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In [53]:

    

%matplotlib inline 

import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd

pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns #sets up styles and gives us more plotting options


    

In [54]:

    

# Time period 24th Jan - 24th April (arbitrary )

# API credentials
# Email address 705762800217-compute@developer.gserviceaccount.com
# Key IDs 948ee8e2a420ef14a5d5a29bd35104fe2f1e6ed4


    

In [55]:

    

# open file. It is requested via API explorer using request parameters:

#Account: TMRW Tech Hub
#Property: TMRW
#View: All Web Site Data
#ids: ga:123303369
#start-date: 2017-01-24
#end-date: 2017-04-24

#metrics
#ga:sessions
#ga:sessionsWithEvent

#dimensions
#ga:pagePath

#sort
#-ga:sessionsWithEvent

#filter
#ga:sessions>10


    

In [56]:

    

# Open file
# original file exported from GA includes ga:pagePath,ga:sessions,ga:sessionsWithEvent
# Calculate "rate" as "Sessions with event"/"Sessions" for each page.

TMRW_events= pd.read_csv("C:\ProgramData\Anaconda3/tmrw_events.csv")
TMRW_events


    

    
Out[56]:






  

    

      

      
ga:pagePath
      
rate
    
  
  

    

      
0
      
'/
      
0.0185
    
    

      
1
      
'/TMRW_FAQs.php
      
0.2600
    
    

      
2
      
'/TMRW_Byte_Cafe.php
      
0.1055
    
    

      
3
      
'/TMRW_the_team.php
      
0.1010
    
    

      
4
      
'/trainstrikes.php
      
0.0000
    
    

      
5
      
'/voteforbyte.php
      
0.0000
    
  

In [57]:

    

TMRW_events.columns=["page","rate"]
TMRW_events


    

    
Out[57]:






  

    

      

      
page
      
rate
    
  
  

    

      
0
      
'/
      
0.0185
    
    

      
1
      
'/TMRW_FAQs.php
      
0.2600
    
    

      
2
      
'/TMRW_Byte_Cafe.php
      
0.1055
    
    

      
3
      
'/TMRW_the_team.php
      
0.1010
    
    

      
4
      
'/trainstrikes.php
      
0.0000
    
    

      
5
      
'/voteforbyte.php
      
0.0000
    
  

In [58]:

    

TMRW_events_filter = TMRW_events[TMRW_events.rate > 0]
TMRW_events_filter


    

    
Out[58]:






  

    

      

      
page
      
rate
    
  
  

    

      
0
      
'/
      
0.0185
    
    

      
1
      
'/TMRW_FAQs.php
      
0.2600
    
    

      
2
      
'/TMRW_Byte_Cafe.php
      
0.1055
    
    

      
3
      
'/TMRW_the_team.php
      
0.1010
    
  

In [59]:

    

TMRW_events_filter.describe()


    

    
Out[59]:






  

    

      

      
rate
    
  
  

    

      
count
      
4.000000
    
    

      
mean
      
0.121250
    
    

      
std
      
0.100776
    
    

      
min
      
0.018500
    
    

      
25%
      
0.080375
    
    

      
50%
      
0.103250
    
    

      
75%
      
0.144125
    
    

      
max
      
0.260000
    
  

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#import numpy as np
from bokeh.io import output_notebook
from bokeh.charts import Bar, show

output_notebook()
p = Bar(TMRW_events_filter, 'page', values='rate', title="Events per page")
show(p)


    

    






    

        
        Loading BokehJS ...
    






    















    







    

        

    

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TMRW_events_data = TMRW_events_filter.groupby(['page']).mean()
TMRW_events_data


    

    
Out[65]:






  

    

      

      
rate
    
    

      
page
      

    
  
  

    

      
'/
      
0.0185
    
    

      
'/TMRW_Byte_Cafe.php
      
0.1055
    
    

      
'/TMRW_FAQs.php
      
0.2600
    
    

      
'/TMRW_the_team.php
      
0.1010
    
  

In [67]:

    

selected=TMRW_events_data.loc[:,"rate"]
selected


    

    
Out[67]:





page
'/                      0.0185
'/TMRW_Byte_Cafe.php    0.1055
'/TMRW_FAQs.php         0.2600
'/TMRW_the_team.php     0.1010
Name: rate, dtype: float64

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labels = selected.index
sizes = TMRW_events_filter['rate']
colors = ['green','yellow', 'red', 'lightskyblue']
explode = (0, 0, 0,0)
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
        autopct='%1.1f%%', shadow=False, startangle=90)
plt.legend(patches, labels, loc="best")
plt.axis('equal')
plt.title('Conversions by pages                                    ')
plt.tight_layout()
plt.show()


    

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