<jupyter_start><jupyter_text>DatasetThe dataset contains information about 227 countries. This dataset has lots of interesting information on each country, such as the country's birth rates, death rates, and its gross domestic product (GDP). GDP is the value of all the goods and services produced in a year, expressed as dollars per person.<jupyter_code>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()<jupyter_output><empty_output><jupyter_text>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.<jupyter_code>sns.scatterplot(x=countries['GDP ($ per capita)'], y=countries['Literacy (%)'])
plt.show()<jupyter_output><empty_output><jupyter_text>how many countries are in each region of the world?<jupyter_code>sns.countplot(y=countries['Region'])
plt.show()<jupyter_output><empty_output>