Data cleaning and exploratory analysis

A/B Testing in Python

Moe Lotfy, PhD

Principal Data Science Manager

Cleaning missing values

Missing values
- Drop, ignore, impute

# Calculate the mean order value
checkout.order_value.mean()

30.0096

# Replace missing values with zeros and get mean
checkout['order_value'].fillna(0).mean()

25.3581

Cleaning duplicates

Duplicates
- Identical rows should be dropped

# Check for duplicate rows due to logging issues 
print(len(checkout))
print(len(checkout.drop_duplicates(keep='first')))

9000
9000

Cleaning duplicates

Duplicates
- Duplicate users should be handled with care.

# Unique users in group B
print(checkout[checkout['checkout_page'] == 'B']['user_id'].nunique())
# Unique users who purchased at least once
print(checkout[checkout['checkout_page'] == 'B'].groupby('user_id')['purchased'].max().sum())
# Total purchase events in group B
print(checkout[checkout['checkout_page'] == 'B']['purchased'].sum())

2938
2491.0
2541.0

EDA summary stats

Mean, count, and standard deviation summary

checkout.groupby('checkout_page')['order_value'].agg({'mean','std','count'})

                    mean     count    std
checkout_page            
        A        24.956437    2461    2.418837
        B        29.876202    2541    7.277644
        C        34.917589    2603    4.869816

EDA plotting

Bar plots

sns.barplot(x=checkout['checkout_page'], y=checkout['order_value'], estimator=np.mean)
plt.title('Average Order Value per Checkout Page Variant')
plt.xlabel('Checkout Page Variant')
plt.ylabel('Order Value [$]')

Bar plot of Average Order Value per Checkout Page Variant

EDA plotting

Histograms

sns.displot(data=checkout, x='order_value', hue = 'checkout_page', kde=True)

Histogram of order value

EDA plotting

Time series (line plots)

sns.lineplot(data=AdSmart,x='date', y='yes', hue='experiment', ci=False)

Time series plot of proportion of users responding yes to an ad

¹ Adsmart Kaggle dataset: https://www.kaggle.com/datasets/osuolaleemmanuel/ad-ab-testing

Let's practice!

A/B Testing in Python