Merge and Clean Customer Order Data for Analysis

customers

+----+---------+---------+ | id | name | country | +----+---------+---------+ | 1 | Alice | US | | 2 | Bob | UK | | 3 | Charlie | NULL | +----+---------+---------+

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orders

+-----+-------------+--------+ | id | customer_id | amount | +-----+-------------+--------+ | 101 | 1 | 250.50 | | 102 | 2 | 99.99 | | 103 | 2 | NULL | +-----+-------------+--------+

Scenario

A retail company needs to combine customer and order datasets, clean nulls, and prepare the data for downstream analysis.

Question

In Python/pandas, merge the two datasets on customer_id, keep all customers, and add a column total_spent that replaces NULL amounts with 0. Fill missing country values with the mode of existing countries. Return the resulting DataFrame sorted by total_spent descending.

Hints

Use merge, fillna, and groupby/transform for the mode.