Excel BI - PowerQuery Challenge 205

Sum

Illustration for Excel BI - PowerQuery Challenge 205

Challenge Description

Sum

Solutions

R Solution

library(tidyverse)
library(readxl)

path = "Power Query/PQ_Challenge_205.xlsx"
input1 = read_excel(path, range = "A2:B13")
input2 = read_excel(path, range = "D2:E13")
test = read_excel(path, range = "H2:L8")

input = left_join(input1, input2, by = "Item") 

result = input %>%
  arrange(desc(YesNo), Item) %>%
  mutate(nr = row_number(), .by = YesNo) %>%
  mutate(nr_rem = nr %% 2,
         nr_int = ifelse(nr_rem == 1, nr %/% 2 + 1,  nr %/% 2)) %>%
  select(-nr) %>%
  pivot_wider(names_from = nr_rem, values_from = c(Item, Value), 
              values_fill = list(Value = 0)) %>%
  mutate(Sum = Value_0 + Value_1) %>%
  select(YesNo, Item1 = Item_1, Item2 = Item_0, Sum) %>%
  mutate(`%age` = Sum/sum(Sum), .by = YesNo) 

identical(result, test)
# [1] TRUE

R Analysis

• 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.

Python Solution

import pandas as pd
import numpy as np

path = "PQ_Challenge_205.xlsx"
input1 = pd.read_excel(path, usecols="A:B", skiprows=1)
input2 = pd.read_excel(path, usecols="D:E", skiprows=1)
input2.columns = input2.columns.str.replace(".1", "")
test  = pd.read_excel(path, usecols="H:L", skiprows=1, nrows = 6)
test.columns = test.columns.str.replace(".1", "")
test = test.fillna("")

input = pd.merge(input1, input2, on="Item", how="inner")\
    .sort_values(by=["YesNo", "Item"], ascending=[False, True])\
    .reset_index(drop=True)

input["nr"] = input.groupby("YesNo").cumcount() + 1
input["nr_rem"] = input["nr"] % 2
input["nr_int"] = np.where(input["nr_rem"] == 1, input["nr"] // 2 + 1, input["nr"] // 2)

input = input.pivot(index=["YesNo", "nr_int"], columns="nr_rem", values=["Item", "Value"]).reset_index()
input.columns = [f"{a}{b}" for a, b in input.columns]
input["Value0"] = input["Value0"].fillna(0)
input["Sum"] = input["Value0"] + input["Value1"]
input["%age"] = input.groupby("YesNo")["Sum"].transform(lambda x: x / x.sum())

input.drop(columns=["Value0", "Value1", "nr_int"], inplace=True)
input = input[["YesNo", "Item1", "Item0", "Sum", "%age"]]
input = input.rename(columns={"Item0": "Item2"})\
    .sort_values(by="YesNo", ascending=False)\
    .reset_index(drop=True)\
    .fillna("")

print(input.equals(test))   # True

Python

• 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

  • Applies the rule iteratively until the output is complete

• 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.

Related Challenges

Explore more challenges from the same series:

• Excel BI - PowerQuery Challenge 204