Compute specialty spend share and top age band

You are given healthcare claims data split across member tables.

Tables

Assume the following schemas (types may be adapted to your SQL dialect):

mem1

• member_id INT PRIMARY KEY
• age_band VARCHAR

mem2

• member_id INT PRIMARY KEY
• age_band VARCHAR

claim

• claim_id INT PRIMARY KEY
• member_id INT
• specialty VARCHAR
• paid_amt DECIMAL(12,2)
• claim_date DATE

risk (provided but not necessarily needed)

• member_id INT
• risk_score DECIMAL(10,4)

Assume claim.member_id joins to either mem1.member_id or mem2.member_id. Assume claim_date is in UTC and “year = 2017” means calendar year 2017.

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Part A (SQL)

For each specialty, compute:

1. total_paid_amt = total paid amount for that specialty
2. paid_share = that specialty’s share of total paid amount across all claims

Output columns: specialty, total_paid_amt, paid_share.

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Part B (SQL)

Considering only claims in calendar year 2017:

1. Union mem1 and mem2 into a single member set.
2. Join members to claims.
3. Compute claim counts per age_band.
4. Return the age band(s) with the maximum number of claims (keep ties).

Output columns: age_band, claim_cnt.

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Part C (Python / Pandas)

You are given a Pandas DataFrame df_claims with columns:

• paid_amt (numeric)
• gender (values are 'M' or 'F')
• claim_timestamp (string or datetime-like)

Tasks:

1. Recode gender from M/F to male/female.
2. Compute total paid_amt for each month in 2017, grouped by gender and month.

Output: a DataFrame with columns like gender, month (1–12), total_paid_amt (and optionally year if you keep it).