Omid - Challenge 181

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 181

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)
library(janitor)

path = "files/CH-181 Table Transformation.xlsx"
input = read_excel(path, range = "C2:C41", col_types = "text")
test  = read_excel(path, range = "E2:H9") %>%
  mutate(From = as.Date(From),
         To = as.Date(To))

result = input %>%
  mutate(row = cumsum(str_detect(Name, "^[A-Z]{3}$"))) %>%
  fill(row, .direction = "down") %>%
  group_by(row) %>%
  mutate(Name1 = first(Name[str_detect(Name, "^[A-Z]{3}$")]),
         prop = ifelse(Name %in% c("From", "To", "Status"), Name, NA)) %>%
  fill(prop, .direction = "down") %>%
  filter(Name != prop | is.na(prop)) %>%
  pivot_wider(names_from = prop, values_from = Name) %>%
  mutate(From = excel_numeric_to_date(as.numeric(From)), 
         To = excel_numeric_to_date(as.numeric(To))) %>%
  ungroup() %>%
  select(Name = Name1, From, To, Status) 

all.equal(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
import numpy as np

path = "CH-181 Table Transformation.xlsx"

input = pd.read_excel(path, usecols="C", skiprows=1, nrows=40, dtype=str)

test = pd.read_excel(path, usecols="E:H", skiprows=1, nrows=7).rename(columns=lambda x: x.split('.')[0])
test['From'] = pd.to_datetime(test['From'], errors='coerce').dt.date
test['To'] = pd.to_datetime(test['To'], errors='coerce').dt.date

input['row'] = input['Name'].str.match(r'^[A-Z]{3}$').cumsum()
input['row'] = input['row'].ffill()

grouped = input.groupby('row')
input['Name1'] = grouped['Name'].transform(lambda x: x[x.str.match(r'^[A-Z]{3}$')].iloc[0])
input['prop'] = np.where(input['Name'].isin(['From', 'To', 'Status']), input['Name'], np.nan)
input['prop'] = input['prop'].ffill()

filtered = input.groupby('row').apply(lambda x: x[~((x['Name'] == x['prop']) & x['prop'].notna())]).reset_index(drop=True)
filtered['prop'] = np.where(filtered['Name'] == filtered['Name1'], 'Name', filtered['prop'])

result = filtered.pivot(index='row', columns='prop', values='Name').reset_index(drop=True)
result['From'] = pd.to_datetime(result['From'], errors='coerce').dt.date
result['To'] = pd.to_datetime(result['To'], errors='coerce').dt.date

result = result[['Name', 'From', 'To', 'Status']]
result = result.rename_axis(None, axis=1)

print(result.equals(test)) # 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

  • 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.