Excel BI - PowerQuery Challenge 144

excel-challenges
power-query
Group Divide the data set for a group in 2 halves. In case of odd number of entries say n, first half will be (n+1)/2 and second half will be (n-1)/2.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 144

Challenge Description

Group Divide the data set for a group in 2 halves. In case of odd number of entries say n, first half will be (n+1)/2 and second half will be (n-1)/2.

Solutions

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_144.xlsx", range = "A1:B16")
test  = read_excel("Power Query/PQ_Challenge_144.xlsx", range = "E1:G16")

result = input %>%
  group_by(Group) %>%
  mutate(Half = ifelse(row_number() <= ceiling(n()/2), "First", "Second")) %>%
  ungroup() %>%
  group_by(Group, Half) %>%
  mutate(`Running Total` = cumsum(Value)) %>%
  ungroup() %>%
  select(-Half)

identical(result, test)
#> [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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

input_data = pd.read_excel("PQ_Challenge_144.xlsx", usecols="A:B", nrows=16)
test = pd.read_excel("PQ_Challenge_144.xlsx", usecols="E:G", nrows=16)

result = input_data.copy()
group_pos = result.groupby("Group").cumcount() + 1
group_size = result.groupby("Group")["Value"].transform("size")
result["Half"] = ["First" if pos <= (size + 1) // 2 else "Second" for pos, size in zip(group_pos, group_size)]
result["Running Total"] = result.groupby(["Group", "Half"])["Value"].cumsum()
result = result.drop(columns="Half")

print(result.equals(test))

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Applies the rule iteratively until the output is complete

  • 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 moderate:

  • It combines reshaping, grouping, or parsing steps that are common in Power Query style problems.

  • The main challenge is reproducing the workbook output structure exactly.