Excel BI - PowerQuery Challenge 160

excel-challenges
power-query
x, y coordinates are given for different cities. Prepare the distance between cities grid where distance between 2 cities are calculated by usual distance formula of coordinate geometry which is represented as SQRT((x2-x1)2+(y2-y1)2) in Excel.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 160

Challenge Description

x, y coordinates are given for different cities. Prepare the distance between cities grid where distance between 2 cities are calculated by usual distance formula of coordinate geometry which is represented as SQRT((x2-x1)2+(y2-y1)2) in Excel.

Solutions

library(tidyverse)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_160.xlsx", range = "A1:C8")
test  = read_excel("Power Query/PQ_Challenge_160.xlsx", range = "F1:M8")

grid = crossing(city1 = input$Cities, city2 = input$Cities) %>%
  mutate(x1 = input$x[match(city1, input$Cities)],
         y1 = input$y[match(city1, input$Cities)],
         x2 = input$x[match(city2, input$Cities)],
         y2 = input$y[match(city2, input$Cities)], 
         dist = round(sqrt((x2 - x1)^2 + (y2 - y1)^2),2)) %>%
  select(Cities = city1, city2, dist) %>%
  pivot_wider(names_from = city2, values_from = dist)

identical(test, grid)
# [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data into the structure required by the result table

    • Builds helper columns that drive the final output

  • Strengths:

    • The R solution stays close to the workbook logic and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the workbook layout and selected ranges remain stable.

  • Gem:

    • The best part of the solution is choosing the right intermediate shape before formatting the final output.
import numpy as np
import pandas as pd

input_data = pd.read_excel("PQ_Challenge_160.xlsx", usecols="A:C", nrows=8)
test = pd.read_excel("PQ_Challenge_160.xlsx", usecols="F:M", nrows=8)

rows = []
for _, r1 in input_data.iterrows():
    row = {"Cities": r1["Cities"]}
    for _, r2 in input_data.iterrows():
        dist = round(np.sqrt((r2["x"] - r1["x"]) ** 2 + (r2["y"] - r1["y"]) ** 2), 2)
        row[r2["Cities"]] = dist
    rows.append(row)
result = pd.DataFrame(rows)

print(result.equals(test))

  • Logic:

    • Reads the workbook range needed for the challenge

    • Applies the rule iteratively until the output is complete

  • Strengths:

    • The Python version follows the same workbook rule in a direct pandas-oriented implementation.

  • Areas for Improvement:

    • As with the R version, any workbook layout change would require small adjustments.

  • Gem:

    • The implementation stays close to the source challenge instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • It combines reshaping, grouping, or parsing steps that are common in Power Query style problems.

  • The main challenge is reproducing the workbook output structure exactly.