Leetcode #2752: Customers with Maximum Number of Transactions on Consecutive Days
In this guide, we solve Leetcode #2752 Customers with Maximum Number of Transactions on Consecutive Days 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 | +------------------+------+ | transaction_id | int | | customer_id | int | | transaction_date | date | | amount | int | +------------------+------+ transaction_id is the column with unique values of this table. Each row contains information about transactions that includes unique (customer_id, transaction_date) along with the corresponding customer_id and amount.
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 |
+------------------+------+
| transaction_id | int |
| customer_id | int |
| transaction_date | date |
| amount | int |
+------------------+------+
transaction_id is the column with unique values of this table.
Each row contains information about transactions that includes unique (customer_id, transaction_date) along with the corresponding customer_id and amount.
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
s AS (
SELECT
customer_id,
DATE_SUB(
transaction_date,
INTERVAL ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY transaction_date
) DAY
) AS transaction_date
FROM Transactions
),
t AS (
SELECT customer_id, transaction_date, COUNT(1) AS cnt
FROM s
GROUP BY 1, 2
)
SELECT customer_id
FROM t
WHERE cnt = (SELECT MAX(cnt) FROM t)
ORDER BY customer_id;""").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.