Excel BI - PowerQuery Challenge 357

excel-challenges
power-query
Pivot the table as shown. Sort Region & Year and also Categories column.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 357

Challenge Description

Pivot the table as shown. Sort Region & Year and also Categories column.

Solutions

library(tidyverse)
library(readxl)

path <- "Power Query/300-399/357/PQ_Challenge_357.xlsx"
input <- read_excel(path, range = "A1:D51")
test <- read_excel(path, range = "F1:L11")

result = input %>%
  mutate(Categories = str_split(Categories, ";")) %>%
  rowwise() %>%
  mutate(no_cat = length(Categories)) %>%
  ungroup() %>%
  mutate(cat_amount = Amount / no_cat) %>%
  mutate(
    Region = ifelse(is.na(Region), "Unknown", Region),
    Year = year(OrderDate)
  ) %>%
  unnest(Categories) %>%
  pivot_wider(
    id_cols = c(Region, Year),
    names_from = Categories,
    names_sort = T,
    values_from = cat_amount,
    values_fn = sum,
    values_fill = 0
  ) %>%
  arrange(Region, Year) %>%
  mutate(across(-c(Region, Year), ~ round(.x, 2)))

all.equal(result, test)
# almost identical, differences on rounding.

  • 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

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

  • 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/300-399/357/PQ_Challenge_357.xlsx"
input = pd.read_excel(path, usecols="A:D", nrows = 50)
test  = pd.read_excel(path, usecols="F:L", nrows = 10).rename(columns=lambda col: col.replace('.1', ''))

result = (
    input
      .assign(
          Categories=lambda d: d["Categories"].fillna("").astype(str).str.split(";"),
          no_cat=lambda d: d["Categories"].str.len(),
          Region=lambda d: d["Region"].fillna("Unknown"),
          Year=lambda d: pd.to_datetime(d["OrderDate"]).dt.year
      )
      .assign(cat_amount=lambda d: d["Amount"] / d["no_cat"])
      .explode("Categories")
      .groupby(["Region", "Year", "Categories"], as_index=False)
      .agg(cat_amount=("cat_amount", "sum"))
      .pivot(
          index=["Region", "Year"],
          columns="Categories",
          values="cat_amount"
      )
      .fillna(0)
      .reset_index()
      .sort_values(["Region", "Year"])
)

num_cols = result.columns.difference(["Region", "Year"])
result[num_cols] = result[num_cols].round(2)

print(result.equals(test))
# Discrepancies based on rounding.

  • 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 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.