Excel BI - PowerQuery Challenge 202

excel-challenges
power-query
Transpose the problem table into result table.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 202

Challenge Description

Transpose the problem table into result table.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_202.xlsx"
input = read_excel(path, range = "A1:C18")
test  = read_excel(path, range = "E1:F18")

result = input %>% 
  mutate(L1 = cumsum(!is.na(Name1))) %>%
  mutate(L2 = cumsum(!is.na(Name2)), .by = L1) %>%
  mutate(L3 = cumsum(!is.na(Name3)), .by = c(L1, L2)) %>%
  mutate(across(starts_with("L"), ~ ifelse(. == 0, NA, .))) %>%
  mutate(across(everything(), ~  as.character(.))) %>%
  rowwise() %>%
  mutate(Names = coalesce(Name3, Name2, Name1), 
         Serial = case_when(
           !is.na(L3) ~ paste(L1, L2, L3, sep = "."),
           !is.na(L2) ~ paste(L1,L2, sep = "."),
           !is.na(L1) ~ L1
         )) %>%
  ungroup() %>%
  select(Serial, Names)

identical(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

path = "PQ_Challenge_202.xlsx"
input = pd.read_excel(path, usecols="A:C")
test = pd.read_excel(path, usecols="E:F")

result = input.copy()
result["L1"] = result["Name1"].notna().cumsum()
result["L2"] = result.groupby("L1")["Name2"].transform(lambda x: x.notna().cumsum())
result["L3"] = result.groupby(["L1", "L2"])["Name3"].transform(lambda x: x.notna().cumsum())

result[["L1", "L2", "L3"]] = result[["L1", "L2", "L3"]].astype("Int64").astype(str).replace("0", pd.NA)
result["Names"] = result["Name3"].combine_first(result["Name2"]).combine_first(result["Name1"])
result["Serial"] = result[["L1", "L2", "L3"]].apply(lambda x: ".".join(x.dropna()), axis=1)
result = result[["Serial", "Names"]]

print(result.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.