Excel BI - PowerQuery Challenge 146

excel-challenges
power-query
Group For a group -
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 146

Challenge Description

Group For a group -

Solutions

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_146.xlsx", range = "A1:D14")
test  = read_excel("Power Query/PQ_Challenge_146.xlsx", range = "F1:I7")

result = input %>%
  group_by(Group) %>%
  mutate(Category = ifelse(Value == Threshold, "Equal",
                           ifelse(Value > Threshold, "High", "Low"))) %>%
  ungroup() %>%
  filter(Category != "Equal") %>%
  group_by(Group, Category) %>%
  mutate(valid = ifelse(Category == "High", min(Value), max(Value))) %>%
  ungroup() %>%
  filter(Value == valid) %>%
  select(-valid, -Category)

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_146.xlsx", usecols="A:D", nrows=14)
test = pd.read_excel("PQ_Challenge_146.xlsx", usecols="F:I", nrows=7)

result = input_data.copy()
result["Category"] = result.apply(
    lambda r: "Equal" if r["Value"] == r["Threshold"] else ("High" if r["Value"] > r["Threshold"] else "Low"),
    axis=1,
)
result = result[result["Category"] != "Equal"].copy()
result["valid"] = result.groupby(["Group", "Category"])["Value"].transform("min")
low_mask = result["Category"] == "Low"
result.loc[low_mask, "valid"] = result.loc[low_mask].groupby(["Group", "Category"])["Value"].transform("max")
result = result[result["Value"] == result["valid"]].drop(columns=["valid", "Category"]).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

  • 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.