the solution of the problem using a command that in pandas library
\n# Code\n\nimport pandas as pd\n\ndef rearrange_products_table(products: pd.DataFrame) -> pd.DataFrame:\n    # 1. Saves output in new DataFrame\n    df = (\n  
\n# Code\n```\nimport pandas as pd\n\ndef rearrange_products_table(products: pd.DataFrame) -> pd.DataFrame:\n    # 1. Saves output in new DataFrame\n    df = (\n        # 2. .melt() lets you unpivot the table\n        products.melt(\n            # 3. Columns you want to leave unchanged\n            id_vars = [\'product_id\'],\n            # 4. Columns you want to unpivot\n            value_vars = [\'store1\', \'store2\', \'store3\'], \n            # 5. This names the store column\n            var_name = \'store\',\n            # 6. This names the price column\n            value_name = \'price\'\n            )\n        # 7. .dropna() lets you drop the null values\n        .dropna() \n    )\n    return df\n```
['Python3', 'Pandas']