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In [1]:

    
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white")
sns.set_context("talk")



In [2]:

    
df = pd.read_csv('raw/2016-17-ClassCentral-Survey-data-noUserText.csv', decimal=',', encoding = "ISO-8859-1")



In [3]:

    
reason = df[['Which region of the world are you in?', 'Reasons: Learning skills for current career',
               'Reasons: Learning skills for new career', 'Reasons: School credit', 'Reasons: Personal interest',
               'Reasons: Access to reference materials']]
reason.head()









    Out[3]:







  
    
      
      Which region of the world are you in?
      Reasons: Learning skills for current career
      Reasons: Learning skills for new career
      Reasons: School credit
      Reasons: Personal interest
      Reasons: Access to reference materials
    
  
  
    
      0
      United Kingdom
      0
      0
      0
      1
      0
    
    
      1
      Western Europe (except UK)
      1
      1
      0
      1
      0
    
    
      2
      Eastern Europe
      0
      1
      0
      1
      0
    
    
      3
      United States
      0
      0
      0
      1
      0
    
    
      4
      NaN
      0
      0
      0
      1
      0



In [4]:

    
multi_reason = pd.melt(reason, id_vars='Which region of the world are you in?', 
                       var_name='select', value_name='score')
multi_reason.head()









    Out[4]:







  
    
      
      Which region of the world are you in?
      select
      score
    
  
  
    
      0
      United Kingdom
      Reasons: Learning skills for current career
      0
    
    
      1
      Western Europe (except UK)
      Reasons: Learning skills for current career
      1
    
    
      2
      Eastern Europe
      Reasons: Learning skills for current career
      0
    
    
      3
      United States
      Reasons: Learning skills for current career
      0
    
    
      4
      NaN
      Reasons: Learning skills for current career
      0



In [5]:

    
grouped_reason = multi_reason.groupby(['Which region of the world are you in?', 'select'], as_index=False).sum()
grouped_reason.head()









    Out[5]:







  
    
      
      Which region of the world are you in?
      select
      score
    
  
  
    
      0
      Africa
      Reasons: Access to reference materials
      64
    
    
      1
      Africa
      Reasons: Learning skills for current career
      164
    
    
      2
      Africa
      Reasons: Learning skills for new career
      156
    
    
      3
      Africa
      Reasons: Personal interest
      149
    
    
      4
      Africa
      Reasons: School credit
      38



In [6]:

    
sns.factorplot(x='score', y='select', hue='Which region of the world are you in?', 
                   data=grouped_reason, kind='bar', size=8, aspect=1.5)
    sns.plt.title('Which of the following are important reasons for you to take MOOCs?')
    sns.plt.show()

	Which region of the world are you in?	Reasons: Learning skills for current career	Reasons: Learning skills for new career	Reasons: Personal interest
0	United Kingdom	0	0	1
1	Western Europe (except UK)	1	1	1
2	Eastern Europe	0	1	1
3	United States	0	0	1
4	NaN	0	0	1

	Which region of the world are you in?	select	score
0	United Kingdom	Reasons: Learning skills for current career	0
1	Western Europe (except UK)	Reasons: Learning skills for current career	1
2	Eastern Europe	Reasons: Learning skills for current career	0
3	United States	Reasons: Learning skills for current career	0
4	NaN	Reasons: Learning skills for current career	0

	Which region of the world are you in?	select	score
0	Africa	Reasons: Access to reference materials	64
1	Africa	Reasons: Learning skills for current career	164
2	Africa	Reasons: Learning skills for new career	156
3	Africa	Reasons: Personal interest	149
4	Africa	Reasons: School credit	38