Excel BI - PowerQuery Challenge 175

excel-challenges
power-query
Name Family Generation No Next Generation Relantionship Joseph
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 175

Challenge Description

Name Family Generation No Next Generation Relantionship Joseph

Solutions

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_175.xlsx", range = "A1:C16")
test  = read_excel("Power Query/PQ_Challenge_175.xlsx", range = "E1:H19") %>%
  mutate(Relantionship = str_remove_all(Relantionship, " ")) # cleaned for purpose of validation

result = input %>%
  left_join(input, by = c("Family" = "Family")) %>%
  filter(`Generation No.x` == `Generation No.y` - 1) %>%
  # there is mispronunciation in the challenge, it should be "Relationship" not "Relantionship"
  unite("Relantionship", `Generation No.x`, `Generation No.y`, sep = "-") %>% 
  select(Name = `Name.x`,Family,`Next Generation` = `Name.y`, Relantionship  ) %>%
  arrange(Family, Relantionship  , Name, `Next Generation`)

identical(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Builds helper columns that drive the final output

    • Uses direct pattern parsing where the workbook encodes logic in text

    • Applies the rule iteratively until the output is complete

  • 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 re

input = pd.read_excel("PQ_Challenge_175.xlsx",  usecols="A:C", nrows=15)
test = pd.read_excel("PQ_Challenge_175.xlsx", usecols="E:H", nrows=19)
test["Relantionship"] = test["Relantionship"].str.replace(" ", "")
test.columns = ["Name", "Family", "Next Generation", "Relantionship"]

result = pd.merge(input, input, left_on="Family", right_on="Family")
result = result[result["Generation No_x"] == result["Generation No_y"] - 1]
result["Relantionship"] = result["Generation No_x"].astype(str) + "-" + result["Generation No_y"].astype(str)
result = result[["Name_x", "Family", "Name_y", "Relantionship"]].rename(columns={"Name_x": "Name", "Name_y": "Next Generation"})
result = result.sort_values(by=["Family", "Relantionship", "Name", "Next Generation"]).reset_index(drop=True)

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

  • 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.