LeetCode #3564 — MEDIUM

Seasonal Sales Analysis

Move from brute-force thinking to an efficient approach using core interview patterns strategy.

The Problem

Problem Statement

Table: sales

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| sale_id       | int     |
| product_id    | int     |
| sale_date     | date    |
| quantity      | int     |
| price         | decimal |
+---------------+---------+
sale_id is the unique identifier for this table.
Each row contains information about a product sale including the product_id, date of sale, quantity sold, and price per unit.

Table: products

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| product_id    | int     |
| product_name  | varchar |
| category      | varchar |
+---------------+---------+
product_id is the unique identifier for this table.
Each row contains information about a product including its name and category.

Write a solution to find the most popular product category for each season. The seasons are defined as:

Winter: December, January, February
Spring: March, April, May
Summer: June, July, August
Fall: September, October, November

The popularity of a category is determined by the total quantity sold in that season. If there is a tie, select the category with the highest total revenue (quantity × price). If there is still a tie, return the lexicographically smaller category.

Return the result table ordered by season in ascending order.

The result format is in the following example.

Example:

Input:

sales table:

+---------+------------+------------+----------+-------+
| sale_id | product_id | sale_date  | quantity | price |
+---------+------------+------------+----------+-------+
| 1       | 1          | 2023-01-15 | 5        | 10.00 |
| 2       | 2          | 2023-01-20 | 4        | 15.00 |
| 3       | 3          | 2023-03-10 | 3        | 18.00 |
| 4       | 4          | 2023-04-05 | 1        | 20.00 |
| 5       | 1          | 2023-05-20 | 2        | 10.00 |
| 6       | 2          | 2023-06-12 | 4        | 15.00 |
| 7       | 5          | 2023-06-15 | 5        | 12.00 |
| 8       | 3          | 2023-07-24 | 2        | 18.00 |
| 9       | 4          | 2023-08-01 | 5        | 20.00 |
| 10      | 5          | 2023-09-03 | 3        | 12.00 |
| 11      | 1          | 2023-09-25 | 6        | 10.00 |
| 12      | 2          | 2023-11-10 | 4        | 15.00 |
| 13      | 3          | 2023-12-05 | 6        | 18.00 |
| 14      | 4          | 2023-12-22 | 3        | 20.00 |
| 15      | 5          | 2024-02-14 | 2        | 12.00 |
+---------+------------+------------+----------+-------+

products table:

+------------+-----------------+----------+
| product_id | product_name    | category |
+------------+-----------------+----------+
| 1          | Warm Jacket     | Apparel  |
| 2          | Designer Jeans  | Apparel  |
| 3          | Cutting Board   | Kitchen  |
| 4          | Smart Speaker   | Tech     |
| 5          | Yoga Mat        | Fitness  |
+------------+-----------------+----------+

Output:

+---------+----------+----------------+---------------+
| season  | category | total_quantity | total_revenue |
+---------+----------+----------------+---------------+
| Fall    | Apparel  | 10             | 120.00        |
| Spring  | Kitchen  | 3              | 54.00         |
| Summer  | Tech     | 5              | 100.00        |
| Winter  | Apparel  | 9              | 110.00        |
+---------+----------+----------------+---------------+

Explanation:

Fall (Sep, Oct, Nov):
- Apparel: 10 items sold (6 Jackets in Sep, 4 Jeans in Nov), revenue $120.00 (6×$10.00 + 4×$15.00)
- Fitness: 3 Yoga Mats sold in Sep, revenue $36.00
- Most popular: Apparel with highest total quantity (10)
Spring (Mar, Apr, May):
- Kitchen: 3 Cutting Boards sold in Mar, revenue $54.00
- Tech: 1 Smart Speaker sold in Apr, revenue $20.00
- Apparel: 2 Warm Jackets sold in May, revenue $20.00
- Most popular: Kitchen with highest total quantity (3) and highest revenue ($54.00)
Summer (Jun, Jul, Aug):
- Apparel: 4 Designer Jeans sold in Jun, revenue $60.00
- Fitness: 5 Yoga Mats sold in Jun, revenue $60.00
- Kitchen: 2 Cutting Boards sold in Jul, revenue $36.00
- Tech: 5 Smart Speakers sold in Aug, revenue $100.00
- Most popular: Tech and Fitness both have 5 items, but Tech has higher revenue ($100.00 vs $60.00)
Winter (Dec, Jan, Feb):
- Apparel: 9 items sold (5 Jackets in Jan, 4 Jeans in Jan), revenue $110.00
- Kitchen: 6 Cutting Boards sold in Dec, revenue $108.00
- Tech: 3 Smart Speakers sold in Dec, revenue $60.00
- Fitness: 2 Yoga Mats sold in Feb, revenue $24.00
- Most popular: Apparel with highest total quantity (9) and highest revenue ($110.00)

The result table is ordered by season in ascending order.

Step 01

Brute Force Baseline

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: General problem-solving

Example 1

{"headers":{"sales":["sale_id","product_id","sale_date","quantity","price"],"products":["product_id","product_name","category"]},"rows":{"sales":[[1,1,"2023-01-15",5,10.00],[2,2,"2023-01-20",4,15.00],[3,3,"2023-03-10",3,18.00],[4,4,"2023-04-05",1,20.00],[5,1,"2023-05-20",2,10.00],[6,2,"2023-06-12",4,15.00],[7,5,"2023-06-15",5,12.00],[8,3,"2023-07-24",2,18.00],[9,4,"2023-08-01",5,20.00],[10,5,"2023-09-03",3,12.00],[11,1,"2023-09-25",6,10.00],[12,2,"2023-11-10",4,15.00],[13,3,"2023-12-05",6,18.00],[14,4,"2023-12-22",3,20.00],[15,5,"2024-02-14",2,12.00]],"products":[[1,"Warm Jacket","Apparel"],[2,"Designer Jeans","Apparel"],[3,"Cutting Board","Kitchen"],[4,"Smart Speaker","Tech"],[5,"Yoga Mat","Fitness"]]}}

Step 02

Core Insight

What unlocks the optimal approach

No official hints in dataset. Start from constraints and look for a monotonic or reusable state.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #3564: Seasonal Sales Analysis
// Auto-generated Java example from py.
class Solution {
    public void exampleSolution() {
    }
}
// Reference (py):
// # Accepted solution for LeetCode #3564: Seasonal Sales Analysis
// import pandas as pd
// 
// 
// def seasonal_sales_analysis(
//     products: pd.DataFrame, sales: pd.DataFrame
// ) -> pd.DataFrame:
//     df = sales.merge(products, on="product_id")
//     month_to_season = {
//         12: "Winter",
//         1: "Winter",
//         2: "Winter",
//         3: "Spring",
//         4: "Spring",
//         5: "Spring",
//         6: "Summer",
//         7: "Summer",
//         8: "Summer",
//         9: "Fall",
//         10: "Fall",
//         11: "Fall",
//     }
//     df["season"] = df["sale_date"].dt.month.map(month_to_season)
//     seasonal_sales = df.groupby(["season", "category"], as_index=False).agg(
//         total_quantity=("quantity", "sum"),
//         total_revenue=("quantity", lambda x: (x * df.loc[x.index, "price"]).sum()),
//     )
//     seasonal_sales["rk"] = (
//         seasonal_sales.sort_values(
//             ["season", "total_quantity", "total_revenue"],
//             ascending=[True, False, False],
//         )
//         .groupby("season")
//         .cumcount()
//         + 1
//     )
//     result = seasonal_sales[seasonal_sales["rk"] == 1].copy()
//     return result[
//         ["season", "category", "total_quantity", "total_revenue"]
//     ].sort_values("season")

// Accepted solution for LeetCode #3564: Seasonal Sales Analysis
// Auto-generated Go example from py.
func exampleSolution() {
}
// Reference (py):
// # Accepted solution for LeetCode #3564: Seasonal Sales Analysis
// import pandas as pd
// 
// 
// def seasonal_sales_analysis(
//     products: pd.DataFrame, sales: pd.DataFrame
// ) -> pd.DataFrame:
//     df = sales.merge(products, on="product_id")
//     month_to_season = {
//         12: "Winter",
//         1: "Winter",
//         2: "Winter",
//         3: "Spring",
//         4: "Spring",
//         5: "Spring",
//         6: "Summer",
//         7: "Summer",
//         8: "Summer",
//         9: "Fall",
//         10: "Fall",
//         11: "Fall",
//     }
//     df["season"] = df["sale_date"].dt.month.map(month_to_season)
//     seasonal_sales = df.groupby(["season", "category"], as_index=False).agg(
//         total_quantity=("quantity", "sum"),
//         total_revenue=("quantity", lambda x: (x * df.loc[x.index, "price"]).sum()),
//     )
//     seasonal_sales["rk"] = (
//         seasonal_sales.sort_values(
//             ["season", "total_quantity", "total_revenue"],
//             ascending=[True, False, False],
//         )
//         .groupby("season")
//         .cumcount()
//         + 1
//     )
//     result = seasonal_sales[seasonal_sales["rk"] == 1].copy()
//     return result[
//         ["season", "category", "total_quantity", "total_revenue"]
//     ].sort_values("season")

# Accepted solution for LeetCode #3564: Seasonal Sales Analysis
import pandas as pd


def seasonal_sales_analysis(
    products: pd.DataFrame, sales: pd.DataFrame
) -> pd.DataFrame:
    df = sales.merge(products, on="product_id")
    month_to_season = {
        12: "Winter",
        1: "Winter",
        2: "Winter",
        3: "Spring",
        4: "Spring",
        5: "Spring",
        6: "Summer",
        7: "Summer",
        8: "Summer",
        9: "Fall",
        10: "Fall",
        11: "Fall",
    }
    df["season"] = df["sale_date"].dt.month.map(month_to_season)
    seasonal_sales = df.groupby(["season", "category"], as_index=False).agg(
        total_quantity=("quantity", "sum"),
        total_revenue=("quantity", lambda x: (x * df.loc[x.index, "price"]).sum()),
    )
    seasonal_sales["rk"] = (
        seasonal_sales.sort_values(
            ["season", "total_quantity", "total_revenue"],
            ascending=[True, False, False],
        )
        .groupby("season")
        .cumcount()
        + 1
    )
    result = seasonal_sales[seasonal_sales["rk"] == 1].copy()
    return result[
        ["season", "category", "total_quantity", "total_revenue"]
    ].sort_values("season")

// Accepted solution for LeetCode #3564: Seasonal Sales Analysis
// Rust example auto-generated from py reference.
// Replace the signature and local types with the exact LeetCode harness for this problem.
impl Solution {
    pub fn rust_example() {
        // Port the logic from the reference block below.
    }
}

// Reference (py):
// # Accepted solution for LeetCode #3564: Seasonal Sales Analysis
// import pandas as pd
// 
// 
// def seasonal_sales_analysis(
//     products: pd.DataFrame, sales: pd.DataFrame
// ) -> pd.DataFrame:
//     df = sales.merge(products, on="product_id")
//     month_to_season = {
//         12: "Winter",
//         1: "Winter",
//         2: "Winter",
//         3: "Spring",
//         4: "Spring",
//         5: "Spring",
//         6: "Summer",
//         7: "Summer",
//         8: "Summer",
//         9: "Fall",
//         10: "Fall",
//         11: "Fall",
//     }
//     df["season"] = df["sale_date"].dt.month.map(month_to_season)
//     seasonal_sales = df.groupby(["season", "category"], as_index=False).agg(
//         total_quantity=("quantity", "sum"),
//         total_revenue=("quantity", lambda x: (x * df.loc[x.index, "price"]).sum()),
//     )
//     seasonal_sales["rk"] = (
//         seasonal_sales.sort_values(
//             ["season", "total_quantity", "total_revenue"],
//             ascending=[True, False, False],
//         )
//         .groupby("season")
//         .cumcount()
//         + 1
//     )
//     result = seasonal_sales[seasonal_sales["rk"] == 1].copy()
//     return result[
//         ["season", "category", "total_quantity", "total_revenue"]
//     ].sort_values("season")

// Accepted solution for LeetCode #3564: Seasonal Sales Analysis
// Auto-generated TypeScript example from py.
function exampleSolution(): void {
}
// Reference (py):
// # Accepted solution for LeetCode #3564: Seasonal Sales Analysis
// import pandas as pd
// 
// 
// def seasonal_sales_analysis(
//     products: pd.DataFrame, sales: pd.DataFrame
// ) -> pd.DataFrame:
//     df = sales.merge(products, on="product_id")
//     month_to_season = {
//         12: "Winter",
//         1: "Winter",
//         2: "Winter",
//         3: "Spring",
//         4: "Spring",
//         5: "Spring",
//         6: "Summer",
//         7: "Summer",
//         8: "Summer",
//         9: "Fall",
//         10: "Fall",
//         11: "Fall",
//     }
//     df["season"] = df["sale_date"].dt.month.map(month_to_season)
//     seasonal_sales = df.groupby(["season", "category"], as_index=False).agg(
//         total_quantity=("quantity", "sum"),
//         total_revenue=("quantity", lambda x: (x * df.loc[x.index, "price"]).sum()),
//     )
//     seasonal_sales["rk"] = (
//         seasonal_sales.sort_values(
//             ["season", "total_quantity", "total_revenue"],
//             ascending=[True, False, False],
//         )
//         .groupby("season")
//         .cumcount()
//         + 1
//     )
//     result = seasonal_sales[seasonal_sales["rk"] == 1].copy()
//     return result[
//         ["season", "category", "total_quantity", "total_revenue"]
//     ].sort_values("season")

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(1)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.