Excel BI - PowerQuery Challenge 255

excel-challenges
power-query
Transpose the data given in problem table to result table.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 255

Challenge Description

Transpose the data given in problem table to result table.

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_255.xlsx"
input = read_excel(path, range = "A1:C14")
test  = read_excel(path, range = "E1:J4")

result = input %>%
  pivot_wider(names_from = "Task", values_from = "Date Time") %>%
  mutate(across(`2`:`6`, 
                ~if_else(!is.na(.), 
                         round(as.numeric(difftime(., `1`, units = "hours")),2), 
                         NA_real_), 
                .names = "{.col}-1")) %>%
  select(-c(`1`:`6`)) %>%
  rename(Task = Ticket)

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 = "PQ_Challenge_255.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=14)
test = pd.read_excel(path, usecols="E:J", nrows=3)

input = input.pivot(index='Ticket', columns='Task', values='Date Time')

for col in range(2, 7):
    input[f'{col}-1'] = input.apply(
        lambda row: round((row[col] - row[1]).total_seconds() / 3600, 2) if pd.notna(row[col]) else None, axis=1
    )

input.drop(columns=range(1, 7), inplace=True)
input.reset_index(inplace=True)
input.rename(columns={'Ticket': 'Task.1'}, inplace=True)

print(input.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

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

    • 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.