Join orders with customers and payments.

Read Pipeline State done

Add Node SOURCE orders.csv awaiting approval

Add Node SOURCE customers.csv done

Add Node SOURCE payments.csv done

Run Node orders.csv done

Read Node Profile customer_id, order_date

Add Node JOIN join_orders_customers

Add Node JOIN join_orders_payments

Run paused 1 node awaiting approval

Generated script review before run

Add Node VALIDATE validate_every_order_has_customer

All assertions passed 1 passed 0 failed

Add Node TRANSFORM customer_churn_score

customer_churn_score 240 rows 9 columns

Add Node OUTPUT top_customers_by_churn

Join orders with customers and payments.

Read Pipeline State done

Add Node SOURCE orders.csv awaiting approval

Add Node SOURCE customers.csv done

Add Node SOURCE payments.csv done

Run Node orders.csv done

Read Node Profile customer_id, order_date

Add Node JOIN join_orders_customers

Add Node JOIN join_orders_payments

Run paused 1 node awaiting approval

Generated script review before run

Add Node VALIDATE validate_every_order_has_customer

All assertions passed 1 passed 0 failed

Add Node TRANSFORM customer_churn_score

customer_churn_score 240 rows 9 columns

Add Node OUTPUT top_customers_by_churn

import duckdb

con = duckdb.connect()

out = con.execute("""

SELECT

df_0.*, df_1."name", df_1."plan"

FROM df_0 LEFT JOIN df_1

ON df_0."customer_id" = df_1."customer_id"

""").df()

work_df['order_date'] = pd.to_datetime(work_df['order_date'], errors='coerce')

log_step(1, 'order_date, attempted_at', 'convert to datetime', rows_before, len(work_df))

customer_orders = orders_df.groupby('customer_id', as_index=False).agg(

total_orders=('order_id', 'nunique'),

last_order_date=('order_date', 'max')

mask_failed = df['name'].isna()

failed_rows = int(mask_failed.sum())

passed = failed_rows == 0

import duckdb

con = duckdb.connect()

out = con.execute("""

SELECT

df_0.*, df_1."name", df_1."plan"

FROM df_0 LEFT JOIN df_1

ON df_0."customer_id" = df_1."customer_id"

""").df()

work_df['order_date'] = pd.to_datetime(work_df['order_date'], errors='coerce')

log_step(1, 'order_date, attempted_at', 'convert to datetime', rows_before, len(work_df))

customer_orders = orders_df.groupby('customer_id', as_index=False).agg(

total_orders=('order_id', 'nunique'),

last_order_date=('order_date', 'max')

mask_failed = df['name'].isna()

failed_rows = int(mask_failed.sum())

passed = failed_rows == 0