library(tidyverse)
library(readxl)
path = "files/200-299/246/CH-246 Table Transformation.xlsx"
input = read_excel(path, range = "B2:H6")
test = read_excel(path, range = "B10:E14")
result = input %>%
select_if(~ any(str_detect(., "\\*")))
all.equal(result, test)
#> [1] TRUEOmid - Challenge 246
data-challenges
advanced-exercises
🔰 Table Transformation!

Challenge Description
🔰 Table Transformation!
Solutions
Logic:
Reads the workbook ranges needed for the challenge
Parses the text patterns directly instead of relying on manual cleanup
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 = "200-299/246/CH-246 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B:H", skiprows=1, nrows=5)
test = pd.read_excel(path, usecols="B:E", skiprows=9, nrows=5)
result = input.loc[:, input.astype(str).apply(lambda col: col.str.contains(r"\*").any())]
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 core logic is clear, but the correct transformation pattern is not obvious from the raw input.
The challenge combines multiple reshaping, grouping, or parsing steps.