Excel BI - Excel Challenge 700

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excel-formulas
🔰 Answer Expected Data Alphabet Value A-234, E-256, D-3673 A Q-897, R-1, S-87 D T-90, A-308, R-45 E
Published

March 24, 2026

Illustration for Excel BI - Excel Challenge 700

Challenge Description

🔰 Answer Expected Data Alphabet Value A-234, E-256, D-3673 A Q-897, R-1, S-87 D T-90, A-308, R-45 E

Solutions

library(tidyverse)
library(readxl)

path = "Excel/700 Pivot Data.xlsx"
input = read_excel(path, range = "A2:A6")
test = read_excel(path, range = "C2:D10")

result = input %>%
  separate_rows(Data, sep = ", ") %>%
  separate(Data, into = c("Alphabet", "Value"), sep = "-", convert = TRUE) %>%
  summarise(Value = sum(Value), .by = Alphabet) %>%
  arrange(Alphabet)

r2 = result %>%
  add_row(Alphabet = "TOTAL", Value = sum(result$Value))

all.equal(r2, test)
#> [1] TRUE
  • Logic: Read the workbook ranges needed for the challenge; Parse the packed text or string structure; Aggregate or rank the data at the required grouping level; Reshape the result into the workbook output format.
  • Strengths: The reshaping step mirrors the workbook output closely instead of forcing extra post-processing.
  • 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: The last reshape turns a raw transformation into something that already looks like a report.
import pandas as pd

path = "700 Pivot Data.xlsx"
input = pd.read_excel(path, usecols="A", skiprows=1, nrows=5)
test = pd.read_excel(path, usecols="C:D", skiprows=1, nrows=9)

result = (
    input["Data"]
    .str.extractall(r"(\w+)-(\d+)")
    .rename(columns={0: "Alphabet", 1: "Value"})
    .astype({"Value": int})
    .groupby("Alphabet", as_index=False)["Value"]
    .sum()
)

r2 = pd.concat([result, pd.DataFrame([{"Alphabet": "TOTAL", "Value": result["Value"].sum()}])], ignore_index=True)

print(r2.equals(test))  # True

The Python version follows the same grouped logic and keeps the transformation explicit in a dataframe pipeline.

Difficulty Level

Medium

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