Omid - Challenge 162

data-challenges
advanced-exercises
🔰 Challenge 162: Extract From Text
Published

March 24, 2026

Illustration for Omid - Challenge 162

Challenge Description

🔰 Challenge 162: Extract From Text

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-162 Extract from Text.xlsx"
input = read_excel(path, range = "B2:C7")
test  = read_excel(path, range = "E2:I7")

r1 = input %>%
  mutate(Value = str_replace_all(Value, "(\\d),\\{", "\\1},\\{")) %>%
  mutate(Value = str_replace_all(Value, "\\{+", "{")) %>%
  mutate(Value = str_replace_all(Value, "\\}+", "}")) %>%
  separate_rows(Value, sep = "(?<=\\}),(?=\\{)") %>%
  mutate(rn = row_number(), .by = ID) %>%
  pivot_wider(names_from = rn, values_from = Value) %>%
  setNames(colnames(test))

all.equal(r1, test, check.attributes = FALSE)
# TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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-162 Extract from Text.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=6)
test = pd.read_excel(path, usecols="E:I", skiprows=1, nrows=6)

def clean_value(value):
    value = re.sub(r"(\d),\{", r"\1},{", value)
    value = re.sub(r"\{+", "{", value)
    value = re.sub(r"\}+", "}", value)
    return value

input['Value'] = input['Value'].apply(clean_value)
input = input.assign(Value=input['Value'].str.split(r"(?<=\}),(?=\{)")).explode('Value')
input['rn'] = input.groupby('ID').cumcount() + 1
r1 = input.pivot(index='ID', columns='rn', values='Value').reset_index()
r1.columns = test.columns

print(r1.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • Aggregates or ranks values at the relevant grouping level

    • Builds the intermediate columns that drive the final result

  • 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.