Omid - Challenge 16

data-challenges
advanced-exercises
🔰 Challenge 16: Transform Data Format!
Published

March 24, 2026

Illustration for Omid - Challenge 16

Challenge Description

🔰 Challenge 16: Transform Data Format!

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-016 .xlsx", range = "B2:C15")
test  = read_excel("files/CH-016 .xlsx", range = "F2:I6")
                                                                                                                                                                      
result = input %>% 
  mutate(name = ifelse(Info...1 == "Name", 1, 0)) %>%
  mutate(name = cumsum(name)) %>%
  pivot_wider(names_from = Info...1, values_from = Info...2, 
              values_fn =  ~ paste(.x, collapse = " and ")) %>%
  select(-name)

identical(result, test)
#> [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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-016 .xlsx"
input_data = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=14)
test = pd.read_excel(path, usecols="F:I", skiprows=1, nrows=5)

input_data["name"] = (input_data.iloc[:, 0] == "Name").cumsum()
result = (
    input_data.groupby(["name", input_data.columns[0]], as_index=False)[input_data.columns[1]]
    .agg(lambda s: " and ".join(s.astype(str)))
    .pivot(index="name", columns=input_data.columns[0], values=input_data.columns[1])
    .reset_index(drop=True)
)

print(result.equals(test))

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • Aggregates or ranks values at the relevant grouping level

  • 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.