Omid - Challenge 337

data-challenges
advanced-exercises
🔰 Challenge 337: Rows Grouping!
Published

March 24, 2026

Illustration for Omid - Challenge 337

Challenge Description

🔰 Challenge 337: Rows Grouping!

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/337/CH-337 Rows Grouping.xlsx"
input <- read_excel(path, range = "B2:C11")
test <- read_excel(path, range = "G2:H7")

result = input %>%
  separate_wider_delim(
    cols = Level,
    delim = " ",
    names = c("Level1", "Level2"),
    too_few = "align_start"
  ) %>%
  summarise(
    Level = paste0(first(Level1), " ", paste(Level2, collapse = ",")) %>%
      str_trim(),
    .by = `Issue ID`
  )

all.equal(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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
import numpy as np

excel_path = "300-399/337/CH-337 Rows Grouping.xlsx"
input = pd.read_excel(excel_path, usecols="B:C", skiprows=1, nrows=10)
test = pd.read_excel(excel_path, usecols="G:H", skiprows=1, nrows=5)
test.columns = [col.replace('.1', '') for col in test.columns]

input[["Level1", "Level2"]] = input["Level"].str.split(" ", n=1, expand=True)
grouped = (
    input
    .groupby("Issue ID", as_index=False)
    .agg({
        "Level1": "first",
        "Level2": lambda x: ",".join(x.dropna())
    })
)
grouped["Level"] = (grouped["Level1"] + " " + grouped["Level2"]).str.strip()
result = grouped[["Issue ID", "Level"]]

print(result.equals(test))
# True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Applies the rule iteratively until the output stabilizes

  • 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.