Excel BI - PowerQuery Challenge 232

excel-challenges
power-query
Store Date Quantity A B C
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 232

Challenge Description

Store Date Quantity A B C

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_232.xlsx"
input = read_excel(path, range = "A1:C7")
test  = read_excel(path, range = "E1:G13")

result = input %>%
  group_by(Store) %>%
  complete(Date = seq(min(Date), max(Date), by = "day")) %>%
  ungroup() %>%
  mutate(has_val = cumsum(!is.na(Quantity))) %>%
  fill(Quantity) %>%
  mutate(Quantity = cumsum(Quantity), .by = c(Store, has_val)) %>%
  select(-has_val)

all.equal(result, test, check.attributes = FALSE)
#> [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

path = "PQ_Challenge_232.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=6)
test = pd.read_excel(path, usecols="E:G", nrows=13).rename(columns=lambda x: x.split('.')[0])

input['Date'] = pd.to_datetime(input['Date'])
input['max_date'] = input.groupby('Store')['Date'].transform('max')
input['min_date'] = input.groupby('Store')['Date'].transform('min')

date_range = pd.date_range(input['Date'].min(), input['Date'].max(), freq='D')
complete = pd.MultiIndex.from_product([input['Store'].unique(), date_range], names=['Store', 'Date']).to_frame(index=False)

result = complete.merge(input, on=['Store', 'Date'], how='left')
result[['max_date', 'min_date']] = result.groupby('Store')[['max_date', 'min_date']].ffill()
result = result.query('min_date <= Date <= max_date')
result['Quantity_has_value'] = result['Quantity'].isna().apply(lambda x: not x)
result['Cumulative'] = result.groupby('Store')['Quantity_has_value'].cumsum()
result['Quantity'] = result.groupby('Store')['Quantity'].ffill()
result['Quantity'] = result.groupby(['Store', 'Cumulative'])['Quantity'].cumsum().astype('int64')
result = result[['Store', 'Date', 'Quantity']].reset_index(drop=True)

print(result.equals(test)) # True

  • 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.