Excel BI - PowerQuery Challenge 227

excel-challenges
power-query
T1 T2 Sequence Name Weight Bonus %
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 227

Challenge Description

T1 T2 Sequence Name Weight Bonus %

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_227.xlsx"
input1 = read_excel(path, range = "A2:D13")
input2 = read_excel(path, range = "F2:H6")
test  = read_excel(path, range = "J2:N11") %>%
  arrange(Sequence, Name)

input2 = input2 %>%
  mutate(pattern_seq = c("^1.*", "321", ".*", ".*8$"),
         pattern_name = c("^M.*", "^S.*", ".*[aA]$", ".*"))

input = input1 %>%
  cross_join(input2) %>%
  mutate(check_seq = str_detect(string = Sequence.x, pattern = pattern_seq),
         check_name = str_detect(string = Name.x, pattern = pattern_name),
         both_conditions = check_seq & check_name) %>%
  filter(both_conditions) %>%
  select(Sequence = Sequence.x,Name = Name.x, Weight, `Bonus %`, Salary) %>%
  arrange(Sequence, Name)

all.equal(input, test, check.attributes = FALSE)
#> [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Builds helper columns that drive the final output

    • Uses direct pattern parsing where the workbook encodes logic in text

  • 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
import re

path = "PQ_Challenge_227.xlsx"
input1 = pd.read_excel(path,  usecols="A:D", skiprows=1, nrows=11)
input2 = pd.read_excel(path,  usecols="F:H", skiprows=1, nrows=4)
test = pd.read_excel(path, usecols="J:N", skiprows=1, nrows=9).rename(columns=lambda x: x.replace('.1', '').replace('.2', ''))\
    .sort_values(by=['Sequence', 'Name']).reset_index(drop=True)

input2['pattern_seq'] = ["^1.*", "321", ".*", ".*8$"]
input2['pattern_name'] = ["^M.*", "^S.*", ".*[aA]$", ".*"]

input = input1.assign(key=1).merge(input2.assign(key=1), on='key').drop('key', axis=1)

input['both_conditions'] = input.apply(
    lambda row: bool(re.match(row['pattern_seq'], str(row['Sequence']))) and 
                bool(re.match(row['pattern_name'], str(row['Name']))), axis=1)

input = input[input['both_conditions']].copy().reset_index(drop=True)
input = input[['Sequence', 'Name', 'Weight', 'Bonus %', 'Salary']].sort_values(by=['Sequence', 'Name']).reset_index(drop=True)

print(input.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Builds helper columns that drive the final output

    • Uses direct pattern parsing where the workbook encodes logic in text

  • 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.