Review this generated segment-revenue pipeline before it processes uploaded order files.
Read one CSV from a fixed order directory, retain rows with an optional missing region, derive revenue and tax, enforce a many-to-one customer join, retain unmatched customers as their own segment, and aggregate through one visible lazy execution boundary.
Python
from pathlib import Path
import polars as pl
def segment_revenue(file_name, customers):
path = Path("/srv/orders") / file_name
orders = pl.read_csv(path).lazy()
report = (
orders
.with_columns(
(pl.col("quantity") * pl.col("unit_price")).alias("revenue"),
(pl.col("revenue") * 0.2).alias("tax"),
)
.filter(pl.col("region") != None)
.join(customers.lazy(), on="customer_id", how="left")
.group_by("segment")
.agg((pl.col("revenue") + pl.col("tax")).sum())
.collect()
)
return report
generated code is illustrative, not from any one model