Data Visualization

The Code of visualization is provided below

 # -*- coding: utf-8 -*-  
 """  
 Created on Sun Oct 4 20:25:31 2015  
 @author: Abhishek  
 """  
 import pandas  
 import numpy  
 import seaborn  
 import matplotlib.pyplot as plt  
 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()  
 # Female Employment Rate  
 print('Describe Female Employee Rate of G20 countries')  
 desc1 = dataG20['femaleemployrate'].describe()  
 print(desc1)  
 seaborn.distplot(dataG20['femaleemployrate'].dropna(),kde=False);  
 plt.xlabel('Female Employment Rate')  
 plt.title('Female Employment Rate in G20 Countries')  
 # Income Per Person  
 print('Describe Female Employee Rate of G20 countries')  
 desc2 = dataG20['incomeperperson'].describe()  
 print(desc2)  
 seaborn.distplot(dataG20['incomeperperson'].dropna(),kde=False);  
 plt.xlabel('Income Per Person')  
 plt.title('Income Per Person in G20 Countries')  
 dataG20['polityscorecat'] = dataG20['polityscore'].astype('category')  
 seaborn.distplot(dataG20['polityscorecat'].dropna(),kde=False);  
 plt.xlabel('Polity Score')  
 plt.title('Polity Score of G20 Countries')  
 scat1 = seaborn.regplot(x="incomeperperson", y="femaleemployrate", fit_reg=False, data=dataG20)  
 print(scat1)  

The Uni-variate graph result

My research question was if income per person is related to female employment rate. It seems like though income female employment rate has a positive relationship to income per person in G20 countries, the relationship is weak.