Excel BI - Excel Challenge 796

excel-challenges

excel-formulas

🔰 Draw the given pattern.

Published

March 24, 2026

Illustration for Excel BI - Excel Challenge 796

Challenge Description

🔰 Draw the given pattern.

Solutions

library(tidyverse)
library(readxl)

path = "Excel/700-799/796/796 Pattern Drawing.xlsx"
test  = read_excel(path, range = "B2:R18", col_names = FALSE) %>%
  as.matrix()

bowtie_matrix = function(n) {
  rows = 2 * n - 1
  cols = 2 * n - 1
  mat = matrix(NA, nrow = rows, ncol = cols)
  
  for (r in 1:rows) {
    k = if (r <= n) r else 2 * n - r
    left_vals = k:1
    mat[r, 1:k] = left_vals
    right_vals = 1:k
    mat[r, (cols - k + 1):cols] = right_vals
  }
  mat
}

result = bowtie_matrix(9)
all.equal(test, result, check.attributes = FALSE)
# TRUE

Logic: Read the workbook ranges needed for the challenge; Iterate through the sequence until the rule is satisfied.
Strengths: The algorithm is explicit about the sequence rule, so the control flow is easy to validate against the prompt.
Areas for Improvement: The solution assumes the workbook layout and selected ranges remain stable, so any structural change in the sheet would require small adjustments.
Gem: The non-obvious part is the local rule inside the loop, because that rule determines the whole output.

import pandas as pd
import numpy as np

path = "700-799/796/796 Pattern Drawing.xlsx"
test = pd.read_excel(path, header=None, usecols="B:R", skiprows=1, nrows=17).values

def bowtie_matrix(n):
    size = 2 * n - 1
    mat = np.full((size, size), np.nan) 
    for r in range(size):
        k = r + 1 if r < n else size - r  
        mat[r, :k] = np.arange(k, 0, -1)
        mat[r, size - k:] = np.arange(1, k + 1)
    return mat

result = bowtie_matrix(9)
print(np.array_equal(test, result, equal_nan=True)) # True

The Python version keeps the algorithm explicit, which helps when the challenge depends on a greedy or iterative rule.

Difficulty Level

Easy / Medium

The business rule is clear, though the workbook still needs a few transformation steps to reach the expected output.