/countries-of-the-world.csv')
countries.head()
sns.scatterplot(x=countries['GDP ($ per capita)'], y=countries['Phones (per 1000)'])
plt.show()
this plot does not show a linear relationship between GDP and percent literate, countries with a lower GDP do seem more likely to have a lower percent of the population that can read and write.
sns.scatterplot(x=countries['GDP ($ per capita)'], y=countries['Literacy (%)'])
plt.show()
sns.countplot(y=countries['Region'])
plt.show()
Sub-Saharan Africa contains the most countries in this list.