You are given a table radiology_claims with columns claim_id, procedure_group, service_dt, and paid_amt. Each row represents one radiology claim. The paid_amt column is numeric and may be negative; a blank value means the amount is unknown and must be treated as 0 before any aggregation. The service_dt column is text, usually formatted as YYYY-MM-DD HH:MM:SS, but it may be missing or invalid.
Using Python/pandas, read the input file with explicit dtypes and parse dates safely. For each procedure_group, produce paid_amt_sum as the two-decimal sum of the cleaned paid_amt values. Do not filter out any group. Then compute pct_of_total for each group on a 0 to 100 scale, rounded to two decimals, using a single cleaned total across all rows.
Next, convert service_dt to a datetime column. The conversion should be vectorized and must not iterate row by row in Python. Add integer fiscal_month where a fiscal year starts on October 1: October is 1, November is 2, December is 3, January is 4, and so on through September as 12. If a date is missing or cannot be parsed, set the parsed datetime to NULL/NaT and set fiscal_month to 0. Show the resulting dtypes.
Provide equivalent PostgreSQL queries for the two grouped aggregations, and a row-level query that returns claim_id, procedure_group, the original service_dt, the safely parsed timestamp, and fiscal_month, sorted by claim_id ascending.
Example 1:
Input:
claim_id,procedure_group,service_dt,paid_amt
3001,CT,2021-11-02 10:15:00,150.00
3002,MRI,2021-03-14 08:30:00,300.00
3003,CT,2021-11-11 16:45:00,-50.00
3004,Ultrasound,2021-12-20 09:00:00,90.00
3005,MRI,2021-02-05 12:00:00,
Output:
procedure_group,paid_amt_sum,pct_of_total
CT,100.00,20.41
MRI,300.00,61.22
Ultrasound,90.00,18.37
Explanation: The blank paid_amt for the MRI row is treated as 0, so the cleaned total is 490.00.
Example 2:
Input:
claim_id,procedure_group,service_dt
4001,XRay,2022-10-01 08:15:00
4002,CT,2022-06-18 23:59:00
4003,MRI,not_a_date
4004,CT,NULL
4005,Ultrasound,2022-01-31 00:00:00
Output:
claim_id,procedure_group,service_dt,service_ts,fiscal_month
4001,XRay,2022-10-01 08:15:00,2022-10-01 08:15:00,1
4002,CT,2022-06-18 23:59:00,2022-06-18 23:59:00,9
4003,MRI,not_a_date,NULL,0
4004,CT,NULL,NULL,0
4005,Ultrasound,2022-01-31 00:00:00,2022-01-31 00:00:00,4
Constraints:
claim_id is an integer with procedure_group is a non-empty string of at most 30 charactersservice_dt is a string of at most 25 characters and may be missing or invalidpaid_amt fits in DECIMAL(12,2); blank values must be treated as 0claim_id ascending