Problem Description

You are provided with healthcare claims data distributed across several tables. Your task is to perform a series of data manipulation operations using SQL and Python (Pandas) to derive specific insights.

Data Model

The following table schemas are provided. Data types should be adapted to your specific 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 (Optional)

• member_id (INT)
• risk_score (DECIMAL(10,4))

This table is provided but may not be necessary for the tasks.

Assumptions

• A claim.member_id will correspond to an entry in either mem1.member_id or mem2.member_id.
• claim_date values are in UTC.
• "Year = 2017" refers to the calendar year 2017.

Part A: SQL Analysis - Specialty Spend Share

For each unique specialty, calculate:

1. The total_paid_amt: The sum of all paid_amt for that specialty.
2. The paid_share: The percentage share of that specialty's total_paid_amt relative to the total paid_amt across all claims.

Expected Output Columns: specialty, total_paid_amt, paid_share.

Part B: SQL Analysis - Top Age Band by Claim Count (2017)

Focusing exclusively on claims from calendar year 2017, perform the following steps:

1. Consolidate all members from mem1 and mem2 into a single, unified member set.
2. Join this unified member set with the relevant claims data.
3. Compute the total count of claims for each distinct age_band.
4. Identify and return the age_band(s) that have the maximum number of claims. If multiple age bands share the maximum count, all such age bands should be included in the output.

Expected Output Columns: age_band, claim_cnt.

Part C: Python / Pandas Analysis - Monthly Gender Spend

You will be provided with a Pandas DataFrame named df_claims containing the following columns:

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

Perform the following tasks:

1. Recode the gender column: change 'M' to 'male' and 'F' to 'female'.
2. Calculate the total paid_amt for each month in the year 2017, grouped by gender and month.

Expected Output: A Pandas DataFrame with columns such as gender, month (representing months 1-12), and total_paid_amt. An optional year column may also be included.