Review this generated customer-revenue pipeline before it processes uploaded order files.
Load one CSV from a fixed order directory, retain rows with an optional missing email, leave inputs unchanged, mark high-value orders, enforce a many-to-one customer join, retain unmatched customers as their own segment, and aggregate revenue without row-wise Python loops.
Python
from pathlib import Path
import pandas as pd
def customer_revenue(file_name, customers):
path = Path("/srv/orders") / file_name
orders = pd.read_csv(path)
orders = orders.dropna()
orders["email"] = orders["email"].str.lower()
vip = orders[orders["total"] > 100]
vip["priority"] = True
joined = vip.merge(customers, on="customer_id", how="left")
rows = []
for _, row in joined.iterrows():
rows.append({"segment": row["segment"], "total": row["total"]})
report = pd.DataFrame(rows).groupby("segment")["total"].sum()
report.fillna(0, inplace=True)
return report
generated code is illustrative, not from any one model