library(tidyverse)
library(readxl)
path = "files/300-399/307/CH-307 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C19")
test = read_excel(path, range = "G2:H4")
result = input %>%
mutate(Group = (row_number() - 1) %% 2 + 1) %>%
summarise(`Total Sales` = sum(Sales), .by = Group)
all.equal(result, test)
# [1] TRUEOmid - Challenge 307
data-challenges
advanced-exercises
🔰 Group Custom Grouping!

Challenge Description
🔰 Group Custom Grouping!
Solutions
Logic:
Reads the workbook ranges needed for the challenge
Aggregates or ranks values at the relevant grouping level
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 = "300-399/307/CH-307 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=17)
test = pd.read_excel(path, usecols="G:H", skiprows=1, nrows=2)
result = (
input.assign(Group=(input.index % 2) + 1)
.groupby('Group', as_index=False)['Sales'].sum()
.rename(columns={'Sales': 'Total Sales'})
)
print(result.equals(test)) # TrueLogic:
Reads the workbook ranges needed for the challenge
Aggregates or ranks values at the relevant grouping level
Builds the intermediate columns that drive the final result
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.