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()
Country Region Population Area (sq. mi.) Pop. Density (per sq. mi.) Coastline (coast/area ratio) Net migration Infant mortality (per 1000 births) GDP ($ per capita) Literacy (%) Phones (per 1000) Arable (%) Crops (%) Other (%) Climate Birthrate Deathrate Agriculture Industry Service
0 Afghanistan ASIA (EX. NEAR EAST) 31056997 647500 48,0 0,00 23,06 163,07 700.0 36,0 3,2 12,13 0,22 87,65 1 46,6 20,34 0,38 0,24 0,38
1 Albania EASTERN EUROPE 3581655 28748 124,6 1,26 -4,93 21,52 4500.0 86,5 71,2 21,09 4,42 74,49 3 15,11 5,22 0,232 0,188 0,579
2 Algeria NORTHERN AFRICA 32930091 2381740 13,8 0,04 -0,39 31 6000.0 70,0 78,1 3,22 0,25 96,53 1 17,14 4,61 0,101 0,6 0,298
3 American Samoa OCEANIA 57794 199 290,4 58,29 -20,71 9,27 8000.0 97,0 259,5 10 15 75 2 22,46 3,27 NaN NaN NaN
4 Andorra WESTERN EUROPE 71201 468 152,1 0,00 6,6 4,05 19000.0 100,0 497,2 2,22 0 97,78 3 8,71 6,25 NaN NaN NaN
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()

how many countries are in each region of the world?

sns.countplot(y=countries['Region'])
plt.show()

Sub-Saharan Africa contains the most countries in this list.