Data Management

It turns out that I have already done some data management in my previous assignment. While calculating the frequency distribution of female employment rate, I grouped the employment rates into 4 groups. And while calculating GDP, I applied similar grouping techniques. I feel the Gapminder data set is rather straight forward and does not require much management. Instead of random groups, I will break down female employment rate and GDP into quartiles for this assignment.

 # -*- coding: utf-8 -*-  
 """  
 Created on Sun Oct 4 20:25:31 2015  
 @author: Abhishek  
 """  
 import pandas  
 import numpy  
 data = pandas.read_csv('gapminder.csv', low_memory=False)  
 pandas.set_option('display.float_format', lambda x: '%f'%x)  
 data['femaleemployrate'] = data['femaleemployrate'].convert_objects(convert_numeric=True)  
 data['incomeperperson'] = data['incomeperperson'].convert_objects(convert_numeric=True)  
 data['polityscore'] = data['polityscore'].convert_objects(convert_numeric=True)  
 dataG20Copy = data[(data['country'] == 'Argentina') |  
       (data['country'] == 'Australia') |  
       (data['country'] == 'Brazil') |  
       (data['country'] == 'Canada') |  
       (data['country'] == 'China') |  
       (data['country'] == 'France') |  
       (data['country'] == 'Germany') |  
       (data['country'] == 'India') |  
       (data['country'] == 'Indonesia') |  
       (data['country'] == 'Italy') |  
       (data['country'] == 'Japan') |  
       (data['country'] == 'Mexico') |  
       (data['country'] == 'Russia') |  
       (data['country'] == 'Saudi Arabia') |  
       (data['country'] == 'South Africa') |  
       (data['country'] == 'Korea, Rep.') |  
       (data['country'] == 'Turkey') |  
       (data['country'] == 'United Kingdom') |  
       (data['country'] == 'United States')]  
 # Not always necessary but can eliminate a setting with copy warning that is displayed  
 dataG20 = dataG20Copy.copy()  
 print('FEMALE EMP RATE: 4 Quartiles')  
 data['femaleemployrate4'] = pandas.qcut(data.femaleemployrate,4,labels=["1=25%tile","2=50%tile","3=75%tile","4=100%tile"])  
 qF = data['femaleemployrate4'].value_counts(sort=True,dropna=True,normalize=True) * 100  
 print(qF)  
 #print(pandas.crosstab(data['femaleemployrate'],data['femaleemployrate4']))  
 print('INCOME PER PERSON: 4 Quartiles')  
 dataG20['incomeperperson4'] = pandas.qcut(dataG20.incomeperperson,4,labels=["1=25%tile","2=50%tile","3=75%tile","4=100%tile"])  
 gdpQ = dataG20['incomeperperson4'].value_counts(sort=False,dropna=True, normalize=True) * 100  
 print(gdpQ)