Pandas

import codecademylib
import pandas as pd

ad = pd.read_csv('ad_clicks.csv')

adgroup = ad.groupby('utm_source').user_id.count().reset_index()
#print(adgroup)
ad['is_click'] = ~ad.ad_click_timestamp.isnull()
clsor = ad.groupby(['utm_source','is_click']).user_id.count().reset_index()
clsor_p = clsor.pivot(columns='is_click',values='user_id',index='utm_source').reset_index()

clsor_p['percent'] = clsor_p[True]/(clsor_p[True]+clsor_p[False])

cl = ad.groupby(['is_click','experimental_group']).user_id.count().reset_index()
cl_p = cl.pivot(columns='experimental_group',values='user_id',index='is_click').reset_index()
#print(cl_p)
a = ad[ad.experimental_group=='A']
b = ad[ad.experimental_group=='B']

aclick = a.groupby(['is_click','day']).user_id.count().reset_index()
aclick_p = aclick.pivot(columns='is_click',values='user_id',index='day').reset_index()

bclick = b.groupby(['is_click','day']).user_id.count().reset_index()
bclick_p = bclick.pivot(columns='is_click',values='user_id',index='day').reset_index()

aclick_p['percent'] = aclick_p[True]/(aclick_p[False]+aclick_p[True])
bclick_p['percent'] = bclick_p[True]/(bclick_p[False]+bclick_p[True])
print(aclick_p)
print(bclick_p)