Testy A/B w Pythonie
Moe Lotfy, PhD
Principal Data Science Manager


Kategoryzacja ilościowa
Stabilna/odporna na nieistotne różnice
Czuła na istotne zmiany
Mierzalna w ramach ograniczeń logowania
Odporna na manipulacje

checkout.groupby('gender')['purchased'].mean()
gender
F 0.908056
M 0.780009
Name: purchased, dtype: float64
checkout[(checkout['browser']=='chrome')|(checkout['browser']=='safari')]\
.groupby('gender')['order_value'].mean()
gender
F 29.814161
M 30.383431
Name: order_value, dtype: float64
checkout.groupby('browser')[['order_value', 'purchased']].mean()
order_value purchased
browser
chrome 30.016625 0.839088
firefox 29.887491 0.851725
safari 30.119808 0.844337
Testy A/B w Pythonie