Omid - Challenge 208

data-challenges
advanced-exercises
šŸ”° Create a formula that, for any ā€˜n x nā€™ matrix, calculates all the values on the primary diagonal as z.
Published

March 24, 2026

Illustration for Omid - Challenge 208

Challenge Description

šŸ”° Create a formula that, for any ā€˜n x nā€™ matrix, calculates all the values on the primary diagonal as z.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-208 Matrix Calculation.xlsx"
input1 = read_excel(path, range = "C3:D4", col_names = FALSE) %>% as.matrix()
input2 = read_excel(path, range = "C6:E8", col_names = FALSE) %>% as.matrix()
input3 = read_excel(path, range = "C10:G14", col_names = FALSE) %>% as.matrix()
test1  = read_excel(path, range = "K3", col_names = FALSE) %>% pull()
test2  = read_excel(path, range = "K6", col_names = FALSE) %>% pull()
test3  = read_excel(path, range = "K10", col_names = FALSE) %>% pull()

result1 = sum(diag(input1))
result2 = sum(diag(input2))
result3 = sum(diag(input3))

all.equal(result1, test1) # TRUE
all.equal(result2, test2) # TRUE
all.equal(result3, test3) # TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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 = "CH-208 Matrix Calculation.xlsx"

input1 = pd.read_excel(path, usecols="C:D", skiprows=2, nrows=2, header=None).to_numpy()
input2 = pd.read_excel(path, usecols="C:E", skiprows=5, nrows=3, header=None).to_numpy()
input3 = pd.read_excel(path, usecols="C:G", skiprows=9, nrows=5, header=None).to_numpy()
test1 = pd.read_excel(path, usecols="K", skiprows=2, nrows=1, header=None).iloc[0, 0]
test2 = pd.read_excel(path, usecols="K", skiprows=5, nrows=1, header=None).iloc[0, 0]
test3 = pd.read_excel(path,  usecols="K", skiprows=9, nrows=1, header=None).iloc[0, 0]

result1 = np.trace(input1)
result2 = np.trace(input2)
result3 = np.trace(input3)

print(np.isclose(result1, test1))  # TRUE
print(np.isclose(result2, test2))  # TRUE
print(np.isclose(result3, test3))  # TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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.