Omid - Challenge 214

data-challenges
advanced-exercises
🔰 Question Result ID Numbers Y QW Letters Before Letters After
Published

March 24, 2026

Illustration for Omid - Challenge 214

Challenge Description

🔰 Question Result ID Numbers Y QW Letters Before Letters After

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-214Column Splitting.xlsx"
input = read_excel(path, range = "B2:B7")
test  = read_excel(path, range = "D2:F7")

generate_substrings <- function(str) {
  n <- str_length(str)
  map_dfr(1:n, function(start) {
    map_dfr(start:n, function(end) {
      tibble(substring = str_sub(str, start, end))
    })
  })
}

subs = generate_substrings("1234567890")
subs = subs %>%
  filter(str_length(substring) > 1) 

result = crossing(ID = input$ID, match = subs$substring) %>%
  filter(str_detect(ID, fixed(match))) %>%
  slice_max(str_length(match), by = ID) %>
  mutate(
    before = str_extract(ID, paste0(".*(?=", match, ")")),
    after = str_extract(ID, paste0("(?<=", match, ").*"))
  ) %>%
select(before, match, after)

colnames(result) = colnames(test)

all.equal(result, test)
#> [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
import itertools

path = "CH-214Column Splitting.xlsx"
input_data = pd.read_excel(path, usecols="B", skiprows=1, nrows=6, names=["ID"])
test = pd.read_excel(path, usecols="D:F", skiprows=1, nrows=6)

def generate_substrings(s):
    n = len(s)
    substrings = []
    for start in range(n):
        for end in range(start + 1, n + 1):
            substrings.append(s[start:end])
    return substrings

subs = pd.DataFrame({"substring": generate_substrings("1234567890")})
subs = subs[subs["substring"].str.len() > 1]

result = pd.DataFrame(
    itertools.product(input_data["ID"], subs["substring"]),
    columns=["ID", "match"]
)
result = result[result.apply(lambda row: row["match"] in row["ID"], axis=1)]
result["match_length"] = result["match"].str.len()
result = result.sort_values("match_length", ascending=False).drop_duplicates("ID")
result[["before", "after"]] = result.apply(
    lambda row: pd.Series(row["ID"].split(row["match"])), axis=1
)
result = result.sort_values("ID").reset_index(drop=True)
result = result[["before", "match", "after"]]
result["match"] = result["match"].astype("int64")
result.columns = test.columns

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.