Excel BI - PowerQuery Challenge 297

excel-challenges
power-query
Cage Animals & Count Cage No Animal Count Cage10
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 297

Challenge Description

Cage Animals & Count Cage No Animal Count Cage10

Solutions

library(tidyverse)
library(readxl)

path = "Power Query/200-299/297/PQ_Challenge_297.xlsx"
input = read_excel(path, range = "A1:B6")
test  = read_excel(path, range = "D1:F15")

result = input %>%
  separate_longer_delim(`Animals & Count`, delim = ", ") %>%
  separate_wider_delim(`Animals & Count`, delim = "-", names = c("Animal", "Count"), too_few = "align_start", ) %>%
  mutate(Count = coalesce(as.numeric(Count),1)) %>%
  arrange(parse_number(Cage), Animal) %>%
  mutate(Cage = if (n() == 1) Cage else paste0(Cage, "_", row_number()), .by = Cage) 

all.equal(result, test, check.attributes = FALSE)

  • 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
import numpy as np

path = "200-299/297/PQ_Challenge_297.xlsx"
input = pd.read_excel(path, usecols="A:B", nrows=5)
test = pd.read_excel(path, usecols="D:F", nrows=15)

input_long = input.assign(**{
    'Animals & Count': input['Animals & Count'].str.split(', ')
}).explode('Animals & Count')

split = input_long['Animals & Count'].str.split('-', n=1, expand=True)
input_long['Animal'] = split[0]
input_long['Count'] = split[1].astype(float)

input_long['Count'] = input_long['Count'].fillna(1).astype(int)

input_long['Cage_num'] = input_long['Cage'].str.extract(r'(\d+)').astype(int)
input_long = input_long.sort_values(['Cage_num', 'Animal']).drop(columns='Cage_num')

input_long['Cage No'] = input_long.groupby('Cage')['Cage'].transform(
    lambda x: x if len(x) == 1 else x + '_' + (np.arange(1, len(x)+1)).astype(str)
)

result = input_long[['Cage No', 'Animal', 'Count']].reset_index(drop=True)

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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 easy to moderate:

  • The transformation rule is readable, but the final layout still requires a careful implementation.