Excel BI - PowerQuery Challenge 272

excel-challenges
power-query
Date ID1 ID2 ID3 ID4 ID5
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 272

Challenge Description

Date ID1 ID2 ID3 ID4 ID5

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_272.xlsx"
input = read_excel(path, range = "A1:F5")
test  = read_excel(path, range = "H1:N8")

result = input %>%
  pivot_longer(-Date, names_to = "IDS", values_to = "Value") %>%
  separate_rows(Value, sep = ",") %>%
  na.omit() %>%
  mutate(Value = trimws(Value), rn = row_number(), .by = c(Date, IDS)) %>%
  pivot_wider(names_from = IDS, values_from = Value) %>%
  arrange(rn, Date) %>%
  select(-rn) %>%
  mutate(Seq = row_number(), .before = everything())

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

    • 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_272.xlsx"

input = pd.read_excel(path, usecols="A:F", nrows=5)
test = pd.read_excel(path, usecols="H:N", nrows=8).rename(columns=lambda x: x.split(".")[0])

result = (
    input.melt(id_vars=["Date"], var_name="Attribute", value_name="Value")
    .dropna(subset=["Value"])
    .assign(Value=lambda df: df["Value"].str.split(", "))
    .explode("Value")
    .assign(RowNumber=lambda df: df.groupby(["Date", "Attribute"]).cumcount() + 1)
    .pivot(index=["Date", "RowNumber"], columns="Attribute", values="Value")
    .reset_index()
    .sort_values(by=["RowNumber", "Date"])
    .drop(columns=["RowNumber"])
    .reset_index(drop=True)
)
result.columns.name = None
result.insert(0, "Seq", result.index + 1)

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.