Excel BI - PowerQuery Challenge 215

excel-challenges
power-query
Assuming today’s date is 7-Sep-24, pivot the table as shown for Outstanding loans. If Paid Date is not blank, it means that loan is not outstanding. For outstanding loans, outstanding days = Today’s date (7-Sep-24) - Due Date. If Due Date > Today’s Date then outstanding days is 0.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 215

Challenge Description

Assuming today’s date is 7-Sep-24, pivot the table as shown for Outstanding loans. If Paid Date is not blank, it means that loan is not outstanding. For outstanding loans, outstanding days = Today’s date (7-Sep-24) - Due Date. If Due Date > Today’s Date then outstanding days is 0.

Solutions

library(tidyverse)
library(readxl)
library(gt)

path = "Power Query/PQ_Challenge_215.xlsx"
input = read_excel(path, range = "A1:E20")
test  = read_excel(path, range = "G1:J15")

result = input %>%
  mutate(out_day = case_when(
    !is.na(`Paid Date`) ~ NA_real_,
    `Due Date` > today() ~ 0,
    TRUE ~ as.numeric(difftime(today(), `Due Date`, units = "days"))
  )) %>%
  filter(!is.na(out_day)) %>%
  arrange(`Branch ID`, Customer, `Due Date`) %>%
  select(-`Paid Date`) %>%
  group_by(`Branch ID`)  %>%
  gt() %>%
  # change column names
  cols_label(Customer = "Branch ID / Customer",
             `Due Date` = "Due Date",
            `Loan Amt` = "Total Loan Amount",
             out_day = "Total Outstanding Days") 

result

  • 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
from datetime import datetime

path = "PQ_Challenge_215.xlsx"
input = pd.read_excel(path, usecols="A:E")
test = pd.read_excel(path, usecols="G:J", nrows=14)

today = pd.Timestamp(datetime.today().date())

result = input.assign(out_day=pd.Series(dtype=float))
result['out_day'] = result['Paid Date'].apply(lambda x: None if pd.isnull(x) else 0)
result['out_day'] = result['out_day'].combine_first(result['Due Date'].apply(lambda x: 0 if x > today else (today.date() - x.date()).days))
result = result[result['Paid Date'].isnull()]
result = result.sort_values(by=['Branch ID', 'Customer', 'Due Date']).reset_index(drop=True)
result = result.drop(columns=['Paid Date']).reset_index(drop=True)
result = result.set_index(['Branch ID', 'Customer'])

print(result)

  • Logic:

    • Reads the workbook range needed for the challenge

    • 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.