Excel BI - PowerQuery Challenge 234

excel-challenges
power-query
Employee StartDate1 EndDate1 StartDate2 EndDate2 StartDate3
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 234

Challenge Description

Employee StartDate1 EndDate1 StartDate2 EndDate2 StartDate3

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_234.xlsx"
input = read_excel(path, range = "A1:G5")
test  = read_excel(path, range = "A10:B14")

result = input %>%
  pivot_longer(-c(1), names_to = c(".value", "number"), names_pattern = "(.*)(\\d)") %>%
  na.omit() %>%
  mutate(seq = map2(StartDate, EndDate, ~seq.Date(from = as.Date(.x), to = as.Date(.y), by = "day"))) %>%
  unnest(cols = seq) %>%
  mutate(Weekday = wday(seq, week_start = 1)) %>%
  filter(Weekday %in% c(1:5)) %>%
  summarise(TotalLeaves = n_distinct(seq), .by = Employee) %>%
  arrange(Employee)

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

    • 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_234.xlsx"
input = pd.read_excel(path, usecols="A:G", nrows=5)
test = pd.read_excel(path, usecols="A:B", skiprows=9, nrows=5)

input_long = input.melt(id_vars=['Employee'], var_name='variable', value_name='value')
input_long[['variable', 'number']] = input_long['variable'].str.extract(r'(\D+)(\d+)')
input_long = input_long.dropna().pivot(index=['Employee', 'number'], columns='variable', values='value').reset_index()
input_long = input_long.assign(seq=input_long.apply(lambda row: pd.date_range(start=row['StartDate'], end=row['EndDate']), axis=1))\
    .explode('seq').reset_index(drop=True)
input_long = input_long[input_long['seq'].dt.weekday < 5]

result = input_long.groupby('Employee').agg(TotalLeaves=('seq', 'nunique')).reset_index()

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

    • Aggregates or ranks values at the relevant grouping level

    • 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.