Excel BI - Excel Challenge 741

excel-challenges

excel-formulas

🔰 Transform the problem table into result table.

Published

March 24, 2026

Illustration for Excel BI - Excel Challenge 741

Challenge Description

🔰 Transform the problem table into result table. Here numbers are sum for

Solutions

library(tidyverse)
library(readxl)

path = "Excel/700-799/741/741 Pivot.xlsx"
input = read_excel(path, range = "A2:A10")
test = read_excel(path, range = "C2:F5")

result = input %>%
  mutate(
    Item = str_extract(Data, "Item\\d+"),
    Group = str_extract(Data, "(?<=Group )\\w")
  ) %>%
  mutate(Data = str_remove_all(Data, "Item\\d+|Group [ABC]")) %>%
  mutate(Data = str_extract_all(Data, "\\d+")) %>%
  mutate(Data = map_dbl(Data, ~ sum(as.numeric(.)))) %>%
  summarise(Total = sum(Data), .by = c("Item", "Group")) %>%
  pivot_wider(names_from = Item, values_from = Total)

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

Logic: Read the workbook ranges needed for the challenge; Derive the required intermediate columns; Parse the packed text or string structure; Aggregate or rank the data at the required grouping level.
Strengths: The solution stays close to the text pattern itself, which makes the extraction logic easy to audit.
Areas for Improvement: The solution assumes the workbook layout and selected ranges remain stable, so any structural change in the sheet would require small adjustments.
Gem: A small number of well-targeted text patterns does most of the heavy lifting.

import pandas as pd
import re

path = "700-799/741/741 Pivot.xlsx"
input_df = pd.read_excel(path, usecols="A", skiprows=1, nrows=8, names=["Data"])
test = pd.read_excel(path, usecols="C:F", skiprows=1, nrows=3)

input_df["Item"] = [re.search(r'Item\d+', s).group(0) if re.search(r'Item\d+', s) else None for s in input_df["Data"]]
input_df["Group"] = [re.search(r'Group (\w)', s).group(1) if re.search(r'Group (\w)', s) else None for s in input_df["Data"]]
input_df["Data"] = [sum(map(int, re.findall(r'\d+', re.sub(r'Item\d+|Group [ABC]', '', s)))) for s in input_df["Data"]]

result = input_df.pivot_table(index="Group", columns="Item", values="Data", aggfunc="sum").reset_index()

print(test.equals(result)) # True

The Python version expresses the core extraction rule directly and keeps the pattern matching easy to review.

Difficulty Level

Medium

The individual steps are manageable, but the correct transformation pattern is not obvious from the raw data.