In [1]:

    

# Settings and imports
%matplotlib inline
from collections import OrderedDict
from IPython.display import display, Markdown
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import learn
from monitoring.data import get_production_data

sns.set()


    

In [2]:

    

# Load data from local cache, fetch and store if not available.
data = get_production_data('2018-01-01')


    

    




Data loaded from cache (/home/xeffenb1/projects/robomission/.prodcache/robomission-2018-01-01/).

In [3]:

    

# Example: look at the data
ts = data['task_sessions']
ts.tail()


    

    
Out[3]:







  

    

      

      
student
      
task
      
solved
      
start
      
end
      
time_spent
    
    

      
id
      

      

      

      

      

      

    
  
  

    

      
1800
      
8694
      
21
      
True
      
2017-12-29T11:26:48.203415Z
      
2017-12-29T11:27:46.170989Z
      
57
    
    

      
1794
      
8694
      
44
      
True
      
2017-12-29T11:09:36.585977Z
      
2017-12-29T11:10:33.592476Z
      
57
    
    

      
1795
      
8694
      
26
      
True
      
2017-12-29T11:11:01.153506Z
      
2017-12-29T11:11:15.674378Z
      
14
    
    

      
1801
      
8694
      
18
      
True
      
2017-12-29T11:27:50.108638Z
      
2017-12-29T11:29:33.879208Z
      
103
    
    

      
1802
      
8694
      
41
      
True
      
2017-12-29T11:29:39.062362Z
      
2017-12-29T11:33:29.109295Z
      
230
    
  

In [4]:

    

# Example: plot histogram of log-times of task sessions
ts.time_spent[ts.time_spent > 0].apply(np.log).hist()


    

    
Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f62e241a470>






    

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