Omid - Challenge 210

data-challenges
advanced-exercises
🔰 Challenge 210: Removing characters !
Published

March 24, 2026

Illustration for Omid - Challenge 210

Challenge Description

🔰 Challenge 210: Removing characters !

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-210Removing a character.xlsx"
input = read_excel(path, range = "B2:B6")
test  = read_excel(path, range = "D2:E6")

result = input %>%
  mutate(rn = row_number()) %>%
  separate_rows(Text, sep = "") %>%
  filter(Text != "") %>%
  mutate(counter = row_number(), .by = Text) %>%
  mutate(rem = ifelse(counter > 1, "Removed chars", "Revised Text")) %>%
  select(-counter) %>%
  pivot_wider(names_from = rem, values_from = Text, values_fn = list(Text = toString))

  • 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

  • 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-210Removing a character.xlsx"

input = pd.read_excel(path, usecols="B", skiprows=1, nrows=5)
test = pd.read_excel(path, usecols="D:E", skiprows=1, nrows=5)

input = input.rename(columns={input.columns[0]: "Text"}).dropna(subset=["Text"])
input = input.assign(rn=range(1, len(input) + 1), characters=input["Text"].apply(list))
input = input.explode("characters").assign(counter=lambda x: x.groupby("characters").cumcount() + 1)
input["rem"] = input["counter"].eq(1).map({True: "Revised Text", False: "Removed chars"})
input = input.groupby(["rn", "rem"])["characters"].apply("".join).unstack().reset_index()
input = input.rename_axis(None, axis=1)[["Revised Text", "Removed chars"]]
print(input)

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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.