Omid - Challenge 285

data-challenges
advanced-exercises
🔰 : Text Matching!
Published

March 24, 2026

Illustration for Omid - Challenge 285

Challenge Description

🔰 : Text Matching!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/285/CH-285 Text Matching.xlsx"
input = read_excel(path, range = "B2:B9")
test  = read_excel(path, range = "D2:E11") %>% arrange(`ID 1`, `ID 2`)

result = input %>%
mutate(rn = row_number(), IDc = str_remove_all(ID, "-")) %>%
  expand_grid(., ., .name_repair = "unique") %>%
  filter(rn...2 > rn...5) %>%
  transmute(ID_left = ID...1, ID_right = ID...4,
            a = IDc...3, b = IDc...6) %>%
  mutate(m1 = map2_int(a, b, ~ sum(str_detect(.x, str_split(.y, "")[[1]]))),
         m2 = map2_int(b, a, ~ sum(str_detect(.x, str_split(.y, "")[[1]])))) %>%
  filter(pmax(m1, m2) >= 3) %>%
  transmute(ID1 = ID_right, ID2 = ID_left) %>%
  arrange(ID1, ID2)

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

    • 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
from itertools import combinations

path = "200-299/285/CH-285 Text Matching.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=7)
test = pd.read_excel(path, usecols="D:E", skiprows=1, nrows=10)\
    .sort_values(['ID 1', 'ID 2']).reset_index(drop=True)

def score(a, b):
    a, b = a.replace("-", ""), b.replace("-", "")
    m1 = sum(ch in a for ch in list(b))
    m2 = sum(ch in b for ch in list(a))
    return max(m1, m2)

pairs = [(i, j) for i, j in combinations(input["ID"], 2) if score(i, j) >= 3]
result = pd.DataFrame(pairs, columns=["ID 1", "ID 2"])\
    .sort_values(["ID 1", "ID 2"]).reset_index(drop=True)

print(result.equals(test)) # True

  • 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.