Omid - Challenge 350

data-challenges
advanced-exercises
🔰 Challenge 350: Filter!
Published

March 24, 2026

Illustration for Omid - Challenge 350

Challenge Description

🔰 Challenge 350: Filter!

Solutions

library(tidyverse)
library(readxl)
library(charcuterie)

path <- "300-399/350/CH-350 Filter.xlsx"
input <- read_excel(path, range = "B3:B10")
test <- read_excel(path, range = "F3:F6")

check_pattern <- function(text) {
  chars <- unique(charcuterie::chars(text))
  patterns <- combn(chars, 2, simplify = TRUE, FUN = function(p) {
    str_c(p[1], ".*", p[2], ".*", p[1], ".*", p[2])
  })
  any(str_detect(text, patterns))
}

result = input %>%
  filter(map_lgl(ID, check_pattern))

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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
from itertools import combinations

path = "300-399/350/CH-350 Filter.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=2, nrows=8)
test = pd.read_excel(path, usecols="F", skiprows=2, nrows=3).rename(columns=lambda col: col.replace('.1', ''))

def check_pattern(text):
    chars = list(dict.fromkeys(str(text)))
    for p in combinations(chars, 2):
        pattern = f"{re.escape(p[0])}.*{re.escape(p[1])}.*{re.escape(p[0])}.*{re.escape(p[1])}"
        if re.search(pattern, str(text)):
            return True
    return False

result = input[input['ID'].apply(check_pattern)].reset_index(drop=True)

print(result.equals(test)) 
# True

  • Logic:

    • Reads the workbook ranges needed for the challenge

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

    • 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.