Omid - Challenge 147

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 147

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)
library(janitor)

path = "files/CH-147 Table Transformation.xlsx"
input = read_excel(path, range = "C2:C27", col_types = "text")
test  = read_excel(path, range = "E2:G12") %>%
  mutate(Date = as.Date(Date, format = "%Y-%m-%d"))


result = input %>%
  mutate(Date = str_extract(`Column 1`, "\\d{5}")) %>%
  fill(Date, .direction = "down") %>%
  filter(`Column 1` != Date) %>%
  mutate(clmn = rep(c("Product", "Quantity"), length.out = n()),
         group = cumsum(clmn == "Product")) %>%
  pivot_wider(names_from = clmn, values_from = `Column 1`) %>%
  select(-group) %>%
  mutate(Quantity = as.numeric(Quantity),
         Date = excel_numeric_to_date(as.numeric(Date)))

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

    • 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-147 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="C", skiprows=1, nrows=25, dtype=str)
test = pd.read_excel(path, usecols="E:G", skiprows=1, nrows=10, parse_dates=['Date'])

input['Date'] = pd.to_datetime(input['Column 1'], errors='coerce').ffill()

input['Group_Index'] = input.groupby('Date').cumcount()
input = input[input['Group_Index'] != 0].reset_index()

input['clmn'] = ['Product', 'Quantity'] * (len(input) // 2) + ['Product'] * (len(input) % 2)
input['group'] = (input['clmn'] == 'Product').cumsum()

result = input.pivot_table(index=['Date', 'group'], columns='clmn', values='Column 1', aggfunc='first').reset_index()
result = result.drop(columns=['group'])
result.columns.name = None
result['Quantity'] = result['Quantity'].astype('int64')

print(result.equals(test)) # 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

  • 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.