Your code is missing the `.agg()` step — it’s grouping but not summing the amounts. How would you fix this?

The issue is that after calling groupBy("customer_id") you need to specify an aggregation function to compute a value for each group. Without .agg(), the DataFrame simply contains the grouping keys and no aggregated metrics. The fix is to add an aggregation, such as summing the amount column. A corrected snippet would be:

from pyspark.sql.functions import sum as _sum

result = (
    df.groupBy("customer_id")
      .agg(_sum("amount").alias("total_amount"))
)

This groups the rows by customer_id and then sums the amount column for each group, producing a DataFrame with columns customer_id and total_amount. If you need additional aggregates, you can chain them inside the agg() call. The missing .agg() was the root cause of the error.