Merge CSVs and build revenue pivot with pandas

You receive four CSVs and must replicate an Excel VLOOKUP + PivotTable workflow using Python/pandas.

CSV samples:

customers.csv
customer_id,signup_date,channel
1,2025-06-02,Organic
2,2025-06-10,Ads
3,2025-07-05,Referral

orders.csv
order_id,customer_id,order_date,total_amount
101,1,2025-06-12,120
102,2,2025-06-28,80
103,1,2025-07-15,200
104,3,2025-08-02,50

refunds.csv order_id,refund_amount 102,20 104,50

targets.csv
month,channel,revenue_target
2025-06,Organic,100
2025-06,Ads,120
2025-07,Organic,150
2025-08,Referral,60

Task: Write pandas code to (a) robustly load these CSVs (assume one file path may initially fail—gracefully retry or fall back without crashing), (b) compute net_revenue per order = total_amount - COALESCE(refund_amount,0), (c) roll up to monthly net revenue by channel for months 2025-06 through 2025-08 based on order_date, (d) left-join the result to targets.csv on (month, channel) to compute target_gap = net_revenue - revenue_target (treat missing targets as 0), and (e) produce a pivot table with index=month (YYYY-MM), columns=channel, values=net_revenue, plus an additional similarly-shaped table for target_gap. Ensure date parsing is correct, missing refunds are handled, and channels with no orders in a month still appear with 0 in the pivot.

Monthly Net Revenue Pivot by Channel with Targets and Gap

You are given four tables that mirror four CSV files: customers, orders, refunds, and targets.

Compute monthly net revenue by channel for months 2025-06 through 2025-08 (inclusive), where:

• net_revenue per order = total_amount - COALESCE(refund_amount, 0)
• month is derived from orders.order_date as YYYY-MM

Then left-join this monthly net revenue to targets on (month, channel) and compute:

• target_gap = net_revenue - COALESCE(revenue_target, 0)

Finally, output a pivoted result with one row per month and these columns:

• organic_net_revenue, ads_net_revenue, referral_net_revenue
• organic_target_gap, ads_target_gap, referral_target_gap

Requirements:

• Include all month/channel combinations for months 2025-06, 2025-07, 2025-08 and channels appearing in either customers or targets.
• If a channel has no orders in a month, its net revenue should be 0.
• If a (month, channel) target is missing, treat revenue_target as 0.
• Use the sample data below; the expected output should match it.

Table customers

column	type	constraints
customer_id	INT	PRIMARY KEY, NOT NULL
signup_date	DATE	NOT NULL
channel	VARCHAR(50)	NOT NULL

Sample data:

channel	customer_id	signup_date
Organic	1	2025-06-02
Ads	2	2025-06-10
Referral	3	2025-07-05

Table orders

column	type	constraints
order_id	INT	PRIMARY KEY, NOT NULL
customer_id	INT	NOT NULL
order_date	DATE	NOT NULL
total_amount	DECIMAL(10,2)	NOT NULL

Sample data:

customer_id	order_date	order_id	total_amount
1	2025-06-12	101	120
2	2025-06-28	102	80
1	2025-07-15	103	200
3	2025-08-02	104	50

Table refunds

column	type	constraints
order_id	INT	PRIMARY KEY, NOT NULL
refund_amount	DECIMAL(10,2)	NOT NULL

Sample data:

order_id	refund_amount
102	20
104	50

Table targets

column	type	constraints
month	VARCHAR(7)	NOT NULL
channel	VARCHAR(50)	NOT NULL
revenue_target	DECIMAL(10,2)	NOT NULL

Sample data:

channel	month	revenue_target
Organic	2025-06	100
Ads	2025-06	120
Organic	2025-07	150
Referral	2025-08	60

Expected output

One row per month (2025-06 to 2025-08) with pivoted net revenue and pivoted target gap columns for Organic, Ads, and Referral. Missing month/channel combinations must appear with 0s.

ads_net_revenue	ads_target_gap	month	organic_net_revenue	organic_target_gap	referral_net_revenue	referral_target_gap
60	-60	2025-06	120	20	0	0
0	0	2025-07	200	50	0	0
0	0	2025-08	0	0	0	-60

Hints

1. Compute net revenue at the order level by left joining refunds and using COALESCE(refund_amount, 0).
2. To ensure missing month/channel combinations appear, build a month x channel grid via CROSS JOIN, then left join aggregates/targets onto it.
3. Pivot using conditional aggregation (SUM(CASE WHEN channel = ... THEN value ELSE 0 END)).