Omid - Challenge 27

data-challenges
advanced-exercises
🔰 : Extract Numbers!
Published

March 24, 2026

Illustration for Omid - Challenge 27

Challenge Description

🔰 : Extract Numbers!

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-027 Extract Numbers.xlsx", range = "B1:B13")
test  = read_excel("files/CH-027 Extract Numbers.xlsx", range = "E1:h13", col_names = T)
colnames(test) = c("V1", "V2", "V3", "V4")

result = input %>%
  mutate(strings = str_extract_all(`Question Tables`, "\\((\\d+)\\)")) %>%
  unnest_wider(strings, names_sep = "") %>%
  mutate(across(-`Question Tables`, ~ str_remove_all(., "\\(|\\)") %>% as.numeric())) %>%
  select(-`Question Tables`) 

colnames(result) = c("V1", "V2", "V3", "V4")

identical(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

input = pd.read_excel("CH-027 Extract Numbers.xlsx", sheet_name="Sheet1", usecols="B", nrows = 13)
test = pd.read_excel("CH-027 Extract Numbers.xlsx", sheet_name="Sheet1", usecols="E:H", nrows = 13)
test.columns = ['Number_1', 'Number_2', 'Number_3', 'Number_4']

extracted_numbers = input["Question Tables"].str.extractall(r'\((\d+)\)').groupby(level=0)[0].apply(list)
extracted_numbers = extracted_numbers.apply(pd.Series)
extracted_numbers.columns = [f"Number_{i+1}" for i in extracted_numbers.columns]

result = pd.concat([input, extracted_numbers], axis=1)
result = result.iloc[:, 1:]
result = result.astype(float)

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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.