Omid - Challenge 145

data-challenges
advanced-exercises
🔰 Find the largest length
Published

March 24, 2026

Illustration for Omid - Challenge 145

Challenge Description

🔰 Find the largest length

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-145 Length of Pattern.xlsx"
input = read_excel(path, range = "B2:D32")
test  = read_excel(path, range = "F2:G5")

result = input %>%
  summarise(result = str_c(Result, collapse = ""), .by = Product) %>%
  mutate(`Largest Length` = map_dbl(result, ~ max(str_length(str_extract_all(.x, "(?:\\+\\+-)+(?:\\+)?")[[1]]), 0))) %>%
  select(Product, `Largest Length`)

all.equal(result, test)
#> [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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 re

path = "CH-145 Length of Pattern.xlsx"
input = pd.read_excel(path, usecols="B:D", skiprows=1, nrows=30)
test = pd.read_excel(path, usecols="F:G", skiprows=1, nrows=3).rename(columns=lambda x: x.split('.')[0])

def largest_length(result):
    patterns = re.findall(r"(?:\+\+-)+(?:\+)?", result)
    return max(map(len, patterns), default=0)

input['Result'] = input.groupby('Product')['Result'].transform(lambda x: ''.join(x))
input = input.drop_duplicates(subset=['Product'])
input['Largest Length'] = input['Result'].apply(largest_length)
result = input[['Product', 'Largest Length']].reset_index(drop=True)

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

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

  • 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.