library(tidyverse)
library(readxl)
path = "files/CH-083 Custom splitter 3.xlsx"
input = read_excel(path, range = "B2:B3")
test = read_excel(path, range = "D5:F27")
result = input %>%
separate_rows(Info, sep = ";") %>%
separate(Info, into = c("Date","Product","Quantity"), sep = ", ") %>%
mutate(Quantity = as.numeric(Quantity))
identical(result, test)
#> [1] TRUEOmid - Challenge 83
data-challenges
advanced-exercises
🔰 Result Question Info A B C Date Product

Challenge Description
🔰 Result Question Info A B C Date Product
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-083 Custom splitter 3.xlsx"
input = pd.read_excel(path, usecols="B", nrows = 1, skiprows = 1)
test = pd.read_excel(path, usecols="D:F", skiprows = 4)
result = input['Info'].str.split(';', expand=True).stack().str.split(", ", expand=True)\
.rename(columns={0: 'Date', 1: 'Product', 2: "Quantity"}).reset_index(drop=True)
result['Quantity'] = result['Quantity'].astype('int64')
print(result.equals(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.