Omid - Challenge 32

data-challenges
advanced-exercises
🔰 : Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 32

Challenge Description

🔰 : Transformation!

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-032 Transformation.xlsx", range = "B2:H15", col_names = FALSE)
test  = read_excel("files/CH-032 Transformation.xlsx", range = "J2:L38")

input_header = input %>% 
  filter(!...1 %in% c(1:12)) %>%
  t() %>%
  as.data.frame() %>%
  fill(1, .direction = "down") %>%
  unite("header", V1, V2, sep = "_", na.rm = T) %>%
  pull()

colnames(input) = input_header

input_table = input %>%
  filter(Month %in% c(1:12)) %>%
  pivot_longer(cols = c(2,4,6), names_to = "Year", values_to = "Sales") %>%
  select(-c(2,3,4)) %>%
  separate(col = "Year", into = c("Year", "M")) %>%
  mutate(across(everything(), as.numeric)) %>%
  select(-M) %>%
  arrange(Year, Month)
  
identical(test, input_table)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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

input = pd.read_excel("CH-032 Transformation.xlsx", usecols="B:H", skiprows=1, nrows= 14, header=None) 
test  = pd.read_excel("CH-032 Transformation.xlsx", usecols="J:L", skiprows=1, nrows=38)
test.columns = ["Month", "Year", "Sales"]

input.columns = input.iloc[0].astype("str").fillna('') + '' + input.iloc[1].fillna('')
input = input.drop([0,1], axis=0).reset_index(drop=True)
input = input.loc[:, ~input.columns.str.contains('%')]
input = input.melt(id_vars=["Month"], var_name="Year", value_name="Sales")
input["Year"] = input["Year"].str.extract('(\d+)').astype("int")
input["Month"] = input["Month"].astype("int")
input["Sales"] = input["Sales"].astype("int")

print(input == test) # all 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.