Excel BI - PowerQuery Challenge 271

excel-challenges
power-query
Group From first group, pick up top 1 on the basis of revenue, from second group, pick up top 2 and so on. First group is that which appears first, second group appears second….(Data is already sorted on Group). Insert a Rank column also.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 271

Challenge Description

Group From first group, pick up top 1 on the basis of revenue, from second group, pick up top 2 and so on. First group is that which appears first, second group appears second….(Data is already sorted on Group). Insert a Rank column also.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_271.xlsx"
input = read_excel(path, range = "A1:C19")
test  = read_excel(path, range = "E1:H12")

result = input %>%
  mutate(group_num = consecutive_id(Group)) %>%
  mutate(Rank = dense_rank(-Revenue), .by = group_num) %>%
  filter(group_num >= Rank) %>%
  arrange(Group,Rank, Company) %>%
  select(Group, Company, Revenue, Rank)

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

  • Logic:

    • Reads the workbook range needed for the challenge

    • Builds helper columns that drive the final output

  • Strengths:

    • The R solution stays close to the workbook logic and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the workbook layout and selected ranges remain stable.

  • Gem:

    • The best part of the solution is choosing the right intermediate shape before formatting the final output.
import pandas as pd

path = "PQ_Challenge_271.xlsx"
input = pd.read_excel(path,  usecols="A:C", nrows=19)
test = pd.read_excel(path, usecols="E:H", nrows=11).rename(columns=lambda col: col.split('.')[0])

input['Rank'] = (
    input.groupby(
        (input['Group'] != input['Group'].shift()).cumsum()
    )['Revenue']
    .rank(method='dense', ascending=False)
    .astype('int64')    
)

result = (
    input[input['Rank'] <= (input['Group'] != input['Group'].shift()).cumsum()]
    .sort_values(['Group', 'Rank', 'Company'])
    [['Group', 'Company', 'Revenue', 'Rank']]
    .reset_index(drop=True)
)

print(test.equals(result)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • Strengths:

    • The Python version follows the same workbook rule in a direct pandas-oriented implementation.

  • Areas for Improvement:

    • As with the R version, any workbook layout change would require small adjustments.

  • Gem:

    • The implementation stays close to the source challenge instead of adding unnecessary abstraction.

Difficulty Level

This task is easy to moderate:

  • The transformation rule is readable, but the final layout still requires a careful implementation.