In [18]:

    

import numpy as np, requests, pandas as pd


    

In [24]:

    

countries=pd.read_html('http://www.geonames.org/countries/',header=0,infer_types=False)[1]
countries.columns=['ISO2','ISO3','ISONUM','FIPS','Country','Capital','Area','Population','Continent']
countries.set_index('Country',drop=True,inplace=True)
countries.head(5)


    

    
Out[24]:






  

    

      

      
ISO2
      
ISO3
      
ISONUM
      
FIPS
      
Capital
      
Area
      
Population
      
Continent
    
    

      
Country
      

      

      

      

      

      

      

      

    
  
  

    

      
Andorra
      
 AD
      
 AND
      
  20
      
 AN
      
 Andorra la Vella
      
    468.0
      
    84000
      
  EU
    
    

      
United Arab Emirates
      
 AE
      
 ARE
      
 784
      
 AE
      
        Abu Dhabi
      
  82880.0
      
  4975593
      
  AS
    
    

      
Afghanistan
      
 AF
      
 AFG
      
   4
      
 AF
      
            Kabul
      
 647500.0
      
 29121286
      
  AS
    
    

      
Antigua and Barbuda
      
 AG
      
 ATG
      
  28
      
 AC
      
       St. John's
      
    443.0
      
    86754
      
 nan
    
    

      
Anguilla
      
 AI
      
 AIA
      
 660
      
 AV
      
       The Valley
      
    102.0
      
    13254
      
 nan
    
  

In [35]:

    

codes={}
for i in countries.index:
    #codes[countries.loc[i]['ISONUM']]=[countries.loc[i]['Population'],countries.loc[i]['ISO3']]
    try: codes[int(countries.loc[i]['ISONUM'])]=[float(countries.loc[i]['Population']),str(countries.loc[i]['ISO3'])]
    except: pass


    

In [94]:

    

import re
codes={}
for i in ids.index:
    try: 
        a=[i]
        a.append(round(float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                                    ["Human Development Index (HDI) Value, 2013"])),3))
        a.append(round((float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                                     ["Life expectancy at birth (years), 2013"]))-20)/(85-20),3))
        a.append(round((float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                                     ["Mean years of schooling (years), 2012 a"]))/15+\
                  float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                               ["Expected years of schooling (years), 2012 a"]))/18)/2,3))
        a.append(round((np.log(float(re.sub(r'[^\d.]+', '',hdi.loc[country_name_converter(i)]\
                        ["Gross national income (GNI) per capita (2011 PPP $), 2013"])))-np.log(100))\
                        /(np.log(75000)-np.log(100)),3))
        a.append(round((a[2]*a[3]*a[4])**(1.0/3.0),3))
        a.append(round((a[2]*a[3]*a[4])**(1.0/3.0),3))
        codes[repr(ids.loc[i][0])]=a
    except: pass


    

In [37]:

    

import json
file('../pop.json','w').write(json.dumps(codes))