Omid - Challenge 81

data-challenges
advanced-exercises
🔰 The distance between cities and staff travels are provided in the question distance and travel tables.
Published

March 24, 2026

Illustration for Omid - Challenge 81

Challenge Description

🔰 The distance between cities and staff travels are provided in the question distance and travel tables.

Solutions

library(tidyverse)
library(readxl)


path = "files/CH-081 Compaint grouping.xlsx"
input1 = read_xlsx(path, range = "B2:F6")
input2 = read_xlsx(path, range = "H2:J14")
test  = read_xlsx(path, range = "K2:K14")

result1 = input1 %>%
  pivot_longer(cols = -c(`From-To`), names_to = "To", values_to = "Distance")

result2 = input2 %>%
  separate_rows(Path, sep = ",") %>%
  mutate(to = lead(Path), .by = c(Date, `Staff ID`)) %>%
  na.omit()

result = result2 %>%
  left_join(result1, by = c("Path" = "From-To", "to" = "To")) %>%
  summarise(Distance = sum(Distance, na.rm = TRUE), .by = c(Date, `Staff ID`))

identical(result$Distance, test$Distance)
#> [1] TRUE

  • Logic:

    • Reshapes the data into the grain required by the task

    • Aggregates or ranks values at the relevant grouping level

    • Builds the intermediate columns that drive the final result

  • 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

path = "CH-081 Compaint grouping.xlsx"

input1 = pd.read_excel(path, skiprows=1, usecols="B:F", nrows= 4)
input2 = pd.read_excel(path, skiprows=1, usecols="H:J")
test = pd.read_excel(path, skiprows=1, usecols="K:K")

result1 = input1.melt(id_vars=["From-To"], var_name="To", value_name="Distance")

result2 = input2.assign(Path=input2["Path"].str.split(",")).explode("Path")
result2["to"] = result2.groupby(["Date","Staff ID"])["Path"].shift(-1)
result2 = result2.dropna()

result = result2.merge(result1, left_on=["Path", "to"], right_on=["From-To", "To"])
result = result.groupby(["Date", "Staff ID"]).agg({"Distance": "sum"}).reset_index()

print(result["Distance"].equals(test["Distance"])) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • Aggregates or ranks values at the relevant grouping level

    • Builds the intermediate columns that drive the final result

  • 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 core logic is clear, but the correct transformation pattern is not obvious from the raw input.

  • The challenge combines multiple reshaping, grouping, or parsing steps.