Excel BI - Excel Challenge 698

excel-challenges
excel-formulas
🔰 Answer Expected Alphabets D A B C E Align the data as shown.
Published

March 24, 2026

Illustration for Excel BI - Excel Challenge 698

Challenge Description

🔰 Answer Expected Alphabets D A B C E Align the data as shown. Generate header row also.

Solutions

library(tidyverse)
library(readxl)

path = "Excel/698 Alignment of Data.xlsx"
input = read_excel(path, range = "A2:A24")
test = read_excel(path, range = "C2:J7")

result = input %>%
  mutate(
    rn = row_number(),
    rn2 = match(Alphabets, LETTERS),
    .by = Alphabets
  ) %>%
  pivot_wider(names_from = rn, values_from = Alphabets) %>%
  arrange(rn2) %>%
  select(-rn2)

all.equal(result, test)
#> [1] TRUE
  • Logic: Read the workbook ranges needed for the challenge; Derive the required intermediate columns; 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 = "698 Alignment of Data.xlsx"
input = pd.read_excel(path, usecols="A", skiprows=1, nrows=23)
test = pd.read_excel(path, usecols="C:J", skiprows=1, nrows=5)

input['rn'] = input.groupby('Alphabets').cumcount() + 1                                 
input['rn2'] = input['Alphabets'].apply(lambda x: ord(x) - ord('A') + 1)
result = input.pivot(index='rn2', columns='rn', values='Alphabets').reset_index(drop=True)
result = result.sort_index().reset_index(drop=True)

result.columns.name = None
print(result.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.