VoxCharta Part II

In [2]:

    

%%html
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In [3]:

    

%pylab inline
import numpy as np
from numpy.random import randn
import pandas as pd
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from pandas import Series, DataFrame


    

    




Populating the interactive namespace from numpy and matplotlib

In [5]:

    

institutes = pd.read_csv('data/institutes.csv', na_values='-', parse_dates=['DateStarted'])
users = pd.read_csv('data/users.csv', parse_dates=['DateUser'], index_col='User')
users.head()


    

    
Out[5]:






  

    

      

      
Votes
      
Up
      
Down
      
Comments
      
Posts
      
DateUser
      
Affiliation
    
    

      
User
      

      

      

      

      

      

      

    
  
  

    

      
tomo
      
 2348
      
 2348
      
 0
      
  0
      
 0
      
2012-07-05
      
                          NTHU
    
    

      
Reiko
      
 1902
      
 1902
      
 0
      
 21
      
 1
      
2011-11-13
      
                    AIfA-Cosmo
    
    

      
Franz Bauer
      
 1384
      
 1382
      
 2
      
  0
      
 0
      
2011-03-26
      
 PUC-Institute of Astrophysics
    
    

      
Brian Siana
      
 1361
      
 1361
      
 0
      
  0
      
 0
      
2010-08-18
      
                           UCR
    
    

      
Andrew Zentner
      
 1214
      
 1212
      
 2
      
  0
      
 3
      
2011-02-15
      
                 U. Pittsburgh
    
  

In [6]:

    

users['Rank'] = users.Votes.rank(method='first', ascending=False)


    

In [11]:

    

affiliations = users.groupby(by='Affiliation')


    

In [24]:

    

institutes.set_index('Institute', inplace=True)


    

In [25]:

    

institutes['membership'] = affiliations.size()


    

In [34]:

    

sns.jointplot('Users', 'Votes', data=institutes, kind='scatter', xlim=[0,60], ylim=[0,2000])


    

    
Out[34]:





<seaborn.axisgrid.JointGrid at 0x115c371d0>






    

In [35]:

    

institutes['meanness'] = institutes.Down.div(institutes.Votes)


    

In [46]:

    

sns.jointplot('Votes', 'meanness', data=institutes, xlim=[100, 5000], ylim=[0, 0.2])
mean_place = (institutes.Votes > 2000) & (institutes.meanness > 0.14)

print institutes.index[mean_place].values[0]


    

    




unaffiliated






    
Out[46]:





'\nplt.annotate(institutes.index[mean_place].values[0], \n             xy=(institutes[\'Votes\'][mean_place], institutes[\'meanness\'][mean_place]), \n             textcoords=\'offset points\',\n             fontsize=16.0,\n             arrowprops=dict(arrowstyle="fancy", #linestyle="dashed",\n                color="0.5",\n                shrinkB=9,\n                connectionstyle="arc3,rad=0.3",\n                ),\n            )\n'






    

In [50]:

    

users['meanness'] = users.Down.div(users.Votes)
users.head()


    

    
Out[50]:






  

    

      

      
Votes
      
Up
      
Down
      
Comments
      
Posts
      
DateUser
      
Affiliation
      
Rank
      
meanness
    
    

      
User
      

      

      

      

      

      

      

      

      

    
  
  

    

      
tomo
      
 2348
      
 2348
      
 0
      
  0
      
 0
      
2012-07-05
      
                          NTHU
      
 1
      
 0.000000
    
    

      
Reiko
      
 1902
      
 1902
      
 0
      
 21
      
 1
      
2011-11-13
      
                    AIfA-Cosmo
      
 2
      
 0.000000
    
    

      
Franz Bauer
      
 1384
      
 1382
      
 2
      
  0
      
 0
      
2011-03-26
      
 PUC-Institute of Astrophysics
      
 3
      
 0.001445
    
    

      
Brian Siana
      
 1361
      
 1361
      
 0
      
  0
      
 0
      
2010-08-18
      
                           UCR
      
 4
      
 0.000000
    
    

      
Andrew Zentner
      
 1214
      
 1212
      
 2
      
  0
      
 3
      
2011-02-15
      
                 U. Pittsburgh
      
 5
      
 0.001647
    
  

In [62]:

    

from __future__ import division
tot_peeps = users.Votes[users.Votes > 30].count()
print tot_peeps
neg_voters = (users.Down > 0) & (users.Votes > 30)
mean_peeps = np.sum(neg_voters)
print mean_peeps
print mean_peeps/tot_peeps

sns.jointplot('Votes', 'Down', data=users[neg_voters], xlim=[20, 1000], ylim=[0, 20])


    

    




813
94
0.115621156212






    
Out[62]:





<seaborn.axisgrid.JointGrid at 0x123073850>