library(tidyverse)
library(readxl)
path = "files/200-299/256/CH-256 Table Transformation.xlsx"
input = read_excel(path, range = "B2:H6")
test = read_excel(path, range = "B10:E14")
result = cbind(
input[1],
set_names(
map_dfc(
c("A", "B", "C"),
~ rowSums(input[startsWith(names(input), .x)], na.rm = TRUE)
),
c("A", "B", "C")
)
)
all.equal(result, test, check.attributes = FALSE)
# > [1] TRUEOmid - Challenge 256
data-challenges
advanced-exercises
🔰 Table Transformation!

Challenge Description
🔰 Table Transformation!
Solutions
Logic:
- Reads the workbook ranges needed for the challenge
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 = "200-299/256/CH-256 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B:H", nrows=5, skiprows=1)
test = pd.read_excel(path, usecols="B:E", nrows=5, skiprows=9)
result = (input.groupby('Date').sum()
.T.groupby(lambda x: x[0]).sum()
.T.reset_index())
for col in result.columns:
if col != 'Date':
result[col] = result[col].astype(int)
print(result.equals(test)) # TrueLogic:
Reads the workbook ranges needed for the challenge
Aggregates or ranks values at the relevant grouping level
Applies the rule iteratively until the output stabilizes
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.