Excel BI - PowerQuery Challenge 221

excel-challenges
power-query
Project Task Activity Project_Index Task_Index Activity_Index
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 221

Challenge Description

Project Task Activity Project_Index Task_Index Activity_Index

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_221.xlsx"
input = read_excel(path, range = "A1:C20")
test  = read_excel(path, range = "E1:J20")

result = input %>%
  mutate(Project_Index = as.numeric(as.factor(Project))) %>%
  mutate(Task_Index = as.numeric(paste0(Project_Index,".",as.numeric(as.factor(Task)))) , .by = Project) %>%
  mutate(Activity_Index = paste0(Task_Index,".", as.numeric(as.factor(Activity))), .by = c(Project, Task)) 

all.equal(result, test)
# [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
import numpy as np

path = "PQ_Challenge_221.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=20)
test = pd.read_excel(path, usecols="E:J", nrows=20).rename(columns=lambda x: x.replace('.1', ''))
test["Task_Index"] = test["Task_Index"].astype(str)

input['Project_Index'] = (input['Project'].astype('category').cat.codes + 1).astype(np.int64)
input['Task_Index'] = (input['Project_Index'].astype(str) + "." +
                          input.groupby('Project')['Task']
                          .transform(lambda x: (x.astype('category').cat.codes + 1).astype(str)))
input['Activity_Index'] = (input['Task_Index'] + "." +
                              input.groupby(['Project', 'Task'])['Activity']
                              .transform(lambda x: (x.astype('category').cat.codes + 1).astype(str)))

print(input.equals(test))   # 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.