Excel BI - PowerQuery Challenge 235

excel-challenges
power-query
Country Year-Quarter Population Male Female 2022-Q3
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 235

Challenge Description

Country Year-Quarter Population Male Female 2022-Q3

Solutions

library(tidyverse)
library(readxl)

path = "Excel/PQ_Challenge_235.xlsx"
input = read_excel(path, range = "A1:E10")
test  = read_excel(path, range = "H1:N10")

result = input %>%
  pivot_longer(cols = -c(Country, `Year-Quarter`), names_to = "Category", values_to = "Value") %>%
  unite("CV", Category, Value) %>%
  pivot_wider(names_from = `Year-Quarter`, values_from = CV) %>%
  unnest(c(starts_with("202"))) %>%
  separate("2022-Q3", into = c("2022-Q3", "Value1"), sep = "_") %>%
  separate("2022-Q4", into = c("2022-Q4", "Value2"), sep = "_") %>%
  separate("2023-Q1", into = c("2023-Q1", "Value3"), sep = "_") 

result = result %>%
  mutate(across(starts_with("Value"), as.numeric))

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_235.xlsx"
input = pd.read_excel(path, usecols="A:E", nrows=10)
test = pd.read_excel(path, usecols="H:N", nrows=10).rename(columns=lambda x: x.split('.')[0]).sort_values(by="Country").reset_index(drop=True)

result = (input.melt(id_vars=["Country", "Year-Quarter"], var_name="Category", value_name="Value")
          .assign(CV=lambda x: x["Category"] + "_" + x["Value"].astype(str))
          .pivot(index=["Country", "Category"], columns="Year-Quarter", values="CV")
          .reset_index()
          .drop(columns=["Category"])
          .rename_axis(None, axis=1))

for col in ["2022-Q3", "2022-Q4", "2023-Q1"]:
    result[[col, f"Value{col[-1]}"]] = result[col].str.split("_", expand=True)

result = result.rename(columns={"Value3": "Value1", "Value4": "Value2", "Value1": "Value3"})
result = result[["Country", "2022-Q3", "Value1", "2022-Q4", "Value2", "2023-Q1", "Value3"]]
result = result.sort_values(by=["Country", "2022-Q3"], ascending=[True, False]).reset_index(drop=True)

for col in result.columns:
    if "Value" in col:
        result[col] = result[col].astype("int64")

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

    • Builds helper columns that drive the final output

    • 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.