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()

	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.