Excel BI - PowerQuery Challenge 145

excel-challenges
power-query
Date Store Sale Column1 A B
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 145

Challenge Description

Date Store Sale Column1 A B

Solutions

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_145.xlsx", range = "A1:C16")
test  = read_excel("Power Query/PQ_Challenge_145.xlsx", range = "F1:I16")

result = input %>%
  group_by(Store) %>%
  mutate(min_date = min(Date),
         year = case_when(
           between(Date, min_date, min_date + years(1)) ~ 1,
           between(Date, min_date + years(1), min_date + years(2)) ~ 2,
           between(Date, min_date + years(2), min_date + years(3)) ~ 3,
           between(Date, min_date + years(3), min_date + years(4)) ~ 4,
           between(Date, min_date + years(4), min_date + years(5)) ~ 5
         )) %>%
  ungroup()  %>%
  group_by(Store, year) %>%
  mutate(Column1 = cumsum(Sale)) %>%
  ungroup() %>%
  select(-year, -min_date)

identical(result, test)
#> [1] 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

  • 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

input_data = pd.read_excel("PQ_Challenge_145.xlsx", usecols="A:C", nrows=16)
test = pd.read_excel("PQ_Challenge_145.xlsx", usecols="F:I", nrows=16)

result = input_data.copy()
result["Date"] = pd.to_datetime(result["Date"])
result["min_date"] = result.groupby("Store")["Date"].transform("min")
result["year"] = ((result["Date"] - result["min_date"]).dt.days / 365.25).floordiv(1).astype(int) + 1
result["Column1"] = result.groupby(["Store", "year"])["Sale"].cumsum()
result = result.drop(columns=["year", "min_date"])

print(result.equals(test))

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • 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.