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import wbdata
import pandas
import matplotlib.pyplot as plt
 
#set up the countries I want
countries = ["CL","UY","HU"]
 
#set up the indicator I want (just build up the dict if you want more than one)
indicators = {'NY.GNP.PCAP.CD':'GNI per Capita'}
 

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  "name": "Grabbing World Bank Data with the wbdata module and plotting it"
 },
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    {

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library(WDI)
library(ggplot2)

#Grab GNI per capita data for Chile, Hungary and Uruguay

dat = WDI(indicator='NY.GNP.PCAP.CD', country=c('CL','HU','UY'), start=1960, end=2012)

#a quick plot with legend, title and lable

ggplot(dat, aes(year, NY.GNP.PCAP.CD, color=country)) + geom_line() 

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Country Name	2005	2006	2007	2008	2009	
Afghanistan	46.6	46.9	47.2	47.5	47.9	
Albania	        76.1	76.3	76.5	76.6	76.8	
Algeria	        71.6	71.9	72.2	72.4	72.6	

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import pandas as pd

#read the normalized CSV file
df = pandas.read_csv('lifeexpectancy.csv')

#melt the normalized file, hold the country name and code variables, rename the melted columns
le = pd.melt(df, id_vars=['Country Name','Country Code'], var_name="year", value_name="life_expectancy")

#sort by country name for convenience
le2 =  le.sort(['Country Name'])

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  "name": "unpivoting-csv-pandas"
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This interactive chart shows "survival curves" for females in different countries.

Calcualted from life tables, these survival curves show the expected number of survivors from birth to 85 at 5-year intervals from a hypothetical cohort of 100,000.