Crispo - Excel Challenge 10 2025

excel-challenges
weekly-exercises
Easy Sunday Excel Challenge
Published

March 9, 2025

Illustration for Crispo - Excel Challenge 10 2025

Challenge Description

Easy Sunday Excel Challenge

⭐ Qty Group ⭐Create Qty Group and Sum the Amount

Solutions

library(tidyverse)
library(readxl)

path = "files/Challenge1025.xlsx"
input = read_excel(path, range = "B3:D11")
test  = read_excel(path, range = "F3:G9")

step  = 5
min = floor(min(input$Qty) / step) * step
max = ceiling(max(input$Qty)/ step) * step


breaks = seq(min, max, by = step)
labels <- c(
  paste0(breaks[1], "-", breaks[2]),
  map2_chr(breaks[-length(breaks)], breaks[-1], ~ paste0(.x + 1, "-", .y))
) %>%
  .[-1]

result = input %>%
  mutate(Qty = cut(Qty, breaks = breaks, labels = labels)) %>%
  summarise(Amount = sum(Amount), .by = Qty)
  

r2 = tibble(`Qty Group` = labels) %>%
  left_join(result, by = c("Qty Group" = "Qty")) %>%
  replace_na(list(Amount = 0))

all.equal(r2, test, check.attributes = FALSE)
#> [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the correct grouping level

    • Builds the intermediate helper columns that drive the final answer

  • Strengths:

    • The R solution stays compact and mirrors the workbook logic closely.

  • Areas for Improvement:

    • The code assumes the workbook layout and named ranges remain stable.

  • Gem:

    • The best part of the solution is choosing a tidy intermediate shape before producing the final answer.
import pandas as pd
import numpy as np

path = "files/Challenge1025.xlsx"
input = pd.read_excel(path, usecols="B:D", skiprows=2, nrows=9)
test = pd.read_excel(path, usecols="F:G", skiprows=2, nrows=6).rename(columns=lambda x: x.replace('.1', ''))

step = 5
min_qty = np.floor(input["Qty"].min() / step) * step
max_qty = np.ceil(input["Qty"].max() / step) * step
breaks = np.arange(min_qty, max_qty + step, step)
labels = [f"{int(breaks[i-1] + 1)}-{int(breaks[i])}" for i in range(1, len(breaks))]

input["Qty_group"] = pd.cut(input["Qty"], bins=breaks, labels=labels, right=True, include_lowest=True)
result = input.groupby("Qty_group", observed=True)["Amount"].sum().reset_index()
result.rename(columns={"Qty_group": "Qty Group"}, inplace=True)
all_groups = pd.DataFrame({"Qty Group": labels})
r2 = pd.merge(all_groups, result, on="Qty Group", how="left")
r2["Amount"] = r2["Amount"].fillna(0)
r2["Amount"] = r2["Amount"].astype("int64")

print(test.equals(r2)) # True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the correct grouping level

    • Applies the rule iteratively until the output is complete

  • Strengths:

    • The Python version keeps the same rule in a direct pandas-oriented workflow.

  • Areas for Improvement:

    • As with the R version, any workbook layout change would require small adjustments.

  • Gem:

    • The implementation stays close to the stated challenge instead of adding unnecessary complexity.

Difficulty Level

This task is easy to moderate:

  • The business rule is readable, but the workbook still needs a few careful transformation steps.