Leetcode #2253: Dynamic Unpivoting of a Table
In this guide, we solve Leetcode #2253 Dynamic Unpivoting of a Table in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
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.
Quick Facts
- Difficulty: Hard
- Premium: Yes
- Tags: Database
Intuition
The task is relational in nature, which maps cleanly to DataFrame operations in Python.
By treating tables as DataFrames, joins and group-bys become concise and readable.
Approach
Load the inputs as DataFrames and apply the appropriate merge, filter, or group-by.
Select or rename the columns to match the required output.
Steps:
- Load inputs as DataFrames.
- Apply merge/groupby/filter operations.
- Select the output columns.
Example
+-------------+---------+
| 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.
Python Solution
import duckdb
import pandas as pd
def solution(products: pd.DataFrame) -> pd.DataFrame:
con = duckdb.connect()
con.register("Products", products)
return con.execute("""CREATE PROCEDURE UnpivotProducts()
BEGIN
SET group_concat_max_len = 5000;
WITH
t AS (
SELECT column_name
FROM information_schema.columns
WHERE
table_schema = DATABASE()
AND table_name = 'Products'
AND column_name != 'product_id'
)
SELECT
GROUP_CONCAT(
'SELECT product_id, \'',
column_name,
'\' store, ',
column_name,
' price FROM Products WHERE ',
column_name,
' IS NOT NULL' SEPARATOR ' UNION '
) INTO @sql from t;
PREPARE stmt FROM @sql;
EXECUTE stmt;
DEALLOCATE PREPARE stmt;
END;""").df()
Complexity
The time complexity is O(n log n) (typical). The space complexity is O(n).
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.