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iPython notebook - Daryna analysis - Conversions by website pages

1. Import libraries



In [9]:

    
%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

Settings



In [10]:

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

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



In [11]:

    
# 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 [14]:

    
# 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("files/TMRW_events.csv")
TMRW_events









    Out[14]:






  
    
      
      ga:pagePath
      ga:sessions
      ga:sessionsWithEvent
      cr
    
  
  
    
      0
      '/
      4436
      82
      0.018485
    
    
      1
      '/TMRW_FAQs.php
      100
      26
      0.260000
    
    
      2
      '/TMRW_Byte_Cafe.php
      218
      23
      0.105505
    
    
      3
      '/TMRW_the_team.php
      99
      10
      0.101010
    
    
      4
      '/trainstrikes.php
      13
      0
      0.000000
    
    
      5
      '/voteforbyte.php
      31
      0
      0.000000



In [17]:

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









    Out[17]:






  
    
      
      page
      sessions
      events
      rate
    
  
  
    
      0
      '/
      4436
      82
      0.018485
    
    
      1
      '/TMRW_FAQs.php
      100
      26
      0.260000
    
    
      2
      '/TMRW_Byte_Cafe.php
      218
      23
      0.105505
    
    
      3
      '/TMRW_the_team.php
      99
      10
      0.101010
    
    
      4
      '/trainstrikes.php
      13
      0
      0.000000
    
    
      5
      '/voteforbyte.php
      31
      0
      0.000000



In [18]:

    
TMRW_events_filter = TMRW_events[TMRW_events.rate > 0]
TMRW_events_filter









    Out[18]:






  
    
      
      page
      sessions
      events
      rate
    
  
  
    
      0
      '/
      4436
      82
      0.018485
    
    
      1
      '/TMRW_FAQs.php
      100
      26
      0.260000
    
    
      2
      '/TMRW_Byte_Cafe.php
      218
      23
      0.105505
    
    
      3
      '/TMRW_the_team.php
      99
      10
      0.101010



In [19]:

    
TMRW_events_filter.describe()



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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 ...



In [65]:

    
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



In [68]:

    
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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	sessions	events	rate
count	4.00000	4.000000	4.000000
mean	1213.25000	35.250000	0.121250
std	2149.22612	31.930915	0.100780
min	99.00000	10.000000	0.018485
25%	99.75000	19.750000	0.080379
50%	159.00000	24.500000	0.103257
75%	1272.50000	40.000000	0.144128
max	4436.00000	82.000000	0.260000

	ga:pagePath	ga:sessions	ga:sessionsWithEvent	cr
0	'/	4436	82	0.018485
1	'/TMRW_FAQs.php	100	26	0.260000
2	'/TMRW_Byte_Cafe.php	218	23	0.105505
3	'/TMRW_the_team.php	99	10	0.101010
4	'/trainstrikes.php	13	0	0.000000
5	'/voteforbyte.php	31	0	0.000000

	rate
page
'/	0.0185
'/TMRW_Byte_Cafe.php	0.1055
'/TMRW_FAQs.php	0.2600
'/TMRW_the_team.php	0.1010