Podcast Search Relevance and Weak-Hit Rate

PandasEasyJunior level~10 min

Problem

You are given a DataFrame `searchhits` loaded from `searchhits.csv` with columns `search_term`, `episode`, `slot`, and `stars`. Each row is one episode that appeared in the results for a search term: `slot` (1 = top) is where it appeared and `stars` (1 to 5) is the score a listener gave it. A search term can have many rows.

For each `search_term`, compute two numbers: `relevance`, the average of `stars / slot` across that term's rows, rounded to 2 decimals; and `weak_hit_pct`, the percentage of that term's rows whose `stars` is below 3, rounded to 2 decimals. Output columns: `search_term`, `relevance`, `weak_hit_pct`.

Input data

Example rows — the live problem includes the full dataset.

searchhits

search_term	episode	slot	stars
history	The Roman Road	1	5
history	Lost Empires	2	5
history	Filler Clip	200	1
comedy	Late Set	5	2
comedy	Open Mic	3	3

Expected output

Your answer should return 2 rows with the columns search_term, relevance, weak_hit_pct.

Starter code (Pandas (Python))

import pandas as pd

def search_relevance(searchhits) -> pd.DataFrame:
    # Your code here
    return searchhits

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