In [233]:

    

import augur

# import everything from githubapi.py and ghtorrent.py so we can
# just copy and paste our function later
import json
import re
from dateutil.parser import parse
import pandas as pd
import github
import numpy as np
import sqlalchemy as s
import datetime
import requests
import matplotlib.pyplot as plt
import seaborn as sns
import pylab
from scipy import stats
from pandas.api.types import is_numeric_dtype



%matplotlib inline
# create an Augur application so we can test our function
augur_app = augur.Application()('../augur.cfg')
github = augurApp.github()
ghtorrent = augurApp.ghtorrent()


    

In [234]:

    

df = ghtorrent.issue_comment_time('rails', 'rails')


    

In [235]:

    

df['opened'] = pd.to_datetime(df['opened'],errors='coerce')
df = df.set_index(['opened'])
df = df.dropna()


    

In [236]:

    

test = df[np.abs(df['minutes_to_comment']-df['minutes_to_comment'].mean()<=(5*df['minutes_to_comment'].std()))] #keep only the ones that are within +3 to -3 standard deviations in the column 'Data'.


    

In [237]:

    

dfnew = test.resample('W-MON').mean()


    

In [238]:

    

dfnew2 = dfnew.fillna(0)


    

In [239]:

    

dfnew3 = dfnew2.rolling(window=8, min_periods=0).mean()


    

In [240]:

    

dfnew3.head()


    

    
Out[240]:







  

    

      

      
id
      
pull_request
      
minutes_to_comment
    
    

      
opened
      

      

      

    
  
  

    

      
2009-04-06
      
136626.000000
      
0.0
      
22716.000000
    
    

      
2009-04-13
      
68313.000000
      
0.0
      
11358.000000
    
    

      
2009-04-20
      
91083.666667
      
0.0
      
7585.666667
    
    

      
2009-04-27
      
68312.750000
      
0.0
      
5689.250000
    
    

      
2009-05-04
      
81975.000000
      
0.0
      
4552.400000
    
  

In [241]:

    

max(dfnew3['minutes_to_comment'])


    

    
Out[241]:





60904.67928001276

In [242]:

    

dfnext=pd.DataFrame({'xvalues':dfnew2.index, 'yvalues':dfnew2["minutes_to_comment"]})


    

In [243]:

    

max(dfnext['yvalues'])


    

    
Out[243]:





82184.95555555556

In [244]:

    

pylab.ylim([0,90000])
pylab.plot('xvalues', 'yvalues', data=dfnext, label="Issue Response Time")


    

    
Out[244]:





[<matplotlib.lines.Line2D at 0x1a1b8334e0>]






    

In [ ]: