Dynamic unpivoting of a table

# Table: Products
# 
# +-------------+---------+
# | Column Name | Type    |
# +-------------+---------+
# | product_id  | int     |
# | store_name1 | int     |
# | store_name2 | int     |
# |      :      | int     |
# |      :      | int     |
# |      :      | int     |
# | store_namen | int     |
# +-------------+---------+
# product_id is the primary key for this table.
# Each row in this table indicates the product's price in n different stores.
# If the product is not available in a store, the price will be null in that store's column.
# The names of the stores may change from one testcase to another. There will be at least 1 store and at most 30 stores.
# 
#  
# 
# Important note: This problem targets those who have a good experience with SQL. If you are a beginner, we recommend that you skip it for now.
# 
# Implement the procedure UnpivotProducts to reorganize the Products table so that each row has the id of one product, the name of a store where it is sold, and its price in that store. If a product is not available in a store, do not include a row with that product_id and store combination in the result table. There should be three columns: product_id, store, and price.
# 
# The procedure should return the table after reorganizing it.
# 
# Return the result table in any order.
# 
# The query result format is in the following example.
#
# Example 1:
# Input:
# Products table:
# +------------+----------+--------+------+------+
# | product_id | LC_Store | Nozama | Shop | Souq |
# +------------+----------+--------+------+------+
# | 1          | 100      | null   | 110  | null |
# | 2          | null     | 200    | null | 190  |
# | 3          | null     | null   | 1000 | 1900 |
# +------------+----------+--------+------+------+
# Output:
# +------------+----------+-------+
# | product_id | store    | price |
# +------------+----------+-------+
# | 1          | LC_Store | 100   |
# | 1          | Shop     | 110   |
# | 2          | Nozama   | 200   |
# | 2          | Souq     | 190   |
# | 3          | Shop     | 1000  |
# | 3          | Souq     | 1900  |
# +------------+----------+-------+
# Explanation:
# Product 1 is sold in LC_Store and Shop with prices of 100 and 110 respectively.
# Product 2 is sold in Nozama and Souq with prices of 200 and 190.
# Product 3 is sold in Shop and Souq with prices of 1000 and 1900.

import pandas as pd

def find_valid_users(products: pd.DataFrame) -> pd.DataFrame:
  store_cols = [c for c in products.columns if c != 'product_id']
  result = products.melt(id_vars='product_id', value_vars=store_cols, var_name='store', value_name='price')
  result = result.dropna(subset=['price'])
  return result[['product_id', 'store', 'price']]