Omid - Challenge 157

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 157

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-157 Table Transformation.xlsx"
input = read_excel(path, range = "C2:C17", col_types = "text")
test  = read_excel(path, range = "E2:G12")

result = input %>%
  as.matrix() %>%
  matrix(., ncol = 3, byrow = TRUE) %>%
  as.data.frame() %>%
  separate_rows(V2:V3, sep = ",") %>%
  mutate(V1 = as.Date(as.integer(V1), origin = "1899-12-30") %>% as.POSIXct(), 
         V3 = as.integer(V3)) %>%
  set_names(c("Date", "Product", "Quantity")) 


all.equal(result, test, check.attributes = FALSE)
#> [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 = "CH-157 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="C", skiprows=1, nrows=15, dtype=str)
test = pd.read_excel(path, usecols="E:G", skiprows=1, nrows=10)

input_matrix = input.values.reshape(-1, 3)
result = pd.DataFrame(input_matrix, columns=["V1", "V2", "V3"])
result = result.assign(V2=result['V2'].str.split(','), V3=result['V3'].str.split(',')).explode(['V2', 'V3'])
result['V1'] = pd.to_datetime(result['V1'], errors='coerce')
result['V3'] = pd.to_numeric(result['V3'], errors='coerce').astype('Int64')
result.reset_index(drop=True, inplace=True)
result.columns = test.columns
print(all(result == test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

  • 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.