Omid - Challenge 11

data-challenges
advanced-exercises
🔰 List 1 List 2 List 3 List 4 Extract all item codes that are repeated at least in 3 out of 4 lists presented in the question table.
Published

March 24, 2026

Illustration for Omid - Challenge 11

Challenge Description

🔰 List 1 List 2 List 3 List 4 Extract all item codes that are repeated at least in 3 out of 4 lists presented in the question table.

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-011.xlsx", range = "B2:E16")
test  = read_excel("files/CH-011.xlsx", range = "K2:K6")

result = input %>%
  pivot_longer(cols = everything(), names_to = "columns", values_to = "codes") %>%
  group_by(codes) %>%
  summarise(is_in_col = n_distinct(columns)) %>%
  filter(is_in_col >= 3) %>%
  select(-is_in_col)

identical(result$codes, test$`Item Code`)
# [1] 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

  • 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_data = pd.read_excel("CH-011.xlsx", usecols="B:E", skiprows=1, nrows=15)
test = pd.read_excel("CH-011.xlsx", usecols="K", skiprows=1, nrows=5)

result = (
    input_data.melt(var_name="columns", value_name="codes")
    .groupby("codes", as_index=False)["columns"]
    .nunique()
    .rename(columns={"columns": "is_in_col"})
)
result = result.loc[result["is_in_col"] >= 3, ["codes"]]

print(result["codes"].equals(test["Item Code"]))

  • 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

  • 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.