Benchmark Results

In [16]:

    

import pandas as pd
data = open('results.csv').read()
commit, timestamp, measurements = data.split(',', 2)
measurements = map(float, measurements.split(','))

# Drop the first 10 measurements
df = pd.DataFrame([[i] for i in measurements[10:]], columns=['runtime'])
df.head()


    

    
Out[16]:






  

    

      

      
runtime
    
  
  

    

      
0
      
 1.390475
    
    

      
1
      
 1.423340
    
    

      
2
      
 1.392797
    
    

      
3
      
 1.446573
    
    

      
4
      
 1.424568
    
  

5 rows × 1 columns

In [17]:

    

df.boxplot(vert=False)


    

    
Out[17]:





{'boxes': [<matplotlib.lines.Line2D at 0x54d0410>],
 'caps': [<matplotlib.lines.Line2D at 0x54e8750>,
  <matplotlib.lines.Line2D at 0x54e8d90>],
 'fliers': [<matplotlib.lines.Line2D at 0x54d40d0>,
  <matplotlib.lines.Line2D at 0x54d46d0>],
 'medians': [<matplotlib.lines.Line2D at 0x54d0a50>],
 'whiskers': [<matplotlib.lines.Line2D at 0x54fae10>,
  <matplotlib.lines.Line2D at 0x54e80d0>]}






    

In [18]:

    

df.describe()


    

    
Out[18]:






  

    

      

      
runtime
    
  
  

    

      
count
      
 30.000000
    
    

      
mean
      
  1.430240
    
    

      
std
      
  0.032587
    
    

      
min
      
  1.353380
    
    

      
25%
      
  1.417872
    
    

      
50%
      
  1.432993
    
    

      
75%
      
  1.446338
    
    

      
max
      
  1.502575
    
  

8 rows × 1 columns