Excel BI - PowerQuery Challenge 157

excel-challenges
power-query
Group Except the Group column, for every other column, log the value within a group if value changes.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 157

Challenge Description

Group Except the Group column, for every other column, log the value within a group if value changes.

Solutions

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_157.xlsx", range = "A1:E31")
test  = read_excel("Power Query/PQ_Challenge_157.xlsx", range = "G1:K31") %>%
  mutate(across(everything(), as.character))

log_changes <- function(data) {
  data %>%
    mutate(across(everything(), as.character)) %>%
    group_by(Group) %>%
    mutate(across(everything(),
                  ~if_else(lag(.x) != .x & !is.na(lag(.x)), .x, NA_character_))) %>%
    ungroup()
}

result = log_changes(input) 

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_157.xlsx", usecols="A:E", nrows=31)
test = pd.read_excel("PQ_Challenge_157.xlsx", usecols="G:K", nrows=31).astype(str)

result = input_data.astype(str).copy()

def mark_changes(group):
    out = group.copy()
    for col in out.columns:
        out[col] = out[col].where(out[col].shift().ne(out[col]) & out[col].shift().notna())
    return out

result = result.groupby("Group", group_keys=False).apply(mark_changes).reset_index(drop=True)
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.