User activity

First lets get out sample file data/activity.txt, which includes activity from users as json entries, one for each line.



In [15]:

    
%matplotlib inline

import json
import numpy as np
import pandas as pd
from pandas import DataFrame, Series

activity = [json.loads(s) for s in open('data/activity.txt')]
activity[0]









    Out[15]:





{u'action': u'log-in', u'time': u'00:00:01', u'user': 1}



In [16]:

    
frame = DataFrame(activity)
frame[:5]



In [17]:

    
frame['action'][:2]









    Out[17]:





0      log-in
1    browsing
Name: action, dtype: object



In [18]:

    
action_counts = frame['action'].value_counts()
action_counts









    Out[18]:





browsing    11
log-out      5
log-in       5
dtype: int64



In [19]:

    
action_counts.plot(kind='barh', rot=0)









    Out[19]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f7799209250>



In [20]:

    
results = Series([col for col in frame.action])
results[:5]









    Out[20]:





0      log-in
1    browsing
2    browsing
3    browsing
4     log-out
dtype: object



In [21]:

    
results.value_counts()









    Out[21]:





browsing    11
log-out      5
log-in       5
dtype: int64



In [22]:

    
browsing = np.where(frame['action'].str.contains('browsing'), 'browsing', 'no-browsing')
browsing[:5]









    Out[22]:





array(['no-browsing', 'browsing', 'browsing', 'browsing', 'no-browsing'], 
      dtype='|S11')



In [23]:

    
by_action_browsing = frame.groupby(['user', browsing])
browsing_counts = by_action_browsing.size().unstack().fillna(0)
browsing_counts









    Out[23]:






  
    
      
      browsing
      no-browsing
    
    
      user
      
      
    
  
  
    
      1
      3
      2
    
    
      2
      2
      2
    
    
      3
      3
      4
    
    
      4
      3
      2



In [24]:

    
indexer = browsing_counts.sum(1).argsort()
indexer









    Out[24]:





user
1    1
2    0
3    3
4    2
dtype: int64



In [25]:

    
count_subset = browsing_counts.take(indexer)[-10:]
count_subset









    Out[25]:






  
    
      
      browsing
      no-browsing
    
    
      user
      
      
    
  
  
    
      2
      2
      2
    
    
      1
      3
      2
    
    
      4
      3
      2
    
    
      3
      3
      4



In [26]:

    
count_subset.plot(kind='barh', stacked=True)









    Out[26]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f779904a690>



In [27]:

    
normed_subset = count_subset.div(count_subset.sum(1), axis=0)
normed_subset.plot(kind='barh', stacked=True)









    Out[27]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f7798f8d110>

	action	time	user
0	log-in	00:00:01	1
1	browsing	00:10:00	1
2	browsing	00:08:00	1
3	browsing	00:05:00	1
4	log-out	00:00:01	1