Omid - Challenge 171

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 171

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-171 Table Transformation.xlsx"
input = read_excel(path, range = "C2:C148")
test = read_excel(path, range = "E2:H8") %>%
  mutate(across(c(From, To), ~ str_replace(., "availabe", "available")))

r1 = input %>% filter(row_number() %% 2 == 1)
r2 = input %>% filter(row_number() %% 2 == 0) %>% rename(Value = Name)

result = cbind(r1, r2) %>%
  filter(Name %in% c('From', 'To', 'Status') | Value == "Process") %>%
  mutate(group = ifelse(Value == "Process", Name, NA)) %>%
  fill(group) %>%
  filter(group != Name) %>%
  pivot_wider(names_from = Name, values_from = Value)

all.equal(result, test, check.attributes = FALSE)
# [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

    • 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 = "CH-171 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="C", skiprows=1, nrows=148, names=["Name"])
test = pd.read_excel(path, usecols="E:H", skiprows=1, nrows=6).rename(columns=lambda x: x.split('.')[0])
test[['From', 'To']] = test[['From', 'To']].replace("availabe", "available", regex=True)
test = test.sort_values(by='Name').reset_index(drop=True)

even_rows, odd_rows = input.iloc[::2].reset_index(drop=True), input.iloc[1::2].reset_index(drop=True)
result = pd.DataFrame({'Name': even_rows['Name'], 'Value': odd_rows['Name']})
result = result[(result['Name'].isin(['From', 'To', 'Status'])) | (result['Value'] == "Process")]
result['group'] = result.apply(lambda row: row['Name'] if row['Value'] == "Process" else None, axis=1).ffill()
result = result[result['Name'] != result['group']]

pivot_result = result.pivot(index='group', columns='Name', values='Value').reset_index().rename(columns={'group': 'Name'})
pivot_result = pivot_result[['Name', 'From', 'To', 'Status']]

print(pivot_result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • Parses the text patterns directly instead of relying on manual cleanup

  • 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.