Omid - Challenge 138

data-challenges
advanced-exercises
🔰 Result Question Date Qty Product A C B
Published

March 24, 2026

Illustration for Omid - Challenge 138

Challenge Description

🔰 Result Question Date Qty Product A C B

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-138 Periodic Sales Summary.xlsx"
input = read_excel(path, range = "C2:E27")
test  = read_excel(path, range = "G2:J6")

result = input %>%
  mutate(Month = month(Date),
         day = day(Date), 
         decade_days = paste0("P",ifelse(ceiling(day / 10) == 4, 3, ceiling(day/10)))) %>%
  summarise(`Total Qty` = sum(Qty, na.rm = TRUE), .by = c(decade_days, Month)) %>%
  pivot_wider(names_from = decade_days, values_from = `Total Qty`, values_fill = 0) %>%
  select(Month, P1, P2, P3)

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

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • Aggregates or ranks values at the relevant grouping level

    • 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 = "CH-138 Periodic Sales Summary.xlsx"
input = pd.read_excel(path, usecols="C:E", skiprows=1, nrows=26)
test = pd.read_excel(path, usecols="G:J", skiprows=1, nrows = 4 )

input['Month'] = input['Date'].dt.month
input['decade_days'] = 'P' + (((input['Date'].dt.day - 1) // 10 + 1).clip(upper=3)).astype(str)

result = input.pivot_table(index = "Month", columns = "decade_days", values = "Qty", aggfunc = "sum").reset_index()
result = result.fillna(0).astype("int64")
result.columns.name = None

print(test.equals(result)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

  • 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 core logic is clear, but the correct transformation pattern is not obvious from the raw input.

  • The challenge combines multiple reshaping, grouping, or parsing steps.