library(tidyverse)
library(readxl)
path = "files/CH-168 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C26")
test = read_excel(path, range = "G2:I26")
result = input %>%
mutate(group = cumsum(cummin(`Stock price`) != lag(cummin(`Stock price`), default = first(cummin(`Stock price`)))) + 1)
all.equal(result, test, check.attributes = FALSE)
#> [1] TRUEOmid - Challenge 168
data-challenges
advanced-exercises
🔰 Group 68: Custom Grouping!

Challenge Description
🔰 Group 68: Custom Grouping!
Solutions
Logic:
Reads the workbook ranges needed for the challenge
Builds the intermediate columns that drive the final result
Strengths:
- The R solution stays close to the workbook rule and keeps the transformation compact.
Areas for Improvement:
- The code assumes the sheet structure and source ranges remain stable.
Gem:
- The strongest part of the solution is choosing the right intermediate representation before shaping the final output.
import pandas as pd
path = "CH-168 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=25)
test = pd.read_excel(path, usecols="G:I", skiprows=1, nrows=25).rename(columns=lambda x: x.split('.')[0])
input['Group'] = ((input['Stock price'].cummin() != input['Stock price'].cummin().shift().fillna(input['Stock price'].cummin().iloc[0])).cumsum() + 1)
print(all(input == test)) # TrueLogic:
- Reads the workbook ranges needed for the challenge
Strengths:
- The Python version follows the same rule in a direct dataframe-oriented implementation.
Areas for Improvement:
- The code assumes the workbook layout remains stable, so any sheet redesign would require small adjustments.
Gem:
- The implementation stays close to the original workbook rule instead of adding unnecessary abstraction.
Difficulty Level
This task is moderate:
- The business rule is readable, but the workbook still requires careful implementation to reach the expected layout.