python
20 lines · 1 tab
Dr. Elena Vasquez
Apr 2026
1 tab
import pandas as pd
orders = pd.read_parquet('orders.parquet')
orders['ordered_at'] = pd.to_datetime(orders['ordered_at'])
reference_date = orders['ordered_at'].max() + pd.Timedelta(days=1)
features = (
orders.groupby('customer_id')
.agg(
recency_days=('ordered_at', lambda values: (reference_date - values.max()).days),
frequency=('order_id', 'nunique'),
monetary=('amount', 'sum'),
avg_basket=('amount', 'mean'),
)
.reset_index()
)
features['monetary_per_order'] = features['monetary'] / features['frequency'].clip(lower=1)
print(features.head())
1 file · python
Explain with highlit
Tabular models improve fast when you encode behavior rather than raw events. Recency, frequency, and monetary aggregates are durable baseline features for retention, fraud, and conversion use cases. I usually build them in pure pandas first, then port them to a scheduled feature pipeline once the signal is proven.
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