Omid - Challenge 20

data-challenges
advanced-exercises
🔰 Hierarchy Transformation transform this table to the result table wich each level is displayed in separate columns .
Published

March 24, 2026

Illustration for Omid - Challenge 20

Challenge Description

🔰 Hierarchy Transformation transform this table to the result table wich each level is displayed in separate columns .

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-020 Transform Higherarchey format.xlsx", range = "B2:C18")
test  = read_excel("files/CH-020 Transform Higherarchey format.xlsx", range = "E2:H10")

result = input %>%
  mutate(level = str_length(Code), 
         first_digit = str_sub(Code, 1,1)) %>%
  pivot_wider(names_from = level, values_from = Description) %>%
  group_by(first_digit) %>%
  fill(everything(), .direction = "down")  %>%
  ungroup() %>%
  filter(str_length(Code) == 3) %>%
  select(Code, `Lvel 1` = `1`, `Lvel 2` = `2`, `Lvel 3` = `3`)

identical(result, test)
# [1] TRUE

  • 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

    • 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

input_data = pd.read_excel("CH-020 Transform Higherarchey format.xlsx", usecols="B:C", skiprows=1, nrows=17)
test = pd.read_excel("CH-020 Transform Higherarchey format.xlsx", usecols="E:H", skiprows=1, nrows=9)

result = input_data.assign(
    level=input_data["Code"].astype(str).str.len(),
    first_digit=input_data["Code"].astype(str).str[0],
)
result = result.pivot(index=["Code", "first_digit"], columns="level", values="Description").reset_index()
result = result.sort_values(["first_digit", "Code"])
result[["1", "2", "3"]] = result.groupby("first_digit")[["1", "2", "3"]].ffill()
result = result.loc[result["Code"].astype(str).str.len() == 3, ["Code", "1", "2", "3"]]
result.columns = ["Code", "Lvel 1", "Lvel 2", "Lvel 3"]

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

    • 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 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.