Top Marketing Channel by Season
Problem
You are given two DataFrames. `assets` has columns `asset_id`, `asset_name`, and `channel` (the marketing channel the asset belongs to). `spots` has columns `spot_id`, `asset_id`, `run_date`, `clicks`, and `cpc` (cost per click); each row is one ad spot.
Group the calendar into four seasons by the month of `run_date`: Winter (12, 1, 2), Spring (3, 4, 5), Summer (6, 7, 8), Fall (9, 10, 11).
For each season, find the single channel with the most total clicks. If two channels tie on clicks, prefer the one with the higher total spend (clicks x cpc); if still tied, prefer the alphabetically earlier channel name.
Return a DataFrame with columns `season`, `channel`, `total_clicks`, and `total_spend`, ordered by `season`.
Input data
Example rows — the live problem includes the full dataset.
| asset_id | asset_name | channel |
|---|---|---|
| 1 | Winter Banner | Social |
| 2 | Denim Promo | Social |
| 3 | Knife Set Card | Search |
| 4 | Speaker Reel | Display |
| 5 | Mat Carousel |
| spot_id | asset_id | run_date | clicks | cpc |
|---|---|---|---|---|
| 1 | 1 | 2023-01-15 | 5 | 10.0 |
| 2 | 2 | 2023-01-20 | 4 | 15.0 |
| 3 | 3 | 2023-03-10 | 3 | 18.0 |
| 4 | 4 | 2023-04-05 | 1 | 20.0 |
| 5 | 1 | 2023-05-20 | 2 | 10.0 |
Expected output
Your answer should return 4 rows with the columns season, channel, total_clicks, total_spend.
Starter code (Pandas (Python))
import pandas as pd
def top_marketing_channel_by_season(assets, spots) -> pd.DataFrame:
# Your code here
return assetsSolve this Pandas question free
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