Omid - Challenge 152

data-challenges
advanced-exercises
🔰 Challenge 152: Extract From Text Separate the values inside the curly braces { }, but ignore any nested curly braces (which are highlighted in red and blue).
Published

March 24, 2026

Illustration for Omid - Challenge 152

Challenge Description

🔰 Challenge 152: Extract From Text Separate the values inside the curly braces { }, but ignore any nested curly braces (which are highlighted in red and blue).

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-152 Extract from Text.xlsx"
input = read_excel(path, range = "B2:C7")
test  = read_excel(path, range = "E2:H7")

split_on_top_level <- function(s) {
  chars <- strsplit(s, "")[[1]]
  level <- 0
  current <- ""
  result <- c()
  for (char in chars) {
    if (char == '{') level <- level + 1
    if (char == '}') level <- level - 1
    if (char == ',' && level == 0) {
      result <- c(result, current)
      current <- ""
    } else {
      current <- paste0(current, char)
    }
  }
  if (nchar(current) > 0) result <- c(result, current)
  return(result)
}

result = input %>%
  mutate(components = map(Value, split_on_top_level)) %>%
  select(-Value) %>%
  unnest_wider(components, names_sep = "") %>%
  rename_with(~paste0("List.", seq_along(.)), starts_with("components"))

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

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

    • Applies the rule iteratively until the output stabilizes

  • 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

path = "CH-152 Extract from Text.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=6)
test = pd.read_excel(path, usecols="E:H", skiprows=1, nrows=6)

def split_on_top_level(s):
    chars = list(s)
    level = 0
    current = ""
    result = []
    for char in chars:
        if char == '{':
            level += 1
        if char == '}':
            level -= 1
        if char == ',' and level == 0:
            result.append(current)
            current = ""
        else:
            current += char
    if current:
        result.append(current)
    return result

input['components'] = input['Value'].apply(split_on_top_level)
components = pd.DataFrame(input['components'].tolist())
components.columns = [f"List.{i+1}" for i in range(components.shape[1])]
result = pd.concat([input.drop(columns=['Value', 'components']), components], axis=1)
test.columns = result.columns

result = result.fillna('')
test = test.fillna('')

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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.