Leetcode #2252: Dynamic Pivoting of a Table
In this guide, we solve Leetcode #2252 Dynamic Pivoting 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 | varchar | | price | int | +-------------+---------+ (product_id, store) is the primary key (combination of columns with unique values) for this table. Each row of this table indicates the price of product_id in store.
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 | varchar |
| price | int |
+-------------+---------+
(product_id, store) is the primary key (combination of columns with unique values) for this table.
Each row of this table indicates the price of product_id in store.
There will be at most 30 different stores in the table.
price is the price of the product at this store.
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 PivotProducts()
BEGIN
SET group_concat_max_len = 5000;
SELECT GROUP_CONCAT(DISTINCT 'MAX(CASE WHEN store = \'',
store,
'\' THEN price ELSE NULL END) AS ',
store
ORDER BY store) INTO @sql
FROM Products;
SET @sql = CONCAT('SELECT product_id, ',
@sql,
' FROM Products GROUP BY product_id');
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.