Excel BI - PowerQuery Challenge 189

excel-challenges
power-query
Seq Code Result Fail Pass Yes
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 189

Challenge Description

Seq Code Result Fail Pass Yes

Solutions

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_189.xlsx", range = "A1:B11")
test  = read_excel("Power Query/PQ_Challenge_189.xlsx", range = "D1:F8")

result = input %>%
  mutate(Result = if_else(lead(Code) == "Yes", "Pass", NA)) %>%
  mutate(Result = if_else(is.na(Result) & str_detect(Code, "\\d"), "Fail", Result)) %>%
  filter(!is.na(Result)) %>%
  mutate(Code = as.numeric(Code))

identical(result, test)
# [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

input = pd.read_excel("PQ_Challenge_189.xlsx", usecols="A:B", nrows = 10)
test = pd.read_excel("PQ_Challenge_189.xlsx", usecols="D:F", nrows = 7)
test.columns = test.columns.str.replace(".1","")

result = input.copy()
result["Result"] = result["Code"].shift(-1).eq("Yes").replace({True: "Pass", False: pd.NA})
result["Result"] = result["Result"].fillna(result["Code"].apply(lambda x: "Fail" if re.search(r"\d", str(x)) else pd.NA))
result = result.dropna(subset=["Result"]).reset_index(drop=True)
result["Code"] = pd.to_numeric(result["Code"])

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • 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.