Excel BI - PowerQuery Challenge 245

excel-challenges
power-query
Names Subjects Days Arts English Maths
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 245

Challenge Description

Names Subjects Days Arts English Maths

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_245.xlsx"
input = read_excel(path, range = "A1:B6")
test = read_excel(path, range = "D1:G9")

r1 = input %>%
  separate_rows(Subjects, sep = ", ") %>%
  group_by(Subjects) %>%
  summarise(Names = list(sort(Names)), .groups = 'drop')

weekdays = c("Mon", "Tue", "Wed", "Thu", "Fri")
weekday_n = length(weekdays)

subjects_list = r1 %>%
  summarise(Names = list(unlist(Names)), .by = Subjects) %>%
  deframe()

longest_subject = max(map_int(r1$Names, length))

first_col = rep("", longest_subject * ceiling(weekday_n / longest_subject))
subjects = map(subjects_list, ~ rep(.x, ceiling(weekday_n / length(.x))))

df = tibble(
  Days = c(weekdays, map_chr(seq_along(first_col) - weekday_n, ~paste0("Backup", .x))),
  Arts = c(subjects[["Arts"]], rep(NA, length(first_col) - length(subjects[["Arts"]]))),
  English = c(subjects[["English"]], rep(NA, length(first_col) - length(subjects[["English"]]))),
  Maths = c(subjects[["Maths"]], rep(NA, length(first_col) - length(subjects[["Maths"]])))
)

all.equal(df, test, check.attributes = FALSE)
# TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • 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 numpy as np

path = "PQ_Challenge_245.xlsx"
input = pd.read_excel(path, usecols="A:B", nrows=6)
test = pd.read_excel(path, usecols="D:G", nrows=9).fillna('')

input = input.assign(Subjects=input['Subjects'].str.split(', ')).explode('Subjects')
r1 = input.groupby('Subjects')['Names'].apply(lambda x: sorted(x.tolist())).reset_index()
weekdays = ["Mon", "Tue", "Wed", "Thu", "Fri"]
weekday_n = len(weekdays)
subjects_list = r1.set_index('Subjects')['Names'].apply(lambda x: np.array(x)).to_dict()
longest_subject = max(map(len, subjects_list.values()))
first_col = [""] * (longest_subject * -(-weekday_n // longest_subject))
subjects = {k: np.tile(v, -(-weekday_n // len(v))) for k, v in subjects_list.items()}

df = pd.DataFrame({
    'Days': weekdays + [f"Backup{i}" for i in range(1, len(first_col) - weekday_n + 1)],
    **{subject: np.concatenate([names, [''] * (len(first_col) - len(names))]) for subject, names in subjects.items()}
})

print(df.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds helper columns that drive the final output

    • 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.