Excel BI - PowerQuery Challenge 341

excel-challenges
power-query
Pivot the table as shown.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 341

Challenge Description

Pivot the table as shown.

Solutions

library(tidyverse)
library(readxl)

path <- "Power Query/300-399/341/PQ_Challenge_341.xlsx"
input <- read_excel(path, range = "A1:D11")
test  <- read_excel(path, range = "G1:K11")

result = input %>%
  t() %>%
  as.data.frame() %>%
  rownames_to_column("dept") %>%
  pivot_longer(-dept, names_to = "col", values_to = "score") %>%
  mutate(col = (parse_number(col)+1) %/% 2) %>%
  mutate(rn = row_number(), .by = c(dept, col)) %>%
  pivot_wider(names_from = rn, values_from = score) %>%
  select(-col) %>%
  separate_wider_delim(`2`,delim = ", ", names = c("Age", "Nationality","Salary")) %>%
  na.omit() %>%
  distinct() %>%
  select(Dept = dept, Employee = '1', Age, Nationality,Salary) %>%
  mutate(across(c(Age, Salary), as.numeric))

all.equal(result, 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

    • 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 = "Power Query/300-399/341/PQ_Challenge_341.xlsx"
input = pd.read_excel(path,  usecols="A:D", nrows=11)
test = pd.read_excel(path, usecols="G:K", nrows=11).rename(columns=lambda x: x.rstrip())

input_t = input.T
input_t["Dept"] = input_t.index
input_long = input_t.reset_index(drop=True).melt(id_vars="Dept", var_name="col", value_name="score")

input_long["col"] = input_long["col"].astype(str)
input_long["col_group"] = input_long["col"].str.extract(r"(\d+)").astype(int)  // 2

input_long["rn"] = input_long.groupby(["Dept", "col_group"]).cumcount()

pivoted = input_long.pivot_table(
    index=["Dept", "col_group"],
    columns="rn",
    values="score",
    aggfunc="first"
).reset_index()

pivoted = pivoted.drop(columns="col_group")
pivoted = pivoted.rename(columns={0: "Employee", 1: "data"})
data_split = pivoted["data"].str.split(", ", expand=True)
data_split.columns = ["Age", "Nationality", "Salary"]

data_split["Age"] = pd.to_numeric(data_split["Age"], errors="coerce")
data_split["Salary"] = pd.to_numeric(data_split["Salary"], errors="coerce")

pivoted = pd.concat([pivoted.drop(columns="data"), data_split], axis=1)
pivoted = pivoted.drop_duplicates()

pivoted = pivoted.sort_values(by=["Dept", "Employee"]).reset_index(drop=True)
test = test.sort_values(by=["Dept", "Employee"]).reset_index(drop=True)

print(pivoted.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

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

  • 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.