Leaderboard Shake-Up

Problem

You are given two DataFrames. `coffee_shops` has columns `shop_id`, `shop_name`, and `revenue` (`shop_id` is unique). `revenue_adjustments` has columns `shop_id` and `revenue_delta` (`shop_id` is unique, one row per shop).

A shop's rank is by `revenue` descending, breaking ties by `shop_name` ascending (ties share the lowest rank, RANK-style). After adding each shop's `revenue_delta` to its `revenue`, the ranking is recomputed the same way. Return `shop_id`, `shop_name`, and `rank_shift` = old rank minus new rank (positive means the shop climbed, negative means it fell). Row order does not matter.

Input data

Example rows — the live problem includes the full dataset.

Expected output

Your answer should return 4 rows with the columns shop_id, shop_name, rank_shift.

Starter code (Pandas (Python))

import pandas as pd

def leaderboard_shake_up(coffee_shops, revenue_adjustments) -> pd.DataFrame:
    # Your code here
    return coffee_shops

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