Problem Description

You are provided with two Pandas DataFrames: users and orders. Your task is to perform a series of data manipulation operations including merging, aggregation, pivoting, and conditional labeling, reusing the intermediate merged DataFrame across steps.

Input DataFrames

users DataFrame

This DataFrame contains user demographic information.

Example users data:

user_id | is_member | state | age
--------|-----------|-------|----
101     | 1         | CA    | 29
102     | 0         | NY    | 41
103     | 1         | CA    | 35
104     | 0         | TX    | 50

orders DataFrame

This DataFrame contains order details.

Example orders data:

order_id | user_id | channel | amount | status
---------|---------|---------|--------|-----------
7001     | 101     | SMS     | 12.00  | delivered
7002     | 102     | Email   | 5.00   | delivered
7003     | 103     | SMS     | 7.00   | delivered
7004     | 103     | Email   | 4.00   | delivered
7005     | 101     | Organic | 3.50   | delivered
7006     | 104     | SMS     | 6.00   | undelivered

Tasks

You must complete the following steps sequentially, using the merged DataFrame as the base for subsequent operations where applicable.

Step 1: Merge and Aggregate Revenue

1. Perform a left join of the orders DataFrame with the users DataFrame on user_id.
2. From this merged DataFrame, compute two separate outputs:
   • (a) Total Delivered Revenue by Channel: Calculate the sum of amount for all orders with status == 'delivered', grouped by channel.
   • (b) Delivered Revenue by Channel for Members Only: Calculate the sum of amount for orders with status == 'delivered' placed by users where is_member == 1, grouped by channel.
3. Both results should be presented as Pandas DataFrames.

Step 2: Create a 2x2 Pivot Table

1. Using the merged DataFrame (from Step 1), create a pivot table focusing on delivered revenue.
2. The pivot table should have is_member (0 or 1) as its index and channel as its columns, specifically restricted to ['SMS', 'Email'].
3. The values in the pivot table should be the sum of amount for delivered orders.
4. Missing cells in the pivot table should be filled with 0.
5. Utilize the pivot_table function with aggfunc='sum'.

Step 3: Compute Per-State Counts

1. From the merged DataFrame, calculate the following metrics for each state:
   • Total number of orders (size).
   • Number of unique purchasers (nunique of user_id).
2. Return the top 2 states based on their total number of orders, sorted in descending order.

Step 4: Add a Binary High-Value Flag

1. Add a new column named high_value_flag to the merged DataFrame.
2. Set high_value_flag = 1 if a user meets either of the following conditions, otherwise set it to 0:
   • The user's total lifetime delivered amount is greater than or equal to 15.
   • The user has placed two or more delivered orders via the 'SMS' channel.
3. Ensure you use np.where and leverage prior groupby aggregations to efficiently compute these conditions and avoid SettingWithCopyWarning issues.
4. Display the head of the final DataFrame, including the newly added high_value_flag column and other relevant user/order details.