Omid - Challenge 243

data-challenges
advanced-exercises
🔰 Age Group Challenge 243: Custom Grouping!
Published

March 24, 2026

Illustration for Omid - Challenge 243

Challenge Description

🔰 Age Group Challenge 243: Custom Grouping!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/243/CH-243 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C16")
test = read_excel(path, range = "F2:G7")

result = input %>%
  count(
    `Age Group` = cut(
      Age,
      c(0, 30, 40, 50, 60, Inf),
      right = FALSE,
      labels = c("<30", "[30-40)", "[40-50)", "[50-60)", ">60")
    ),
    name = "Count"
  ) %>%
  complete(`Age Group`, fill = list(Count = 0)) %>%
  mutate(`Age Group` = as.character(`Age Group`))

all.equal(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

  • Strengths:

    • The R solution stays close to the workbook rule and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the sheet structure and source ranges remain stable.

  • Gem:

    • The strongest part of the solution is choosing the right intermediate representation before shaping the final output.
import pandas as pd

path = "200-299/243/CH-243 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=14)
test = pd.read_excel(path, usecols="F:G", skiprows=1, nrows=5)

input['Age Group'] = pd.cut(
    input['Age'],
    bins=[0, 30, 40, 50, 60, float('inf')],
    right=False,
    labels=["<30", "[30-40)", "[40-50)", "[50-60)", ">60"]
)

result = (
    input.groupby('Age Group')
    .size()
    .reindex(["<30", "[30-40)", "[40-50)", "[50-60)", ">60"], fill_value=0)
    .reset_index(name='Count')
)
result['Age Group'] = result['Age Group'].astype(str)

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • Strengths:

    • The Python version follows the same rule in a direct dataframe-oriented implementation.

  • Areas for Improvement:

    • The code assumes the workbook layout remains stable, so any sheet redesign would require small adjustments.

  • Gem:

    • The implementation stays close to the original workbook rule instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • The business rule is readable, but the workbook still requires careful implementation to reach the expected layout.