Omid - Challenge 318

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 318

Challenge Description

🔰 Table Transformation!

Solutions

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

path = "files/300-399/318/CH-318 Table Transformation.xlsx"
input = read_excel(path, range = "B2:E10", col_names = FALSE)
test  = read_excel(path, range = "G2:J7")

m = as.matrix(input)
m[str_starts(m, "Column")] = NA

df = m %>%
    as_tibble() %>%
    map_df(~ {
        x = na.omit(.x)
        c(x, rep(NA, nrow(m) - length(x)))
    }) %>%
    row_to_names(1) %>%
    drop_na() %>%
    mutate(Date = excel_numeric_to_date(as.numeric(Date)) %>% as.POSIXct(),
        Quantity = as.numeric(Quantity))

all.equal(result, test)

  • 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
import numpy as np

path = "300-399/318/CH-318 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B:E", skiprows=1, nrows=9, header=None).to_numpy()
test = pd.read_excel(path, usecols="G:J", skiprows=1, nrows=5).rename(columns=lambda c: c.replace('.1', ''))  

m = np.where(np.char.startswith(input.astype(str), 'Column'), np.nan, input)

for j in range(m.shape[1]):
    col = m[:, j][~pd.isna(m[:, j])]
    m[:len(col), j] = col
    m[len(col):, j] = np.nan

result = pd.DataFrame(m, columns=test.columns)
result = result.iloc[1:][~pd.isna(result['Date'])].reset_index(drop=True)
if 'Date' in result.columns:
    result['Date'] = pd.to_datetime(result['Date']).dt.strftime('%Y-%m-%d %H:%M')

print((result == test).all().all())

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Applies the rule iteratively until the output stabilizes

  • 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.