Excel BI - PowerQuery Challenge 194

excel-challenges
power-query
Date Amt1 Amt2 Amt3 RESULT PROBLEM
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 194

Challenge Description

Date Amt1 Amt2 Amt3 RESULT PROBLEM

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ Challenge_194.xlsx"
input = read_xlsx(path, range = "A1:D10")
test  = read_xlsx(path, range = "F1:I10")

result = input %>%
  pivot_longer(cols = -c(1), names_to = "Amt", values_to = "Value") %>%
  mutate(val = lag(Value, default = 0),
         diff = Value - val) %>%
  select(-c(Value, val)) %>%
  pivot_wider(names_from = Amt, values_from = diff)

identical(result, test)  
# [1] TRUE

  • Logic:

    • 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_194.xlsx'
input = pd.read_excel(path, usecols = "A:D")
test  = pd.read_excel(path, usecols = "F:I") 
test.columns = input.columns

result = input.melt(id_vars=['Date'], var_name='Amt', value_name='Value') \
    .sort_values(['Date', 'Amt']).reset_index(drop = True) \
    .assign(val=lambda x: x['Value'].shift(fill_value=0),
            diff=lambda x: x['Value'] - x['val']) \
    .drop(columns=['Value', 'val']) \
    .pivot(index='Date', columns='Amt', values='diff').reset_index(drop=False) 

print(result.equals(test)) # 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 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.