Omid - Challenge 263

data-challenges
advanced-exercises
🔰 Group Challenge 263: UnGrouping!
Published

March 24, 2026

Illustration for Omid - Challenge 263

Challenge Description

🔰 Group Challenge 263: UnGrouping!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/263/CH-263 UnGrouping.xlsx"
input = read_excel(path, range = "B2:C5")
test  = read_excel(path, range = "G2:H17")

result = input %>%
  separate_wider_delim(Group, "-", names = c("From", "To")) %>%
  mutate(Date = map2(From, To, ~seq(as.Date(.x), as.Date(.y), by = "day"))) %>%
  unnest(Date) %>%
  mutate(n_days = n_distinct(Date),
         Sales = `Total Sales` / n_days,
         Date = as.POSIXct(Date), .by = From) %>%
  select(Date, Sales)

all.equal(result, test, check.attributes = FALSE)
# > [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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 = "200-299/263/CH-263 UnGrouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=3)
test = pd.read_excel(path, usecols="G:H", skiprows=1, nrows=16)

input[['From', 'To']] = input['Group'].str.split('-', expand=True)
result = (
    input
    .explode('Date', ignore_index=True)
    if 'Date' in input.columns else
    input.assign(Date=input.apply(lambda r: pd.date_range(r['From'], r['To']), axis=1)).explode('Date')
)
result['Sales'] = input.loc[result.index, 'Total Sales'].values / result.groupby('From')['Date'].transform('nunique')
result = result[['Date', 'Sales']].reset_index(drop=True)
print(result.equals(test))

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.