Omid - Challenge 217

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 217

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-217 Table Transformation.xlsx"
input = read_excel(path, range = "B2:C17")
test  = read_excel(path, range = "E2:G11") %>% arrange(Date, Product, Quantity)

result = input %>%
  mutate(Product = ifelse(is.na(`Column 2`), `Column 1`, NA) %>% str_extract(".{1}$")) %>%
  fill(Product) %>%
  mutate(`Column 2` = ifelse(str_detect(`Column 2`, "^[0-9]+$"), `Column 2`, NA)) %>%
  na.omit() %>%
  select(Date = `Column 1`, Product, Quantity = `Column 2`) %>%
  mutate(Date = as.Date(as.numeric(Date), origin = "1899-12-30") %>% as.POSIXct(),
         Quantity = as.numeric(Quantity)) %>%
  arrange(Date, Product, Quantity)

all.equal(result, test, check.attributes = FALSE) 
#> True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

    • Parses the text patterns directly instead of relying on manual cleanup

  • 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-217 Table Transformation.xlsx"

input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=16)
test = pd.read_excel(path, usecols="E:G", skiprows=1, nrows=9).sort_values(by=["Date", "Product", "Quantity"]).reset_index(drop=True)

input['Product'] = input.apply(
    lambda row: row['Column 1'][-1] if pd.isna(row['Column 2']) and isinstance(row['Column 1'], str) else None,
    axis=1
)
input['Product'] = input['Product'].ffill()
input = input[input['Column 2'].apply(lambda x: str(x).isdigit() and len(str(x)) == 1)]
input.rename(columns={"Column 1": "Date", "Column 2": "Quantity"}, inplace=True)
input['Quantity'] = input['Quantity'].astype('int64')

input = input[["Date", "Product", "Quantity"]].sort_values(by=["Date", "Product", "Quantity"])
input.reset_index(drop=True, inplace=True)
input['Date'] = pd.to_datetime(input['Date'])

print(input.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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.