In [10]:

    

x=9
y=6
z=y**x
print "z=", z


    

    




z= 10077696

In [42]:

    

%pylab


    

    




Using matplotlib backend: MacOSX
Populating the interactive namespace from numpy and matplotlib

In [54]:

    

import statsmodels.api as sm
import matplotlib.pyplot as plt


    

In [29]:

    

import pandas as pd

filename = 'namebench_2013-12-27_0205.csv'

data = pd.read_csv(filename, header=0, 
                   names=[u'IP', u'Name', u'Test_Num', u'Record', u'Record_Type', u'Duration', u'TTL', u'Answer_Count', u'Response','Foo'])
data.shape, data.keys()


    

    
Out[29]:





((1000, 10),
 Index([u'IP', u'Name', u'Test_Num', u'Record', u'Record_Type', u'Duration', u'TTL', u'Answer_Count', u'Response', u'Foo'], dtype=object))

In [30]:

    

data.head()


    

    
Out[30]:






  

    

      

      
IP
      
Name
      
Test_Num
      
Record
      
Record_Type
      
Duration
      
TTL
      
Answer_Count
      
Response
      
Foo
    
  
  

    

      
0
      
 156.154.71.1
      
 UltraDNS-2
      
 0
      
 nl.youtube.com.
      
 A
      
 264.336109
      
  300
      
 2
      
 youtube-ui.l.google.com. -> 173.194.44.1, 173....
      
 NaN
    
    

      
1
      
 156.154.71.1
      
 UltraDNS-2
      
 0
      
 www.flickr.com.
      
 A
      
 402.462006
      
  133
      
 5
      
 flickr-panda-police.a00.yahoodns.net. -> geoyc...
      
 NaN
    
    

      
2
      
 156.154.71.1
      
 UltraDNS-2
      
 0
      
    news.qq.com.
      
 A
      
 316.584110
      
 6088
      
 4
      
 www.qq.com. -> qq.com.edgesuite.net. -> a1574....
      
 NaN
    
    

      
3
      
 156.154.71.1
      
 UltraDNS-2
      
 0
      
  get.adobe.com.
      
 A
      
 307.471991
      
 2908
      
 2
      
              get.wip4.adobe.com. -> 192.150.16.58
      
 NaN
    
    

      
4
      
 156.154.71.1
      
 UltraDNS-2
      
 0
      
  www.yandex.ru.
      
 A
      
 337.362051
      
   49
      
 1
      
        213.180.193.3, 213.180.204.3, 93.158.134.3
      
 NaN
    
  

In [37]:

    

opendns_durations=data.Duration[data.Name=='OpenDNS']
google_durations=data.Duration[data.Name=='Google Public DNS']


    

In [39]:

    

set(data.Name)


    

    
Out[39]:





{'DynGuide-2', 'Google Public DNS', 'OpenDNS', 'UltraDNS-2'}

In [40]:

    

google_durations.shape


    

    
Out[40]:





(250,)

In [46]:

    

clf()
hist(google_durations,bins=20)


    

    
Out[46]:





(array([ 202.,   20.,    6.,    5.,    5.,    5.,    0.,    1.,    0.,
          0.,    2.,    0.,    0.,    0.,    1.,    0.,    0.,    0.,
          0.,    3.]),
 array([  134.5410347 ,   302.81398296,   471.08693123,   639.35987949,
         807.63282776,   975.90577602,  1144.17872429,  1312.45167255,
        1480.72462082,  1648.99756908,  1817.27051735,  1985.54346561,
        2153.81641388,  2322.08936214,  2490.36231041,  2658.63525867,
        2826.90820694,  2995.1811552 ,  3163.45410347,  3331.72705173,
        3500.        ]),
 <a list of 20 Patch objects>)

In [47]:

    

figure(2)
hist(opendns_durations,bins=20)


    

    
Out[47]:





(array([ 179.,   32.,   12.,    8.,    3.,    1.,    7.,    0.,    3.,
          0.,    0.,    1.,    0.,    0.,    1.,    0.,    0.,    0.,
          0.,    3.]),
 array([  114.35484886,   283.63710642,   452.91936398,   622.20162153,
         791.48387909,   960.76613665,  1130.0483942 ,  1299.33065176,
        1468.61290932,  1637.89516687,  1807.17742443,  1976.45968199,
        2145.74193954,  2315.0241971 ,  2484.30645466,  2653.58871222,
        2822.87096977,  2992.15322733,  3161.43548489,  3330.71774244,
        3500.        ]),
 <a list of 20 Patch objects>)

In [58]:

    

fig=figure(3)
clf()
ecdf = sm.distributions.ECDF(google_durations)
x = np.linspace(min(google_durations),max(google_durations))
y = ecdf(x)
ax = fig.add_subplot(111)
ax.set_ylabel('%')
ax.set_xlabel('ms')
ax.step(x,y)
plt.show()


    

In [51]:

    

ecdf


    

    
Out[51]:





<statsmodels.distributions.empirical_distribution.ECDF at 0x10dff9210>

In [ ]: