Omid - Challenge 365

data-challenges
advanced-exercises
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Published

March 24, 2026

Illustration for Omid - Challenge 365

Challenge Description

πŸ”° 🧩 Adding Explanations If you want to include explanations or extra notes: 🌐 Sharing External Content πŸ—£ Feedback I always appreciate your feedback β€” feel free to share an…

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/365/CH-365 Matrix Calculation.xlsx"
input <- read_excel(path, range = "C4:G7", col_names = FALSE) %>%
  as.matrix()
test <- read_excel(path, range = "K4:O7", col_names = FALSE) %>%
  as.matrix()

add_surrounding = function(mat) {
  result <- matrix(0, nrow = nrow(mat), ncol = ncol(mat))
  for (i in 1:nrow(mat)) {
    for (j in 1:ncol(mat)) {
      rows <- max(1, i - 1):min(nrow(mat), i + 1)
      cols <- max(1, j - 1):min(ncol(mat), j + 1)
      result[i, j] <- sum(mat[rows, cols]) - mat[i, j]
    }
  }
  return(result)
}
result = add_surrounding(input)
all.equal(result, test, check.attributes = FALSE)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Applies the rule iteratively until the output stabilizes

  • 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 numpy as np
path = "300-399/365/CH-365 Matrix Calculation.xlsx"

input = pd.read_excel(path, usecols="C:G", skiprows=3, nrows=4, header=None).to_numpy()
test = pd.read_excel(path, usecols="K:O", skiprows=3, nrows=4, header=None).to_numpy()

def add_surrounding(mat):
    result = np.zeros_like(mat)
    for i in range(mat.shape[0]):
        for j in range(mat.shape[1]):
            rows = slice(max(0, i - 1), min(mat.shape[0], i + 2))
            cols = slice(max(0, j - 1), min(mat.shape[1], j + 2))
            result[i, j] = np.sum(mat[rows, cols]) - mat[i, j]
    return result
result = add_surrounding(input)

comparison = np.allclose(result, test, equal_nan=True)
print(comparison) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • 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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.