Feature engineering for recency, frequency, and monetary behavior

                  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())