Excel BI - PowerQuery Challenge 260

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

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 260

Challenge Description

Group Transpose the problem table into result

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_260.xlsx"
input = read_excel(path, range = "A1:C16")
test  = read_excel(path, range = "E1:N4") %>% mutate(across(everything(), as.character))

names_ = names(test)

df_sum <- input %>% 
  group_by(Group, Dept) %>% 
  summarize(Team = paste(Team, collapse = ", "), .groups = "drop") %>% 
  arrange(Team) %>% 
  mutate(rn = row_number(), .by = Group)

r1 <- df_sum %>% 
  pivot_wider(id_cols = rn, names_from = c(Group, Dept), values_from = Team, names_sep = "  ") %>% 
  left_join(df_sum %>% 
              select(-Team) %>% 
              pivot_wider(names_from = Group, values_from = Dept),
            by = "rn") %>% 
  select(-rn) %>% 
  rename_with(~ifelse(nchar(.) == 2, ., str_trim(substr(., 4, nchar(.))))) %>% 
  select(any_of(names_))

all.equal(r1, test, check.attributes = FALSE)
#> [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data into the structure required by the result table

    • 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

path = "PQ_Challenge_260.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=16)
test = pd.read_excel(path, usecols="E:N", nrows=3).astype(str).applymap(lambda x: x.replace('.0', ''))
names_ = list(test.columns)

df_sum = (
    input
    .groupby(['Group', 'Dept'], as_index=False)
    .agg({'Team': lambda x: ", ".join(x.astype(str)) if len(x) > 1 else x.iloc[0]})
    .sort_values('Team', key=lambda x: x.astype(str))
)
df_sum['rn'] = df_sum.groupby('Group').cumcount() + 1

r1_left = df_sum.pivot(index='rn', columns=['Group', 'Dept'], values='Team').reset_index()
r1_left.columns = ['rn'] + [f"{g}  {d}" for g, d in r1_left.columns[1:]]
r1_right = df_sum.drop(columns='Team').pivot(index='rn', columns='Group', values='Dept').reset_index()
r1_merge = pd.merge(r1_left, r1_right, on='rn').drop(columns='rn').rename(columns=lambda x: x[3:].strip() if len(x) > 2 else x)
r1_final = r1_merge[[col for col in names_ if col in r1_merge.columns]].astype(str)

print(r1_final.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data into the structure required by the result table

    • 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.