Aggregate radiology spend and derive fiscal month

Using Python/pandas, complete the tasks below. Assume the following CSV-like input, where service_dt is object dtype and amounts may be negative (adjustments). Treat missing paid_amt as 0 when aggregating, but do not create NaNs during type conversion.

radiology.csv (toy data)
claim_id,procedure_group,service_dt,paid_amt
1001,CT,2020-07-01 13:05:00,120.00
1002,MRI,2020-10-15 09:30:00,250.00
1003,CT,2020-10-20 11:00:00,-20.00
1004,XRay,2020-12-05 08:00:00,80.00
1005,MRI,2020-01-02 14:10:00,

Tasks

1. Load the file robustly (explicit dtypes, parse_dates) and aggregate paid_amt by procedure_group to produce columns: procedure_group, paid_amt_sum (rounded to 2 decimals).
2. Add each group's percentage of total paid_amt as pct_of_total (0–100 with two decimals). Ensure total uses post-cleaning values and is not double-counted.
3. Convert service_dt from object to datetime and add an integer fiscal_month column where fiscal year starts on October 1 (Oct=1, Nov=2, …, Sep=12). Validate on the sample rows so that 2020-10-15 maps to fiscal_month=2. Show the resulting dtypes and demonstrate that the transformation is vectorized (no per-row Python loops). Handle bad or missing dates gracefully (coerce to NaT, then impute fiscal_month with 0 for unknown).

Aggregate paid amounts by procedure group

You are given a table of radiology claims. The paid_amt column can contain negative values (adjustments), and some paid_amt values may be missing (NULL). Treat missing paid_amt as 0 when aggregating, and include negative values in the arithmetic sum.

Write a SQL query to aggregate paid_amt by procedure_group and return one row per procedure_group with the following columns:

• procedure_group
• paid_amt_sum: the sum of paid_amt for that procedure_group, treating NULL as 0, rounded to 2 decimal places.

Do not filter out any procedure_group values. The result can be returned in any row order.

Table radiology_claims

column	type	constraints
claim_id	INT	PRIMARY KEY, NOT NULL
procedure_group	VARCHAR(20)	NOT NULL
service_dt	VARCHAR(19)
paid_amt	DECIMAL(10,2)

Sample data:

claim_id	paid_amt	procedure_group	service_dt
1001	120	CT	2020-07-01 13:05:00
1002	250	MRI	2020-10-15 09:30:00
1003	-20	CT	2020-10-20 11:00:00
1004	80	XRay	2020-12-05 08:00:00
1005	NULL	MRI	2020-01-02 14:10:00

Expected output

One row per procedure_group with the sum of paid_amt (NULL treated as 0) rounded to 2 decimal places. Using the sample data, CT has 120 + (-20) = 100, MRI has 250 + 0 = 250, and XRay has 80.

paid_amt_sum	procedure_group
100	CT
250	MRI
80	XRay

Hints

1. Use COALESCE (or a similar function) to treat NULL paid_amt as 0 before summing.
2. Apply ROUND(..., 2) to the aggregated sum, not to individual rows.

Add percentage of total paid amount per procedure group

Using the same radiology_claims table, extend the previous aggregation.

Compute, for each procedure_group:

• paid_amt_sum: the sum of paid_amt (treating NULL as 0), rounded to 2 decimals.
• pct_of_total: that group’s percentage share of the overall paid_amt total, on a 0–100 scale, rounded to 2 decimals.

The overall total must be calculated from the same cleaned paid_amt values (NULL treated as 0) and must not be double-counted. Return one row per procedure_group with columns:

• procedure_group
• paid_amt_sum
• pct_of_total

The result can be returned in any row order.

Table radiology_claims

column	type	constraints
claim_id	INT	PRIMARY KEY, NOT NULL
procedure_group	VARCHAR(20)	NOT NULL
service_dt	VARCHAR(19)
paid_amt	DECIMAL(10,2)

Sample data:

claim_id	paid_amt	procedure_group	service_dt
1001	120	CT	2020-07-01 13:05:00
1002	250	MRI	2020-10-15 09:30:00
1003	-20	CT	2020-10-20 11:00:00
1004	80	XRay	2020-12-05 08:00:00
1005	NULL	MRI	2020-01-02 14:10:00

Expected output

One row per procedure_group with its sum of paid_amt and that sum as a percentage of the total sum across all groups. Using the sample data, group sums are: CT=100, MRI=250, XRay=80; total=430. Percentages rounded to 2 decimals: CT≈23.26, MRI≈58.14, XRay≈18.60.

paid_amt_sum	pct_of_total	procedure_group
100	23.26	CT
250	58.14	MRI
80	18.6	XRay

Hints

1. First compute per-group sums in a CTE or subquery, then compute the overall total in a separate step.
2. Use a CROSS JOIN (or scalar subquery) to make the total available to each group when calculating pct_of_total.

Derive fiscal month from service date with October fiscal year start

Derive a fiscal month from a string service date (fiscal year starts in October)

You are given a single table, radiology_claims, where the service_dt column stores the service date and time as a string (e.g. '2020-10-15 09:30:00'). Some rows may have a service_dt that is NULL or that is not a valid YYYY-MM-DD HH:MI:SS timestamp.

Write one PostgreSQL query that, for every row in radiology_claims, returns:

1. service_ts — service_dt safely parsed into a timestamp. If service_dt is NULL or cannot be parsed as a valid YYYY-MM-DD HH:MI:SS value, service_ts must be NULL (the query must not error on a bad string).
2. fiscal_month — an integer giving the fiscal month under a fiscal year that starts on October 1:
   • October = 1, November = 2, December = 3, January = 4, ..., September = 12.
   • When service_ts is NULL (unknown / invalid date), set fiscal_month = 0.

Required output columns (in this order)

• claim_id
• procedure_group
• service_dt (the original string)
• service_ts (parsed timestamp, or NULL)
• fiscal_month (integer)

Sorting

Return one row per claim, ordered by claim_id ascending.

Note: the original problem assumed a SQL-Server TRY_CONVERT/MONTH(...) dialect. This is graded on PostgreSQL, so use a regex guard plus to_timestamp(...) for the safe parse and EXTRACT(MONTH FROM ...) for the month.

Table radiology_claims

column	type	constraints
claim_id	INT	PRIMARY KEY, NOT NULL
procedure_group	VARCHAR(20)	NOT NULL
service_dt	VARCHAR(19)
paid_amt	DECIMAL(10,2)

Sample data:

claim_id	paid_amt	procedure_group	service_dt
1001	120	CT	2020-07-01 13:05:00
1002	250	MRI	2020-10-15 09:30:00
1003	-20	CT	2020-10-20 11:00:00
1004	80	XRay	2020-12-05 08:00:00
1005	NULL	MRI	2020-01-02 14:10:00
1006	60	XRay	not-a-date
1007	45	CT	NULL

Expected output

One row per claim with the original string service_dt, the safely parsed service_ts (NULL when invalid/missing), and the integer fiscal_month under an October-start fiscal year (NULL date -> 0); ordered by claim_id.

claim_id	fiscal_month	procedure_group	service_dt	service_ts
1001	10	CT	2020-07-01 13:05:00	2020-07-01T13:05:00+00:00
1002	1	MRI	2020-10-15 09:30:00	2020-10-15T09:30:00+00:00
1003	1	CT	2020-10-20 11:00:00	2020-10-20T11:00:00+00:00
1004	3	XRay	2020-12-05 08:00:00	2020-12-05T08:00:00+00:00
1005	4	MRI	2020-01-02 14:10:00	2020-01-02T14:10:00+00:00
1006	0	XRay	not-a-date	NULL
1007	0	CT	NULL	NULL

Hints

1. PostgreSQL has no TRY_CAST that yields NULL on failure — guard the conversion with a regex (service_dt ~ '^[0-9]{4}-...$') and only then call to_timestamp(service_dt, 'YYYY-MM-DD HH24:MI:SS'); otherwise produce NULL.
2. Use EXTRACT(MONTH FROM service_ts) for the calendar month, then a CASE: months >= 10 subtract 9, months <= 9 add 3.
3. Check service_ts IS NULL first in the outer CASE so invalid/missing dates yield fiscal_month = 0.