Excel BI - Excel Challenge 736

excel-challenges

excel-formulas

🔰 Find out the missing entries in the word squares.

Published

March 24, 2026

Illustration for Excel BI - Excel Challenge 736

Challenge Description

🔰 Find out the missing entries in the word squares.

Solutions

library(tidyverse)
library(readxl)

path = "Excel/700-799/736/736 Word Square Missing Entries.xlsx"
read_as_matrix <- function(range) {
  read_excel(path, range = range, col_names = FALSE) %>% as.matrix()
}

input1 = read_as_matrix("B2:C3")
input2 = read_as_matrix("B5:D7")
input3 = read_as_matrix("B9:E12")
input4 = read_as_matrix("B14:F18")
input5 = read_as_matrix("B20:G25")
test1 = read_as_matrix("I2:J3")
test2 = read_as_matrix("I5:K7")
test3 = read_as_matrix("I9:L12")
test4 = read_as_matrix("I14:M18")
test5 = read_as_matrix("I20:N25")

fill_matrix <- function(mat) {
  na_idx <- which(is.na(mat), arr.ind = TRUE)
  mat[na_idx] <- mat[na_idx[,2:1]]
  mat
}

all.equal(fill_matrix(input1), test1)
all.equal(fill_matrix(input2), test2)
all.equal(fill_matrix(input3), test3)
all.equal(fill_matrix(input4), test4)
all.equal(fill_matrix(input5), test5)

Logic: Read the workbook ranges needed for the challenge.
Strengths: The code maps the workbook rule into a compact, reproducible pipeline.
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 elegant part is how little code is needed once the correct intermediate representation is chosen.

import numpy as np
import pandas as pd

path = "700-799/736/736 Word Square Missing Entries.xlsx"

def read_as_matrix(cell_range):
    df = pd.read_excel(path, sheet_name=0, header=None, 
                       usecols=cell_range.split(':')[0][0]+':'+cell_range.split(':')[1][0], 
                       skiprows=int(cell_range.split(':')[0][1:])-1, 
                       nrows=int(cell_range.split(':')[1][1:])-int(cell_range.split(':')[0][1:])+1)
    return df.values

input1 = read_as_matrix("B2:C3")
input2 = read_as_matrix("B5:D7")
input3 = read_as_matrix("B9:E12")
input4 = read_as_matrix("B14:F18")
input5 = read_as_matrix("B20:G25")
test1 = read_as_matrix("I2:J3")
test2 = read_as_matrix("I5:K7")
test3 = read_as_matrix("I9:L12")
test4 = read_as_matrix("I14:M18")
test5 = read_as_matrix("I20:N25")

def fill_matrix(mat):
    mat = mat.copy()
    na_idx = np.argwhere(pd.isna(mat))
    for i, j in na_idx:
        mat[i, j] = mat[j, i]
    return mat

print((fill_matrix(input1) == test1).all())
print((fill_matrix(input2) == test2).all())
print((fill_matrix(input3) == test3).all())
print((fill_matrix(input4) == test4).all())
print((fill_matrix(input5) == test5).all())

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.