Leetcode #2986: Find Third Transaction
In this guide, we solve Leetcode #2986 Find Third Transaction 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: Transactions +------------------+----------+ | Column Name | Type | +------------------+----------+ | user_id | int | | spend | decimal | | transaction_date | datetime | +------------------+----------+ (user_id, transaction_date) is column of unique values for this table. This table contains user_id, spend, and transaction_date.
Quick Facts
- Difficulty: Medium
- 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 |
+------------------+----------+
| user_id | int |
| spend | decimal |
| transaction_date | datetime |
+------------------+----------+
(user_id, transaction_date) is column of unique values for this table.
This table contains user_id, spend, and transaction_date.
Python Solution
import duckdb
import pandas as pd
def solution(transactions: pd.DataFrame) -> pd.DataFrame:
con = duckdb.connect()
con.register("Transactions", transactions)
return con.execute("""WITH
T AS (
SELECT
*,
RANK() OVER (
PARTITION BY user_id
ORDER BY transaction_date
) AS rk,
spend > (
LAG(spend) OVER (
PARTITION BY user_id
ORDER BY transaction_date
)
)
AND spend > (
LAG(spend, 2) OVER (
PARTITION BY user_id
ORDER BY transaction_date
)
) AS st
FROM Transactions
)
SELECT user_id, spend AS third_transaction_spend, transaction_date AS third_transaction_date
FROM T
WHERE rk = 3 AND st = 1;""").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.