Omid - Challenge 105

data-challenges
advanced-exercises
🔰 In the question table, where some passwords are provided, extract the 6 most commonly used characters across all the passwords and count their repetitions.
Published

March 24, 2026

Illustration for Omid - Challenge 105

Challenge Description

🔰 In the question table, where some passwords are provided, extract the 6 most commonly used characters across all the passwords and count their repetitions.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-105 Character Repetition.xlsx"
input = read_excel(path, range = "B2:B12")
test = read_excel(path, range = "D2:E8")

result = input %>%
  mutate(Password = Password %>%
           str_to_lower()) %>%
  separate_rows(Password, sep = "") %>%
  select(Character = Password) %>%
  summarise(Repitation = n() %>% as.numeric(), .by = Character) %>%
  filter(Character != "") %>%
  arrange(desc(Repitation), Character) %>%
  head()

identical(result, test)
#> [1] TRUE

  • 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

    • 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

path = "CH-105 Character Repetition.xlsx"
input = pd.read_excel(path, usecols = "B", skiprows = 1, nrows = 10)
test  = pd.read_excel(path, usecols = "D:E", skiprows = 1, nrows = 6)


result = (
    input
    .assign(Password=lambda df: df['Password'].str.lower())
    .assign(Character=lambda df: df['Password'].str.split(''))
    .explode('Character')
    .groupby('Character', as_index=False)
    .size()
    .rename(columns={'size': 'Repitation'})
    .query("Character != ''")
    .sort_values(by=['Repitation', 'Character'], ascending=[False, True])
    .reset_index(drop=True)
    .head(6)
)

print(result.equals(test))  # True

  • 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.